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

Abstract: The 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)

Cloud computing (ClC) has become a more popular computer paradigm in the preceding few years. Quality of Service (QoS) is becoming a crucial issue in service alteration because of the rapid growth in the number of cloud services. When evaluating cloud service functioning using several performance measures, the issue becomes more complex and non-trivial. It is therefore quite difficult and crucial for consumers to choose the best cloud service. The user's choices are provided in a quantifiable manner in the current (...) methods for choosing cloud services. Hence, this study attempts to achieve this objective through construction. decision-making framework so-called cloud services evaluator (CSsEv). The main indicator and its sub-indicators are formed as nodes at levels(n) in tree soft sets (TSSs). Thereafter Single Value Neutrosophic Sets (SVNSs) as branch of neutrosophic sets which conjunction with the Multi-Criteria Decision Making (MCDM) technique to facilitate analysis and evaluation process for the available Cloud services providers. Hence, entropy is employed to obtain indicators and sub_indicators weights and Complex Proportional Assessment utilizes these weights to facilitate the decision process of selecting optimal ClSPs. (shrink)

This edited volume is designed to explore different perspectives of culture, identity and social development using the impact of the digital age as a common thread, aiming at interdisciplinary audiences. Cases of communities and individuals using new technology as a tool to preserve and explore their cultural heritage alongside new media as a source for social orientation ranging from language acquisition to health-related issues will be covered. Therefore, aspects such as Art and Cultural Studies, Media and Communication, Behavioral Science, Psychology, (...) Philosophy and innovative approaches used by creative individuals are included. From the Aboriginal tribes of Australia, to the Maoris of New Zealand, to the mystical teachings of Sufi brotherhoods, the significance of the oral and written traditions and their current relation to online activities shall be discussed in the opening article. The book continues with a closer look at obesity awareness support groups and their impact on social media, Facebook usage in language learning context, smartphone addiction and internet dependency, as well as online media reporting of controversial ethical issues. The Digital progress has already left its dominating mark as the world entered the 21st century. Without a doubt, as technology continues its ascent, society will be faced with new and altering values in an effort to catch-up with this extraordinary Digitization, adapt satisfactorily in order to utilize these strong developments in everyday life. (shrink)

BACKGROUND: In today’s world of increased awareness regarding the concepts of corporate social responsibility (CSR) and corporate governance (CG), many firms in the developed countries consider noncompliance with CSR and CG standards as an important source of risk to their reputations with stakeholders. OBJECTIVE: The aim of this study is to investigate the relationship between the corporate social responsibility disclosure (CSRD) index and corporate factors, namely, board size, board independence, board meetings, CEO duality, a firm’s size, leverage, profitability and age. (...) This is the first known study in the case of Saudi Arabia to use the GRI 4th edition indicators to construct the CSRD index and evaluate Saudi listed firms. Results: The results show that profitability and size factor have positive and significant association with CSR disclosure in listed Saudi firms. While CG characteristics have no impact on CSR disclosure except board independence which has a negative impact. Conclusion: The average of CSRD index among Saudi firms is too low, it is about 11% that means Saudi firms disclose 11% of the information that they have to provide for stockholders according to GRI guidelines. Furthermore, the study concludes that the most polluted sectors “Ener. (shrink)

This paper seeks to determine the extent of anti-corruption information disclosure in the sustainability reports originating from Gulf countries. Focus primarily on the fight against corruption, this study utilizes a deeply-rooted content analysis technique of corporate sustainability reporting, covering 66 Gulf Cooperation Council (GCC) firms during 2014. Strengthened by the application of institutional theory, insight into the results points to a state of limited maturity regarding the disclosure of anti-corruption procedures in the region. More specifically, the results highlight the compliance (...) in the reporting of conduct code, while reporting information on whistle-blowing was significantly less in comparison. Firms in Qatar and UAE ultimately release better informed reports; inclusive of detailed information on internal anti-corruption practices. (shrink)

