This study aimed to identify the extent of the application of Cyber Security at the Ministry of Interior and National Security from the point of view of workers in the computer and information technology units. 70 employees, and the study tool (questionnaire) was distributed, and the comprehensive survey method was used, as (61) questionnaires were retrieved at a rate of (87.1%), and they were unloaded and analyzed using the SPSS statistical package. The study reached several results, including: There was a (...) high degree of agreement on the degree of Cyber Security application, which amounted to (74.8%). With a relative weight of (74.8%), and The Dimension efforts to monitor threats, it received a high degree of approval and a relative weight of (73.4%). Privacy has a high degree of approval with a relative weight of (78.4%).The study presented a set of recommendations, the most important of which are: the need to pay attention to providing the Ministry of Interior and National Security with a budget in order to activate and improve the Cyber Security system, and the need for the Ministry of Interior and National Security to use a protection network capable of detecting all threats, and the cooperation of the Ministry of Interior and National Security with external institutions to limit penetration of network systems, In addition to the need to continuously strengthen and develop the laws and legislations of the Ministry of Interior in line with the development of Cyber Security, and to enhance the capabilities of the public beneficiaries of the ministry's electronic services to reduce security gaps in the field of Cyber Security. (shrink)

American Sign Language, or ASL as its acronym is commonly known, is a fascinating language, and many people outside of the Deaf community have begun to recognize its value and purpose. It is a visual language consisting of coordinated hand gestures, body movements, and facial expressions. Sign language is not a universal language; it varies by country and is heavily influenced by the native language and culture. The American Sign Language alphabet and the British Sign Language alphabet are completely contrary. (...) Fingerspelling is one-handed in ASL and two-handed in BSL, with the exception of the letter C. AI technologies, particularly deep learning, can play an important role in breaking down communication barriers between deaf or hearing-impaired people and other communities, significantly contributing to their social inclusion. Recent advances in sensing technologies and AI algorithms have paved the way for the development of a wide range of applications aimed at meeting the needs of the deaf and hearing-impaired communities. To that end, the purpose of this paper is By attempting to translate sign language using artificial intelligence algorithms, we focused in this paper on a transfer learning technique based on deep learning using a ResNet algorithm and compared it to previous papers in which we used VGG19[1]and mobileNet [2], where we get in the ResNet algorithm on the degree of Accuracy 93,48% e number of images (43500 in the dataset in size 64*64 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch. (shrink)

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)

Abstract: Age and gender classification has been around for a long time, and efforts are still being made to improve the findings. This has been the case since the inception of social media platforms. Visible understanding has become more important in the computer vision society with the emergence of AI increase in performance and help train a model to achieve age and gender classification. Although these networks built for the mobile platform are not always as accurate as the larger, more (...) resource- intensive networks we've come to know and love, they stand out when it comes to the accuracy trade-off. Despite the importance of these attributes in our daily lives, the ability to estimate them efficiently from face images is still far from meeting the requirements of commercial applications. (shrink)

The aim of the research is to identify decision support systems and their impact on the development of the organizational structure in Palestinian higher education institutions. The research was applied to four Palestinian universities (Al-Aqsa University, the Islamic University, Al-Azhar University, the University of Palestine), and a sample of academics with an administrative position. The analytical descriptive approach was used and secondary data was obtained through a survey list distributed to the research community of academics in an administrative position using (...) the stratified random sample according to the university, and (250) survey lists were distributed and (216) were recovered with a recovery rate of (86.4%). The research concluded with a set of results, the most important of which is that there is a statistically significant relationship at the level of significance (α ≤ 0.05) between the dimensions of decision support systems: the potentials (material, human, technical, organizational) available for the use of decision support systems, the approval of senior management for the use of decision support systems. Training process and organizational structure development in Palestinian higher education institutions. The results also confirmed through the results of multiple linear regression that there is no effect of the two variables (available material capabilities, available human capabilities) in developing the organizational structure in Palestinian higher education institutions, while it was found that there is an effect of the following variables (technical capabilities available for using decision support systems, organizational capabilities). Available for the use of decision support systems, the approval of senior management for the use of decision support systems, the training process) in developing the organizational structure in Palestinian higher education institutions. The results confirmed that there were no statistically significant differences at the level (α ≤ 0.05) between the averages of the respondents' opinions about decision support systems and their impact on the development of the organizational structure in Palestinian higher education institutions due to the variable (scientific degree) and the variable (years of service). The research reached a set of recommendations, the most important of which is the need for the higher management of universities to pay attention to decision support systems, and to make intensive and continuous efforts to support and enhance efforts aimed at enriching and enhancing the use of decision support systems in universities. And that universities actively contribute to increasing interest in providing material and technical capabilities and making them available to all users, while continuing to keep pace with modern technological means and techniques. And increasing the interest in the organizational capabilities available through an independent unit of decision support systems that reports directly to the senior management in Palestinian universities, and allowing the organizational structure to easily flow information between different faculties, departments and departments. Focusing on training as a major component of decision support systems, allocating a special budget for training on the systems to be applied, and linking the training system to the workers’ training needs accurately, in order to improve and develop their skills, which increases the quality and efficiency of their performance. He urged universities to involve their employees when designing the organizational structure, by communicating with all colleges and departments and holding brainstorming sessions, workshops and periodic meetings with all departments. (shrink)

