Journal of Scientific Research and Technology

Application of Deep Learning Technique for Tomato Maturity Stage Prediction

Ankit — 2025-07-01

Tomatoes are a major crop worldwide, and accurately classifying their maturity is important for many agricultural
applications, such as harvesting, grading, and quality control. Primary objective to develop an automated system
capable of identifying various defects in tomatoes and providing relevant treatment suggestions. Leveraging deep
learning techniques, a CNN model is trained to classify tomatoes into four categories: Damaged, Old, Ripen, and
Unripen. The implementation involves training a convolutional neural network and testing dataset of tomato images,
for classification, and deploying model intended real-time predictions. Project has potential to improve effectiveness of
tomato harvesting & reduce waste. The CNN model then predicts the stage of the tomato, providing the probability of
the prediction. Alongside the classification result, the system offers detailed information on the cause of the defect,
appropriate treatment methods, and nutritional content, aiding users in making informed decisions regarding the
tomatoes usability. The implementation ensures a robust and efficient detection mechanism, even in varying lighting
conditions and backgrounds. An Accuracy of 94.48% was attained when taught sculpt was used to make prediction on
the test dataset.

Multifunctional Agricultural Robot

Neelambika — 2025-07-01

This paper presents the design, development, and implementation of a multipurpose agricultural robot aimed at
automating essential farming tasks to enhance productivity and reduce labor. The robot is capable of digging soil,
levelling mud, and spraying water and fertilizers, operating on a combination of battery and solar energy. Key
components include a relay switch, Bluetooth modules for user interaction, and various sensors to ensure precise
operation. The hands-free, efficient design addresses the increasing need for innovative solutions in agriculture, driven
by the challenges of labor intensive traditional farming. By leveraging advanced technology, the proposed system
demonstrates significant potential in improving farming efficiency and sustainability, particularly in the context of the
growing interest in autonomous agricultural vehicles. The robot's hands-free, autonomous capabilities make it a
valuable asset for farmers, reducing the physical effort and time required for traditional farming practices.

A Novel Approach for Real-Time Audio-to-Sign Language Translation using Naive Bayes classifier and Natural Language Processing Technique

Poornima — 2025-07-01

Real-time translation from spoken language to sign language, crucial for the deaf and hard of hearing, is difficult employing machine learning advances, this research suggests employing Naive Bayes to instantaneously translate audio input into sign language motions. The initiative provides precise translations between spoken and visual language to improve accessibility and inclusion. The system generates sign language animations from audio inputs using natural language processing and deep learning. Language extraction, tense and context classification, and proficient sign language synthesis are key aspects. The research innovates by using a Naive Bayes model trained on varied spoken language and sign language gesture datasets to ensure robust performance across contexts and user situations. To enhance realism and comprehension, the system integrates a 3D animated avatar to render the translated signs visually. A custom-built gesture mapping mechanism aligns each keyword with its corresponding sign language motion. The tool operates in real-time, supporting both sentence-level and word-level translations. This approach contributes to bridging communication gaps and supports inclusive digital interaction for the hearing-impaired community.

Automated Detection of Image Forgery with Deep Learning

Shreedevi S Patil — 2025-07-01

The image forgery techniques are used to provide the particular image in the form of computerized pictures with no errors in capturing of digital image information which shows the original image. While compared to normal images the edited images are difficult to find out the forged images. The image should maintain the authenticity and integrity to secure the picture from unauthorized users. In this paper, we have compared the digital image forgery and JPEG is the most common format used by the photographic images and the digital camera devices. These operations are performed in an adobe photo-shop using with the content of image security to restore some digital image with an authenticity and integrity to detect the digital image forgery using active and passive techniques.

Smart Traffic Control Approach For Emergency Vehicles

Dr Rashmi Patil — 2025-07-01

Traffic congestion problem is a phenomenon which contributed huge impact to the transportation system in our country. This causes many problems especially when there are emergency cases at traffic light intersections which are always busy with many vehicles. A traffic light assistance system is designed in order to solve these problems. This system was designed to be operated when it received signal from emergency vehicles based on radio frequency (RF) transmission and used the Programmable Arduino Atmega 328 microcontroller to controls the LEDs used in the traffic signals. The use of hazard LED in the system which helps the emergency vehicles to pass the traffic easily. This system will reduce accidents which often happen at the traffic light intersections because of another vehicle had to huddle for given a special route to emergency vehicle. As the result, this project successful analyzing and implementing the traffic assistance system for emergency vehicles.

CNN-Based Tomato Leaf Disease Detection With Smart Spraying Mechanism

Swati V Partapur — 2025-07-01

Indian agriculture plays a vital role in crop production and economic development, providing employment to a significant portion of the population. The health of plants is crucial for better yield and profit, requiring continuous monitoring to prevent diseases that can severely impact crop productivity. Traditional disease detection relies on manual observation, which is time-consuming and inefficient. To address this, an automated plant disease identification and prevention system is introduced, integrating IoT-based agricultural monitoring with Machine Learning algorithms. Various IoT sensors, such as thermal, moisture, humidity, and color sensors, help detect plant diseases at an early stage. The system enables timely intervention through automated medicine spraying, improving efficiency and crop health.

