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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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: 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)

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)

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)

Malaria is a life-threatening disease spread to humans by some types of mosquitoes. It is mostly found in tropical countries. It is preventable and curable. The infection is caused by a parasite and does not spread from person to person. Symptoms can be mild or life-threatening. Mild symptoms are fever, chills and headache. Severe symptoms include fatigue, confusion, seizures, and difficulty breathing. Infants, children under 5 years, pregnant women, travelers and people with HIV or AIDS are at higher risk of (...) severe infection. Discovering this disease is a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help to avoid spreading this disease. The expert system diagnoses malaria disease correctly to facilitate doctors to find the correct treatment based on the appropriate diagnosis. Objectives: An expert system has been established based on CLIPS to diagnose malaria disease. (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)

The research aims to identify the impact of the management requirements on operating of computerized management information systems to improve performance, and discuss the perceptions of respondents to develop the performance of employees in the Gaza Electricity Distribution Company, the researchers used the stratified sample method, (360) questionnaires were distributed on the study sample, (306) questionnaires were recoved with a percentage of (85%). The most important findings of the study: computerized MI have a positive impact on the development of performance (...) in the Gaza Electricity Distribution Company systems. The is statistical significance impact between “the physical, software supplies, human and organizational” requirements for the management and operation of computerized management information systems and performance development in the Gaza Electricity Distribution Company. -/- The research also concluded a series of recommendations: the need to strengthen the company's management interest in the use of computerized management information systems in all its components and elements of being an important variable which contributes to the effect on performance development. It's essential that we develop the infrastructure for information technology in general, and computerized management information systems, in particular for the development of performance. Increased interest in providing the material of equipment and devices used in the computerized management information system requirements. Interest in providing technical inputs to the computerized management information systems through keeping pace with technological means and modern techniques and work on the training of personnel to use those systems. And increased attention to human inputs of computerized management information systems "specialists technicians and end-users" through a variety of disciplines working in the field of computerized management information systems. (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)

Abstract Alzheimer's disease has spread insanely throughout the world. Early detection and intervention are essential to improve the chances of a positive outcome. This study presents a new method to predict a person's likelihood of developing Alzheimer's using a neural network model. The dataset includes 373 samples with 10 features, such as Group,M/F,Age,EDUC, SES,MMSE,CDR ,eTIV,nWBV,Oldpeak,ASF.. A four-layer neural network model (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 98.10% and an average error (...) of 0.01. This study provides a valuable tool for early detection and intervention of Alzheimer's, thus contributing to the field of health and medicine. (shrink)

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

Abstract: 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)

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)

A lot of infants have significant food-related problems, as well as spitting up, rejecting new foods, or not accepting to eat at specific times. These issues are frequently ordinary and are not a sign that the baby is unwell. According to the National Institutes of Health, 25% of generally developing infants and 35% of babies with neurodevelopmental disabilities are tormented by some sort of feeding problem. Some, for example rejecting to eat specific foods or being overly finicky, are momentary and (...) don’t cause any health dangers. This paper proposes an expert system that can be used to successfully diagnose Feeding problems in infants and children. The suggested systems were found to be beneficial approach in addition to existing impartial ones. So far as the authors are aware, this is the initial effort of using an expert system in attaining good performance in a real world application. This expert system was designed and implemented to help parents diagnose these problems and get a recommendation of how to deal with infants and children. (shrink)

The paper depicts the blueprint of an electronic wise indicating system for demonstrating learning French to understudies to overcome the inconveniences they go up against. The fundamental idea of this structure is a proficient introduction into learning French. The system shows the purpose of learning French and coordinates thusly made issues for the understudies to clarify. The system is logically balanced at run time to the understudy’s individual progress. The system gives unequivocal help to adaptable presentation to learners.

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)

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)

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)

Abstract: Rice, as a paramount staple crop worldwide, sustains billions of lives. Precise classification of rice types holds immense agricultural, nutritional, and economic significance. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing rice type classification accuracy and efficiency. This research explores rice type classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 18,188 entries and key rice grain attributes, we develop and evaluate a neural network model. Our neural network, featuring a (...) single hidden layer, achieves remarkable results—a staggering 100% accuracy and a minute average error rate of 0.000001. Beyond performance metrics, we delve into the intricacies of rice type classification through feature importance analysis. The most influential features—'id,' 'Area,' 'MajorAxisLength,' 'MinorAxisLength,' 'Eccentricity,' 'ConvexArea,' 'EquivDiameter,' 'Extent,' 'Perimeter,' 'Roundness,' and 'AspectRatio'—uncover the physiological traits underpinning accurate rice classification. This research contributes to advancing rice classification methods and highlights the potential of ANNs in optimizing agricultural practices, ensuring food safety, and bolstering global trade. (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)

