Professor, School of Computing and Electrical Engineering and Chairperson of the Centre for Human Computer Interaction, Indian Institute of Technology (IIT) , India
Authors - Hector Rafael Morano Okuno Abstract - Mechatronics is an interdisciplinary field that draws on mechanics, electronics, and computer science. In recent years, the term biomechatronic has been used with increasing frequency; it is also a multidisciplinary field that in volves biological sciences and, therefore, bioinformatics. With the development of AI, bioinformatics provides data to biomechatronic systems, enabling appli cations ranging from agriculture to medicine. This article explores how bio mechatronics and CFD simulations can help monitor a person's health status. The objectives of this research were: 1) to determine whether, using biomarkers such as hemoglobin, fibrinogen, and low-density lipoprotein (LDL), among others, and CFD simulations, it is possible to obtain blood flow velocity pro files; and 2) to investigate whether the information from CFD simulations can be used to feed a biomechatronic system to monitor a person's health condi tions. Among the results, it was found that it is necessary to have models that allow relating the main biomarkers to determine the state of health of a person, as well as with suitable sensors to measure each variable according to the orien tation of the application that is to be developed, for example, for physical train ing or for the monitoring of nutrition.
Authors - Radha Gawande, Supriya Nara Abstract - Complicated nature of the intensive care unit (ICU), immediate and accurate decision-making is vital to the survival of the patient. The problems that healthcare providers are struggling with are the overload of information, slowness of the decision making process, and the human factor due to growing amount of various patient information. Recent development in artificial intelligence (AI) offers promising solutions since they facilitate effective analysis of data, pattern detection and predictive modelling. This changes the provision of critical care. In this paper, the changing application of AI in ICUs is discussed. It talks about its usage, merits and demerits, as well as technological basis. It also discusses AI methods such as machine learning (ML), deep learning (DL), natural language process (NLP), and expert system, predictive analytics, early sepsis detection, clinical decision support system, automated monitoring and insight-based treatments by documentation fueled by natural language processing, are but a few of the practical methods of applying AI. The advantages of automation and robotics to enhance productivity and patient care are also discussed, which are AI-based medication delivery system and robotics helper. Nonetheless, a number of challenges to implement AI in critical care units are a lack of consensus, algorithm bias, understanding model decisions, and various data, personalized AI-driven care in the ICU, integration of edge computing and internet of medical things (IoMT), reinforcement learning in adapting patient management are some of the future prospects[1].
Authors - Priyanka Patel, Ashvi Padshala, Moxa Patel Abstract - This paper surveys recent advances in the application of data analysis, machine learn ing, artificial intelligence, and big data techniques for climate pattern detection. It covers sources of climate data, analytical methods, computational architectures, key challenges, and emerging trends. The focus is on identifying how integrated data-driven methods enhance the understanding, prediction, and interpretability of climate phenomena.
Authors - Rohan Dafare, Supriya Narad Abstract - The quick spread of big data and the rising need for instant analytics have shown the built-in limits of old-school relational database management systems (RDBMS). NoSQL ("Not SQL") databases give schema-less design, side-to-side growth, and adaptable data shaping making them a better fit for handling messy and semi-messy data on a big scale. This paper looks at the edge NoSQL has over SQL systems by checking out key traits like how flexible the data model is how well it works under high output how easy it is to grow sideways, and how well it fits with cloud-native setups. Using a careful review of NoSQL teaching and use, we boil down real-world findings and suggest ways to pick the right database tech based on what the app needs. Our talk ends with a plan to help pros and teachers get when and why to use NoSQL fixes instead of, or along with classic SQL databases. Modern data intensive workloads driven by real time analytics, large scale user interactions, IoT streams, and unstructured content. It demands storage system capable of delivering high throughput, scalability and flexible data models. Traditional SQL databases continue to offer strong consistency, ACID guarantees and structured schema support, making them ideal for transactional applications and environments requiring strict data integrating. However, as data volume, variety and velocity increase, NOSQL databases have emerged as powerful alternative, providing horizontal scalability, schema-less design and optimized performance for distributed and semi-structured data processing.
Authors - Anshuman Prajapati, Madhav Desai, Priyanka Patel Abstract - Analysis of facial skin conditions is essential for both dermatological and cosmetic evaluation; however, inter-class similarity and localized texture variations make multi-label classification of characteristics like wrinkles, dark circles, enlarged pores, hyperpigmentation, pimples, and fine lines difficult. The effectiveness of transfer learning for this task is examined in this paper, and an attention-enhanced framework based on EfficientNet-B0 is proposed. In order to highlight the importance of pre-trained feature representations, we first assess a bespoke convolutional neural network (CNN) as a baseline. Using the Convolu tional Block Attention Module (CBAM), which combines channel and spatial attention processes to enhance discriminative feature localization while maintain ing computational efficiency, we build upon this by using EfficientNet-B0 as the transfer learning backbone. According to experimental data, our CBAM augmented EfficientNet achieves better class-balanced performance in macro-F1 score than both the baseline EfficientNet and the bespoke CNN. Consistent in creases are confirmed by per-class analysis and confusion matrices, even for dif ficult settings. Additionally, Grad-CAM visualizations show that by concentrat ing activation on pertinent facial regions, the attention mechanism improves in terpretability. These results imply that a promising avenue for multi-label derma tological image analysis is attention-guided transfer learning.
Professor, School of Computing and Electrical Engineering and Chairperson of the Centre for Human Computer Interaction, Indian Institute of Technology (IIT) , India
Authors - Fredy Gavilanes-Sagnay, Edison Loza-Aguirre, Luis Castillo-Salinas, Narcisa de Jesus Salazar Alvarez Abstract - Ayurveda, India's ancient system of medicine, is full of inter-connected knowledge about diseases, their symptoms, herb and formulation (compounds). However, texts such as Charaka Samhita are mostly unstructured and cannot be readily analysed computationally. This work presents AyurKOSH which is a machine-readable, high-quality Ayurvedic dataset that is designed as a Knowledge Graph (KG) in order to support Artificial Intelligence driven research. The dataset is represented as subject–predicate–object triplets, which enables semantic interoperability, graph traversal, and multi-hop inferencing across entities. The dataset is designed by following schema-driven ontology which standardizes relationships between various nodes such as diseases, symptoms, pharmacological attributes, and compound formulations. DB Schema ensures consistency and computational tractability. AyurKOSH has the structured data of diseases and related symptoms, drug preparations, herbs and the detailed pharmacological properties are Rasa, Guna, Virya, Vipaka, Karma. The graph structure shows real-world biomedical network characteristics such as high sparsity and low average degree, which makes it suitable for embedding-based learning, graph neural networks, and explainable AI frameworks. Moreover, there is botanical metadata and herb-substitution relationships added for the prediction of synergy and repurposing of drugs. The dataset facilitates applications in biomedical NLP, and automated reasoning systems and clinical decision assistance, and pedagogy in integrative medicine. AyurKOSH became available for academic and non-commercial research under CC BY-NC-SA 4.0 license.
Authors - W M I T Warnasooriya, T D Jayadeera, A M G S Adhikari, M A F Zumra, A J Vidanaralage, M Samaraweera Abstract - The integration of large language models (LLMs) into primary educa tion remains limited in low resource, diglossic languages like Sinhala. General purpose models often produce grammatically inconsistent or cognitively over whelming output for young learners. This paper introduces a grade-adaptive, con straint-driven framework for automated Sinhala story and quiz generation target ing Grades 1-5. Building upon an 8-billion-parameter Sinhala-adapted LLaMA 3 model, we apply Quantized Low-Rank Adaptation (QLoRA) using a curated multi-task educational dataset. The system enforces tier-specific linguistic con straints separating conversational Sinhala for lower grades from formal written Sinhala for upper grades while embedding strict structural rules such as con trolled sentence counts (5-6 vs. 7-8) and validated multiple-choice formats (3 vs. 4 options). Evaluation on 100 structured prompts demonstrated substantial im provements over a zero-shot baseline: structural compliance increased from 64% to 93%, and hallucination-related failures decreased from 31% to 8%. Further more, evaluation against 50 unseen real-world classroom prompts yielded a 0.0% crash rate and 95% register adherence, confirming robust qualitative perfor mance. Results demonstrate that diglossia-aware dataset engineering and con straint-aware fine-tuning enable reliable, pedagogically aligned deployment of LLMs in low-resource primary learning environments.
Authors - S. M. Mizanoor Rahman Abstract - Removable USB storage devices are widely used in day-to day computing, but they also introduce risks such as unauthorized data transfer and misuse of external media. Understanding how these devices are used on a system is important during forensic investigations, espe cially when analyzing potential data leakage incidents. On Windows sys tems, traces of USB activity are not stored in a single location. Instead, they are distributed across registry entries, system logs, and file system records. Examining these sources individually often makes it difficult to form a clear picture of events. This paper introduces a forensic frame work that brings together USB-related artifacts from multiple system components and analyzes them in a unified manner. The method gath ers data from sources such as registry entries, Plug-and-Play logs, and f ile system structures, and then aligns them based on their timestamps. A Python-based implementation is used to automate this process and to relate device connection events with file operations. Experiments con ducted on a Windows setup show that the framework can identify device usage and reconstruct the sequence of related activities with clarity. By combining evidence into a single timeline, the approach helps simplify analysis and supports consistent interpretation of results.
Authors - Shamita Jagarlamudi, Soormayee Joshi, Aman Aditya, Anushka Gangwar, Pratvina Talele Abstract - Federated Learning (FL) is a privacy-preserving, distributed learning framework where models are trained locally on client devices, and only the trained parameters are shared with a central server. Nevertheless, FL encounters substantial obstacles in real-world applications due to data heterogeneity, such as non-IID distributions leading to local inconsistencies and client drift thereby diminishing global model efficacy. To tackle these challenges, we propose a Federated Prox Drift Correction (FedPDC), an effective and practical method designed to mitigate client drift and local overfitting through the use of drift correction and proximal terms. Comprehensive experiments conducted on public datasets demonstrate that FedPDC performance is superior compared to state-of-the-art methods.
Authors - U. A. Walke, G. A. Kulkarni, Pranav Mungankar, Om Kale, Tejas Kadam Abstract - Digitizing damaged historical texts requires multiple processing steps that can propagate semantic noise through the workflow. Efforts have been made to improve the recognition, correction, and normalization steps of the pipeline, but few studies have quantified model-level effects in isolation under a controlled architecture setup. Here we present Probanza, an extensible staged evaluation framework that decouples preprocessing normalization from semantic modeling to facilitate clean comparisons between LLMs. We perform super-resolution, contextual correction, and historical normalization before English translation. We selected 30 total degraded pages from the Florentine Codex and digitized them with three LLM configurations: GPT-5, GPT-4o, and Gemini 3 Flash. Co sine similarity was computed between model predictions and archival baseline translations to measure semantic accuracy. A one-way repeated-measures ANOVA was done to examined differences across configurations. The analysis revealed a significant main effect of LLM configuration. Gemini 3 Flash pro duced the highest mean similarity (M = .881, SD = .075), while GPT-5 (M = .783, SD = .147) and GPT-4o (M = .769, SD = .135) which were not significantly dif ferent from one another. Our results demonstrate that significant differences exist between LLM configurations for the task of digitizing damaged historical texts when preprocessing is held constant. Probanza allows an isolating model-level effects comparison in LLM-based historical digitization workflows.