Advanced Structural Analysis And Design Optimization Of High- Rise Building Utilizing Building Information Modeling (BIM) Techniques

Ritik Yadav — 2025-07-01

In this study, we will be exploring the advanced structural analysis and design optimization techniques for a G+16 high-rise RCC building using STAAD.Pro V8i SS6. Based on IS codes, the building is evaluated for dead, live, and seismic loads, with manual calculations and software evaluations being utilized to evaluate the data. This involves evaluating the design elements, including stresses on beams and columns, reinforcement detailing, and structural stability. Other important considerations are also given. These findings provide evidence that STAAD.Pro can design high-rise buildings with accuracy, safety, and affordability without requiring manual methods, while also being an excellent substitute for advanced software tools.

Studying The Strength And Durability Properties Of Steel Fiber Reinforced Self -Compacting Concrete

Agrim — 2025-07-01

This study investigates the strength and durability characteristics of steel fiber reinforced self-compacting concrete (SFRSCC). The primary objective is to evaluate the influence of varying steel fiber contents on the mechanical performance and durability performance of self-compacting concrete. Tests conducted include compressive strength, splitting tensile strength, flexural strength, water absorption, sorptivity, and resistance to chloride ion penetration. The experimental results demonstrate that the inclusion of steel fibers significantly enhances the tensile and flexural strengths, improves the concrete's crack resistance, and reduces permeability, thereby augmenting its durability. The findings suggest that steel fiber reinforcement is an effective method to improve the structural integrity and longevity of self-compacting concrete in various construction applications. The optimal fiber content balancing workability, strength, and durability is also discussed, providing valuable insights for structural engineers and material scientists aiming to design durable, high-performance concrete composites.

Research On The Development Of Eco-Friendly, High-Strength, And Durable Concrete Materials Used In Modern Construction

Devdhar Dwivedi — 2025-07-01

The construction industry is increasingly seeking sustainable solutions to address environmental concerns while meeting the demands for high-performance structural materials. Traditional concrete, although widely used, significantly contributes to carbon emissions and resource depletion. This research explores the development of eco-friendly concrete composites that combine high strength and durability for modern construction applications. Emphasis is placed on incorporating supplementary cementitious materials such as fly ash, slag, and silica fume, as well as innovative eco-friendly binders and admixtures that reduce the reliance on Portland cement. The study evaluates the mechanical properties, durability factors, and environmental impacts through experimental testing, including compressive strength, tensile strength, water absorption, and resistance to chemical attacks. Sustainable approaches such as the utilization of recycled aggregates and biogenic materials are also examined. The findings demonstrate that optimized mixes can achieve comparable or superior performance to conventional concrete, with significant reductions in carbon footprint and resource consumption. The research underscores the potential of eco-friendly, high-strength, and durable concrete as a cornerstone for sustainable infrastructure development, contributing to environmentally responsible construction practices.

Agricultural Pesticides Spraying Remote Control Robot

Asst. Prof. Nandini — 2025-07-02

Automatic pesticide sprayers offer several significant advantages in modern agriculture. They enhance efficiency by covering larger areas quickly and reducing labor costs. These machines also improve precision in pesticide application, minimizing wastage and environmental impact. Furthermore, they enhance safety foe farmers by reducing exposure to harmful chemicals. By optimizing resource use and minimizing risks, automatic sprayers contribute to sustainable and productive agricultural practices. In conclusion, agriculture pesticides spraying smart remote-control robots offer a range of benefits, including increased efficiency, improved safety, enhanced accuracy, cost-effectiveness, and environmental benefits. These robots can help farmers optimize pesticide application, reduce waste, and promote healthier crops. As technology continues to evolve, we can expect to see further advancements in this field, leading to more sustainable and productive agriculture practices.

A Machine Learning Approach To Classify Medicinal Plant Leaf By Using Random Forest And KNN

Bhagyashree — 2025-07-02

Numerous plant and herb types partake remained cast-off broadly in traditional medicine due to their medicinal properties and cost-effectiveness compared to modern prescription drugs. Nevertheless, accurately identifying medicinal plants poses a significant challenge in the realm of machine learning. This project leverages a comprehensive dataset of leaf images from various medicinal plants to train and evaluate a classification model.The primary impartial of this explore is to develop an advanced ML model capable of accurately identifying different Indian medicinal plants. By focusing on several vulnerable species, the study employs the Random Forest and K-Nearest Neighbors algorithms to classify the plants. The KNN algorithm achieved an accuracy of 84.33%, while the Random Forest algorithm outperformed it with an accuracy of 85.82%, demonstrating its superior effectiveness.Beyond mere identification, the project explores the medicinal properties & aids related with individually recognized plant. Given the visual similarities among many plant species, classification is predominantly stimulating, expressly when commerce with extensive datasets. This research aims to bridge the gap between traditional botanical knowledge and modern technological advancements, thereby contributing to the preservation and dissemination of valuable medicinal information.By utilizing a Random Forest-based predictive model, this structure not only aids in the accurate identification of medicinal plants but also supports the conservation of ancient and traditional medicinal knowledge. This integration of historical wisdom with contemporary machine learning techniques ensures that valuable botanical information is preserved and made accessible for future generations

An Intelligent Video Analysis Framework For Classifying And Prioritizing Harmful Social Media Content With CNNs

