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

The study aimed to identify Design Thinking and its use in NGOs in Gaza Strip. In order to achieve the objectives of the study and to test its hypotheses, the analytical descriptive method was used, relying on the questionnaire as a main tool for data collection. The study society was one of the decision makers in the local NGOs in the Gaza Strip. The study population reached 78 local NGOs in Gaza Strip. The overall inventory of the possible study community (...) was based mainly on the use of the SPSS in processing and analyzing the data obtained through the survey tool. Smart-PLS was also used to construct the structural equation model (SEM) Analyzing the relationship between study variables. The study found that Design Thinking mediates the relationship between the management of process design and decision making with a holistic effect. The study showed the interest of local NGOs in creating a good mental image in the local community. And the ownership of local NGOs to the expertise and technical skills required to implement the projects, and showed the adoption of local NGOs in their activities to meet the needs of the beneficiaries and their wishes, and local NGOs analyze the problem, and causes, through data relevant to the decision, based on reference data for decision-making. The main recommendations of the study are: The need for senior management in the local NGOs in Gaza Strip to adopt the methodology of design thinking because of its impact on the sustainability of the projects, design the technical feasibility study and meet the wishes of the beneficiaries. (shrink)

The study aimed to identify the reality of decision-making in the local NGOs in Gaza Strip. In order to achieve the objectives of the study and to test its hypotheses, the analytical descriptive method was used, relying on the questionnaire as a main tool for data collection. The study society was one of the decision makers in the local NGOs in Gaza Strip. The study population reached 78 local NGOs in Gaza Strip. A Census Method of the possible study community (...) was adopted, and the use of the Statistical Package for Social Sciences (SPSS) was mainly based on the analysis and analysis of the data obtained through the survey tool. The study reached a set of results, the most important of which are: NGOs follow a decision-making mechanism. NGOs examine the problems and causes of the organization. Organizations are less concerned with stakeholder participation. Make decision. The main recommendations of the study are: To promote systematic methods of decision-making in NGOs in Gaza Strip: Local NGOs in Gaza Strip should continue to develop their competencies in order to make sound and correct decisions and encourage them to follow the scientific methodologies in the mechanism of taking Decisions. (shrink)

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained using (...) data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey lung (...) cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. (shrink)

The study aimed at identifying the personal variables and their effect in promoting job creation in Gaza Strip through business incubators. The researchers used the descriptive analytical approach to achieve the study objectives. The study population consisted of 92 of the pilot projects benefiting from the three business incubators in Gaza Strip (Palestinian Information Technology Incubator, UCAS Technology Incubator and Business and Technology Incubator). The study reached a number of results, the most important of which are the existence of statistically (...) significant differences on entrepreneurship attributed to each age as most of them are between the ages of 22-30, Gender for males, business incubator, scientific qualification for the specialties of information technology and engineering, and years of experience. Based on the findings, the researchers recommend focusing on university students in guiding them towards entrepreneurship and helping new graduates to start entrepreneurship. And to guide students to scientific disciplines that help them in entrepreneurship after graduation, whether starting a small business or self-employment, support females to entrepreneurship as most of the entrepreneurs are male, in addition to stimulating males as well. (shrink)

The computer science (CS) graduated students suffered from documenting their projects and specially from coordinating it. In addition, students’ supervisors faced difficulties with guiding their students to an efficient process of documenting. In this paper, we will offer a suggestion as a solution to the mentioned problems; that is an application to make the process of documenting computer science (CS) student graduation project easy and time-cost efficient. This solution will decrease the possibility of human mistakes and reduce the effort of (...) documenting process. (shrink)

This paper describes an Intelligent Tutoring System for helping users with ADO.NET called ADO-Tutor. The Intelligent Tutoring System was designed and developed using (ITSB) authoring tool for building intelligent educational systems. The user learns through the intelligent tutoring system ADO.NET, the technology used by Microsoft.NET to connect to databases. The material includes lessons, examples, and questions. Through the feedback provided by the intelligent tutoring system, the user's understanding of the material is assessed, and accordingly can be guided to different difficulty (...) level of exercises and/or the lessons. The Intelligent Tutoring System was evaluated by a group of users and the results were more than satisfactory in terms of the quality of the material and the design of the system. (shrink)

