International Journal of Engineering and Science Research
Secure Her : A Voice Controlled Women Safety App
Ensuring personal security, especially for women, has become an increasingly important concern in modern society. To address this challenge, this project proposes an intelligent women safety application designed to provide immediate assistance during emergency situations. The system integrates modern web technologies, real-time communication, and artificial intelligence to create a responsive and reliable safety platform. Users can activate emergency alerts either manually through an SOS button or through voice commands, ensuring accessibility even when physical interaction with the device is difficult.The application utilizes technologies such as Firebase Authentication, the Geolocation API, and Google Maps services to enable secure user login and precise location identification. When the SOS function is triggered, the system automatically captures the user's real-time geographical coordinates and generates a shareable map link. This information is instantly transmitted to pre-registered emergency contacts through an automated SMS service, enabling faster response and accurate location tracking during critical situations.To enhance usability, the system includes an AI-based voice recognition component capable of detecting emergency phrases like “HELP” or “SOS.” This functionality allows the application to operate in a hands-free mode, which can be essential in threatening circumstances. Additionally, the platform incorporates a real-time monitoring interface supported by Folium that visually displays the user’s movement on an interactive map, improving situational awareness for emergency responders and trusted contacts.By combining cloud authentication services, geospatial technologies, artificial intelligence, and automated communication tools, the proposed solution enhances emergency response efficiency and promotes proactive personal safety. The system demonstrates a scalable and user-friendly approach to modern safety applications, encouraging the integration of smart technologies into everyday personal security practices.
Women Safety, SOS Alerts, Voice Recognition, Real-Time Tracking, Geofencing, Crime Data Analysis, AI Safety Scoring, Danger Zone Detection, Safer Route Suggestions, Live Location Sharing, Emergency Contacts, Flutter, Firebase Authentication, Google Maps API, Twilio SMS Service.
Rithisha Aritakula,Samiha Sarwar,Shaik Mahin
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
Preparing for job interviews has become increasingly important for students and job seekers in the rapidly evolving technology sector. However, many candidates face difficulties due to the absence of realistic interview practice environments and personalized feedback. Conventional preparation approaches, such as reading interview questions or solving coding problems, often fail to replicate real interview conditions and therefore provide limited insight into a candidate’s performance.This paper presents AI MEET, an Artificial Intelligence–driven mock interview preparation platform designed to simulate real interview experiences and provide structured performance evaluation. The system integrates voice-based interaction with AI-powered interviewers that conduct dynamic interviews in real time. Users can configure interviews based on type (technical, behavioral, or hybrid), preferred technology stack, and experience level.The platform employs speech-to-text technology to convert spoken responses into textual data, which is then processed using large language models to analyze the quality of responses. Based on this analysis, the system generates detailed feedback evaluating various dimensions such as communication ability, technical competency, analytical thinking, confidence, and cultural alignment.Beyond mock interviews, AI MEET offers additional preparation features including resume-driven question generation, AI-based quizzes covering multiple technical domains, and company-oriented question repositories for major technology firms. The platform also includes an intelligent conversational chatbot available around the clock to assist users with interview strategies, resume guidance, and career advice. Furthermore, the system provides performance analytics dashboards that track interview attempts, quiz scores, and improvement trends over time.The proposed system aims to enhance interview preparedness by delivering an interactive and personalized learning environment powered by modern artificial intelligence technologies. By combining automated interview simulation, adaptive feedback mechanisms, and performance analytics, AI MEET supports candidates in identifying weaknesses, strengthening skills, and improving their chances of success in competitive hiring processes.
Artificial Intelligence, Mock Interview System, Voice-Based Interviewing, Career Preparation, Speech Recognition, AI-Driven Feedback, Resume-Based Question Generation, Conversational AI, Technical Interview Training, Performance Analytics.
Aligeti Puneetha,Vuppala Sai Srija,Routhu Shriya
UPI Guard: Intelligent UPI Fraud Detection System
The rapid growth of digital payment platforms has transformed financial transactions by enabling fast and convenient money transfers. Among these platforms, the Unified Payments Interface (UPI) has gained widespread adoption due to its simplicity and real-time transaction capability. However, the increasing reliance on UPI has also led to a surge in fraudulent activities such as phishing attacks, unauthorized transfers, and scam-based payment requests. Detecting these fraudulent activities in real time has become a critical challenge for financial systems.This paper proposes a machine learning-based UPI fraud detection system designed to identify suspicious transactions and prevent financial losses. The system evaluates transaction attributes including amount, time, and receiver information to estimate the probability of fraud. A classification model is trained to categorize transactions as legitimate or fraudulent based on learned behavioral patterns. To improve detection accuracy, rule-based validation is integrated alongside machine learning prediction. Suspicious receiver identifiers and unusually high transaction values are flagged as potential threats. Transactions identified as fraudulent are automatically blocked, and repeated fraudulent attempts lead to account suspension. The system is implemented using Python, Flask, and MySQL, with an administrative dashboard for monitoring fraud trends and high-risk users. The proposed solution enhances digital payment security through intelligent and real-time fraud detection.
UPI Fraud Detection, Machine Learning, Digital Payments, Fraud Prevention, Transaction Analysis, Real-Time Detection, Cybersecurity.
Gajula Sadhika, Nelapatla Sahithi, Kanduri Sri Navya
Swarm technology in robotics is an emerging field inspired by the collective behavior of natural systems such as ant colonies, bird flocks, and bee swarms. The concept focuses on developing multiple simple robots that work together in a decentralized manner to accomplish complex tasks that are difficult for a single robot to achieve. This project aims to design and implement a swarm robotic system capable of demonstrating autonomous coordination, adaptability, and scalability in real-time environments. Each robot in the swarm is equipped with basic sensors, wireless communication modules, and simple processing capabilities. Through local interactions and rule-based algorithms, the robots collectively perform tasks such as exploration, path optimization, obstacle avoidance, and cooperative object transport. The decentralized control ensures robustness, as the system can still function effectively even if individual units fail.The project highlights the advantages of swarm robotics in terms of efficiency, fault tolerance, and cost-effectiveness compared to conventional single-agent systems. Potential applications include search and rescue operations, environmental monitoring, precision agriculture, and defense surveillance. By bridging bio-inspired algorithms with embedded system design, this project contributes to the advancement of intelligent, scalable, and cooperative robotic systems for real-world applications.
