In many areas, technology has facilitated many things, diagnosing diseases, regulating traffic and teaching students in schools rely on Intelligent systems to name a few. At present, traditional classroom-based education is no longer the most appropriate in schools. From here, the idea of intelligent e-learning for students to increase their culture and keep them updated in life began. E-learning has become an ideal solution, relying on artificial intelligence, which has a footprint in this through the development of systems based on (...) education without a teacher taking into account the individual differences of students. This study describes intelligent tutoring system for teaching the Data Manipulation Language (DML) Commands Using SQLite to non IT students to overcome the difficulties they face. The System was built as education system by using the researcher own authoring tool that based on mobile and web App. The basic idea of this system is a systematic introduction into the concept of Database and its DML query. The system presents the topic of DML Commands, help student to execute what they are learn from the system content using the practical part of the system and administers automatically adapted at run time to the student’s individual growth. The system was assessed by a group of teachers and students and the outcome showed as 91% from the database specialists, 94% from Diploma students group and 89% from the secondary school students group. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative study method is (...) suitable for the evaluation of car acceptability forecasting, can also be extended to all other areas. (shrink)

Abstract: Background: In spite of the fact that computers continue to improve in speed and functions operation, they remain complex to use. Problems frequently happen, and it is hard to resolve or find solutions for them. This paper outlines the significance and feasibility of building a desktop PC problems diagnosis system. The system gathers problem symptoms from users’ desktops, rather than the user describes his/her problems to primary search engines. It automatically searches global databases of problem symptoms and solutions, and (...) also allows ordinary users to contribute exact problem reports in a structured manner. Objectives: The main goal of this Knowledge Based System is to get the suitable problem desktop PC symptoms and the correct way to solve the errors. Methods: In this paper the design of the proposed Knowledge Based System which was produced to help users of desktop PC in knowing many of the problems and error such as : Power supply problems, CPU errors, RAM dumping error, hard disk errors and bad sectors and suddenly restarting PC. The proposed Knowledge Based System presents an overview about desktop PC hardware errors are given, the cause of fault are outlined and the solution to the problems whenever possible is given out. CLIPS Knowledge Based System language was used for designing and implementing the proposed expert system. Results: The proposed PC desktop troubleshooting Knowledge Based System was evaluated by IT students and they were satisfied with its performance. (shrink)

Abstract: This research delves into the utilization of Artificial Neural Networks (ANNs) as a powerful tool for predicting the overall ratings of books by leveraging a diverse set of attributes. To achieve this, we employ a comprehensive dataset sourced from Goodreads, enabling us to thoroughly examine the intricate connections between the different attributes of books and the ratings they receive from readers. In our investigation, we meticulously scrutinize how attributes such as genre, author, page count, publication year, and reader reviews (...) influence a book's overall rating. Through rigorous analysis and experimentation, we construct an advanced ANN model tailored for predictive analysis in the realm of book ratings. The outcomes of our study reveal the remarkable potential of ANNs in this domain. The ANN model exhibits an impressive level of accuracy when it comes to forecasting book ratings, underlining the efficacy and promise of artificial neural networks in enhancing our understanding and prediction of book evaluations. This research opens up new avenues for leveraging machine learning techniques to gain deeper insights into the dynamics of book ratings and reader preferences. (shrink)

Abstract: Pistachio nuts are a valuable source of nutrition and are widely cultivated for commercial purposes. The accurate classification of different pistachio varieties is important for quality control and market analysis. In this study, we propose a new model for the classification of different pistachio varieties using Convolutional Neural Networks (CNNs). We collected a dataset of pistachio images form Kaggle depository with two varieties (Kirmizi and Siirt). The images were then preprocessed and used to train a CNN model based on (...) VGG16. We evaluated the performance of the VGG16 on a held-out test set. Our results show that the CNN-based model VGG16 performed very well in terms of accuracy and efficiency. The VGG16 model was able to accurately classify different pistachio varieties with an accuracy of over 99.91%. Additionally, the VGG16 was able to generalize well to new and unseen data, demonstrating its ability to handle variability in the pistachio images. This study highlights the potential of CNNs for the classification of different pistachio varieties, and has important implications for the pistachio industry. The results suggest that this approach can be used to improve the quality control process and enhance market analysis. (shrink)

In computer science, Artificial Intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Deep Learning is a new field of research. One of the branches of Artificial Intelligence Science deals with the creation of theories and algorithms that (...) allow the machine to learn by simulating neurons in the human body. Most in-depth learning research focuses on finding high-level methods. The strippers analyze a large data set using linear and nonlinear transformations. The method of deep learning is used in the detection of several diseases including blood cell diseases and their classification using the radiography of blood cells to help decision makers to know the type of blood cell and its associated diseases and the results will be presented in detail and discussed. This thesis is using python language and deep learning to detect blood cell diseases and their classifications. The proposed deep learning model was trained, validated and the tested. The accuracy of proposed model was 98.00%. (shrink)

