Results for 'Mary Hesse's network model'

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  1. Phenomenology, Empiricism, and Constructivism in Paolo Parrini's Positive Philosophy.Andrea Pace Giannotta - 2019 - In Federica Buongiorno, Vincenzo Costa & Roberta Lanfredini (eds.), Phenomenology in Italy. Authors, Schools, Traditions. Springer. pp. 161-178.
    In this work, I discuss the role of Husserl’s phenomenology in Paolo Parrini’s positive philosophy. In the first section, I highlight the presence of both empiricist and constructivist elements in Parrini’s anti-foundationalist and anti-absolutist conception of knowledge. In the second section, I stress Parrini’s acknowledgement of the crucial role of phenomenology in investigating the empirical basis of knowledge, thanks to its analysis of the relationship between form and matter of cognition. In the third section, I point out some lines of (...)
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  2. Fenomenologia, empirismo e costruttivismo nella filosofia positiva di Paolo Parrini.Andrea Pace Giannotta - 2018 - In Federica Buongiorno, Vincenzo Costa & Roberta Lanfredini (eds.), La fenomenologia in Italia. Autori, scuole, tradizioni. Roma: Inschibboleth. pp. 255-283.
    In this work, I discuss the role of Husserl’s phenomenology in Paolo Parrini’s philosophical view. In the first section, I highlight the presence of both empiricist and constructivist elements in Parrini’s anti-foundationalist and anti-absolutist conception of knowledge. In the second section, I stress Parrini’s acknowledgement of the crucial role of phenomenology in investigating the empirical basis of knowledge, thanks to its analysis of the relationship between form and matter of cognition. In the third section, I point at some lines of (...)
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  3. Granger and science as network of models.Sergio Volodia Marcello Cremaschi - 1987 - Manuscrito 10 (2):111-136.
    The discovery of the role of models in science by Granger parallels the analogous discovery made by Mary Hesse and Marx Wartofsky. The role models are granted highlights the linguistic dimension of science, resulting in a 'softening' of Bachelard's rationalistic epistemology without lapsing into relativism. A 'linguistic' theory of metaphor, as contrasted with Bachelard's 'psychological' theory, is basic to Granger's account of models. A final paragraph discusses to what extent Granger's 'mature' theory of models would imply a revision of (...)
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  4. Polarization and Belief Dynamics in the Black and White Communities: An Agent-Based Network Model from the Data.Patrick Grim, Stephen B. Thomas, Stephen Fisher, Christopher Reade, Daniel J. Singer, Mary A. Garza, Craig S. Fryer & Jamie Chatman - 2012 - In Christoph Adami, David M. Bryson, Charles Offria & Robert T. Pennock (eds.), Artificial Life 13. MIT Press.
    Public health care interventions—regarding vaccination, obesity, and HIV, for example—standardly take the form of information dissemination across a community. But information networks can vary importantly between different ethnic communities, as can levels of trust in information from different sources. We use data from the Greater Pittsburgh Random Household Health Survey to construct models of information networks for White and Black communities--models which reflect the degree of information contact between individuals, with degrees of trust in information from various sources correlated with (...)
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  5. Models and Analogies in Science.Mary Hesse - 1965 - British Journal for the Philosophy of Science 16 (62):161-163.
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  6. Modele teoretyczne.Mariusz Mazurek - 2015 - Filozofia i Nauka. Studia Filozoficzne I Interdyscyplinarne 3:141-157.
    I analyse three most interesting and extensive approaches to theoretical models: classical ones—proposed by Peter Achinstein and Michael Redhead, and the rela-tively rareanalysed approach of Ryszard Wójcicki, belonging to a later phase of his research where he gave up applyingthe conceptual apparatus of logical semantics. I take into consideration the approaches to theoretical models in which they are qualified as models representing the reality. That is why I omit Max Black’s and Mary Hesse’s concepts of such models, as those (...)
