Authors - Sohesh Gandhe, Aditya Shirwalkar, Prathmesh Jomde, Shreyash Dhavale, Anil M. Bhadgale Abstract - Automatically generating Unified Modeling Language (UML) diagrams from unstructured software requirements remains one of the persistent challenges in modern software engineering. This paper introduces an intelligent project management framework that transforms client-provided requirement documents into accurate UML diagrams with minimal human intervention. Our system leverages Optical Character Recognition (OCR) to extract text from various document formats, employs a fine-tuned model for intelligent prompt synthesis, and utilizes a fine-tuned CodeLLaMA 7B model trained on prompt-to-MermaidJS code mappings. The generated diagrams—including sequence diagrams, flow charts, and Gantt charts—are rendered in real time through an integrated Mermaid Live Editor, providing immediate visual feedback within the project management interface. The experimental evaluation demonstrates substantial improvements in automation efficiency, reduced manual modeling effort, and improved consistency in UML generation. Our approach bridges the gap between natural language requirements and formal system design artifacts, offering a practical solution for automated software documentation and project planning at scale.
Authors - Trupti Shripad Tagare, K.L.Sudha, Nagendra Kumble, Sanketh T S, Belliappa M Abstract - The current developments in the design of aircraft have remarkably improved their overall performance. The parameter Rate of Climb (RoC) plays a very vital role in planning the trajectory of the flight, optimum fuel utilization and flight safety and is of significance for both technicians and pilots. The factors affecting RoC are weight of the aircraft, its design, and the atmospheric state. In this study, the estimation of real time RoC using predictive AI and deep learning is presented. The model is trained on real time flight data collected from Radome Technologies, Bengaluru. The parameters like drag, thrust, weight, climb angle and airspeed are provided as inputs to the model after preprocessing. The results show that the system achieves an enhanced predication accuracy with R2 of 0.9396, Root Mean Squared Error (RMSE) of 861.69 feet per minute and Mean Absolute Error (MAE) of 659 feet per minute. The efficiency and capability of several aircrafts can be measured and analysed using the rate of climb. The work greatly finds its important role in ground-based flight planning tools and in onboard decision-support systems. The fuel requirements for the aircraft can be reduced significantly by setting an optimum ROC. This will result in reduced costs and sustainable solutions. This work contributes to overall performance and safety, as the aircraft will maintain the optimal ascent using AI driven climb profile optimization.
Authors - Glenn Erick Zambrano Estupinan, Maria Genoveva Moreira Santos Abstract - Virtual Reality (VR) has gradually become an increasingly relevant technological tool in higher education, not only because of its innovative nature, but also due to its ability to create immersive experiences capable of capturing students’ attention and generating meaningful emotional responses. In this con-text, the aim of this study was to analyze the immediate emotional impact produced by a virtual reality experience on university students, using data mining techniques to identify patterns within the collected responses. The research followed a quantitative approach, with a descriptive–correlational and cross-sectional design, and included the participation of 305 students from the Faculty of Computer Sciences at the Technical University of Manabí. Each participant engaged in an immersive experience lasting approximately five minutes using the Meta Quest 2 device. After the activity, a Likert-type questionnaire, with a scale ranging from 1 to 5, was applied in order to evaluate variables such as perceived immersion, realism of the environment, level of attention, emotional interaction, empathy, and enthusiasm before and after the experience. The collected data were subsequently analyzed through exploratory and correlational analysis, as well as through several data mining techniques, including Principal Component Analysis (PCA), k-means clustering, and Apriori association rules. Overall, the results suggest that the virtual reality experience generated predominantly positive emotional responses among the students.