Authors - Kushall Pal Singh, Vijay Kumar, Monu Verma, Dinesh Kumar Tyagi, Santosh Kumar Vipparthi Abstract - Hybrid enterprise environments spanning on-premises systems and public cloud services increase exposure to credential abuse, lateral movement, and misconfiguration-driven attack paths, motivating continuous verification and policy enforcement beyond perimeter assumptions. This paper presents an Azure-native, AI-enhanced Zero Trust framework that integrates identity-first enforcement (Microsoft Entra Conditional Access, Continuous Access Evaluation, and Privileged Identity Management), telemetry centralization (Microsoft Sentinel with UEBA), and an Azure Machine Learning classifier that outputs a probability-derived 0–100 trust score. Because identity policy engines consume bounded native signals, the framework binds external scoring to enforcement using SOAR automation that updates policy-targeted identity group membership via Microsoft Graph. A controlled A/B evaluation compares a static baseline (non-adaptive enforcement) with an adaptive mode (ML-in-the-loop scoring and automated score-to-policy binding) using MITRE ATT&CK-aligned scenarios: impossible travel sign-in, privilege escalation attempts via privileged activation workflows, and lateral movement via remote access/filesharing pathways. Quantitative outcomes are reported using median (P50) and tail (P95) time-to-detect, decision latency, and false-positive rate. To technically validate the adaptive control loop, the paper also reports an instrumented latency decomposition (trigger delay, playbook runtime, ML scoring call duration, and score-to-policy execution time) to show which components dominate end-to-end delay.
Authors - Karuppasamy E, Krithika V, Harish P, Pravinbaalaa V, Satheeskumar Abstract - The large online data consist of duplication and plagiarized contents. Due to Artificial Intelligence, data generation has become very easy. But, it may also lack an ethical data generation process. Hence, there is a need of validating plagiarism free data for authentic usage. In this research work, authors focus on word-level plagiarism detection methods in Natural Language Processing. The proposed method uses a comparative analysis of cosine similarity, Euclidean distance and Manhattan distance methods for word-level plagiarism detection for different n-gram sizes. The inculcation of n-gram size improved the accuracy compared to unigram based methods. The experimental results of the cosine similarity method outperform Euclidean and Manhattan distance methods by achieving an average accuracy range of 88 % to 92 % and 75 % to 80 % for direct plagiarism and lightly paraphrased text respectively. The future work is to identify reused images and visual contents.
Authors - Nagaraj.M, V. Balamurugan, Matam Veera Chandra Kundan, M.J. Mathesh, V. Vijairam Abstract - Academic credential fraud is a global issue that undermines institutional trust. Although blockchain solutions provide immutability, they are generally reactive, securing documents only after potential errors or fraud have already occurred. This paper proposes a proactive approach to prevent inconsistencies before degree issuance. We introduce a hybrid model that integrates Digital Twins as a preventive validation layer and Multichain as an immutable ledger. The Digital Twin operates as a virtual sensor during the degree creation process at Universidad El Bosque, simulating and validating academic, financial, and national exam data (Saber Pro) in real time; if inconsistencies are detected, “red flags” are triggered prior to issuance. Once validated, the degree’s hash is anchored to a Multichain network. A functional prototype developed in Python achieved a 100% detection rate of inconsistent records during testing. The pro-posed model transforms the academic certification process into a proactive, se-cure, and trustworthy ecosystem by combining preventive validation with block-chain immutability.
Authors - S. M. Mizanoor Rahman Abstract - Driver fatigue is a major cause of accidents on the road that generates major safety issues for drivers as well as passengers. Real-time detection of driver fatigue can help avert accidents by warning the driver about impending lapses in his attention. This paper proposes a real-time automated system for the detection of driver fatigue through observation of eye blink and yawn, which are major notifications for fatigue. The system uses a combination of deep learning models that give high accuracy levels in detecting a drowsy driver. Eye blink is detected by using a state-of-the-art object detection model that is trained to locate the open and closed states of the eyes accurately using correct coordinate mapping methods, giving an accuracy level of 96 percent. Yawning is detected using a combination of CNN and LSTM models that allow it to analyze spatial information as well as temporal information obtained through videos, giving an accuracy level of 98 percent. Both of these modules work on real-time camera inputs, which makes it possible for a constant monitoring of the alertness of the driver. Whenever the driver is found dozing off due to either excessive blinking or yawning, the system releases a real time auditory warning alert to caution the driver. The result of the experiments has justified that the capability of the combined system works well while operating reliability with low-latency responses in real time. This study has shown that the hybrid detection strategy with spatial and temporal analysis is quite effective in detecting a dozy driver on the road and developing such a system that can be helpful in increasing the safety of the road.
Authors - Kaniska D, Shreya J V, Srinidhi K, Sudhakar K S, Bagavathi Sivakumar P, Krishna Priya G Abstract - Language modeling of clinical text in healthcare pens down a necessitated context along with a high level of security measure for sensitive patient information. A few large language models have shown very good clinically related performance in documentation, summarization, and these models have been rolled out freely. Therefore, these models generate hallucinated or non verifiable outputs. Retrieval augmented approaches thus fix the problem by limiting the answer to the evidences retrieved. However, majority of the existing systems rely on the textual records only and the integration of the diagnostic imaging is not done systematically. In this paper, we put forward a retrieval grounded multimodal clinical modeling framework that unifies structured clinical text with imaging-derived contextual features. A patient specific vector indexing approach is used for isolated retrieval and a modality aware visual analytics approach turn imaging outputs into structured signals, hence language generation. The entire framework is performed fully offline, thus supporting privacy preserving deployment in resource-limited clinical settings. Experimental results show steady multimodal integration as well as the semantic consistency alignment between the retrieved evidence and the generated output.
Authors - Pratham Vasa, Amishi Desai, Chahel Gupta, Avani Bhuva, Mohini Reddy Abstract - Content Delivery Networks (CDNs) play an essential role in enhancing the content delivery speed by caching frequently requested data in edge servers distributed across geographical regions. Traditional CDNs utilize rule-based pol icy and machine learning approaches for optimizing the cache. Machine learning is performed centrally, and the cache optimization is performed using the traffic logs collected by the central server. Although the use of central learning ap proaches is beneficial, it poses certain limitations, including data privacy and high communication cost. The central learning approach aggregates raw data, which poses data privacy issues. This paper proposes an architecture for secure federated learning, which is utilized for cache hit prediction in CDNs. The proposed archi tecture is evaluated using a synthetic dataset containing 1,30,548 records, and the features include temporal and network features. The proposed architecture is com pared with the traditional central learning approach, and the results reveal that the secure federated learning model achieves an accuracy of 70.15%, which is com parable to the central learning approach. The proposed architecture is found to reduce data privacy exposure by 30%.
Authors - Syed Shanika Zaida, Kamineni Leela Tapaswi, Kilari Dhana Malikarjuna Rao, Adarapu Sandeep, Amar Jukuntla Abstract - Removable USB storage devices are widely used in day-to day computing, but they also introduce risks such as unauthorized data transfer and misuse of external media. Understanding how these devices are used on a system is important during forensic investigations, espe cially when analyzing potential data leakage incidents. On Windows sys tems, traces of USB activity are not stored in a single location. Instead, they are distributed across registry entries, system logs, and file system records. Examining these sources individually often makes it difficult to form a clear picture of events. This paper introduces a forensic frame work that brings together USB-related artifacts from multiple system components and analyzes them in a unified manner. The method gath ers data from sources such as registry entries, Plug-and-Play logs, and f ile system structures, and then aligns them based on their timestamps. A Python-based implementation is used to automate this process and to relate device connection events with file operations. Experiments con ducted on a Windows setup show that the framework can identify device usage and reconstruct the sequence of related activities with clarity. By combining evidence into a single timeline, the approach helps simplify analysis and supports consistent interpretation of results.
Authors - Sanchi Mahajan, Nandini Jain, Evangelin G, Jansi K R, Shivam Shivam Abstract - The issue of efficient work planning in heterogeneous multi-cloud in frastructures is still an open issue due to scalability limitations, data privacy, and latency sensitivity. The conventional centralized scheduling approach requires data aggregation, which is associated with critical privacy challenges and com munication cost. The proposed work aims to design a privacy-preserving feder ated multi-cloud task scheduling framework for smart mobility applications to overcome the limitations of conventional approaches. The proposed framework employs a decentralized scheduler for separate cloud regions. The proposed framework employs a novel task abstraction approach to transform real-time traffic data into task-scheduling forms. The proposed framework eliminates the requirement to communicate raw traffic data by employing a federated learning based aggregation approach. The proposed framework employs a federated ag gregation approach, which is associated with scalability, routing, and multi cloud coordination while ensuring data locality. The proposed framework is evaluated by conducting experiments on Random, Rule-Based, Local-ML ap proaches using a Smart Mobility dataset. As can be observed from the results, considerable reductions in communication overhead and privacy leakage are achieved with the preservation of competitive execution latency and SLA com pliance. The strategy has been observed to scale well with an increase in cloud regions, as the communication scalability results indicate. It is the ability to sup port federated, scalable, and privacy-aware job scheduling for smart traffic sys tems without central data sharing that makes this work interesting.
Authors - Thota Neha, Napa. Sai Gopi, R. Aarthi Abstract - The increasing realism of deepfake media has raised signifi cant concerns regarding the authenticity of digital content. Most existing detection methods rely on audio–visual fusion, which often introduces ad ditional complexity and may degrade performance when one modality is unavailable or unreliable. This work presents a dual-stream deep learning framework that pro cesses audio and video independently, avoiding explicit fusion. The au dio stream employs a CNN–BiLSTM model on log-Mel spectrograms to capture temporal and spectral artifacts, while the video stream uses EfficientNet-B0 with BiLSTM to model spatial inconsistencies and tem poral variations in facial sequences. Experiments conducted on multiple benchmark datasets, including ASVspoof 2019, WaveFake, LJSpeech, FaceForensics++, and Celeb-DF (v2), demon strate that the proposed approach achieves competitive detection perfor mance. In addition, the framework maintains robustness under missing modality conditions and offers improved interpretability compared to fusion-based methods. These results indicate that independent modality-specific learning pro vides a practical and effective alternative for deepfake detection in real world scenarios.
Authors - Ankit Podder, Piyush Ranjan Das, Soham Acharya, Ayushmaan Singh, Soumitra Sasmal, Partho Mallick Abstract - Static perimeter-based security architectures are now inef fective in the current threat scenario. The ability of attackers to obtain legitimate credentials and the presence of zero-day exploits often cause real-time breaches of the network perimeter. An area of concern is the real-time monitoring of these systems. In the current scenario, security monitoring is performed in a segregated manner, where network analysts analyze time-stamped network logs and identity analysts analyze time stamped login attempts, without cross-referencing in real time between these two domains. The proposed solution is a fusion platform capable of ingestion of raw network transport data and real-time human element monitoring data. This is achieved through the integration of two dif ferent threat detection mechanisms using a FastAPI backend. The first threat detection system will be the Network Threat Detector (NTD), im plemented in Python and using the Scapy library to parse deep packet data in real time for flow analysis. The second threat detection system will be a JavaScript tracker designed for monitoring digital behavioral indicators and calculating real-time metrics such as mouse velocities, ac celerations, kinematic jerk, and typing speeds. Real-time monitoring will be achieved through a machine learning framework with three different modules for inferring user intent using the Random Forest algorithm, detecting anomalous statistical patterns using the Isolation Forest algo rithm, and detecting malicious plaintext syntax using Logistic Regres sion. The system has been tested in a lab scenario and has been able to classify user session states into four different states: Engaged, Con fused, Frustrated and Suspicious with accuracy exceeding 95%. These digital behavioral indicators will be fed into the Network Transport Data (NTD), allowing the computation of a real-time risk score.
Authors - Lavu Uha Saranya, T.V.S.S. Reddy, I.V.M.K. Sarma, Dipesh Kumar Kushwaha, T.N.V.D. Sai Krishna Abstract - Digital Forensic investigations have typically focused on the identification of private browsing at the application layer using artifacts from memory and disk, as well as the fact that modern browsers rely extensively on the operating system for fundamental capabilities such as rendering, input processing, and networking. This paper extends the forensic scope by demonstrating that session Data related to private Sessions remain in shared Subsystems of the OS in Volatile Memory. In particular, This paper examines the three primary components of the linux desktop environment: the display compositor (GNOME shell); the Input Pipeline (IBus Daemon); and the network resolver (systemdresolved). utilizing physical memory acquisitions via LiME on an ubuntu 25.04 System, This paper monitored the migration of high entropy inputs across these subsystems. The results of this research indicate that critical session data including: Window metadata associated with wayland sessions; Plaintext keystroke data received through D-Bus; and fallback queries made via DNS-over-HTTPS were found to remain in OS Managed Memory for extended periods of time after the conclusion of the private browsing session. The author provides a reproducible framework for analysis of memory associated with the OS level and demonstrates that browser based privacy controls are structurally insufficient to fully sanitize volatile memory.
