Samy S. Abu-Naser

Warts and verrucas are common skin conditions caused by the human papillomavirus (HPV) infection. They present as raised, rough, or bumpy growths on the hands, feet, or other areas subjected to friction or pressure. Plantar warts exhibit a rough surface with small black dots, while genital warts have a cauliflower-like appearance. Pain or itchiness may accompany these lesions. Factors such as close contact with infected individuals and immune compromise can impact the severity and spread of warts. Diagnosis is primarily based on clinical examination, and referral to a dermatologist may be necessary for uncertain cases. Treatment options range from over-the-counter remedies to professional interventions. Persistent or symptomatic lesions should be evaluated by a healthcare professional. Improved understanding and effective management of warts and verrucas are essential for optimal patient outcomes.

Mango is an economically significant fruit crop cultivated in various tropical and subtropical regions around the world. However, the productivity and quality of mangoes can be severely impacted by a range of pests. This research paper introduces an innovative approach to identify mango pests using an expert system. The expert system integrates knowledge from entomology and plants to provide accurate identification of common mango pests. The paper outlines the development and implementation of the expert system using Clips shell, which utilizes rule-based techniques to analyze symptoms associated with mango pests. By inputting observed symptoms, such as leaf spots, fruit damage, leaf mild and stem cankers the system can accurately identify the specific pest species affecting the mango tree. For each pest, the paper discusses their specific symptoms and Favorable conditions that affect the mango plant. By understanding the identification and symptoms of these pests, mango growers and agricultural professionals can implement appropriate pest management strategies to mitigate the economic losses caused by these insects. The research presented in this paper aims to contribute to the body of knowledge on mango pest management, facilitating sustainable mango production and ensuring the availability of high-quality mangoes in global markets.

Malaria is a life-threatening disease spread to humans by some types of mosquitoes. It is mostly found in tropical countries. It is preventable and curable. The infection is caused by a parasite and does not spread from person to person. Symptoms can be mild or life-threatening. Mild symptoms are fever, chills and headache. Severe symptoms include fatigue, confusion, seizures, and difficulty breathing. Infants, children under 5 years, pregnant women, travelers and people with HIV or AIDS are at higher risk of severe infection. Discovering this disease is a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help to avoid spreading this disease. The expert system diagnoses malaria disease correctly to facilitate doctors to find the correct treatment based on the appropriate diagnosis. Objectives: An expert system has been established based on CLIPS to diagnose malaria disease.

This abstract is a synopsis of the paper "An Expert System for Diagnosing Whooping Cough Using CLIPS." The bacterium Bordetella pertussis causes whooping cough, a highly infectious respiratory ailment with several phases of symptoms. An accurate and timely diagnosis is critical for effective treatment and the avoidance of future transmission. The construction of an expert system for detecting whooping cough using the CLIPS (C Language Integrated Production System) architecture is highlighted in this abstract. The expert system provides standardized and systematic evaluation, which reduces the chance of misdiagnosis and improves patient outcomes. Maintenance and upgrades are required to keep the system current with growing medical knowledge. More study in this area has the potential to advance expert systems in identifying and managing a variety of medical disorders.

Tinnitus is a common condition characterized by the perception of sound in the absence of an external source, with potential negative physical and psychological impacts. Accurate and efficient diagnosis of tinnitus is crucial for appropriate treatment and management. Traditional diagnostic methods have limitations in terms of time, cost, and accuracy. To address these challenges, expert systems have emerged as a promising tool for tinnitus diagnosis. This paper explores the application of expert systems in tinnitus diagnosis, highlighting their potential to improve accuracy and efficiency. By incorporating a knowledge base and rule-based decision-making, expert systems can provide valuable insights for accurate diagnosis and appropriate management of tinnitus. Further research and development in this area can enhance the clinical assessment and treatment of tinnitus, ultimately improving the quality of life for affected individuals.

Lung cancer is a serious and deadly disease that affects the lungs, which are responsible for taking in oxygen and expelling carbon dioxide from the body. The disease can develop in any part of the lungs and is usually caused by smoking or exposure to certain chemicals. The main Objective: of this expert system is to provide an accurate diagnosis of lung cancer and the appropriate treatment options. In this paper, Methods: we present the design and implementation of an expert system that can assist physicians in diagnosing lung cancer and determining the best course of treatment. The system provides an overview of the disease, its causes, symptoms, diagnosis, and treatment, along with relevant medical information. Results: The system was developed using the Clips expert system language and evaluated by medical professionals who found it to be an effective tool for diagnosing and treating lung cancer. Conclusion: The proposed expert system has the potential to improve patient outcomes and reduce healthcare costs associated with the treatment of lung cancer.

