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)

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.

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)

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)

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: 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)

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)

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)

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)

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)

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)

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)

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)

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: 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.

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)

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines or software that work and react like humans. Some of the activities computers with artificial intelligence are designed for include, Speech, recognition, Learning, Planning and Problem solving. Deep learning is a collection of algorithms used in machine learning, It is part of a broad family of methods used for machine learning that are based on learning representations of data. Deep learning is a technique used (...) to produce Pneumonia detection and classification models using x-ray imaging for rapid and easy detection and identification of pneumonia. In this thesis, we review ways and mechanisms to use deep learning techniques to produce a model for Pneumonia detection. The goal is find a good and effective way to detect pneumonia based on X-rays to help the chest doctor in decision-making easily and accuracy and speed. The model will be designed and implemented, including both Dataset of image and Pneumonia detection through the use of Deep learning algorithms based on neural networks. The test and evaluation will be applied to a range of chest x-ray images and the results will be presented in detail and discussed. This thesis uses deep learning to detect pneumonia and its classification. (shrink)

Programming artificial intelligence to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair or fair. It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing (...) to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector. The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec—which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out. (shrink)

Infants and children are suffering from a lots of nausea and vomiting problems. Doctors, usually face various difficulties dealing with these problems because of their similarities. In this paper, we present an expert system to help users in getting the correct diagnosis of problems of nausea and vomiting in infants and children (Gastro-esophageal reflux, Gastroenteritis, Systemic Infection, Bowel obstruction, Tumors, A bleeding disease, tonsillitis, and Hepatitis pharynx). Furthermore, this expert system provide information about the disease and how to deal with (...) it. SL5 Object expert system language was used to design and implement this expert system. (shrink)

(No abstract is available for this citation).

The best-known argument for Evidential Decision Theory (EDT) is the ‘Why ain’cha rich?’ challenge to rival Causal Decision Theory (CDT). The basis for this challenge is that in Newcomb-like situations, acts that conform to EDT may be known in advance to have the better return than acts that conform to CDT. Frank Arntzenius has recently proposed an ingenious counter argument, based on an example in which, he claims, it is predictable in advance that acts that conform to EDT will do (...) less well than acts that conform to CDT. We raise two objections to Arntzenius’s example. We argue, first, that the example is subtly incoherent, in a way that undermines its effectiveness against EDT; and, second, that the example relies on calculating the average return over an inappropriate population of acts. (shrink)

Background: The castor bean is a large grassy or semi-wooden shrub or small tree. Any part of the castor plant parts can suffering from a disease that weakens the ability to grow and eliminates its production. Therefore, in this paper will identify the pests and diseases present in castor culture and detect the symptoms in each disease. Also images is showing the symptom form in this disease. Objectives: The main objective of this expert system is to obtain appropriate diagnosis of (...) the disease. Methods: In this paper, the expert system is designed for the ability of agricultural engineers to detect and diagnose disease of castor like as: seeding blight, alternaria blight, cercospora leaf spot, powdery mildew and wilt. This system presents the disease symptoms, survival and spread, favorable conditions and image for each disease. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of castor diseases was assessed by farmers and agricultural engineers and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for farmers and people have experience in the plant of castor to detect and diagnosis the symptoms that may face this plant from several disease. (shrink)

We describe the earliest occurrences of the Liar Paradox in the Arabic tradition. e early Mutakallimūn claim the Liar Sentence is both true and false; they also associate the Liar with problems concerning plural subjects, which is somewhat puzzling. Abharī (1200-1265) ascribes an unsatisfiable truth condition to the Liar Sentence—as he puts it, its being true is the conjunction of its being true and false—and so concludes that the sentence is not true. Tūsī (1201-1274) argues that self-referential sentences, like the (...) Liar, are not truth-apt, and defends this claim by appealing to a correspondence theory of truth. Translations of the texts are provided as an appendix. (shrink)

Abstract: The Spotify Status Dataset is a valuable resource that provides real-time insights into the operational status and performance of Spotify, a popular music streaming platform. This dataset contains a wide array of information related to server uptime, user activity, service disruptions, and more, serving as a critical tool for both Spotify's internal monitoring and the broader data analysis community. As digital services like Spotify continue to play a central role in music consumption, understanding the platform's status becomes crucial for (...) ensuring a seamless user experience. This dataset offers numerous research and analysis opportunities, whether you're interested in exploring service availability trends or investigating user behavior patterns during service disruptions. This study highlights the significance of the Spotify Status Dataset, its potential applications, and the valuable insights it can offer to various stakeholders, from Spotify engineers working to enhance service reliability to researchers studying the effects of downtime on user engagement. It invites readers on a journey through this dataset to uncover the secrets of Spotify's operational status and its impact on music enthusiasts worldwide. (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)