Authors - N. Revathy, V. Latha Sivasankari, Nikileshwar V, Surendhiran G, Abijith M, Sheik Mohamed S Abstract - Enterprise networks face escalating cyber threats as cloud, IoT, and remote work adoption expand attack surfaces. Traditional signature-based detection and manual response suffer average breach detection intervals of 287 days, failing to scale against rising alert volumes [9]. CyberSentinel addresses this through an autonomous pipeline processing Windows Security Event Logs: Isolation Forest anomaly detection on engineered behavioral features, large language model (LLM) threat explanations via local Ollama inference, and automated remediation including account deactivation, process termination, and firewall adjustment. A Flask web dashboard provides real-time threat visualization. Evaluation across 72 hours on a controlled Windows 10 Enterprise testbed with 28 injected anomalies confirms an F1-score of 0.78, 84.2% remediation success, and mean end-to-end latency of 24.7 seconds. The modular Python architecture enables fully autonomous operation on standard Windows hosts without dedicated SOC infrastructure.
Authors - Palgulla Rangaswami Reddy, Palla Maheswara Rao, Gogineni Hari Prasad, Guthikonda Akhila, T.V. Sai Krishna Abstract - The implementation and design of a covert communication channel that embeds hidden information within TCP/IP packet headers rather than within the actual payload of the packets is presented as a project. This is different than traditional embedding methods (steganography), which typically embed data into multime dia files, in that steganography in this case utilizes header fields that are not cur rently in use or can be modified so that TCP/IP packets can transmit hidden data. The fields that are used to transmit hidden data are the IP Identification Field, TCP Sequence Number, TCP Acknowledgment Number, and TCP Window Size. The sender module encodes and generates packets, and the receiver retrieves packets, extracts encoded bits, and reassembles data from the encoded bits found in the packets. The integrity of the data is verified using a checksum (SHA-256) and packet loss is reported. The lack of a payload will further enhance the stealth various data transmission methods may enjoy as it will circumvent conventional intrusion detection techniques (which primarily examine the payload data within packets). This project will demonstrate the ability to use this or similar covert communication channels to implement covert communication systems. In addi tion, covert communication channels can be used for different types of files and demonstrate the security and educational value of covert channel research in net work security.
Authors - Busrat Jahan, Kevin Osei-Onomah, Mansi Bhavsar, Hermela Dessie, Apu Chandra Bhowmik Abstract - In the global health sector, Diabetes is a major concern which needs accurate and effective models for early prediction. This work is quantitative re-search work. The dataset was collected from CDC Diabetes Health Indicators, and we used Light Gradient Boosting Machine (LightGBM) model for predicting diabetes. Since this research work is binary classification-based work, in our data preprocessing stage, we used Synthetic Minority Oversampling Technique (SMOTE) for controlling class imbalance and for feature selection we used Chi-square test to improve the model performance. The proposed LightGBM model showed its ability to recognize complex correlation between diabetes-related health indicators with the training accuracy of 92% and a ROC-AUC score of 0.97 on the test dataset. Overall, the findings highlight that predictive accuracy is significantly improved after applying both imbalance data controlling and most correlated feature selection techniques.
Authors - Ruby Bisht, Amit Kumar Uniyal Abstract - Digital transformation is reshaping education systems worldwide, with significant implications for rural and underserved regions. In India, initiatives aligned with the National Education Policy (2020) have promoted online learning platforms, digital classrooms, and technology-enabled teacher training to enhance access, equity, and quality in education. However, rural schools continue to face structural challenges such as limited infrastructure, digital divides, and inadequate teacher preparedness, which influence the effectiveness of digital integration.This conceptual paper examines the transformation of rural education in India from traditional teacher-centred classrooms to digitally enabled learning ecosystems. Grounded in Constructivist Learning Theory, the Technology Acceptance Model (TAM), Diffusion of Innovation Theory, and the TPACK framework, the study proposes an integrated conceptual model linking digital infrastructure, pedagogical innovation, and teacher competence to improved access, engagement, and learning outcomes. The paper argues that digital transformation represents a systemic pedagogical and institutional reform rather than a mere technological shift. Its success depends on inclusive infrastructure development, sustained teacher capacity building, and context-sensitive implementation in rural settings.
