Research Digest on Engineering Management and Social Innovations
The Research Digest on Engineering Management and Social Innovations (RDEMS) (p-ISSN: 3117-4418 e-ISSN: 3117-4426) is a peer-reviewed, interdisciplinary journal dedicated to advancing knowledge at the intersection of engineering management and social innovation. The journal publishes high-quality research articles, case studies, reviews, and thought leadership pieces that explore innovative management practices, sustainable engineering solutions, and strategies for driving social impact. RDEMSI serves as a platform for academics, practitioners, and policymakers to share insights on improving organizational efficiency, fostering technological innovation, and addressing societal challenges through collaborative engineering and management approaches. The journal emphasizes the practical application of research findings, promoting solutions that integrate technological advancement with social responsibility. Focus Areas Include: Engineering management strategies and organizational leadership Sustainable and socially responsible innovation practices Case studies highlighting social innovation through engineering solutions Interdisciplinary research connecting engineering, management, and society Policy and framework development for technological and social impact RDEMS aims to bridge the gap between theory and practice, providing a forum where innovative ideas, empirical research, and applied solutions converge to shape the future of engineering and societal development.
Multi-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study
This study presents a multi-class stress detection system based on heart rate variability (HRV) analysis using a deep neural network (DNN) framework. Stress is a significant physiological and psychological factor that affects human health, productivity, and overall well-being, making its timely detection essential in healthcare and human-centered applications. Conventional stress detection methods often rely on binary classification or handcrafted statistical models, which may fail to capture the complex nonlinear patterns present in physiological signals. To address these limitations, the proposed work utilizes HRV features extracted from electrocardiogram (ECG) signals to classify multiple stress levels, enabling a more detailed and realistic assessment of an individual’s stress state. A deep neural network is employed to automatically learn discriminative patterns from HRV data and perform robust multi-class classification of stress conditions such as relaxed, low stress, moderate stress, and high stress. The proposed framework includes signal preprocessing, HRV feature extraction, feature normalization, and DNN-based classification for accurate stress recognition. Experimental evaluation demonstrates that the deep learning-based approach outperforms conventional machine learning models in terms of classification accuracy, robustness, and generalization. The proposed system offers a reliable and intelligent solution for continuous stress monitoring and has significant potential in wearable healthcare, mental health assessment, and real-time physiological monitoring applications.
Stress detection, Heart rate variability, HRV analysis, Deep neural network, Multi-class classification, Electrocardiogram, ECG signal processing, Physiological signal analysis, Mental stress monitoring, Wearable healthcare, Deep learning, Biomedical signal processing, Stress level classification, Real-time health monitoring, Intelligent healthcare systems.
Copyright (c) 2026 R.Nagesh, Uppari Hari (Author)
Smart Agricultural Subsidy recommendation Approach for identifying appropriate formers using ML Techniques
Subsidy on various types of items is more important in agriculture filed to optimize risk factors in production and also support to farmer to cultivate farming with extra benefits and with additional security. The traditional subsidy allocation technique heavily depends on physical verifications, checking large number of rules, verifying a greater number of constraints, these factors delayed the complete subsidy process that in turn facing the issue of identifying appropriate subsidy beneficiaries, and also shows inefficiency in utilizing government subsidy schemes. The farmers data samples are increased day by day with many features and this provide an opportunity to develop an intelligent technique to identify and allocate subsidies to needy farmers on time. In this research we propose an ML based SSR (Smart Subsidy Recommending) approach for allocation various kinds of subsidies, and the main objective is to develop efficient and transparent subsidy recommending techniques by analyzing complete farmer list. In our model, we have taken certain features like previous repayment histories, farmers economical situations, climatic conditions for crap, availability of farming tools, features of soil, characteristics of land, and farmer conditions to allocate various types of subsidies. The ML-SSR technique starts with preprocessing of farmers data, identification of feature vectors, remove irrelevant features with help of feature engineering approaches, allocation of data samples to various stages of ML, calculates various types of indices based on farmers data samples, and finally decide the list of eligible formers for different types of subsidies. Performance of ML-SSRS method is calculated and compared with RBEEA, EAA, ABSS, and TLR subsidy recommended approaches. The method we developed is useful to many government agencies to identify list of eligible farmers without any discrepancies and also prepare the list by maintaining transparency.
Machine Learning, Agriculture Subsidy Schemes, RBEEA, EAA, ABSS, TLR, Recommendation of Subsidies, ML-SSR
Copyright (c) 2026 K.G. Ponnambalam, Dr.J. Sridhar (Author)
A Robust Machine Learning Framework for Phishing Attack Detection Using RF, DT, and XGBoost
Phishing attacks have emerged as one of the most serious cyber threats, particularly in the domain of social engineering, affecting individuals and organizations in their daily activities and business operations. These attacks often result in severe financial losses and the compromise of sensitive information. Traditional phishing detection methods, such as blocklist-based approaches, are ineffective against newly emerging phishing websites due to delays in updating blocklist databases. In recent years, Machine Learning (ML) has demonstrated significant potential across various domains, including cybersecurity, by enabling the development of intelligent and adaptive threat detection systems. This study proposes a phishing attack detection framework using three machine learning algorithms: Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting (XGBoost). To enhance the performance of these models, various feature selection and classification techniques are incorporated. The primary objective of this research is to develop a robust and efficient phishing detection model capable of providing improved accuracy and reliability in identifying phishing attacks. The performance of the proposed models is evaluated using key metrics such as Accuracy, Precision, and Recall. The proposed approach offers notable improvements over traditional phishing detection methods and contributes to advancing cybersecurity solutions for safeguarding individuals and organizations against phishing threats.