West Nile virus (WNV) is a mosquito-borne flavivirus that was first identified in 1937 in the West Nile district of Uganda. The virus is now widely distributed throughout the world and is considered a significant public health concern. WNV is primarily transmitted to humans through the bite of infected mosquitoes, with birds serving as the primary reservoir host. Most people infected with WNV will not experience any symptoms, but approximately 1 in 5 will develop a fever, and a smaller percentage may develop more severe symptoms such as meningitis or encephalitis. There is no specific treatment for WNV infection, and prevention efforts focus on mosquito control measures and personal protective measures to avoid mosquito bites. While WNV is not typically considered a major threat to human health, outbreaks have occurred sporadically in various parts of the world, and ongoing surveillance and research are necessary to better understand and control the virus. Objectives: This paper will solve the problems of treatment of West Nile virus (WNV) through correct diagnosis and treatment. Methods: In this research, we provide an expert system for the diagnosis of West Nile virus (WNV) which will help doctors to explore everything related to the problems of West Nile virus (WNV). We look forward to providing simplified answers to West Nile virus (WNV).

There is no doubt that Computer troubleshooting is important for organizations and companies and for personal use level. Sound cards troubles is one of the most annoying problems in computers. It causes damage and troubles in computers to persons, organizations and firms. Correctly, expert systems can greatly help to avoid damage to these computers. designed to diagnose and troubleshoot issues related to sound cards in computer systems. The expert system is developed using a combination of rule-based and machine learning approaches, which allows for accurate and efficient diagnosis of a wide range of sound card problems. Objectives: An expert system has been established based on CLIPS to diagnose Computer Problems

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of heart palpitations, 2) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes.

Vertigo is a common symptom that can result from various underlying diseases and conditions, ranging from benign to severe. Accurate and timely diagnosis of the cause of vertigo is crucial for appropriate management and treatment. In this research, we propose the development of an expert system for vertigo diseases diagnosis, utilizing artificial intelligence (AI) and the proposed Expert System which was produced to help assist healthcare professionals in diagnosing the cause of vertigo based on a patient's symptoms, medical history, and other relevant clinical information. The proposed expert system presents an overview about vertigo diseases are given, the cause of disease is outlined and the treatment or recommendation of disease whenever possible is given out. CLIPS language was used for designing and implementing the proposed expert system. The potential of the proposed expert system lies in its ability to enhance the accuracy and efficiency of vertigo diagnosis, as well as assist in the proper referral and management of patients.

The study aimed to investigate the reality of spreading the culture of entrepreneurship at Al-Azhar University from the point of view of students of the Faculty of Engineering and Information Technology and diagnose the most important obstacles that limit its activation. The researchers used the descriptive approach (survey) to achieve the objectives of the study, and relied on the questionnaire as a tool for applied study. The study concluded that: The reality of spreading the culture of entrepreneurship at Al-Azhar University from the point of view of the students participating in the study came with an average degree of approval, while the most important obstacles and possible proposals for activating it came with a high degree of approval, which indicates the existence of strengths of Al-Azhar University in spreading the culture of entrepreneurship among students. Deficiencies must be corrected through proposals. In light of the results of the study, it was possible to develop a number of recommendations that can contribute to activating the culture of entrepreneurship at Al-Azhar University. Including the establishment of a specialized center for entrepreneurship within the university, develop a declared strategic plan to spread and develop the culture of entrepreneurship, allocate a sufficient budget to sponsor and support entrepreneurial ideas and projects for students and provide specialized cadres and certified trainers in the field of entrepreneurship.

This study aimed to highlight the nature of the relationship between knowledge management and the strategic decision-making process, considering that strategic decisions are formulated and made based on a specific knowledge perspective. The study targeted the university management, deans of faculties, and college directors at Al-Azhar University - Gaza. The study followed a descriptive-analytical approach, and data was collected through a questionnaire designed to cover six dimensions related to knowledge management processes and an axis related to strategic decision-making. The data was analyzed using various statistical methods. The study results showed a statistically significant positive relationship between the role of knowledge management processes and activating the strategic decision-making process at Al-Azhar University - Gaza. The study recommended that the university pay more attention to the processes of knowledge awareness and application as they serve as the link between existing knowledge and the creation of good knowledge. Furthermore, these processes are considered the essential means through which the university improves the effectiveness of strategic decisions and enhances its position.