Authors - Ruby Bisht, Amit Kumar Uniyal Abstract - The rapid growth of Information and Communication Technologies (ICT) has profoundly altered educational systems by redefining teaching practices, institutional processes, and professional expectations. Within the broader context of sustainable development and smart education, ICT has emerged as an important facilitator of efficiency, accessibility, and innovation. This paper presents a conceptual analysis of how ICT can contribute to sustainable development through its influence on teachers’ work–life balance and job satisfaction in ICT-enabled learning environments. While ICT adoption has the potential to enhance instructional flexibility, autonomy, and efficiency, excessive digital connectivity, intensified workload, and blurred work–life boundaries may adversely affect teachers’ well-being. The paper identifies work life balance as a key mediating factor linking ICT use to job satisfaction and long term professional sustainability. Furthermore, the study situates teachers’ well being within the broader framework of sustainable development, emphasizing its relevance to Sustainable Development Goals such as SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), and SDG 8 (Decent Work and Economic Growth). The analysis underscores the need for human-centred, policy-driven, and ethically oriented ICT integration strategies that prioritize teacher well-being alongside technological advancement. The paper contributes to the discourse on sustainable and intelligent education systems by highlighting that the long-term effectiveness of ICT-driven educational transformation depends on balanced digital practices that support teachers’ work–life balance and job satisfaction.
Authors - Vasumathi R, Kalpana Y Abstract - Graduate communication competency gaps represent a critical barrier to the workforce readiness in the Indian higher education, yet existing assessment infrastructure measures a credential completion rather than the skill trajectories over time. This paper presents a LSTM-CDSF (Long Short-Term Memory Communication Demand and Skill Forecasting), a temporal deep learning based framework that predicts the future communication skill demand from the sequential monthly assessment records and also quantifies per skill gaps against the industry benchmarks. The framework operates on a synthetic dataset of 240 students observed over a period of 18 months calibrated to published NASSCOM and India Skills Report statistics. LSTM-CDSF achieves a Mean Absolute Error of 1.468, RMSE of 1.837, MAPE of 2.61%, and R² of 0.9249 on a held-out test set of 480 sequences, demonstrating consistent performance improvements over the Linear Regression, ARIMA, and a naïve baseline across all the evaluated metrics. Gap analysis reveals that the Digital Communication (gap: 25.4 points) and the Intercultural Communication (gap: 23.5 points) requires the most urgent curriculum interventions.
Authors - M. Kamaraju, B. Rajasekhar, V.N.V.R. Karthik, V.N.L. Mahima, Y.H.V. Satya Narayana, R. Pujitha Abstract - This manuscript presents a dedicated Application-Specific Integrated Circuit (ASIC) architecture purpose-designed for computing eigenvalues of two-dimensional square matrices in resource-constrained embedded systems. The fundamental challenge motivating this work stems from the computational intensity of eigenvalue decomposition in digital signal processing, robotics control systems, and embedded analytics, where conventional software implementations incur unacceptable latency and power overhead. The proposed solution lever-ages the closed-form algebraic solution inherent to 2×2 matrices, eliminating iterative numerical methods and their associated performance penalties. Our design employs a direct characteristic-equation approach mapped onto dedicated arithmetic circuits including parallel multipliers, adders, and a specialized square-root computation unit implementing the non-restoring digit-re-currence algorithm. The Verilog RTL synthesized using Cadence Genus in a 180 nm CMOS standard cell library yields a compact silicon footprint of 1,703 square micrometers utilizing 196 standard cells, with measured power dissipation of 0.5738 milliwatts at 100-megahertz operation. Timing closure is achieved with positive slack under worst-case process-voltage-temperature conditions. The high dynamic-to-static power ratio of 98.66 percent to 1.34 percent indicates activity-dominated power behavior, confirming successful implementation of low-leakage design principles. These metrics demonstrate that the proposed architecture constitutes an effective hardware acceleration solution for eigenvalue computation in battery-powered and always-on applications where conventional approaches prove infeasible.