Authors - Venkata Saikumar Thalupuru, Shubham Kumar, Santhoshini Pranathi Singaraju, Vishal Gupta Abstract - As the use of online banking and digital payments grew faster, that has also left the institution at risk of becoming the victims of credit card fraud, which has become a major challenge for traditional banks and other financial institutions. This huge discrepancy in transaction datasets is one of the greatest challenges in fraud analytics wherein only the rare fraudulent activity takes up a tiny fraction of the total transaction. Traditional machine learning models are often quite accurate but not great at detecting occasional frauds. To overcome this limitation, this study proposes a cost-aware hybrid framework comprising Attention-based Long Short-Term Memory (Attention-LSTM) and ensemble-based machine learning. This method will take care to preprocess the data, maintain balance among classes using SMOTE, select features based on mutual information by leveraging a soft-voting ensemble of the Logistic Regression, Random Forest, and the XGBoost models. Cost-aware learning is coupled with decision threshold enhancement to minimize false negative predictions. Additionally, SHAP-based explainability is added on top for enhanced transparency and interpretability of the model. The experimental results show 99.3% accuracy, 0.905 precision, 0.892 recall, 0.898 F1-score, and 0.98 ROC-AUC, indicating that our new framework is effective in detecting genuine financial fraud.
Authors - Ismail Suleiman, Dinesh Reddy Vemula, Abhaya Kumar Pradhan Abstract - This paper presents the evaluation and demonstration phases of a Design Science Research Methodology (DSRM) study that produced the Organisational Security Culture Framework (OSCF) for Namibian Public Enterprises. An empirical needs assessment established a three-tier security culture maturity deficit: a 40% policy awareness gap; a widespread misconception among non-IT staff that cybersecurity is solely an IT responsibility; and a training gap in which 25% of staff had received no formal security training in the preceding year. The OSCF comprises five interrelated components: Risk Assessment, Security Policy and Enforcement, Security Compliance, Training and Awareness, and Ethical Conduct. Demonstration was executed across four staged phases: baseline assessment, component testing, pilot integration, and full-scale deployment. Evaluation employed a dual approach: expert panel review against eight criteria and Key Performance Indicator (KPI) measurement across five strategic objectives. Results confirm that the OSCF closed the 40% policy awareness gap, achieving 95% staff awareness post-implementation, and significantly reduced phishing susceptibility. Seven evidence based refinements evolved the OSCF from a static policy model into a continuous security culture maturity loop. The framework’s modular, tiered architecture supports long-term sustainability of behavioural change and scalable deployment across organisations of varying cybersecurity maturity, including federated multi-institutional environments.
Authors - Thomas K P, Sherly K K Abstract - Permanent Magnet Synchronous Motors (PMSMs) are commonly utilized in electric vehicle (EV) traction systems because of its high efficiency, power density, and reliability. Conventional field-oriented control (FOC) schemes require accurate rotor position and speed information, typically obtained from mechanical sensors, which increase cost and reduce system reliability. Sensor less control techniques based on observer theory have therefore gained significant attention. Among them, sliding mode observers (SMOs) offer strong robustness against parameter variations and external disturbances but suffer from chattering and noise sensitivity. This paper presents an advanced sensor less FOC strategy for PMSM drives using a super-twisting SMO (ST-SMO) for rotor position sensing and estimation of speed. The proposed approach employs a ST-SMO algorithm to achieve the convergence in finite-time while significantly reducing chattering effects. The observer is integrated into a standard FOC framework and evaluated under EV-relevant operating conditions, including low-speed operation and load transients. Comparative performance discussion demonstrates the suitability and the effectiveness of the proposed method for high-efficiency EV traction.
Authors - N. V. Naik, Raga Madhuri Dhulipudi, Marisetti Sandhya, Jadda Anjan Kumar Abstract - Distributed systems rely on data replication to ensure availability, fault tolerance, and scalability across multiple nodes in modern cloud environments. Replication enables systems to maintain continuity even when individual nodes fail or experience network disruptions. However, replication often introduces synchronization delays between primary and replica nodes, known as replication delay. These delays can cause temporary data inconsistency, stale reads, and increased response latency, degrading application performance and user experience. As infrastructures scale to larger clusters, communication overhead, network latency, and workload variability further amplify replication delays, making efficient synchronization increasingly challenging. Traditional replication mechanisms typically rely on static synchronization intervals or sequential update propagation strategies. These approaches fail to adapt to dynamic network conditions and fluctuating workloads, resulting in inefficient data propagation and delayed consistency across nodes. In large scale systems, such limitations may cause bottlenecks, reduced reliability, and inconsistent states during high workload periods or network congestion. Addressing replication delay is critical for maintaining reliability and consistency in distributed environments. Recent research emphasizes intelligent synchronization mechanisms capable of adapting to changing conditions. Adaptive synchronization strategies that monitor network latency, workload intensity, and node communication patterns offer improvements in replication efficiency. By enabling replication decisions that respond dynamically to system behavior, such approaches reduce synchronization delays and improve data consistency across clusters. Enhanced replication efficiency ultimately strengthens reliability, scalability, and operational performance in modern distributed computing platforms operating under variable workload conditions.
Authors - Shaik Shafi, C Santhoshi Abstract - In the recent past, vehicle theft in India has increasing nearly 2.5 times, with more than 2 lakh vehicles stolen annually. The Delhi NCR region alone accounts for over 30% of reported cases, and in Delhi, a vehicle is reportedly stolen approximately every 14 minutes. These alarming trends highlight the ur-gent need for stronger and smarter vehicle security mechanisms. Traditionally, vehicle anti-theft technologies have relied largely on non-biometric approaches such as GPS–GSM tracking modules. Thus, biometric authentication is an emerging security approach that limits vehicle access to authorized individuals by verifying unique biological traits such as fingerprints, facial features, iris pat-terns, or voice. Although this technology significantly strengthens vehicle security, its widespread deployment still faces certain technical and social constraints. Thus in this paper, an IoT enabled biometric ignition system with security alerts is proposed. The proposed model makes use of an ESP32 micro controller and fingerprint sensor to replace traditional keys. The system operates in two stages: first secure door access and secondly engine ignition authorization. Any unauthorized attempts trigger real-time alerts with GPS location via IoT protocols like MQTT or HTTP. Further, cloud integration enables remote monitoring, data storage, and scalability, making suitable for modern intelligent transport systems. In the same way, the fingerprint-based vehicle starter grants the privilege of starting the vehicle only to the registered users, thus deterring theft and ensuring safety. Over all, biometric vehicle ignition is a dependable, economical, and hassle-free solution to access control as well as theft prevention.
Authors - A.Sree Rama Chandra Murthy, T.Gamya Sri, B.Harshitha, G.Vincent Paul Abstract - Accurate forecasting of drug demand is one of the challenging areas in the healthcare service to reduce waste as well as shortages. Some recent studies focused only on predicting drug use demand for regions and hospitals, missing an overall way to combine these forecasts. In this study, a multilevel machine learning framework is presented that merges regional tender demand predictions with monthly and seasonal order forecasting in hospitals and pharmacies. With historical drug usage, the system captures time-based changes, seasonal demands, and also location specific behaviors . Models for regional tenders predict yearly procurement, but models at hospitals and pharmacies try to tell the need of each month, allowing better resource distribution. The rigorous experimental process showed better estimates and forecasting with less error than just making a single-level prediction. This framework helps to make better purchasing decisions and ensures a stable drug supply across healthcare systems. Health departments, hospital chains, and pharmacy groups can benefit from using a model.
Authors - Seamus Lyons Abstract - Methane (CH4) emission from rice paddies is a significant source of greenhouse gas emissions from agriculture. Currently, most models for methane prediction from rice paddies depend on collecting field data and sending it to a server. In this new paradigm, several privacy concerns arise, model scalability is restricted, and a large number of data points are exposed to the attacker. This paper addresses all privacy con cerns by providing an edge-based solution for modeling methane emis sions from rice paddies that leverages data from edge sensors at respec tive locations, while keeping individual sensor data private. The method employs different machine learning (ML) algorithms, including Linear Regression, Random Forest, XGBoost, and a Feedforward Neural Net work (FNN), implemented using TensorFlow Federated (TFF) in both centralized and federated learning (FL) frameworks. The FL-based FNN achieved an R2 score of 0.91, which was superior to both centralized classical and centralized FL models, especially for highly non-IID client side data distributions in sensor datasets. In summary, this paper extends the current literature on modeling methane emissions from rice paddies and provides a comprehensive evaluation of our proposed FL system ar chitecture, an in-depth discussion of the communication resources re quired for FL implementation, and an examination of the effects of abla tion studies on clients’ data heterogeneity. Therefore, the proposed FL approach is efficient and scalable, enabling safe, privacy-preserving modeling of methane emissions from rice paddies to effectively imple ment Climate Smart Agriculture (CSA) and mitigate global warming while supporting sustainable rice cultivation.
Authors - Gia Nghi Thoi, My An Tran, Tram Thi Tuyet Le, Nhat Van Hoang Nguyen, Long Hong Buu Nguyen, Dien Dinh Abstract - Medical diagnosis using Small Language Models (SLMs) of ten suffers from hallucinations and knowledge inconsistency. While re inforcement learning (RL) from knowledge graph feedback offers a po tential solution, pure reinforcement learning strategies often encounter challenges related to sample inefficiency and poor exploration. To address this, a hybrid training pipeline that combines supervised alignment with structural reinforcement is proposed. The method applies knowledge guided supervised fine-tuning (SFT) with hard negatives to refine deci sion boundaries and employs a bipartite-specific reward model to capture interactions between symptoms and diseases. Experiments on multiple medical datasets, including DXY, GMD, and MED-D, demonstrate that this hybrid approach outperforms pure RL methods. By incorporating knowledge graph (KG) information as a structural regularizer, the model achieves improved accuracy, stronger cross-dataset generalization, and reduced overfitting while maintaining strict adherence to diagnostic out put constraints
Authors - Mustafa Icel, Ochilbek Rakhmanov, Ergul Gunerhan, Muhammad Qasim Abstract - Artificial intelligence driven adaptive learning systems progressively operate as knowledge management platforms by collecting, refining, and using learner knowledge to personalize instruction. However, empirical evidence demonstrating how managed knowledge translates into measurable student achievement remains as a question to answer. This study examines the effective ness of AI driven adaptive learning as a knowledge management system in a high school setting. Using de-identified archival data from 182 students across three academic years, the study explores relationships among AI-managed knowledge mastery, engagement, course performance, and standardized assessment out comes. Learning analytics techniques, including descriptive statistics and Pear son correlation analysis, were employed to examine knowledge–performance re lationships. Predictive modeling using multivariable linear regression and Ran dom Forest classification was performed to assess the extent to which knowledge management indicators predict end-of-course achievement and performance lev els. Results indicate that final knowledge mastery is moderately associated with standardized assessment outcomes and is a stronger predictor of achievement than time-on-task alone. While predictive models demonstrate modest accuracy, findings suggest that AI driven knowledge management supports student achievement when integrated within instructional contexts.
Authors - Akshay Kumar, Reena Satpute, Kumar Gaurav, Sanjit Kumar, Edidiong Akpabio, Sudhir Agarmore Abstract - Recent literature has posed LLMs as nonlinear dynamical systems. LLM safety, in these modern LLMs is about the systematic and critical monitoring of logit based oscillations, hidden state rotations and entropy fluctuations. Many of these important factors are spectral proxies for the generation of imaginary eigenvalues. These imaginary eigenvalues are, in a way, determinants of the latent oscillation energy. Though the system in its original state space is inherently nonlinear, through the Koopman operator, we can linearize the evolution in the lifted space of observables. We design a spectral jailbreak detector that has a Sparsely regularized koopman autoencoder as its backbone. We obtain the koopman operator through this SR-KAE, and also obtain the imaginary component of the eigenvalues of that spectral operator, A new risk score metric is proposed that is used to classify prompts as either jailbreak or safe. This becomes a physics-style stability classifier on prompts. We present several test cases, while we discuss the strengths and limitations of this new system.
