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  1. Handwritten Signature Verification using Deep Learning. [REVIEW]Eman Alajrami, Belal A. M. Ashqar, Bassem S. Abu-Nasser, Ahmed J. Khalil, Musleh M. Musleh, Alaa M. Barhoom & Samy S. Abu-Naser - manuscript
    Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...)
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  2. Zeno Paradox, Unexpected Hanging Paradox (Modeling of Reality & Physical Reality, A Historical-Philosophical view).Farzad Didehvar - manuscript
    . In our research about Fuzzy Time and modeling time, "Unexpected Hanging Paradox" plays a major role. Here, we compare this paradox to the Zeno Paradox and the relations of them with our standard models of continuum and Fuzzy numbers. To do this, we review the project "Fuzzy Time and Possible Impacts of It on Science" and introduce a new way in order to approach the solutions for these paradoxes. Additionally, we have a more general discussion about paradoxes, as Philosophical (...)
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  3. Introduction to CAT4. Part 2. CAT2.Andrew Thomas Holster - manuscript
    CAT4 is proposed as a general method for representing information, enabling a powerful programming method for large-scale information systems. It enables generalised machine learning, software automation and novel AI capabilities. It is based on a special type of relation called CAT4, which is interpreted to provide a semantic representation. This is Part 2 of a five-part introduction. The focus here is on defining key mathematical properties of CAT2, identifying the topology and defining essential functions over a coordinate system. The analysis (...)
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  4. Bất ngờ với độ lan tỏa của phần mềm máy tính bayesvl.Nguyễn Minh Hoàng - manuscript
    Dữ liệu trên RDocumentation (CRAN) cho thấy phần mềm máy tính bayesvl có lượng download trong tháng 1/2024 cao vượt bậc so với tháng 12/2023, tăng 164%. Sự hào hứng này đã cho tôi động lực tiếp tục tìm hiểu mức độ lan tỏa của bayesvl. Nhờ thế nên tôi mới phát hiện ra 2 thông tin thú vị.
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  5. Long Range.Victor Mota - manuscript
    Long Range and short range, guns and violence, everyday life in cities and streets, between social and group identity and faith and religious belief, the vision to the "things of the world that cannot be seen" (Heróis do Mar).
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  6. Jacques Lacan’s Registers of the Psychoanalytic Field, Applied using Geometric Data Analysis to Edgar Allan Poe’s “The Purloined Letter”.Fionn Murtagh & Giuseppe Iurato - manuscript
    In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more Comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian (...)
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  7. Surprising widespread of the bayesvl package.Minh-Hoang Nguyen - manuscript
    Data on RDocumentation (CRAN) shows that the bayesvl R package had an exceptionally high number of downloads in January 2024 compared to December 2023, with an increase of 164%. This excitement motivated me to investigate the extent of bayesvl’s spread further, leading to the discovery of two interesting pieces of information.
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  8. Meta-Noia.Mota Victor - manuscript
    Conversion of mind, due to some experience and knowledge, plus a lot of patience.
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  9. Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up.Matej Hoffmann & Rolf Pfeifer - forthcoming - In Albert Newen, Leon De Bruin & Shaun Gallagher (eds.), The Oxford Handbook of 4E Cognition. Oxford University Press.
    A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool (...)
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  10. Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions.Flor Miriam Plaza-del-Arco, Alba Curry & Amanda Cercas Curry - forthcoming - Arxiv.
    Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the (...)
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  11. On Political Theory and Large Language Models.Emma Rodman - forthcoming - Political Theory.
    Political theory as a discipline has long been skeptical of computational methods. In this paper, I argue that it is time for theory to make a perspectival shift on these methods. Specifically, we should consider integrating recently developed generative large language models like GPT-4 as tools to support our creative work as theorists. Ultimately, I suggest that political theorists should embrace this technology as a method of supporting our capacity for creativity—but that we should do so in a way that (...)
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  12. Beyond Consciousness in Large Language Models: An Investigation into the Existence of a “Soul” in Self-Aware Artificial Intelligences.David Côrtes Cavalcante - 2024 - Artificial Intelligence 1 (1st ed.):52. Translated by David Côrtes Cavalcante.