Swarm Robotics; Multi-Agent Systems; Decentralized Control; Autonomous Coordination; Bio-inspired Algorithms; Obstacle Avoidance; Cooperative Robotics; Scalability.
Customer churn prediction is an important area of interest for businesses in competitive and subscription based business models where customer retention is a key determinant for business success. Accurate prediction of customers who are likely to churn can help businesses design proactive strategies for retaining customers and reducing business losses. In this paper, a design and implementation of a Customer Churn Prediction System using machine learning techniques is presented to accurately predict customers as Churned or Stayed and their probability of Churning The proposed system uses a range of customer data, including demographic characteristics, service usage behavior, billing and contract details, and service engagement. A structured data processing pipeline is designed for accurate data processing and modeling. To overcome common problems in churn prediction models related to class imbalance issues in machine learning models, a hybrid model using SMOTE ENN is implemented for accurate class imbalance handling. Further, a machine learning model using a random forest algorithm is implemented for accurate Predictions. The random forest algorithm is a popular ensemble machine learning technique known for its robustness and high accuracy in dealing with a wide range of data types. The performance of the model can be evaluated using different metrics like accuracy, precision, recall, and F1-score. However, it should be noted that recall should be given prime importance to effectively identify high-risk customers. The proposed system utilizes SQL for storing data in a structured manner and Flask for deploying the application. Interactive visualization has been achieved through Plotly. The proposed system has been designed to effectively cater to the problem of customer churn prediction in an effective manner. The proposed approach utilizes advanced sampling techniques and ensemble learning to effectively predict customer churn, and through the integration of visualization techniques, it can be effectively utilized to take effective decisions regarding retaining customers within an organization.
Customer Churn, Machine Learning, Predictive Analytics, Data Preprocessing, SMOTE-ENN, Classification, Customer Retention, Business Intelligence, Flask Web Application, Predictive Analytics, Interactive Visualization.
CH. Pranitha, S. Sai Leela, V. Varun Kumar Reddy, A. Naveen Kumar
Grid Connected Energy System To Profit From Net Metering And Variable Rate Electricity
The rising demand for electricity, coupled with the growing adoption of net metering policies, necessitates efficient energy monitoring and management systems. This paper presents a grid-connected energy storage and monitoring system designed to optimize energy utilization and financial benefits under net metering schemes. The proposed system enables precise measurement, calculation, and display of both consumed and exported energy in a residential environment.The system uses a dual energy meter configuration to measure bidirectional energy flow: one meter tracks electricity supplied to the grid, while the other monitors electricity drawn from the utility. A microcontroller processes data from both meters, calculating net energy consumption and billing for each period. Stable operation is ensured through a regulated power supply and crystal oscillator.By providing real-time monitoring via an LCD display, the system enhances consumer awareness, promotes efficient energy usage, and ensures transparent billing. The proposed approach offers economic advantages to both consumers and utilities while improving system reliability and supporting renewable energy integration.
Net Metering, Energy Monitoring, Grid-Connected System, Bidirectional Energy Measurement, Microcontroller, Renewable Energy, Energy Efficiency, Billing Accuracy
Underwater Data Communication Using Li-Fi Technology
The demand for reliable underwater communication systems has increased rapidly due to advancements in marine research, underwater robotics, environmental monitoring, and defense applications. Conventional underwater communication techniques, such as radio frequency (RF) and acoustic communication, suffer from significant limitations including high attenuation, limited bandwidth, signal distortion, and high latency. These limitations reduce the efficiency of real‑time underwater data transmission and restrict the performance of underwater monitoring and exploration systems.Experimental observations indicate that Li‑Fi based communication performs effectively for short‑range underwater communication. The system demonstrates low latency, reduced power consumption, improved data transmission speed, and enhanced security compared to traditional methods. The results confirm that Li‑Fi technology is a promising solution for future underwater communication applications including oceanographic data acquisition, underwater robotics, and remote monitoring systems.