Soumya — 2025-07-02

Social media platform have develop essential channels intended communication, content sharing, and community building. However, the widespread distribution of user-generated content introduces significant challenges in monitoring and managing harmful, inappropriate, or violative material. Addressing these challenges is crucial to maintaining a safe and respectful online environment. This project presents a comprehensive framework for detecting and rating violative user content in social media, employing advanced computer vision techniques to analyze video content. The framework extracts frames from videos and utilizes (CNNs) to classify various forms of violations with high accurateness. A robust dataset, encompassing diverse categories of violations, is employed to train the model, ensuring its effectiveness across different contexts and platforms. The framework's evaluation component is thorough, incorporating system of measurement such as accurateness, precision, recall, & F1-score to assess model performance. Confusion matrices & classification reports offer detailed insights into the system's effectiveness. The model's capability to process video frames in real- time simplifies its integration into prevailing social media monitoring systems, providing a scalable solution for content moderation. In addition to detection, the framework includes a rating mechanism that evaluates the severity of detected violations. This rating system aids in prioritizing content review processes, ensuring that the most harmful material is addressed promptly. The use of advanced machine learning algorithms and comprehensive training data allows the framework to adapt to evolving content trends and emerging threats effectively. Overall, this project delivers a scalable, accurate, and efficient solution for detecting and rating violative user content on social media platforms. By enhancing content moderation capabilities, it contributes significantly to the creation of safer and more respectful online communities.

Advanced CNN Approach To Automated Squint Eye Detection And Comprehensive Early Intervention

Prakruti — 2025-07-02

Squint eye, also recognized as strabismus, is condition in which eyes do not properly align by each other while looking at an object. This misalignment can result in both eyes pointing in different directions, leading to double vision or a loss of depth perception. Early detection & treatment of squint eye are crucial to prevent long-term visual impairments & improve superiority of life intended affected individuals. Traditional methods for diagnosing squint eye involve subjective assessments by medical professionals, which can be time-consuming & prone to variability. To address these challenges, a novel approach using CNN intended automated squint eye detection has been developed. This study presents a comprehensive framework for detecting squint eye using CNNs, leveraging the powerful capabilities of deep learning to analyze and classify eye images with high accuracy. The proposed system involves the collection and preprocessing of bulky dataset of eye metaphors, which be subsequently worn to train a CNN model. Model is intended to identify key features indicative of squint eye, such as positioning of pupils & alignment of eyes. Once trained, replica is capable of analyzing new eye images to establish presence of squint eye with high precision. Performance of developed CNN model is evaluated using variety of metrics, including accurateness, sympathy, specificity, & F1-score. Results exhibit that representation achieves noteworthy improvement over traditional diagnostic methods, providing a reliable and efficient tool for squint eye detection. Additionally, the study explores the potential applications of this technology in clinical settings, highlighting its benefits for early diagnosis and intervention. Regarding licensing regulations, it imperative to note that individuals with squint eye may face restrictions when it comes to obtaining a driving license. In many regions, the Road Transport Office (RTO) does not issue driving licenses to individuals with squint eye due to prospective risks associated with impaired vision, such as double vision and reduced depth perception. Use of automated detection systems can aid in early diagnosis & appropriate management of this condition, ensure that pretentious individuals receive necessary treatment and support.

AI KNOWLEDGE HUB 2.0

Swati S Kagi — 2025-07-02

The AI Knowledge Hub is an interactive application that enables users to access and analyze multimedia content like PDFs, YouTube videos, and images. It uses advanced AI models and natural language processing to extract relevant information from these sources. The system extracts text from PDFs using PyPDF2, retrieves video transcripts via the YouTube Transcript API, and processes images using Optical Character Recognition (OCR). The extracted text is processed using LangChain and stored in FAISS, a vector database optimized for fast similarity searches. The Gemini AI model analyzes user queries, generating context-aware responses. The application supports multilingual translation using Googletrans and a text-to-speech (TTS) feature powered by gTTS. The intuitive Streamlit-based interface allows for quick navigation. The AI Knowledge Hub is designed for real-time processing, making it useful for students, researchers, and professionals. Future enhancements include support for additional file formats, cloud storage integration, advanced video analysis, offline processing capabilities, and compatibility with voice assistants like Alexa and Google Assistant.

Designing And Implementing Arduino Based Nutrition Feeding Automation System Of A Prototype Scaled Nutrient Film Technique (NFT) Hydroponics Using Total Dissolved Solids (TDS) Sensor

Rashmi Bangar — 2025-07-03

Hydroponics is a new breakthrough in farming because it no longer uses soil as a planting medium, and uses water instead. In the hydroponic system, the fertilizer used is mixed into water, which is then referred to as hydroponic nutrition or nutrient solution. The nutrient concentration in the solution, which is then indicated by the electrical conductivity (EC), is very influential on crop production. The nutrient concentration usually mixed manually by combining the fertilizer and the water in the right amount. Therefore, through this research, a nutrition feeding automation system of a prototype scaled Nutrient Film Technique (NFT) hydroponics is prepared. The system is designed with a control center using the NODE MCU R3 board. The system is equipped with SR04 proximity sensor as a water level detector, TDS sensor as a detector of electrical conductivity of the nutrient solution, and servo motor as an opening device of the faucet in the nutrient container. The research resulted that the system is capable of performing water delivery automatically when the water level is less than the minimum level, and add the nutrients automatically when the nutrient solution concentration is below 800ppm.

