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.