The scholarly search engine Google Scholar (G.S.) has problems that make it not a 100% trusted search engine. In this research, we discussed a few drawbacks that we noticed in Google Scholar, one of them is related to how does it perform (add articles) option for adding new articles that are related to the registered researchers. Our suggestion is an attempt for making G.S. more efficient by improving the searching method that it uses and finally having trusted statistical results.

Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the dynamics (...) of the pandemic. However, this paper underscores the critical importance of a comprehensive evaluation beyond accuracy, including metrics such as sensitivity, specificity, and the area under the ROC curve (AUC-ROC), to assess the model's performance robustness. The research paper offers detailed insights into the architecture of the ANN model, encompassing critical hyperparameters, data preprocessing techniques, and regularization strategies employed to optimize model accuracy. Ethical considerations surrounding data privacy and potential biases within the COVID-19 dataset are also addressed. While the achieved accuracy is a significant milestone, this study underscores the dynamic and evolving nature of the pandemic, necessitating continuous model refinement and validation. Furthermore, it emphasizes the importance of considering false positives and false negatives in the context of public health decision-making. In conclusion, this research contributes to the arsenal of tools available for pandemic management by showcasing the potential of ANNs in COVID-19 case forecasting. It encourages ongoing exploration and adaptation of predictive models to enhance their applicability in real-world public health scenarios, ultimately contributing to more effective pandemic control and response efforts. (shrink)

The study aims to identify the efficiency of the university education performance from the perspective of postgraduate and undergraduate students in international and Palestinian universities. The analytical descriptive approach was used for this purpose and the questionnaire was used as a main tool for data collection. The study community consists of: post graduate students, (23850) graduate students and (146355) undergraduate students. The sample of the study was 378 graduate students and 383 undergraduate students. The random stratified sample was used. The (...) Statistical Package of Social Sciences (SPSS) was also used for data analysis. The study reached a number of results, the most important of which are: The level of efficiency of educational performance in Palestinian and international universities from the point of view of postgraduate and undergraduate students was high. And that there are significant differences between the average views of the sample of the study on the efficiency of educational performance in Palestinian and international universities attributed to the University and to the benefit of international universities. The study concluded many recommendations, the most important of which is the necessity of continuing to develop e-learning strategies that affect the efficiency of educational performance and research commensurate with the university's position in the local and international community, which puts it on the best classification between local and international universities through e-learning. (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)

Causal finitism, the view that the causal history of any event must be finite, has garnered much philosophical interest recently—especially because of its applicability to the Kalām cosmological argument. The most prominent argument for causal finitism is the Grim Reaper argument, which attempts to show that, if infinite causal histories are possible, then other paradoxical states of affairs must also be possible. However, this style of argument has been criticized on the grounds of (i) relying on controversial modal principles, and (...) (ii) providing a false diagnosis of the paradoxes involved. In this paper, I develop a new kind of Grim Reaper argument immune to these criticisms. I show that, by using insights from the literature on time travel, causal finitists should instead argue that infinite causal histories are problematically inexplicable, as they entail the possibility of unexplained foiling mechanisms. The fruits of this paper are that (i) a novel supporting argument for the Kalām is developed, and (ii) along the way of building this argument, it is shown that the literatures on time travel and causal finitism are deeply and intimately connected. (shrink)

The Successive Addition Argument (SAA) is one of the key arguments espoused by William Lane Craig for the thesis that the universe began to exist. Recently, Malpass, Mind, 131(523), 786–804 (2021) has developed a challenge to the SAA by way of constructing a counterexample that originates in the work of Fred Dretske. In this paper, I show that the Malpass-Dretske counterexample is in fact no counterexample to the argument. Utilizing a distinction between properties of members and properties of collections, I (...) argue that Malpass’ counterexample has no bearing on the soundness of the SAA. I also develop a novel parity argument against Malpass’ argument that I demonstrate can only be resolved by way of the aforementioned analysis. (shrink)