In this paper a Knowledge-Based System (KBS) for determining the appropriate students major according to his/her preferences for sophomore student enrolled in the Faculty of Engineering and Information Technology in Al-Azhar University of Gaza was developed and tested. A set of predefined criterions that is taken into consideration before a sophomore student can select a major is outlined. Such criterion as high school score, score of subject such as Math I, Math II, Electrical Circuit I, and Electronics I taken during (...) the student freshman year, number of credits passed, student cumulative grade point average of freshman year, among others, were then used as input data to KBS. KBS was designed and developed using Simpler Level Five (SL5) Object expert system language. KBS was tested on three generation of sophomore students from the Faculty of Engineering and Information Technology of the Al-Azhar University, Gaza. The results of the evaluation show that the KBS is able to correctly determine the appropriate students major without errors. (shrink)

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

As a type of evidence glass can be very useful contact trace material in a wide range of offences including burglaries and robberies, hit-and-run accidents, murders, assaults, ram-raids, criminal damage and thefts of and from motor vehicles. All of that offer the potential for glass fragments to be transferred from anything made of glass which breaks, to whoever or whatever was responsible. Variation in manufacture of glass allows considerable discrimination even with tiny fragments. In this study, we worked glass classification (...) and testing of artificial neural network model created by the JustNN. The aim of the study is help investigator in identifying the type of glass found in arena of the crime. The Neural Network model was trained and validated using the type of glass dataset. The accuracy of model in predicting the type of glass reached 96.7%. Thus neural network is suitable for predicating type of glasses. (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.

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)

Male genital problems and injuries may occur quite simply because of the scrotum and penis are not protected like other organs. Genital problems and injuries normally happen through: recreational activities (like Football, Hooky, biking, basketball), workrelated tasks (like contact to irritating chemicals), downhill drop, and sexual activity. A genital injury frequently causes harsh pain that typically disappear fast without causing enduring harm. Home handling is generally all that is required for trivial problems or injuries. Pain, inflammation, staining, or rashes that (...) are coexist with other symptoms might be a source for concern. Genital problems in men is one of the most common problems which requires fast intervention. In this paper we present an expert system that help men diagnose their genital problems and give them the proper treatment. SL5 Object expert system language was used to design and implement this expert system. (shrink)

Lung cancer is a serious and deadly disease that affects the lungs, which are responsible for taking in oxygen and expelling carbon dioxide from the body. The disease can develop in any part of the lungs and is usually caused by smoking or exposure to certain chemicals. The main Objective: of this expert system is to provide an accurate diagnosis of lung cancer and the appropriate treatment options. In this paper, Methods: we present the design and implementation of an expert (...) system that can assist physicians in diagnosing lung cancer and determining the best course of treatment. The system provides an overview of the disease, its causes, symptoms, diagnosis, and treatment, along with relevant medical information. Results: The system was developed using the Clips expert system language and evaluated by medical professionals who found it to be an effective tool for diagnosing and treating lung cancer. Conclusion: The proposed expert system has the potential to improve patient outcomes and reduce healthcare costs associated with the treatment of lung cancer. (shrink)

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

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)

There is no doubt that Computer troubleshooting is important for organizations and companies and for personal use level. Sound cards troubles is one of the most annoying problems in computers. It causes damage and troubles in computers to persons, organizations and firms. Correctly, expert systems can greatly help to avoid damage to these computers. designed to diagnose and troubleshoot issues related to sound cards in computer systems. The expert system is developed using a combination of rule-based and machine learning approaches, (...) which allows for accurate and efficient diagnosis of a wide range of sound card problems. Objectives: An expert system has been established based on CLIPS to diagnose Computer Problems. (shrink)

The study aimed to identify the level of promotion of entrepreneurship in the technical colleges in Palestine. The analytical descriptive method was used in the study. A questionnaire of 41 items was randomly distributed to the technical colleges in the Gaza Strip. The random sample consisted of (275) employees from the mentioned colleges, and the response rate were (74.5%). The results of the study showed that the technical colleges achieved a high level of promotion of entrepreneurship with a relative weight (...) of 73.45%. The results of the study showed that there is a high level of promotion of entrepreneurship (risk, preparedness, proactive competition, innovation orientation) in the technical colleges in Gaza Strip. The field of competition came in first place with a relative weight of 76.65%. In the second place came the field (the trend towards innovation) and relative weight (74.96%). In the third place came the field of pre-emptive preparedness with a relative weight of 74.07%. In the fourth and last place came the field of risk and a relative weight of 68.39%. The results confirmed that there are statistically significant differences in the promotion of entrepreneurship in the technical colleges in Gaza Strip due to the college variable in favor of UCAS. The results confirmed that there is no statistically significant relationship in the promotion of entrepreneurship in technical colleges in Gaza Strip due to the variable level of employment. The researchers suggest a set of recommendations, the most important of which is to draw the attention of the technical colleges to the importance of promoting entrepreneurship, because of their role in reducing the problem of unemployment, the importance of linking technical education and promoting entrepreneurship to the Palestinian society in general and Gaza Strip in particular. The importance of urging decision-makers in technical colleges to promote interest in leadership and to put their own courses in all technical education programs in these colleges, as well as enhancing the technical, technological and technical capabilities of technical education and keeping pace with the latest international standards by providing the necessary material resources. There is a need to urge researchers to conduct further studies of the future which deal with the same variables of the current study in the field of entrepreneurship and applied to other sectors. (shrink)