Website Classification, Threat Intelligence, Malicious URL Detection, Data Protection, Online Fraud Prevention.
Copyright (c) 2026 Bindu Madhavi.N, R.G. Kumar (Author)
Quantum Intelligence Platform: A Hybrid Quantum-Inspired Framework for Intelligent Decision Prediction and Optimization
The rapid advancement of artificial intelligence (AI) and quantum computing has introduced new paradigms for solving computationally complex problems beyond the capabilities of classical systems. This paper presents a Quantum Intelligence Platform (QIP), a hybrid quantum-inspired computational framework designed to enhance intelligent decision prediction and optimization in complex environments. The proposed system integrates quantum probabilistic modelling concepts such as superposition and interference with classical machine learning algorithms to improve prediction accuracy and adaptive decision-making. A hybrid architecture combining quantum circuit simulation and classical deep learning modules is developed to enable scalable experimentation without requiring physical quantum hardware. The platform is evaluated on decision prediction and optimization tasks, demonstrating improvements in convergence stability and predictive performance compared to conventional models. Experimental results show that quantum-inspired state representation enhances feature expressiveness and probabilistic reasoning. The proposed framework bridges the gap between theoretical quantum computation and practical intelligent systems, offering a scalable pathway for future quantum-enhanced AI applications.
Quantum Intelligence, Quantum-Inspired Computing, Hybrid Architecture, Machine Learning, Decision Prediction, Optimization, Quantum Simulation.
Copyright (c) 2026 G Saikumar, G Indiravathi (Author)
An Interpretable Hybrid AI Framework Combining Machine Learning and Large Language Models for Clinical Diabetes Prediction
The Hybrid System is comprised of three classifier models (Logistic Regression, Gradient Boosting, Random Forest) and uses 100 input parameters within the Diagnostic Schema to create a synthetic cohort of 1000 patients that are demographically, clinically, lifestyle-wise, and lab-wise representative of the real world population of patients. Accuracy, AUC, Precision, Recall, and F1 scores were used to evaluate the performance of the classifiers. The performance of the LLM (Large Language Model) part of the hybrid system was evaluated and compared across the GPT-4, Claude 3.5 Sonnet, and Gemini 1.5 Pro LLM platforms for tasks such as Structured Data Extraction, Schema Validation, and Explanation Generation. All three classifiers had an AUC greater than .960 and F1 Score greater than 0.969; Logistic Regression had the largest AUC (0.972) and most equally-balanced Precision (0.979) and Recall (0.984) values of the three classifiers. Additionally, the GPT-4 platform demonstrated the greatest degree of schema adherence (96.2%) and explanation faithfulness (94.8%) when compared to the other two LLM platforms, though Claude 3.5 Sonnet demonstrated a similar degree of both metrics (Schema Adherence = 95.6% and Explanation Faithfulness = 93.7%). The Hybrid System allows for the separation of the generative language abilities of the LLM from the numerical predictive abilities of the classifiers, thereby providing a means of supporting transparent clinical decision making. Therefore, the proposed hybrid system has the ability to leverage the performance advantages of calibrated ML classifiers and the enhanced clinical usability of an LLM-enabled interface to facilitate early risk assessment for diabetes while simultaneously providing transparency into the clinical decision-making process.
Diabetes mellitus; Machine learning; Large language model; Clinical decision support; Risk prediction; Explainable AI; GPT-4; Claude; Gemini
Copyright (c) 2026 Radha A, Mallika R.M (Author)
An Interpretable DL Approach to Early Identification of Skin Diseases by Combined Using FC-ResNet with Lesion Indexing Framework
Skin diseases affect millions of people worldwide every year, and early detection is important to prevent severe health complications. Traditional skin disease prediction methods mainly depend on dermatology experts, making the diagnosis process time-consuming and dependent on expert availability. Deep Learning (DL) techniques can analyze skin disease symptoms with high accuracy and effectively handle inter-class similarities and inconsistent image patterns. In this research, a hybrid deep learning model combining FC-ResNet and Lesion Index Calculation Unit (LICU) is proposed for accurate skin disease classification and lesion severity analysis. FC-ResNet extracts important spatial features, while LICU measures lesion intensity and pathological characteristics. Experimental results on different datasets show that the proposed FC-ResNet + LICU model achieved 95.9% accuracy, 95.4% precision, 95.1% recall, and 95.5% F1-score. The proposed model outperformed existing models such as VGG16, ResNet50, EfficientNet-B0, DenseNet121, DeepLabV3+, and Attention U-Net.
Deep Learning, Skin Disease, Prediction Model, Convolutional Residual Network, Dice Score, Classification of Melanoma, Medical Imaging, Dermatological Features
Copyright (c) 2026 T.Pushpalatha, Dr.N.Babu (Author)
Enhanced Stock Market Prediction Using Multi-Source Multiple Instance Learning
The stock market is influenced by a multitude of dynamic and often interdependent factors, making accurate prediction a complex task. Traditional machine learning models typically rely on single-source data and assume precise instance-level labels, which may not effectively capture the multifaceted nature of financial markets. In this project, we propose a novel approach for stock market prediction using Multi-Source Multiple Instance Learning (MS-MIL), which enables the model to learn from grouped data instances (bags) derived from various heterogeneous sources such as historical stock prices, financial news, social media sentiment, and macroeconomic indicators. By treating each source as a distinct set of instances and aggregating them under the multiple instance learning framework, the model can better handle weak supervision and uncertainty inherent in financial data. Our MS-MIL framework integrates both numerical and textual data, applying advanced feature extraction and attention mechanisms to learn discriminative representations. Experimental results demonstrate that the proposed method achieves superior performance in predicting stock movement direction and market trends compared to traditional learning models. This approach offers enhanced robustness, adaptability, and interpretability, making it a promising tool for investors and analysts in making informed decisions.