The study aimed to study the reality of the application of human resource information systems in the major hospitals in the southern Palestinian governorates. Department directors, department heads, and computer engineers), totaling (320) male and female employees during the year (2022), where the researchers prepared a questionnaire to achieve the purpose of the study on human resource information systems (Individual Performance Assessment, Organizational Performance Assessment, Performance Management, Training Development, And Knowledge Management). The most important findings of the two researchers were (...) that human resources management is more effective in the event of application of human resources information systems, and that applications of human resources information systems include individual performance evaluation, institutional performance evaluation, performance management, knowledge dissemination, training and development, decision-making, and appointment. Attraction, problem solving, team building, and quality team building. The study recommended making use of human resources information systems in providing periodic reports that include all activities in the hospital, and enhancing reliance on human resources information systems in building work teams and involving employees in setting the hospital's vision for change management and benefiting from various experiences in leading change. (shrink)

The cucumber is a creeping vine that roots in the ground and grows up trellises or other supporting frames, wrapping around supports with thin, spiraling tendrils. The plant may also root in a soilless medium, whereby it will sprawl along the ground in lieu of a supporting structure. The vine has large leaves that form a canopy over the fruits. Among these common diseases, we single out the diseases that affect the cucumber, which is affected by about 22 diseases, with (...) different symptoms for each disease. Today, technology is facilitating human life in all areas of life, and among these facilities are expert systems that have become an integral part of human life as they contain several systems and areas, for example: Artificial Intelligence (AI), which refers to systems or devices that simulate Human intelligence to perform tasks that can improve itself based on some human information, and other areas, and with reference to expert systems and their importance to humans, an integrated expert system has been created in the agricultural field that diagnoses cucumber diseases using CLIPS Expert System language Delphi language. The system was used to design and implement the proposed expert system. The system facilitates the diagnosis of cucumber-related diseases. There is no doubt that this expert system will help farmers and those involved in the agricultural field to diagnose cucumber-related diseases. Objectives: is to help farmers diagnose pear diseases in the correct way and how to treat these diseases. Method: The system contains a program that diagnoses 22 diseases that affect cucumber. Results: The expert system was evaluated by farmers and praised for helping them with it. Conclusion: The expert system for diagnosing cucumber diseases is effective and usable. (shrink)

Background: Mint is a grassy, perennial plant, belonging to the oral platoon, fast growing and spreading, its leaves are green in color, fragrant, tart, refreshing, square-shaped leg, bifurcated, erect, ranging in height from (10 - 201 cm). Home to Europe and Asia. The mint plant has many benefits, the most important of which are pain relief, treatment of gallbladder disorders, the expulsion of gases, anti-inflammatory, and relaxing nerves. While the mint plant is the ideal option for the start of gardens, (...) it is prone to some common diseases that affect the plant's growth. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper, the design of the proposed Expert System was produced to help Farmers and those interested in agriculture in diagnosing many of the Mint diseases such as Mint rust, Verticillium wilt, Anthracnose, Powdery mildew, Black Stem Rot, Stem and stolon canker, Septoria leaf spot. The proposed expert system presents an overview of mint diseases are given, the cause of diseases outlined and the treatment of disease whenever possible is given out. CLIPS Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Mint diseases diagnosis expert system was evaluated by Agricultural Students at AL Azhar University and some friends interested in agriculture and they were satisfied with its performance. Conclusions: The proposed expert system is very useful for Farmers and those interested in agriculture. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...) was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...) to classify memory loss and to know the patient at what level and his assessment of Alzheimer's disease through CT scans of the brain. In this thesis, we review ways and techniques to use deep learning classification to classifying the Alzheimer's Disease The proposed method used to improve patient care, reduce costs, and allow fast and reliable analysis in large studies. The model will be designed using Python language for implementing the system, which is very useful for doctors, classifying the Alzheimer's Disease, was used. The model used 70% from image for training and 30% from image for validation, our trained model achieved an accuracy of 100% on a held-out test set. (shrink)