Journal of Science Engineering Technology and Management Science

In this research paper, write-up explores how Information Systems (IS) has acted as an engine of entrepreneurial prosperity among small and medium sized firms (SMEs) of Nagpur region in India. In the descriptive research design, 384 entrepreneurs in different sectors were sampled in collecting data using a structured questionnaire. In the analysis, it was made using descriptive and inferential statistical methods; such as correlation and regression analysis. The conclusion shows that there is a great correlation between IS adoption and entrepreneurial success that is quite significant in the improvement of operation efficiency, innovations, and decision-making processes. The paper finds that IS plays a very seminal role in facilitating business functionality and success with respect to sustainability in novel entrepreneurial ecosystems. Recommendations are made on what should be practiced by entrepreneurs, policymakers and technology providers based on the results to enhance IS integration to nature local business practices

Keywords : Information Systems, Entrepreneurial Success, SMEs, Operational Efficiency, Innovation, Strategic Decision-Making, Technology Adoption, etc

Author : Dr. Leelavathi. K

Title : INFORMATION SYSTEMS AS A CATALYST FOR ENTREPRENEURIAL SUCCESS: AN EMPIRICAL STUDY

Volume/Issue : 2025;02(07)

Page No : 511-516

Understanding human interactions and relationships requires human activity recognition (HAR). Extraction is difficult since it reveals a person's identity, personality, and mental state. Human activity movement forecasting is HAR. HAR is used in many applications since cellphones and video cameras can collect human activity data. Digital devices, apps, and AI advances have made deep learning data extraction for reliable detection and interpretation possible. This integration has strengthened our understanding of HAR's three pillars—acquisition devices, artificial intelligence, and their applications—which are expanding rapidly. While various review papers have covered the basics of HAR, few have compared all HAR devices and even fewer have examined the impact different AI designs. This assessment covers 2006–2021 and analyses HAR's three pillars. The study also suggests ways to improve HAR design for reliability and stability. Five main conclusions: (1) HAR is built on devices, AI, and apps; (2) HAR dominates the healthcare business; (3) hybrid AI models are still developing and require significant improvement to provide stable and reliable designs. Additionally, these models must have reliable predictions, high precision, effective generalisation, and the ability to achieve application objectives without bias; there has been little research on anomaly detection in human activities; and movement prediction has advanced little. The three core components of the HAR industry—electronic devices, apps, and artificial intelligence—will continue to evolve, and AI will shape the future of the sector. This paper summarises current human activity classification research. We classify human activity recognition methods and evaluate their pros and cons. Two main types of human activity classification techniques use one or more modalities. Subcategories within each category show how they represent human activities and which ones they highlight. We also exhaustively evaluate publically accessible human activity recognition datasets and ideal HAR dataset criteria. This paper highlights sensor-based and video-based human activity detection development, emphasizing on core technologies, identification systems, and applications from low-level to highlevel representations

Keywords : HAR, Artificial Intelligence, datasets, electronic devices, healthcare

Author : Anireddy. Sridhar Reddy

Title : AN ADVANCED REVIEW ON HUMAN ACTIVITY RECOGNITION USING ARTIFICIAL INTELLIGENCE

Volume/Issue : 2025;02(07)

Page No : 517-525

Background: The increasing complexity of software systems and accelerated development cycles have intensified the need for intelligent test management systems that optimize decisionmaking during software testing processes. Objective: This narrative review examines the current state of intelligent test management systems, evaluating their effectiveness in optimizing testing decisions, identifying key technologies, and assessing implementation challenges. Methods: We conducted a comprehensive review of 25 verified studies published between 2007-2024, sourced from IEEE Xplore, ACM Digital Library, ScienceDirect, and other reputable databases. Studies were selected based on their focus on AI/ML applications in software testing and empirical validation of intelligent decision-making systems. Results: Our analysis reveals that AI-powered test management systems achieve 85-95% accuracy in automated testing decisions, with 40-60% reduction in test execution time and 20- 50% cost savings. Machine learning classification techniques dominate current implementations, followed by genetic algorithms and natural language processing. The ITMOS framework demonstrates industrial viability with F1-scores of 0.9163 for CI configuration and 0.9463 for test automation decisions. Conclusions: Intelligent test management systems show significant promise for transforming software testing practices through automated decision support, predictive analytics, and adaptive optimization. However, challenges remain in integration complexity, tool reliability, and organizational adoption