Stock Market Prediction, Multi-Source Learning, Multiple Instance Learning (MIL), Financial Time Series Analysis, Sentiment Analysis, Machine Learning, Deep Learning, Feature Extraction, Attention Mechanism, Market Trend Prediction.
Copyright (c) 2026 Samson Paul, Kuruva Ravi Teja (Author)
A Hybrid Deep Learning Model To Predict High-Risk Students In Virtual Learning Environments
This project presents “A Hybrid Deep Learning Model to Predict High-Risk Students in Virtual Learning Environments”, an intelligent web-based prediction system developed to identify students who are at risk of low engagement and poor academic performance in online learning platforms. With the rapid growth of virtual education systems, monitoring student participation and learning behavior has become a major challenge for educational institutions. The proposed system uses machine learning and deep learning techniques to analyze student interaction data collected from virtual learning environments and accurately predict student engagement levels. The system considers multiple behavioral and academic features such as weekly study time, quiz score average, forum participation, percentage of video content watched, assignment submission count, login frequency, average session duration, device type, course difficulty, and regional information. The collected dataset is preprocessed using techniques such as missing value removal, label encoding for categorical features, and standard scaling for numerical normalization to improve prediction performance. After preprocessing, the dataset is divided into training and testing datasets for model development and evaluation. Multiple algorithms including XGBoost, Convolutional Neural Network (CNN), Random Forest, and Decision Tree are implemented and compared to determine the most effective prediction model
Hybrid Deep Learning, High-Risk Student Prediction, Virtual Learning Environment (VLE), Student Performance Analysis, Educational Data Mining, Machine Learning in Education, Learning Analytics, Academic Risk Detection, Artificial Intelligence, Student Behavior Analysis, Predictive Analytics, Online Learning Systems, Deep Neural Networks, E-Learning Analytics, Intelligent Education Systems
Copyright (c) 2026 M. Ahanibasha, Shaik Mohammed Arif (Author)
AI-Based Android Malware Detection System Using Machine Learning For Mobile Security
The rapid growth of Android applications has increased the risk of malware attacks on mobile devices, leading to threats such as data theft, unauthorized access, and privacy violations. Traditional signature-based malware detection techniques are often ineffective against newly emerging and evolving malware attacks. To address this issue, this project proposes an AI-Based Android Malware Detection System Using Machine Learning for Mobile Security that intelligently detects malicious Android applications using machine learning algorithms. The system is developed using the Django framework and integrates user authentication, dataset uploading, machine learning model training, and malware prediction functionalities. In this system, Android application datasets are uploaded and processed using the Pandas library. The dataset is divided into training and testing data using the train-test split method. A Support Vector Machine (SVM) classifier is used to train the model for identifying malicious and safe applications. The trained model is then stored using Joblib for future predictions. The proposed system allows authenticated users to upload test datasets and predict whether applications are malware or safe. The prediction results display the total number of applications analyzed along with malware and safe application counts. The system improves mobile security by providing fast, accurate, and automated malware detection. Experimental results demonstrate that the machine learning-based approach achieves high accuracy and effectively enhances Android malware detection compared to traditional methods. The project provides a scalable and user-friendly solution for securing Android devices against malicious applications and contributes to the advancement of intelligent mobile security systems using artificial intelligence and machine learning techniques.
Android Malware Detection, Machine Learning, Artificial Intelligence, Mobile Security, Django Framework, Support Vector Machine (SVM), Malware Classification, Safe Applications Detection, Dataset Processing, Pandas Library, Train-Test Split, Model Training, Joblib Serialization, Android Application Security, Cybersecurity, Intelligent Threat Detection, Data Mining, Feature Extraction, Predictive Analytics, Automated Malware Detection, Scalable Security System, Mobile Threat Prevention, AI-Based Security System.
Copyright (c) 2026 M.G.K.Priyanka, Kalapari Ranga Swamy (Author)
Heart Disease Prediction Using Machine Learning Algorithms
Heart disease is one of the major causes of death across the world, which makes early diagnosis extremely important. This project focuses on developing a prediction system using machine learning techniques to identify the risk of heart disease in patients. The system analyzes medical and demographic data such as age, cholesterol level, blood pressure, and heart rate.Different machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine are applied to study patterns within the dataset. The effectiveness of these machine learning models is measured using evaluation metrics such as accuracy, precision, recall, and F1-score to understand how well the models predict heart disease. Among the tested algorithms, ensemble methods like Random Forest usually provide better accuracy and stability.The developed system is intended to support healthcare professionals in diagnosing heart disease more quickly and effectively. By assisting doctors in early detection, the system can help improve patient health outcomes and reduce medical costs.
Machine Learning, Heart Disease Prediction, Classification, Healthcare Analytics, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Data Mining, Predictive Modeling, Medical Diagnosis, Feature Selection, Accuracy, Precision, Recall.
