Samah A. Z. Hassan

Breast cancer remains one of the most prevalent and life-threatening diseases among women worldwide. Early and accurate diagnosis have been shown to enhance treatment effectiveness and patient survival rates. This study presents an enhanced breast cancer classification framework by leveraging Machine Learning (ML) techniques and feature selection methods. The methodology involves data preprocessing, feature selection using the Binary Particle Swarm Optimization (BPSO), and classification through advanced ML models, including Random Forest (RF), Logistic Regression (LR), Gradient Boosting (GB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). The proposed approach is rigorously evaluated using key performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. By reducing the feature set from 30 to 13, BPSO enhances both model efficiency and predictive performance. Among the classifiers evaluated, RF achieved the highest accuracy of 99.2%, accompanied by a perfect ROC-AUC score of 1.0. The results demonstrate the potential of ML-driven breast cancer classification in revolutionizing healthcare by enabling more accurate, efficient, and personalized treatment strategies.

Green Internet of Things (IoT) is a crucial area of research that addresses the substantial energy consumption resulting from the multitude of high-performance and advanced devices interconnected within IoT systems. As the number of IoT devices increases, the energy demand becomes a pressing concern. Therefore, the current research lies in survey reducing the energy consumption of IoT-based systems to establish a sustainable environment. Green IoT systems and Energy Harvesting (EH) technologies provide promising solutions to provide energy to IoT devices in an environmentally friendly manner. This paper highlights the latest research in the field of energy optimization for IoT through the use of EH techniques, the goal is to take advantage of these technologies to harvest and use energy from the surrounding environment, thus alleviating dependence on traditional energy sources and reducing the overall energy consumption of IoT devices. Also this paper discussed the Green IoT EH (GIoTEH) architecture, which is a robust framework designed to create a sustainable and energy-efficient IoT ecosystem.

The primary purpose of this paper is to establish a healthcare ecosystem framework for COVID-19, CronaSona. Unlike some studies that focus solely on detection or forecasting, CronaSona aims to provide a holistic solution, for managing data and/or knowledge, incorporating detection, forecasting, expert advice, treatment recommendations, real-time tracking, and finally visualizing results. The innovation lies in creating a comprehensive healthcare ecosystem framework and an application that not only aids in COVID-19 diagnosis but also addresses broader health challenges. The main objective is to introduce a novel framework designed to simplify the development and construction of applications by standardizing essential components required for applications focused on addressing diseases. CronaSona includes two parts, which are stakeholders and shared components, and four subsystems: (1) the management information subsystem, (2) the expert subsystem, (3) the COVID-19 detection and forecasting subsystem, and (4) the mobile tracker subsystem. In the proposed framework, a CronaSona app. was built to try to put the virus under control. It is a reactive mobile application for all users, especially COVID-19 patients and doctors. It aims to provide a reliable diagnostic tool for COVID-19 using deep learning techniques, accelerating diagnosis and referral processes, and focuses on forecasting the transmission of COVID-19. It also includes a mobile tracker subsystem for monitoring potential carriers and minimizing the virus spread. It was built to compete with other applications and to help people face the COVID-19 virus. Upon receiving the proposed framework, an application was developed to validate and test the framework’s functionalities. The main aim of the developed application, CronaSona app., is to develop and test a reliable diagnostic tool using deep learning techniques to avoid increasing the spread of the disease as much as possible and to accelerate the diagnosis and referral of patients by detecting COVID-19 features from their chest X-ray images. By using CronaSona, human health is saved and stress is reduced by knowing everything about the virus. It performs with the highest accuracy, F1-score, and precision, with consecutive values of 97%, 97.6%, and 96.6%. Graphical Abstract