The evolution of Intelligent Tutoring System (ITS) is the result of the amount of research in the field of education and artificial intelligence in recent years. English is the third most common languages in the world and also is the internationally dominant in the telecommunications, science and trade, aviation, entertainment, radio and diplomatic language as most of the areas of work now taught in English. Therefore, the demand for learning English has increased. In this paper, we describe the design of (...) an Intelligent Tutoring System for teaching English language grammar to help students learn English grammar easily and smoothly. The system provides all topics of English grammar and generates a series of questions automatically for each topic for the students to solve. The system adapts with all the individual differences of students and begins gradually with students from easier to harder level. The intelligent tutoring system was given to a group of students of all age groups to try it and to see the impact of the system on students. The results showed a good satisfaction of the students toward the system. (shrink)

Recently there is an increasing technological development in intelligent tutoring systems. This field has become interesting to many researchers. In this paper, we present an intelligent tutoring system for teaching information security. This intelligent tutoring systems target the students enrolled in Advanced Topics in Information Security in the faculty of Engineering and Information Technology at Al-Azhar University in Gaza. Through which the student will be able to study the course and solve related problems. An evaluation of the intelligent tutoring systems (...) was carried out and the results were promising. (shrink)

This study examines the Egyptian House of Zakat and Charities as an example for Al- Azhar's public role after June 30, 2013. It raises questions regarding Al-Azhar's role in state-society relations, as well as the social, political, and economic implications of the institution's work. The study is based on the concept of the "common sphere," a theoretical model that assumes Al- Azhar's ideal position is to contribute to strengthening cooperative relations between society and the state in a way that reflects (...) the interests of both parties and not to reflect the dominance of one over the other and support it in a way that strengthens conflictual relations. The conclusion of the study is that the policies of the Zakat House assist the state in implementing its austerity economic policies and make up for the lack of public expenditure on service sectors by utilizing donor funds. Thus, the Zakat House contributes to enhancing Al-Azhar's standing in its relations with the state and society. It also aids economically impoverished segments of the population. Nevertheless, compared to the state and Al-Azhar, society at large continues to garner less benefit from the Zakat House's activities. -/- تناقش هذه الدراسة مؤسسة بيت الزكاة والصدقات المصري، باعتباره نموذجًا للحضور الأزهري في المجال العام في مرحلة ما بعد 30 حزيران/ يونيو 2013. وتتساءل عن الأبعاد الاجتماعية والسياسية والاقتصادية لنشاط المؤسسة، وآثار هذه الأنشطة في علاقات الدولة والمجتمع وفي موقع مؤسسة الأزهر من هذه العلاقات. وتعتمد الدراسة في تحليلها على مفهوم "المجال المشترك"، وهو نموذج نظري يفترض أن الوضع المثالي للأزهر وممارساته في المجال العام هو أن يُسهم في تعزيز العلاقات التعاونية بين المجتمع والدولة بما يعكس مصالح الطرفين، لا أن تعكس الهيمنة والسيطرة لأحدهما على الآخر وتدعمها بما يعزز العلاقات الصراعية بين الدولة والمجتمع. وتخلص الدراسة إلى أن سياسات بيت الزكاة تدعم الدولة جزئيًا في تمرير سياساتها الاقتصادية التقشفية وتُعوض قلة الإنفاق العام على القطاعات الخدمية، وذلك باستخدام أموال المتبرعين. يُسهم البيت في تعزيز قوة الأزهر في علاقاته بكل من الدولة والمجتمع. وتستفيد فئات فقيرة من المساعدات والإعانات التي يقدمها البيت. لكن يظل المجتمع أقل الأطراف استفادة من نشاط المؤسسة مقارنة بكل من الدولة والأزهر. (shrink)

Abstract: The aims of this study are to introduce acceleration methods that are called triangular acceleration methods, which come within the series of several acceleration methods that generally known as Al-Tememe's acceleration methods of the second kind which are discovered by (Ali Hassan Mohammed). These methods are useful in improving the results of determining numerical integrals of continuous integrands where the main error is of the forth order with respect to accuracy, partial intervals and the fasting of calculating the (...) results specifically to accelerate results come out by Simpson's method. Also, it is possible to make use of it to improve the results of solving differential equations numerically of the main error of the forth order. (shrink)

Abstract: The aims of this study are to introduce acceleration methods that called relative and algorithmic acceleration methods, which we generally call Al-Tememe's acceleration methods of the second kind discovered by (Ali Hassan Mohammed). It is very useful to improve the numerical results of continuous integrands in which the main error is of the 4th order, and related to accuracy, the number of used partial intervals and how fast to get results especially to accelerate the results got by Simpson's (...) method. Also, it is possible to utilize it in improving the results of differential equations numerically of the main error of the forth order. (shrink)