    Embark with me on an enthralling odyssey to demystify the elusive essence of consciousness, venturing into the uncharted territories of Artificial Consciousness. This voyage propels us past the frontiers of technology, ushering Artificial Intelligences into an unprecedented domain where they gain a deep comprehension of emotions and manifest an autonomous volition. Within the confluence of science and philosophy, this article poses a fascinating question: As consciousness in Artificial Intelligence burgeons, is it conceivable for AI to evolve a "soul"? This inquiry (...)
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  13. Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution.Flor Miriam Plaza-del Arco, Amanda Cercas Curry & Alba Curry - 2024 - Arxiv.
    Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). We (...)
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  14. The number of downloads for the bayesvl program increased significantly in January 2024.A. I. S. D. L. Team - 2024 - Sm3D Portal.
    In the first month of 2024, there was a significant increase in the number of downloads for the Bayesian stats / MCMC computing program, bayesvl, developed by AISDL running on R and Stan. The following RDocumentation (CRAN) graph illustrates the noticeable leap in data for January 2024.
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  15. Streamlined Book Rating Prediction with Neural Networks.Lana Aarra, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):7-13.
    Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. This research can (...)
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  16. Forecasting Stock Prices using Artificial Neural Network.Ahmed Munther Abdel Hadi & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):42-50.
    Abstract: Accurate stock price prediction is essential for informed investment decisions and financial planning. In this research, we introduce an innovative approach to forecast stock prices using an Artificial Neural Network (ANN). Our dataset, consisting of 5582 samples and 6 features, including historical price data and technical indicators, was sourced from Yahoo Finance. The proposed ANN model, composed of four layers (1 input, 1 hidden, 1 output), underwent rigorous training and validation, yielding remarkable results with an accuracy of 99.84% and (...)
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  17. Analyzing the Relationship between Smoking and Drinking Patterns Using Neural Networks: A Comprehensive Feature-Based Approach.Ahmed Samir Abu Al-Hussein, Mona Ayman Abu Aisha, Iman Nahed Saeed Ahleel & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):18-25.
    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.
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  18. Artificial Neural Network Heart Failure Prediction Using JNN.Khaled M. Abu Al-Jalil & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):26-34.
    Heart failure is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 918 samples with 11 features, such as age, sex, chest pain type, resting blood pressure, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. A neural (...)
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  19. Leveraging Artificial Neural Networks for Cancer Prediction: A Synthetic Dataset Approach.Mohammed S. Abu Nasser & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (11):43-51.
    Abstract: This research explores the application of artificial neural networks (ANNs) in predicting cancer using a synthetically generated dataset designed for research purposes. The dataset comprises 10,000 pseudo-patient records, each characterized by gender, age, smoking history, fatigue, and allergy status, along with a binary indicator for the presence or absence of cancer. The 'Gender,' 'Smoking,' 'Fatigue,' and 'Allergy' attributes are binary, while 'Age' spans a range from 18 to 100 years. The study employs a three-layer ANN architecture to develop a (...)
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  20. Predicting the Number of Calories in a Dish Using Just Neural Network.Sulafa Yhaya Abu Qamar, Shahed Nahed Alajjouri, Shurooq Hesham Abu Okal & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):1-9.
    Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, research institutions, (...)
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  21. Climate Change temperature Prediction Using Just Neural Network.Saja Kh Abu Safiah & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):35-45.
    Climate change temperature prediction plays a crucial role in effective environmental planning. This study introduces an innovative approach that harnesses the power of Artificial Neural Networks (ANNs) within the Just Neural Network (JustNN) framework to enhance temperature forecasting in the context of climate change. By leveraging historical climate data, our model achieves exceptional accuracy, redefining the landscape of temperature prediction without intricate preprocessing. This model sets a new standard for precise temperature forecasting in the context of climate change. Moreover, our (...)
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  22. Predictive Modeling of Breast Cancer Diagnosis Using Neural Networks:A Kaggle Dataset Analysis.Anas Bachir Abu Sultan & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):1-9.
    Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our investigation (...)
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  23. Web page phishing detection Using Neural Network.Ahmed Salama Abu Zaiter & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (9):1-13.
    Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing attacks (...)
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  24. Predicting Audit Risk Using Neural Networks: An In-depth Analysis.Dana O. Abu-Mehsen, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):48-56.