Authors - Humma Ghaffar, Usman Ali, Muhammad Arfan, Sajid, Muhmmad Mujeeb Akbar Abstract - The growing mental health challenges around the globe need access to scalable, available, and safety conscious digital interventions. The paper describes a mental health support platform, based on AI, which combines conversational intelligence, multi-therapeutic persona modeling, structured mood analytics, proactive crisis identification, multi-lingual interaction, and voice-based access in a secure full stack design. The system, which runs on the Google Gemini AI, provides context-sensitive therapeutic dialogue and performs four-dimensional mood analysis of anxiety, stress, depression, and wellbeing, allowing longitudinal assessment by providing interactive dashboards and automated reporting. A safety-first crisis override system offers validated emergency capacity in the high-risk situations. The platform also includes multilingual voice feedback to facilitate inclusion of the visually impaired users and non-English speaking communities in providing inclusive digital mental health care. The proposed system is capable of changing the prevalent perception that AI and its applications may never be responsible and scalable because it integrates therapeutic diversity, structured analytics, accessibility features, and proactive safety controls into a single framework.
Authors - Pranay Kavthankar, Rutuj Koli, Ronit Ghadi, Yug Mora, Abhijit Joshi Abstract - Speech-to-Speech Translation (S2ST) has evolved from cas caded pipelines into end-to-end neural architectures. However, preserv ing emotion, prosody, and speaker identity across languages remains challenging. This survey examines state-of-the-art emotion and identity preserving S2ST and neural TTS systems, covering discrete-representation models, end-to-end systems, and cascaded pipelines. We analyze architec tures including Translatotron, VQ-Translatotron, SeamlessM4T, VALL E, VALL-E X, VITS, YourTTS, StyleTTS2, and XTTSv2. The survey discusses speaker identity preservation (x-vectors, d-vectors, codec repre sentations), prosody modeling (pitch, duration, energy), emotion reten tion (categorical, dimensional, embeddings), datasets, evaluation met rics, and challenges including data scarcity, cross-lingual emotion trans fer, and computational costs. We propose future directions toward large scale expressive datasets, improved cross-lingual modeling, and respon sible AI practices.
Authors - Maykin Warasart, Pallop Piriyasurawong, Panita Wannapiroon, Prachyanun Nilsook Abstract - This paper introduces an AI-based investment assistant that helps users to understand the fundamental principles of the financial markets. This work is mainly focused on stock market data to provide accurate insights and helps in various decision-making purposes. The rising volatility in the financial markets, massive data set, and the complexity of financial instruments, makes decision-making in financial sectors more difficult to individual investors.In order to cope with this problem, our model integrates time series forecasts, large language model intelligence with real-time financial information with interactive visualizations and personalized insights. The suggested system will interpret user queries in natural language with the help of a Large Language Model (Gemini 2.5 Flash) and extracts the corresponding stock tickers and financial objects and transforms them into structured inputs to be used in predictive analysis. Past and current stock market data are retrieved with the help of yfinance API and fed into an LSTM-based time-series predictive model that predicts future price fluctuations.The results predicted are presented in interactive charts created with Plotly, which users can analyze trends easily and compare several stocks. The system can also give personalized recommendations, textual summaries of stock movements (moving up or down), multi-turn chatbot conversations, portfolio, wishlist and real time price moves besides forecasting. The proposed investment assistant improves the gap between complicated financial information and practical results by incorporating natural language comprehension, deep learningbased prediction, and intuitive visualization etc. The system promotes user knowledge and helps them in effective decision making .