Role Of IT Industry In Driving India's Economic Growth

Ms. Sathyapriya R — 2025-07-04

The Information Technology (IT) industry has emerged as a cornerstone of India's economic transformation, particularly following the economic liberalization reforms of 1991. This paper analyzes the pivotal role of the IT sector in driving India's economic growth by examining its contributions to GDP, exports, and employment generation. Post-1991, the IT industry experienced exponential growth due to policy support, global demand, and a skilled English-speaking workforce. It has since become one of the largest contributors to India's service exports and a significant source of direct and indirect employment. The sector's impact extends beyond economic indicators, fostering urban development, innovation, and global integration. This study explores the structural evolution of the IT industry, assesses its economic contributions quantitatively and qualitatively, and highlights the challenges and opportunities in sustaining its momentum in the digital era.

Smart Helmet For Mining Workers

Prof. Poornima — 2025-07-04

The proposed project aims to develop a smart helmet for mining workers, which integrates multiple sensors, communication modules, and emergency features to enhance the safety of workers in hazardous working environments. The smart helmet incorporates a gas sensor MQ2 to detect hazardous gases, DHT11 temperature and humidity sensor to monitor environmental conditions, an emergency switch to trigger an alert in case of an emergency, a 16*2 LCD to display real-time data, and a buzzer to sound an alarm in case of a critical situation. Additionally, the smart helmet integrates a GSM modem to send emergency SMS messages to predefined numbers, GPS location tracking, and an ESP8266 WiFi module for IoT communication. The Blynk app is used for data visualization, which allows users to remotely monitor the real-time data from the helmet. Thus, the smart helmet provides an advanced safety system for mining workers, which improves situational awareness, reduces accident risks, and enables rapid response in case of an emergency.

Smart Composting IOT Solution For Waste Reduction

Asst. prof. Sujata — 2025-07-04

By analyzing the environmental protection agency’s study, the amount of waste material thrown on the landfills is increases annually on a large scale. Thus, in developing countries, the organic waste becomes a global issue which becomes a big problem. As these waste materials emit methane gas and other toxic pollutants, the pollution rises as a result. These gasses cause global warming as it continues to heat the earth. Practices in waste management are not consistent between countries, regions and residential. Therefore, a new technique for smart compost system is proposed here whereby we can crush the organic waste for the composting process and can also achieve required compost by maintaining and controlling its parameters. Composting means keeping a waste in a close unit and let the micro-organism to grow to breakdown to make product ready for fertilizer. This Fertilizers or ready product can be used for agriculture or farming purpose. In this proposed system, the temperature, humidity, gas availability of the compost and the water level, requirement of the soil/waste is monitored with the help of
Arduino, Node MCU, relays and various sensors. Thus, by utilizing the system process, the garbage or organic waste Pollution can be controlled.

Child Safety Device With GPS Tracking And Alert Messaging

Smita. K — 2025-07-04

The project's goal is to create a wearable child safety tool that may be applied to put an end to bullying and violence. The gadget has numerous sensors and technologies, such as a GPS (Global Positioning System), GSM (Global System for Mobile) Module, and Alarm, in addition to real- time tracking, emergency alert messages, live streaming, and the capacity to stun assailants. The gadget tracks children's health using a mobile app. The device is perfect for daily use due to its small size, easy to conceal, and ease of use. The proposed method offers a reliable and practical means of enhancing child safety and reducing the probability of kidnapping and child trafficking situations involving children. It just requires one button to be pressed to turn it on in an emergency.

Smart Waste Management System

Prof. Poonam Reddy — 2025-07-05

With the rapid growth of urbanization and population, waste generation has become a major concern for cities worldwide. Traditional waste disposal methods, including manual sorting, are inefficient, error-prone, and contribute to environmental pollution by allowing recyclable and biodegradable waste to be dumped in landfills. To address this issue, this project presents a Smart Waste Management System that automates the segregation of waste using sensor technology and embedded systems. The proposed model utilizes an Arduino-based control system equipped with sensors and a servo-controlled rotating mechanism to identify and segregate waste into three categories: wet, dry, and metallic. The upper chamber acts as an intake unit where waste is dropped. Sensors analyze the waste type, and the rotator aligns the top bin over the correct container (green for wet, yellow for dry, red for metal), allowing gravity to drop the waste into the corresponding bin. The system minimizes manual intervention and ensures effective separation at the source. This prototype is energy-efficient, cost-effective, and scalable for household, institutional, or community-level deployment. The project demonstrates how automation and smart electronics can revolutionize waste management practices and contribute to a cleaner, sustainable environment.