Abstract: The efficiency and performance of sorting algorithms play a crucial role in various applications and industries. In this research paper, we present a comprehensive comparative analysis of popular sorting algorithms on datasets of different sizes and characteristics. The aim is to evaluate the algorithms' performance and identify their strengths and weaknesses under varying scenarios. We consider six commonly used sorting algorithms: QuickSort, TimSort, MergeSort, HeapSort, RadixSort, and ShellSort. These algorithms represent a range of approaches and techniques, including divide-and-conquer, hybrid (...) sorting, and simple comparison-based methods. To assess their performance, we employ a diverse set of datasets, including the Iris dataset (1K), student dataset (5.8K), Wine dataset (6.5K), Uniform (10K), Normal (10K), Exponential (10K), Bimodal (10K), Yelp dataset (10K), MNIST dataset (42K), Uniform (100K), Normal (100K), Exponential (100K), Bimodal (100K), Uniform (500K), Normal (500K), Exponential (500K), Bimodal (500K), Uniform (1M), Normal (1M), Exponential (1M), and Bimodal (1M). These datasets cover a wide range of sizes and characteristics, allowing us to analyze the algorithms' performance across different dimensions. We measure and compare several key metrics, including execution time, memory usage, algorithmic complexity and stability. By analyzing these metrics, we gain insights into the efficiency and suitability of each algorithm for different dataset sizes and characteristics. We also discuss the implications of the findings in practical applications. Our results reveal important trade-offs among the sorting algorithms. While some algorithms excel in certain scenarios, others demonstrate better scalability or memory efficiency. We identify the best-performing algorithms for specific dataset characteristics and highlight their strengths and limitations. This research can assist developers and practitioners in selecting appropriate sorting algorithms based on their specific requirements and dataset characteristics. In conclusion, this comparative analysis provides a valuable contribution to the understanding of sorting algorithm performance. The findings contribute insights into the efficiency and suitability of popular sorting algorithms across datasets of different sizes and characteristics. By evaluating key metrics and discussing the implications, we offer guidance for selecting the most appropriate sorting algorithm in various practical scenarios. (shrink)

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing attacks (...) are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (shrink)

Abstract: Accurate stock price prediction is essential for informed investment decisions and financial planning. In this research, we introduce an innovative approach to forecast stock prices using an Artificial Neural Network (ANN). Our dataset, consisting of 5582 samples and 6 features, including historical price data and technical indicators, was sourced from Yahoo Finance. The proposed ANN model, composed of four layers (1 input, 1 hidden, 1 output), underwent rigorous training and validation, yielding remarkable results with an accuracy of 99.84% and (...) an average error of 0.005. Additionally, we conducted a feature importance analysis, identifying the key drivers of stock price prediction, such as "High," "Low," "Open," "Volume"," and "Date." This study not only offers a highly accurate stock price prediction model but also contributes valuable insights into the influential factors affecting stock prices, enhancing the field of financial analysis and investment decision-making that are suitable for the task. (shrink)

This study employs a neural network to analyze the connection between smoking, drinking, and various health-related factors using a dataset of 5148 samples. Achieving an impressive 99.94% accuracy and an average training error of 0.0016, the model identifies influential factors such as serum aminotransferases, serum creatinine, sex, weight, and triglyceride levels. These findings enhance our understanding of lifestyle choices and their impact on health. This research underscores the potential of machine learning in studying complex health phenomena.

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)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

The study aimed at measuring the dominant pattern of leadership and its relation to the performance of the administrative staff in the Palestinian universities. The study community consists of all the administrative staff from Al-Azhar University and the Islamic University, and through the census of the study society it was found to consist of (655) administrative staff. In order to achieve the objectives of the study, the researchers used the method of random sample in the study, and the study was (...) conducted on a sample of (221) administrative staff of the surveyed universities and the response rate was (82.35%). The study found that there is a high degree of satisfaction with the Dominant Pattern of Leadership in the Palestinian universities in Gaza Strip from the point of view of the administrative staff, which reached (71.67%). There is a high level of performance from the point of view of administrative staff, with a percentage of (82.53%). There is a direct correlation between the dominant pattern of leadership and the functional Performance of Administrative Staff, the absence of differences between the sample according to the gender variable in their perception of the dominant pattern of leadership and the functional Performance of Administrative Staff, the absence of differences in the perception of employees of the dominant pattern of leadership and the functional Performance of Administrative Staff depending on the age variable. There are differences of statistical significance according to the variable of scientific qualification in the Dominant Pattern of Leadership, while there were no differences in performance, the differences in the dominant pattern of leadership according to the scientific qualification were in favor of those who obtained the diploma degree compared to other practical qualifications, the absence of differences in the perception of employees of the dominant pattern of leadership and the functional Performance of Administrative Staff depending on the variable years of service, the absence of differences in the perception of employees of the dominant pattern of leadership and the functional Performance of Administrative Staff depending on the variable level of career (Director, Head of Department, and Administrative Officer). There were differences in the perception of workers to the dominant pattern of leadership depending on the variable of the workplace, where there were differences in favor of workers in deanships and colleges compared with the workers in the technical departments. While there were no differences in job performance according to the workplace variable, the existence of differences in the perception of workers to the dominant pattern of leadership depending on the university in which they work in favor of the Islamic University and the absence of differences in performance between the Islamic University and Al-Azhar University. The study reached a number of recommendations, the most important of which is that the interest of the managements of the Palestinian universities in improving the dominant pattern of leadership, to give universities the opportunity to participate in decision-making, the continued administration of universities interest and continuous improvement of the performance of its employees, enhance the periodic evaluation of job performance and to inform employees and express their opinion, solving workers' problems and giving them the opportunity to contribute to solving their own problems, the use of the staff rotation method periodically and strengthening the democratic the dominant pattern of leadership and empowering university staff. -/- . (shrink)