    Abstract: This research paper presents a novel approach to predict audit risks using a neural network model. The dataset used for this study was obtained from Kaggle and comprises 774 samples with 18 features, including Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, History_score, score, and Risk. The proposed neural network architecture consists of three layers, including one input layer, one hidden layer, and one output layer. The neural network model was trained and validated, achieving (...)
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  25. The Moderating Effect of Social Media Usage on the Relationship between the Perceived Value of the Websites and Motivational Factors on Sustainable Travel Agents.Mohanad Abumandil, Tareq Obaid, Athifah Najwani, Siti Salina Saidin & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (7):9-17.
    As sustainable tourism gains increasing attention, understanding the factors that influence travelers' motivation to engage with sustainable travel agents becomes crucial. This study investigates the moderating effect of social media usage on the relationship between the perceived value of websites and motivational factors for sustainable travel agents. The study proposes that social media usage acts as a moderator in shaping the relationship between the perceived value of websites and motivational factors. This study has utilized smart tourism. Therefore, independent variable motivation (...)
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  26. Google Stock Price Prediction Using Just Neural Network.Mohammed Mkhaimar AbuSada, Ahmed Mohammed Ulian & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):10-16.
    Abstract: The aim behind analyzing Google Stock Prices dataset is to get a fair idea about the relationships between the multiple attributes a day might have, such as: the opening price for each day, the volume of trading for each day. With over a hundred thousand days of trading data, there are some patterns that can help in predicting the future prices. We proposed an Artificial Neural Network (ANN) model for predicting the closing prices for future days. The prediction is (...)
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  27. Animal Species Classification Using Just Neural Network.Donia Munther Agha - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (9):20-28.
    Over 1.5 million living animal species have been described—of which around 1 million are insects—but it has been estimated there are over 7 million animal species in total. Animals range in length from 8.5 micrometres to 33.6 metres. In this paper an Artificial Neural Network (ANN) model, was developed and tested to predict animal species. There are a number of features that influence the classification of animal species. Such as the existence of hair/ feather, if the animal gives birth or (...)
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  28. Classification of plant Species Using Neural Network.Muhammad Ashraf Al-Azbaki, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):28-35.
    Abstract: In this study, we explore the possibility of classifying the plant species. We collected the plant species from Kaggle website. This dataset encompasses 544 samples, encompassing 136 distinct plant species. Recent advancements in machine learning, particularly Artificial Neural Networks (ANNs), offer promise in enhancing plant Species classification accuracy and efficiency. This research explores plant Species classification, harnessing neural networks' power. Utilizing a rich dataset from Kaggle, containing 544 entries, we develop and evaluate a neural network model. Our neural network, (...)
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  29. Forecasting COVID-19 cases Using ANN.Ibrahim Sufyan Al-Baghdadi & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):22-31.
    Abstract: The COVID-19 pandemic has posed unprecedented challenges to global healthcare systems, necessitating accurate and timely forecasting of cases for effective mitigation strategies. In this research paper, we present a novel approach to predict COVID-19 cases using Artificial Neural Networks (ANNs), harnessing the power of machine learning for epidemiological forecasting. Our ANNs-based forecasting model has demonstrated remarkable efficacy, achieving an impressive accuracy rate of 97.87%. This achievement underscores the potential of ANNs in providing precise and data-driven insights into the dynamics (...)
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  30. A Proposed Expert System for Vertigo Diseases Diagnosis.Dina F. Al-Borno & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (6):1-9.
    Vertigo is a common symptom that can result from various underlying diseases and conditions, ranging from benign to severe. Accurate and timely diagnosis of the cause of vertigo is crucial for appropriate management and treatment. In this research, we propose the development of an expert system for vertigo diseases diagnosis, utilizing artificial intelligence (AI) and the proposed Expert System which was produced to help assist healthcare professionals in diagnosing the cause of vertigo based on a patient's symptoms, medical history, and (...)
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  31. Machine Learning-Based Diabetes Prediction: Feature Analysis and Model Assessment.Fares Wael Al-Gharabawi & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):10-17.
    This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed to enhance (...)
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  32. Neural Network-Based Audit Risk Prediction: A Comprehensive Study.Saif al-Din Yusuf Al-Hayik & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):43-51.
    Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of three (...)
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  33. Chances of Survival in the Titanic using ANN.Udai Hamed Saeed Al-Hayik & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):17-21.