Authors - Gabriel M. da Silva, Nicolas O. da Rocha, Heloise V. C. Brito, Joao V. N. M. da Silva, Sergio A. S. da Silva, Anderson R. de Souza, Carlos A. O. de Freitas, Vandermi Joao da Silva Abstract - Spiking Neural Networks (SNNs) have been investigated as a biologically inspired alternative for efficient information processing, particularly in energy-sensitive applications. This work presents a comparative evaluation of the energy efficiency of different SNN techniques, including Liquid State Machines (LSM), Recurrent Spiking Neural Networks (RSNN), Spiking Convolutional Neural Networks (SCNN), and learning based on Spike-Timing Dependent Plasticity (STDP). The experiments were conducted on conventional hardware plat-forms, namely an Android smartphone and a notebook, using simulated implementations of SNNs without dedicated neuromorphic acceleration. The analysis considered different network scales by varying the number of neurons and was based on neural activity metrics, particularly the total number of generated spikes, employed as a proxy for the indirect estimation of energy consumption during audio signal processing. The results demonstrate a consistent relationship between neural activity and estimated energy consumption, as well as an energy saturation behavior as network complexity increases. Differences among the an-alyzed techniques are more pronounced in small-scale configurations, whereas larger networks exhibit convergent patterns of neural activity and energy consumption. Although conducted in a digital simulation environment, this study highlights the limitations of conventional platforms for the efficient execution of SNNs and reinforces the potential of dedicated neuromorphic hardware for embedded and low-power applications.
Authors - Maykin Warasart, Veerasith Wongkarn, Phonesavanh Nammakone, Duangtavanh Thatsaphone Abstract - Manual correction of written examination scripts is still the default practice in many institutions, but it is slow, tiring for evaluators, and not always consistent, especially when large numbers of papers must be graded in a short time. In this work we look at how recent advances in optical character recognition (OCR), machine learning (ML), and natural language processing (NLP) can be used together to support automatic evaluation of both objective and descriptive answers. In this paper We study a two–stage system: first, a handwriting recognizer based on convolutional and recurrent neural networks (CRNN) is used to read handwritten responses from scanned answer sheets; next, the recognized text is scored using semantic and syntactic similarity measures driven by transformer-based language models. By training the recognizer on a mixture of public handwriting corpora and locally collected scripts, and by combining keyword features with sentence-level embeddings, the system is able to approximate faculty grading patterns with good accuracy. This study examines the way that real tests are administered, including variations in writing styles, background noise in scans, the arrangement of answers on paper, and terms related to specific subjects. We clearly address each of those factors in our approach. Teachers won’t vanish because of this setup; instead, it aims to ease their ongoing tasks while offering fairness and consistency across student results.
Authors - Hai D. Nguyen, Nguyen Ngoc Quan, Viet H. Le, Mai T. Nguyen, Nguyen Huy Trung, Le Duc Huy, Nhu Son Nguyen Abstract - Military forces launch offensive operations to defeat and destroy enemy. Battlefield surveillance enables provisioning of timely and correct battle space information to commanders, both prior and during the launch of offensive operations. Static battlefield surveillance devices have certain limitations which restrict their usage during offensive operations. In the current paper, we review the requirement of surveillance devices during various periods of offensive operations, the limitations of static surveillance devices and efficacy of Unmanned Aerial Vehicles (UAVs) as prime battlefield surveillance device for offensive operations. We then explore the possibility of connecting UAVs with existing cellular base stations and with vehicle mounted cellular base stations which can be moved into enemy territory with the progress of offensive operations. Furthermore, a UAV communication model for enhanced battlefield surveillance during offensive operations is presented after analyzing various antenna techniques utilized to achieve desired data rates for UAV operations.
Authors - Quan Nguyen, Chau Vo, Phung Nguyen Abstract - In order to create reliable connectivity where there is no direct line-of-sight (LOS) path between ground terminals, this study provides the design and performance evaluation of a dual-hop Unmanned Aerial Vehicle (UAV) assisted free space optical communication system. The proposed ground–UAV–UAV–ground architecture enables non-LOS communication by employing aerial relays to bypass physical obstructions and extend transmission coverage. Three modulation formats—Non-Return to Zero (NRZ), Return to Zero (RZ), and Carrier-Suppressed Return to Zero (CSRZ)—under various weather conditions and turbulence regimes are used to assess the system performance. While all modulation schemes perform closely for different attenuation level, differences in performance is prominent under turbulence, CSRZ demonstrates superior robustness, followed by NRZ and RZ.
