SM ADAI, BN RASHID, Journal of Current Researches on Social Sciences, 2023

One of the most serious health disasters in recent memory is the COVID-19 epidemic. Several restriction rules have been forced to reduce the virus spreading. Masks that are properly fitted can help prevent the virus from spreading from the person wearing the mask to others. Masks alone will not protect against COVID-19; they must be used in conjunction with physical separation and avoidance of direct contact. The fast spread of this disease, as well as the growing usage of prevention methods, underscore the critical need for a shift in biometrics-based authentication schemes. Biometrics systems are affected differently depending on whether are used as one of the preventive techniques based on COVID-19 pandemic rules. This study provides an

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

The extraction process of chlorophyll from dehydrated and pulverized alfalfa plant were studied by percolation method. Two  solvent systems were used for the extraction namely; Ethanol-water and Hexane-Toluene systems . The effect of circulation rate, solvent concentration, and solvent volume to solid weight ratio were studied. In  both ethanol water, and Hexane-Toluene systems it appears that solvent concentration is the most effective variable.

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select