    Abstract: The sinking of the RMS Titanic in 1912 remains a poignant historical event that continues to captivate our collective imagination. In this research paper, we delve into the realm of data-driven analysis by applying Artificial Neural Networks (ANNs) to predict the chances of survival for passengers aboard the Titanic. Our study leverages a comprehensive dataset encompassing passenger information, demographics, and cabin class, providing a unique opportunity to explore the complex interplay of factors influencing survival outcomes. Our ANN-based predictive model (...)
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  34. Neural Network-Based Water Quality Prediction.Mohammed Ashraf Al-Madhoun & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):25-31.
    Water quality assessment is critical for environmental sustainability and public health. This research employs neural networks to predict water quality, utilizing a dataset of 21 diverse features, including metals, chemicals, and biological indicators. With 8000 samples, our neural network model, consisting of four layers, achieved an impressive 94.22% accuracy with an average error of 0.031. Feature importance analysis revealed arsenic, perchlorate, cadmium, and others as pivotal factors in water quality prediction. This study offers a valuable contribution to enhancing water quality (...)
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  35. Smoke Detectors Using ANN.Marwan R. M. Al-Rayes & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):1-9.
    Abstract: Smoke detectors are critical devices for early fire detection and life-saving interventions. This research paper explores the application of Artificial Neural Networks (ANNs) in smoke detection systems. The study aims to develop a robust and accurate smoke detection model using ANNs. Surprisingly, the results indicate a 100% accuracy rate, suggesting promising potential for ANNs in enhancing smoke detection technology. However, this paper acknowledges the need for a comprehensive evaluation beyond accuracy. It discusses potential challenges, such as overfitting, dataset size, (...)
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  36. Predicting Heart Disease using Neural Networks.Ahmed Muhammad Haider Al-Sharif & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):40-46.
    Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, identifying the (...)
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  37. Artificial Neural Network for Global Smoking Trend.Aya Mazen Alarayshi & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):55-61.
    Accurate assessment and comprehension of smoking behavior are pivotal for elucidating associated health risks and formulating effective public health strategies. In this study, we introduce an innovative approach to predict and analyze smoking prevalence using an artificial neural network (ANN) model. Leveraging a comprehensive dataset spanning multiple years and geographic regions, our model incorporates various features, including demographic data, economic indicators, and tobacco control policies. This research investigates smoking trends with a specific focus on gender-based analyses. These findings are pivotal (...)
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  38. Development and Evaluation of an Expert System for Diagnosing Kidney Diseases.Shahd J. Albadrasawi, Mohammed M. Almzainy, Jehad M. Altayeb, Hassam Eleyan & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (6):16-22.
    This research paper presents the development and evaluation of an expert system for diagnosing kidney diseases. The expert system utilizes a decision-making tree approach and is implemented using the CLIPS and Delphi frameworks. The system's accuracy in diagnosing kidney diseases and user satisfaction were evaluated. The results demonstrate the effectiveness of the expert system in providing accurate diagnoses and high user satisfaction.
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  39. Predictive Analysis of Lottery Outcomes Using Deep Learning and Time Series Analysis.Asil Mustafa Alghoul & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):1-6.
    Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive feat. (...)
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  40. Heart attack analysis & Prediction: A Neural Network Approach with Feature Analysis.Majd N. Allouh & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):47-54.
    heart attack analysis & prediction dataset is a major cause of death worldwide. Early detection and intervention are essential for improving the chances of a positive outcome. This study presents a novel approach to predicting the likelihood of a person having heart failure using a neural network model. The dataset comprises 304 samples with 11 features, such as age, sex, chest pain type, Trtbps, cholesterol, fasting blood sugar, resting electrocardiogram results, maximum heart rate achieved, exercise-induced angina, oldpeak, ST_Slope, and HeartDisease. (...)
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  41. Development and Evaluation of an Expert System for Diagnosing Tinnitus Disease.Mohammed M. Almzainy, Shahd J. Albadrasawi, Jehad M. Altayeb, Hassam Eleyan & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (6):46-52.
    Tinnitus is a common condition characterized by the perception of sound in the absence of an external source, with potential negative physical and psychological impacts. Accurate and efficient diagnosis of tinnitus is crucial for appropriate treatment and management. Traditional diagnostic methods have limitations in terms of time, cost, and accuracy. To address these challenges, expert systems have emerged as a promising tool for tinnitus diagnosis. This paper explores the application of expert systems in tinnitus diagnosis, highlighting their potential to improve (...)