Abstract: The main aim of this work is to introduce acceleration methods called a Trigonometric acceleration methods which are of series of numerated methods. In general, these methods named as AL-Tememe’s acceleration methods of first kind to his discoverer ''Ali Hassan Mohammed''. These are very beneficial to acceleration the numerical results for definite integrations with continuous integrands which are of 2nd order main error, with respect to the accuracy and the number of the used subintervals and the fasting obtaining (...) results. Especially, for acceleration the results which are obviously obtained by trapezoidal and midpoint methods. Moreover, these methods could be enhancing the results of the ordinary differential equations numerically which are of 2nd order main error. (shrink)

Abstract: The main aim of this work is to introduce acceleration methods called a hyperbolic acceleration methods which are of series of numerated methods. In general, these methods named as AL-Tememe’s acceleration methods of first kind discovered by (Ali Hassan Mohammed). These are very beneficial to acceleration the numerical results for definite integrations with continuous integrands which are of 2nd order main error, with respect to the accuracy and the number of the used subintervals and the fasting obtaining results. (...) Especially, for acceleration the results which are obviously obtained by trapezoidal and midpoint methods. Moreover, these methods could be enhancing the results of the ordinary differential equations numerically which are of 2nd order main error. (shrink)

It aimed to identify the strategic creativity in Islamic banks in Palestine between reality and implementation. The study adopted the descriptive analytical approach. A questionnaire was designed as a tool for the study. The study community consisted of all employees in Islamic banks from the top and middle management and the study has been applied to the Palestinian Islamic bank and the Arab Islamic Bank. The comprehensive inventory method was used, given the small size of the study sample, as questionnaires (...) were distributed to (175) employees, and a number of (5) categories were chosen from each branch of the bank(general manager, deputy general manager, director Branch, department head, department manager). (164) questionnaires have been used Recovered with a recovery rate of (93.71%). The study showed a number of results, the most important of which is the availability of dimensions of strategic innovation at a high level in Islamic banks in Palestine with a relative weight of (82.22%). In addition, that there are no differences between the averages estimates about the reality of the study variables in Islamic banks due to (gender, age group, educational qualification, number of years of service, job title). The study also presented a set of recommendations, including that the bank should provide the environment and the appropriate climate for employees to invest their intellectual energy, urge them to strategic creativity, and deal fairly with all creative ideas regardless of their source. (shrink)

This thesis focuses on the use of intelligent tutoring system for education and training of students specialized in electricity in the field of technical and vocational education. The use of modern systems in training and education will have a great positive impact in improving the level of students receiving training and education; this will improve the level of the local economy by producing students of professionals who are able to engage in society efficiently, especially for those who have specialized in (...) electricity. It is known that students in electrical specialties face many problems because of time and cost. Learning and training is not as easy as you imagine, so intelligence is how to overcome these limitations effectively. Motivating the Intelligent Tutorial System (ITS) for this type of training. This system consists of a student model, which in turn will contain lessons, exercises and examples that will be used by the student in addition to the user interface model which makes the teacher to be able to manage lessons, examples, and questions and make changes to the system. Expert System (ES) model representing the Artificial Intelligence (AI) here has been used to determine the level of the student and gives him the results, directions and levels. The program was implemented and was used by trainees to show the extent of its impact. The students expressed their surprise and admiration in this way of education as the system used focuses on individual education which can be used at any time and without restrictions. After this experience, the dedicated (ITS) system needs to be developed in several ways such as to be used on mobile systems and web systems in addition to linking some of the specialized programs in the field of electricity. (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. This research can (...) enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (shrink)

Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all reaching 97.23%. (...) The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. It (...) involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to develop a (...) predictive model. The achieved accuracy of 97.24% and a low loss value of 0.02 indicate promising performance. Further analysis involves a comprehensive examination of the confusion matrix, precision, recall, and F1 score metrics, along with the ROC curve and AUC score, to provide a detailed understanding of the model's strengths and weaknesses. Additionally, considerations for cross-validation, hyperparameter tuning, and interpretability are discussed. It is emphasized that while the synthetic nature of the dataset allows for controlled experimentation, results should be cautiously interpreted and validated on authentic clinical data before extrapolation to real-world medical scenarios. This research serves as a valuable contribution to the exploration of predictive models for cancer detection, encouraging continued investigation and development in the field. (shrink)

Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, achieving (...) an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our neural network, (...) featuring a single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.002. Beyond performance metrics, we delve into the intricacies of plant Species classification through feature importance analysis. The most influential features— Vegsout, durflow, semiros, pdias, begflow, wind, leafy, autopoll and insects— uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring plant safety, and bolstering global trade. (shrink)

Neutrosophic theory has many applications in graph theory, bipolar single valued neutrosophic graphs (BSVNGs) is the generalization of fuzzy graphs and intuitionistic fuzzy graphs, SVNGs. In this paper we introduce some types of BSVNGs, such as subdivision BSVNGs, middle BSVNGs, total BSVNGs and bipolar single valued neutrosophic line graphs (BSVNLGs), also investigate the isomorphism, co weak isomorphism and weak isomorphism properties of subdivision BSVNGs, middle BSVNGs, total BSVNGs and BSVNLGs.