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  7. Metafore, modelli, linguaggio scientifico: il dibattito postempirista.Sergio Volodia Marcello Cremaschi - 1988 - In Virgilio Melchiorre (ed.), Simbolo e conoscenza. Milano: Vita e Pensiero. pp. 31-102.
    I discuss Mary Hess’s interaction view of scientific metaphor, outline an alternative view and show how it may prove fruitful when applied to chapters of the history of science. I start with a reconstruction of the discussion on the nature of scientific models and their relationship to metaphors that took place in the Anglo-Saxon philosophy of Science starting from the Fifties; the discovery began with Stephen Pepper and Kenneth Burke, reaching Thomas Kuhn, Marx Wartofsky, and George Lakoff via Max (...)
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  8. Mary Slessor’s Legacy: A Model For 21st Century Missionaries.Ekpenyong Nyong Akpanika - 2015 - American Journal of Social Issues and Humanities 5 (3).
    The story of Miss Mary Mitchell Slessor is not a story of a clairvoyant legend who existed in an abstract world but a historical reality that worked around the then Old Calabar estuary and died on the 15th of January, 1915 at Ikot Oku Use, near Ikot Obong in the present day Akwa Ibom State and was buried at “Udi Mbakara” (Whiteman’s grave) in Calabar, Cross River State. Mary was one of those early missionaries that went to villages (...)
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  9. Ernst Mach’s Contribution to the Philosophy of Science in Light of Mary B. Hesse’s Postempiricism.Pietro Gori - 2021 - Hopos: The Journal of the International Society for the History of Philosophy of Science 11 (2):383-411.
    Ernst Mach’s definition of the relationship between thoughts and facts is well known, but the question of how Mach conceived of their actual relationship has received much less attention. This paper aims to address this gap in light of Mary B. Hesse’s view of a postempiricist approach to natural science. As this paper will show, this view is characterized by a constructivist conception of the relationship between theory and facts that seems to be consistent with Mach’s observations on scientific (...)
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    Metafore, modelli, linguaggio scientifico: il dibattito postempirista.Sergio Volodia Marcello Cremaschi - 1988 - In Virgilio Melchiorre (ed.), Simbolo e conoscenza. Milano: Vita e Pensiero. pp. 31-102.
    I start with a reconstruction of the discussion on the nature of scientific models and on their relationship to metaphors that has taken place in the Anglo-Saxon philosophy of Science starting from the Fifties; the discovery started with Stephen Pepper and Kenneth Burke, reaching Thomas Kuhn, Marx Wartofsky, and George Lakoff via Max Black's and Mary Hesse's interaction view. I argue that Hesse's view has a number of weak points: uncritically accepting Black's idea of "interaction", for keeping (...)
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  11. Deidealization: No Easy Reversals.Tarja Knuuttila & Mary S. Morgan - 2019 - Philosophy of Science 86 (4):641-661.
    Deidealization as a topic in its own right has attracted remarkably little philosophical interest despite the extensive literature on idealization. One reason for this is the often implicit assumption that idealization and deidealization are, potentially at least, reversible processes. We question this assumption by analyzing the challenges of deidealization within a menu of four broad categories: deidealizing as recomposing, deidealizing as reformulating, deidealizing as concretizing, and deidealizing as situating. On closer inspection, models turn out much more inflexible than the reversal (...)
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  12. Symbol Systems as Collective Representational Resources: Mary Hesse, Nelson Goodman, and the Problem of Scientific Representation.Axel Gelfert - 2015 - Social Epistemology Review and Reply Collective 4 (6):52-61.
    This short paper grew out of an observation—made in the course of a larger research project—of a surprising convergence between, on the one hand, certain themes in the work of Mary Hesse and Nelson Goodman in the 1950/60s and, on the other hand, recent work on the representational resources of science, in particular regarding model-based representation. The convergence between these more recent accounts of representation in science and the earlier proposals by Hesse and Goodman consists in the recognition (...)