Authors - Kamala L, Mohan K G Abstract - This paper presents the error performance of digital commu nication systems operating over α-Beaulieu-Xie (α-BX) and its extreme variant, the α-BXe fading channel. A generalized noise model, additive white generalized Gaussian noise (AWGGN), is adopted to account for various practical scenarios including impulsive and Laplacian environ ments. We derive closed-form average bit error rate (ABER) expressions utilizing the Fox-H function. The mathematical expressions derived are validated through numerical integration for binary phase shift keying (BPSK) and binary frequency shift keying (BFSK) modulation schemes. Our results demonstrate the degradation caused by Laplacian noise and characterize the irreducible error floors inherent in the α-BXe model, providing a robust tool for system designers in complex fading environ ments.
Authors - Akshay Kumar, Deepa Thilak Abstract - Smart city apps are growing quickly, which means that there are more real-time, latency-sensitive, and privacy-critical workloads that are hard for traditional single-cloud computing models to handle. In particular, smart mobility and traffic management systems generate large volumes of geographically distributed data that require efficient processing with minimal delay and high reliability. This project proposes a multi-cloud task scheduling framework that protects privacy and uses federated learning to solve these problems. The suggested system turns real-time smart mobility traffic data into abstract scheduling tasks and sends them to different cloud regions using a lightweight, decision-free task broker. Each cloud region has its own local federated scheduler that uses only data that is available in that region to schedule tasks based on latency and congestion. Federated learning is used to work together to improve scheduling policies by safely combining local model updates without sharing raw data. This keeps data private and meets data sovereignty requirements. The system enables improved scalability, reduced response time, fault tolerance, and avoidance of vendor lock-in compared to centralized scheduling approaches. Using a smart mobility dataset to test the proposed method shows that it works well for scheduling tasks quickly and with privacy in mind in multi-cloud settings.
Authors - Thanh-Phuong Ngo, Van-Thanh Huynh, Thai-Son Nguyen Abstract - This paper presents a novel Reversible Data Hiding (RDH) method for dual images. First, secret data is converted into a binary sequence of equal length and then divided into shorter segments to control the amount of data embedded into each pixel. The embedding process uses two copies of the original image to distribute the data, reducing the impact on each image while maintaining overall image quality. During recovery, the original image is restored by averaging the pixel values at corresponding locations in the two stego images, while the embedded data is recovered through a reverse process. Experimental results on grayscale images demonstrate that the method maintains good image quality, achieving a high Peak Signal-to-Noise Ratio (PSNR) across different embedding levels while ensuring accurate recovery of both the secret data and the original image.
Authors - Yasir Abdullah R, Lakshmana Kumar T, Vijaykumar M, Thirunavukkarasu C, Saravanagukhan P, Hariharasuthan M Abstract - In the recent past, vehicle theft in India has increasing nearly 2.5 times, with more than 2 lakh vehicles stolen annually. The Delhi NCR region alone accounts for over 30% of reported cases, and in Delhi, a vehicle is reportedly stolen approximately every 14 minutes. These alarming trends highlight the ur-gent need for stronger and smarter vehicle security mechanisms. Traditionally, vehicle anti-theft technologies have relied largely on non-biometric approaches such as GPS–GSM tracking modules. Thus, biometric authentication is an emerging security approach that limits vehicle access to authorized individuals by verifying unique biological traits such as fingerprints, facial features, iris pat-terns, or voice. Although this technology significantly strengthens vehicle security, its widespread deployment still faces certain technical and social constraints. Thus in this paper, an IoT enabled biometric ignition system with security alerts is proposed. The proposed model makes use of an ESP32 micro controller and fingerprint sensor to replace traditional keys. The system operates in two stages: first secure door access and secondly engine ignition authorization. Any unauthorized attempts trigger real-time alerts with GPS location via IoT protocols like MQTT or HTTP. Further, cloud integration enables remote monitoring, data storage, and scalability, making suitable for modern intelligent transport systems. In the same way, the fingerprint-based vehicle starter grants the privilege of starting the vehicle only to the registered users, thus deterring theft and ensuring safety. Over all, biometric vehicle ignition is a dependable, economical, and hassle-free solution to access control as well as theft prevention.
Authors - Amol Dhumane, Jitendra Chavan, Arijit Dutta, Priyanka Paygude, Aditi Sharma, Datta Takale, Yashwant Dongre Abstract - Depression is a psychiatric condition that is largely common all over the world and greatly influences the emotional stability, cognitive performance and behavior functioning. Computational techniques that can detect the condition early can help to prevent psychological dangers in the long term and ensure timely treatment of the disease. This paper refers to a complete machine learning architecture of automated depression recognition of textual information based on hybrid feature engineering and ensemble learning approaches. The suggested methodology is a combination of text preprocessing, Term Frequency / Inverse Document Frequency (TF -IDF) vectorization, unigram and bigram features, hand-crafted statistics and sentiment-based indicators, and several classification models such as Logistic Regression, Random Forest, XGBoost, and LightGBM. The issue of class imbalance is tackled using Synthetic Minority Over-sampling Technique (SMOTE) and compared. The original dataset of 7,489 samples was cleaned and narrowed down to 7,486 valid cases. Accuracy, Precision, Recall, F1 score, ROC-AUC and 5-fold cross-validation were used to evaluate the performance. There are experimental results to show that ensemble models are more effective compared to traditional linear classifiers. XGBoost performed best in the overall performance of 94.59% accuracy and F1-score of 0.8323. The hybrid-based feature fusion technique has a considerable improvement on the classification performance and does not sacrifice the level of interpretability and computational efficiency, which is why the framework is applicable to scalable mental health analytics services.
Authors - Armie E. Pakzad, Nathanael Adrian T. Cua, Louie T. Que, Alvin Josh T. Valenciano, Jana Johannes Valenzuela, Abbasali Pakzad Abstract - Emotional Support Conversation (ESC) seeks to lessen users’ emotional dis tress through sympathetic communication. Current approaches concentrate on comprehending present emotional states and combining support techniques to generate responses. But they fail to take into account an important factor: emotional trajectories (how users’ feelings change over time). Two people expe riencing the same feeling may need essentially different answers depending on whether they are in a therapeutic window (gradually improving), a depressed spiral (continuous hopelessness), or a crisis escalation (rapidly worsening). We propose TRAGEDY (TRAjectory-Guided Emotional Dialogue System), a sys tem that explicitly models clinical patterns and emotional trajectories in order to direct response creation. We present: (1) a trajectory encoder that records the temporal dynamics of emotion and intensity sequences; (2) a clinical pat tern detector that recognizes five psychologically grounded patterns (normal progression, therapeutic window, resistance pattern, depressed spiral, and crisis escalation); and (3) pattern-aware generation that bases responses on trajectories found. Experiments on the ESConv benchmark show that TRAGEDY provides interpretable trajectory insights while outperforming robust baselines, across standard generation metrics. Our approach opens new avenues for trajectory aware conversational AI and emphasizes the significance of temporal dynamics in emotional support.
Authors - Akhil P, Mallikharjuna Rao K. Abstract - Cloud storage platforms support diverse multimedia and col laborative workloads across organizations, yet conventional methods ne glect user behavior’s role in shaping access patterns. Privacy regulations prohibit centralized aggregation of interaction traces, while standard fed erated learning algorithms like FedAvg fail under statistical heterogene ity from varied user roles. This paper introduces FedPAE (Federated Per sonalized AutoEncoder), an unsupervised framework for behavior-aware user profiling in federated settings. FedPAE employs a shared global encoder for common patterns and private local decoders for individual adaptation, augmented by an Adaptive Fine-Tuning (AF) mechanism to mitigate encoder drift and preserve global semantics, without sharing any raw user data with the server. Evaluated on the CMU CERT benchmark and anonymized cloud storage logs, FedPAE surpasses FedAvg, FedProx, and FedPer in anomaly detection accuracy across all thresholds (e.g., F1 gains of 5–13% points over FedAvg across all precision thresholds), con f irming that the approach holds across heterogeneous client populations.
Authors - Yarragunta Babu , Challa Yuva Prasanthi, Vadapalli Sparjan, Sanagapati Venkata Siva Naga Sai Jayanth Abstract - Distributed systems rely on data replication across multiple nodes to ensure high availability, fault tolerance, and scalability. While replication improves system reliability, it also introduces temporary inconsistencies between primary and replica nodes during data propagation. This phenomenon, commonly referred to as consistency drift, occurs when distributed nodes maintain slightly different states before synchronization is completed. As distributed infrastructures grow in scale and complexity, consistency drift becomes increasingly significant due to network latency, workload variability, and communication overhead between nodes. Traditional synchronization mechanisms typically rely on static replication intervals or fixed update propagation strategies that do not adapt effectively to dynamic system conditions. Such approaches may allow drift to accumulate before synchronization occurs, resulting in delayed consistency and inefficient resource utilization. Managing consistency drift therefore becomes a critical challenge in distributed computing environments where maintaining accurate and synchronized data states is essential. This research addresses the problem of consistency drift in distributed systems by examining the factors that contribute to state divergence among nodes and exploring mechanisms for dynamic drift management. The proposed framework focuses on monitoring system behavior, including workload intensity, network latency, and node communication patterns, to regulate synchronization behavior more effectively. By enabling adaptive synchronization strategies that respond to real time system conditions, the framework aims to reduce drift accumulation and improve overall data consistency across distributed clusters. Effective management of consistency drift ultimately enhances system reliability, operational stability, and performance in modern distributed computing platforms operating under dynamic workloads.
Authors - Olutayo V. A., Agbele K. K., Ogundimu O. E., Dudu M. T. Abstract - As online shopping has become increasingly popular, companies must utilize social media to develop and improve customer experience. This study examined customer interaction sentiment regarding online shopping through automated systems to classify comments on social media sites like Twitter, Facebook, and Instagram. This research study compared three machine learning and natural language processing (NLP) techniques: Bidirectional Gated Recurrent Units (GRUs), Random Forests, and Naïve Bayes. Customer reviews were classified as positive, negative, and neutral, as well as analyzed for time-related patterns. The classification framework was constructed by using sentiment analysis, feature extraction, and data preprocessing techniques. Furthermore, model training and performance assessment were executed through Naïve Bayes and Support Vector Machines. Of all the models studied, the Bidirectional GRU had the best performance with an accuracy of 88.08 %. The results of this study help companies understand customer preferences better, and thereby refine their products, services, and marketing techniques.
Authors - Akbar Kushanoor, Sanjay K. Sahay Abstract - Traditional tree classification methods are inefficient, requiring tremendous effort, time, and labor. To address this, the primary objective of this research was to develop and implement a machine learning model that utilizes 3D Light Detection and Ranging (LiDAR) data, acquired via an unmanned aerial vehicle (UAV), for the accurate classification of tree species in the Philippines. Then, the collected data was pre-processed in preparation for the next portions of the methodology. Once completed, the features used in preparation for machine learning were extracted for the creation and training of the model. Ground truth data, validated by two licensed foresters, were used to ensure species accuracy, focusing on the five most abundant tree species in the dataset. Several machine learning algorithms were evaluated, with the XGBoost model achieving the best performance, reaching an overall accuracy of 85.63%, a mean class accuracy of 84.98%, and a Kappa accuracy of 81.57%. All producers’ accuracy exceeded 70%, indicating robust model reliability. Additionally, a user interface was developed to visualize the LiDAR data, tree attributes, and classification results. The findings demonstrate that LiDAR data obtained from UAVs can effectively be used for tree species classification in the Philippines, supporting forest inventory initiatives and reforestation efforts. Future work may include expanding the dataset, incorporating more species, and testing additional machine learning algorithms to further enhance classification accuracy.