This study focused on the adaptability and attitude of Grade 6 students in the Modular Learning Approach. This study aims to create a comprehensive review of the adaptability and attitude of elementary students in the Modular Learning Approach Especially. It seeks to describe and analyze the following: the profile of the respondents in terms of sex and education of the parents; the adaptability of the respondents toward the relationship between the student's profile in terms of their sex and educational attainment (...) of parents; the attitude of the respondent's significant relationship between sex and educational attainment. The researcher used the quantitative survey questionnaires to gather data from 128 Grade 6 students in five Elementary Schools in Bongabon, Nueva Ecija who enrolled during the School Year 2020-2021. The study revealed that the respondent's demographic profile in sex has no significant relationship to their adaptability and attitude in the modular learning approach. Yet the parents' educational attainment of the respondents has a significant relationship to adaptability in terms of learning assistance and support in the modular learning approach. The results further signified that parents' educational attainment is a basis for adaptability in modular learning. -/- . (shrink)

It is a common intuition in scientific practice that positive instances confirm. This confirmation, at least based purely on syntactic considerations, is what Nelson Goodman’s ‘Grue Problem’, and more generally the ‘New Riddle’ of Induction, attempt to defeat. One treatment of the Grue Problem has been made along Bayesian lines, wherein the riddle reduces to a question of probability assignments. In this paper, I consider this so-called Bayesian Grue Problem and evaluate how one might proffer a solution to this problem (...) utilizing what I call a phenomenological approach. I argue that this approach to the problem can be successful on the Bayesian framework. (shrink)

In this paper, Taylor series is used to solve neutrosophic multi-objective programming problem (NMOPP). In the proposed approach, the truth membership, Indeterminacy membership, falsity membership functions associated with each objective of multi-objective programming problems are transformed into a single objective linear programming problem by using a first order Taylor polynomial series. Finally, to illustrate the efficiency of the proposed method, a numerical experiment for supplier selection is given as an application of Taylor series method for solving neutrosophic multi-objective programming problem (...) at end of this paper. (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)

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: 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)

The purpose of this article is to discuss Safavid period Islamic philosopher Sadraddin Shirazi’s philosophical methodology and the sources of the school founded by him. The article relies on research conducted on Shirazi philosophy. It shows that Shirazi through synthesizing the methods of the earlier schools that existed in Islam to acquire knowledge devised a new mechanism for acquiring knowledge. Before coming to Shirazi, intellectual movements formed during Islam’s classical period, such as peripateticism, illuminationism, theology and Gnosticism, were of different (...) views regarding the best way to reach the truth. For example, for peripatetics reason, for theologians revelation, for gnostics/mystics intuition or personal experience were important, but ishraqi school was somewhere between peripatetic and Gnostic schools. Sadraddin Shirazi, though, considered all these agents necessary, for they complement each other, and said they should be utilized altogether to obtain ultimate reality. Similarly, considering his philosophical sources, we see that he is influenced almost by all philosophical schools as well as religious sources. However, Ibn Sina and Ibn Arabi’s influence on him was more profound compared to other philosophers. (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)

This paper describes an Intelligent Tutoring System for helping users with ADO NET called ADO-Tutor. The IntellTutoring System was designed and developed using (ITSB) authoring tool for building intelligent educational systems. The user learns through the intelligent tutoring system ADO NET, the technology used by Microsoft NET to connect to databases. The material includes lessons, examples, and questions. Through the feedback provided by the intelligent tutoring system, the user's understanding of the material is assessed, and accordingly can be guided to (...) different difficulty level of exercises and/or the lessons. The Intelligent Tutoring System was evaluated by a group of users and the results were more than satisfactory in terms of the quality of the material and the design of the system. (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)

