ملخص البحث :

With the rapid development in the field of information and communication technology, taking advantage of this development has become an urgent necessity in most areas such as the military, engineering, agriculture, and health life. The developers have provided an appropriate environment that accommodates most of these areas, which take on responsibility for looking for speed of implementation, speed of access, remote management, and other features that are in high demand and need. This paper introduces one of the important applications in the field of the Internet of Things (IoT) that is related to human life and health. a smart ambulance system implemented. Patient data collected by sensors are distributed in an ambulance and continuously received at the hospital side based on cloud computing, then it is sent to the specialists to give decisions, that may help to save critical cases according to the received information and it may need quick access and intervention by them Access to the patient shortens the time, especially during peak times. The ambulance tracking is done through the navigation monitoring system.

ملخص البحث :

Breast cancer is a significant public health concern worldwide, and early detection is crucial for itstreatment. Although breast cancer has been extensively studied, there is still room for improvementin its classification accuracy. This study aims to improve the classification accuracy of breast cancerby applying information gain feature selection and machine learning techniques to the WisconsinDiagnostic Breast Cancer (WDBC) dataset. The information gain method is utilized to reduce featurecharacteristics, and machine learning algorithms such as support vector machine (SVM), naive Bayes(NB), and C4.5 decision tree are employed for breast cancer classification. The study also conducts acomparison analysis based on accuracy value. The proposed model achieves maximum classificationaccuracy (100%) and a weighted average for precision (100%) and recall (100%) using a C4.5 decisiontree, while SVM accuracy (98.42%) and weighted average for precision (98.17%) and recall (98.58%)are achieved using a C4.5 decision tree. The NB algorithm attains an accuracy of 96%, with a weightedaverage for precision (18.57%) and recall (50%). The proposed model's results are compared to similarstudies and demonstrate significant progress, indicating new opportunities for breast cancer detection (PDF) A Comparative Analysis of Methods for Detecting and Diagnosing Breast Cancer Based on Data Mining. Available from: https://www.researchgate.net/publication/372958879_A_Comparative_Analysis_of_Methods_for_Detecting_and_Diagnosing_Breast_Cancer_Based_on_Data_Mining [accessed Feb 05 2024].