The study aimed to know the relationship between the nature of the work and the type of communication among the Employees in the Palestinian universities. A comparative study between Al-Azhar University and Al-Aqsa University. The researchers used the analytical descriptive method through a questionnaire that is randomly distributed among the employees of Al-Azhar and Al-Aqsa universities in Gaza Strip. The study was conducted on a sample of (176) administrative employees from the surveyed universities. The response rate was (85.79%). The study (...) reached a number of results, the most important of which is that there is a high degree of satisfaction with the nature of work prevailing in the Palestinian universities in Gaza Strip from the point of view of the administrative staff, where the percentage was (68.15%). There is an Mean level of communication from the point of view of administrative staff, with a percentage of (67.50%). There is a direct correlation between the nature of the work and the prevailing pattern of communication. There is an absence of differences between the sample according to the gender variable in their perception of the nature of work and the prevailing pattern of communication. There is an absence of differences in the perception of Employees nature of work and the pattern of communication prevailing depending on the variables (age, years of service, job level, and university). There are statistically significant differences between Al-Azhar University and Al-Aqsa University in favor of Al-Azhar University. The study reached a number of recommendations, the most important of which is that the interest of the management of the Palestinian universities in Gaza Strip in general, and Al-Aqsa and Al-Azhar Universities in particular should be provided with a good nature of work and communication. There is a need for continuing the management of universities to pay attention and continuous improvement of the performance of employees. There is an importance of solving the problems of Employees and giving them the opportunity to contribute to solving their own problems. Staff rotation should be used periodically and the need to strengthen the democratic leadership style and empower university Employees. (shrink)

Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the (...) nuanced dynamics of student performance. The key influential features identified in this study offer valuable insights into the complex factors shaping student achievement. These insights are vital for educators, policymakers, and institutions seeking to enhance educational outcomes and promote equitable access to quality education. This research provides a data-driven foundation for proactive interventions, personalized learning strategies, and support systems, ultimately contributing to improved student performance and academic success. The high accuracy of the predictive model and the feature analysis it provides empower decision-makers in the education sector. This model holds significant potential for applications in student performance monitoring, early intervention, and tailored educational strategies. By adopting a data-driven approach, this work advances the field of educational analytics and contributes to the goal of fostering student success and educational equity. (shrink)

Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...) helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task. (shrink)

Our gums (gingiva) act as a significant barricade in guarding our teeth and their neighboring support structures. A tiny known fact is that gum disease is the foremost cause of tooth loss in grown persons. Healthy, strong, teeth are reliant on on healthy gums. The core culprit for gum problems is bacteria in dental plaque. The bacteria in plaque yield harmful toxins that generate an inflammatory process that happens in the gum tissue. If left for a sufficient period of time, (...) bacterial plaque causes hurt to our teeth as well as our gums. In this paper, we present a knowledge based system that help people with teeth and gums problems to diagnose their problems and get recommendation for the treatment. This expert system was designed and implemented using SL5 Object language for knowledge based system. (shrink)

Organizations are increasingly finding it challenging to balance economic and environmental performance particularly those that face competitive, regulatory and community pressure. With the increasing pressures for environmental sustainability, this calls for the new formulation of strategies by the manufacturers in order to minimize their products and services negative impact on the environment. Hence, Green Human Resource Management (GHRM) continues to be an important research agenda among the researchers. In Palestine, green issues are new and still developing. Constant study is needed (...) to fully understand and update information regarding this area. Objective: The aim of this paper is to explore the views and level of acceptance of GHRM practices among manufacturing firms in Palestine. Results: Through the use of e-mail survey, 121 responses were obtained to generate the results of the study. The result showed GHRM practices have been practiced to somewhat to a greater extent a firms in Palestine. Findings can be extended to study on the issues in further. Academicians and practitioners can apply this result to their research and business strategies on how to improve sustainable performance and to effectively implement GHRM practices. (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)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying the (...) presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City with 97.50 (...) % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

The study aimed to identify the availability of the resource and partnership standard as one of the possibilities of excellence in Palestinian universities according to the European model. The study used the analytical descriptive method. The study was conducted on the university leadership at Al - Azhar and Islamic Universities, where the study population consisted of (282) individuals. The study sample consisted of (135) individuals, (119) of them responded, and the questionnaire was used in collecting the data. The study has (...) reached a number of results, the most important of which is the existence of a high standard of resources and partnership in the Palestinian public universities operating in the southern governorates. The fields of the resource and partnership criteria were as follows: (79.8%) management of internal and external partnerships; management of technical resources (technology) (76.4%), Buildings, equipment and resources (76%), Finance Management (72.8%). The study presented a number of recommendations, the most important of which is: Increasing the interest of universities in applying the criteria of resources and partnership as one of the criteria of excellence, increasing the interest of universities in managing finance and obtaining funding for their various activities, periodic maintenance of buildings and equipment and ensuring their suitability to the requirements of the educational process. (shrink)