Authors - Monir El Mounaoui, Kunale Kudagba, Mohamed Yassin Chkouri Abstract - This paper presents PricePulse, a web-based price comparison system that supports consumers with real-time multi-platform price analysis and AI-powered shopping insights. The system aggregates product data from Amazon, Flipkart, and Meesho via SerpAPI’s Google Shopping API and enriches results with recommendations generated by Google’s Gemini AI. Built on Next.js and Flask, PricePulse addresses gaps in the e-commerce ecosystem by eliminating manual price comparison across platforms. The system uses JWT-based authentication, maintains search history in SQLite, and provides an intuitive interface with React and Tailwind CSS. Evaluation shows average response times under 2 seconds and 95% accuracy in price extraction, demonstrating significant potential to help consumers make informed purchasing decisions and save on purchases.
Authors - Tiurida Lily Anita, Siti Nahdiah, Muslikhin Muslikhin, Mohd. Nor Shahizan Ali Abstract - Despite the importance of Allied Healthcare professionals in healthcare service delivery, low professional development opportunities, a high turnover rate, and a shortage of workers in India are some of the challenges that are affecting Allied Healthcare professionals’ retention. The purpose of this research is to explore the po tential of Internet of Things (IoT) solutions and Big Data analytics, coupled with infor mation and communication technology (ICT) as a solution to Allied Healthcare profes sionals’ retention strategies. The purpose of this paper is to propose a conceptual frame work that can be achieved by utilizing Internet of Things solutions coupled with Big Data analytics as a solution to Allied Healthcare professionals’ retention strategies by utilizing theories such as Technology Acceptance Model theory, Job Demands-Re sources theory, Social Exchange Theory, among others. The paper concludes that ICT is a resource that can be utilized to reduce job stress, enhance effective communication, and provide career opportunities for Allied Healthcare professionals; whereas Big Data analytics coupled with Internet of Things solutions can be utilized to predict potential risks that may affect Allied Healthcare professionals’ retention. The proposed concep tual framework offers a theoretical understanding of the digital revolution of human resource management practices in healthcare services.
Authors - Soji Binu Mathew, A. Hepzibah Christinal Abstract - Permanent Magnet Synchronous Motors (PMSMs) are commonly utilized in electric vehicle (EV) traction systems because of its high efficiency, power density, and reliability. Conventional field-oriented control (FOC) schemes require accurate rotor position and speed information, typically obtained from mechanical sensors, which increase cost and reduce system reliability. Sensor less control techniques based on observer theory have therefore gained significant attention. Among them, sliding mode observers (SMOs) offer strong robustness against parameter variations and external disturbances but suffer from chattering and noise sensitivity. This paper presents an advanced sensor less FOC strategy for PMSM drives using a super-twisting SMO (ST-SMO) for rotor position sensing and estimation of speed. The proposed approach employs a ST-SMO algorithm to achieve the convergence in finite-time while significantly reducing chattering effects. The observer is integrated into a standard FOC framework and evaluated under EV-relevant operating conditions, including low-speed operation and load transients. Comparative performance discussion demonstrates the suitability and the effectiveness of the proposed method for high-efficiency EV traction.
Authors - Mohammed Mudassir, Irene Joseph, Jyothi Mandala, Sandeep J Abstract - This study introduces a Bidirectional Long Short-term Memory based multichannel speech enhancement framework that operates in the short-time Fourier transform domain using time-varying complex spectral masking. The pro-posed approach predicts channel-specific complex masks, allowing adaptive frame-wise suppression of noise in reverberant and multi-noise environments. A comprehensive dataset was created using multiple noise sources, and experiments were carried out at different signal-to-noise ratios. The proposed method outperformed the Relative Transfer Matrix and Deep Multichannel Active Noise Control techniques in perceptual speech quality and intelligibility across all test conditions, indicating its potential for real-world speech enhancement applications.
Authors - Gauri P Nair, Vinaya V, Dona Sebastian, Kavitha K V Abstract - Reliable stock price forecasting remains challenging due to the noisy, nonlinear, and non-stationary characteristics of financial time-series data. Traditional statistical methods and deep learning models that rely solely on raw price data often struggle to capture short-term fluctuations and evolving market dynamics. To address these limitations, this study proposes a hybrid forecasting framework that integrates causal time-domain filtering, time–frequency feature extraction, and deep learning–based temporal modeling. The proposed approach employs Savitzky–Golay and Kalman filters to sup press high-frequency market noise while preserving important price trends in a causality-aware manner suitable for real-time forecasting. Localized spectral fea tures representing transient and time-varying market behavior are then extracted using the Short-Time Fourier Transform (STFT). These enhanced time-domain and frequency-domain features are combined and modeled using a Long Short Term Memory (LSTM) network, which effectively captures long-range depend encies and nonlinear temporal patterns in financial data. The framework is evaluated using standard performance metrics, including RMSE, MAPE, and R². Experimental results demonstrate that integrating causal filtering with STFT-based features significantly improves forecasting accuracy and robustness compared to baseline models, providing a reliable and practical solution for short-term and multi-step stock price prediction.
Authors - Zubair Zaland, Mumtaz Begum Mustafa, Miss Laiha Mat Kiah, Hua-Nong Ting, Zuraidah M Don, Saravanan Muthaiyah Abstract - As digital marketing expands in Oman, many organizations struggle to transform large volumes of customer data into actionable insights. This study presents an AI-driven marketing intelligence framework designed for non-technical users, combining automated customer segmentation, sentiment analysis, and personalized recommendations. The framework employs an autoencoder-based feature extraction approach to capture key behavioral patterns, followed by K-Means clustering to define meaningful customer segments (Berahmand et al., 2024). A fine-tuned BERT model analyzes multilingual feedback in Arabic and English to assess customer sentiment (Manias et al., 2023). The framework was evaluated using 12 months of campaign data from 450 customers across multiple Omani businesses. Analysis revealed four distinct customer groups and an overall positive sentiment of +0.55. Controlled A/B experiments demonstrated that AI-guided campaigns outperformed traditional methods, increasing conversion rates by 27%, improving retention by 15%, and generating a threefold return on marketing spend. These results indicate that accessible AI tools can deliver measurable marketing benefits in emerging markets and provide a scalable solution for Gulf-region businesses.
Authors - B.Usha Rani, M.Sudhakar, A.Srivani, Y.Surya Praveen Abstract - The purpose of Diabetic Retinopathy Prediction is to use computer technology to identify early stages of retinal damage caused by diabetes. Since diabetic retinopathy can lead to blindness or permanent vision impairment if not treated in a timely manner, accurate and rapid diagnosis is vital. Recent tech niques for diagnosing diabetic retinopathy require an ophthalmologist to perform a manual examination of the eye’s retina with the use of fundus photography. The diagnostic process can be costly, time-consuming, and vary significantly from one person to another. A large percentage of diabetes patients live in rural areas, where it is difficult or impossible for them to have periodic screening by a diabetic specialist or receive healthcare services. There is a need to develop a solution to these problems, and the Diabetic Retinopathy Prediction System uses deep learning based techniques to analyze retinal fundus images and produce pre dictions regarding diabetic retinopathy. Analysis of the retinal fundus images will include preprocessing, feature extraction using CNNs, and automated classifica tion into diabetic retinopathy by degree and severity. This approach increases the accuracy and consistency of diabetic retinopathy diagnosis while minimizing the need for human input. The proposed system will allow for early identification of diabetic retinopathy in resource poor environments, support large scale screening programs and aid in clinical decision making by ophthalmologists. Additionally, the system has potential integration into mobile health systems and tele-ophthal mology networks. Experimental results indicate the proposed system is capable of accurately detecting diabetic retinopathy with high levels of specificity and sensitivity.
Authors - Bai B Mathura, Narra Dhanalakshmi Abstract - This paper presents a novel Reversible Data Hiding (RDH) method for dual images. First, secret data is converted into a binary sequence of equal length and then divided into shorter segments to control the amount of data embedded into each pixel. The embedding process uses two copies of the original image to distribute the data, reducing the impact on each image while maintaining overall image quality. During recovery, the original image is restored by averaging the pixel values at corresponding locations in the two stego images, while the embedded data is recovered through a reverse process. Experimental results on grayscale images demonstrate that the method maintains good image quality, achieving a high Peak Signal-to-Noise Ratio (PSNR) across different embedding levels while ensuring accurate recovery of both the secret data and the original image.
Authors - Priyanka Khalate, Satish S. Banait, Chandrakant Kokane, Dnyanada Shinde, Madhumati Pol, Pravinkumar M. Sonsare Abstract - The emerging use of digital deepfake technology is creating a myriad of obstacles in verifying the authenticity of digital media. Most of today’s detection methods yield satisfactory results when applied to clean samples of content, however, they are still susceptible to adversarial perturbations specifically created to bypass these detection methods. The current research paper introduces DC-DAFDN, a dual-stream architecture for detecting fraudulent digital content, which fuses frequency-domain analysis using the Discrete Cosine Transform (DCT) with Space-Attention Mechanisms. The current architecture uses adversarial training to develop more robust features. The proposed model uses EfficientNet-B4 as a backbone, augmented with Spatial Reduction Attention Blocks and Forged Fea tures Attention Modules to detect manipulation artifacts in the spatial domain, while the parallel DCT stream analyzes inconsistencies in the frequency-domain. Through an adversarial training procedure using Fast Gradient Sign Method (FGSM)-induced adversarial perturbations, the model learns robust feature sets that are resistant to evasion attacks. When evaluated on Face-Forensics++ dataset, DC-DAFDN significantly improves upon the original Dual Attention for Deepfake Detection Network (DAFDN) in terms of adversarial robustness. When attacked with large adversarial perturbations (e.g., FGSM with ϵ ranging from 0.1 to 0.25), the DC-DAFDN architecture maintained greater than average accu racy enhancements from +2.74% up to +3.61%, for an average accuracy increase of +3.36%, for the tested att, from all strengths. Our findings suggest that fusing frequency-domain analysis with adversarial training provides measurable improvement in the model’s robustness to adversarial attacks and simultaneously preserves the detection capabilities of the dual-attention method.
Authors - Cristian Castillo-Olea, Clemente Zuniga Gil, Angelica Huerta Abstract - Question paper preparation in educational institutions is conventionally manual and time-consuming, often generating question papers of uneven difficulty and less diversity. This project solves the problem of automatic question paper generation from voluminous academic content available in multiple formats. The motivation for this work is reducing human effort and enhancing efficiency, ensuring fair and balanced assessment generation, while supporting modern digital learning environments. Input content, in the form of text documents, portable document files, presentation slides, images, audio recordings, and video lectures, forms the bedrock of the proposed system; first, it gets preprocessed into a unified textual format through document parsing, optical character recognition, and speech-to-text techniques. Natural language processing approaches like sentence segmentation, tokenization, stop word removal, and extraction of key concepts are subsequently applied on the meaningful and relevant identification of the contents. It follows a hybrid approach relying on the Transformer architecture: a classification model that assesses the importance of a sentence, relevance of concepts, and difficulty level; and a generation model providing question types such as multiple choice, short answer, long answer, case studies, reasoning, fill-in-blanks, and programming. The proposed model goes through training and fine-tuning using publicly available datasets of question-answer pairs and pre- processed information in textbooks. In the experimental results, the proof of efficiency by the proposed approach is shown in generating accurate and diverse question papers with high relevance. Such an approach would definitely ensure much better outcomes for the question papers and the assessment.
