Journal of Future Internet and Hyperconnectivity (e-ISSN: 3048-9210)

AI Intelligent Deep Learning-Driven Computational Pathology Framework for Automated Breast Cancer Detection from Histopathology Images

Mon, 30 Mar 2026 00:00:00 +0000

One of the major reasons for death among women is breast cancer, and early diagnosis is essential for survival. The proposed study is an AI Intelligent Deep Learning, Driven Computational Pathology Framework that can automatically detect breast cancer from histopathological images. This Framework uses advanced convolutional neural networks (CNNs) to analyze the microscopic images in detail and classify them as either benign or malignant automatically. Pre-processing methods like image normalization, augmentation, and noise reduction are utilized in order to enhance the quality of data and make the model more resistant to changes. Feature extraction is done by deep learning layers, which help in the accurate identification of complicated cellular patterns without requiring manual feature engineering. The system introduced here uses transfer learning and fine, tuning strategies for best results when working with very few medical datasets. Besides accuracy, precision, recall, and F1, score, the model assessment is carried out to be sure of reliable clinical applicability of the proposed model. Experimental findings show that this framework not only reaches high levels of diagnostic accuracy but also cuts down the time needed for pathological assessment. This is achieved by equipping pathologists with fast and steady analysis, thus lowering the chances of human error and enabling early, stage breast cancer detection. This smart computational pathology method is a step forward in AI, powered healthcare solutions, and thereby helps in better patient outcomes.

Design and Development of a Web-Based System for Managing the Rental of Farming Equipment

M. P. Chaudhari, Atharv Burange, Yash Deshmukh, Om Parthe — Mon, 30 Mar 2026 00:00:00 +0000

Agriculture plays a significant role in the economic development of rural regions, yet many small-scale farmers face financial limitations in acquiring modern agricultural machinery. To address this challenge, this paper presents the design and development of a Web-Based Farming Equipment Rental Management System that enables farmers to access essential farming tools through an online platform. The proposed system provides a centralized interface where users can register, browse available equipment, check pricing, and book machinery based on rental duration and availability. The system is developed using HTML, CSS, JavaScript for the frontend, Node.js for backend processing, and MySQL for database management. It incorporates secure authentication, real-time availability updates, and administrative inventory control. The platform aims to reduce idle equipment time, eliminate manual booking errors, and promote resource sharing among farmers. Experimental evaluation and system testing demonstrate improved efficiency, transparency, and accessibility compared to traditional rental methods. The proposed solution offers a scalable and cost-effective approach to digitizing agricultural equipment management, thereby supporting sustainable farming practices and rural digital transformation.

Self-Adaptive Cloud Cost Management Through Reinforcement-Driven Intelligence

Bala Shanmukha Sowmya Javvadhi, Usha Bhargavi Gummala, Darapu Uma — Mon, 30 Mar 2026 00:00:00 +0000

Cloud computing has transformed the current IT infrastructure by offering the ability to scale and on-demand resources. Nevertheless, the dynamic workloads changes and sophisticated pricing schemes frequently result in the inefficient use of the resources and the excessive operating expenses. The conventional rule-based auto-scaling and heuristic optimization methods are not flexible and do not react well to real-time variations in the workload. The paper will suggest an independent cloud cost optimization framework based on Reinforcement Learning (RL) and dynamically balancing the resource provisioning to ensure compliance with the Service Level Agreement (SLA). The optimization problem is stated as a Markov Decision Process (MDP) where the scaling actions are carried out by the RL agent, who monitors the states of the system (CPU utilization, memory consumption, and request rates) and applies the scaling actions to minimize the cost without compromising the performance. A reward functionality is intended to achieve a balance between the cost-cutting and penalties in SLA violations to make smart trade-off decisions. The given method involves the use of a Deep Reinforcement Learning model to train the best scaling policies for continuous interaction with a simulated cloud environment. Experimental analysis has shown a great amount of cost saving over the traditional threshold-based and predictive scaling techniques without experiencing much of the response time and utilization efficiency being affected. Findings show that the RL-based framework is able to adjust to the variations in workload and minimize instances of over-provisioning and under-provisioning. The paper identifies the promise of self-directed, learning-oriented processes to improve cloud resource management and attain sustainable cost-efficiency in the current cloud infrastructures.

MediConnect: Smart Medicine Distribution System

Mon, 30 Mar 2026 00:00:00 +0000

Today’s fast-paced healthcare environment, managing medicine supply, patient appointments, and pharmacy coordination has become increasingly complex. Delays in medicine delivery, poor communication between suppliers and pharmacies, and inefficient patient management often result in disrupted healthcare services and reduced patient satisfaction. To address these challenges, this paper introduces Mediconnect, an intelligent Android-based system designed to streamline medicine distribution, pharmacy management, and patient appointment scheduling. Mediconnect provides a centralized platform where suppliers, pharmacies, and patients can interact efficiently, track orders, and manage medicine inventory in real time. An integrated intelligent assistant monitors deliveries, sends reminders, and optimizes schedules to ensure timely medicine supply and patient care. The system is developed using Android Studio (Java & XML) for the frontend, with XAMPP (MySQL) for database management and Postman for API testing, ensuring secure, responsive, and reliable performance. Mediconnect improves supply chain coordination, reduces delays, enhances patient satisfaction, and supports pharmacies and suppliers in delivering healthcare services more effectively. Overall, the proposed system leverages modern mobile technologies and intelligent assistance to create an organized, cost-effective, and user-friendly solution for healthcare management.

A Study on Cloud Computing Technologies, Applications, and Associated Challenges

Adhiraj S. Kolekar, Shreyas G. Malvadkar, Shubhangi G. Bhaigade — Tue, 31 Mar 2026 00:00:00 +0000

Cloud computing, which enables users to access shared resources like servers, storage, and software over the Internet whenever needed, has emerged as a crucial tool in contemporary information systems. Cloud-based solutions provide more scalability, flexibility, and cost-effectiveness for managing data and applications than traditional methods, which depend on significant investment in physical infrastructure. It operates through large-scale data centers managed by service providers, where virtualization technology enables efficient resource utilization and multi-user access. In order to deliver dependable and effective services, cloud architecture is made up of back-end systems like servers, databases, and networking components, as well as front-end components like user devices and interfaces. Cloud computing can be divided into service models like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), as well as deployment methods like public, private, and hybrid clouds. The technology supports various applications across domains like education, healthcare, and business, enhancing collaboration and accessibility. Notwithstanding these developments, problems with data security, user privacy, and network dependability still present serious obstacles. This essay offers a thorough introduction to cloud computing technologies, their uses, and the difficulties they provide.