A newborn baby is an infant within the first 28 days of birth. Diagnosis and treatment of infant diseases require specialized medical resources and expert knowledge. However, there is a shortage of such professionals globally, particularly in low-income countries. To address this challenge, a knowledge-based system was designed to aid in the diagnosis and treatment of neonatal diseases. The system utilizes both machine learning and health expert knowledge, and a hybrid data mining process model was used to extract knowledge from a clinical dataset. The PART algorithm achieved the highest performance result with 98.06% accuracy under 10-fold cross-validation, and the generated rules were used to develop the knowledge-based system. The system achieved 90.9% accuracy in system performance testing and 89.2% in user acceptance testing, and is intended to serve as an assistant tool for healthcare experts.

Mouth ulcers, also known as canker sores, are a common oral health issue affecting a significant portion of the population. Early and accurate diagnosis of mouth ulcers is crucial for effective treatment and prevention of complications. This paper presents an expert system developed using CLIPS (C Language Integrated Production System) to diagnose mouth ulcer disease. The expert system utilizes a rule-based approach, incorporating a comprehensive knowledge base consisting of symptoms, risk factors, and medical literature related to mouth ulcers. By employing an inference engine, the system evaluates patient input against the rules and generates a diagnosis based on the matching symptoms and relevant factors. The system also provides detailed explanations and recommendations for each diagnosis, aiding healthcare professionals and patients in understanding the condition and selecting appropriate treatment options. The development and validation of the expert system involved a multi-step process, including knowledge acquisition, rule formulation, and testing against a diverse dataset of mouth ulcer cases. The system's performance was evaluated by comparing its diagnoses with those made by medical professionals, demonstrating a high level of accuracy and consistency. The implementation of an expert system for diagnosing mouth ulcer disease offers several advantages, including the ability to provide timely and accessible healthcare services, reduce misdiagnosis rates, and assist healthcare professionals in decision-making processes. Moreover, it empowers individuals to make informed decisions about their health and seek appropriate medical intervention. This paper contributes to the field of medical expert systems by showcasing the practical application of CLIPS in the diagnosis of mouth ulcer disease. The results demonstrate the potential of expert systems as valuable tools for healthcare professionals and patients alike, enhancing the efficiency and accuracy of diagnosis in oral health care.

Diagnosing flatulence involves a thorough assessment of an individual's symptoms, medical history, and, if necessary, the use of diagnostic tests. Healthcare providers gather information about the patient's medical background and conduct a physical examination to identify any signs of gastrointestinal issues. Dietary habits are evaluated, and potential triggers are identified through an elimination diet. Diagnostic tests such as breath tests, stool analysis, or imaging studies may be performed to further investigate the underlying causes of excessive flatulence. Accurate diagnosis is crucial for effective management and treatment of flatulence, enabling individuals to gain control over their symptoms and address any underlying conditions.

Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) Confirm the diagnosis of a brain tumor, 2) Consider the possibility of a brain tumor that has metastasized, and 3) Consider the possibility of a brain tumor. Our expert system offers a user-friendly interface, enabling users to select symptoms and receive a diagnosis based on the provided information. This paper discusses the expert system's development, implementation, and evaluation, highlighting its potential to facilitate brain tumor diagnosis and decision-making in clinical settings. Additionally, we provide a literature review that contextualizes our expert system within the broader landscape of rule-based expert systems for brain tumor diagnosis, examining their effectiveness, limitations, and challenges.

