Early diagnosis and treatment of Alzheimer's disease (AD) is necessary for the patient safety. Computer-aided diagnosis (CAD) is a useful tool for early diagnosis of Alzheimer's disease (AD). We make two contributions to the solution of this problem in this study. To begin with, we are the first to propose an Alzheimer's disease diagnosis solution based on the MATLAB that does not require any magnetic resonance imaging (MRI) pre-processing. Second, we apply recent deep learning object detection architectures like YOLOv2 to the diagnosis of Alzheimer's disease. A new reference data set containing 300 raw data points for Alzheimer's disease detection/normal control and severe stage (MCI/AD/NC) deep learning is presented. Primary screening cases for each category from the Alzheimer's disease neuroimaging initiative (ADNI) dataset. The T1-weighted Dicom MRI slice in the MP-Rage series in 32-bit DICOM image format and 32-bit PNG are included in this dataset. By using MATLAB’s image label tool, the test data were marked with their appropriate class label and bounding box. It was possible to achieve a detection accuracy of 0.98 for YOLOv2 in this trial without the usage of any MRI preprocessing technology.

Particle swarm optimization (PSO) has become universal due to its simplicity and effectiveness in solving many problems in various applications with low computational cost. This algorithm consumes time as dealing with large tasks programs. The main goal of this paper is to introduce a parallel particle swarm optimization (PPSO) on multi-core processing kernel to decrease the determination. In order to ease transfer information among particles of shared area and exchange information by switching randomly. Most of serial PSO algorithms allow updating information among particles which takes a long time during the implementation period. The algorithm was applied to the standard optimization test set CEC (Congress on Evolutionary Computation) 2014 and gave good results compared to the previous algorithm. The empirical results show the execution time of Shared-PSO is more efficient than the serial PSO's. The proposed algorithm using a multicore CPU technique to improve it via parallelization and enhanced the efficiency of an algorithm by increase the range of PSO application.

Biometrics became fairly important to help people identifications persons by their individualities or features. In this paper, gait recognition has been based on a skeleton model as an important indicator in prevalent activities. Using the reliable dataset for the Chinese Academy of Sciences (CASIA) of silhouettes class C database. Each video has been discredited to 75 frames for each (20 persons (10 males and 10 females)) as (1.0), the result will be 1,500 frames. After Pre-processing the images, many features are extracted from human silhouette images. For gender classification, the human walking skeleton used in this study. The model proposed is based on morphological processes on the silhouette images. The common angle has been computed for the two legs. Later, principal components analysis (PCA) was applied to reduce data using feature selection technology to get the most useful information in gait analysis. Applying two classifiers artificial neural network (ANN) and Gaussian Bayes to distinguish male or female for each classifier. The experimental results for the suggested method provided significant accomplishing about (95.5%), and accuracy of (75%). Gender classification using ANN is more efficient from the Gaussian Bayes technique by (20%), where ANN technique has given a superior performance in recognition.

An Ad-hoc network is collection of mobile nodes without the necessity of existing any centralized access points. In this paper introduce novel protocol that used to improveAdHoc protocol with salp swarm algorithm to routed flying Ad Hoc Network (FANET) by method of simulation. FANET predicated unmanned aerial vehicles (UAV), it designed wireless network has nodes withwith high mobility, actively changing topology and movement in 3D space. The main problem for FANET manner of routing packets among managed nodes. The new protocol based on salp swarm algorithm called “SalpAdHoc” protocol to solve routing problem and less the conjunction, thesimulation results of an experimental study confirming the feasibility of using salp swarm algorithms for routing in FANET are presented.

Protecting maritime borders is crucial to ensuring overall border security. Law enforcement agencies make great use of analyzing images of underwater debris to gather intelligence and detect illicit materials. Underwater image improvement contributes to better data quality and analytical. Nevertheless, underwater image analysis poses greater challenges compared to analyzing images taken above the water, factors like refraction of light and darkness contribute to the degradation of underwater image quality. In this paper, a novel approach is proposed to enhance underwater images, the proposed approach involves splitting underwater colored image to its three basic components, Subsequently, a point spread function is created for each component to describes image blurring factor, The deblurring process is then applied by using wiener filter, the result sharped by sharping filter to clarify edges, contrast linear stretch is performed to improve contrast and visual details. and the resulting image is finally reassembled from the three basic components. The proposed method showed effective results in evaluating the main metrics and gave better results when compared to a number of different methods. These results prove the effectiveness of the proposed method and its ability to practical applications in improving image quality.

The world is now more connected through technology and this has given rise to cyber threats, and malware is one of the threats that a system and data need to guard against. In this paper, we propose a detailed framework wherein state of the art ML methodologies can be employed for malware categorization and identification. In the evaluation of the performances of various ML algorithms, we analyze Random Forest, CatBoost, XGBoost, K-Nearest Neighbors (KNN), Histogram-based Gradient Boosting (Hist GB), and AdaBoost. The algorithms are assessed in this study using an assembly command dataset and a static and dynamic analysis approach to improve the detection rate and stability. Out of all algorithms discussed, Random Forest, CatBoost, XGBoost, Hist GB are ranked highest with 99% accuracy. As for the accuracy, KNN yielded an accuracy of 97%. Performance analysis based on metrics shows that Random Forest, CatBoost, and Hist GB not only have high accuracy but also precision, recall, and F1-score. Particularly, the accuracy of Random Forest was 99% for both the precision, recall, and F1-score. These results confirm the use of ML-based solutions in the analysis and counteraction of modern malware threats and their advantages over traditional detection methods as well as strengthening cybersecurity