Keywords : intelligent test management, software testing automation, artificial intelligence, machine learning, decision support systems, test optimization, continuous integration, test case prioritization

Author : Manjeet Kour,Er. Shilpa

Title : INTELLIGENT TEST MANAGEMENT SYSTEMS FOR OPTIMIZING DECISION-MAKING DURING SOFTWARE TESTING: A NARRATIVE REVIEW

Volume/Issue : 2025;02(07)

Page No : 526-544

The design and analysis of multicavity cap moulds using ANSYS Fluent for the simulation of the injection molding process. The study investigates three distinct multicavity cap designs: 2-cavity threaded, 8-cavity threaded, and 8-cavity flip-top moulds, commonly used in manufacturing caps for packaging, pharmaceuticals, and consumer goods. The primary aim is to optimize the molding process by evaluating key parameters such as melt flow, temperature distribution, cooling efficiency, and pressure drop during both the injection and cooling phases. Using SolidWorks, 3D models of the cap moulds are created and analyzed through CFD simulations in ANSYS Fluent to study the fluid dynamics, heat transfer, and material behavior within the mould. The simulations help in understanding fill patterns, melt front progression, and the impact of cooling channel layouts on temperature uniformity across the cavities. The performance of each mould configuration is evaluated in terms of cycle time, energy consumption, and potential defect formation (such as warping, short shots, and air traps), offering insights into optimizing the mould design for improved efficiency and part quality. The findings highlight the importance of integrated CFD analysis in multicavity mold design, emphasizing the role of cooling system optimization to reduce defects and improve overall efficiency in industrial manufacturing processes

Keywords : Cap mold, Multicavity, Ansys fluent

Author : Saikumar Amda, Dr. N.V. Srinivasulu, Dr. L.Sivarama Krishna

Title : ANALYSIS OF MULTICAVITY CAPMOULD USING ANSYS FLUENT

Volume/Issue : 2025;02(07)

Page No : 545-559

Customer loyalty has emerged as a major concern of any business in the current competent and fastpaced market. Customer retention and brand loyalty are two of the key concepts which determine the success of a business in the long run. This research paper provide comparison of the two factors within Fast-Moving Consumer Goods (FMCG) sector and Technology (Tech) sector. The central goal is to learn the approach companies in the industries can achieve brand loyalty and keep their consumers in the long-run. The analysis aims at determining some of the major factors involved in influencing loyalty and retention-customer satisfaction, trust, product quality, emotional connection, and service experience. The statistical analysis conducted utilizing primary data gathered with the use of survey among 200 respondents (100 in both FMCG and Tech) involves the use of such statistical tools as descriptive analysis and hypothesis testing in order to determine the patterns and relationships. The findings demonstrate that although brand loyalty has a tremendous influence on customer retention across the two industries, the level of their interactions is stronger in the Tech sector. The study is rich in information that can prove helpful to anybody involved in marketing, brand management and business strategy interested in enhancing the relationship they have with the customers and maintaining growth in the industry of their products

Keywords : Brand loyalty, customer retention, FMCG, technology sector, customer satisfaction, consumer behavior

Author : Dr. Rajesh G. Walode

Title : BRAND LOYALTY AND CUSTOMER RETENTION: A COMPARATIVE ANALYSIS OF FMCG AND TECH SECTORS

Volume/Issue : 2025;02(07)

Page No : 560-567

Natural fibers, particularly flax, are recognized as an eco-friendly alternative to synthetic fibers in composite materials. Their use is growing due to their low cost, biodegradability, and sustainability. This study investigates the effect of alkali treatment on the load-bearing capacity of natural flax fiber epoxy composites. Flax fibers were treated with 5% and 10% NaOH solutions to enhance the fibermatrix interaction, and the composites were fabricated using the hand layup technique with an epoxy resin matrix. A series of mechanical tests were conducted, including low-velocity impact, drop weight tests, quasi-static indentation, and water absorption tests. The results demonstrated a significant improvement in the load-bearing capacity of the alkali-treated composites, particularly in low-velocity impact and indentation resistance, with 10% NaOH-treated fibers exhibiting the highest performance. Alkali treatment enhanced the interfacial bonding between the flax fibers and epoxy resin, leading to better stress transfer and increased energy absorption during impact. However, the water absorption test showed a slight reduction in moisture uptake, suggesting that alkali treatment improves the composite's moisture resistance. Despite the enhanced performance, higher NaOH concentrations (10%) caused slight fiber degradation, affecting the elongation at break. Overall, the findings suggest that alkali treatment effectively improves the mechanical properties and durability of flax fiber composites, making them suitable for high-performance, eco-friendly applications, provided that the NaOH concentration is optimized