Li Fi, Underwater Communication, Visible Light Communication, Optical Communication, Photodiode, LED Communication, Marine Monitoring
Tarunya Kondapalli,Tulasi Chatlaparthi,Pallapu Venkata Jyothirmayee
Explainable Ensemble Machine Learning Framework for Customer Churn Prediction: An Intelligent Business Analytics Approach
Customer churn prediction has emerged as one of the key challenges for companies, which aim at increasing their revenue and retaining their customers in long term. Conventional statistical methods usually lack the ability to model complex nonlinear behaviors in contemporary industrial data. Machine learning capabilities offer strong prediction, but often are not transparent enough for the kinds of management decisions involved. The framework presented in this work is an interpretable ensemble machine learning technique for intelligent churn prediction in business intelligence systems. Logistic Regression Decision Tree Random Forest Gradient Boosting XGBoost Voting and Stacking are integrated into the framework. Model’s stability and generalization are leveraged by performing categorical encoding, feature scaling and stratified sampling as well. The evaluation of the model’s performance are Accu-racy, F1-score, ROC-AUC, Cross validation, Confusion matrix analysis and Fea-ture importance interpretation. Our empirical studies show that stacked ensemble models with Gradient Boosting ensemble learners can lead to better classification accuracy than using only single learners. Explainability analysis suggests tenure, monthly charges and total charges are most influential in churn prediction. The resultant method increase predictive confidence and interpretability for strategic decision-making. These results demonstrate the potential expressiveness of explainable ensemble learning for data-driven customer relationship management applications. DOI: https://doi-ds.org/doilink/04.2026-69377646
Customer Churn, Ensemble Learning, Gradient Boosting, Machine Learning, Business Analytics, Predictive Modeling, ROC- AUC, Explainable AI (XAI)
Tauqeer Alam, Roshaan Raza Khan, Amarjeet Yadav, Suman, Apoorv Jain, Arun K. Tripathi
The Future-Ready Enterprise: Reimagining Business Models with AI &Analytics
With the increasing pace of digital transformations in various industries, the challenge of changing the business models of enterprises to remain competitive, cost-effective, and customer-centric has become a major challenge. Artificial Intelligence (AI) and analytics play an important role in changing the way enterprises innovate their revenues, manage their costs, improve customer experiences, and become more data-driven in their decision-making processes. However, challenge of understanding how AI and analytics can be leveraged in the strategic management of enterprises remains while addressing the ethical, organizational, and skill-based challenges. This paper seeks to understand the role of AI and analytics in the digital revolution of the enterprise business model through a thorough examination of the implications of AI in the following areas: revenue innovation, cost optimization, customer experiences through automation, and the organizational implications of AI and analytics. The paper will also cover the implications of machine learning in the decision-making of enterprises, the implications of big data in the accuracy of forecasts, and the implications of ethical issues in AI and analytics. The paper has established that AI enterprises have the advantage of dynamic revenue models, efficient cost management through AI-powered analytics, personalized customer experiences through automation, and strategic decision-making through data intelligence. The paper has also established the emerging skill requirements of the workforce and the industries likely to be disrupted by AI and analytics, which include the finance, healthcare, manufacturing, and retail industries. The paper has concluded that AI will be a complementary tool to the managerial role in the future, and enterprises need to become digital enterprises to be future-ready to remain competitive in the long term. DOI: https://doi-ds.org/doilink/04.2026-55656478
Artificial Intelligence; Business Model Innovation; Predictive Analytics; Future-Ready Enterprises
Ms. Teena Sharma, Ms. Shweta, Dr. Anand Kumar Gupta, Dr. Sarika Agarwal
AI-Enabled Digital Platform for Strengthening Academia–Industry–Government Collaboration
In cyberland, the productive trinity of Academia, Industry and Government has been the magic formula for both innovation and economic dynamism. Academia produces the knowledge and the human capital, Industry converts the knowledge into commercial applications, and Government provides the assistance and resources. Although all three sectors depend on each other, the successful partnership of Academia, Industry, and Government has been impeded by communication gaps, the misalignment of research and industry needs, and the wasteful use of funds and data-based coordination. This really crippled the collaboration. AI, in specific, holds enormous promise to transcend these obstacles and enable the AIG partnership to achieve its full potential by imbuing it with intelligent decision-making, matchmaking and prediction abilities. See how artificial intelligence can help the Academy, Industry and Government work. The person who wrote this thinks that artificial intelligence can be used to help figure out what research is most important to both schools and companies to keep track of skills that are emerging to decide how to spend money and to understand when new ideas are coming out. This paper was really smart to think that artificial intelligence could help the Academy, Industry and Government work to come up with new ideas faster and make the economy grow in a way that is good for everyone. This shows that artificial intelligence can be a help, in making a smart and sustainable way for the Academy, Industry and Government to work together. Artificial intelligence can help the Academy, Industry and Government collaborate in a way. DOI: https://doi-ds.org/doilink/04.2026-85844599
Artificial Intelligence, Academia–Industry–Government Collaboration, Innovation Ecosystem, Technology Transfer, Tech-Driven Growth
Mr. Satyam Rai, Md Waquar Hassan, Mr. A. Vasanth Reddy, Dr. Anand Kumar Gupta, Mr. Yaduvir Singh
Artificial Intelligence, Students, and Educational Management: A Contemporary Analysis
This paper explores the impacts of the use of Generative Artificial Intelligence (Gen AI) tools on students, the patterns of their use, the purposes, and potential effects on academic behaviour and learning. The increasing presence of AI-based apps is driving students to constantly use Gen AI systems to assist them with homework, develop content, conduct research, and explain concepts. On the one hand, these tools significantly improve efficiency, accessibility and personalised learning, but on the other hand, they are also associated with problems of academic integrity, over-reliance and the development of critical thinking skills. It is founded on a survey distributed to students to obtain primary feedback concerning their impressions and perceptions on how generative AI is applied in a classroom. The survey will examine the frequency of AI utilization, academic activities, and the purpose of AI, and the perception of the benefits and disadvantages by the students. The students provide valuable information on the impacts of these technologies on their study behaviours, learning performance, and engagement of their brains due to their status as the prime users of the Gen AI tool. Based on the results, most students regard generative AI as an effective learning tool in enhancing understanding and time-saving. However, the reduction in originality, reliance on the content created by AI and damage to problem-solving ability are also of concern. The paper highlights the importance of using AI responsibly and focuses the attention on duality of generative AI in education. To ensure that the Gen AI tools are integrated in the education system in a manner that facilitates learning without undermining intellectual growth and academic honesty. DOI: https://doi-ds.org/doilink/04.2026-71773491
Generative Artificial Intelligence, learning, AI Ethics.
Ishant Bhati, Ajay Gangele, Yaduvir Singh, Lalit Kumar Saraswat
The growing demand for electrical energy and the widespread adoption of net metering policies require efficient systems capable of monitoring and managing bidirectional energy flow between consumers and the utility grid. This paper presents a grid-connected energy storage and monitoring system designed to maximize benefits from net metering and variable electricity tariffs. The proposed system enables accurate measurement, computation, and display of both energy consumption and energy export within a residential environment.The system utilizes a home grid configuration that connects household loads with the utility supply. Two energy meters are employed to perform bidirectional energy measurement. One meter records the electrical energy exported to the grid, while the second meter measures the energy imported from the utility mains. This dual-meter arrangement ensures reliable tracking of both energy flows.A microcontroller serves as the central processing unit, collecting data from both meters and calculating consumed and exported energy during each billing cycle. A regulated power supply and crystal oscillator provide stable system operation and timing accuracy. The processed information enables precise net energy computation and transparent billing. The calculated net units are displayed on an LCD in real time.The proposed system improves billing transparency, increases consumer awareness, and encourages efficient energy utilization. It also supports renewable energy integration and offers economic advantages for both consumers and utility providers.