Authors - Y. C. A. Padmanabha Reddy, Panigrahi Srikanth, Kavita Goura Abstract - Advances in Artificial Intelligence, Machine Learning and Internet of Things technologies have enabled wearable devices to sense as well as process and respond to human behaviour in real time. While most wearable devices today are used for health and fitness tracking. Many people face communication challenges such as language barriers, difficulty understanding emotions or social cues, social anxiety and accessibility issues for individuals with hearing or speech impairments. Existing systems often collect data but fail to provide meaningful, real-time assistance during actual human interactions. This research paper presents a literature-based study on AI powered wearable devices designed to support and enhance human communication. The research papers are focusing on intelligent wearables that use multimodal sensors such as microphones, cameras and sensors. These systems apply AI techniques to interpret speech, gestures, facial expressions and emotional signals in real time. The wearable devices considered include everyday consumer-oriented systems such as smart eyewear that provides audio visual assistance and wrist worn wearables that offer haptic feedback. The key focus of this study is to examine how such devices can deliver subtle, real-time support through visual prompts, audio cues or vibrations to improve conversational awareness and user confidence. The expected outcome is to identify current capabilities, practical limitations and design considerations for developing human centric wearable technologies that move beyond passive tracking toward meaningful communication support.
Authors - Anisha Panja, Ranjita Kumari Dash, Biswajit Sahoo Abstract - Singer identification is a challenging task because of pitch and me lodic variations, tempo, vibrato, and adaptive singing styles. This paper propos es a novel approach towards singer identification and classification by adapting a model originally meant for speaker recognition. Specifically, this work utiliz es vector representations extracted from a pretrained Speech Brain Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Net work (ECAPA-TDNN) model. The research pipeline processes a custom curated dataset of four prominent Indian playback singers into fixed, 8 second audio clips, with mono channel sampled at 16 kHz and exported as wav files. The Speech Brain Emphasized Channel Attention, Propagation and Aggrega tion (ECAPA) encoder transforms these labelled clips into fixed embeddings which are unique vector representations of voice characteristics of each audio clips. A suite of classical machine learning classifiers is trained on these em beddings. The study evaluates four of them namely, Logistic Regression, Sup port Vector Machines, Random Forests, and a Multi-Layer Perceptron (MLP). The MLP achieved the highest accuracy of 99.38% on held-out test data. Sup porting this result, both confusion matrix analysis and t-SNE projection clearly demonstrate clear cluster separation based on individual singer identities. These findings thus collectively validate that ECAPA embeddings contain sufficient identity-bearing structure on a singing voice. This analysis thus concludes that adaptation of speaker recognition models with appropriate classifiers is a great ly effective and efficient approach for singer identification.
Authors - Arpita Choudhury, Pinki Roy, Sivaji Bandyopadhyay Abstract - Modern agriculture faces several challenges such as uncertain crop selection, inefficient fertilizer usage, and changing soil conditions. To address these issues, this research proposes an integrated AI/MLbased system that combines crop recommendation, fertilizer recommendation, and time-series prediction. The system utilizes IoT sensor data, including soil nutrients (N, P, K) and environmental parameters such as temperature and humidity, to support data-driven decision-making. Random Forest models are used for crop and fertilizer recommendation, while an LSTM-based model is applied for predicting future soil conditions using time-series data. Basic preprocessing techniques are used to ensure data quality, and results are presented through a simple and user-friendly dashboard. Experimental results demonstrate strong performance, with 96% accuracy for crop recommendation and reliable prediction trends for time-series forecasting. Designed for offline use with minimal computational requirements, the system is suitable for deployment in rural and resource-constrained environments, highlighting the practical role of AI/ML in modern precision agriculture.
Authors - Sharayu Mirasdar, Mangesh Bedekar Abstract - Ayurveda, India's ancient system of medicine, is full of inter-connected knowledge about diseases, their symptoms, herb and formulation (compounds). However, texts such as Charaka Samhita are mostly unstructured and cannot be readily analysed computationally. This work presents AyurKOSH which is a machine-readable, high-quality Ayurvedic dataset that is designed as a Knowledge Graph (KG) in order to support Artificial Intelligence driven research. The dataset is represented as subject–predicate–object triplets, which enables semantic interoperability, graph traversal, and multi-hop inferencing across entities. The dataset is designed by following schema-driven ontology which standardizes relationships between various nodes such as diseases, symptoms, pharmacological attributes, and compound formulations. DB Schema ensures consistency and computational tractability. AyurKOSH has the structured data of diseases and related symptoms, drug preparations, herbs and the detailed pharmacological properties are Rasa, Guna, Virya, Vipaka, Karma. The graph structure shows real-world biomedical network characteristics such as high sparsity and low average degree, which makes it suitable for embedding-based learning, graph neural networks, and explainable AI frameworks. Moreover, there is botanical metadata and herb-substitution relationships added for the prediction of synergy and repurposing of drugs. The dataset facilitates applications in biomedical NLP, and automated reasoning systems and clinical decision assistance, and pedagogy in integrative medicine. AyurKOSH became available for academic and non-commercial research under CC BY-NC-SA 4.0 license.
Authors - Liz Huancapaza Hilasaca, Maria Cristina Ferreira de Oliveira, Rosane Minghim Abstract - The abstract of the study emphasizes the thorough discussion of cussword usage in Hollywood films over a period of thirty five years, from 1990 to 2025, particularly in genres such as Action, Comedies, and Romances. On the basis of a carefully selected dataset of cusswords from Kaggle along with a considerable subtitle file dataset (.srt), the results have been obtained to determine whether profanity has been used over the years with an appropriate level of intensity in the respective genres of films.
Authors - Lanja Azeez Abdalqadir, Aram Mahmood Ahmed, Rozha Kamal Ahmed, Dirk Draheim Abstract - This study explores advanced metaheuristic optimization algorithms to improve smart home energy management under constrained electricity supply, aiming to reduce costs and enhance energy efficiency. It addresses challenges such as dynamic pricing and unstable supply, particularly common in developing regions. Five algorithms—Particle Swarm Optimization (PSO), Bat Algorithm (BAT), Fitness Dependent Optimization (FDO), Marine Predators Algorithm (MPA), and Single Candidate Optimization (SCO)—are evaluated, along with enhanced versions of MPA, FDO, and SCO incorporating Lévy flight and Oppo-sition-Based Learning (OBL). OBL improves exploration and exploitation in FDO and MPA, while Lévy flight enhances SCO’s ability to escape local optima. A novel cyclic rebounding technique is introduced to manage appliance sched-ules exceeding 24-hour limits. Tested across three scheduling scenarios, results show that MPA-OBL consistently achieves the lowest energy costs. Overall, the proposed enhancements significantly improve energy optimization in supply-constrained environments.
Authors - Purva Trivedi, Arun Parakh, Shurbhit Surage Abstract - Awareness regarding consumer sentiments will benefit a business en tity and/or a company in making their marketing strategies more effective and engaging in the current digital marketing context. In traditional marketing sce narios, since there is a lack of actual emotional aspect in expressing views in real time contexts, it has always been challenging for a business to perform a signifi cant adjustment in their marketing campaigns and achieve a greater success rate. The proposed idea focuses on AI and ML-based approaches for sentiment analy sis in digital marketing. The framework is made up of seven core steps: data collection, preprocessing and data cleaning, sentiment analysis models, feature extraction and model train ing, sentiment classification and analysis, insights and decision-making, and ap plication in digital marketing. From social media to e-commerce reviews to online discussions, consumer sentiment data comes from many digital sources. The text for analysis is standardized, and noise is cleaned in data prepara tion. Then, apart from other artificial intelligence-based sentiment classification models, sentiments are classified as positive, negative, or neutral using lexicon based, machine learning, and deep learning approaches. The learned knowledge enables businesses to react dynamically to consumer sentiment, target advertise ments, and adjust marketing strategies. Businesses will be able to conduct more profitable promotions, communicate with customers better, and monitor real-time sentiment through this AI-driven sentiment analysis platform. The paper emphasizes the benefit of incorporating artificial intelligence in decision-making within digital marketing, even in ad dressing issues like ambiguous sentiment expression management and multi-lan guage data. This paper provides a strategic way towards maximum customer in teraction and brand loyalty and also emphasizes the need for sentiment analysis that is sustained by available data in modern digital marketing.
Authors - Soumyadeep Basak, Shubham Sahu, Sankur Kundu, Ankita Ray Chowdhury Abstract - Hyperspectral image (HSI) classification requires effective modeling of high-dimensional spectral signatures and fine-grained spa tial structures while maintaining computational efficiency for real-world deployment. Although recent Transformer- and state-space-based ap proaches enhance long-range dependency modeling, they often introduce substantial architectural complexity and computational overhead. To ad dress these challenges, we propose MF-HSINet, a lightweight dual branch framework that enables adaptive spectral–spatial fusion via se lective state-space modeling. The spectral branch captures inter-band de pendencies, the spatial branch extracts local structural patterns, and the proposed Mamba-Enhanced Attention Fusion (MAF) module integrates these complementary representations through selective state updates, cross-attention, and adaptive gating to achieve pixel-wise feature balanc ing. This design preserves discriminative local details while strengthen ing global contextual modeling with reduced parameter complexity. Ex tensive experiments on nine benchmark hyperspectral datasets demon strate that MF-HSINet achieves competitive and consistent performance in terms of Overall Accuracy, Average Accuracy, and Kappa coefficient, while offering improved efficiency and inference speed, making it suitable for practical and resource-constrained HSI applications.
Authors - N. Revathy, Tamilmani M, Naveena P, Mariya Nisha S, Mega varshini V, Karthik B Abstract - Virtual Learning Environments (VLEs) are commonly evaluated through expert-driven frameworks that lack reproducibility and objective prioritization of defining features. This study proposes a data-driven framework integrating a Systematic Literature Review (SLR) and the iKeyCriteria method to identify and logically classify core VLE characteristics. A corpus of peer-re-viewed studies was analyzed and divided into VLE-focused (P) and contrastive non-VLE (Q) contexts. Criteria extraction and validation were conducted using tfidf (Term Frequency-Inverse Document Frequency) weighting and Boolean logical matrices to determine necessary and sufficient conditions. Results indicate that structured delivery of learning materials (91.5% in P vs. 12.7% in Q) and shared collaborative workspaces (82.1% vs. 18.2%) function as sufficient but not necessary discriminators of VLEs. In contrast, self-assessment and summative assessment appear frequently across both contexts and are therefore non-distinctive. The proposed framework provides a reproducible and bias-reduced mechanism for distinguishing defining VLE features, bridging systematic review methodologies with logical condition analysis. These findings support evidence-based prioritization in the design and evaluation of digital learning systems and contribute to advancing objective classification approaches in educational technology research.
Authors - Tegawende Brigitte KIENTEGA, Sadouanouan MALO Abstract - Navigation of mobile robots using GPS is widely available but use of GPS is sometimes either costly, not suitable for security reason, not available in indoor environments, or underground operational fields. This work provides a greedy method of path planning for a mobile robot from a starting point to the given destination point in a GPS-denied field where a set of access points (AP) are deployed randomly. Using these APs, the robot is able to calculate its current position at any moment as well as it chooses the next position to move further towards the destination. An efficient algorithm is designed to guide the robot to reach to its destination successfully taking into account that all the holes are convex, if exists within the field of interest. An analysis of the deployment strategy of the APs is provided in order to guarantee the successful path planning by the robot without backtracking any sub path.
Authors - Ambati Abhinavya, Jarupula Sunitha, Raparthy Navya, Rama Valupadasu Abstract - Internet of Things (IoT) devices are growing in domains because of their reliability and efficiency in monitoring, real-time detection and automated support. However, these IoT systems have also introduced security challenges. These devices are vulnerable to cyber threats, where attackers exploit weak points in the system to steal sensitive information. One of the attacks is the Distributed Denial of Service (DDoS) attack, which disrupts services by overwhelming systems and making them inaccessible to legitimate users. IoT devices are resource-constrained, so reducing feature dimensionality is essential to lower computational overhead and complexity. IoT devices generate data for detecting cyber-attacks, but sharing such data across organizations raises privacy concerns. To address these challenges, the proposed approach is designed in two phases. In the first phase, a hybrid feature selection technique using mutual information, permutation feature importance, and Greedy wrapper-based feature selection with cross-validation is applied to extract relevant features. In the second phase, Federated Learning (FL) is applied to train the model without sharing raw data among clients. Within the FL framework, Random Forest (RF) algorithm is utilized for training due to its robustness and classification capability. The proposed model is evaluated under two data distribution scenarios: mildly non-IID and strongly non-IID conditions. Experimental results demonstrate that the model achieved an accuracy of 99.69% in a mildly non-IID scenario and 98.36% under strongly non-IID conditions, highlighting the effectiveness and reliability of the proposed framework for secure IoT-based DDoS attack detection.
