Daily Archives: March 9, 2026

Authors: Himanshu Kaspate, Tejas More, Atharv Sanas, Pruthviraj Sarade, Prof.Vijay Mohite

Abstract: Agriculture remains a primary source of livelihood in many developing regions, yet many farmers face challenges in accessing modern agricultural machinery due to high purchasing costs and limited availability. Small and medium-scale farmers often cannot afford expensive equipment such as tractors, harvesters, and other farming tools, which affects productivity and efficiency. To address this issue, this research presents KhetSetGo – Empowering Farmers and Machine Owners, a web-based platform designed to connect farmers who require agricultural machinery with machine owners willing to rent their equipment. The platform enables machine owners to post available machinery with relevant details, while farmers can easily browse, view machine information, and place booking requests according to their agricultural needs. The proposed system is developed using Java Server Pages (JSP), Servlets, MySQL database, HTML, CSS, and JavaScript, and is deployed on the Apache Tomcat server. The platform includes features such as user authentication, OTP-based password recovery, machine listing with media support, and booking management between farmers and machine owners. By enabling an online rental marketplace for agricultural equipment, the system helps reduce machinery costs for farmers while improving equipment utilization for owners. The implementation of KhetSetGo demonstrates how digital platforms can support smart farming practices and improve accessibility to agricultural resources through an efficient and user-friendly system.

Published by: vikaspatanker

Authors: I.Sravani, D. Lakshmi, M.Ushaswini, L.Aswini, C. Subramanyam

Abstract: Malware is any type of program that is intended to wreak havoc to the computer system and network. Examples of malware are bot, ransomware, adware, keyloggers, viruses, trojan horses, worms and others. The exponential growth of malware is posing a great danger to the security of confidential information. The problem with many of the existing classification algorithms is their low performance in term of their ability to detect and prevent malware from infecting the computer system. There is an urgent need to evaluate the performance of the existing Machine Learning classification algorithms used for malware detection. This will help in creating more robust and efficient algorithms that have the capacity to overcome the weaknesses of the existing algorithms. This study did the performance evaluation of some classification algorithms such as J45, LMT, Naïve Bayes, Random Forest, MLP Classifier, Random Tree, REP Tree, Bagging, AdaBoost, KStar, SimpleLogistic, IBK, LWL, SVM, and RBF Network. The performance of the algorithms was evaluated in terms of Accuracy, Precision, Recall, Kappa Statistics, F-Measure, Matthew Correlation Coefficient, Receiver Operator Characteristics Area and Root Mean Squared Error using WEKA machine learning and data mining simulation tool. Our experimental results showed that Random Forest algorithm produced the best accuracy of 99.2%. This positively indicates that the Random Forest algorithm achieves good accuracy rates in detecting malware.

DOI: https://doi.org/10.5281/zenodo.18921650

Published by: vikaspatanker

Authors: Shruti Dhage, Heena Barach, Sanakruti Jadhav, Vaishnavi Shivsharan, Shravani Pichake, Suchita Barkund

Abstract: Authentication is a critical component of digital systems, ensuring that only authorized users gain access to sensitive information and services. Traditional text-based password mechanisms, while widely used, suffer from vulnerabilities such as weak password selection, reuse across platforms, and susceptibility to brute-force and phishing attacks. To address these issues, this research presents the Graphical Password Authentication System, a web-based platform designed to enhance security by combining conventional password hashing with graphical pattern verification. The proposed system is developed using Java Server Pages (JSP), Servlets, MySQL database, HTML, CSS, and JavaScript, and deployed on the Apache Tomcat server. It includes features such as secure user registration, SHA-256 password hashing, graphical password setup and validation, OTP-based password recovery, and session management with duplicate login prevention. By introducing a dual-layer authentication mechanism, the system reduces risks of impersonation and unauthorized access while providing a user-friendly interface. The implementation demonstrates how graphical authentication can strengthen digital identity management and improve usability in academic, corporate, and community environments.

Published by: vikaspatanker

Authors: Ms.S.Kanimozhi, P.Guruprasaath, S.Lingaraj, S.Kanagaraj

Abstract: In modern healthcare, patient medical records are often distributed across multiple hospitals, resulting in repeated medical tests, delayed diagnosis, and poor continuity of care. This project proposes a Reference Electronic Health Record (EHR) System that provides secure and centralized access to patient records across hospitals. The system includes two login modules: users (patients) can view their medical history, disease descriptions, and prescriptions, while hospitals manage multiple doctors who select their own name to add new records for specific users. All medical records are maintained in a reference based manner, ensuring that previous information remains preserved, while doctors from different hospitals can view earlier records as reference to support accurate diagnosis and treatment. The system promotes better coordination among healthcare providers by maintaining a consistent and complete patient medical history. By offering structured storage and role-based access to sensitive medical information, the system enhances continuity of care, minimizes redundancy in medical testing, and improves overall efficiency within the healthcare process.

DOI: https://doi.org/10.5281/zenodo.18920808

Published by: vikaspatanker

Authors: Shravani Mali, Yukta Koli, Mayuri Mohite, Nilam Patil

Abstract: This study presents an AI-driven allergy checker designed explicitly for skincare. It reviews the user's allergies, skin type, and skin conditions and suggests a product and skincare routine based on those factors. The random forest model is used to classify skin type while the Light GBM model evaluates the skincare routine recommendations. Then a K-Nearest Neighbors (KNN) algorithm uses the allergy information the user provides to make the recommendations. A YOLOv8 model also analyzes the image the user provides and determines if there are skin conditions visible to the naked eye. In review, the system developed is able to provide appropriate personalized data-driven recommendations for skincare products and routines with a lower likelihood of allergic body complications, while also allowing for informed selections of skincare according to allergenic history.

Published by: vikaspatanker

Authors: Mr. Meenakshi, Dr. Brij Mohan Goel

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed the field of healthcare, particularly in cancer detection, diagnosis, and treatment. With the advancement of digital pathology, large clinical datasets, and powerful computational techniques, AI has become a crucial tool in oncology research and clinical practice. Deep learning algorithms can analyse high-resolution histopathology images, genomic data, and electronic health records to detect patterns that may not be visible to human experts. These technologies enable early cancer detection, risk prediction, accurate diagnosis, and personalized treatment planning. Additionally, AI-based approaches such as Natural Language Processing (NLP), radiomics, and biomarker discovery have enhanced the analysis of complex medical data. Cloud-based AI platforms further facilitate large-scale data processing and collaborative research. Despite these benefits, the integration of AI into cancer care also faces technical and ethical challenges, including data privacy concerns, lack of standardized datasets, algorithm bias, and interpretability issues. This paper explores the applications of AI in cancer prediction, diagnosis, and treatment while discussing the technical and ethical challenges associated with its implementation. The study highlights the future potential of AI-driven precision medicine and its role in improving cancer care outcomes.

DOI: https://doi.org/10.5281/zenodo.18919879

Published by: vikaspatanker