Net Metering, Bidirectional Energy Measurement, Energy Monitoring System, Grid-Connected Systems, Renewable Energy Integration, Microcontroller-Based System, Smart Energy Management, Energy Billing Transparency.
AI-Enabled Education Platform For Personalized Learning
The AI-Enabled Educational Platform for Personalized Learning is a web-based solution developed to enhance the effectiveness and accessibility of online education. The platform offers a unified environment where learners can access course materials, instructional videos, quizzes, and notes without relying on multiple external sources. Conventional online learning often requires students to navigate various websites to gather resources and resolve doubts, which can reduce learning efficiency. This proposed system addresses these limitations by integrating essential educational services into a single interface.Instructors are provided with tools to upload and manage course content, while students can enroll in courses and progress at their own pace. A personalized dashboard enables learners to monitor their academic progress, track quiz performance, and manage enrolled courses. The system also incorporates an AI-powered chatbot that provides instant responses to course-related queries. The chatbot explains concepts in simplified language, assisting students in understanding complex topics and supporting continuous learning.Additionally, the platform utilizes intelligent recommendation techniques to suggest relevant courses based on learner interests and activity patterns. By combining centralized content management, personalized learning analytics, and artificial intelligence features, the proposed system improves learning efficiency and promotes accessible education. Overall, the platform demonstrates how AI-driven technologies can create an adaptive and student-centric learning environment.
Artificial Intelligence, Personalized Learning, Educational Platform, AI Chatbot, Online Learning, Course Recommendation, Adaptive Learning, Learning Analytics
AI-Driven Predictive Maintenance In IoT-Enabled Conveyor Belt Systems
Conveyor belt systems are critical in industrial operations, yet traditional maintenance approaches often lead to unplanned downtime, high operational costs, and reduced equipment lifespan. This paper presents an AI-driven predictive maintenance framework for IoT-enabled conveyor belt systems. IoT sensors continuously monitor operational parameters such as vibration, temperature, motor current, and belt speed, generating real-time data for analysis. Machine learning and deep learning algorithms process this data to detect anomalies, predict potential failures, and estimate the remaining useful life of critical components. The proposed framework enables proactive maintenance, minimizes downtime and maintenance costs, and enhances overall system reliability and safety. A user-friendly dashboard provides real-time alerts and actionable recommendations, facilitating timely maintenance decisions. Implementing AI-based predictive maintenance in IoT-enabled conveyor systems aligns with Industry 4.0 objectives, supporting smarter, more efficient, and reliable industrial operations.
Predictive Maintenance, IoT, Conveyor Belt Systems, Machine Learning, Deep Learning, Industry 4.0, Anomaly Detection, Remaining Useful Life (RUL).
G Bramarambica Devi, A Greeshma, A Manogna
The Smart Baby Cradle Monitoring System is an Internet of Things (IoT) based solution developed to enhance infant safety, comfort, and continuous supervision while minimizing manual effort from caregivers. The system incorporates multiple sensors to track important parameters such as infant movement, crying, ambient temperature, and bed wetness. When discomfort is detected, particularly through crying, the cradle automatically initiates a gentle rocking motion to calm the infant. Environmental conditions are continuously monitored to ensure a safe and comfortable atmosphere.If abnormal situations such as excessive crying, temperature variations, or inactivity are identified, instant alerts are transmitted to parents or caregivers through a mobile application or SMS notifications. The system supports real-time monitoring and remote accessibility, making it suitable for working parents and healthcare environments. By integrating embedded systems with IoT technology, the proposed design offers a reliable and cost-effective approach for infant care.The system also features a user-friendly interface that allows caregivers to observe the infant’s condition remotely. Automated cradle movement combined with timely alerts reduces the risk of delayed attention and improves emergency response. The use of low-power sensors and economical microcontrollers makes the solution energy-efficient and affordable. Future enhancements such as live video streaming, cloud-based data storage, and health analytics can further improve system scalability and performance.
Smart Baby Cradle, IoT Infant Monitoring, Arduino, Cry Detection, Automatic Rocking, GSM Alert System, Moisture Detection, Embedded Systems, Baby Activity Monitoring.
Manglaram Akshaya, Kasa Deepika, Shavva Jhansi
Analysis of Electrical Systems In CNC Machines At Bharat Dynamics Limited
Computer Numerical Control (CNC) machines rely heavily on robust electrical systems to ensure precision, reliability, and productivity in advanced manufacturing environments. This paper presents a detailed examination of the electrical architecture of CNC machines installed at Bharat Dynamics Limited (BDL), a major defence manufacturing organization. The study investigates key components including power distribution networks, control circuits, drive mechanisms, and safety interlock systems, with particular focus on operational performance, energy utilization, and fault detection capabilities.An in-depth analysis of power quality parameters such as voltage regulation, harmonic distortion, and transient behavior identifies opportunities for improving equipment reliability and extending component life. The work further evaluates the role of Programmable Logic Controllers (PLCs), Human–Machine Interfaces (HMIs), and closed-loop feedback mechanisms, demonstrating their influence on machining accuracy and process consistency.The research also reviews protective arrangements including overload protection, grounding schemes, earth-fault monitoring, and surge suppression to ensure safe and stable operation within an industrial defence production environment. Using system modelling, on-site observations, and comparative analysis, the study proposes recommendations for optimized electrical configuration, enhanced grounding practices, and implementation of advanced monitoring solutions. The findings indicate that systematic evaluation and improvement of CNC electrical systems can significantly enhance operational efficiency, minimize downtime, and support compliance with stringent defence manufacturing requirements.