Authors - Kalidasu Lochani Krishna Priya, Nupur Ajit Kale, Apeksha Pandurang Mujumale, Anagha Vijaysinha Rajput Abstract - The large online data consist of duplication and plagiarized contents. Due to Artificial Intelligence, data generation has become very easy. But, it may also lack an ethical data generation process. Hence, there is a need of validating plagiarism free data for authentic usage. In this research work, authors focus on word-level plagiarism detection methods in Natural Language Processing. The proposed method uses a comparative analysis of cosine similarity, Euclidean distance and Manhattan distance methods for word-level plagiarism detection for different n-gram sizes. The inculcation of n-gram size improved the accuracy compared to unigram based methods. The experimental results of the cosine similarity method outperform Euclidean and Manhattan distance methods by achieving an average accuracy range of 88 % to 92 % and 75 % to 80 % for direct plagiarism and lightly paraphrased text respectively. The future work is to identify reused images and visual contents.
Authors - Neeraj Mathur, Jiby Mariya Jose Abstract - Material Control Systems (MCS) serve as a critical software layer that coordinates material flow by issuing transport commands, tracking material lo-cations, and interfacing with factory equipment and automated handling systems. Although the term may appear to focus primarily on inventory management, it is most commonly used in high-tech environments such as semiconductor manufacturing to describe the software layer that manages, directs, and optimizes the movement, storage, and routing of materials (e.g., wafers and carriers) within a production or logistics environment. This paper presents the development and implementation of a novel Physical AI–based Material Control System. Unlike traditional MCS architectures that rely on rigid rule-based dispatching, the proposed approach leverages a Physical AI plat-form to enable unified and adaptive control across heterogeneous hardware, including stockers, Autonomous Mobile Robots (AMRs), and Overhead Hoist Transport (OHT) systems. By integrating real-time sensor fusion and adaptive motion planning, the proposed system enhances process logistics in semiconductor backend facilities, where high-mix production requires highly dynamic coordination between storage and transport resources.
Authors - Maryam Ghazi Ali, Bindu V. R Abstract - The Internet of Things (IoT) has spread rapidly, significantly increasing several secu-rity vulnerabilities, as traditional detection systems are becoming insufficient to manage the vol-ume and diversity of traffic that characterizes modern networks. The review provides a compre-hensive analysis of recent advances in learning-based intrusion detection systems (IDS), focusing primarily on deep learning, traditional learning, machine learning, and hybrid frameworks. Through critically evaluating a diverse range of state-of-the-art studies, the review explores dif-ferent methodological solutions, data, and performance measurement in the field. The available empirical results show that, although deep learning models are better at identifying complex pat-terns in the data, traditional machine learning algorithms require less computational power. In addition, hybrid and ensemble models often outperform single-method options, but often with high computational cost. The review outlines a number of important challenges, including the issue of class imbalance and the fact that models are not very interpretable. It argues that light-weight and interpretable AI systems should be a priority in future studies, and the gap between theoretical academic frameworks and practical IoT applications would be minimized.
Authors - Aditi Jha, Ravi Shankar Pandey Abstract - Indoor air quality (IAQ) is a frequently overlooked determinant of health in rural villages, where the extensive use of solid fuels for cooking and space-heating generates elevated concentrations of airborne pollutants. This study presents an integrated, low-cost protocol for improving IAQ in rural dwellings, combining real-time environmental monitoring, simplified digital modelling and passive strategies of ventilation and biophilic design. The methodology can be structured into three steps: Conceptual digital twin, feedback interface, ventilation strategies, biophilic integration. Conceptual digital twin is based on the mapping of each dwelling linked to Arduino low-cost, stand-alone sensors (CO₂, PM₂.₅, temperature and relative humidity) that collect data at temporal resolution of one minute. An immediate feedback interface based on visual and/or acoustic indicators that prompt residents to take corrective actions (selective opening of windows, activation of cross-breezes), when exposure thresholds - derived from WHO Air Quality Guidelines - are exceeded. Data-driven natural-ventilation strategies – optimal ventilation windows identified through time-series analysis of sensor data, calibrated to local weather conditions and occupancy profiles to maximise air exchange while minimising heat losses. Biophilic integration implies the introduction of resilient plant species with proven phytoremediation capacity, as Epipremnum aureum) which could reduce CO₂ level, with quantitative guidance on density (two to three plants per main room) and optimal placement. Using low-cost IoT sensors, the protocol monitors environmental parameters and pollutant concentrations in real time. The system targets specific safety and comfort thresholds, aiming to maintain CO₂ levels below 700 ppm and PM₂.₅ below 50 μg/m³ to optimize occupant health (Wu et al, 2021). These thresholds, derived from World Health Organization (WHO) guidelines, are essential to ensure occupant satisfaction and well-being. The ultimate objective is to define a scalable and replicable intervention model capable of combining digital technologies and natural solutions for the sustainable regeneration of fragile territories.
Authors - Atul Pawar, Ganesh Deshmukh, Rajesh Lomte, Sahil Ambokar, Vedant Bankewar, Sanket Ahirrao Abstract - This study explored teachers’ perspectives on the need for an interac tive digital storytelling application to support English language learning at the primary level. Using a teacher-based needs analysis, data were collected through expert review of research instruments and in-depth interviews with English teachers working in international school contexts. The findings reveal that teach ers perceive digital storytelling as an effective approach for enhancing student engagement, motivation, and contextualized language learning. Teachers high lighted the importance of integrating interactive elements such as narrative audio, visuals, game-based tasks, immediate feedback, and reward systems to support vocabulary development, comprehension, and learner autonomy. The results also indicate a need for applications that are curriculum-aligned, age-appropriate, and easy to use in classroom settings. Based on the identified needs, the study pro vides design implications for the development of an interactive digital storytell ing application that combines storytelling and game-based learning principles. This research contributes to the growing body of literature on digital storytelling and offers practical guidance for educators and developers seeking to design ef fective language learning applications.
Authors - Veenu Singh, Saurabh Singhal Abstract - Many AI agents store observations, summaries, and retrieved content in persistent memory, then reuse that material in later planning and action. This creates a failure mode that standard incident response does not fully address. If malicious content is written into durable memory, patching the vulnerable component, rotating credentials, and restarting the agent do not remove the poisoned state. The agent can restart clean, retrieve the same memory, and act on it again. We call this provenance laundering: external-origin content is later consumed with authority it should not have. We formalize this mechanism, show that remediation without memory purge leaves residual impact over time, and examine seven production memory architectures against this threat model. We then define a containment primitive based on provenance metadata, namespace separation, and an inference-time non-escalation gate, and evaluate it with ablation across two frameworks. In our experiments, unauthorized behavior persisted after standard remediation and stopped only after memory purge. These results suggest that incident response for persistent-memory agents should treat purge as a required step rather than an optional cleanup action.
Authors - Nitesh Varman V R, Sanjith Ganesa P, Rahul Veeramachaneni, Korapati Mohan Aditya, Bagavathi Sivakumar Abstract - With the development of cloud computing and big data technology, data handling particularly in handling big data, while also mentioning the dangers of privacy and security violations in delegating the processing of sensitive data to cloud computing has increased. The conventional encryption method that demands the decryption of data for processing, which could result in the leakage of sensitive data and performance inefficiencies are no longer valid. The paper introduces the Optimized Privacy-Preserving Cryptographic Processing Algorithm (OPCPA), which reduces computational complexity through the use of light-weight encryption, adaptive data partitioning, hierarchical key management, and parallel processing of encrypted data. The proposed algorithm is compared to conventional methods using the KDD Cup 1999 dataset and outperforms them in terms of processing speed, throughput, and resource utilization.
Authors - Kashish Goyal, Parteek Kumar, Karun Verma Abstract - The clinical deployment of continuous epileptic seizure forecasting systems is severely hindered by the cold-start problem. Current state-of-the-art deep learning models require patient-specific fine-tuning, necessitating the recording of multiple seizures from a newly admitted patient before the system becomes operational. To achieve immediate clinical utility, forecasting models must operate in a zero-shot capacity. This paper presents a Zero-Shot Cross-Patient Transfer Framework, leveraging the Horizon-Aware Graph Transformer as a universal feature extractor, coupled with the Strict Discipline Protocol as a rigid domain adaptation layer. By anchoring the batch normalization layers to a global source distribution and utilizing a brief interictal calibration phase, the framework mitigates the severe covariate shift inherent in cross-patient electroencephalogram signals. Experimental validation on the CHB-MIT dataset demonstrates a sensitivity of 87.3% with a false alarm rate of 0.28 per hour, achieving a Time-to-Utility of exactly 10 minutes, a 99.9% reduction compared to conventional patient-specific approaches requiring 5-14 days of monitoring. The framework successfully bypasses patientspecific training, offering immediate clinical interoperability while minimizing alarm fatigue through disciplined feature scaling.
Authors - The Quan Trong, Nguyen Trong Nhan Abstract - The integration of large language models (LLMs) into primary educa tion remains limited in low resource, diglossic languages like Sinhala. General purpose models often produce grammatically inconsistent or cognitively over whelming output for young learners. This paper introduces a grade-adaptive, con straint-driven framework for automated Sinhala story and quiz generation target ing Grades 1-5. Building upon an 8-billion-parameter Sinhala-adapted LLaMA 3 model, we apply Quantized Low-Rank Adaptation (QLoRA) using a curated multi-task educational dataset. The system enforces tier-specific linguistic con straints separating conversational Sinhala for lower grades from formal written Sinhala for upper grades while embedding strict structural rules such as con trolled sentence counts (5-6 vs. 7-8) and validated multiple-choice formats (3 vs. 4 options). Evaluation on 100 structured prompts demonstrated substantial im provements over a zero-shot baseline: structural compliance increased from 64% to 93%, and hallucination-related failures decreased from 31% to 8%. Further more, evaluation against 50 unseen real-world classroom prompts yielded a 0.0% crash rate and 95% register adherence, confirming robust qualitative perfor mance. Results demonstrate that diglossia-aware dataset engineering and con straint-aware fine-tuning enable reliable, pedagogically aligned deployment of LLMs in low-resource primary learning environments.
Authors - Maria Veronica Alderete Abstract - This study extends the empirical literature on the relationship between intention to use Artificial Intelligence (AI), the digital divide, and regional ine-qualities in Latin America. To the best of our knowledge, no prior research has examined the AI gap by combining data at the subnational (regional) level across countries. The analysis relies on a sample of 208 regions from 10 Latin American countries. A structural equation model is estimated to assess the relationships among digital infrastructure, socioeconomic factors, and intention to use ChatGPT. The results show that household internet access has a positive and statistically significant effect on intention to use ChatGPT. Data center presence indirectly re-inforces AI intention use through its positive association with internet access, while rurality exerts a negative effect. Education levels and platform-based em-ployment (e.g., Uber) are also positively associated with intention to use AI. The findings suggest that AI adoption is structurally conditioned by foundational digi-tal infrastructure, regional human capital, and exposure to platform-based labor markets. Although the expansion of the gig economy fosters intention to use AI, AI diffusion simultaneously increases the importance of formal education.