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  13. Why It Is Time To Move Beyond Nagelian Reduction.Marie I. Kaiser - 2012 - In D. Dieks, S. Hartmann, T. Uebel & M. Weber (eds.), Probabilities, Laws and Structure. Springer. pp. 255-272.
    In this paper I argue that it is finally time to move beyond the Nagelian framework and to break new ground in thinking about epistemic reduction in biology. I will do so, not by simply repeating all the old objections that have been raised against Ernest Nagel’s classical model of theory reduction. Rather, I grant that a proponent of Nagel’s approach can handle several of these problems but that, nevertheless, Nagel’s general way of thinking about epistemic reduction in terms (...)
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  14. Real Film.Reid Perkins-Buzo - 2007 - Semiotics:142-158.
    Recent work by Ian Aitken and others has sought to re-establish a "Realist approach" to the documentary film in reaction to the postmodernist, pragmatist approach popular in the 1970s and 80s. The Saussurian/Lacanian orientation o f the semiotics that played a large role in the older film theory is rejected and replaced by an analytic theory of representation based on the work of Mary Hesse, Hilary Putnam and W.V.O. Quine. Although this may seem a setback vis-a-vis semiotics, it actually (...)
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  15. How and when are topological explanations complete mechanistic explanations? The case of multilayer network models.Beate Krickel, Leon de Bruin & Linda Douw - 2023 - Synthese 202 (1):1-21.
    The relationship between topological explanation and mechanistic explanation is unclear. Most philosophers agree that at least some topological explanations are mechanistic explanations. The crucial question is how to make sense of this claim. Zednik (Philos Psychol 32(1):23–51, 2019) argues that topological explanations are mechanistic if they (i) describe mechanism sketches that (ii) pick out organizational properties of mechanisms. While we agree with Zednik’s conclusion, we critically discuss Zednik’s account and show that it fails as a general account of how and (...)
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  16. Classification of Alzheimer's Disease Using Convolutional Neural Networks.Lamis F. Samhan, Amjad H. Alfarra & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (3):18-23.
    Brain-related diseases are among the most difficult diseases due to their sensitivity, the difficulty of performing operations, and their high costs. In contrast, the operation is not necessary to succeed, as the results of the operation may be unsuccessful. One of the most common diseases that affect the brain is Alzheimer’s disease, which affects adults, a disease that leads to memory loss and forgetting information in varying degrees. According to the condition of each patient. For these reasons, it is important (...)
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  17. Why interdisciplinary research in AI is so important, according to Jurassic Park.Marie Oldfield - 2020 - The Tech Magazine 1 (1):1.
    Why interdisciplinary research in AI is so important, according to Jurassic Park. -/- “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” -/- I think this quote resonates with us now more than ever, especially in the world of technological development. The writers of Jurassic Park were years ahead of their time with this powerful quote. -/- As we build new technology, and we push on to see what can actually (...)
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  18. Alzheimer: A Neural Network Approach with Feature Analysis.Hussein Khaled Qarmout & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (10):10-18.
    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% (...)
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  19. 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 (...)
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  20. THE INVOLVEMENT OF ENTREPRENER's NETWORKS IN OPPORTUNITIES EXPLORATION AND EXPLOITATION OF INTERNATIONAL NEW VENTURES.Mai Phuong Ha - 2014 - Dissertation, University of Vaasa
    The role and importance of entrepreneur’s network for International New Ventures (INVs) are highlighted in much research. However, there is a lack of more profound studies on how different perspectives of network influence INVs. Therefore, this thesis aims to develop a deeper understanding of the multiple aspects of entrepreneurs’ networks involvement in INVs with regard to opportunity development process. Theoretical framework constitutes of three aspects of entrepreneur’s networks: type, strength and functions of relationships, put in the context of (...)
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  21. The Virtuous Ensemble: Socratic Harmony and Psychological Authenticity.Paul Carron & Anne-Marie Schultz - 2014 - Southwest Philosophy Review 30 (1):127-136.
