Advancements in Telehealth: Enhancing Breast Cancer Detection and Health Automation through Smart Integration of IoT and CNN Deep Learning in Residential and Healthcare Settings

Journal of Advanced Research in Applied Sciences and Engineering Technology, Год журнала: 2024, Номер 45(2), С. 214 - 226

Опубликована: Май 24, 2024

The rapid evolution of telehealth, or telemedicine, has spurred crucial technological advancements aimed at addressing the early stages complex cancer conditions, where conventional diagnostic methods face challenges. This research introduces a detection system that utilizes Internet Things (IoT)-based patient records and machine learning. primary objective is to automate real-time breast monitoring in residential institutions smart hospitals, thus enhancing delivery quality healthcare. Background: Traditional methods, particularly physical inspection, exhibit inherent limitations identifying stages. responds this challenge by leveraging innovative technologies, such as IoT deep learning-based techniques, overcome constraints approaches. Objective: goal study develop implement integrates IoT-based learning for healthcare settings. Method: employs synergistic combination technology collecting images users Convolutional Neural Network (CNN), technique, prediction. focus lies on contributing overall well-being individuals who may unknowingly be living with cancer. Result: Simulated outcomes after 25 epochs are presented, emphasizing training accuracy model its validation using proposed VGG16 classifier. Graphical representations results indicate consistent performance metrics, both exceeding 99%. Specifically, measures an impressive 99.64%, while stands 99.12%. Main Findings: demonstrates effectiveness integrated techniques achieving high rates findings affirm potential approach assist dermatologists malignancies treatable Conclusion: establishes foundational framework integration presenting promising avenue advancing systems. holds significant improving risk

Язык: Английский

Binary northern goshawk optimization for feature selection on micro array cancer datasets

Evolving Systems, Год журнала: 2024, Номер 15(4), С. 1551 - 1565

Опубликована: Апрель 10, 2024

Язык: Английский

Analysis of Inverted Planar Perovskite Solar Cells with Graphene Oxide as HTL using L9 OA Taguchi Method

Journal of Advanced Research in Micro and Nano Engieering, Год журнала: 2024, Номер 16(1), С. 48 - 60

Опубликована: Март 22, 2024

In this work, the Taguchi Method approach is used to optimize graphene oxide (GO) as hole transport layer (HTL) in inverted perovskite solar cells (IPSC). By using method, data from numerical modelling Solar Cell Capacitance Simulator-One Dimensional (SCAPS-1D) was optimized. While it has distinct parameter results and diverse causes, also takes a long time complete analysis process. The method reported be able find most significant factor reduce variations less time. algorithm experiment because based on orthogonal array (OA) experiments, which provide substantially smaller variance for with optimal control values. SCAPS-1D software simulate IPSC GO HTL. obtained are then analysed compared performance of cell. final show that optimized HTL achieved better Power Conversion Efficiency (PCE) previous researchers, efficiency increasing 18.53%.to 23.408%.

Язык: Английский