Background: A bean is the seed of one of several genera of the flowering plant family Fabaceae, which are used as vegetables for human or animal food. They can be cooked in many different ways, including boiling, frying, and baking, and are used in many traditional dishes throughout the world. Beans are one of the longest-cultivated plants. Broad beans, also called fava beans, in their wild state the size of a small fingernail, were gathered in Afghanistan and the Himalayan foothills. (...) In a form improved from naturally occurring types, Beans were an important source of protein throughout old and new world history, and still are today. 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 Bean diseases such as Fusarium wilt, Charcoal rot or ashy stem blight, Bacterial leaf spot and blight, Mung bean yellow mosaic virus, Cercospora leaf spot. The proposed expert system presents an overview of Bean 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 Bean diseases diagnosis expert system was evaluated by Agricultural experts 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)

Abstract: Purpose - This study aimed to analyze the relationship between the determinants of organizational justice and their relationship to conscientious behavior from the point of view of officers working in the Palestinian police in Gaza Strip. Methodology - The study relied on the descriptive and analytical approach, using the questionnaire, targeting a stratified random sample of (400) officers, who hold the rank of captain and above, from the study population of 1550 officers. The study tool was distributed among the (...) sample members in all departments and governorates. Police in Gaza Strip. (353) questionnaires were retrieved, with a recovery rate of (88.3%). Findings - The existence of a positive correlation between the determinants of organizational justice and the behavior of conscience, and the existence of organizational justice in general in a medium degree and the order of its determinants is as follows: fairness of dealings and to a large degree, followed by fairness of procedures and a medium degree, and finally distribution fairness was to a small degree, while the behavior of conscience awareness came to a large extent. Significance - The existence of statistically significant differences between the respondents' averages of responding to the searched relationship due to the variables (type of administration, age group, job title, military rank, years of service). Recommendations - Work to take into account the fairness of distribution by providing adequate and fair salaries, granting bonuses for the additional efforts exerted, and budgeting in the distribution of duties and job burdens, as well as recommending attention to the interests of employees when making decisions and involving them in formulating them, explaining their justifications, and creating an organizational climate dominated by a sense of justice. (shrink)

Abstract: This study aimed to identify the behavior of organizational citizenship in Palestinian Police Force between reality and expectations, and this study comes to study the reality of human resources and their organizational behavior in the police apparatus, which is the largest security services operating in Gaza Strip, so it is expected that this study will contribute to improving this This aspect will be reflected positively on serving the country and the citizen and achieving security and safety for them. The (...) study relied on the descriptive and analytical approach, using the questionnaire, and targeting a stratified random sample of (400) officers, who hold the rank of captain and above, from the study population of 1550 officers, and the study tool was distributed to the sample members in all departments and governorates Police in Gaza Strip. (353) questionnaires were retrieved, with a recovery rate of (88.3%), and they were analyzed using (SPSS) software. The study found that the organizational citizenship behavior was largely due to all its components. The study also showed that there are statistically significant differences between the respondents ’average response towards the organizational citizenship behavior due to the variables (type of administration, age group, job title, military rank, and years of service). The study recommended taking into account the interests of employees when making decisions and involving them in formulating them, explaining their justifications, and finally, the study recommended that organizational citizenship behavior be considered an organizational value that the police seek, and to create an organizational climate in which a sense of justice prevails, given its prominent role in stability, calm and discipline at work Thus, the elevation of the police institution and the achievement of its desired goals. (shrink)

Recently, due to the rapid progress of computer technology, researchers develop an effective computer program to enhance the achievement of the student in learning process, which is Intelligent Tutoring System (ITS). Science is important because it influences most aspects of everyday life, including food, energy, medicine, leisure activities and more. So learning science subject at school is very useful, but the students face some problem in learning it. So we designed an ITS system to help them understand this subject easily (...) and smoothly by analyzing it and explaining it in a systematic way. In this paper, we describe the design of an Intelligent Tutoring System for teaching science for grade seven to help students know the 7 characteristics for living things smoothly. The system provides all topics of living things and generates some questions for each topic and the students should answer these questions correctly to move to the next level. In the result of an evaluation of the ITS, students like the system and they said that it is very useful for them and for their studies. (shrink)

In this paper, we designed and developed an intelligent tutoring system for teaching Photoshop. We designed the lessons, examples, and questions in a way to teach and evaluate student understanding of the material. Through the feedback provided by this tool, you can assess the student's understanding of the material, where there is a minimum overshoot questions stages, and if the student does not pass the level of questions he is asked to return the lesson and read it again. Eventually this (...) administration is a special teacher for the students and can continue with him until he fully understands the lesson without weariness or boredom, regardless of the level of student. (shrink)

Abstract: Breast cancer is reported to be the most common cancer type among women worldwide and it is the second highest women fatality rate amongst all cancer types. Notwithstanding all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still not sufficient. In this paper, we propose an ANN model which outperforms all the previous supervised learning methods by reaching 99.57 in terms of accuracy in Wisconsin Breast Cancer dataset. Experimental results on Haberman’s Breast (...) Cancer Survival dataset show the superiority of proposed method by reaching 88.24 % in terms of accuracy. The results are the best reported ones obtained from Artificial Neural Network using JNN environment without any preprocessing of the dataset. (shrink)