Copyright (c) 2026 S. Sravanthi, M. Shanmukha Priya, T. Sai Lakshmi, Y .Blessee Devamani, Dr .N. Ramesh Babu (Author)
Enhancing Phishing Detection: A Machine Learning Approach with Feature Selection and Deep Learning Models
Phishing attacks continue to pose significant threats to online security, with the potential to steal sensitive user data and compromise systems. To address this issue, this project introduces a machine learning-based approach for enhancing phishing detection by integrating feature selection techniques and deep learning models. The project first utilizes a dataset of phishing and legitimate URLs, containing various features such as URL length, number of dots, and subdomain levels. Through feature selection methods like SelectKBest and mutual information, the most relevant features are identified, which improve the model’s ability to distinguish between phishing and legitimate sites. Subsequently, Multi- Layer Perceptron (MLP) models are employed to learn complex relationships between the features and accurately classify URLs as phishing or legitimate. The MLP architecture consists of multiple layers of neurons, enabling it to model non- linear patterns in the data, which enhances its performance for phishing detection. The proposed model is trained and evaluated using standard performance metrics, including accuracy, precision, recall, and F1 Score. Experimental results demonstrate the effectiveness of combining feature selection with MLP-based deep learning for phishing detection, yielding high detection accuracy and low false-positive rates. This method offers an enhanced solution for real-time phishing threat detection and can be integrated into web browsers or security systems to mitigate phishing attacks and protect user data. This approach underscores the potential of machine learning and deep learning in cybersecurity, paving the way for more adaptive and efficient phishing-detection systems.
Phishing Detection, Machine Learning, Multi- Layer Perceptron (MLP), Feature Selection, Deep Learning, URL Classification, Phishing Websites, Model Evaluation, Accuracy, Precision, Recall, F1-Score, Cybersecurity, Data Mining.
Copyright (c) 2026 S. Sadik Basha, S. Shahrukh, M. Thrishul, M. Mohan, P. Sai Ganesh, Dr. N Ramesh Babu (Author)
Verbal-To-Visual AI Forensic Face Reconstruction System
The Verbal-to-Visual AI Forensic Face Reconstruction System is a project that focuses on converting verbal descriptions of a person into a visual facial representation and comparing it with a stored face database to identify possible matches. The system helps reduce the limitations of traditional manual forensic sketching, which is time-consuming and highly subjective. In this project, user-provided descriptions are processed to generate a facial image using artificial intelligence techniques. The generated face is then analyzed and matched against a database of facial images to determine similarity levels. The system presents the matching results in a ranked manner along with confidence percentages, helping investigators identify potential leads more efficiently. This project is developed for educational and research purposes and demonstrates the practical application of artificial intelligence in forensic investigations, highlighting its potential to support faster and more consistent suspect identification. The proposed system is designed with a simple and user-friendly interface, making it easy for users to provide inputs and analyze results. It emphasizes decision support rather than final identification, ensuring that the generated outcomes are treated as investigative leads. By automating the face reconstruction and matching process, the system improves efficiency and consistency while demonstrating how emerging AI technologies can assist forensic analysis in a controlled and ethical manner for academic use.
Copyright (c) 2026 Dr. K. Vijayalakshmi, R. Revathi, K. Tamil Selvi (Author)
Implications of Human Resource Analytic Practices on Telangana State Pharmaceutical Industry
This study investigates the implications of HR analytics practices on organizational performance in Telangana's pharmaceutical industry. Primary data was collected from 250 HR professionals across pharmaceutical companies in Hyderabad and Genome Valley. Multiple regression analysis revealed that HR analytics adoption (β=0.54, p<0.001), organizational preparedness (β=0.36, p<0.01), and technological readiness (β=0.31, p<0.01) significantly influence HR effectiveness and organizational performance, explaining 71% of variance. Correlation analysis confirmed strong positive relationships between HR analytics and talent acquisition efficiency (r=0.77), employee retention (r=0.72), and performance management (r=0.74). ANOVA results demonstrated that Genome Valley companies with mature analytics achieve 32% faster recruitment, 26% lower turnover, and 24% cost reduction. The findings underscore the strategic importance of data-driven HR practices in Telangana's ₹1,26,450 crore pharmaceutical ecosystem. Organizations adopting comprehensive HR analytics frameworks demonstrate enhanced talent management outcomes and competitive advantage in India's fastest-growing pharmaceutical hub.
HR Analytics, Telangana Pharmaceutical Industry, Talent Management, Organizational Performance, Genome Valley.
Copyright (c) 2026 V Vijaya Kumar, A.Sarveswara Reddy, G.Ashok (Author)
Deadline Constrained Modified Min-Min algorithm for Cloudlet Scheduling
Cloud computing provides scalable and cost-effective computing services through virtualization and distributed resource sharing. Efficient task scheduling and load balancing are important challenges in cloud environments, as improper cloudlet allocation may increase Makespan, reduce resource utilization, and cause deadline violations. Most existing scheduling algorithms mainly focus on minimizing completion time and often ignore important factors such as current VM load, computing capability, expected execution time of cloudlets on different virtual machines, and task deadlines. This paper proposes a Deadline-Constrained Modified Min-Min (DCMM) scheduling algorithm for efficient cloudlet allocation and dynamic load balancing in cloud computing environments. The proposed algorithm initially allocates cloudlets using a modified Min-Min approach by selecting the VM with minimum completion time. A rescheduling mechanism is then applied to balance workload by migrating tasks from heavily loaded virtual machines to lightly loaded ones whenever better completion time can be achieved. The algorithm also considers task deadlines and reduces the number of missed deadlines during scheduling. The proposed method is implemented using the CloudSim simulation framework and compared with the Smarter Round Robin (SRR) algorithm. Experimental results show that the DCMM algorithm achieves better Makespan, improved load balancing, higher resource utilization, and more tasks meeting their deadlines.