Computer Numerical Control (CNC), Electrical Systems, Power Quality, Programmable Logic Controller (PLC), Human–Machine Interface (HMI), Harmonic Distortion, Voltage Regulation, Fault Detection, Industrial Automation, Operational Efficiency.
Analysis Of Thermal Power Plant Components And Electrical Protection In KTPP
This study presents a comprehensive analysis of power generation, transmission, and operational efficiency at the Kakatiya Thermal Power Plant (KTPP). The plant has an installed capacity of 1,100 MW, comprising two generating units: Unit-1 rated at 500 MW and Unit-2 rated at 600 MW. The station operates with a plant load factor of 85.36% and consumes approximately 16,000 tons of coal per day. The project aims to examine the complete power generation cycle, beginning with coal handling and progressing through boiler operation, steam production, turbine rotation, and the conversion of mechanical energy into electrical energy using synchronous generators.The study evaluates unit ratings and installed capacity to assess the overall output and operational performance of the plant. In addition, the research highlights the importance of preventive and predictive maintenance practices applied to electrical motors and auxiliary equipment. These maintenance strategies involve systematic monitoring of parameters such as temperature and vibration to detect early signs of deterioration. By employing diagnostic techniques, plant personnel can anticipate failures, extend equipment lifespan, and minimize unplanned shutdowns. Predictive maintenance also contributes to reduced maintenance costs and improved operational reliability through efficient planning and timely intervention.Overall, this work provides a practical understanding of large-scale thermal power generation systems and emphasizes the role of efficient maintenance strategies in enhancing plant performance and reliability at Kakatiya Thermal Power Plant.
Thermal Power Plant, Power Generation, Kakatiya Thermal Power Plant, Plant Load Factor, Coal-Based Power Generation, Steam Turbine, Synchronous Generator, Predictive Maintenance, Preventive Maintenance, Operational Efficiency.
Automation Of Control And Protection Systems Using Scada
The Srisailam Hydropower Plant is one of the major hydroelectric generating stations in India and plays an important role in maintaining grid stability and supporting peak load demand. To ensure reliable and efficient operation, Supervisory Control and Data Acquisition (SCADA) systems are widely employed for automation, monitoring, and protection of plant equipment. The SCADA system provides real-time monitoring of critical parameters such as turbine speed, generator voltage, current, water level, gate position, temperature, and protection relay status.Through centralized control, operators can remotely supervise and operate turbines, generators, transformers, and circuit breakers. The integration of protection systems with SCADA helps safeguard equipment against abnormal conditions including overloads, short circuits, over-voltage, and mechanical faults. The system continuously collects data from field sensors and transmits it to PLCs/RTUs, which further communicate with the SCADA server through communication networks.SCADA also performs performance analysis by comparing real-time values with predefined safe limits. When abnormal conditions are detected, alarms are generated and protective actions such as tripping of equipment are automatically executed. In addition, historical data is stored for analysis of power generation efficiency, fault occurrence, equipment performance, and load variations. This data supports predictive maintenance and operational optimization. By enabling automation and data-driven decision making, SCADA enhances reliability, reduces downtime, improves safety, and ensures continuous power generation in hydropower plants.
SCADA, Hydropower Plant, Automation, PLC/RTU, Protection System, Real-Time Monitoring, Fault Detection, Data Acquisition, Predictive Maintenance.
V.Vaishnavi, M.Hari Priya, M.Divya Sudha
A Detailed Study On Srirampur Open Cast-II Expansion Project Mine Review
Coal mining remains a cornerstone in fulfilling the growing energy requirements essential for industrial and economic progress. This study presents an in-depth analysis of operational practices implemented in the SRP OC-II Expansion Project, focusing on open-cast mining techniques and coal extraction processes. The research aims to elucidate the sequence of mining operations and evaluate the methods employed to achieve safe, efficient, and cost-effective production.The study outlines the key stages of mining activities, including site preparation, drilling, blasting, loading, and coal transportation. The application of mechanized equipment—such as excavators, dumpers, drilling rigs, and conveyor systems—is emphasized for enhancing productivity and minimizing manual labor. The functioning of the Coal Handling Plant (CHP) is examined, highlighting processes such as coal crushing, conveying, storage, and dispatch.Furthermore, the study underscores the critical role of electrical infrastructure, particularly the 33 kV/3.3 kV substation, which supplies power to heavy mining machinery. Essential components including transformers, circuit breakers, current and potential transformers, insulators, and lightning arresters are discussed in the context of maintaining a safe and reliable electrical network. Environmental and safety considerations are also addressed, encompassing dust suppression, water management, waste handling, and protective measures for personnel and equipment.Overall, this study provides a comprehensive overview of open-cast mining operations, demonstrating how strategic planning, mechanization, and robust electrical systems collectively contribute to efficient and safe coal extraction.
Open-Cast Mining, Coal Extraction, Drilling and Blasting, Coal Handling Plant (CHP), Mining Equipment, Electrical Substation.
Short Circuit Analysis Of 33kv/11kv Distribution Substation
This paper presents a comprehensive short-circuit analysis of a 33/11 kV distribution substation, an essential component of modern electrical power distribution networks. The study investigates various fault conditions that may occur within the system and evaluates the corresponding fault currents at key locations, including the 33 kV busbar, power transformer, and 11 kV busbar. Both symmetrical faults (three-phase faults) and unsymmetrical faults such as line-to-ground (L-G), line-to-line (L-L), and double line-to-ground (L-L-G) are examined. The symmetrical components technique is employed to simplify the analysis of unbalanced fault conditions and to obtain accurate current estimations. Fault current calculations are carried out using standard analytical methods and commonly accepted assumptions to determine the magnitude and severity of faults. The analysis indicates that the highest fault current occurs at the 11 kV busbar, making it a critical point for the selection and coordination of protective equipment such as circuit breakers and protective relays. The findings emphasize the importance of reliable protection schemes to ensure secure and continuous operation of distribution substations. Furthermore, the study highlights how accurate fault analysis contributes to improved system stability and reduces the risk of equipment damage under abnormal operating conditions. Proper coordination of protective devices is also discussed to enable effective fault isolation and minimize outage duration. The integration of modern monitoring technologies, including SCADA-based systems, is considered to enhance real-time supervision, control, and rapid response to fault events, thereby improving overall operational efficiency.Future enhancements of this work may include detailed simulation using power system analysis software and implementation of advanced SCADA-based monitoring for real-time fault detection and system performance evaluation. Overall, the study provides a strong technical foundation for understanding fault analysis and protection strategies in contemporary power distribution networks. DOI: https://doi-ds.org/doilink/04.2026-94395456
Short-Circuit Analysis, Power Distribution System, Symmetrical Components, Fault Analysis, Protective Relays, SCADA Systems.
