Volume 8 Issue 11
Author's Name: Santhosh, S.Srilekha
Abstract—This study introduces a deep learning-based approach for classifying astrocytoma grades using MRI scans. Astrocytoma, a type of brain tumor, varies in severity, making accurate grading essential for effective treatment. Traditional diagnosis relies on manual MRI interpretation, which can be subjective and time-consuming. To address this, we implement a Convolutional Neural Network (CNN) to automate tumor classification, improving precision and efficiency. The model is trained on a dataset of MRI images and compared with conventional methods like Support Vector Machines (SVM) and Random Forest (RF). Experimental findings reveal that CNN significantly enhances accuracy, sensitivity, and specificity. This approach reduces reliance on manual diagnosis and contributes to AI-driven advancements in medical imaging.
Author's Name: Ramsai, P.Bhavani
Abstract—This paper presents Mentoring Academy is a program which aims to integrate students of the Instituto Politécnico de Bragança in the academy through peer mentoring and peer tutoring. This program needed an application that permits its execution. Motivated by this demand, we aimed at proposing a web application that complies with the Mentoring Academy requirements. The system requirements were defined together with the program stakeholders. Based on the literature review, we opted for the technologies ASP.NET Core and MySQL, for the backend, and Angular 6, for the frontend.
Author's Name: SG. Vasudeva, A. Dattu
Abstract—Cloud storage has become an essential part of modern digital life, yet existing cloud services pose privacy risks, impose storage limitations, and require recurring payments for additional space. This project aims to develop a secure, cost-effective personal cloud storage system using a Raspberry Pi and Next cloud to provide users with full control over their data. The proposed system will enable users to store, manage, and access files remotely without relying on third-party cloud providers. The Raspberry Pi will act as a mini cloud server, integrated with an external hard drive for expandable storage. To address security concerns, the system will incorporate Next cloud encryption to protect stored data and a real-time notification system via the LINE app to alert users of unauthorized access or file modifications. The implementation involves setting up a Raspberry Pi with Next cloud, configuring an external storage device, and ensuring secure remote access through encrypted connections. Key applications include file sharing, data backup, multimedia streaming,
Author's Name: SK.Mukesh, R.Manikanth
Abstract—Cloud computing supports a variety of platforms, systems, and applications and offers functionality for distributed, ubiquitous, and pervasive information data management. In this work, a mobile system that uses cloud computing to store, update, and retrieve electronic health data is implemented. The mobile application, which supports JPEG2000 coding and the DICOM format, manages patient health records and medical images and was created with Google's Android operating system. Amazon's S3 cloud service has been used to assess the developed system. This article provides an overview of the implementation process and shows the system's first operational outcomes
Author's Name: SRam kumar
Abstract—This Our economy is extremely relying on agriculture production. One of the recent research topics is recognition, classification and detection of diseases from the leaf image of a plant. Plants are affected by various kinds of diseases, which causes great harm to the agricultural plant production. Today artificial intelligence and machine learning techniques is capable enough to detect and identify the plant disease. One of the biggest advantages of using AI and ML in agriculture is to reduce the hectic work of constant monitoring of crops field spread over hundreds of acres. Machine learning algorithms detect diseases in early stages as soon as they emerge on the plant surface. The aim of this research is to develop a software system that mechanically finds and classify diseases. The techniques like “image acquisition, pre-processing, segmentation, extraction and classification” are used in it. The leaves picture is captured to detect the plant disease. Thus, use of image processing techniques to detect and classify diseases in agricultural application is useful.
Author's Name: SKada Manideep Kumar, P.Ravinder Rao
Abstract—Electronic voting (e-voting) systems can enhance electoral processes with improved accessibility, transparency, and efficiency. Traditional e-voting systems are plagued with security vulnerabilities, voter anonymity, and tampering. The current study proposes a blockchain-based e- voting system tailored to deliver secure, transparent, and tamper-proof elections. Employing blockchain technology, smart contracts, and cryptographic techniques, the suggested system enhances the voting process integrity with a decentralized, immutable, and verifiable vote record[3]. Double voting can be easily eliminated, voters are anonymous, and real-time verification of votes is possible without loss of privacy. Deployed as Ethereum smart contracts, the solution makes centralized electoral authorities redundant while ensuring trust in the electoral process[5]. Performance evaluation demonstrates the feasibility of the system with its security, scalability, and efficiency. This study extends the advancement of secure digital democracy with a beneficial solution for prospective future electoral systems.
Author's Name: SV.Neha Sri, A.Shivaprasan
Abstract—Most people such as working professionals, students, and house makers often find lack of your time and time management as problems for successful task accomplishment. One among the key reasons for failure in task accomplishment is inefficient planning of the tasks. It's vital to look for a tool that can assist in schedule and manage the time, meetings, and appointments. Why this project is important work stems from the necessity of leveraging a single React Native Application that may be accustomed control many of the activities such as tasks, appointments and meetings for people and also for teams and tiny companies. This application uses Flow Time methodology in handling activities. This program will reduce the purchasing costs incurred through different uses.
Author's Name: SM. Suvishal Sen Kasyap, K. Anjali Reddy
Abstract—Vision related diseases like glaucoma, diabetic retinopathy, macular degeneration, and cataracts are becoming increasingly common, making early diagnosis essential to prevent blindness and improve quality of life. However, traditional diagnostic methods can be time-consuming, reliant on expert ophthalmologists, and sometimes subject to human error. In many resource-limited areas, access to quality eye care is even more restricted, contributing to the growing challenge of preventable blindness. This project explores an innovative solution leveraging machine learning, particularly deep learning techniques like Convolutional Neural Networks (CNNs), to automate eye disease diagnosis. The goal is to develop a reliable, efficient, and scalable system that can analyze retinal images to detect and classify various eye conditions. By enabling accurate early detection, this approach has the potential to significantly reduce the risk of permanent vision loss and expand access to quality eye care worldwide