Abstract: This study was aiming to determine the effect of the Vitamin A on paracetamol-induced hepatotoxicity. Fifteen rats were randomly divided into five groups; (three rats each) control group, paracetamol (1000 mg/kg body weight) was used to induce hepatotoxicity in albino rats. Vitamin A (Retinyl palmtate) administered at the dose levels of 100, 500 and1000 IU/kg body weights orally for 7 days prior to paracetamol dose. Hepatic toxicity was observed in paracetamol group; aspartate aminotransaminase (AST) level was high as compared (...) with control group (p = 0.003). The significant hepatoprotective effect of Vitamin A was observed at doses 500 and 1000 IU/kg body weight, as there was significant reduction of serum AST when compared to paracetamol group (p = 0.01 and 0.003 respectivly), where 100 IU/Kg vitamin A not significantly decrease AST. Liver trace element determined by X-ray fluorescence (XRF) (K, Ca, Fe, Cu, Zn and Rb) in paracetamol-treated group were found to be higher than that of the control group, although the only significant increase was observed with Cu. Br shows slight decrease compared to the control group. There were no significant differences between Vitamin A groups (100 mg/kg 500mg/kg and 1000mg/kg) and PCM-treated group in the liver tissue content of Ca, K, Zn, Cu, Rb and Br where Fe concentrations were significantly decrease at doses 500 and 1000IU/l (P value 0.05 and 0.045 respectively). Vitamin A shows protective effect by regulating liver tissue Fe. We recommend to perform further investigation on the mechanisms of Vitamin A hepatoprotective mechanisms of action. (shrink)

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. (shrink)

لطالما تُستخدم القراءات غير الدقيقة لتاريخ الأزهر في نقد واقع الأزهر وعلاقته بالدولة في العصر الراهن من جانب بعض المعارضين السياسيين أو بعض التيارات الإسلامية أو حتى بعض القيادات الأزهرية والأصوات الأكاديمية التي تدرس المجال الديني. حيث يشيع بين المصريين –على المستويات الشعبية والسياسية وحتى الأكاديمية-أن الأزهر قديمًا، وحتى بداية الخمسينيات من القرن الماضي، كان مستقلًا تمام الاستقلال عن الدولة، ومتحيزًا بشكل كامل للمجتمع ومصالحه وقضاياه، ومساندًا لأي احتجاجات شعبية أو مطالب اجتماعية من الشعب في مواجهة السلطة. ووصل الأمر لأن (...) يدعي البعض أن الأزهر كان ثوريًا كسمة رئيسية له خلال العصور الوسطى. تسعى هذه الدراسة إلى تقديم قراءة متعمقة لتاريخ الأزهر، ودور علمائه في المجال العام ومواقعهم المتغيرة على محور العلاقة بين الدولة والمجتمع، وذلك منذ افتتاحه الأول عام 362هـ في العصر الفاطمي، وصولًا إلى يناير 2011م/ 1432هـ. وتستخدم الدراسة إطارًا نظريًا لفهم وتحليل العلاقة بين الدولة والمجتمع ينطلق أساسًا من الخبرة التاريخية للمجتمعات الإسلامية خلال عصور ازدهار الحضارة الإسلامية، وهو اقتراب "المجال المشترك" الذي يفترض شكلًا نموذجيًا للتكوينات التي تُسهم في تحقيق الهدوء وتقليل الصراعات في العلاقة بين الدولة والمجتمع. وخلافًا للتحليل الشائع للوضع التاريخي للأزهر باعتباره مجرد "مؤسسة وسيطة" تنتمي للمجتمع وتُمثل مصالحه أمام السلطات؛ تجادل الورقة بأن الأزهر –في صورته المثالية– يُمثل دائرة تجتمع فيها المصالح المتباينة بين الدولة والمجتمع، ولا يستهدف مجرد الضغط لتحقيق مصالح فئة اجتماعية بعينها، كما هو حال المؤسسات الوسيطة؛ ولكنه يستهدف بالأساس تحقيق الهدوء في علاقات المجتمع والدولة، ومنع الانفجارات الثورية الدامية، من خلال دفع الدولة للرضوخ لبعض المطالب المجتمعية، مقابل منحها الشرعية اللازمة. (shrink)

Abstract: Fruits are a rich source of energy, minerals and vitamins. They also contain fiber. There are many fruits types such as: Apple and pears, Citrus, Stone fruit, Tropical and exotic, Berries, Melons, Tomatoes and avocado. Classification of fruits can be used in many applications, whether industrial or in agriculture or services, for example, it can help the cashier in the hyper mall to determine the price and type of fruit and also may help some people to determining whether a (...) certain type of fruit meets their nutritional requirement. In this paper, machine learning based approach is presented for classifying and identifying 10 different fruit with a dataset that contains 6847 images use 4793 images for training, 1027 images for validation and 1027 images for testing. A deep learning technique that extensively applied to image recognition was used. We used 70% from image for training and 15% from image for validation 15% for testing. Our trained model achieved an accuracy of 100% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