Secure And Real-Time 1-To-N Face Recognition System For Web-Based User Authentication
Traditional authentication mechanisms primarily rely on textual credentials such as usernames, passwords, or identification numbers. These approaches are prone to security risks including password theft, guessing attacks, and user forgetfulness. Although face recognition has been explored as an alternative many existing solutions depend on static image storage or manual verification, which limits automation and real-time usability. Furthermore, such systems often lack dynamic retrieval of user-specific information, leading to inefficient and less secure identification processes.To address these limitations, this paper proposes a real-time face capture and identification framework integrated with OpenCV for intelligent user authentication. During the registration phase, the system captures facial data live through a web-based interface connected to a webcam using OpenCV. The acquired facial images are securely stored in the server-side database or file system. During authentication, a new live image is captured and processed using face similarity algorithms to compare it with previously stored templates. Once a match is confirmed, the system dynamically retrieves associated user details from the database and presents them securely on the interface.The proposed architecture combines real-time camera integration, secure storage mechanisms, and efficient facial comparison techniques to provide reliable identity verification. By ensuring that access is granted only to authenticated individuals, the system enhances security while improving usability. The modular design allows seamless deployment in modern web-based applications requiring automated and intelligent authentication.
Face Recognition, OpenCV, Real-Time Authentication, Biometric Security, Image Processing, Identity Verification.
A. Yashwanth,B. Prabhas,B. Vishnu Vardhan
Secure And Private Analytics Of Healthcare Records In Multi-Tenant Cloud Environments Using Blockchain
The growing volume of digital healthcare data and increasing incidents of data breaches have intensified concerns regarding the protection of sensitive patient information. Healthcare analytics systems must therefore balance privacy preservation with the ability to generate meaningful insights. This paper presents a secure framework that integrates privacy-preserving mechanisms, zero-knowledge proofs (zk-SNARKs), blockchain technology, and a multi-tenant cloud architecture to address these challenges. The proposed approach safeguards healthcare records during analytical computations by applying advanced cryptographic techniques, enabling verification without revealing underlying data.Within the framework, anonymized healthcare datasets are processed by a privacy-preserving analytics engine that generates zk-SNARK proofs to validate computations. These proofs are recorded on a blockchain network, creating a transparent and tamper-resistant ledger that enhances trust and security in healthcare data transactions. Such a mechanism is particularly beneficial in telemedicine scenarios, where secure sharing and processing of patient information are critical. The implementation of the proposed model in a telemedicine application demonstrates its scalability, reliability, and effectiveness. Overall, the framework provides a practical solution for secure healthcare analytics while maintaining strict privacy guarantees.
Healthcare Data Security, Privacy-Preserving Analytics, Zero-Knowledge Proofs (zk-SNARKs), Blockchain Technology, Cloud Computing, Data Privacy.
Machine Learning Enhanced By Sentiment Analysis For Cyberbullying Detection Using NLP
Cyberbullying has become a significant concern in the modern digital environment, negatively impacting individuals and society at large. Detecting harmful interactions on social media platforms is therefore essential, as these platforms represent a major channel of online communication. Conventional detection techniques relying on machine learning methods and pre-trained language models often struggle with computational overhead and limited capability to interpret subtle linguistic variations. To address these limitations, this study introduces an enhanced cyberbullying detection framework that integrates Natural Language Processing (NLP) techniques with Long Short-Term Memory (LSTM) networks.The proposed approach incorporates comprehensive text preprocessing procedures, including tokenization, stop-word elimination, stemming, and lemmatization, to generate clean and meaningful input data. Semantic and sentiment-based features are extracted using embedding strategies that preserve contextual relationships among words. These representations are subsequently processed by an LSTM model, which is capable of learning sequential dependencies and temporal patterns in textual data, thereby improving the detection of complex cyberbullying expressions.Furthermore, to mitigate class imbalance in multi-class classification scenarios, appropriate resampling strategies are applied, enhancing model stability and reducing bias. Experimental observations indicate that the integration of deep learning with structured NLP preprocessing improves both accuracy and contextual understanding, making the proposed framework effective for identifying cyberbullying content in online communications.
Cyberbullying Detection, Natural Language Processing (NLP), Long Short-Term Memory (LSTM), Text Classification, Sentiment Analysis, Deep Learning.
