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

Authors: Mrs.V.Maria Subaitha, Dr. R. Vijayalakshmi

Abstract: The fishing industry in Rameshwaram, a coastal town in Tamil Nadu, India, is facing significant challenges, including declining fish stocks, rising operational costs, and regulatory restrictions. This study aims to identify and analyze the existing livelihood options available to the fishermen community in Rameshwaram, with a focus on understanding the socio-economic implications of these options. A mixed-methods approach was employed, combining surveys, interviews, and focus group discussions with fishermen and other stakeholders. The study found that fishermen in Rameshwaram have diversified their livelihood options beyond traditional fishing, including fish processing and marketing, tourism-related activities, and alternative livelihoods such as agriculture and small-scale industries. However, these options are often characterized by low incomes, limited job security, and inadequate social protection. The study highlights the need for targeted interventions to promote sustainable livelihoods for fishermen in Rameshwaram, including vocational training, credit facilities, and social protection programs. The findings of this study have important implications for policymakers, development practitioners, and researchers working on livelihood promotion and poverty reduction initiatives in coastal communities.

 

 

Published by: vikaspatanker

Authors: Mr. K.Karthick, Pradeepkumar K, Prathap P, Surendar S

Abstract: Efficient waste collection plays a crucial role in maintaining clean urban environments while reducing operational costs. Traditional garbage collection systems follow fixed schedules, which often lead to unnecessary trips to halfempty bins or delayed pickups of overflowing bins. This project proposes a Smart Waste Monitoring and Collection System that uses ultrasonic sensors to continuously measure the fill level of garbage bins and transmit the data to a cloud platform. The collected data is visualized on a webbased dashboard that enables administrators to monitor the status of each bin in real time and assign optimized routes to garbage collection vehicles. The system includes an intelligent route optimization module that prioritizes bins requiring urgent attention, reducing fuel consumption and travel time. A key enhancement in this project is the integration of a predictive overflow feature. By analyzing historical filllevel patterns, the system forecasts when a bin is likely to reach its capacity. This prediction enables proactive scheduling of collection before overflow occurs, which improves cleanliness and resource utilization. The proposed solution enhances overall waste management efficiency through datadriven decision making. The system is scalable, costeffective, and suitable for implementation in smart city initiatives.

DOI: https://doi.org/10.5281/zenodo.18832421

 

Published by: vikaspatanker

Authors: Priyanshu Singh, Priyanshu Soni, Manmohan Singh Yadav

Abstract: One of the significant resources used in agriculture is water whose availability is extremely low. Most people use traditional irrigation techniques, which are manual, and these techniques tend to cause wastage of water. The current paper has introduced a smart irrigation system, which is founded on soil moisture detection to enhance water management. The type of system employed in the specified project in this paper is based on a soil moisture sensor and an ultrasonic sensor that are linked to an Arduino UNO microcontroller. It constantly measures soil conditions and regulates automatically a water pump with a relay module. When the soil gets dry the pump is switched ON and when the moisture potential is achieved accordingly the pump is switched OFF. The system also offers real time updates to the farmers via the mobile notification and also show the information on an LCD screen. The solution saves water, less human labour, and enhances crop output. It is also affordable, simple to operate and can be applied in small and medium-scale farmers hence is an effective solution to current day farming.

 

 

Published by: vikaspatanker

Authors: Priyanshu Singh, Priyanshu Soni, Manmohan Singh Yadav

Abstract: One of the significant resources used in agriculture is water whose availability is extremely low. Most people use traditional irrigation techniques, which are manual, and these techniques tend to cause wastage of water. The current paper has introduced a smart irrigation system, which is founded on soil moisture detection to enhance water management. The type of system employed in the specified project in this paper is based on a soil moisture sensor and an ultrasonic sensor that are linked to an Arduino UNO microcontroller. It constantly measures soil conditions and regulates automatically a water pump with a relay module. When the soil gets dry the pump is switched ON and when the moisture potential is achieved accordingly the pump is switched OFF. The system also offers real time updates to the farmers via the mobile notification and also show the information on an LCD screen. The solution saves water, less human labour, and enhances crop output. It is also affordable, simple to operate and can be applied in small and medium-scale farmers hence is an effective solution to current day farming.

 

 

Published by: vikaspatanker