The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.

In this paper, a management system for smart electric vehicle is introduced using software engineering models and installed Sensor Network (SN). Two software engineering models are proposed to construct information exchange and available resource management algorithms, in which the required performance of vehicles is obtained. The resource management algorithm adopts the LeNet-5 deep-learning model in choosing the best driving mode. The datset is achieved from the simulated sensor Network (SN). The results show the satisfied performance of the electric cars in terms of information exchange and resource management. The MQTT broker server is employed for monitoring the information exchange algorithm, where the delay time is less than 1 sec for transmitting 1000 message. The proposed system saves power by 1-8 Kwh and a storage capacity by 9-95 MB for driving 100Km.

The detection of people that are infected with COVID-19 is critical issue due to the high variance of appearing the symptoms between them. Therefore, different medical tests are adopted to detect the patients, such as Polymerase Chain Reaction (PCR) and SARS-CoV-2 Antibodies. In order to produce a model for detecting the infected people, the decision-making techniques can be utilized. In this paper, the decision tree technique based Decisive Decision Tree (DDT) model is considered to propose an optimized decision-making approach for detecting the infected people with negative PCR test results using SARS-CoV-2 antibodies and Complete Blood Count (CBC) test. Moreover, the fever and cough symptoms have been adopted as well to improve the design of decision tree, in which the precision of decision is increased as well. The proposed DDT model provide three decision classes of Infected (I), Not Infected (NI), and Suspected (S) based on the considered parameters. The proposed approach is tested over different patients? samples in off and real-time simulation, and the obtained results show a satisfactory decision class accuracy ratio that varies from 95% to 100%.

It is well known that with the growing of the humanity and all the development in technologies, there is an increasing in need for recognition systems. These systems can recognize people from distinct characteristics in which these are unique for each one individually. The researchers went to the finger print and eye recognition methods to be adopted as the dominated approaches, yet, these methods suffers from numerous health risks due to diseases transferring. Therefore, the walking step recognition method has been adopted recently. This is because each person has different walking style from others.This paper proposed a human walking step recognition system that adopts group of weight sensors distributed amongst carpet. The reading data from sensors has been transmitted to the information center for processing. The data is transmitted through out a wired sensor network that includes sensor nodes and sink node. The latest node is used to collect the reading data from the sensor nodes. At the information center, the received data is processed using the proposed recognition algorithm. This algorithm gives two decisions; either matching with full information about the intruder or no matching. On the other hand, the proposed system has been designed and implemented using MATLAB simulator. Throughout this simulator, a database matrix is generated randomly to cover all the probability of walking step patterns available for humans. This matrix consists of three dimensions; one for users, second for sensor readings (walking patterns), and third for tries. Each user records numerous walking patterns by passing over the designed carpet several times at different modes just to cover the slightly changes in walking style in terms of modes. It is important to note, that the carpet include the sensors in between of two layers.The simulation results show the successful performance of the proposed system with high efficiency and recognition accuracy. In addition, statistical analysis has been obtained using sampling theorem by adopting sample of 100 employees at University of Technology. This is done by distributing a questioner form over the employees to evaluate the acceptation of the proposed system by people in terms of health issues and ease of use. The outcome results show high ratio of accepted people in comparison with rejected.