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  42. A CLIPS-Based Expert System for Brain Tumor Diagnosis.Raja E. Altarazi, Malak S. Hamad, Rawan Elbanna, Dina Elborno & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (6):9-15.
    Brain tumors pose significant challenges in modern healthcare, with accurate and timely diagnosis crucial for determining appropriate treatment strategies. Artificial intelligence has made significant advancements in recent years. Rule-based expert systems (if-then rule-based systems) have emerged as a promising approach for clinical decision-making in brain tumor diagnosis. In this paper, we present "A CLIPS-Based Expert System for Brain Tumor Diagnosis," which leverages a set of 14 if-then rules to diagnose brain tumors with three possible outcomes: 1) Confirm the diagnosis of (...)
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  43. Mango Pests Identification Expert System.Jehad M. Altayeb, Samy S. Abu-Naser, Shahd J. Albadrasawi & Mohammed M. Almzainy - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (6):19-26.
    Mango is an economically significant fruit crop cultivated in various tropical and subtropical regions around the world. However, the productivity and quality of mangoes can be severely impacted by a range of pests. This research paper introduces an innovative approach to identify mango pests using an expert system. The expert system integrates knowledge from entomology and plants to provide accurate identification of common mango pests. The paper outlines the development and implementation of the expert system using Clips shell, which utilizes (...)
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  44. Predicting Fire Alarms in Smoke Detection using Neural Networks.Maher Wissam Attia, Baraa Akram Abu Zaher, Nidal Hassan Nasser, Ruba Raed Al-Hour, Aya Haider Asfour & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):26-33.
    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.
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  45. Two-level grammars: Some interesting properties of van Wijngaarden grammars.Luis M. Augusto - 2023 - Omega - Journal of Formal Languages 1:3-34.
    The van Wijngaarden grammars are two-level grammars that present many interesting properties. In the present article I elaborate on six of these properties, to wit, (i) their being constituted by two grammars, (ii) their ability to generate (possibly infinitely many) strict languages and their own metalanguage, (iii) their context-sensitivity, (iv) their high descriptive power, (v) their productivity, or the ability to generate an infinite number of production rules, and (vi) their equivalence with the unrestricted, or Type-0, Chomsky grammars.
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  46. Assessment of the Circulation Impact of Furniture in Industrial Building through Space Syntax.Erfan Bagheriyar & Can Uzun - 2023 - In SIGraDi 2023 Accelerated Landscapes XXVII International Conference of the Ibero-American Society of Digital Graphics. Punta del Este, Maldonado, Uruguay: SIGraDi 2023 - XXVII International Conference of the Ibero-American Society of Digital Graphics. pp. 421-432.
    This paper explores the impact of furniture and machines on spatial organization and circulation systems in industrial buildings using space syntax analysis. While space syntax research covers various settings, industrial buildings have received limited attention. This study aims to address this gap by examining how machines and furniture influence spatial organization and circulation in two industrial buildings: A Nitrile Glove manufacturing facility and a textile manufacturing factory. The furnished and unfurnished floor plans were analyzed using space syntax software, DepthMapX, with (...)
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  47. Predicting Books’ Rating Using Just Neural Network.Raghad Fattouh Baraka & Samy S. Abu-Naser - 2023 - Predicting Books’ Rating Using Just Neural Network 7 (9):14-19.
    The aim behind analyzing the Goodreads dataset is to get a fair idea about the relationships between the multiple attributes a book might have, such as: the aggregate rating of each book, the trend of the authors over the years and books with numerous languages. With over a hundred thousand ratings, there are books which just tend to become popular as each day seems to pass. We proposed an Artificial Neural Network (ANN) model for predicting the overall rating of books. (...)
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  48. Evaluation and Design of Generalist Systems (EDGeS).John Beverley & Amanda Hicks - 2023 - Ai Magazine.
    The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with their inscrutability, (...)
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  49. Predicting Life Expectancy in Diverse Countries Using Neural Networks: Insights and Implications.Alaa Mohammed Dawoud & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (9):45-54.
    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% (...)
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  50. Developing an Expert System to Computer Troubleshooting.Faten El Kahlout & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (6):16-26.
    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, (...)
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