B.Siddarth Reddy,M.Havish Reddy,M.Nikhitha
Dual Axis Solar Tracker Without Microcontroller
Solar energy is one of the most widely used renewable energy sources, but its efficiency depends on the alignment of the solar panel with the sun. In fixed panels, maximum energy is received only for a short duration. To overcome this limitation, this project presents a dual axis solar tracker that automatically adjusts the position of the solar panel to follow the sun’s movement in both directions. The system is designed without using a microcontroller, making it simple, cost-effective and easy to implement. It uses multiple Light Dependent Resistor (LDR) sensor modules placed around the setup to detect the intensity and direction of sunlight. When light falls unevenly on these sensors, a difference in output is generated, which is used to control the movement of the panel. An L298N motor driver module is used to control two DC motors. One motor is used for horizontal rotation (left–right movement) and the other is used for vertical tilt (up–down movement) of the solar panel. Based on the signals from the LDR sensors, the motors rotate the panel until it reaches the position of maximum light intensity. The system also includes a power supply unit consisting of a rectifier, filter capacitor and voltage regulator to provide a stable DC supply. A battery is used to power the circuit and LED indicators are used to show the working condition of the system and sensor outputs. The solar panel mounted on a mechanical structure is capable of adjusting its position smoothly using the motor arrangement. As the panel continuously tracks the sun, it receives more sunlight compared to a fixed panel, thereby improving overall efficiency. This project demonstrates a practical and effective way of increasing solar energy utilization using simple components without any programming. Additionally, the system is easy to build, understand and maintain, making it suitable for student projects and educational purposes. With further improvements, this model can be developed into a real-time solar tracking system for better energy generation in practical applications.
Dual Axis Solar Tracker, Solar Energy, Light Dependent Resistor (LDR), L298N Motor Driver, DC Motors, Sun Tracking System, Renewable Energy, Automatic Panel Alignment, Energy Efficiency, Microcontroller-Free Design, Solar Panel Optimization, Analog Control System
Blockchain-Enabled Comprehensive Security Framework For Industrial Iot
The Industrial Internet of Things (IIoT) is rapidly transforming modern industries by enhancing operational efficiency and productivity. However, the exponential growth and diversity of connected devices expose IIoT systems to significant security vulnerabilities. Traditional centralized security mechanisms struggle with scalability, data integrity, and reliability, leaving industrial environments susceptible to cyberattacks. To address these challenges, this research proposes a decentralized, blockchain-based security framework for IIoT systems. By integrating blockchain technology, smart contracts, and SHA-256 encryption, the framework provides robust authentication, authorization, and data integrity without reliance on a central authority. Smart contracts dynamically enforce access policies and respond in real time to suspicious activities, while SHA-256 ensures strong data confidentiality. Moreover, IoT devices are leveraged to manage encryption keys and data access, enabling a lightweight and scalable security solution tailored to dynamic industrial environments. The hybrid use of public and private blockchains ensures both high performance and confidentiality, offering a practical and adaptive approach to securing IIoT infrastructures. Experimental results demonstrate the proposed framework’s effectiveness in enhancing data security, scalability, and operational reliability.
Industrial Internet of Things (IIoT), Blockchain Technology, Cybersecurity, Decentralized Security Framework, Smart Contracts, SHA-256 Encryption, Data Integrity, Authentication and Authorization, Secure Data Transmission, IoT Security, Public and Private Blockchain, Access Control, Encryption Key Management, Industrial Automation Security, Scalable Security Solutions.
Suraj Kumar Pal,Mohammad Azharuddin,V Prajval
Industry Parameters Monitoring And Controlling System Based On Embedded Web Server
In today’s world,Industrial automation and remote monitoring have become crucial in modern industry to enhance productivity, improve safety, and minimize manual intervention. This project proposes the development of an Industry Parameters Monitoring and Controlling System Based on a Webserver, which offers an efficient and real-time solution to monitor and control various industrial parameters such as temperature, humidity, gas concentration, voltage, and more. The system is built around a microcontroller unit (such as ESP8266 or ESP32) equipped with built-in Wi-Fi capabilities, which connects to multiple sensors installed in the industrial environment. These sensors continuously collect data on various parameters and transmit it to the microcontroller. The microcontroller acts as a mini webserver and hosts a web-based dashboard accessible through any standard web browser on a computer or mobile device. The web interface provides a user-friendly dashboard that displays real-time sensor values and system status. Additionally, it includes interactive control options that allow users to take corrective actions such as turning on cooling fans, activating alarms, or shutting down machinery if any monitored parameter exceeds the predefined safety threshold. This two-way communication supports both monitoring and control functions without the need for a physical presence on-site. The system enhances safety by reducing the risk of equipment damage or hazardous conditions through early detection and timely response. It also supports data logging, enabling trend analysis, fault diagnosis, and performance optimization. The webserver-based approach eliminates the need for external cloud platforms or expensive SCADA systems, making it a cost-effective, lightweight, and scalable solution for small and medium-scale industries. This project demonstrates the power of IoT in industrial automation and showcases how web-based technologies can be leveraged to build reliable, real-time, and remotely accessible monitoring and control systems.
Industrial Automation, IoT (Internet of Things), Embedded Web Server, ESP8266, ESP32, Remote Monitoring, Real-Time Control, Sensor Networks, Data Logging, Smart Industry, Wireless Communication, Industrial Safety
Akula Kalyani, Dannaram Manisha, Manikyala Meghana
Intelli Drive+ Multi-Modal Fatigue Detection And Adaptive Alert System
Driver fatigue is a leading cause of road accidents worldwide, contributing to an estimated 20% of all traffic fatalities. Existing single-modality detection systems, which primarily rely on visual cues like eye closure, suffer from poor robustness under real-world conditions (e.g., low lighting, occlusions) and high false-positive rates due to a lack of personalization. This paper presents IntelliDrive+, a novel multi-modal fatigue detection system that integrates visual, vocal, and vehicle behavioral data to achieve accurate, real-time driver state monitoring. The system employs a hybrid deep learning architecture combining Vision Transformers (ViTs) for global spatial feature extraction and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for temporal sequence modeling. A key contribution is an adaptive online learning module that personalizes detection thresholds to individual driver baselines, significantly reducing false alarms. Implemented on resource-constrained edge devices (NVIDIA Jetson Nano), the prototype achieves 94% classification accuracy, an average latency of 200 ms, and an 85% user-perceived alert relevance rate. This work establishes a robust, scalable framework for intelligent, human-centered fatigue monitoring, advancing both road safety and personalized well-being.
Driver Fatigue Detection, Multi-Modal Fusion, Vision Transformers, CNN-LSTM, Edge Computing, Adaptive Alert System, Intelligent Transportation Systems.