Cloudlet Scheduling, Load Balancing, Min Min Algorithm, Deadline
Copyright (c) 2026 Lipika Datta, Sudip Ghosh, Siddhartha Chakraborty, Siddhartha Sahu, Subham Nandi (Author)
Drishti - An Autonomous Surveillance Bot Using Raspberry Pi
This paper introduces Drishti, an intelligent autonomous monitoring robot built on the Raspberry Pi 4 platform to provide productive and effective, real-time observation. The system combines several sensor modules; vision, ultrasonic distance sensing, inertial measurement, and GPS/GSM communication; to support self-navigation, collision avoidance, and independent decision-making. By integrating computer vision techniques with lightweight deep learning algorithms, Drishti can identify items, recognize faces, and evaluate potential threats within its surroundings. A hybrid SLAM architecture, including DROID-SLAM, SuperGlue, and a Neo4j-based Graph Retrieval-Augmented Generation (GraphRAG) network, enables precise mapping and context-aware localization. The robot moreover features a custom battery management and encryption system that enhances energy productivity and data security. Experimental validation shows that Drishti delivers consistent and stable operation, low-latency wireless communication, and stable performance in dynamic environments The proposed design provides a compact, low-cost, and scalable approach for intelligent monitoring across residential, industrial, and public-safety domains.
Autonomous robot, Raspberry Pi, Surveillance system, SLAM, Path planning, Real-time navigation.
Copyright (c) 2026 Tilak Kumar L, Vinay Shivananda Jumanal, Sinchana R, Srihari K S, Sikander K Irfan (Author)
Automatic Real Time Multi Hazard Detection and Prevention System for Railway Safety using Machine Learning and IoT
Railway transportation is one of the most essential modes of travel and goods movement, but it is highly vulnerable to accidents caused by track cracks obstacles, fires, and unauthorized access. This paper proposes an intelligent real-time multi-hazard detection and prevention system that combines the Internet of Things (IoT) with Machine Learning (ML) to enhance railway safety. The system employs multiple sensors and a camera module to monitor the track and surrounding environment continuously. Detected hazards trigger immediate alerts through wireless communication modules, enabling timely preventive action. Machine learning algorithms are utilized to improve detection accuracy and reduce false alarms. The proposed model operates autonomously, minimizing human intervention while ensuring faster response times. Experimental results confirm that the system is cost-effective, scalable, and capable of significantly improving railway safety and reliability.
Internet of Things (IoT), Machine Learning (ML), Railway Safety, Multi-hazard Detection, Real-Time Monitoring, Automation, Predictive Analytics, Smart Transportation
Copyright (c) 2026 Rajeshwari Shetty, Dr. Shazia Sulthana, Nandita Krishna K, Lokeshwari BN, Tejaswini V (Author)
Smart Vehicle Parking Reservation System Using Web And Mobile Technologies
The rapid increase in urban population and vehicle usage has intensified the problem of finding suitable parking spaces, especially during peak hours. Traditional parking systems are inefficient, time-consuming, and contribute to traffic congestion and environmental pollution. To address these challenges, this project proposes an "Online Vehicle Parking Reservation System" that allows users to view, book, and manage parking spaces in real time through a web or mobile interface. This system enables vehicle owners to check space availability, make advance bookings, and pay online, thereby saving time and avoiding the stress of finding parking spots manually. For parking administrators, the system provides efficient space allocation, real-time monitoring, and utilization analysis, ensuring maximum occupancy and streamlined operations. Integrated with location-based services and automated notifications, this intelligent parking management solution enhances user convenience, reduces waiting times, and contributes to smarter urban mobility. The system is designed to be scalable and can be integrated with IoT sensors and QR-based check-ins for enhanced automation and accuracy.
Smart Parking System, Vehicle Parking Reservation, Internet of Things (IoT), Mobile Application, Web-Based System, Real-Time Parking Availability, Cloud Computing, GPS Navigation, Parking Slot Detection, Smart City Infrastructure, Sensor-Based Parking Management, Digital Payment Integration, Location-Based Services, Traffic Congestion Reduction, Intelligent Transportation Systems (ITS).
Copyright (c) 2026 1Dr.B.Subba Reddy, B Karthik (Author)
Empowering Women: A Creative Approach To Integrated Safety With Machine Learning Algorithms
Ensuring the safety and security of women in public and private spaces remains a critical global challenge. Traditional safety measures, while important, often lack real-time responsiveness and predictive capabilities to effectively prevent incidents before they occur. This project presents a creative and integrated approach to women’s safety by leveraging advanced machine learning algorithms to detect, predict, and respond to potential threats dynamically.The proposed system combines data from multiple sources such as real-time video surveillance, social media signals, location tracking, and emergency alerts to create a comprehensive safety network. Machine learning models analyze these data streams to identify suspicious behavior, abnormal patterns, and early warning signs of danger. By integrating various data inputs, the system aims to provide timely alerts and actionable insights to both users and law enforcement agencies.Key machine learning techniques utilized in this project include supervised learning for threat classification, anomaly detection to highlight unusual activities, and natural language processing to analyze text-based inputs like social media posts or emergency messages. These models are trained on diverse datasets to improve accuracy and reduce false alarms, thereby ensuring the system's reliability and effectiveness.Moreover, the solution incorporates a user-friendly mobile application that empowers women to instantly report incidents, share their location, and receive real-time safety notifications based on predictive analysis.