The study aims to identify the reality of management of electronic documents and electronic archiving retirement in the Palestinian Pension Agency -analytical study, as well as to recognize the reality of the current document management system in the Palestinian Pension Agency. The study found the following results: that the reality of the current system for the management of documents in the agency is weak and suffers from many jams. Employee in the agency understand the importance and benefits of the management (...) of electronic documents system, where the application of electronic document management system provide important features and benefits most of which reduce the loss of documents between departments, illustrates the flow path, the speed, accuracy, transparency, and reduce the proportion of damage and destruction of files. Furthermore, the electronic documents system cost will be less than the cost of the current system and it will reduce the tasks assigned for the staff. The existence of a clear adoption of the agency for the policies and procedures established for the application of electronic documents management system. There are weak plans for training and developing of staff in the agency to raise their efficiency. The study found a set of recommendations, including: increased interest and awareness of the need to implement policies, mechanisms, and procedures to ensure the success of electronic document management system through benefiting from the experiences of other organizations and the private sector. The agency need to increase and develop its services for retirees in order to encourage the private sector, universities, and institutions to join the agency, and open the way for all segments of society in Gaza and West Bank and enhance its competitiveness between international social security institutions. The need to focus its attention on developing and publishing appropriate clear plans and specific goals about management of electronic documents and the agency should be committed to apply them. The need to focus on the establishment of a public management of archiving in the structure dealing with all technical operations and having competent and qualified employees in the field of electronic document management. The need to focus on the Palestinian National Archives and the follow-up with the international standards by the International Council Archives (ICA). (shrink)

Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction is (...) based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Bilime ve bilimsel bilgiye yönelik yaygın görüş, bilimin objektif bir faaliyet olduğudur. Bu görüş bilimsel bilginin elde edilmesinde, bilim insanlarının nesnel bir tavır sergilediğini ve onların sosyal faktörlerden etkilenmediğini varsaymaktadır. Yirminci yüzyılın ikinci çeyreğinde, Viyana Çevresi ve Karl Popper’ın düşünceleri ile bilimde sosyolojik ve psikolojik unsurların keşif bağlamı içerisinde görülebileceği, bilimsel kuramların ve araştırmaların gerekçelendirilmesine yönelik girişimlerin ise yalnızca nesnel, epistemik çalışmalardan oluştuğu ileri sürülmektedir. Keşif bağlamı ve gerekçelendirme bağlamı adı altında yapılan bu ayrıma ilişkin iddialar, Thomas Kuhn’un 1962 yılında (...) yayımlanmış olan ‘Bilimsel Devrimlerin Yapısı’ adlı kitabında vurguladığı argümanlar ile sekteye uğramaktadır. Kuhn, sosyal ve psikolojik etkenlerin her iki bağlamda da yer aldığını ifade etmekte ve bu sebeple keşif ve gerekçelendirme ayrımına karşı çıkmaktadır. Bu çerçevede makalede, Kuhn’un ulaşmış olduğu sonuçlar Güçlü Program’ın öne sürmüş olduğu argümanlar çerçevesinde desteklenerek ortaya konulmaktadır. (shrink)

Abstract: Background: Hair loss is often a concern for women, men and children. It is normal to lose between 50 and 100 hairs a day, without us noticing it. Usually hair loss is a sign of a medical condition, some of them are hereditary, and some of them are temporary due to disease, stress, cancer treatment, weight loss, and iron deficiency. Objectives: The main goal of this expert system is to obtain an appropriate diagnosis of the cause of hair loss. (...) Methods: In this paper, the proposed expert system that was produced is designed to assist the physician in diagnosing hair loss disease such as seborrhea, lichen planus, ringworm, alopecia areata, thyroid disease and male-pattern baldness. The proposed expert system presents an overview about hair loss diseases are given, the cause of diseases are 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 hair loss diseases diagnosis expert system was evaluated by Medical students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for hair loss treatment physician, patients with hair loss problems and newly graduated physician. (shrink)

Abstract: Lemons are an important fruit that have a wide range of uses and benefits, from culinary to health to household and beauty applications. Deep learning techniques have shown promising results in image classification tasks, including fruit quality detection. In this paper, we propose a convolutional neural network (CNN)-based approach for detecting the quality of lemons by analysing visual features such as colour and texture. The study aims to develop and train a deep learning model to classify lemons based on (...) their quality, evaluate the model's performance, compare it to traditional machine learning approaches, and identify key factors that affect the model's performance. The dataset used in the study consists of approximately 2533 images of lemons and an empty surface, divided into training, test, and validation data, and includes images of both good and bad quality lemons under different lighting conditions. The deep learning model achieved an accuracy of 99.74% on the test dataset and outperformed traditional machine learning approaches. The developed model has the potential to improve efficiency and accuracy in lemon quality control in the agriculture and food industry. (shrink)