Migraine is a complex neurological disorder characterized by recurrent moderate to severe headaches, accompanied by additional symptoms such as nausea, sensitivity to light and sound, and visual disturbances. Accurate and timely diagnosis of migraines is crucial for effective management and treatment. However, the diverse range of symptoms and overlapping characteristics with other headache disorders pose challenges in the diagnostic process. In this research, we propose the development of an expert system for migraine diagnosis using artificial intelligence and the CLIPS (C Language Integrated Production System) framework. The expert system utilizes a rule-based inference engine to analyze patient-reported symptoms and provide reliable diagnoses or probability scores indicating the likelihood of migraine. The knowledge base of the expert system is designed based on expert knowledge obtained from medical professionals specializing in migraines. The collected knowledge is translated into a structured format suitable for the CLIPS inference engine, incorporating rules and facts to represent the diagnostic criteria and associated symptoms. The system prompts users to provide relevant information about their symptoms, medical history, and potential triggers. It applies the defined rules and facts to evaluate the likelihood of migraine and generate accurate diagnoses or probability scores. Preliminary evaluation results demonstrate the potential of the expert system as a valuable tool for diagnosing migraines. A dataset of anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by the expert system were compared against the known diagnoses, and a high level of accuracy was observed, with 90% of cases correctly diagnosed as migraines. These results highlight the effectiveness and reliability of the system in assisting medical professionals in the diagnosis of migraines. The proposed expert system offers several advantages for migraine diagnosis. It leverages the collective knowledge and expertise of experienced migraine specialists, providing a standardized and consistent approach to diagnosis. The system can handle large amounts of patient data and effectively analyse complex relationships between symptoms, risk factors, and diagnostic criteria. Furthermore, it offers real-time feedback and recommendations, supporting medical professionals in their clinical decision-making process. Future work involves refining the expert system based on feedback from medical experts, expanding the knowledge base to encompass a wider range of symptoms and risk factors, and conducting further evaluations to enhance its accuracy and applicability in clinical settings. The development of an expert system for migraine diagnosis has the potential to improve the diagnostic process, leading to more effective management and treatment strategies for individuals suffering from migraines.

The study aimed to identify the reality of total quality management in university colleges in Gaza Strip, through its dimensions, which are (commitment of senior management; organizational building; employee empowerment; continuous improvement; focus on beneficiaries; management by facts), and to detect differences in the responses of sample members according to For personal variables (type; educational qualification; college; nature of work; years of service). The researchers used the descriptive analytical method, and used a main tool to collect information: the questionnaire consisted of (42) phrases, and the study population reached (596) academic and administrative employees distributed to (5) university colleges in Gaza Strip, and it was chosen A stratified random sample of them consisted of (240) employees, approximately (40.3%) of the study population. (SPSS) was used to analyze the data. The study concluded a set of results, the most important of which are: (1) The application of the requirements of total quality management in university colleges in Gaza Strip is generally appropriate in terms of: (the commitment of senior management to total quality management, organizational building, facts management, focus on beneficiaries, employee empowerment, continuous improvement), where the response of the sample members was about the dimensions Total quality management with relative weight (69.93%). (2) There are differences at the level of significance (α =.05) between the averages of the respondents' responses to total quality management in university colleges in Gaza Strip due to gender and differences in favor of the male category. And differences attributed to the college and in favor of the University College of Professional Sciences. (3) There are no differences between the averages of the respondents' responses to total quality management in university colleges in Gaza Strip due to (Educational Qualification, Nature of Work, and Years of Service). Among the most important recommendations of the study: The need for university colleges to ensure: (achieving job satisfaction, employee participation when setting regulations and policies and making important decisions, adhering to the requirements of scientific research and encouraging scientific achievements and job creativity among employees, while providing material and moral facilities for scientific research and development, developing Mechanisms and patterns of electronic work). And that university colleges give attention to handling complaints and grievances through: (assigning a competent and competent committee to handle complaints and grievances in all fairness and transparency, adopting and following up on the results of implementing complaints and grievances, protecting the complainants from any arbitrary procedure against them).

UNRWA’s Mediation Process is a key element in the organization’s efforts to strengthen its internal justice system. The research aims to study the reality of mediator competencies, emotional intelligence and case characteristics variables, (UNRWA) Gaza. The research also aims at identifying the differences between respondents as attributed to their professional and personal traits of age, gender, educational level, distribution of department, dispute resolution experience and representation party. The researchers adopted mixed data collection methods; quantitative and qualitative. For qualitative, a semi-structured interview was conducted with (9) officials including management, staff unions and expert external mediators. (63) Questionnaires were distributed to a systematic random sample population; (56) were received, with a response rate 88.9%. The results proved that there are no significant differences among respondents attributed to personal and professional traits of age, academic background, gender, department, years’ of experience, educational degree and representation party. The research has presented some recommendations concerning the factors affecting the mediation outcomes of workplace disputes resolution. The recommendations include: UNRWA should adopt mediation as a method to resolve disputes at workplace instead of following litigation before the courts, as many organizations today increasingly started to adopt mediation as a method to resolve disputes at workplace. This will help decreasing the high cost, non-flexibility, and threatening features of disputes and conflicts resolution before courts, as well as enhance the relationship between disputant parties. Mediators are essentially recommended to attend regular training such as guiding negotiation processes, employing group sessions properly, overcoming obstacles, summarizing & analytical skills and selecting emotional words and phrases, it is also important to encourage the mediation experts' training workshops and meetings. Regular training sessions will not only improve skills and abilities, but will also imply variety and flexibility. This leads to having competent qualified mediators who are able to resolve organization’s workplace disputes in a way, which satisfies the need of disputants.