Authors - Rajesh Kapoor, Vishal Goyal, Aasheesh Shukla Abstract - This paper presents a systematic review of visual sarcasm detection research with a focus on learning-based approaches. The review examines input representations, feature extraction methods, model architectures, datasets, and evaluation practices reported in the literature. Studies are analyzed with respect to the use of visual information, including images and image–text pairs, along with associated deep learning frameworks such as convolutional, transformer-based, and hybrid models. A structured search strategy, defined inclusion criteria, and an analytical framework are employed to ensure consistency and reproducibility of the review process. The findings are synthesized to identify prevailing research patterns, methodological limitations, and gaps related to visual feature representation, model design, and experimental consistency. By organizing and comparing existing approaches, this systematic review provides a consolidated reference and supports future research in visual sarcasm detection.
Authors - G. Sabera, Kanajam Murali Krishna, N. Sabitha, Tummala Purnima, A. Naresh, Shaik Janbhasha Abstract - Complementing the continuous deep integration of culture and tour-ism, the tourism market environment and visitor consumption demand are constantly evolving, with cultural theme attractions playing an increasingly prominent role in tourism industry development. Tourism resources constitute the basic foundation of scenic destination development, while scientific and effective tour-ism marketing provide a key factor in enhancing market competitiveness and achieving sustainable development. Relying on the cultural resources of the Song Dynasty and martial arts culture, The Song Dynasty of Kungfu City has formed a distinctive thematic identity against the background of cultural–tourism integration and has gained a particular level of market attention. However, its tourism marketing practices still face practical challenges such as brand strengthening, intensified market competition, and changing visitor expectations. This study takes The Song Dynasty of Kungfu City as the research object and analyzes the current status of its tourism marketing, exploring the developmental foundation and practical challenges faced by the scenic area under the contemporary tourism market environment. A qualitative research approach is adopted. Relevant data were collected through field observation and in-depth interviews to review the scenic area’s tourism marketing activities. Based on this, the SWOT analytical framework was applied to systematically examine the strengths, weaknesses, opportunities, and threats associated with the tourism marketing status of The Song Dynasty of Kungfu City.
Authors - Sambhram Pattanayak, Akankasha Kathuria, Shreesha Mairaru Abstract - Reliable prediction of rare critical events is a key enabler for modern risk management, civil protection, and decision support sys tems, yet it remains challenging due to extreme class imbalance and strict requirements on false alarm rates. We present an ensemble learn ing framework that combines a deep feed-forward neural network with a Random Forest classifier, complemented by temporal feature engineering and precision-oriented optimization. The approach addresses three ob jectives: extracting informative temporal and regional patterns from raw event logs, learning calibrated probabilistic scores under severe imbalance using focal loss, and tuning per-region decision thresholds to achieve high precision while preserving acceptable recall. As a case study we apply the framework to air alert prediction over 25 administrative regions across 38 months, totalling 774,125 hourly observations. The system attains 96.13% accuracy, 75.1% precision, and 77.9% recall, demonstrating that high-precision early warning is feasible in strongly imbalanced settings. The framework is applicable to a wide range of safety-critical rare event prediction tasks.