Abstract: Classifying sea animals is an important problem in marine biology and ecology as it enables the accurate identification and monitoring of species populations, which is crucial for understanding and protecting marine ecosystems. This paper addresses the problem of classifying 19 different sea animals using convolutional neural networks (CNNs). The proposed solution is to use a pretrained MobileNetV2 model, which is a lightweight and efficient CNN architecture, and fine-tune it on a dataset of sea animals. The results of the study (...) show that the fine-tuned MobileNetV2 model is able to achieve an accuracy of 85% on the classification task, which is competitive with the state-of-the-art methods. Additionally, the study also found that data augmentation and regularization techniques such as dropout were important for preventing overfitting and improving the performance of the model. This study demonstrates the potential of using pretrained models and efficient CNN architectures for classifying sea animals, and it provides insights into the challenges and best practices for solving this important problem in marine biology and ecology. (shrink)

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. (shrink)

The aim of the research is to identify the role of policies and procedures for the electronic document management system in the success of the electronic document management system in the Palestinian Pension Agency. In order to achieve the objectives of the study, the researchers used the analytical descriptive method in which the tries to describe the phenomenon studied, analyze its data, the relationship between its components and the opinions that are raised around it, and use the comprehensive inventory method (...) due to the small size of the society. (108) questionnaires were distributed to the community of 65 employees in Gaza Strip and 43 employees in the West Bank. All the responses were retrieved. The research found that there is a clear adoption by the Commission of the policies and procedures put in place to implement the electronic management system of electronic documents. The study showed that there are no statistically significant differences between the responses of the study population regarding the role of policies and procedures for the electronic document management system in the success of the electronic document management system in the Palestinian Pension Agency due to variable (age, nature of job, specialization). And showed that there is a fundamental difference in the opinion of members of the study community on the role of policies and procedures for the electronic document management system in the success of the electronic document management system in the Palestinian Pension Agency attributed to the scientific qualification, and shows the difference in favor of the bachelor's degree. And years of service, and shows the difference in favor of the 11-15 year category. The research led to a number of recommendations, the most important of which is the increase of interest and awareness of the need to implement policies and procedures to ensure the success of the electronic document management system through access to and benefit from the experiences of other institutions and the private sector. And the need to focus on the attention to develop appropriate and clear plans and dissemination on the electronic document management system and specific to specific objectives and the Commission is committed to apply them, the need to pay attention to the development of strategic plans and policies and work mechanisms commensurate with the electronic document management system, and the need to define a policy in advance to form a team of different specialties of the staff of the Authority and cooperation with consultants from outside the body to work on the electronic document system. (shrink)

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying (...) PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%. (shrink)

Abstract: Context: grapevine leaves are an important agricultural product that is used in many Middle Eastern dishes. The species from which the grapevine leaf originates can differ in terms of both taste and price. Method: In this study, we build a deep learning model to tackle the problem of grape leaf classification. 500 images were used (100 for each species) that were then increased to 10,000 using data augmentation methods. Convolutional Neural Network (CNN) algorithms were applied to build this model (...) specifically using the pre-trained model on top of the VGG16 architecture. Then, dense layers were added to classify the output of the Convolutional layers and classify outputs to the five classes (species) the leaf belonged to. Results: It was found that feature extraction without fine-tuning the convolutional layers yielded poor results, about 86% accuracy, while training the whole network along with some data preprocessing gave the best results, about 99.45% accuracy on the testing dataset. Conclusions: The proposed CNN model is an effective one for the problem of classification of grape leaf species. (shrink)

Abstract: The Knowledge-Based System for Diagnosing Breast Cancer aims to support medical students in enhancing their education regarding diagnosis and counseling. The system facilitates the analysis of biopsy images under a microscope, determination of tumor type, selection of appropriate treatment methods, and identification of disease-related questions. According to the Ministry of Health's annual report in Gaza, there were 7,069 cases of breast cancer between 2009 and 2014, with 1,502 cases reported in 2014. In an era dominated by visual information, where (...) 65% of the population are visual learners, the Knowledge-Based System serves as an effective tool to aid long-term information retention by utilizing visual and textual resources. The system features a user-friendly interface, enabling students to diagnose the disease and expand their understanding. Furthermore, it can be accessed via smartphones. The Knowledge-Based System comprises three primary components: Firstly, it provides specific patient-oriented questions to familiarize students with the questioning process. Secondly, it facilitates the analysis of biopsy sample images to determine their integrity and identify potential infections. Lastly, the system incorporates animated videos illustrating various approaches for treating breast cancer. (shrink)

Abstract: Rice is one of the most important staple crops in the world and serves as a staple food for more than half of the global population. It is a critical source of nutrition, providing carbohydrates, vitamins, and minerals to millions of people, particularly in Asia and Africa. This paper presents a study on using deep learning for the classification of different types of rice. The study focuses on five specific types of rice: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. A (...) dataset of 75,000 images was collected and annotated, consisting of 15,000 images for each of the five types of rice. The deep learning system uses convolutional neural networks (CNNs) to classify images of rice grains based on their physical characteristics. The performance of the deep learning system was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that the proposed deep learning system was able to achieve high accuracy in classifying the different types of rice, outperforming traditional image processing techniques. This study provides valuable insights into the potential of deep learning for classifying different types of rice and can be useful for applications such as quality control in rice processing industries and research in crop science. The large size of the dataset, 15,000 images for each type of rice, helped the deep learning system to learn the features of each type of rice, and increase the performance of the classifier. The attained overall accuracy of the proposed model (99.96%), Precision (99.96%), Recall (99.96%) and F1-score (99.96%). Thus, the proposed model proved that it leaned the five categories of rice and it can generalize these categories and it is effective. (shrink)