The study aimed to identify the effect of Total Quality Management in achieving the requirements of the quality of job life among university college employees, and the researchers used the descriptive and analytical approach, and used a main tool to collect information, which is: the questionnaire. The study population reached (596) academic and administrative employees distributed among (5) University colleges in Gaza Strip, and a stratified random sample of (240) employees was selected, approximately (40.3%) of the study population. SPSS software was used to analyze the data. The study concluded that the application of the requirements of total quality management in university colleges in Gaza Strip is appropriate in general in terms of: (the commitment of the top management to total quality management, organizational structure, management with facts, focus on beneficiaries, staff empowerment, and continuous improvement). The sample on the dimensions of total quality management with a relative weight is (69.93%). And that the elements of job quality in university colleges in Gaza Strip are generally adequate in terms of: (moral working conditions, wages and rewards, participation in decision-making, handling of complaints and grievances, benefits provided to employees), where the response of the sample respondents was about the dimensions of job quality with a relative weight (63.30%), and the existence of a strong positive relationship between Total Quality Management in university colleges in Gaza Strip. And that there is a statistically significant effect of the variables of comprehensive quality management and quality of career in university colleges in Gaza Strip at a significance level (α = 0.05). And that the dimensions of total quality management most influencing the dimensions of job quality are: (empowering employees; continuous improvement; focusing on beneficiaries; managing with facts). The absence of differences between the answers of the sample members about the impact of Total Quality Management in achieving the requirements of the quality of the job life of the employees in university colleges due to the variable (academic qualification, years of service). And the existence of differences attributed to a variable (gender in favor of the male category, the college in favor of the university college of applied sciences category, and the nature of work attributed to an academic category with an administrative position). Among the most important recommendations of the study is the necessity for university colleges to be keen on: (achieving job satisfaction, employee participation when setting regulations and policies and making important decisions, adhering to scientific research requirements and encouraging scientific achievements and career creativity among employees, while providing material and moral facilities for scientific research and development, developing mechanisms and patterns of electronic work). University colleges must provide benefits to employees such as: (providing health care, organizing leisure trips, contributing to solving economic problems, providing club subscriptions). And the necessity for university colleges to pay attention to handling complaints and grievances through: (Assigning an efficient and competent committee to handle complaints and grievances in all fairness and transparency, adopting and following up the results of the application of handling complaints and grievances, protecting complainants from any arbitrary action against them). And the need for the Ministry of Education and Higher Education to develop the evaluation role of the National Authority for Accreditation, Quality and Quality of Higher Education Institutions to evaluate the performance of university colleges, to ensure the application of academic and administrative quality standards in colleges, and to seek the help of quality and excellence experts in evaluating the application of Total Quality Management in the academic and administrative f ield in accordance with regional performance standards And global.

This study aims to identify enhancing the competitive advantage in the University of Palestine: Reality and Ambition, as the researchers used the descriptive and analytical method, through a questionnaire distributed to a sample of employees at the University of Palestine, where the size of the study population is (234) employees, and the size of the sample (117) employees, of whom (90) responded. The study provided a theoretical framework for what the writers and researchers presented about the study variable, as well as an analytical practical framework for the opinions of employees at the University of Palestine. The study reached a set of results, the most important of which are: the presence of a high degree of competitive advantage in the University of Palestine from the viewpoint of the employees, and the absence of differences in the opinions of the study sample about the study variables for the demographic variables. The study presented a set of recommendations, the most important of which are: The necessity of working to enhance the university's competitive advantage by enhancing the human capital and knowledge of the university.