Keywords : alkali treatment, Hand layup technique, flax fiber, epoxy resin composite

Author : Vijay Mohan Shetty ,M. Madhavi, P. Ramesh Babu

Title : FECT OF ALKALI TREATMENT ON LOAD BEARING CAPACITY OF NATURAL FLAX FIBER EPOXY COMPOSITES

Volume/Issue : 2025;02(07)

Page No : 568-583

This study investigates the synergistic effects of alkali post-treatment and fiber orientation on the impact behavior of woven flax fiber-reinforced epoxy laminates. Six distinct layup configurations— were fabricated using the hand layup technique. Post-curing, the laminates were chemically treated with 5% and 10% sodium hydroxide (NaOH) to enhance fiber–matrix interfacial bonding. Mechanical performance under impact loading was assessed through drop weight (low-velocity impact) and quasistatic indentation (QSI) tests at impact energies of 5 J, 10 J, and 15 J. The 5% NaOH-treated composites exhibited superior energy absorption across all layups, with maximum QSI absorption efficiency reaching 78.7% in symmetric hybrid (D) and 78.6% in group hybrid (E) configurations at 15 J. Angle-ply (F) and hybrid stacking (C) configurations also demonstrated comparable resistance (78.3%–78.4%). These improvements were attributed to enhanced fiber–matrix adhesion and favorable in-plane load distribution enabled by the multi-angled orientations. In contrast, the 10% NaOH treatment, while improving bonding, led to marginal fiber degradation, slightly reducing energy absorption efficiency (typically 75.7%–75.9%). Unidirectional stacks (A) consistently showed the lowest performance due to directional stress concentration and limited delamination control. The results underscore the importance of optimizing both chemical surface modification and laminate architecture to achieve high-impact resistance in natural fiber composites. This study provides a pathway for developing sustainable, lightweight, and structurally resilient materials for automotive and aerospace applications where energy absorption and eco-efficiency are critical

Keywords : alkali treatment, Hand layup technique, flax fiber, epoxy resin composite

Author : Vijay Mohan Shetty ,M. Madhavi, P. Ramesh Babu

Title : MECHANICAL RESPONSE OF POST-TREATED WOVEN FLAX EPOXY LAMINATES UNDER LOW-VELOCITY IMPACT AND QUASI-STATIC INDENTATION

Volume/Issue : 2025;02(07)

Page No : 584-597

Biodegradable composites reinforced with plant-based fibers are emerging as promising candidates for lightweight and sustainable material solutions. Among these, Calotropis gigantea an underutilized plant-based fiber holds potential as a sustainable reinforcement material. This study investigates the mechanical characterization of Calotropis gigantea stem fibers, using corn flour as an eco-friendly matrix to develop biodegradable composites. Fibers were extracted via the retting process and incorporated into corn flour in varying weight ratios. Composite laminates with different fiber-to-resin proportions were fabricated to check the stability for the novel fiber composite, among which 44.4% w/w fiber content was found optimal for mechanical testing due to superior laminate stability. A series of five replicates were tested to ensure accuracy and repeatability. The results revealed a tensile strength of 31.34 MPa, flexural strength of 97.20 MPa, and a notably high impact strength of 28.5 kJ/m², indicating the material's potential for impact-sensitive applications. Although marginal gains were seen in tensile and flexural performance, the composite demonstrated significant enhancement in impact resistance, highlighting its practical viability in lightweight structural, packaging, and insulation components where energy absorption is critical.