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)

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)

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)

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)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and compared with (...) others. (shrink)

This thesis analyses Deleuze & Guattari’s notion of becoming through certain performative encounters in contemporary political art, and re-conceptualizes them as “art(s) of becoming”. Art(s) of becoming are actualizations of a non-representational –minoritarian– mode of becoming and creation as well as the political actions of fleeing quanta. The theoretical aim of the study is, on the one hand, to explain how Platonic Idealism is overturned by Deleuze’s reading of Nietzsche and Leibniz, and on the other hand, how Cartesian dualism of (...) mind and body is surpassed by following a Spinozistic theory of affects. In this respect, the dissertation has both theoretical and practical dimensions. Since art(s) of becoming are bodies without organs which constitute their own lines of flight through a process of minoration, the concepts of body, affect, becoming, and intensity are central to this study. For the same reason, this is an attempt to show the intersections of philosophical, political and aesthetic domains in Deleuze’s theory of sensation which is part of his general practice of philosophy, that is, a quest for establishing an ontology of immanence as opposed to identitarian metaphysics. (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)

In this paper, an artificial neural network (ANN) model was developed and validated to predict whether a couple is going to get divorced or not. Prediction is done based on some questions that the couple answered, answers of those questions were used as the input to the ANN. The model went through multiple learning-validation cycles until it got 100% accuracy.

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)

Abstract: A complex parasite such as human Plasmodium is likely to generate a variety of substances that injure the hosts directly or cause immunopathology. In malaria, a blood concentration of anti-inflammatory cytokines, such as interleukin (IL-10) is increased. The present study was performed to analyze IL-10 levels in patients with malaria falciparum and healthy controls individuals and correlate with malaria density infection as well as age groups. It is a cross sectional study was carried out in Khartoum state /Sudan, a (...) total of 70 febrile patients and 20 healthy volunteers as controls were included in this study. Blood samples were collected from both patients and control candidates in (EDTA) containers for parasitological and immunological tests. Immunochromatography test and microscopic examination for both thick and thin blood films were used to detect the positive samples for Plasmodium falciparum. Enzyme linked Immunosorbant Assay (ELISA), was used for the determination of IL-10 levels. The result showed that of IL-10 levels in malaria patients were 1598.50 and SD 4771.64 while the levels in healthy participants the level was 10.39 and SD was 124.33 the difference was found to be statistically significant (P= 0.007). The levels of cytokines mentioned above were observed to be raised in malaria individual compared to healthy control candidates. (shrink)

Abstract: This study has effectively tackled the critical challenge of accurate calorie prediction in dishes by employing a robust neural network-based model. With an outstanding accuracy rate of 99.32% and a remarkably low average error of 0.009, our model has showcased its proficiency in delivering precise calorie estimations. This achievement equips individuals, healthcare practitioners, and the food industry with a powerful tool to promote healthier dietary choices and elevate awareness of nutrition. Furthermore, our in-depth feature importance analysis has shed light (...) on the indispensable role played by specific nutritional attributes in calorie estimation. This unveiling of crucial factors provides valuable insights for further research endeavors and practical applications. Notably, this research extends its impact beyond the immediate context by making substantial contributions to the domains of nutrition science and dietary planning. It underscores the transformative potential of artificial intelligence, demonstrating how it can revolutionize our approach to food, nutrition, and health. As the world grapples with the challenges of diet-related health issues and environmental concerns, the accuracy and precision achieved in calorie prediction through neural networks represent a significant stride towards more informed and conscientious dietary practices. In this era of data-driven decision-making, our research paves the way for healthier lifestyles, heightened nutritional awareness, and a more health-conscious society. (shrink)