The study aimed at identifying the intermediate role of the criterion of emphasis on students and beneficiaries in the relationship between adopting the criterion of partnership and resources and achieving the satisfaction of the society. The study used the analytical descriptive method. The study was conducted on university leadership in Al-Azhar, Islamic and Al-Aqsa Universities. The sample of the study consisted of (200) individuals, 182 of whom responded, and the questionnaire was used in collecting the data. The study reached a (...) number of results, the most important of which were: The level of adoption by the Palestinian universities of the criterion of partnership and resources came to a large extent. The level of adoption by the Palestinian universities of the criterion of emphasis on the beneficiary students was very high. The criterion of partnership and resources to achieve the satisfaction of the community surrounding the Palestinian universities, the existence of a statistically significant impact of the adoption of the criterion of partnership and resources to strengthen the focus on students benefiting in Palestinian universities, The focus on the students benefiting from the satisfaction of the community surrounding the Palestinian universities, the existence of a statistically significant impact on the adoption of the criterion of partnership and resources to achieve the satisfaction of the community surrounding the Palestinian universities, The study presented a number of recommendations, most important of which are: Increase interest in the resource and partnership criterion through enhancing the university's resources and developing internal and external partnerships to serve the university and the society. Increase the focus on students and beneficiaries by providing an appropriate educational environment and academic programs that meet the needs of the labor market. Through the strengthening of existing community service centers and the establishment of new community centers, strengthening the partnership of industrial companies and promoting scientific research for the benefit of universities and society. -/- . (shrink)

The study aimed to identify the intermediate role of the standard of operations in the relationship between the focus on students and beneficiaries in achieving satisfaction of students in Palestinian universities. The study used the analytical descriptive method. The study was conducted on university leadership in Al-Azhar, Islamic and Al-Aqsa Universities. The study sample consisted of (200) individuals, 182 of whom responded, and the questionnaire was used in collecting the data. The results of the study were as follows: - There (...) is a statistically significant effect on the adoption of the criterion of focus on the students benefiting to achieve the satisfaction of students in the Palestinian universities, where the value of R Square (35.3%), there is a statistically significant effect on the adoption of the criterion of focus on the students benefiting on the use of the standard of operations in Palestinian universities. The value of the R Square is (62.8%), there is a statistically significant impact of the criterion of operations on the satisfaction of students in Palestinian universities. Where the value of the R is (36.2%), there is a significant effect on the adoption of the criterion of emphasis on students benefiting from the satisfaction of students in Palestinian universities with the existence of the standard of operations as an intermediary variable. The role of the mediator in the relationship between the adoptions of the criterion of focus on the students benefiting to achieve the satisfaction of students in Palestinian universities was revealed through the use of Path Analysis. The study presented a number of recommendations, the most important of which is: to increase the focus of the universities on students and beneficiaries in a manner appropriate to achieve the objectives of the university on the one hand, and students and beneficiaries on the other, and in line with the approach of quality and excellence, the universities more attention to the management of operations through the design and development of programs Academic and internal services according to the needs of students, and improving processes in innovative ways to generate added value for students and beneficiaries, the universities have made more efforts to create satisfaction among students and beneficiaries by developing internal performance indicators to measure student satisfaction, Students and other university beneficiaries periodically to improve their satisfaction. (shrink)

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...) large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. (shrink)

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The (...) evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (shrink)

Abstract: Background: Tuberculosis is a major public health problem world-wide, it is contagious disease caused by organism mycobacterium tuberculosis. The aim of this study was to assess the level of zinc and albumin in newly diagnosed patients with pulmonary tuberculosis in Khartoum state. Methods: Fifty blood samples were collected from newly diagnosed pulmonary tuberculosis patients during the period between April to May 2017, chosen randomly from Abu Anga Teaching Hospital and fifty blood samples from apparently healthy individuals serve as control (...) group. Estimation of serum albumin level was performed using spectrophotometer and estimation of serum zinc by using flame atomic absorption spectrophotometer. The results were analyzed by using (SPSS) computer program. Results: The study showed that, serum level of zinc and albumin were significantly decrease in patients compared to control group (mean± SD for cases versus control). For zinc :( 0.16±0.05 versus 0.58± 0.13 mg/l, P. value= 0.000). For albumin: (2.5±0.58 versus 4.1±0.31g/dl, P. value= 0.000). The result of this study showed that, there was statically significant decrease in the mean of BMI in patients compared to control group. Mean ±SD for case versus control (18.7±0.17 versus 21.5±1.7). The result of this study showed that, pulmonary tuberculosis most common among age between (20-39) years (52%) and (48%) of patients between age (40-58) years and this disease most abundant in males (78%) than females (22%). Persons correlation showed that there was no correlation between serum zinc level and age (r =-0.128, p. value=0.374) also there was no correlation between the level of serum albumin and age (r=0.137, p. value =0.344). Conclusion: The serum levels of zinc and albumin were significantly decreased in newly diagnosed patients with pulmonary tuberculosis in Khartoum state. (shrink)