Keywords : Natural Fibers, Calotropis Gigantea, bio-composite, Corn flour, Mechanical Testing

Author : Reeti Mukherjee , M Madhavi , A Krishnaiah

Title : Mechanical Performance of Biodegradable Natural Composites Reinforced with Calotropis Gigantea Fibers

Volume/Issue : 2025;02(07)

Page No : 598-610

Green hydrogen has emerged as an alternative potential energy source for a clean and sustainable future. It is versatile, has high energy content and derived from renewable energy sources. It has the potential to replace traditional energy sources like fossil fuels. In this article, we have introduced hydrogen as future energy carrier with its various types and production methods and storage techniques. We also discussed about its various applications and limitations. This study offers perceptions about overcoming the challenges and accelerating the worldwide adoption of carbon-free and sustainable energy source for future generation.

Keywords : Green Hydrogen, renewable energy, carbon-free, sustainable.

Author : M. Chakraborty

Title : GREEN HYDROGEN: AN ALTERNATIVE CLEAN ENERGY FOR SUSTAINABLE FUTURE

Volume/Issue : 2025;02(07)

Page No : 611-616

The main aim of this work is to implement a design and verification of car parking management system using Verilog and system Verilog . The system is divided into two major parts. The first part involves the design of a counter that continuously monitors and indicates the number of occupied and vacant parking slots, also using water level sensor, smoke sensor, fire sensor for safety and security focuses on improving safety by detecting fire, smoke, and water conditions. The second part focuses on the design of a display and indication unit, where the slot status (filled or vacant) is shown on an LCD to assist drivers and prevent forward collision between vehicles inside the parking area. The system operation is based on sensor detection, where the presence or absence of a vehicle is identified using sensors installed at each parking slot. The LED indications depend on the sensor outputs and visually represent vehicle entry and exit from the parking slots. These LED signals help in identifying the availability of slots and also indicate the presence of vehicles approaching from the opposite direction at the slot entrance, thereby enhancing safety and traffic coordination within the cellar parking area. The system provides real-time information through LCD display and LEDs, and alerts users using a buzzer during emergency conditions The complete system is designed using Verilog and functionally verified using System Verilog with simulation performed in Questa Sim. The proposed design improves parking efficiency, enhances safety, and minimizes human intervention, making it suitable for modern automated parking applications.

Keywords : Questa Sim, Verilog: System Verilog, IR sensors, fire sensor, smoke sensor, soil moisture sensor, LED, LCD display, motors

Author : 1K.Yebu kumar, 2K. Chandu Prasad, 3G. Chandu Bhargav, 4K. V. Siva Naga Rajendra, 5K. N. Srinivas Kumar, 6DR. P. Srinivasa Rao

Title : Design And Verification of Car Parking Management System Using Verilog and System Verilog.

Volume/Issue : 2025;02(07)

Page No : 289-301

The Advanced Obstacle Detection and Automatic Vehicle Control System is a safety-based project designed to detect obstacles and prevent vehicle accidents. The main problem addressed in this project is road accidents caused by human errors such as delayed reaction, distraction, and poor visibility. To rectify this issue, the system uses ultrasonic sensors to continuously monitor the distance between the vehicle and surrounding objects. A microcontroller processes the sensor data and identifies obstacles within a predefined range. When an obstacle is detected, the system alerts the driver through a buzzer or LED indicator, and if no action is taken, it automatically slows down or stops the vehicle. The project uses technologies such as embedded systems, sensor technology, and automation control. Algorithms like distance calculation, threshold detection, and decision-making are implemented for accurate functioning. Components such as Arduino, motor driver, DC motors, and sensors are used in the system. This project is useful in autonomous vehicles, accident prevention systems, and smart transportation. It improves safety, reduces human dependency, and provides an efficient solution for modern vehicle control systems

Keywords : Internet of Things (IoT), Smart Vehicle System, Real-Time Tracking, Remote Monitoring, Sensor Data Processing, Automation, Vehicle Safety

Author : 1D. Sai Latha, 2D. Siva Lakshmi,3GM. Jhansi Lakshmi,4B. Divya, 5G. Harshini, 6DR. B. Kiran Kumar

Title : ADVANCED OBSTACLE DETECTION AND AUTOMATIC VEHICLE CONTROL SYSTEM

Volume/Issue : 2025;02(07)

