Authors: Professor Yash Deshmukh, Aishwarya Bhalekar, Vaibhav Dhok, Atharva Gaikwad, Epshita Gaikwad

Abstract: Industrial machines require continuous monitoring to avoid damage due to overcurrent, overheating, or abnormal voltage conditions. This paper presents a GSM-based machine protection system that monitorsmachine parameters and sends real-time alerts to theoperator using SMS. A microcontroller continuously checks sensor values, and when unsafe conditions are detected, the system automatically shuts down the machine through a relay and informs the user remotely. This system improves safety, reduces maintenance cost, and enables remote monitoring of industrial equipment.

Published by: vikaspatanker

Authors: Om Vichare, Aniket Nale, Soham Deolalikar, Siddhant Kamble

Abstract: Maintaining optimal indoor humidity is essential for human comfort, health, and the safety of electronic devices. Conventional humidifiers lack intelligent monitoring and safety mechanisms, and typically require manual operation. This paper presents the design and implementation of a Smart Room Humidifier using the ESP32 microcontroller. The system continuously monitors ambient humidity using a DHT11 sensor and automatically controls a 5 V ultrasonic mist maker through a transistor-driven switching circuit. A water level sensor prevents dry operation, and a buzzer alerts the user when the water level is low. The ESP32’s built-in Wi-Fi module enables web- based remote ON/OFF control, while a 0.96-inch OLED display provides real-time readings of humidity, temperature, water level status, and system state. The system is powered by a 5 V buck converter; regulated 3.3 V for sensor modules is supplied directly by the ESP32. Experimental testing confirms reliable performance, safe operation, and effective humidity control. The system is low-cost and well-suited for indoor domestic applications.

DOI:

 

 

Published by: vikaspatanker

Authors: Kunal Vishwajit Uke, Shreyas Anil Sonwalkar, Abhishek Avinash Yadav, Krushna Ganesh Vibhute, Prof. Chetna Sharma

Abstract: Industrial automation demands efficient material handling and quality control mechanisms to enhance productivity and reduce operational costs. This paper presents the design and implementation of an automated colour-sorting machine using Arduino microcontroller technology integrated with optical colour sensors. The system employs a conveyor belt mechanism that transports objects through a detection zone where a TCS3200 colour sensor identifies the colour of each item. Based on the detected colour signature, the Arduino controller processes the sensor data and activates corresponding servo motors to divert items into designated collection bins. The proposed system achieves high-speed sorting with accuracy exceeding 95%, significantly reducing manual labour requirements and minimizing classification errors. Experimental results demonstrate the system's effectiveness in sorting multiple colours simultaneously with minimal delay. This automated solution finds applications in food processing, pharmaceutical packaging, recycling industries, and quality control operations where colour-based segregation is essential.

 

 

Published by: vikaspatanker

Authors: Saukhya, Santhosh, Sai Surya, Abdul Rahman Sharikh, Abhijit raj N

Abstract: Auto Go is a tech-enabled autorickshaw rental and fleet management platform designed to address the growing urban transportation challenges in Indian metropolitan cities, with an initial focus on Bengaluru. The project’s core objective is to facilitate affordable and flexible autorickshaw access for independent drivers and small businesses, thereby reducing the financial barrier posed by vehicle ownership and promoting economic opportunity. The service will provide a digital platform — including a website and mobile app — to enable customers to book autorickshaws under daily, weekly, or monthly rental agreements. Additionally, the platform will integrate payment processing, customer support, and vehicle tracking, improving transparency and operational efficiency.

 

 

Published by: vikaspatanker

Mallesh Miryala
Affiliation	SalesForce Technical Head, San Francisco , California, USA
Email-Id:	miryalaca@gmail.com
Publication: Mallesh Miryala. “Engineering-Grade Delivery for Salesforce in Integration-Heavy Enterprises: Metadata Graphs, Contract Tests, and Deterministic Operations”. International Journal of Scientific Research & Engineering Trends, Volume 11 issue 6, 2025. Mallesh Miryala. “Operational Graph Patterns For Continuity And Fulfillment In Large Enterprises: A Field-Based Reference Architecture”. International Journal of Scientific Research & Engineering Trends, Volume 11 issue 6, 2025.

Published by: vikaspatanker

Authors: Priti Birajdar, Ambika Kshirsagar, Shravani Raut, Harshada Raykar, Prajakta Bhadale

Abstract: This paper presents an Artificial Intelligence (AI) based system designed for the early detection of brain-related diseases such as Alzheimer's disease, Parkinson's disease, brain tumors, and stroke using medical imaging and machine learning techniques. Early diagnosis of neurological disorders is critical for effective treatment and improved patient outcomes. Traditional diagnostic approaches rely heavily on manual interpretation of MRI scans, which may lead to delayed detection and human error. The proposed system integrates Deep Learning models, particularly Convolutional Neural Networks (CNN), to analyze MRI images and detect abnormalities at an early stage. The architecture consists of image preprocessing, feature extraction, classification, and result visualization modules. The system aims to assist neurologists by providing accurate and fast predictions.