Authors - Sanchit Prashant Joshi, Vedant Vipin Joshi, Aditya Arun Mangalekar, G.S.Mundada Abstract - Malware classification is essential in cyber-security. It en ables prevention of threats by identifying and accurately classifying ma licious software. It also helps in understanding attacker behavior, enhanc ing threat intelligence, and improving the overall effectiveness of security systems. It is increasingly critical as adversaries now employ obfuscation techniques to avoid detection. Traditional models such as Convolutional Neural Networks (CNN) often struggle with such obfuscated malware samples. In this paper, we propose MalViT, a Vision Transformer (ViT) based framework for robust malware classification using grayscale image representations of malware binaries. The ViT is fine-tuned on a prepro cessed Malimg dataset. To evaluate the robustness of the model, real world obfuscation techniques such as Encryption, Dead code insertion, Random masking and Junk Padding are simulated. ViT model is initially f ine-tuned on the clean samples and later on a combination of the clean and obfuscated samples. Both models are evaluated on the clean and obfuscated test sets to highlight the robustness of the model. The final model achieved a combined accuracy of 94.52 % on both the clean and obfuscated samples. The results demonstrate that MalViT maintains a competitive performance under obfuscation. This project highlights the potential of ViTs in building resilient malware classification systems and provides a foundation for future work in transformer based architecture for malware analysis.
Authors - Samiksha Ganesh Zagade, Arya Mahesh Parkar, Suman Madan 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 - Adnan Hasan, Ishaan Mishra, Jyotiska Bose, Jada Viswa Chaitanya Sai, Jai Kumar, Kaif Akhter, Ranjita Kumari Dash Abstract - In the present-day context, presentations and computer-based interac tion play a crucial role in various domains, particularly in education and business. Traditionally, users have to rely on physical devices such as mouses, keyboards, or laser. Although these devices meet the basic requirements, they still reveal many limitations regarding mobility, continuity, and dependence on battery life. To address these limitations, hand gesture-based presentation control systems have emerged as a promising solution due to their intuitive, natural, and engaging interaction style. This paper proposes a touchless system that enables users to control common desktop operations as well as presentations in a natural manner using hand gestures captured via a standard webcam. The proposed system lev erages OpenCV for real-time video acquisition and preprocessing, while Medi aPipe framework is employed for hand tracking and landmark extraction. From the experiments, our system can process in real-time with the accuracy of approx imately 92%. As a result, users can seamlessly control slides, use virtual mouse operations, annotate presentation content, and engage with the audience in a more interactive and natural way without physical contact.
Authors - Jyoti Chandel, Meenakshi Mittal 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 - P.N. Deorukhakar, V.B. Waghmare, I.K. Mujawar, R.Y. Patil Abstract - Convolutional Neural Networks (CNNs) have been widely and successfully applied to bioacoustic and passive acoustic monitoring tasks, including soundscape classification. However, the high dimension ality of CNN-derived embeddings often results in increased computa tional cost and reduced efficiency, particularly in iterative learning frame works such as Active Learning (AL) and in scenarios with limited labeled data. This work addresses these limitations by proposing a method for adapting CNN architectures to generate compact and discriminative em beddings tailored to soundscape data classification. The proposed ap proach leverages transfer learning and incorporates three progressively reduced dense layers (512, 256, and 128 neurons), enabling dimensional ity reduction to be learned intrinsically during network training rather than applied as a post-processing step. Experimental evaluations con ducted across multiple soundscapes datasets under the Active Learning paradigm demonstrate that the proposed embeddings consistently out perform conventional CNN embeddings (CNNE) in terms of classification performance and the efficient use of labeled data. These results indicate that integrating dimensionality reduction directly into CNN training en hances representation quality and robustness, offering an effective solu tion for soundscape data classification in labeling-constrained environ ments.
Authors - Domenico D’Uva 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 - Kritika Singhal, Khushi Madeshiya, Utkarsh Upadhyay, Siser Pratap Singh, Surendra Kr. Keshari, Veepin Kumar Abstract - The integration of artificial intelligence in the academic en vironment has been rapidly growing since late 2022. One of the most widely adopted artificial intelligence tools in engineering is the large lan guage model. By using large language models, the engineering students can generate assignment answers, solve problems through code, and ex plain engineering concepts. Unlike traditional approaches, the large lan guage models can reduce time and simplify the students’ work. Many researchers have worked on artificial intelligence tools, most specifically large language models for engineers. This paper reviews the literature on the application of artificial intelligence tools in the following five areas of engineering education, which include programming, problem-solving in the core subjects, intelligent tutoring, technical writing, and simula tion support. Further, this paper discusses the main challenges of large language models in engineering education. Finally, this article concludes by outlining the future scope of large language models in engineering.