Page No : 302-310

Driver drowsiness is one of the major causes of road accidents worldwide, resulting in injuries, fatalities, and significant economic losses. Fatigue and lack of concentration reduce the driver's ability to react quickly to road situations, thereby increasing the risk of accidents. The proposed Drowsiness Detection System is designed to monitor the driver's alertness condition continuously and provide real-time warning notifications whenever drowsiness symptoms are identified. The system utilizes sensors and intelligent monitoring techniques to detect eye movement and blink patterns. The collected information is processed through a controller unit that continuously analyzes the driver's behavior. When the detected values exceed predefined threshold levels, the system automatically activates a buzzer alert and initiates safety measures to reduce accident possibilities. GPS technology is integrated to provide location information during emergency situations, while IoT functionality allows real-time monitoring and communication. The proposed system reduces manual intervention, improves road safety, and provides immediate responses to dangerous situations. Experimental results indicate that the system effectively identifies fatigue conditions with high accuracy and supports the development of intelligent transportation and driver assistance systems.

Keywords : Drowsiness Detection, Driver Monitoring System, IoT, Eye Blink Detection, GPS Module, Safety Alert System, Raspberry Pi, Machine Learning, Driver Fatigue Detection, Intelligent Transportation

Author : 1Mannem Sai Teja, 2Guthi Yaswanth, 3Kommanaboyina Bhaswanth, 4 Irapani Lakshmi Narasimha, 5S. Ravindra

Title : Drowsiness Detection System

Volume/Issue : 2025;02(07)

Page No : 311-319

Random Access Memory (RAM) system has been designed and functionally verified using Verilog HDL and SystemVerilog-based verification environment. The RAM module incorporates Error Correction Code (ECC) and Built-In Self-Test (BIST) mechanisms to enhance data integrity and reliability against transient and permanent faults, which are critical in safety-critical and high-reliability systems.The ECC encoder implements a Hamming SEC-DED (Single Error Correction) scheme by generating 5 parity bits for every 16-bit data, resulting in a 21- bit encoded output. During read operations, the ECC decoder computes the syndrome to detect and correct single-bit errors and flag double-bit errors. The BIST controller automates the test process by writing known patterns into memory, reading them back, and comparing the values to detect permanent faults without external test equipment. The top-level module integrates the RAM, ECC encoder/decoder, and BIST controller. It supports normal operation mode and self-test mode, making the system capable of runtime error detection and correction. The entire design is validated using a SystemVerilog testbench to ensure functional correctness under various scenarios, including fault injection and random stimuli. Simulation results confirm the system's ability to correct single-bit errors, detect double-bit errors, and identify memory faults, demonstrating its effectiveness and reliability in fault-prone environments

Keywords :

Author : 1Medikondu Abhinav, 2Abhiram, 3P.Enosh, 4M. Lokesh, 5M.Bhanu, 6DR. J.Kavitha

Title : Design of hybrid memory using ECC, EDC, & BIST using Verilog and verification using system Verilog.

Volume/Issue : 2025;02(07)

Page No : 320-328

A Defence Utility Robot is an advanced robotic system designed to assist military and security forces in performing hazardous tasks such as surveillance, bomb detection, reconnaissance, and rescue operations. The robot integrates sensors, cameras, wireless communication, and control systems to operate in dangerous and inaccessible environments, reducing risks to human life. It can be operated remotely or function autonomously using intelligent algorithms, providing real-time data and improving mission efficiency. This project focuses on developing a compact, reliable, and costeffective robotic solution to enhance safety, accuracy, and operational effectiveness in defence applications.

Keywords : Defence Robot, Surveillance, Bomb Disposal, Wireless Control, Autonomous System, Sensors, Robotics, Military Technology

Author : 1L Hari krishna, 2 I.V.N.M .Sai raman, 3K.Vishnu vardhan, 4K.Prem Kumar, 5 John Varakumar

Title : DEFENCE UTILITY ROBOT

Volume/Issue : 2025;02(07)