Authors - Donald Flywell Malanga, Wallace Chigona Abstract - Mobile Health (mHealth) has been regarded as a potentially transform-ative element for enhancing health service delivery in low-income nations. The effective integration of technology relies on ongoing usage rather than just initial acceptance. While the body of literature on factors influencing continued mHealth use is expanding, post-adoption expectations are proposed as indicators of the success or failure of mHealth implementation. There is limited research on how community health workers' post-adoption expectations influence their inten-tions to persist in using mHealth in developing regions. Consequently, this study explores the effect of post-adoption expectations on satisfaction and ongoing us-age behaviour regarding mHealth among community health workers in Malawi, which represents a developing country context. The research introduces a frame-work that builds upon the expectation confirmation model and incorporates ele-ments from the updated information success model. A mixed-methods conver-gent design was utilised for the study. Data were collected through surveys and semi-structured interviews with community health workers who utilise Cstock. Cstock is an mHealth application that facilitates the ordering of medical supplies via text message. The findings generally support the notion that post-usage use-fulness, along with information quality, system quality, and service quality, pos-itively influences community health workers’ satisfaction and their intention to continue using the Cstock application. The results indicate that the ongoing usage behaviour of mHealth among community health workers is shaped not solely by behavioural expectation beliefs (i.e., post-usage usefulness) but also by objective expectation beliefs, including system quality, service quality, and information quality. Therefore, these findings provide valuable insights to policymakers, practitioners, mHealth developers, and other relevant parties regarding the post-user expectations essential for maintaining future mHealth solutions in develop-ing countries, particularly in Malawi.
Authors - Dhanashri Amol Gore, Satish Narayanrav Gujar Abstract - The wide use of machine learning in the field of medical imaging has caused concern with regard to patient information security, especially when mod els are being trained over multiple health care systems in a distributed manner. Centralized learning requires transferring raw patient data to a central server where there is an extreme risk of data breach and unauthorized access to patients' personal information. Violations of health care regulations (HIPAA and GDPR) can occur in a centralized system because of the transfer of patients' data. Feder ated Learning (FL) addresses these issues by allowing collaborative model de velopment on individual client devices. Therefore, the sensitive patient data will remain at its source institution. This paper provides a thorough comparative study of centralized learning and federated learning methods for detecting pneumonia utilizing chest X-rays from the publicly available Kaggle Chest X-Ray Pneumo nia dataset. Three architecture types (Support Vector Machine (SVM), Convolu tional Neural Network (CNN) and Long Short-Term Memory (LSTM)) were tested in both centralized and federated environments utilizing the FedAvg ag gregation method. Only the model weights were shared between the clients and the central server; therefore, patient data was maintained private through the en tire model training process. Experimental results demonstrated that federated learning produced superior performance than centralized learning for all three architectures (81.1%, 84.6%, and 82.7% for SVM, CNN and LSTM respec tively). The performance metrics for centralized learning were 76.6%, 76.3%, and 81.6%. This superior performance of FL is attributed to the inherent regular ization effect of local class-balancing within the federated clients that reduces the inherent class imbalance in the dataset. Overall, our research demonstrates that FL is not only a viable privacy-preserving solution to centralized training but offers improved generalization in the medical imaging domain with imbalanced classes and is a suitable solution for application in distributed health care envi ronments.
Authors - Vu Nguyen, Chau Vo Abstract - Artificial intelligence (AI) offers powerful capabilities for understanding stakeholder perceptions of corporate sustainability initiatives. This study investigates how AI‑driven sentiment analysis can support sustainable business decision‑making by analyzing secondary data from social media platforms, online re-views, and ESG reports. Using advanced text mining and trans-former‑based sentiment classification techniques, the research identifies patterns in public opinion regarding environmental, social, and governance practices across industries. Topic modeling is applied to detect emerging sustainability themes, while sentiment trend analysis provides actionable insights for improving stakeholder engagement and brand reputation. The findings reveal how organizations can leverage real‑time sentiment data to guide strategic investments, enhance communication strategies, and strengthen commitment to green practices. By integrating AI‑based natural language processing with sustainability management, this research contributes to evidence‑based decision‑making frameworks that enable businesses to respond effectively to societal expectations and achieve long‑term competitive and environ-mental advantages.
Authors - A. Viji Amutha Mary, Ram Swagath B, Ruthresh E, S Jancy, B. Shamreen Ahamed Abstract - As one of the most damaging natural risks, earthquakes require quick situational consciousness for emergency response as well as control. Usual impact assessment methods use larger on field surveys conducted after a disaster, which delays decision making and results in a poor comprehension of damaged zones. An automated analysis pipeline processes high resolution imagery from satellites and land based seismic data to extract land use change patterns, information on terrain change in shape and signs of structural damage. An XGBoost model is then used to classify the extracted spatial features, estimate severe levels and produce dynamic earthquake risk maps. During seismic emergencies, the system supports resource distribution and rescue planning by enabling quicker and more accurate estimation of open areas. The suggested hybrid model greatly outperforms traditional disaster assessment techniques in terms of accuracy, processing speed or scalability, according to experimental evaluation, underscoring its potential to transform preventive earthquake disaster management as well as prepare strategies.
Authors - Shital Waghamare, Swati Shekapure, Girija Chiddarwar, Shital Waghamare Abstract - Public administrations generate extensive administrative data through routine governance processes yet it is weakly based on verifiable evidence. This paper introduces a human-centric policy intelligence system based on execution-level administrative data for provision of accountable and evidence-based policy-making. The framework brings together governance-conscious data ingestion, cryptographic hash-based verification including permissioned blockchain systems to control the integrity of data, cross-domain data harmonisation to overcome administrative silos, and explainable machine learning models to create interpretable supporting insights. The framework is specifically meant as a human-in-the-loop system, maximizing policy foresight, administrative discretion, and accountability to the law. The validation with actual Mahatma Gandhi National Rural Employment Guarantee Act administrative data of the year 2022–2023 proves that the framework can be used to stress the implementation issues and regional inequalities without computerising policy-related decisions. The suggested solution is lightweight, scaled down to fit in the existing open-sector digital infrastructure.
Authors - Zarif Bin Akhtar, Ifat Al Baqee Abstract - Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have accelerated the capabilities of Computer Vision (CV) across domains such as healthcare, autonomous systems, manufacturing, and intelligent surveillance. This research exploration presents a comprehensive investigation into the technological evolution, practical applications, and ethical implications of modern CV systems. Through a mixed-methods approach combining available knowledge analysis, empirical model evaluation, and expert interviews, the study assesses the performance of state-of-the-art architectures including Convolutional Neural Networks (CNN), Vision Transformers, YOLO-based detectors, and diffusion models—across diverse real-world deployment scenarios. Experimental findings highlight significant improvements in image classification, object detection, semantic segmentation, autonomous navigation, driven by techniques such as transfer learning, ensemble modeling, and model optimization for edge devices. Despite these advancements, challenges persist regarding data quality, interpret-ability, bias, and privacy, particularly in high-stakes environments. The study emphasizes the need for responsible AI governance, human-centric design, and standardized regulatory frameworks to ensure safe and equitable adoption of visual AI. Furthermore, emerging trends such as multi-modal learning, edge-based inference, and foundation models are discussed as catalysts for the next generation of contextaware and resource-efficient CV systems. This work provides a holistic perspective on current CV capabilities, identifies key limitations, and outlines strategic future directions for developing robust, sustainable, and ethically aligned AI-driven vision technologies.
Authors - Fernando Latorre, Ivan Becerro, Nuria Sala Abstract - The rapid expansion of interconnected networks, cloud infrastruc tures, and IoT environments has significantly increased the complexity of mod ern cyber threats, necessitating intelligent and adaptive Intrusion Detection Sys tems (IDS). While machine learning and deep learning techniques have im proved detection accuracy, their black-box nature limits transparency, interpret ability, and analyst trust in high-stakes cybersecurity environments. This lack of explainability hinders forensic validation, regulatory compliance, and resilience against adversarial manipulation. To address these challenges, this paper pre sents a comprehensive survey of Explainable Artificial Intelligence (XAI) tech niques applied to IDS and proposes a reference hybrid architecture that inte grates deep packet inspection, dual-model detection, multi-level explanation mechanisms, adversarial robustness monitoring, and governance-aware logging. The architecture combines high-performance deep learning models with inter pretable components and an explanation fusion engine to balance detection ac curacy with transparency. Furthermore, security implications such as explana tion leakage and adversarial manipulation are analyzed. The study highlights evaluation metrics, open challenges, and future research directions toward trustworthy and transparent cybersecurity systems. The findings emphasize that secure explainability is essential for next-generation IDS deployment in distrib uted and resource-constrained environments.
Authors - Sanjay Kumar, Vimal Kumar, Sahilali Saiyed, Pratima Verma, J.R. Ashlin Nimo Abstract - As online shopping has become increasingly popular, companies must utilize social media to develop and improve customer experience. This study examined customer interaction sentiment regarding online shopping through automated systems to classify comments on social media sites like Twitter, Facebook, and Instagram. This research study compared three machine learning and natural language processing (NLP) techniques: Bidirectional Gated Recurrent Units (GRUs), Random Forests, and Naïve Bayes. Customer reviews were classified as positive, negative, and neutral, as well as analyzed for time-related patterns. The classification framework was constructed by using sentiment analysis, feature extraction, and data preprocessing techniques. Furthermore, model training and performance assessment were executed through Naïve Bayes and Support Vector Machines. Of all the models studied, the Bidirectional GRU had the best performance with an accuracy of 88.08 %. The results of this study help companies understand customer preferences better, and thereby refine their products, services, and marketing techniques.
Authors - Tanmoy De, Vimal Kumar, Pratima Verma Abstract - The traditional centralized insurance operation has contributed to insurance fraud due to poor identity verification systems, fragmented data sharing, and slow manual validation, all leading to substantial financial loss and loss of faith in the integrity of the operation. This research aims to develop a framework for an insurance operation that provides security, transparency, intelligence, and improved fraud detection accu- racy while meeting the privacy and interoperability needs of insurers and their related stakeholders. The proposed framework is a decentralized solution that employs blockchain, self-sovereign identity (SSI), artificial intel- ligence (AI), and federated learning to create secure identity cre- ation processes, transparent policy management, and intelligent verification of claims. The results of experimental evaluations of the proposed framework show that it provides increased fraud detection accuracy, reduced duration of processes, and improvements in transparency over current processes. Thus the suggested method improves efficiency and trust in insurance ecosystems and can be applied to real-world implementations with sophisticated identity integration and extensive blockchain networks.
Authors - A. Viji Amutha Mary, S. Chanikya, S Gayathri Sarayu, S Jancy, B. Shamreen Ahamed Abstract - This work presents an intelligent solution to render residential garages more secure and safer. We developed an IoT platform to address frequent. homeowner issues, including leaving the accidentally. garage door open, looking to know whether it is your car, or noticing anything unusual. At its core, the system uses an internet connected ESP 32 microcontroller through Wi-Fi. In order to identify a vehicle inside, we added an ultrasonic sensor which calculates the proximity to the closest object. A simple magnetic switch, mounted on the garage door indicates when the door is ajar or closed. Our software processes these readings, and puts logic to alert you whether the door has been long or long been opened when your car is not home, which poses a possible security threat. An extra optional motion sensor may also be added. guards in case of any unforeseen motion in the garage.
Authors - Ashavaree Das, Dimo Valev, Sambhram Pattanayak, Prashant Kamal Abstract - The rise of short-form video (SFV) platforms like TikTok, Instagram Reels, and YouTube Shorts has caused a fundamental shift in digital marketing, moving from static images to engaging, multimodal strategies. These platforms utilize advanced "interest-graph" algorithms and unique user interfaces that significantly alter consumer attention spans and engagement patterns. Traditional marketing metrics often fall short in these environments, requiring new approaches that emphasize immediacy and authenticity. This paper explores the key intersection of algorithmic recommendation biases, content memorability, and technical video quality. To address these challenges, we propose an integrated framework that combines advanced blind video quality assessment (BVQA) with generative enhancements to optimize content for short-form formats. By incorporating technical insights from affective computing and recommender systems alongside strategic marketing goals, this study explores how "lo-fi" aesthetics and influencer-led credibility influence consumer attitudes. Our findings offer a roadmap for managing user-generated content (UGC) and algorithmic biases to enhance brand resonance and purchase intent in today's digital economy.