Background: With the coming of the Industrial Revolution, the levels of pollution grow significantly. This Technological development contributed to the worsening of shortness breath problems in great shape. especially in infants and children. There are many shortness breath diseases that infants and children face in their lives. Shortness of breath is one of a very serious symptom in children and infants and should never be ignored. Objectives: Along these lines, the main goal of this expert system is to help physician (...) in diagnosing and describe some common causes of shortness of breath in infants and children by diagnosing their cases through our expert system. Moreover, this system which we are presenting will give patient the appropriate diagnosis of disease and the treatment required. Methods: In this paper the strategy of the expert system for diagnosing a number of the existed shortness of breath in infants and children diseases such as (Asthma , Bronchiolitis, Viral Pneumonia, cough, Shortness of breath' dyspnea ', Epiglottitis, Croup, ABSCESS in the tonsil 'peritonsillar abscess', Bronchitis, Viral Bronchitis, Wheezing, sudden infant death syndrome 'SIDS' ) is introduced, an overview about the shortness of breath in children and infants diseases are delineated, the cause of diseases are sketched and the treatment of disease whenever conceivable is given . SL5 Object Expert System language was utilized for designing and implementing the proposed expert system. Results: The proposed shortness of breath in children and infants diseases diagnosis expert system was estimated by Medical students and they were satisfied with its result. Conclusions: The Proposed expert system is very useful for Respiratory physician, pediatrician, recently graduated physician, and for children's parents with shortness of breath problem. (shrink)

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

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)

This research aims to identify the use of decision support systems as an entry point for operations of re-engineering in the Palestinian universities in Gaza Strip. The researchers used the method of questionnaire to collect data, and the researchers used a sample stratified random way, were (350) questionnaire distributed on the research sample and (312) questionnaire were collected back (89.1%). The study results showed that the most important ones are: there exists statistically significant impact at the level of significance (α (...) ≤ 0.05) for physical requirements, human requirements. technical requirements and regulatory requirements. The results showed also, the presence of statistically significant differences between the averages of the study sample estimates on the use of decision support systems as an entry point for operations of re-engineering in the Palestinian universities in Gaza Strip due to the variable sex in favor of males requirements. Furthermore, the results showed the existence of differences to the variable name of the university and each area of study for the benefit of the Islamic University then Al-Azhar University and then Al-Aqsa University. The study also concluded a series of recommendations including: encourage Palestinian universities in Gaza Strip, which are planning to re-engine Information Systems to start implementing the programs in the radical change as soon as possible. The need for the Palestinian universities in Gaza Strip, to develop the infrastructure for information technology in general, and decision support systems, in particular, when doing re-engineering operations. And the need for a separate unit of decision support systems and organizational structure to allow information to flow seamlessly between colleges and various departments and divisions. And investment of available information in building the capacities of integration techniques, in order to enable them to continue with the re-engineering of Information Systems. (shrink)

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

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)

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)

Abstract cantaloupe and honeydew melons are part of the muskmelon family, which originated in the Middle East. When picking either cantaloupe or honeydew melons to eat, you should choose a firm fruit that is heavy for its size, with no obvious signs of bruising. They can be stored at room temperature until you cut them, after which they should be kept in the refrigerator in an airtight container for up to five days. You should always wash and scrub the rind (...) of your melon before you cut it to remove any dirt or bacteria on the outside. In this paper, cantaloupe classification approach is presented with a dataset that contains approximately 1,312 of Cantaloupes and honeydews. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this task. The results found that CNN-driven classification applications when used in farming automation have the latent to enhance crop harvest and improve output and productivity when designed properly. The trained model achieved an accuracy of 99.74% on a held-out test set, demonstrating the feasibility of this approach. (shrink)

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

Abstract: Sign language recognition is one of the most rapidly expanding fields of study today. Many new technologies have been developed in recent years in the fields of artificial intelligence the sign language-based communication is valuable to not only deaf and dumb community, but also beneficial for individuals suffering from Autism, downs Syndrome, Apraxia of Speech for correspondence. The biggest problem faced by people with hearing disabilities is the people's lack of understanding of their requirements. In this paper we try (...) to fill this gap. By trying to translate sign language using artificial intelligence algorithms, we focused in this paper using transfer learning technique based on deep learning by utilizing a MobileNet algorithm and compared it with the previous paper results[10a], where we get in the Mobilenet algorithm on the degree of Accuracy 93,48% but the VGG16 the accuracy was 100% For the same number of images (43500 in the dataset in size 64*64 pixel ) and the same data split training data into training dataset (70%) and validation dataset(15%) and testing dataset(15%) and 20 epoch . (shrink)