Design And Development Of Smart Healthcare Assistant Robot Patients Monitoring

Dr. Sushma Patil — 2025-07-05

The main objective of this project is to present the development of an “Smart Robot for Health Assistance”. Most countries, particularly first world countries, are faced with an increasingly higher percentage of people in need of healthcare assistance, either due to old age or some disabilities or impairments, requiring prolonged care, constant examination, and following up the patients very closely. This pressure, both in number of and time dedicated to the patients, will put a strain on the current healthcare and social systems all over the world. Solving this problem, however, is not a simple matter, not just due to the lack of professionals but also due to the increasing costs of maintaining these services. The implementation of such a system would not only remove some of the workload from the medical professional, but also be more reliable in the long run, not suffering from attention, exhaustion or other issues that often affect us. With this system we intend to show the possibility of using robot platform in this context, paving the way for further development and implementation of such platform in hospitals and other healthcare facilities.

Multipurpose Agriculture Robot

Ningu Annor — 2025-07-05

Agriculture, being the backbone of many economies, is facing critical challenges such as labor shortages, high operational costs, and the need for increased productivity. To address these issues, this project proposes the design and development of a Multipurpose Agriculture Robot capable of performing multiple farming tasks autonomously or semi-autonomously. The primary aim is to reduce manual labor and enhance the efficiency and precision of routine agricultural operations like seed sowing, pesticide spraying, soil monitoring, and weed detection. The robot is built on a robust, all-terrain mobile platform and powered by a microcontroller (Arduino Uno), which coordinates the functions of various sensors and actuators. Sensors such as soil moisture, temperature, and obstacle detection enable real-time monitoring and decision-making. A spraying mechanism is incorporated for precise pesticide application, while a sowing module allows for uniform seed placement. The robot can be controlled remotely using Bluetooth or IoT-based systems, making it accessible and easy to operate. Power is supplied either through rechargeable batteries or solar panels, making it energy-efficient and suitable for remote areas. The design is cost-effective, lightweight, and durable, aimed especially at small and medium-scale farmers. The successful implementation of this multipurpose agriculture robot has the potential to revolutionize traditional farming practices by making them more automated, data-driven, and resource-efficient. It promotes sustainable agriculture by minimizing input waste, reducing chemical overuse, and optimizing manpower. This project serves as a step toward modernizing agriculture through practical and affordable robotic solutions tailored to real-world farming conditions.

Heating And Cooling Suit For Military People

Prof. Soumya — 2025-07-06

The heater / cooler in turn will help us to provide chilling or warming effect inside the uniform which helps the soldier
to bear to any kind of external environment and he can work efficiently without heat stress or cold stress. A heating
and cooling suit with integrated location tracking provides soldiers with enhanced protection and operational
capabilities in diverse and challenging environments. This technology combines thermal regulation with real-time
location monitoring to address issues like extreme temperatures and potential injuries during combat or training. The
system The typically includes a Peltier module for heating and cooling, sensors for monitoring environmental and
physiological data, and GPS/GSM modules for location tracking and communication. This project is operated in two
modes summer mode and winter mode. If the weather condition is too hot then the cooling system will operated and if
it is too cool then the heating system will operated. If this system may fail GPS will find out the position of soldiers and
send messages via GSM to the control station. This project has a significant role in our day to day life. Also it can be
used in various streams of industrial applications The specially designed E-uniforms are very much useful for military
applications especially, in unlike climatic conditions for soldiers and other civilian people.

Solar Based Multipurpose Farming Machine

Dr.M Sasikala — 2025-07-06

Today’s agricultural field demands to find new ways of agricultural operation to improve performance efficiency. In the field of agriculture various problems are faced by the farmers in the operations like seed sowing, pesticide spraying, weeding. Also, the equipment’s used to perform the operations are very heavy. Due to migration of humans in the cities the labor problem occurs. Nowadays robotics technology plays a paramount role in all sections like medical field, industries, and various organizations. In other countries robots are used to perform different operations in the agricultural field. We can make the use of available technologies and the robotics technology in the farming system to reduce the efforts of farmers and to reduce time, energy and required cost. Any autonomous vehicle is going to go wrong at some time and the chance of catastrophic failure should be minimized within the design process. A small light vehicle is inherently safer than the larger one. Redundant, self-checking systems should be built into the system architecture to allow graceful degradation. The vehicle should be in communication with the base station, giving data about current condition and context.

Smart Car Locking System

Prof. Laxmi — 2025-07-06

The main aim of this project is to track down the vehicles position, this system uses GPS receiver/transmitter, GSM receiver/transmitter with a micro controller. Position of the vehicle can be tracked down, by Authorized owner of the vehicle with this module installed in his vehicle. Owner wants to know where his vehicle is, he will send an SMS to that particular SIM number which is present in the modem of the module. The SMS, which has sent, by the owner will reach the vehicle, which is travelling and there it will compare the password and the command. If every thing matches then it will get the coordinates of the vehicle position. In this way we can easily map the vehicle position from the place where they are residing. In our project the PCB is designed by using Express PCB & the circuit is designed by using Proteus software.

Pulsenet: IOT Integrated Patient Paralysis Treatment

Bannamma Patil — 2025-07-06

Paralysis is a condition that severely limits a person's ability to move, speak, or perform basic tasks independently. Patients who suffer from partial or complete paralysis often find themselves heavily dependent on caregivers for even the simplest forms of communication or assistance. The project seamlessly combines multiple technologies to build a system that is not only functional but also highly effective in real-world patient care. Through gesture and voice interfaces, it empowers patients; through real-time health monitoring and emergency alerts, it ensures safety; and through IoT connectivity, it keeps caregivers informed at all times. This blend of hardware and software truly elevates the standard of care for individuals living with paralysis.

