Deep Learning (DL) has rapidly become a methodology of choice for analyzing medical images and increasingly attracts researchers’ attention in the medical research community. Breast cancer is a common disease among women throughout the world. The medical images and especially Breast Ultrasound (BUS) images are of poor quality, low contrast, and ambiguous. To avoid misdiagnosis, a Computer-Aided Diagnosis (CAD) system has been created for the diagnosis of breast cancer. This study discusses a variety of ultrasonic image segmentation approaches, with an emphasis on several methods developed in the recent four years. As a result, breast ultrasound image segmentation remains a difficult and demanding problem because of several ultrasound aberrations, including strong speckle noise, preprocessing, classification, feature extraction, and segmentation technique to find the accuracy. Lastly, this study outlines the current trends and issues in breast ultrasound images diagnosis, segmentation, and classifications. This review may be useful for both clinicians and researchers who utilize CAD systems for early breast cancer detection.

Microarray data plays a major role in diagnosing and treating cancer. In several microarray data sets, many gene fragments are not associated with the target diseases. A solution to the gene selection problem might become important when analyzing large gene datasets. The key task is to better represent genes through optimum accuracy in classifying the samples. Different gene classification algorithms have been provided in past studies; after all, they suffered due to the selection of several genes mostly in high-dimensional microarray data. This paper aims to review classification and feature selection with different microarray datasets focused on swarm intelligence algorithms. We explain microarray data and its types in this paper briefly. Moreover, our paper presents an introduction to most common swarm intelligence algorithms. A review on swarm intelligence algorithms in gene selection profile based on classification of Microarray Data is presented in this paper.

Machin learning (ML) and Deep Learning (DL) technique have been widely applied to areas like image processing and speech recognition so far. Likewise, ML and DL play a critical role in detecting and preventing in the field of cybersecurity. In this review, we focus on recent ML and DL algorithms that have been proposed in cybersecurity, network intrusion detection, malware detection. We also discuss key elements of cybersecurity, the main principle of information security, and the most common methods used to threaten cybersecurity. Finally, concluding remarks are discussed, including the possible research topics that can be taken into consideration to enhance various cyber security applications using DL and ML algorithms.

The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.

Examination timetabling is a discrete, multi-objective and combinatorial optimization problem which tends to be solved with a cooperation of stochastic search approaches such as particle swarm optimization (PSO) and the Genetic Algorithm (GA). PSO is a well-known one of the popular swarm intelligent algorithm. used successfully for several complicated combinatorial optimization problems. Throughout the years, educational institutions have been confronted by the problems related to changing the time to their schedule. In order to compete for the growing number of students, educators must offer three or four final examinations every year. Furthermore, to approach this problem, in this study going to use the enhanced hybrid method for resolving the issue. The proposed study, a GA and PSO algorithms were utilized together to find a solution to the exam scheduling issue. The results of the study show that our approach exceeds the GA and PSO approaches by achieving 90% best of mean. The effectiveness of the approach can be hurt by any change to its parameters.