Talamanchi Madhusudhan, Katta Hari Chandan Prasad, Ramavath Thirupathi, Muntha Sai Raghavendra Prasad, Ramavath Desh Kumar
An Intelligent Robotic System For Borehole Child Rescue Operations
Borewell accidents remain a critical safety issue in rural and semi-urban regions, where abandoned or uncovered borewells pose life-threatening risks, particularly to children. The narrow diameter, significant depth, poor visibility, and limited oxygen availability inside borewells make rescue operations extremely challenging and time-sensitive. Conventional rescue approaches, including rope-based retrieval and excavation of parallel pits, are time-consuming, hazardous, and lack real-time monitoring, thereby reducing the chances of safe recovery. To address these limitations, an intelligent robotic solution is proposed to enhance the efficiency, safety, and success rate of borewell rescue missions.This paper presents the design and implementation of an intelligent borewell rescue robot capable of performing safe and controlled child rescue operations. The system incorporates an ESP32-CAM module for real-time video surveillance within the borewell. Track-based DC motors, controlled through motor drivers, facilitate smooth vertical movement, while a servo-actuated gripper mechanism is designed to securely hold the trapped child. High-intensity LED illumination is integrated to improve visibility in low-light conditions, enabling accurate positioning and monitoring. The ESP32-CAM transmits live video footage to the operator, allowing continuous observation of the child’s condition and robot maneuvering.During operation, the robot is lowered into the borewell using a cable or rope while the operator observes the live video stream. After locating the child, the servo-controlled gripper carefully secures the child, and the robot is gradually lifted to safely bring the child to the surface.The proposed intelligent rescue system significantly improves operational speed, precision, and safety compared to traditional methods. By combining real-time monitoring, remote control capability, and a secure gripping mechanism, the system enhances the probability of successful rescue. Furthermore, the proposed design can be extended for additional applications such as confined-space inspection, underground exploration, and disaster-response operations.
Borewell Rescue Robot, ESP32-CAM, Real-Time Video Monitoring, Robotics, Embedded Systems, Child Rescue System, Remote-Controlled Robot, Servo Motor, DC Motors, Motor Driver, Gripper Mechanism, IoT-Based Surveillance, Confined Space Rescue, Disaster Response Robotics, Wireless Communication, LED Illumination System, Safety Engineering, Automation in Rescue Operations, Smart Rescue Technology.
A Detailed Study On Srirampur Open Cast-Ii Expansion Project Mine Review
Coal mining plays a crucial role in meeting the energy demands of industrial and economic development. This project presents a detailed study of operational methodologies adopted in the SRP OC-II Expansion Project, focusing on open cast mining practices and coal extraction processes. The study aims to understand the sequence of mining operations and the methods used to achieve safe, efficient, and cost-effective production. The project explains various stages of mining operations including site preparation, drilling, blasting, loading, and transportation of coal. It highlights the use of mechanized equipment such as dumpers, excavators, drilling machines, and conveyor systems, which improve productivity and reduce manual effort. The functioning of the Coal Handling Plant (CHP) is also discussed, including coal crushing, conveying, storage, and dispatch systems. In addition, the study emphasizes the importance of electrical systems, particularly the 33kV/3.3kV substation, which plays a key role in stepping down voltage and supplying power to heavy mining equipment. Components such as transformers, circuit breakers, current transformers, potential transformers, insulators and lightning arresters are essential for safe and reliable operation of the electrical network. The project also addresses environmental and safety aspects including dust suppression, water management, waste handling, and the implementation of safety precautions to protect workers and equipment. Overall, this study provides a comprehensive understanding of open cast mining operations and demonstrates how systematic planning, mechanization, and a strong electrical infrastructure contribute to efficient and safe coal extraction.
Open Cast Mining, Coal Extraction, SRP OC-II Expansion Project, Coal Handling Plant (CHP), Mining Operations, Drilling and Blasting, Heavy Earth Moving Machinery (HEMM), Dumpers and Excavators, Conveyor Systems, 33kV/3.3kV Substation, Power Distribution in Mining, Electrical Protection Systems, Transformers and Circuit Breakers, Lightning Arresters, Industrial Power Systems, Mining Safety, Environmental Management, Dust Suppression, Sustainable Mining Practices, Energy Production
Smart Batteries And Battery Monitoring System
A Battery Monitoring System (BMS) plays a critical role in ensuring the reliability, safety, and performance of battery-powered systems used in defense applications. At the Defense Research and Development Organization (DRDO), advanced battery monitoring technologies are developed to support military equipment such as communication systems, unmanned vehicles, missiles, and portable electronic devices. The primary objective of the Battery Monitoring System is to continuously track key battery parameters including voltage, current, temperature, and state of charge (SOC). The system uses sensors and embedded controllers to collect real-time data from individual battery cells and transmit it to a central processing unit for analysis. This enables early detection of abnormalities such as overheating, overcharging, deep discharge, or cell imbalance, thereby preventing potential failures and improving operational safety. Additionally, the monitoring system incorporates diagnostic algorithms to estimate battery health and predict remaining useful life. In DRDO applications, the BMS is designed to operate under harsh environmental conditions, ensuring high reliability and efficiency in mission-critical scenarios. By enabling intelligent battery management and predictive maintenance, the system enhances battery lifespan, optimizes power utilization, and supports uninterrupted operation of defense technologies. Thus, the Battery Monitoring System is an essential component in modern defense energy management systems.
Battery Monitoring System (BMS), Defense Applications, DRDO, Battery Safety, State of Charge (SOC), State of Health (SOH), Real-Time Monitoring, Embedded Systems, Battery Diagnostics, Predictive Maintenance, Energy Management Systems, Thermal Management, Cell Balancing, Power Optimization, Military Electronics, Unmanned Systems, Missile Systems, Fault Detection, Battery Reliability, Smart Battery Systems