Eco Friendly Solar Powered Garbage Disposal Boat

Savitri — 2025-07-06

This article presents the garbage collection robot on the beach using wireless communications. The robot is built on the caterpillar wheels, sizes 52x74x17 cm and the power is supplied from 12V 1.2Ah battery which is connected to 3W solar cells. The user can control a robot via a program developed from MIT app inventor application based on Android. The commands from user are sent via Bluetooth to Arduino for processing. In addition, it is also equipped with an IP camera with added pan/tilt capabilities which relays feedback information to the human operator via Ad-hoc system. The results of robot performances were found that the robot can move with an average speed of 0.5 meters per second on the sand via wireless communication and collect the big garbage with side 12.5 x 49 cm, for example, glass bottles, and plastic, etc. From the experimental results, it can clearly indicate that the proposed robot is superior to handle tasking conveniently, control capability, and operate environmentally friendly.

UNICEF – Strategy For Water, Sanitation And Hygiene (Wash)

Lavanya. K — 2025-07-08

This paper was done to explore the impact of water treatment, hygiene, and sanitary interventions on improving child health outcomes such as absenteeism, infections, knowledge, attitudes, and practices and adoption of point-of-use water treatment. Focusing on the effects of access to safe water, hand washing facilities, and hygiene education among school-age children. Studies included were those that documented the provision of water and sanitation in schools for children less than 18 years of age, interventions which assessed WASH practices. Further research is required to assess the long-term impact of such interventions in different settings.The review concluded that the importance of access to safe water, hand washing facilities, and hygiene education cannot be underscored in abating water-borne illnesses, malnutrition, school absenteeism, and generally improving the quality of life and learning performance in children.

Smart Regulator For Controlling Fan Speed Through Android Mobile

Dr. Rashmi Patil — 2025-07-08

In today’s era of smart electronics, wireless control of home appliances has become essential for convenience and energy efficiency. This project presents a Bluetooth-based fan speed control system using an Arduino microcontroller, I2C LCD, Bluetooth module, motor driver, and a DC fan. The system allows users to control the speed of a fan remotely via a smartphone Bluetooth app. By receiving commands through the Bluetooth module (HC-05), the Arduino processes the input and adjusts the speed of the fan using PWM (Pulse Width Modulation) through the motor driver. The real-time speed level is displayed on the I2C LCD. This system offers a low-cost, flexible, and energy-efficient solution for smart fan control in homes and workplaces.

Integrated Health Management System (IHMS)

Dr. Ayesha Heena — 2025-07-08

Intelligent, scalable, and secure digital solutions that optimize clinical operations and improve patient outcomes are needed as healthcare delivery becomes more complicated. The web-based, unified Integrated Health Management System (IHMS) solves this requirement by managing Electronic Health Records (EHRs), scheduling doctor-patient appointments, and predicting illness using machine learning. IHMS' modular, user-friendly design allows real-time medical data access, secure stakeholder communication, and early illness diagnosis.
Django with SQLite provide a lightweight yet strong backend solution. Supervised machine learning methods like Random Forest, Decision Tree, Naive Bayes, and Logistic Regression forecast diseases. We train and serialize these models using a structured symptom-based dataset. When patients enter symptoms, the system produces a list of likely illnesses and confidence levels, helping doctors diagnose intelligently.
Privacy and security are built into the system. To protect medical data, IHMS uses role-based access restriction, encrypted sessions, and secure authentication. Physicians, patients, and administrative staff may access individualized dashboards and learn quickly thanks to adaptable design and straightforward navigation.
The system offers automatic appointment alerts, real-time patient status updates, and symptom-based triaging in addition to essential healthcare functions. Its lightweight architecture and low technological requirements make it suitable for small clinics and scalable for bigger healthcare facilities or cloud platforms.
The Random Forest model proved its usability and prediction accuracy in actual circumstances with an accuracy of 87.75% after extensive testing. IHMS improves decision-making and efficiency and advances intelligent healthcare management systems for current issues by seamlessly combining predictive analytics with operational modules.

Safe Lane IoT

Swati S Kagi — 2025-07-09

The Accident Detection and Assistance System with Blind Spot Detection is a safety system designed to reduce accidents and improve vehicle safety using a variety of sensors. This system utilizes an Arduino Uno, I2C LCD, ultrasonic sensors for obstacle and blind spot detection, an alcohol sensor for driver condition monitoring, a piezo sensor for accident detection, and a GSM module (SIM900) for emergency communication. The system alerts the driver in case of obstacles, blind spot hazards, alcohol consumption, and accidents. Additionally, it sends SMS notifications to an emergency number in case of critical incidents. This project aims to enhance the safety of the driver and others on the road.