Abū Ishāq al-Ṣaffār was one of scholars of the Western Qarakhānids’ period who followed the Kalām thought of al-Māturīdī (d. 333/944). His theological works Talkhīs al-adilla and Risāla fī al-kalām, his method in kalām, and frequent reference to his works by Ottoman and Arab scholars indicate that al-Ṣaffār is a respected and authorative Māturīdī theologian. The article focuses on his defense of the kalām. By adding a long introduction to Talkhīs about the naming, importance, and religious legitimacy of the science (...) of kalām, Saffār asserted that the kalām should be learned. When systematical vindication of the science of kalām is examined, it is understood that al-Ṣaffār is the first theologian who reserved a private and voluminous part for defensing the kalām among Ḥanafī-Māturīdīs. Even though he does not state its systematic, it can be understood that vindication of kalām in al-Ṣaffār divides into three parts as of explanation, demonstration and refutation. Al-Ṣaffār’s defending the science of kalām shows that there were opposite thoughts against kalām in the 4th/10th and 5th/11th centuries throughout in Transoxania. In this period Aṣḥāb al-Ḥadīth and Ikhwān al-Ṣafāʾ were the opposite fronts of kalām. In addition, he was trying to go beyond the oppositions of Ḥanafī jurists. His vindication is consistent and has scholarly depth because it is able to be against a strong opposition. -/- SUMMARY: Abū Ishāq Ibrahīm b. Ismāil Zāhid al-Ṣaffār al-Bukhārī is a scholar belonging to the Ḥanafī theological tradition which was improved by the contribution of Ḥanafī theologians who used thinking system of Abū Ḥanīfa (d. 150/767) as base and adopted Abū Manṣūr al-Māturīdī (d. 333/944) and his thoughts in historical process. The contents of his theological works Talkhīs al-adilla li-qawāʿid al-tawḥīd and Risāla fī al-Kalām, the method that he used, and references to his works made by Ottoman and Arab scholars indicate that he is an important Māturīdī theologian. The article focuses on his defense of the science of Kalām. -/- In Talkhīs al-adilla, there are two sections including the subject of naming, and importance and necessity of Kalām discipline. Here, the necessity of learning Kalām and its value are defended in detail. -/- Besides, rumors “Abū Ḥanīfa turned away from the science of Kalām in his doomsdays” and that “He prohibited to make occupation with Kalām completely” are evaluated. -/- When systematical vindication of the science of Kalām is examined, it is understood that al-Ṣaffār is the first theologian who reserved a private and voluminous part for defensing the Kalām among Ḥanafī Māturīdīs. Even if Abū Manṣūr al-Māturīdī, Abū al-Yusr al-Bazdawī (d. 493/1100) and Abū Muʿīn al-Nasafī (d. 508/1115) defend that the science of Kalām is not wrong for religion; yet, the vindication of Kalām does not take a place under a separate title and in a detailed manner in any of Māturīdī theologians works. -/- The method that al-Ṣaffār used when he is defending the science of Kalām, differs from strategy of Abū Ḥanīfa. While Abū Ḥanīfa has mentioned that there is a need for the science of Kalām under these new circumstances, al-Ṣaffār has defended theologians who were charged with being Ahl al-Bidaʿ, by trying to prove that the prophets especially the Prophet Ibrāhīm, even the Companions of the Prophet Muḥammad (Ṣaḥāba) and the Successors of the Companions (Tābiʿūn) scholars use their minds and make arguments in religious matters. In this respect, he emphasizes that the Qurʾān orders to think and discuss gently, not to be stay in silence: “And dispute with them, using what is best” (16.125-126). Therefore, he states that this discipline which took a mission to explain and defend the creed of Islam (ʿaqīda), cannot be characterized as an innovation (bidʿah) or illicit. -/- His vindication method can be defined as a more developed type of method that Abu’l-Ḥasan al-Ash’arī’s (d. 324/935) used in Risāla fī istiḥsān al-khawḍ fī ʿilm al-kalām. Even though he does not state its systematic, it can be understood that vindication of Kalām in al-Ṣaffār divides into three parts as of explanation, demonstration and refutation: -/- a) Explanation: Explaining the necessity and importance of the science of Kalām by giving information about its definition, names, value and place among other principles. -/- b) Demonstration: Revealing the religious basics of the science of Kalām from the Qurʾān, the Sunna of the Prophet Muḥammad, the Companions of the Prophet, and the Successors of the