Women Safety, Machine Learning, Threat Detection, Anomaly Detection, Real-Time Monitoring, Predictive Analytics, Emergency Alert System, Natural Language Processing (NLP), Location Tracking, Smart Surveillance, Social Media Analysis, Public Safety, Crime Prevention, Mobile Safety Application, Artificial Intelligence.
Copyright (c) 2026 U. Padmavathi, Mahesh (Author)
Integrating Artificial Intelligence and Blockchain for Reliable Early Disease Diagnosis
The early detection of human diseases plays a vital role in improving treatment effectiveness and reducing healthcare costs. Recent advancements in Artificial Intelligence (AI) and Blockchain technologies provide new opportunities to enhance traditional diagnostic systems. AI techniques such as machine learning and deep learning enable efficient analysis of healthcare datasets, allowing accurate prediction of diseases at early stages. At the same time, blockchain-inspired mechanisms ensure secure, transparent, and tamper-proof handling of sensitive healthcare data. By integrating AI-based predictive models with secure data handling approaches, the proposed system provides a reliable and efficient framework for disease prediction. This work demonstrates a simulation-based system that combines intelligent prediction with secure data management, highlighting the potential of modern technologies in building next-generation healthcare solutions.
Artificial intelligence, blockchain, early disease diagnosis, healthcare security, machine learning, medical data privacy, deep learning, disease prediction, smart healthcare, decentralized healthcare systems.
Copyright (c) 2026 Ahani Basha, Shaik Darga Jabid (Author)
Improving Cloud Data Transmission Security Through Machine Learning Techniques
The rapid adoption of cloud computing has revolutionized data storage and access, but it has also introduced significant challenges in securing data transmission. Traditional security mechanisms, while effective to an extent, are increasingly inadequate in the face of sophisticated cyber threats. This paper explores the integration of machine learning (ML) techniques to enhance the security of data transmission in cloud environments. We propose a hybrid model that employs anomaly detection, encryption optimization, and real-time traffic analysis using supervised and unsupervised ML algorithms. The model dynamically identifies and mitigates potential threats, such as data leakage, man-in-the-middle attacks, and unauthorized access, by learning from historical network patterns and user behavior. Experimental results demonstrate improved accuracy in threat detection and reduced response time compared to conventional methods. The findings highlight the potential of machine learning to provide adaptive, intelligent, and scalable security solutions for cloud data transmission.
Cloud security, data transmission, machine learning, intrusion detection, anomaly detection, data encryption, cybersecurity.
Copyright (c) 2026 G. Sravani Prasad, Devendra Reddy (Author)
SKYWARD SAFEGUARD USING MACHINE LEARNING
The Skyward project represents a groundbreaking fusion of machine learning and web development, aimed at harnessing the power of predictive analytics to forecast cloud bursts based on a myriad of weather parameters. At its core, the project leverages the Django web framework to construct a robust and scalable backend infrastructure, facilitating seamless communication between users and the predictive model. Within this framework, a sophisticated Support Vector Machine (SVM) model, trained on extensive historical weather data, stands as the cornerstone of predictive analysis. This model intricately analyzes a multitude of weather variables, including temperature, humidity, wind speed, and visibility, to discern patterns and correlations indicative of impending cloud bursts. Integrated seamlessly into the Django backend, this machine learning model processes user-inputted weather data in real-time, delivering precise predictions with remarkable accuracy. On the frontend, the project boasts an intuitive and user-friendly interface crafted using HTML templates and enhanced with Bootstrap styling, ensuring a seamless user experience. Through a simple yet elegant form, users can effortlessly input weather parameters and receive instantaneous predictions, empowering them with actionable insights for risk assessment and decision-making. As a testament to the symbiotic relationship between technology and meteorology.
Copyright (c) 2026 P Nageswaramma, Shaik Mohammed Azezul Rahiman (Author)
An Intelligent Deep Learning Approach For Accurate Recyclable Waste Categorization
Efficient waste segregation plays a crucial role in sustainable environmental management and smart city development. Manual waste sorting is often time-consuming, inconsistent, and prone to human error. To overcome these challenges, this research proposes an intelligent deep learning-based waste classification framework capable of accurately identifying and categorizing recyclable waste items. The system employs a Convolutional Neural Network (CNN) architecture trained on image datasets containing multiple recyclable categories such as plastic, paper, glass, and metal. Advanced preprocessing techniques and data augmentation are applied to improve generalization and robustness under varying lighting and background conditions. The model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate a significant improvement in classification efficiency, making the proposed framework a reliable and scalable solution for automated waste management systems. This study highlights the potential of AI-driven technologies in promoting recycling practices and environmental sustainability.
Recyclable Waste Classification, Deep Learning, Convolutional Neural Network (CNN), Waste Segregation, Image Classification, Smart Waste Management, Data Augmentation, Environmental Sustainability, Automated Recycling, Artificial Intelligence.
Copyright (c) 2026 K. Kumara Swamy, Suddala Surya Narayana (Author)
Teacher Student Inter Brain and Behavioral Synchronies in Remote Education
This study explores Teacher–Student Inter-Brain and Behavioral Synchronies in Remote Education, focusing on how neural and behavioral coordination between teachers and students influences learning outcomes in online learning environments. With the rapid growth of remote education, understanding the cognitive and social mechanisms that support effective virtual teaching has become increasingly important. Inter-brain synchrony refers to the alignment of neural activity between individuals during communication, while behavioral synchrony includes coordinated actions such as eye contact, facial expressions, gestures, and response timing. These synchronies play a significant role in enhancing attention, engagement, understanding, and emotional connection during teaching and learning processes.The proposed research investigates how teacher-student interactions in remote settings affect both brain synchronization and behavioral patterns, and how these factors contribute to student engagement and academic performance. Advanced technologies such as EEG (Electroencephalography), video analysis, and machine learning techniques are used to monitor and analyze these synchronies in real-time virtual classrooms. The study aims to identify patterns that distinguish effective teaching sessions from less interactive ones. The findings can help improve online education platforms by designing more interactive and personalized learning experiences, ultimately bridging the gap between traditional classroom teaching and remote education systems.