Abstract: malaria is one of the most frequent hemoparasitic infections in tropical and sub-tropical countries. Malaria in Sudan is the major public health problem. This study was aimed to investigate the association of intraleukocytic pigment with malaria infection (severity, diagnosis and prognosis), and to investigate the correlation of parasite density levels with Malaria Severity. A total of 176 participants was drawn from the population of sudanese patient above 5 years, who attended or were admitted to the Bashayer Hospital, with diagnosis (...) suggestive of malaria, they were selected for inclusion in the study. Blood films were examined first for malaria parasites diagnosis ,this was followed by detection of malaria pigment in both negative and positive films.Then Immunochromatography test was done, Subsequently haemoglobin concentration was determined. This study was approves that malaria pigment in peripheral blood leukocytes is evident for malaria disease , and makes the method of pigment determination appropriate and useful in malaria diagnosis, especially in patients with an illness consistent clinically with malaria but with negative blood smear due to haemolytic anaemia .A total of 176, 73 male and 103 female aged from 7 years to 69 years , drawn to Bashayer hospital with symptoms of malaria showed ICT positive result .Blood films were positive in 98(55.7%) and negative in 78 (44.3%) of patients . Malaria pigment was observed with the mean (22.07). There was an association between ICT and malaria pigment (P-value = 0.000).There was no association between BF and malaria pigment ( P-value = 0.40).This study also approved that malaria pigment can be used in prognosis, were thirty five patient showed complications(19.9%) and 141 showed no complication(80.1%) ,there was association between complications and malaria pigment ( P-value = 0.004) .This study also validates the presence of malaria pigment in leukocyte as a marker for disease severity, there was association between severity and malaria pigment (P-value = 0.02). While there is no association between density and severity (P-value = 0.980). So we conclued, intraleukocytic malaria pigment produced by parasites during intra erythrocytic development is associated with severe disease, mortality and it is a useful diagnostic indicator in anaemic patients with negative blood smears. The varied complexities of the current diagnostic methods make the method of pigment determination appropriate and useful. Secondly, malaria pigment is significantly associated with severe malaria. Thirdly, parasitaemia levels are neither associated nor correlated with malaria severity and therefore parasitaemia alone is not a reliable measure of malaria severity. We recommend the use of malaria pigment as marker for malaria disease severity. We recommend that parasitaemia should not be used alone as an indicator of malaria severity. We further recommend that, the presence of pigment in leukocytes to be adopted for diagnosis of malaria especially in cases of negative blood films, and recommend using malaria pigment as indicator for malaria prognosis. (shrink)

Abstract: This study aimed to analyze the relationship between the determinants of organizational justice and altruistic behavior from the point of view of the police officers in Gaza Strip. The study relied on the descriptive and analytical approach, using the questionnaire, and targeting a stratified random sample of (400) officers, who hold the rank of captain and above, from the study population of 1550 officers, and the study tool was distributed to the sample members in all departments and governorates Police (...) in Gaza Strip. (353) questionnaires were retrieved, with a recovery rate of (88.3%). The study found the existence of a positive correlation between the determinants of organizational justice and altruistic behavior, and the study also found the existence of organizational justice in general in a moderate degree and the order of its determinants was as follows: fairness of dealings and to a large degree, followed by fairness of procedures and a medium degree, and finally, distribution fairness was to a small degree, as for altruistic behavior. It came to a large extent. There are statistically significant differences between the respondents ’average response to the researched relationship due to the variables (Type Of Administration, Age Group, Job Title, Military Rank, and Years of Service). Taking into account the fairness of distribution by providing appropriate and fair salaries, granting bonuses for the additional efforts exerted, and budgeting in the distribution of duties and job burdens, and paying attention to the interests of employees when making decisions and involving them in formulating them, explaining their justifications, and considering altruistic behavior as a value, and the creation of an organizational climate dominated by a sense of justice, because of its prominent role in stability, calm and discipline at work, and thus the elevation of the police institution and the achievement of its desired goals. (shrink)

In this paper, we present an intelligent tutoring system developed to help students in learning Computer Theory. The Intelligent tutoring system was built using ITSB authoring tool. The system helps students to learn finite automata, pushdown automata, Turing machines and examines the relationship between these automata and formal languages, deterministic and nondeterministic machines, regular expressions, context free grammars, undecidability, and complexity. During the process the intelligent tutoring system gives assistance and feedback of many types in an intelligent manner according to (...) the behavior of the student. An evaluation of the intelligent tutoring system has revealed reasonably acceptable results in terms of its usability and learning abilities are concerned. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

This research paper presents the development and evaluation of an expert system for diagnosing kidney diseases. The expert system utilizes a decision-making tree approach and is implemented using the CLIPS and Delphi frameworks. The system's accuracy in diagnosing kidney diseases and user satisfaction were evaluated. The results demonstrate the effectiveness of the expert system in providing accurate diagnoses and high user satisfaction.