Companions thus specifying that it is legitimate for religion. -/- c) Refutation: Replying the claims having aim to weaken the value of Kalām principle and its religious legality. -/- Al-Ṣaffār describes ‘the science of Kalām’ as ‘Knowing the Real with the evidences which help to reach the absolute information’. By allocating the concept of 'Ḥaqq', he repeats the definition of Kalām as “it is to know God with certain evidences” in a part of his work, and “it is to know the principles of religion (uṣūl al-dīn) with certain evidences” in another part. He describes Kalām as “Knowing ḥaqq / God / uṣūl al-dīn by depending on evidence”, and tries to prove that it is wrong to consider a science which performs the stated duty, as illicit and abominable and thus turn away from it. -/- Al-Ṣaffār thinks that it is right to call this principle as ‘Kalām’ because of the certain evidences leading to the truth that this science has used. This thought was defended by other theologians as well. -/- For example, according to Saʿd al-Dīn Masʿūd al-Taftazānī (d. 792/1310) it seems like to say “This is the word (kalām), not the other knowings” by the way of the power of evidences used in Kalām.Thus, it is appropriate to give name Kalām for this science which based on certain evidences. -/- Al-Ṣaffār’s explanations revealed in scope of vindication of the Kalām are adopted by some of following scholars. Ḥusām al-Dīn al-Sighnāqī (d. 714/1314) in his book called al-Tasdīd sharḥ al-Tamhīd fī qawāʿid al-tawḥīd quotes Saffâr’s explanations as the same. -/- Al-Ṣaffār indicates that the method told in the Qurʾān is to reply questions about religion, beliefs and rejection instead of remaining in silence. The duty taken on by Kalām is this vindication activity performed by the prophets whose examples are described in the Qurʾān and which is legal and demanded. His way of thinking and vindication of the Kalām resembles that of Abū Manṣūr al-Māturīdī. Al-Māturīdī says, “Prophets and we were ordered to invite infidels to Islam. When this invitation happens, the respondents will ask for evidence and explanation, and discussion will be inevitable. Therefore, discussing and talking about subjects of Kalām is not objectionable”. -/- Thoughts of al-Māturīdī and al-Ṣaffār regarding the vindication of the Kalām were repeated by Nūr al-Dīn al-Ṣābūnī (d. 580/1184), and Fakhr al-Dīn al-Rāzī (d. 606/1210) later. According to al-Ṣābūnī, the evidences put against deniers and especially the discussion made by the Prophet Ibrāhīm to defend his own belief, prove the legality of Kalām discipline. Likewise, according to al-Rāzī, the duty of Kalām is just an activity performed already in the Qurʾān and ordered to the prophets. At this point of view, since the prophets were leading to Kalām, banning this activity is nonsense. -/- According to al-Ṣaffār, the reason for why Abu Hanifa avoids his son Hammād from these discussions is that Abu Hanifa does not like discussions based on obstinate. Otherwise, it cannot be that Abū Ḥanīfa prohibits to learn Kalām and make discussion about Kalām. According to him, this behavior of Abū Ḥanīfa results from that people discussing with him are ignorant about the subject of Kalām, discussion turns to an obstinate, and coming to an end of discussion seems impossible. This comment of al-Ṣaffār is quoted in Miftāḥ al-saʿāda wa-miṣbāḥ al-siyāda by ʿIṣām al-Dīn Aḥmed b. Muṣṭafā Tashköprüzāde (d. 968/1561) and in Minaḥ al-rawḍ al-azhar fī sharḥ al-Fiqh al-akbar by ʿAlī b. Sulṭān Muḥammad al-Qārī (d. 1014/1606) as same as his words. -/- Al-Ṣaffār’s defending the science of Kalām shows that there were opposite thoughts against Kalām in the 4th/10th and 5th/11th centuries throughout in Transoxania.In this period Aṣḥāb al-Ḥadīth and Ikhwān al-Ṣafāʾ were the opposite fronts of Kalām. In addition, he was trying to go beyond the oppositions of Ḥanafī jurists (fuqahāʾ). Because some of the Ḥanafī jurists thought that Abū Ḥanīfa forsook occupation with the science of Kalām and even he prohibited his son to have interest in this discipline. But some of Ḥanafī theologians such as al-Māturīdī, al-Nasafī and al-Ṣaffār protested this thought which describes Abū Ḥanīfa as a banner for Kalām. -/- His vindication is consistent and has scholarly depth because it is able to be against a strong opposition. -/- His book Talkhīs al-adilla li-qawāʿid al-tawḥīd is a unique source in terms of containing vindication of the Kalām in detail and also influencing the approaches of the next period scholars. (shrink)