Reality and its Shadow, a brief yet powerful essay written in 1948, is the only text where Emmanuel Levinas deals solely with the ontology of art. Already in this early text, we can see how his understanding that ethics is the ground of philosophy drives his discussion. The nature of art is therefore treated in relation to what it does, ethically, to the subject, the maker, and the viewer. Art is the “inhumanity” and “inversion” of ethics. Only philosophical criticism reintegrates (...) its “inhumanity” in the ethical relation. The strength of Levinas’s philosophy issues from a pre-cognitive commitment to the “other”, epitomised in the “face to face” relation. Any philosophy emphasising the primacy of the subject over and above the “other” crumbles under his reading. Yet this same strength implies that those domains where the “face to face” relation is obscured lead to irresponsibility. One such domain is art. In this essay I argue that by applying his mature work to the criticism he advances in Reality and its Shadow we can find ethical value in art in virtue of its “inhumanity” and “inversion”. That is, we can agree with Levinas that art leads to irresponsibility, and yet ascribe to it positive ethical value in Levinas's own terms. This can help concretise the tension between the ethical and unethical aspects of art within a Levinasian framework. (shrink)

The concept of an individual, liberal data subject, who was traditionally at the centre of data protection efforts has recently come under scrutiny. At the same time, the particularly destructive effect of digital technology on Black people establishes the need for an analysis that not only considers but brings racial dimensions to the forefront. I argue that because Afrofuturism situates the Black struggle in persistent, yet continuously changing structural disparities and power relations, it offers a powerful departure point for re-imagining (...) data protection. Sketching an Afrofuturist data subject then centres on radical subjectivity, collectivity, and contextuality. (shrink)

Abstract: Retama raetam (RR) is a traditional medicinal plant belongs to fabaceae family which grows in North Africa and East Mediterranean region. Locally, RR is used in several diseases including diabetes mellitus and hypertension. Thus, this study aims to investigate certain behavioral and central effects of methanolic extract of RR seeds in experimental animals (male Albino adult mice of 20 – 35 gm). Three exploratory behavioral models are used in this study, open field, elevated plus maze and light-dark box models, (...) in addition, picrotoxin induced seizure model in mice. In elevated plus-maze test of anxiety, a dose of 25 mg/kg of RR induced a significant increase in number of open arms entries and time spent on open arms of the maze compared to the control. In light-dark model, time spent on light area and number of light-dark transitions are significantly increased after treatment with 25 mg/kg RR extract of seeds. Open field model also revealed a significant increase in general motor activity of the mouse after 25 mg/kg of RR. On the other hand, a dose of 50 mg/kg of RR significantly induced a profound central and peripheral analgesic responses. In summary, this study concludes that RR seeds have a profound anxiolytic-like effect and analgesic response with delay in latency time of seizure induced by picrotoxin in mice. (shrink)

Ancient Egyptians precisely direct their temples and tombs to specific astronomical points, as attested in the designs of the Old kingdom pyramids and related temples. Likewise, the same approach was used in many religious and funerary buildings across the sequential historical epochs of ancient Egypt. This research introduces what can be called "astronomical design improvements" conducted by ancient Egyptians to secure a precise orientation for a specific direction of religious and funerary monuments. Moreover, this precise orientation requires observatories used for (...) the orientation and monitoring of geographical directions. Therefore, this paper discusses the inquiring; are there any Astronomical Observatories evidences in ancient Egypt? (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)

ABSTRACT: Titu Maiorescu had a special relationship with psychology under the influence of both Kant and Herbart. The following study presents Maiorescu’s answers to the main issues raised by the materialism controversy that broke out in Germany in the mid-1850s century, at a time when he was writing and defending his doctoral thesis in Giessen. Most of these issues were related to the mind–body liaison and the capability of science to explain the mind. KEYWORDS: materialism controversy; mind–body relationship; psychology and (...) metaphysics; materialism vs, spiritualism; transcendental idealism. (shrink)

David Bloor’s thought experiment is taken into consideration to suggest that the rationality of the Other cannot be inferred by way of argument for the reason that it is unavoidably contained as a hidden supposition by any argument engaged in proving it. We are able to understand a different culture only as far as we recognize in it the same kind of rationality that works in our own culture. Another kind of rationality is either impossible, or indiscernible.

Abstract. The neutral monism suggested by Constantin Rădulescu-Motru was a theoretical frame intended to match the general idea of Kant’s apriorism with the results reached by physics and psychology at the beginning of the 20th Key words: transcendental aesthetic; consciousness in general; empirical consciousness; psychophysical parallelism; phenomenal ontology; scientistic ontology.