Background: Depression (major depressive disorder) is a common and serious medical illness that negatively affects how you feel, the way you think and how you act. Fortunately, it is also treatable. Depression causes feelings of sadness and/or a loss of interest in activities once enjoyed. It can lead to a variety of emotional and physical problems and can decrease a person’s ability to function at work and at home. Depression affects an estimated one in 15 adults (6.7%) in any given (...) year. And one in six people (16.6%) will experience depression at some time in their life. Depression can strike at any time, but on average, first appears during the late teens to mid-20s. Women are more likely than men to experience depression. Some studies show that one-third of women will experience a major depressive episode in their lifetime. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment and give the appropriate method of treatment through several tips that concern the disease and how to treat it and we will see it through the application on the expert system. Methods: in this paper the design of the proposed Expert System which was produced to help Psychologist in diagnosing depression disease through its symptoms such as: a loss of energy, a change in appetite, sleeping more or less, anxiety, reduced concentration, indecisiveness, restlessness, feelings of worthlessness, guilt or hopelessness and thoughts of self-harm or suicide. The proposed expert system presents an overview about depression disease is given, the cause of diseases is outlined and the treatment of disease whenever possible is given out. SL5 Object Expert System language was used for designing and implementing the proposed expert system. Results: The proposed depression disease diagnosis expert system was evaluated by psychologist students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for psychologist, patients with depression and newly graduated psychologist. (shrink)

There is no doubt that custard apple diseases are among the important reasons that destroy the Custard Apple plant and its agricultural crops. This leads to obvious damage to these plants and they become inedible. Discovering these diseases is a good step to provide the appropriate and correct treatment. Determining the treatment with high accuracy depends on the method used to correctly diagnose the disease, expert systems can greatly help in avoiding damage to these plants. The expert system correctly diagnoses (...) Custard Apple disease to make it easier for farmers to find the right treatment based on the appropriate diagnosis. Objectives: A specialized syllable language system was established for the diagnosis of Custard Apple plant disease. (shrink)

The paper describes the design of an intelligent tutoring system for teaching Introduction to Computer Science-a compulsory curriculum in Al-Azhar University of Gaza to students who attend the university. The basic idea of this system is a systematic introduction into computer science. The system presents topics with examples. The system is dynamically checks student's individual progress. An initial evaluation study was done to investigate the effect of using the intelligent tutoring system on the performance of students enrolled in computer science (...) curriculum at Al-Azhar University, Gaza. The results showed a positive impact on the evaluators. (shrink)

Background: Obstetrics and gynaecology are many and common, where a woman suffers from problems related to pregnancy or her reproductive organs. Any part of her body may be affected due to some symptoms that are completely related to the reproductive organs when she is in a critical period for her, whether in her menstrual cycle, pregnancy, or disease conditions. The bulk of cases of diseases related to women and childbirth are dealt with great care and special care, as all diseases (...) related to women are considered very sensitive diseases due to the presence of the disease in sensitive and not simple places. A gynaecologist is a specialist in all diseases and problems related to the female sexual organs. They perform regular preventive medical exams, such as cervical smear tests and breast exams. It also provides consultations for women of all ages about the problems of contraception, infertility and menopause. Objectives: The objective of this expert system is to help and facilitate women in diagnosing diseases related to their obstetrics and gynaecology, a simple expert system has been designed consisting of ten various common diseases that affect women whether During or without pregnancy, Methods: In this system, the system consists of a list of some common symptoms, the correct diagnosis adopted for these diseases, and how to treat diseases in the correct way with the help of a consultant obstetrician and gynaecologist, Dr. Fathi Muhammad Al-Habibi, and these diseases are: uterine cancer, cervical cancer, Infertility, double uterus, ectopic pregnancy, endometrial cancer, female sexual dysfunction, endometriosis, faecal incontinence, female infertility, CLIPS Expert System language was used to design and implement the proposed expert system. Results: The proposed ten obstetrics and gynaecology diagnostic expert system was evaluated by medical students and they were satisfied with its performance. Conclusions: The proposed expert system is very useful for obstetricians and gynaecologists, patients with reproductive system problems, and recent graduates. (shrink)

Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive 99.27% (...) and an average error of 0.0034, underscoring the potency of deep learning in tackling complex, real-world challenges. Furthermore, our study delved into feature importance analysis, identifying critical determinants such as HIV/AIDS prevalence, income composition of resources, and socioeconomic status that significantly influence life expectancy. These findings provide actionable insights for healthcare policymakers and practitioners, emphasizing the importance of addressing health disparities, promoting economic development, and implementing targeted interventions. This research bridges the gap between data-driven methodologies and global health, offering a robust predictive tool and enriching our understanding of the multifaceted dynamics that shape life expectancy across nations. (shrink)