In this paper a symmetry argument against quantity absolutism is amended. Rather than arguing against the fundamentality of intrinsic quantities on the basis of transformations of basic quantities, a class of symmetries defined by the Π-theorem is used. This theorem is a fundamental result of dimensional analysis and shows that all unit-invariant equations which adequately represent physical systems can be put into the form of a function of dimensionless quantities. Quantity transformations that leave those dimensionless quantities invariant are empirical and (...) dynamical symmetries. The proposed symmetries of the original argument fail to be both dynamical and empirical symmetries and are open to counterexamples. The amendment of the original argument requires consideration of the relationships between quantity dimensions. The discussion raises a pertinent issue: what is the modal status of the constants of nature which figure in the laws? Two positions, constant necessitism and constant contingentism, are introduced and their relationships to absolutism and comparativism undergo preliminary investigation. It is argued that the absolutist can only reject the amended symmetry argument by accepting constant necessitism. I argue that the truth of an epistemically open empirical hypothesis would make the acceptance of constant necessitism costly: together they entail that the facts are nomically necessary. (shrink)

Physicist Percy Bridgman has been taken by Heather Douglas to be an exemplar defender of an untenable value-free ideal for science. This picture is complicated by a detailed study of Bridgman's philosophical views of the relation between science and society. The normative autonomy of science, a version of the value-free ideal, is defended. This restriction on the provenance of permissible values in science is given a basis in Bridgman's broader philosophical commitments, most importantly, his view that science is primarily an (...) individual commitment to a set of epistemic norms and values. Considerations of external moral or social values are not, on this view, intrinsic to scientific practice, though they have a broader pragmatic significance. What Bridgman takes as the proper relation between science and society is shown through analysis of his many writings on the topic and consideration of his rarely remarked upon involvement in the most problematic example of "Big Science" of his day: the atomic bomb. A reevaluation of Bridgman's views provides a unique characterization of what is at stake in the values in science debate: the normative autonomy of science. (shrink)

Causal Decision Theory reckons the choice-worthiness of an option to be completely independent of its evidential bearing on its non-effects. But after one has made a choice this bearing is relevant to future decisions. Therefore it is possible to construct problems of sequential choice in which Causal Decision Theory makes a guaranteed loss. So Causal Decision Theory is wrong. The source of the problem is the idea that agents have a special perspective on their own contemplated actions, from which evidential (...) connections that observers can see are either irrelevant or invisible. (shrink)

This essay was published in the September 2023 edition of Griffith Observer. It is an examination of the curious friendship between writer Aldous Huxley and astronomer Edwin Hubble and its philosophical basis: empiricism. I hope that by consideration of these two thinker''s lives and work light may be shed on each of their ideas and on the 20th century conceptions of empiricism more broadly.

Throughout this research, imposing the training of an Artificial Neural Network (ANN) to play tic-tac-toe bored game, by training the ANN to play the tic-tac-toe logic using the set of mathematical combination of the sequences that could be played by the system and using both the Gradient Descent Algorithm explicitly and the Elimination theory rules implicitly. And so on the system should be able to produce imunate amalgamations to solve every state within the game course to make better of results (...) of winnings or getting draw. (shrink)

Publication date: 29 September 2016 Source: Author: Samy S. Abu Naser, Mazen J. Al Shobaki, Youssef M. Abu Amuna This paper aims to measure knowledge management maturity in higher education institutions to determine the impact of knowledge management on high performance. Also the study aims to compare knowledge management maturity between universities and intermediate colleges. This study was applied on five higher education institutions in Gaza strip, Palestine. Asian productivity organization model was applied to measure Knowledge Management Maturity. Second dimension (...) which assess high performance was developed by the authors. The controlled sample was. Several statistical tools were used for data analysis and hypotheses testing, including reliability correlation using Cronbach’s alpha, “ANOVA”, Simple Linear Regression and Step Wise Regression. The overall findings of the current study shows that maturity level is in the second level. Findings also support the main hypothesis and it is sub- hypotheses. The most important factors effecting high performance are: Processes, knowledge management leadership, People, knowledge management Outcomes. Furthermore the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first comparative study between universities and intermediate colleges in Gaza Strip that explores the status of knowledge management maturity using the Asian productivity Model. (shrink)

This dissertation draws upon historical studies of scientific practice and contemporary issues in the metaphysics and epistemology of science to account for the nature of physical quantities. My dissertation applies this integrated HPS approach to dimensional analysis—a logic for quantitative physical equations which respects the distinct dimensions of quantities (e.g. mass, length, charge). Dimensional analysis and its historical development serve both as subjects of study and as a sources for solutions to contemporary problems. The dissertation consists primarily of three related (...) papers on: (1) the methodological and metaphysical foundations of dimensional analysis, (2) the use of dimensional analysis in determining physical symmetries, (3) the use of dimensional analysis in securing metrological extension. (shrink)

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

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The dataset (...) used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (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)

Automatic detection of diseases and anatomical landmarks in medical images by the use of computers is important and considered a challenging process that could help medical diagnosis and reduce the cost and time of investigational procedures and refine health care systems all over the world. Recently, gastrointestinal (GI) tract disease diagnosis through endoscopic image classification is an active research area in the biomedical field. Several GI tract disease classification methods based on image processing and machine learning techniques have been proposed (...) by diverse research groups in the recent past. However, yet effective and comprehensive deep ensemble neural network-based classification model with high accuracy classification results is not available in the literature. In this thesis, we review ways and mechanisms to use deep learning techniques to research on multi-disease computer-aided detection about gastrointestinal and identify these images. We re-trained five state-of-the-art neural network architectures, VGG16, ResNet, MobileNet, Inception-v3, and Xception on the Kvasir dataset to classify eight categories that include an anatomical landmark (pylorus, z-line, cecum), a diseased state (esophagitis, ulcerative colitis, polyps), or a medical procedure (dyed lifted polyps, dyed resection margins) in the Gastrointestinal Tract. Our models have showed results with a promising accuracy which is a remarkable performance with respect to the state-of-the-art approaches. The resulting accuracies achieved using VGG, ResNet, MobileNet, Inception-v3, and Xception were 98.3%, 92.3%, 97.6%, 90% and 98.2%, respectively. As it appears, the most accurate result has been achieved when retraining VGG16 and Xception neural networks with accuracy reache to 98% due to its high performance on training on ImageNet dataset and internal structure that support classification problems. (shrink)