As an alternative for causality – which modern science found to be rather construed than objective – Jung developed his idea of synchronicity according to the demands of a modern scientific approach of nature. As I will show in the following paper, even if he promised a complementary principle of explanation, he ended by offering a principle of reality. His attempt gave birth to a pretty vast literature that links Jung’s synchronicity to Bohr’s complementarity. I will show that such a (...) connection, although not entirely groundless, should be treated with caution as long as the two approaches of reality are on completely different bases. Keywords: Jung, Bohr, causality, the measurement problem, quantum epistemology. (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)

Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional (...) neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics. (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)

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)

Where does entitlement to trust come from? When we trust someone to φ, do we need to have reason to trust them to φ or do we start out entitled to trust them to φ by default? Reductivists think that entitlement to trust always “reduces to” or is explained by the reasons that agents have to trust others. In contrast, anti-reductivists think that, in a broad range of circumstances, we just have entitlement to trust. even if we don’t have positive (...) reasons to do so. In this paper, we argue for a version of anti-reductivism. Roughly, we argue that we have default entitlement to trust someone to φ so long as there is an operative norm that requires S to φ. At least in such circumstances (and absent defeaters), we don’t need any positive reasons to trust S to φ. (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)

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)

Trust is a topic of longstanding philosophical interest. It is indispensable to every kind of coordinated human activity, from sport to scientific research. Even more, trust is necessary for the successful dissemination of knowledge, and by extension, for nearly any form of practical deliberation and planning. Without trust, we could achieve few of our goals and would know very little. Despite trust’s fundamental importance in human life, there is substantial philosophical disagreement about what trust is, and further, how trusting is (...) normatively constrained and best theorized about in relation to other things we value. This entry is divided into three sections, which explore key (and sometimes interconnected) ethical and epistemological themes in the philosophy of trust: (1) The Nature of Trust; (2) The Normativity of Trust, and (3) The Value of Trust. (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)

Multinational companies commonly and increasingly undertake their research in low and middle-income countries through commercial clinical research organizations (CROs). The involvement of these scientific middle men complicates the application of the theories of justice. We examine those complexities, and conclude that while the difficulties are not immune to analysis in terms of these theories, the theories have to be deployed in new ways in order to be useful in the new commercial world.

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: 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)

Data access control is a challenging issue in public cloud storage systems. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been adopted as a promising technique to provide flexible, fine-grained and secure data access control for cloud storage with honest-but- curious cloud servers. However, in the existing CP-ABE schemes, the single attribute authority must execute the time-consuming user legitimacy verification and secret key distribution, and hence it results in a single-point performance bottleneck when a CP-ABE scheme is adopted in a large-scale cloud storage (...) system. Users may be stuck in the waiting queue for a long period to obtain their secret keys, thereby resulting in low-efficiency of the system. Although multi authority access control schemes have been proposed, these schemes still cannot overcome the drawbacks of single-point bottleneck and low efficiency, due to the fact that each of the authorities still independently manages a disjoint attribute set. In this paper we propose a system that improves the approach of CP-ABE from text based asymmetric to Image based symmetric approach for faster encryption as well as access to data. We also propose a multiple access policy generation for single user where we will be able to implement one to many and many to many methodology. (shrink)

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: 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)

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)

Recently, the sustainability concept has become a prominent topic in the tourism and hospitality field, particularly after the COVID-19 pandemic. In response, this study undertook a systematic review of 176 articles to identify the research trends in sustainability within the tourism and hospitality area in the post-COVID-19 era. Using thematic analysis, this study identified five key research trends. The findings offer a thorough overview of the changing research landscape regarding sustainability in the field of tourism and hospitality. Consequently, the study (...) provided recommendations for practitioners and researchers aiming to promote sustainability in the industry. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to (...) predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

This paper has two aims. The first is critical: it argues that our mainstream epistemology of disagreement does not have the resources to explain what goes wrong in cases of group-level epistemic injustice. The second is positive: we argue that a functionalist account of group belief and group justification delivers (1) an account of the epistemic peerhood relation between groups that accommodates minority and oppressed groups, and (2), furthermore, diagnoses the epistemic injustice cases correctly as cases of unwarranted belief on (...) the part of the oppressor group. (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 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)

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)