The study aimed to identify the reality of total quality management in university colleges in Gaza Strip, through its dimensions, which are (commitment of senior management; organizational building; employee empowerment; continuous improvement; focus on beneficiaries; management by facts), and to detect differences in the responses of sample members according to For personal variables (type; educational qualification; college; nature of work; years of service). The researchers used the descriptive analytical method, and used a main tool to collect information: the questionnaire consisted of (42) phrases, and the study population reached (596) academic and administrative employees distributed to (5) university colleges in Gaza Strip, and it was chosen A stratified random sample of them consisted of (240) employees, approximately (40.3%) of the study population. (SPSS) was used to analyze the data. The study concluded a set of results, the most important of which are: (1) The application of the requirements of total quality management in university colleges in Gaza Strip is generally appropriate in terms of: (the commitment of senior management to total quality management, organizational building, facts management, focus on beneficiaries, employee empowerment, continuous improvement), where the response of the sample members was about the dimensions Total quality management with relative weight (69.93%). (2) There are differences at the level of significance (α =.05) between the averages of the respondents' responses to total quality management in university colleges in Gaza Strip due to gender and differences in favor of the male category. And differences attributed to the college and in favor of the University College of Professional Sciences. (3) There are no differences between the averages of the respondents' responses to total quality management in university colleges in Gaza Strip due to (Educational Qualification, Nature of Work, and Years of Service). Among the most important recommendations of the study: The need for university colleges to ensure: (achieving job satisfaction, employee participation when setting regulations and policies and making important decisions, adhering to the requirements of scientific research and encouraging scientific achievements and job creativity among employees, while providing material and moral facilities for scientific research and development, developing Mechanisms and patterns of electronic work). And that university colleges give attention to handling complaints and grievances through: (assigning a competent and competent committee to handle complaints and grievances in all fairness and transparency, adopting and following up on the results of implementing complaints and grievances, protecting the complainants from any arbitrary procedure against them).

The aim of the research is to identify the reality of the practice of crisis management in light of The Corona Pandemic, and to achieve the research objectives, the researchers used the descriptive and analytical approach using the comprehensive survey method for the total research community, which numbered (110) individuals, while (90) were recovered: That the level of crisis management practice came with a relative weight (75.60%). Among the most important recommendations made by the research: Work to disburse a health risk premium to all workers in health centers, in addition to paying attention to training and building work teams in various fields, including the crisis team.

The study aimed to identify the leadership features and their relationship to increasing achievement motivation among Palestinian police employees in Gaza Strip in light of the Corona pandemic. To achieve the study’s objectives, the researchers used the descriptive method in its analytical method, using a questionnaire applied to the police officers at the Central Governorate Police Station, whose number is (113) individuals. They were chosen in a stratified, random manner, and the study resulted in a set of results, the most important of which is that the effect of leadership characteristics of Palestinian police employees in Gaza Strip, which came with a relative weight (76.20%), which is, to a large extent, the level of increase in achievement motivation among employees in the Palestinian Police in Gaza Strip. Gaza under the outbreak of the Corona epidemic? Which came with a relative weight (82.40%), which is to a large extent. While it resulted in a positive correlation between the impact of leadership characteristics and the increase in achievement motivation, there are no statistically significant differences between the averages of the study sample's estimates about the impact The leadership characteristics of the Palestinian Police in Gaza Strip are attributed to the variable of the age group, academic qualification, scientific specialization, scientific experience. There are no statistically significant differences between the averages of the estimates of the individuals of the study sample about The achievement motivation in the Palestinian Police in Gaza Strip is attributable to a variable, age group, academic qualification, scientific specialization, scientific experience, and in light of these results the researchers suggested a set of recommendations, the most important of which are: The need to tighten penalties for individuals by the Central Governorate Police Station for violating the instructions issued by the Ministry Interior In light of the outbreak of the Corona epidemic, increased interest in preparing and organizing specialized training courses and programs in the field of dealing with all different segments in order to preserve the lives of people from the Corona epidemic.

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.

Age and gender classification has become relevant to an increasing amount of applications, particularly since the rise of social platforms and social media. Even Nevertheless, contrast to the large performance improvements recently reported for the closely related task of audio. In this research, we show that performance on these tasks can be significantly improved by learning representations using deep convolutional neural networks (CNN). where we get in the ResNet the training accuracy was 98% ,validation accuracy 95%, testing accuracy 96% .Testing dataset organized into one folder (age-gender-test) and contains 1676 audio files related to 39 of age and gender categories and 40 epoch

there is a communication gap between hearing-impaired people and those with normal hearing, sign language is the main means of communication in the hearing-impaired population. Continuous sign language recognition, which can close the communication gap, is a difficult task since the ordered annotations are weakly supervised and there is no frame-level label. To solve this issue, we compare the accuracy of each model using two deep learning models, Inception and Xception . To that end, the purpose of this paper is By attempting to translate sign language using deep learning models, we focused in this paper a comparing between Inception and Xception ¬¬¬¬¬ algorithm and compared it to previous papers in which we used VGG19[1], mobileNet [2], where we get in the Inception and Xception algorithm on the degree of Accuracy 99.32%, 99,43% respectively. number of images (29000 in the dataset in size 75*75 pixel ) and the data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch.

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.

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 .