Teacher-Student Interaction, Inter-Brain Synchrony, Behavioral Synchrony, Remote Education, Online Learning, EEG Analysis, Virtual Classroom, Student Engagement, Neural Synchronization, Educational Technology, Learning Outcomes, Human-Computer Interaction, Real-Time Monitoring, Machine Learning, Cognitive Neuroscience.
Copyright (c) 2026 Dr C Gulzar, Boya Sharath Kumar (Author)
Predicting Hospital Stay Length Using Explainable ML
Predicting the length of hospital stay (LOS) is a critical task in healthcare management, as it directly impacts hospital resource allocation, patient care, and operational efficiency. This project presents a machine learning-based approach to accurately predict the duration of a patient’s hospital stay using clinical and demographic data. Traditional methods rely heavily on physician estimates, which may be subjective and inconsistent. The proposed system utilizes historical patient data, including medical history, diagnosis, lab results, and treatment plans, to train predictive models. Various machine learning algorithms such as Linear Regression, Decision Trees, and Random Forest are employed to analyze patterns and forecast LOS. The system processes data through preprocessing, feature selection, and model training phases to ensure high prediction accuracy. By providing early predictions, hospitals can optimize bed management, reduce overcrowding, and improve patient flow. The system also supports healthcare professionals in decision-making by offering data-driven insights. Ultimately, this solution enhances operational efficiency, reduces healthcare costs, and improves patient satisfaction. The integration of predictive analytics into healthcare systems represents a significant step toward intelligent and efficient hospital management.
Hospital Length of Stay (LOS), Explainable Machine Learning (XML), Healthcare Analytics, Predictive Modeling, Clinical Decision Support Systems, Electronic Health Records (EHR), Feature Importance Analysis, Patient Risk Stratification, Medical Data Mining, Supervised Learning Algorithms, Model Interpretability, SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), Healthcare Resource Optimization, Patient Outcome Prediction.
Copyright (c) 2026 Dr.C.Gulzar, Madhu (Author)
Multi-Modal Deep Learning Framework for Pilot Situation Awareness Assessment Using Eye Gaze and Flight Control Signals
Human factors and pilot situation awareness play a critical role in aviation safety, especially during complex flight operations where cognitive overload and loss of attention may lead to accidents. This project presents a multimodal deep learning framework for pilot situation awareness analysis using gaze position and flight control data. The proposed system integrates human eye-tracking information with aircraft flight parameters such as throttle, altitude, speed, pitch, roll, and yaw to evaluate the cognitive awareness level of pilots in real time. The system is implemented using Python and Flask for web-based interaction, while machine learning algorithms including XGBoost, Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression are used for classification and comparative analysis. The uploaded aviation dataset undergoes preprocessing techniques such as missing value removal, label encoding, and feature standardization before model training. Multiple classifiers are trained and evaluated, and the model with the highest accuracy is automatically selected as the best prediction model. During prediction, the trained system analyzes pilot gaze coordinates and flight control behavior to classify the pilot’s situation awareness level as either High Awareness or Low Awareness. Based on the prediction results, the system also generates intelligent safety recommendations to assist in maintaining stable flight operations and reducing pilot workload. Experimental results demonstrate that the proposed framework effectively improves awareness detection accuracy and provides real-time decision support for aviation safety monitoring systems. The developed application offers a scalable and intelligent human-centered aviation monitoring solution that can contribute to reducing human-error-related incidents in modern aircraft operations.
Pilot Situation Awareness, Multi-Modal Deep Learning, Eye Gaze Tracking, Flight Control Data, Human Factors in Aviation, Pilot Behavior Analysis, Aviation Safety, Deep Neural Networks, Attention Monitoring, Flight Data Analytics.
Copyright (c) 2026 R.Vishnuvardhan, Shaik Sana Ahmmed (Author)
Enhancing Emergency Response in Road Accidents: a severity Prediction Frame work Using RF-RFE and Deep Learning Model
Road accidents remain a major public safety concern, where timely and accurate assessment of accident severity is critical for improving emergency response and reducing fatalities. This study proposes a severity prediction framework that integrates Random Forest–Recursive Feature Elimination (RF-RFE) with a deep learning model to enhance prediction accuracy and decision-making. The RF-RFE technique is employed to identify and select the most relevant features from large and complex accident datasets, effectively reducing dimensionality and improving model efficiency. The selected features are then fed into a deep learning model capable of capturing complex nonlinear relationships among variables to predict accident severity levels. The proposed framework aims to assist emergency services in prioritizing responses, optimizing resource allocation, and minimizing response time. Experimental results demonstrate that the hybrid approach outperforms traditional machine learning models in terms of accuracy, precision, and recall. Overall, this system provides a robust and scalable solution for intelligent traffic management and emergency response enhancement, contributing to safer road environments.