Page No : 329-338

The Automatic Irrigation and Weather Intelligence System is developed to ensure efficient and automated water management for agricultural fields. The system integrates multiple sensors, including a soil moisture sensor and a rain sensor, to monitor environmental conditions that directly influence irrigation needs. A TDS sensor is included to measure soil nutrient levels, enabling farmers to understand soil quality and plan fertilizer requirements. Additionally, an IR sensor is used for theft or intrusion detection, providing security alerts for the farm area. A Raspberry Pi functions as the central controller, acquiring all sensor data and operating the irrigation pump through a relay. The system connects to an IoT cloud platform, where real-time data such as soil moisture, rainfall status, nutrient levels, weather information, and security alerts are stored and visualized. This allows users to remotely monitor field conditions and motor status from anywhere. Cloud intelligence model is used to predict short-term weather conditions such as rainfall, humidity, and temperature. These predictions help the system decide whether irrigation is required, thereby preventing unnecessary watering during rainfall or high soil moisture conditions. All sensor readings, motor status, and weather forecasts are stored in the cloud and can be viewed remotely by the user. By combining multi-sensor monitoring with IoT-based weather intelligence, the system ensures optimal water usage, enhances soil health awareness, reduces manual labour, prevents over-irrigation, and provides an additional layer of farm security. This solution supports modern precision agriculture and offers a reliable, automated approach to improve productivity and resource efficiency.

Keywords : Raspberry PI – W, Soil Moisture Senso, TDS Sensor, Rain Sensor, IR Sensor, Relay Module, Water Pump, Battery, Cloud Intelligence, IOT (Cloud)

Author : 1D. Vamsi, 2B. Prem kumar, 3D. Rohith, 4A. Lakshmi charan, 5B. Trinath, 6Mr. G. Koteswarao

Title : Automatic Irrigation and Weather Intelligence

Volume/Issue : 2025;02(07)

Page No : 339-349

The increasing rate of theft incidents in modern society has created a strong demand for intelligent and automated security systems. Traditional locking mechanisms such as mechanical locks and basic alarms are no longer sufficient to provide reliable protection for valuable assets. These systems often fail to detect unauthorized access in real time or notify the user immediately. To overcome these limitations, advanced embedded and IoTbased solutions are being developed to enhance security and provide smart monitoring capabilities.This project presents an Anti-Theft Security System designed using NodeMCU along with RFID, IR sensor, vibration sensor, GPS module, and a buzzer. The main objective of the system is to detect unauthorized access and trigger instant alerts. The RFID module is used for authentication, ensuring that only authorized users can access the system. If an invalid RFID tag is detected, the system immediately considers it as a threat and activates the security response mechanism. In addition to RFID authentication, the system also uses IR sensors and vibration sensors to detect physical disturbances or motion near the protected object. These sensors continuously monitor the environment and send signals to the NodeMCU microcontroller when abnormal activity is detected. The microcontroller processes the data and determines whether a security breach has occurred. If a threat is confirmed, the system activates a buzzer alarm to alert nearby people.Furthermore, the GPS module plays a crucial role in tracking the exact location of the protected object. In case of theft, the system sends real-time location data to the user through IoT communication using Wi-Fi. This allows the user to take immediate action and recover the stolen item quickly. The integration of multiple sensors with IoT technology ensures high accuracy, reliability, and efficiency in security monitoring. In conclusion, the proposed Anti-Theft Security System provides a smart, costeffective, and efficient solution for modern security challenges. By combining embedded systems and IoT technology, the system enhances real-time monitoring, instant alert generation, and location tracking. This makes it more advanced compared to conventional security methods and suitable for applications such as vehicle protection, locker security, and personal asset safety

Keywords : Anti-Theft Security System, NodeMCU, IoT, RFID Authentication, IR Sensor, Vibration Sensor, GPS Module, Buzzer Alarm, Real-Time Monitoring, Embedded Systems, Wireless Communication, Smart Security System.

Author : 1CH.Rohith, 2D.Yaswanth Kumar, 3CH.Naga Venkat Sai, 4G.Arun Kumar, 5B.Mohan Rao

Title : ANTI THEFT SECURITY SYSTEM

Volume/Issue : 2025;02(07)

Page No : 350-363

The Advanced Driver Monitoring System is an embedded safety platform built using a Raspberry Pi and sensor fusion. The system is designed to detect unsafe driving behaviours in real time to prevent road accidents. A key feature of the system is the use of an IR sensor to monitor the driver for drowsiness by detecting prolonged eye closure. In addition to fatigue monitoring, the system includes a heart rate sensor to track the driver’s physical stress and an alcohol sensor to detect intoxication. By combining these inputs, the Raspberry Pi can identify if a driver is too tired, unwell, or impaired to drive safely.When a risk is detected, the system provides immediate feedback through an LCD, an audible buzzer, and LED indicators. Furthermore, the Raspberry Pi connects to a DC motor control unit to automatically and gradually reduce the vehicle's speed. This sensor-based approach offers a low-cost and efficient alternative to expensive camera systems, providing a reliable way to improve road safety through real-time alerts and controlled deceleration.