Authors - S.Venkata Rakesh, K.Tarun Kumar, A.Lohith, M.Nirupama Bhatt Abstract - One of the world's most destructive types of malware is ransomware, which results in huge financial and data loss around the globe. Current signature-based detection methodologies do not work for the detection of these types of ransomware because they have no way to identify them prior to their creation (zero-day) or when a variant of the ransomware is created (polymorphic). A behaviour-based ransomware detection methodology that involves the use of CPU Hardware Performance Counters (HPC) in combination with machine learning models for the purpose of detecting ransomware activity is the focus of this project. The following HPC metrics will be used to monitor the execution of a program or application while it is executing: instruction count; cache references; cache hits; branch instructions; and CPU cycles. These low-level architectural events will provide information on the unique behaviour characteristics of a ransomware program or application based on the types of behaviours exhibited by the encryption pro-cesses of a ransomware program or application. A labelled dataset of HPC traces of typical programs/applications will be developed by running both standard pro-grams/applications and ransomware in a controlled testing environment. Several supervised learning models such as Random Forest, Support Vector Machines, and Logistic Regression will be trained and validated on the labelled dataset. The experimental results show that ransomware activity causes significantly different HPC metrics, thereby allowing the correct identification of ransomware. The pro-posed methodology will offer a real-time, graphical user interface for real-time monitoring and graphical representation of the detected ransomware program or application.
Authors - Vasavi Ravuri, S. Lalitha Geetanjali, T. Bhavana Sri, V. Praveen, M. Mokshgna Teja Abstract - Unstructured vehicle traffic (i.e. those containing multiple users such as automobile drivers, pedestrians, cyclists, and even animals) creates a significant challenge for road safety. This work presents the development of a real-time road risk assessment (RRA) system for analyzing dashcam video that combines several computer vision techniques: object detection, semantic segmentation, multi-object tracking, and alert classification, into a unified, integrated processing pipeline. Object detection and multi-object tracking are accomplished using the YOLOv8m and ByteTrack with Kalman Filter algorithms. Additionally, semantic segmentation of the road scene is achieved using a SegFormer-B2. Finally, a segmentation-assisted fusion filter and perspective-aware danger zone are applied (to define each point in the field of view as belonging to a zone with certain levels of risk). The Road Intrusion Risk Score (RIRS) is a composite score that quantifies the severity of intrusion accumulated over time, and provides graduated alert levels. Testing of the system on COCO val2017 and four dashcam videos produced reliable object detections with significantly fewer false positives and very close to real-time performance, demonstrating the potential of the system to improve driver assistance systems in unstructured road environments.
Authors - Nathula Dayarathne, Guhanathan Poravi Abstract - This paper presents a novel methodology for predicting bug severity and priority in software development using machine learning models. The approach involves leveraging a manually curated dataset labelled with the support of industry experts, enabling the incorporation of domainspecific knowledge into feature selection and classification. A K-Means clustering method is initially employed to label the collected data, ensuring accurate grouping and feature extraction. The study identifies and utilizes 16 key features for classification and develops separate models for severity and priority prediction. These models, trained on the expertly labelled dataset, achieve high performance with accuracy metrics above 90%. This study uniquely combines K-Means pre-labelling with expert validation to reduce manual annotation while maintaining model accuracy. The proposed method demonstrates the effectiveness of combining clustering techniques with expert-driven labelling for improving bug management processes. By automating severity and priority classification, this research contributes to enhancing the efficiency and reliability of software development workflows.