This study was carried out on the low-grade chromite, Gedarif state, Sudan, at Umm Saqata- Qala Elnahal to achieve full characterization for the ore via Microscopic studies for the polish section samples, X-ray diffraction analysis (XRD), X-ray Fluorescence Analysis (XRF), Scanning electron microscopic with Energy Dispersive X-ray Spectroscopy (SEM-EDX), and physical properties tests. Microscopic study revealed that the liberation size of chromite mineral is in the range (0.30-0.05) mm. also the X-ray diffraction, X-ray fluorescence and scanning electron microscope with the (...) Energy Dispersive X-ray analysis showed that main gangue of the ore is mineral silicates as Antigorite and contains 21.58-19.60% Cr2O3. The physical tests (saturated density and dry density) demonstrated that ore has low density in range 2.54-2.38 g/cm3 because it is low grade chromite ore, this can be attributed to the dominating mineral silicate (waste) which associated with chromite mineral. (shrink)

Abstract: Background: In spite of the fact that computers continue to improve in speed and functions operation, they remain complex to use. Problems frequently happen, and it is hard to resolve or find solutions for them. This paper outlines the significance and feasibility of building a desktop PC problems diagnosis system. The system gathers problem symptoms from users’ desktops, rather than the user describes his/her problems to primary search engines. It automatically searches global databases of problem symptoms and solutions, and (...) also allows ordinary users to contribute exact problem reports in a structured manner. Objectives: The main goal of this Knowledge Based System is to get the suitable problem desktop PC symptoms and the correct way to solve the errors. Methods: In this paper the design of the proposed Knowledge Based System which was produced to help users of desktop PC in knowing many of the problems and error such as : Power supply problems, CPU errors, RAM dumping error, hard disk errors and bad sectors and suddenly restarting PC. The proposed Knowledge Based System presents an overview about desktop PC hardware errors are given, the cause of fault are outlined and the solution to the problems whenever possible is given out. CLIPS Knowledge Based System language was used for designing and implementing the proposed expert system. Results: The proposed PC desktop troubleshooting Knowledge Based System was evaluated by IT students and they were satisfied with its performance. (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)

Abstract - The aim of the study is to identify the degree of administrative transparency in the Palestinian higher educational institutions in the Gaza Strip. In the study, the researchers adopted a descriptive and analytical method. The research population consisted of administrative staff, whether academic or administrative, except for those in senior management or the university council. The study population reached 392 employees. A random sample was selected (197). The number of questionnaires recovered was (160) with a recovery rate of (...) (81.2%). The researchers used a questionnaire for the data collection and were treated using SPSS to obtain the results. The results show that there is no significant difference between male responses and female responses due to gender variable. The results also confirm that there is no significant difference between respondents' responses due to the age variable. The results also showed a significant difference between respondents' responses attributed to the university variable. There is a fundamental difference between respondents' responses attributed to the scientifically qualified variable. The results also confirmed a significant difference between respondents' responses attributed to the management level variable. The results also confirmed a significant difference between respondents' responses due to variable years of service. The research reached a number of recommendations, the most important of which is: The necessity of Palestinian universities to adhere to the application of transparency standards in all university activities. The need to benefit from regional and international experience in the application of transparency systems within universities and to examine the possibility of applying these systems in our universities. As well as the need to engage in the program of teaching transparency in universities, as it is confirmed that only five universities participated in this experiment. The importance of raising awareness among the employees of Palestinian universities to clarify the foundations of building transparency and its dimensions to represent the active supporter through workshops and seminars. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

This discussion note examines a recent argument for the principle that any counterfactual with true components is itself true. That argument rests upon two widely accepted principles of counterfactual logic to which the paper presents counterexamples. The conclusion speculates briefly upon the wider lessons that philosophers should draw from these examples for the semantics of counterfactuals.

The aim of this paper is to evaluate the critical success factors and investigate the benefits that might be gained once implementing Electronic Customer Relationship Management at HEI from employee perspective. The study conducted at Al Quds Open University in Palestine and data collected from (300) employee through a questionnaire which consist of four variables. A number of statistical tools were intended for hypotheses testing and data analysis, including Spearman correlation coefficient for Validity, reliability correlation using Cronbach’s alpha, and Frequency (...) and Descriptive analysis. The overall findings of the current study show that all the features were important for staff and it was critical success factors, at the same time, websites were providing all the features discussed by the theory whereas staff showed their willingness to use those features if provided. It is also discovered that implementing Electronic Customer Relationship Management can cause staff retention, were provided efficiently and needed to be improved. Research limitations: The survey findings were based on QOU employee in Palestine, UAE and KSA branches not included in the study. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep learning (...) is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)