Keywords : Raspberry Pi, IR sensor, Drowsiness detection, Eye closure monitoring, Heart rate sensor, Alcohol sensor, Driver behaviour monitoring, LCD, Buzzer alarm, LED indicators, DC motor speed control.

Author : 1Karna Gnana Sree, 2Katteda Srividhya, 3 Jakkula Sri Divya, 4 Jonnalagadda Lally Sunnaina, 5Gowrabattuni Meghana, 6K. Anusha373

Title : Advanced Driver Monitoring System Using Raspberry Pi

Volume/Issue : 2025;02(07)

Page No : 364-

n Advanced Home Protection System using embedded systems is an intelligent and reliable security solution developed to provide safety and protection for residential environments. The system is designed to continuously monitor different conditions within a home and identify potential threats such as unauthorized access, fire accidents, gas leakage, and unusual temperature variations. Embedded systems are highly suitable for such applications because they provide real-time processing capability, low power consumption, compact size, fast response, and reliable performance. The system uses a microcontroller such as Arduino, ESP32, or Raspberry Pi as the central processing unit, which receives and processes data collected from multiple sensors. Different sensors are integrated to perform specific tasks, including PIR motion sensors for detecting human movement, magnetic door sensors for monitoring the opening and closing of doors and windows, gas and smoke sensors for detecting hazardous gas leaks, and flame or temperature sensors for identifying fire-related conditions. The embedded software continuously analyzes sensor information and compares the received values with predefined limits to determine abnormal situations. When a threat or intrusion is detected, the system immediately activates alarms such as buzzers or sirens and sends alert notifications to homeowners using communication modules like Wi-Fi or GSM. This system provides a costeffective, efficient, and smart approach for enhancing residential security and safety.

Keywords : Embedded Systems, Home Security, Microcontroller, Sensors, PIR Sensor, Gas Sensor, Fire Detection, Intrusion Detection, GSM Module, Wi-Fi Communication, Real-Time Monitoring, Alarm System, Smart Home, Remote Access, Automation

Author : 1Sura Sri Charan Gopi, 2Shaik Abbas, 3Shaik Abdul Reheman, 4Shaik Nazeer, 5K. Lakshmi Prasanna

Title : ADVANCE HOME PROTECTION SYSTEM USING EMBEDDED

Volume/Issue : 2025;02(07)

Page No : 374-383

Flood-induced structural failures of railway bridges create significant threats to transportation safety, infrastructure stability, and uninterrupted railway operations, especially during heavy rainfall and extreme weather events. Early identification and prediction of flood conditions are essential to reduce accidents, minimize structural damage, and ensure operational reliability. This project proposes a hybrid Internet of Things (IoT) and Machine Learning (ML)-based predictive flood risk detection system specifically designed for railway bridge environments. The proposed system combines water-level sensing, vibration monitoring, and environmental gas detection through an MQ-5 sensor integrated with a Raspberry Pi Zero Wbased edge-processing unit. These sensors continuously monitor environmental and structural parameters that influence flood risk and bridge safety. Real-time data are collected through an IoT-enabled wireless sensor network and processed for analysis. A machine learning model is trained using historical flood records along with live sensor data to identify patterns and trends associated with flood occurrences. Unlike conventional threshold-based systems, the ML model categorizes flood conditions into multiple levels such as normal,warning, and danger, enabling predictive risk assessment and early intervention.

Keywords : IoT, Machine Learning, Flood Risk Detection, Railway Bridges, Raspberry Pi, Wireless Sensor Network, Structural Health Monitoring.

Author : 1Kotha Gnana Sathwika, 2Gudivada Harika, 3Khagga Venkata Navya, 4Koppolu Keerthana, 5Kurapati Bhargavi, 6N. Syamala

Title : Hybrid IOT and Machine Learning System for Predictive for Railway Bridge Flood Risk Detection

Volume/Issue : 2025;02(07)

Page No : 384-394