Published by: vikaspatanker

Authors: Ch.Srilakshmi, Nithish Kanth M, Rupesh J, Tharun CR

Abstract: Retinal vessel segmentation is essential for the early diagnosis of diseases such as diabetic retinopathy, hypertensive retinopathy, and age-related macular degeneration. Manual segmentation of fundus images is time-consuming and prone to variability, limiting large-scale screening. This paper presents RETINASEG, a deep learning-based system for automated pixel-level segmentation of retinal vessels from fundus images. The proposed framework combines image enhancement techniques such as contrast normalization, CLAHE, and noise reduction with an encoder–decoder architecture based on U-Net and transformer-enhanced models. To address challenges including thin vessel detection and class imbalance, data augmentation and class-balanced loss functions are employed during training. Experimental results on DRIVE and STARE datasets demonstrate strong performance, achieving high accuracy and robustness across datasets. A web-based interface with real-time visualization and explainable AI support further enhances clinical usability. RETINASEG enables scalable, reliable, and automated retinal analysis for early disease detection and tele-ophthalmology applications.

Published by: vikaspatanker

Authors: Dr. S.Ragul, V.Sivakumar, C.Sudhan Aghash, G.Vishnu Kumar

Abstract: The Poultry farms generate Efficient management of poultry waste is essential for maintaining farm hygiene, reducing labor costs and minimizing environmental impact. This project presents a Smart Poultry Waste Collection System based on an IoT-controlled movable conveyor mechanism designed to automate the collection and monitoring of poultry waste. The proposed system employs sensors to detect waste accumulation levels and environmental conditions, while a microcontroller enabled conveyor system dynamically moves to collect waste from designated areas within the poultry shed. Allowing remote monitoring, system control and performance analysis through a mobile. Automation reduces manual intervention, improves sanitation and helps prevent the spread of disease among poultry. The system is energy-efficient, scalable and adaptable to different poultry farm sizes. Experimental results demonstrate improved waste collection efficiency, reduced labor dependency and enhanced overall farm management, making the solution a practical step toward smart and sustainable poultry farming.

DOI: https://doi.org/10.5281/zenodo.18834843

 

Published by: vikaspatanker

Authors: mr. P.Sudheer Kumar, C.Ashok, Ug Student, m.Premchand, m.S.Sulthan

Abstract: The increasing demand for sustainable construction materials and the growing environmental problems associated with waste tyre disposal have encouraged researchers to explore alternative materials in concrete production. This study presents an experimental investigation on the replacement of natural coarse aggregate with scrap rubber tyre particles in cement concrete. The primary objective is to evaluate the feasibility of utilizing waste rubber as a partial aggregate replacement while maintaining acceptable mechanical performance and promoting eco-friendly construction practices.Concrete mixes were prepared by replacing coarse aggregates with scrap rubber tyre particles at different percentages of 0%, 5%, 10%, and 15% by weight. The mix design was carried out in accordance with IS 10262 guidelines, and specimens were cast and cured under controlled laboratory conditions. Fresh concrete properties were evaluated using the slump test, while hardened concrete properties were assessed through compressive strength and split tensile strength tests at 7 and 28 days of curing.

 

 

Published by: vikaspatanker

Authors: Mr.R. Goplakrishnan, S. Ajay, K. Manikandaprabu, S. Santhosh kumar

Abstract: Transformer rewinding is generally a manual process that depends on skilled operators and predefined winding data, which may result in calculation errors and increased rewinding time. This project presents a bobbin-based automated transformer rewinding system using a microcontroller to improve accuracy and efficiency. The system accepts basic electrical inputs such as input voltage, output voltage, required output current and bobbin dimensions. Based on these inputs, the required number of winding turns is calculated using empirical transformer winding rules and an appropriate copper wire gauge is selected using the current density method. The system also verifies whether the winding can physically fit within the given bobbin dimensions by calculating turns per layer and total winding thickness. An Arduino UNO R4 controls an induction motor through a relay module, while a rotary encoder provides accurate turn counting. The calculated parameters and winding status are displayed on an LCD. This system reduces human dependency, minimizes errors and provides a cost-effective solution for transformer rewinding applications.

DOI: https://doi.org/10.5281/zenodo.18833928

 

Published by: vikaspatanker