Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research. Principal Component Analysis (PCA) is a method to reduce the dimensionality of certain datasets. Improves interpretability but without losing much information. It achieves this by creating new covariates that are not related to each other. Finding those new variables, or what we call the main components, will reduce the eigenvalue /eigenvectors solution problem. (PCA) can be said to be an adaptive data analysis technology because technology variables are developed to adapt to different data types and structures. This review will start by introducing the basic ideas of (PCA), describe some concepts related to (PCA), and discussing. What it can do, and reviewed fifteen articles of (PCA) that have been introduced and published in the last three year

Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization. Note that the MATLAB source codes for GTO will be publicly available after the acceptance of the paper.

Segmentation of the breast region and pectoral muscle are fundamental subsequent steps in the process of Computer-Aided Diagnosis (CAD) systems. Segmenting the breast region and pectoral muscle are considered a difficult task, particularly in mammogram images because of artefacts, homogeneity among the region of the breast and pectoral muscle, and low contrast along the region of breast boundary, the similarity between the texture of the Region of Interest (ROI), and the unwanted region and irregular ROI. This study aims to propose an improved threshold-based and trainable segmentation model to derive ROI. A hybrid segmentation approach for the boundary of the breast region and pectoral muscle in mammogram images was established based on thresholding and Machine Learning (ML) techniques. For breast boundary estimation, the region of the breast was highlighted by eliminating bands of the wavelet transform. The initial breast boundary was determined through a new thresholding technique. Morphological operations and masking were employed to correct the overestimated boundary by deleting small objects. In the medical imaging field, significant progress to develop effective and accurate ML methods for the segmentation process. In the literature, the imperative role of ML methods in enabling effective and more accurate segmentation method has been highlighted. In this study, an ML technique was built based on the Histogram of Oriented Gradient (HOG) feature with neural network classifiers to determine the region of pectoral muscle and ROI. The proposed segmentation approach was tested by utilizing 322, 200, 100 mammogram images from mammographic image analysis society (mini-MIAS), INbreast, Breast Cancer Digital Repository (BCDR) databases, respectively. The experimental results were compared with manual segmentation based on different texture features. Moreover, evaluation and comparison for the boundary of the breast region and pectoral muscle segmentation have been done separately. The experimental results showed that the boundary of the breast region and the pectoral muscle segmentation approach obtained an accuracy of 98.13% and 98.41% (mini-MIAS), 100%, and 98.01% (INbreast), and 99.8% and 99.5% (BCDR), respectively. On average, the proposed study achieved 99.31% accuracy for the boundary of breast region segmentation and 98.64% accuracy for pectoral muscle segmentation. The overall ROI performance of the proposed method showed improving accuracy after improving the threshold technique for background segmentation and building an ML technique for pectoral muscle segmentation. More so, this paper also included the ground-truth as an evaluation of comprehensive similarity. In the clinic, this analysis may be provided as a valuable support for breast cancer identification.

Gene expression profiles could be generated in large quantities by utilizing microarray techniques. Currently, the task of diagnosing diseases relies on gene expression data. One of the techniques which helps in this task is by utilizing deep learning algorithms. Such algorithms are effective in the identification and classification of informative genes. These genes may subsequently be used in predicting testing samples' classes. In cancer identification, the microarray data typically possesses minimal samples number with a huge feature collection size which are hailing from gene expression data. Lately, applications of deep learning algorithms are gaining much attention to solve various challenges in artificial intelligence field. In the present study, we investigated a deep learning algorithm based on the convolutional neural network (CNN), for classification of microarray data. In comparison to similar techniques such as Vector Machine Recursive Feature Elimination and improved Random Forest (mSVM-RFE-iRF and varSeIRF), CNN showed that not all the data have superior performance. Most of experimental results on cancer datasets indicated that CNN is superior in terms of accuracy and minimizing gene in classifying cancer comparing with hybrid mSVM-RFE-iRF.

One of the main causes of increased mortality among women is breast cancer. The ultrasound scan is the most widely used method for diagnosing geological disease i.e. breast cancer. The first step for identifying the abnormality of the breast cancer (malignant from benign), is the extraction of the region of interest (ROI). In order to achieve this, a new approach to breast ROI extraction is proposed for the purpose of reducing false positive cases (FP). The proposed model was built based on the local pixel information and neural network. It includes two stages namely, training and testing. In the training stage, a trained model was built by extracting the number of batches from both ROI and background. The testing stage involved scanning the image with a fixed size window to detect the ROI from the background. Afterwards, a distance transform was used to identify the ROI and remove non-ROI. Experiments were conducted on the on-data set with 250 ultrasound images (150 benign and 100 malignant) the preliminary results show that the proposed method achieves a success rate of about 95.4% for breast contour extraction. The performance of the proposed solution also has been compared with the existing solutions that have been used to segment different types of images.