Chest pain is the pain felt in the chest by infants, children and adolescents. In most cases the pain is not associated with the heart. It is mainly recognized by the observance or report of pain by the infant, child or adolescent by reports of distress by parents or care givers. Chest pain is not unusual in children. Lots of children are seen in ambulatory clinics, emergency rooms and hospitals and cardiology clinics. Usually there is a benign cause for the (...) pain for utmost children. Certain patients have conditions that are serious and perhaps life-threatening. Chest pain in pediatric patients needs careful physical examination and a detailed history that would point to the possibility of a serious cause. Researches of pediatric chest pain are scarce. It has been difficult to create evidence-based guidelines for evaluation. In this paper we propose an expert system to help doctors and parents, and care giver in diagnosing chest pain in infants and children. This expert System is design and implemented in SL5 Object language. (shrink)

Recently, Intelligent Tutoring Systems (ITS) got much attention from researchers even though ITS educational technology began in the late 1960s and ITS is just embryonic from laboratories into the field. In this paper we outline an intelligent tutoring system for teaching basics of the databases system called (MDB). The MDB was built as education system by using the authoring tool (ITSB). MDB contains learning materials as a group of lessons for beginner level which include relational database system and lessons in (...) the process to install and set up a database. MDB system has exams for each level of the Lessons. An evaluation was done to see the effectiveness the MDB among learners and instructors. The outcome of the evaluation was promising. (shrink)

Background: Dengue Disease is a mosquito-borne tropical disease caused by the dengue virus, symptoms typically begin three to fourteen days after infection. This may include a high fever, headache, vomiting, muscle and joint pains, and a characteristic skin rash. Dengue serology is applied in different settings, such as for surveillance, in health care facilities in endemic areas and in travel clinics in non-endemic areas. The applicability and quality of serological tests in dengue endemic regions has to be judged against a (...) background of potential cross reactivity with other flavi-viruses, difficulties in distinguishing primary from secondary infections and technological problems related to the fact that most dengue endemic regions are relatively poor of resources .Objectives: to help doctors and patients in diagnosing Dengue Disease and give them the information of how to prevent Dengue Disease and to be able to understand the signs and symptoms of Dengue Disease. Methods: We collected all relevant material for Dengue Disease. Then we designed and implemented a knowledge based system for diagnosing Dengue Disease using SL5 Object Language. Results: The knowledge based system was evaluated by a group of Patients and specialized doctors and they found it very friendly and easy to use. (shrink)

This paper aims to assess knowledge management maturity at HEI to determine the most effecting variables on knowledge management that enhance the total performance of the organization. This study was applied on Al-Azhar University in Gaza strip, Palestine. This paper depends on Asian productivity organization model that used to assess KM maturity. Second dimension assess high performance was developed by the authors. The controlled sample was (364). 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 suggest that KMM is suitable for measuring and lead to enhance high performance. KMM assessment shows that the university maturity level is in level three. Findings also support the main hypothesis and it is subhypotheses. The most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes, Knowledge Process. Furthermore the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first study at HEI in Palestine explores the status of KMM using the Asian productivity model. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving an impressive (...) accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

In this paper, a predictive artificial neural network (ANN) model was developed and validated for the purpose of prediction whether a watermelon is good or bad, the model was developed using JUSTNN software environment. Prediction is done based on some watermelon attributes that are chosen to be input data to the ANN. Attributes like color, density, sugar rate, and some others. The model went through multiple learning-validation cycles until the error is zero, so the model is 100% percent accurate for (...) the purpose of prediction whether good or bad the watermelon is. (shrink)

There is an increase in the use of in-door wireless networking solutions via Wi-Fi and this increase infiltrated and utilized Wi-Fi enable devices, as well as smart mobiles, games consoles, security systems, tablet PCs and smart TVs. Thus the demand on Wi-Fi connections increased rapidly. Rule Based System is an essential method in helping using the human expertise in many challenging fields. In this paper, a Rule Based System was designed and developed for diagnosing the wireless connection problems and attain (...) a precise decision about the cause of the problem. SL5 Object expert system language was used in developing the rule based system. An Evaluation of the rule based system was carried out to test its accuracy and the results were promising. (shrink)