Road Accident Severity Prediction, Emergency Response, Random Forest, RF-RFE, Deep Learning, Feature Selection, Machine Learning, Traffic Safety, Predictive Analytics, Intelligent Transportation Systems
Copyright (c) 2026 Dr. H.Ateeq Ahmed, kuruva Giribabu (Author)
Li-Ion Battery Life Prediction Using Deep Learning
Lithium-ion (Li-ion) batteries are widely used in electric vehicles, portable electronics, and renewable energy storage systems due to their high energy density and long lifespan. However, accurately predicting battery life and degradation remains a critical challenge for ensuring reliability, safety, and efficient energy management. Traditional methods for battery life prediction rely on empirical models and statistical techniques, which often fail to capture complex nonlinear degradation patterns under varying operating conditions. This paper presents a deep learning-based approach for predicting the remaining useful life (RUL) of Li-ion batteries. The proposed system utilizes advanced deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) to analyze battery performance data, including voltage, current, temperature, and charge-discharge cycles. These models effectively learn temporal and spatial features from historical data, enabling accurate prediction of battery degradation trends. Experimental results demonstrate that the proposed approach outperforms traditional machine learning methods in terms of prediction accuracy and robustness. This study contributes to improving battery management systems, enhancing operational safety, and optimizing maintenance strategies in energy storage applications.
Li-ion battery, battery life prediction, deep learning, remaining useful life (RUL), LSTM, CNN, battery degradation, energy storage systems, predictive maintenance, and time-series analysis.
Copyright (c) 2026 R.Vishnuvardhan, Shaik Hussain Nawazish (Author)
Multi-class adaptive learning for predicting student anxiety
This project presents a machine learning-based approach for predicting student anxiety levels using a multi-class adaptive learning framework. The system analyzes various student-related attributes such as age, gender, course, academic performance, marital status, and mental health indicators like depression and panic attacks to identify different levels of anxiety, including no anxiety, mild, moderate, and severe anxiety. Data preprocessing techniques such as handling missing values, label encoding for categorical features, and normalization are applied to improve data quality and model performance. Multiple classification algorithms, including Decision Tree, Random Forest, and Support Vector Machine, are implemented and compared to achieve accurate predictions. The adaptive learning component enables the system to update and improve its performance as new data is introduced, making it more robust and scalable over time. A user-friendly web application is developed using Flask, allowing administrators to upload datasets, train models, and evaluate performance, while users can input their details to receive real-time anxiety predictions. This system aims to assist in early detection of mental health issues among students, enabling timely intervention and support, thereby contributing to improved academic performance and overall well-being.
Machine Learning, Multi-class Classification, Adaptive Learning, Student Anxiety Prediction, Mental Health Analysis, Data Preprocessing, Random Forest, Support Vector Machine, Decision Tree, Feature Engineering, Classification Algorithms, Predictive Analytics, Flask Web Application, Early Detection, Student Well-being
Copyright (c) 2026 Sravani Prasad, Polakal Shaik Mohammed Arif (Author)
Interpretable Machine Learning For Predicting Climate Change Effect On Agricultural Land Suitability In Eurasia
Climate change is rapidly transforming temperature and precipitation patterns, posing serious challenges to agricultural sustainability across Eurasia. Accurately predicting changes in agricultural land suitability is essential for effective long-term planning. Conventional statistical models often fail to provide both high accuracy and interpretability. This project presents an interpretable machine learning approach to assess climate change impacts on agricultural land suitability. Multiple climate indicators, soil properties, and land-use parameters are used as input features. Machine learning models such as Random Forest and Gradient Boosting are developed for prediction. Model performance is evaluated using standard accuracy and error metrics. To enhance transparency, interpretability techniques such as SHAP are applied. These methods reveal the relative importance of climatic and environmental factors. Spatial analysis is conducted to identify regional variations in suitability changes. The model highlights vulnerable and resilient agricultural zones across Eurasia. The findings support explainable, data-driven agricultural.
Climate Change, Agricultural Land Suitability, Interpretable Machine Learning, Explainable AI, SHAP, Random Forest, Gradient Boosting, Eurasia, Spatial Analysisltural policy and climate adaptation strategies.
Copyright (c) 2026 Dr.C Gulzar, Boya Ramachandra (Author)
Machine Learning-Based Earthquake Emergency Evacuation System
The Estimation in Earthquake Early Warning system is designed to predict and analyze earthquake-related parameters using Machine Learning techniques. The primary objective of the system is to provide an efficient and accurate early warning mechanism that can help reduce the impact of earthquakes on human life and infrastructure. The system collects seismic and environmental data, processes it using data mining and predictive algorithms, and generates warning predictions based on earthquake intensity and probability.The proposed model utilizes advanced Machine Learning algorithms to analyze historical earthquake datasets and identify hidden patterns associated with seismic activities. Various parameters such as magnitude, depth, latitude, longitude, and time are considered for prediction. The system improves prediction accuracy through preprocessing, feature extraction, and model training techniques. A user-friendly web-based interface developed using Django allows users and administrators to monitor predictions, visualize analytical reports, and manage earthquake-related data efficiently.The proposed approach offers advantages such as faster prediction, improved reliability, reduced manual analysis, and real-time monitoring capabilities. By providing early warnings and predictive insights, the system aims to support disaster management authorities and enhance public safety measures during seismic events.
Earthquake Early Warning, Machine Learning, Seismic Data Analysis, Earthquake Prediction, Data Mining, Predictive Analytics, Django Framework, Real-Time Monitoring, Disaster Management, Seismic Activity Detection, Artificial Intelligence, Earthquake Magnitude Estimation, Risk Assessment, Pattern Recognition, Early Warning System.
Copyright (c) 2026 Dr.G.Thippanna, Chinna Musali Chakradhar (Author)