IoT-Powered Emergency Button for Women's Safety

Dr. Sujata Mallapur — 2025-07-09

In a world where women's safety remains a major concern, there is an urgent need for proactive solutions that can provide real-time assistance during emergencies. Traditional mobile applications and emergency contact numbers, while useful, rely heavily on user interaction and internet connectivity, which may not always be accessible in high-stress situations. This project proposes a practical, standalone, and IoT-based solution titled "IoT Powered Emergency Button for Women Safety", designed to address these limitations.
The system consists of a compact and wearable emergency switch built using an ESP8266/ESP32 microcontroller, a NEO-6M GPS module, and a SIM800L GSM module. A simple push button serves as the trigger. When pressed, it activates the system to retrieve real-time location coordinates and send an SMS alert containing a Google Maps location link to pre-configured emergency contacts. A buzzer or LED indicator provides immediate feedback to the user, confirming successful transmission of the alert. The system also integrates Blynk IoT platform support, enabling cloud-based monitoring, logging, and app-based notifications.
The prototype was successfully tested under various real-world conditions, including weak GPS signals and GSM network fluctuations. It demonstrated high reliability in sending alerts, maintaining GPS accuracy, and functioning effectively even without internet access. The system is energy-efficient, battery-powered, and fully portable, making it suitable for everyday use as a wearable accessory.
This project contributes to the domain of smart safety systems by offering a low-cost, independent, and easy-to-use device for women’s personal security. Future improvements such as two-way communication, battery monitoring, fall detection, and mobile app integration are also discussed, paving the way for a scalable, real-world deployment. The integration of embedded systems and IoT technologies into safety applications proves to be a powerful step toward building a safer, more responsive environment for women.

Women Labours In Brick Industries

Dr. Basavant S. Awaradi — 2025-07-10

The unorganized or informal sector has also been termed "The non-farm economic activities" especially in relation to rural economy. This means that there are other economic activities carried out by the peasants and farmers which are not related to the farming economic system or are being carried out in parallel with farming activities. A woman is treated and respected as a mother, a wife, a daughter or a daughter-in-law but never as an independent being. To be born as a woman means to inhabit, from early infancy to the last day of life, a psychological world that is different from that of a man and much of the frustration and discrimination in woman’s life stems from this fact. The non-existence of the above factors may obstruct the resonance industrial relation, demotivate the workers, create job dissatisfaction, and increase labour turnover and absenteeism. No denying the fact that good, favorable socio-economic condition of the workers may act as a booster, which in turn may establish congenial industrial relations. In this backdrop, an attempt has been made to find out the socio-economic conditions of the women workers engaged in the selected brick kiln factories in Gulbarga Taluka. The main objectives of the studies are to understand the problems faced by informal brick kiln units. To understand the nature of work in the selected units. To analyse the socio-economic status of workers the selected units. For the present Research a research method containing data collection and analysis is followed. Both the primary and secondary data are referred for the research. The primary data is collected from the women labourers in the Brick kilns in the form of interview schedule and the secondary data from the journals books, reports published books.

Awareness And Practices Of Menstruation Hygiene Among Adolescent Girls

Dr. Surendra K — 2025-07-10

Adolescent girls and women in India face social and cultural constraints and hidden taboos when it comes to menstruation hygiene. School attendance drops and local illnesses rise when people don't have access to clean toilet facilities and sanitary hygiene supplies. In Assam's tea garden regions, people still have a low level of knowledge about many health issues. The commencement of menarche is a significant life milestone that occurs throughout adolescence; as a result, it is essential to practise excellent hygiene habits during menstruation in order to keep a healthy life overall. The data was gathered by a community-wide house-to-house survey, with questions asked of females using a tailored questionnaire that had been previously developed and tested. Among the topics included in the survey were participants' socio-demographic information, their familiarity with menstruation, their menstrual hygiene routine, and any limits they may have. The teen's parent or legal guardian gave their verbal agreement before the interview, and the teen herself gave her assent. During the course of the research, all participants were given the guarantee that their information would remain private. The girls were informed about menstruation facts and how to stay clean throughout their period at the conclusion of the interview. Teenage girls' menstrual hygiene was found to be inadequate, according to this research. This is why it's so important to teach girls about menstruation and how to stay clean throughout it.

Arnold Chiari Malformation With Meningo Myelocele And Open Spina Bifida For MTP: A Case Report

Kajal Banyal — 2025-07-16

In this case report we report a 39-year-old primigravida with 20 weeks+3 days presenting with abortion. She presented to the antenatal clinic with Amenorrhea since 5 months and bleeding per vagina since 1 day. Her ultrasonography report showed that single live intrauterine gestation of 20 weeks 3 days, fetal cardiac activity presented, liquor was adequate, placenta anteriorly implanted, fetal cranium appeared abnormal with frontal bone flattening with lemon shaped appearance and small posterior cranial fossa. Cerebellum appears banana shaped. She was diagnosed to have Arnold chiari malformation and meningomyelocele with open spina bifida in second trimester and medical termination of pregnancy was carried out.