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)

Abstract. Intelligent Tutoring System Builder (ITSB) is an authoring tool designed and developed to aid teachers in constructing intelligent tutoring systems in a multidisciplinary fields. The teacher is needed to create a set of pedagogical fundamentals, which, in line, are inured to automatically build up a broad tutor framework and construct an intelligent tutoring system. In this paper an explanation of the theory and the architecture of the tool is outlined. A presentation of several system components, the requirements of the (...) different components, integration of these components in ITSB tool are shown. Furthermore, implanting of requirements, cognitive principle, and common design fundamentals in the tool to ease the use of teachers. A variety of design matters, an example of building an intelligent tutoring system for teaching Java language using ITSB tool and an evaluation are presented. (shrink)

The essay presents a novel counterexample to Causal Decision Theory (CDT). Its interest is that it generates a case in which CDT violates the very principles that motivated it in the first place. The essay argues that the objection applies to all extant formulations of CDT and that the only way out for that theory is a modification of it that entails incompatibilism. The essay invites the reader to find this consequence of CDT a reason to reject it.

We sought to compare the implicit and explicit views of a group of Muslim graduates on the fairness of Islamic law. In this preliminary investigation, we used the Electroencephalographic N400 Event Related Potential to detect the participant’s implicit beliefs. It was found that the majority of participants, eight out of ten, implicitly held that Islamic Law was unfair despite explicitly stating the opposite. In seeking to understand what separated these eight participants from the remaining two – the two who both (...) implicitly and explicitly held that Islamic Law was fair – only two distinguishing characteristics could be identified. Both participants had undertaken an in-depth study of a branch of Islamic law that places the spirit of the law above that of a literal interpretation. They had also attended the same seminary, exclusive to the other participants. Of the eight participants, it was discovered that, while they implicitly held Islamic law to be unfair, they also held it to be rational – in the same way they found that it was rational to push a person of a ship in order to save the remaining from drowning, yet unfair. We discuss these preliminary findings and consider theories on how an innate sense of fairness, an aspect of nativism, may come into play when it is not congruent with a participant’s own beliefs. Further, we ask, where such an inconsistency occurs, how does the mind attempt to rectify it – if at all? As a possible contribution to the discussion on theories of nativism vs. empiricism we put forward a hypothesis and methodology for investigation that may produce previously unconsidered data on human nature. (shrink)

Abstract: In, this research embodies the spirit of interdisciplinary collaboration, bringing together data science, healthcare, and public health to address one of the most significant global health challenges in recent history. The achievements of this study underscore the potential of advanced machine learning techniques to enhance our understanding of the pandemic and guide effective decision-making. As we navigate the ongoing battle against COVID-19 and prepare for future health emergencies, the lessons learned from this research serve as a testament to the (...) power of data-driven insights and the unwavering commitment of the global scientific community in the face of adversity. (shrink)

The best justification of time-discounting is roughly that it is rational to care less about your more distant future because there is less of you around to have it. I argue that the standard version of this argument, which treats both psychological continuity and psychological connectedness as reasons to care about your future, can only rationalize an irrational—because exploitable—form of future discounting.

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)

The Black woman has always been portrayed in clichéd images in the white media, stereotyping them in a racist and sexist manner. In Black Women Image Makers, Mary Helen Washington dwells upon such unfair portrayals as the tragic mulatto, the hot blooded exotic whore and the strong Black Mammy. And this is probably why the black mother frequently appears in literature as a figure of towering strength. In Zora Neale Hurston’s Their Eyes Were Watching God (1937), an old grandmother, a (...) former slave, accurately describes her position in this society: "De nigger woman is de mule uh de world so fur as Ah can see". The black mother is not a woman with power, not a liberated woman, but a mule, picking up the burdens that everyone else has thrown down and refused to carry. To outsiders, the Negro mother appears to be the one-dimensional Rock of Gibraltar – strong of back, long of arm, incapable of destruction. The proposed paper would study the black mothers as portrayed in Alice Walker’s novels, The Third Life of Grange Copeland and Meridian. To Walker, the Black mother is an individual – profound, tragic, mysterious, sacred, and unfathomable – strong in many, but not in all ways. She claims that the assertion of the black mother as a superwoman is a myth, and she feels that the black mother isn’t always strong. A black man does have a history of ignoring his responsibilities, but the black mother has had no choice in any matter; she has had to pick up the burdens that no one else would carry. In her fiction, Walker introduces the real-feeling, caring, uncaring, disappointed, indifferent black mother to the world. (shrink)