Convolutional Neural Network Model for Flue Cured Tobacco Classification in Zimbabwe

Arthur Mbendera — 2025-07-16

The flue-cured tobacco sector plays a vital role in Zimbabwe’s economy. Ensuring accurate and efficient grading of tobacco leaves is essential for fair pricing, maintaining quality standards, and optimizing marketing strategies. Traditional grading methods, which rely on manual inspection, are often subjective, labour-intensive, and inconsistent. This study explores the use of Convolutional Neural Networks (CNNs) to automate the classification of flue-cured tobacco leaves cultivated in Zimbabwe. A diverse dataset of high-resolution leaf images, representing various grades and sourced from multiple regions, was compiled. The research evaluates several CNN architectures, including both custom-built models and well-known pre-trained networks such as VGG16, ResNet50, and InceptionV3. To address class imbalance and improve model robustness, data augmentation techniques were applied. Model performance was measured using metrics like accuracy, precision, recall, F1-score, and AUC-ROC. Among the models tested, the fine-tuned ResNet50 achieved the highest accuracy, outperforming the others and significantly exceeding the reliability of manual grading. Additionally, the study employed interpretability tools like Grad-CAM to highlight the specific areas of the tobacco leaves that influenced the model’s decisions. This work presents a reliable and efficient automated grading system that could enhance productivity and profitability in Zimbabwe’s tobacco industry, while also demonstrating the broader potential of deep learning in agricultural applications across developing regions.

A Study On Key Aspects Of Talent Acquisition Practices In The IT Sector

Shubham Dadariya — 2025-07-16

In the IT industry, the strategic goal of talent acquisition is to luring, employing, and keeping skilled personnel in a highly competitive industry. Unlike traditional recruitment, it emphasises long-term personnel planning, company branding, and proactive candidate involvement. Because of the rapid advancement of technology and the increasing demand for specialised skills, IT companies need to employ innovative employment techniques. Employers use a range of sourcing strategies, such as LinkedIn, job portals, college hiring, social media, and employee recommendations. By facilitating efficient applicant identification and selection, AI-driven technologies, predictive analytics, and applicant tracking systems (ATS) combine to maximise recruiting. During the candidate screening process, technical skills like as programming, cybersecurity, cloud computing, and data analytics are assessed with soft skills like communication and problem-solving. Despite the importance of IT talent acquisition, challenges include significant employee turnover, a lack of skilled workers, and shifting technology trends. To enhance employee engagement and retention, companies fund training programs, mentorship initiatives, and structured onboarding processes. Additionally, diversity and inclusion have become top priorities in the creation of high-performing teams. IT firms require a well-defined talent acquisition strategy supported by technology and continuous learning if they are to draw in and retain top personnel, foster innovation, and advance long-term corporate success.

Early Detection Of Melanoma Disease With AI-Driven Skin Cancer Diagnosis Using Deep Learning Approach

Syeda Naba Tanzeem — 2025-07-24

Because of its fast growth and high death rate when not identified early, skin cancer, especially melanoma, is a serious health concern. Conventional diagnostic procedures may be laborious and sometimes need the opinion of a dermatologist, who may be out of reach in certain areas. To accurately and early identify melanoma using dermoscopy skin scans, this research suggests an AI-driven diagnostic system that uses deep learning methods. The system is taught to automatically extract and categorize complicated information, differentiating benign lesions from malignant melanoma using Convolutional Neural Networks (CNNs). Accuracy, sensitivity, and specificity are all well achieved by the model, which is trained and verified using publicly accessible datasets like ISIC. This method, which is based on deep learning, helps dermatologists make better clinical decisions, speeds up screening, and drastically decreases the likelihood of human mistakes. By providing a scalable, non-invasive, and effective method for early melanoma detection, the proposed system showcases the power of artificial intelligence to transform skin cancer diagnostics.

Bone Deformity Identification Using ML

Zaveriya Fatima — 2025-07-24

Accurate detection and measurement of bone deformities—such as lower-limb alignment errors, spinal misalignments, and joint abnormalities—are critical for planning corrective orthopedic treatments. Recent AI-driven methodologies employ deep learning techniques to automatically locate anatomical landmarks on X-rays and CT/MRI scans, significantly improving efficiency and reducing dependency on expert manual assessment. For instance, landmark-based models operating on bi-planar radiographs achieved vertebral detection accuracy rates of up to 98%, coupled with mean absolute landmark and angular errors under 1.8 mm and ~5.6°, respectively. Multi-view convolutional neural networks further enhance 3D deformity assessments of lower limbs, yielding landmark localization errors of around 2.05 mm and angular deviations of below 0.9°. Additionally, segmentation-based deep learning methods targeting knee deformities—like varus/valgus misalignment—achieved an AUC of 0.9839 in angle classification, utilizing hyperparameter-optimized CNN pipelines. These AI systems streamline deformity quantification, reducing time and inter-observer variability while offering accuracy comparable to clinicians. Together, these advances demonstrate the high potential of machine learning in supporting early detection and corrective planning of bone deformities across orthopedic practice.

A Smart IOT Based Emergency Detection System

Dr. Sujata. Mallapur — 2025-07-24

This project aims to create a Smart IoT-Based Emergency Detection System that can identify hazardous environmental conditions in real-time. The system is designed to reduce response time during emergencies and enhance safety in residential, industrial, and public environments. The system uses an ESP32 Dev Kit microcontroller with low power consumption and Wi-Fi capabilities. It integrates various environmental sensors to monitor conditions in real-time. Upon detecting abnormal values, the system activates local alerts via a buzzer and LCD display, and sends remote alerts via a GSM module via SMS. Cloud integration using the Blynk IoT platform allows data visualization and control via mobile applications. The system responds accurately to hazardous conditions and reliably sends alerts within seconds. The project extends to smart homes, factories, schools, hospitals, and warehouses, laying the foundation for future AI-based risk prediction and integration with centralized emergency management systems.