    We discuss two models of virtue cultivation that are present throughout the Republic: the self-mastery model and the harmony model. Schultz (2013) discusses them at length in her recent book, Plato’s Socrates as Narrator: A Philosophical Muse. We bring this Socratic distinction into conversation with two modes of intentional regulation strategies articulated by James J. Gross. These strategies are expressive suppression and cognitive reappraisal. We argue that that the Socratic distinction helps us see the value in cognitive reappraisal (...)
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  22. Self-trust and critical thinking online: a relational account.Lavinia Marin & Samantha Marie Copeland - 2022 - Social Epistemology.
    An increasingly popular solution to the anti-scientific climate rising on social media platforms has been the appeal to more critical thinking from the user's side. In this paper, we zoom in on the ideal of critical thinking and unpack it in order to see, specifically, whether it can provide enough epistemic agency so that users endowed with it can break free from enclosed communities on social media (so called epistemic bubbles). We criticise some assumptions embedded in the ideal of critical (...)
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  23. 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, (...)
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  24. Energy Efficiency Prediction using Artificial Neural Network.Ahmed J. Khalil, Alaa M. Barhoom, Bassem S. Abu-Nasser, Musleh M. Musleh & Samy S. Abu-Naser - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (9):1-7.
    Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based (...)
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  25. Optimization Models for Reaction Networks: Information Divergence, Quadratic Programming and Kirchhoff’s Laws.Julio Michael Stern - 2014 - Axioms 109:109-118.
    This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.
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  26. A Romantic Life Dedicated to Science: André-Marie Ampère’s Autobiography.Dolores Martín Moruno - 2011 - Teorie Vědy / Theory of Science 33 (2):299-322.
    This article explores André-Marie Ampère's autobiography in order to analyse the dynamics of science in early 19th century French institutions. According to recent works that have emphasised the value of biographies in the history of science, this study examines Ampère's public self-representation to show the cultural transformations of a life dedicated to science in post-revolutionary French society. With this aim, I have interpreted this manuscript as an outstanding example of the scientific rhetoric flourishing in early 19th century French Romanticism, which (...)
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  27. Diabetes Prediction Using Artificial Neural Network.Nesreen Samer El_Jerjawi & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 121:54-64.
    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 (...)
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  28. Glass Classification Using Artificial Neural Network.Mohmmad Jamal El-Khatib, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (23):25-31.
    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 (...)
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  29. 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 (...)
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  30. Predicting Kidney Stone Presence from Urine Analysis: A Neural Network Approach using JNN.Amira Jarghon & Samy S. Abu-Naser - 2023 - International Journal of Academic Information Systems Research (IJAISR) 7 (9):32-39.
    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, (...)
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  31. 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, (...)
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  32. 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 (...)
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  33. Plato's Use of Eleusinian Mystery Motifs.Anne Mary Farrell - 1999 - Dissertation, The University of Texas at Austin
    The Eleusinian Mysteries are religious rituals that include rites of initiation, purification, and revelation. The high point of these Mysteries is the moment when a priest reveals the secret of the Mysteries to the newly initiated. Plato frequently uses language and motifs from the Mysteries in his dialogues, yet Plato scholars have not paid much attention to this usage, and those who have done so have not found much philosophical significance in it. I argue that in explaining his epistemology in (...)
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  34. Artificial Neural Network for Forecasting Car Mileage per Gallon in the City.Mohsen Afana, Jomana Ahmed, Bayan Harb, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2018 - International Journal of Advanced Science and Technology 124:51-59.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to (...)
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  35. COLLABORATE FRAMEWORK BASED ON SOFTWARE DEFINED NETWORK IN MANET.S. Praveen Kumar - 2022 - Journal of Science Technology and Research (JSTAR) 3 (1):39-54.
    Create a novel network model for mobile ad hoc network (MANET) nodes and actors in wireless sensor networks to collaborate on event processing. There are two stages in the development of distributed algorithms: setup and negotiation. The first uses weighted proportional max-min fairness to initially allocate MANET nodes across event zones, whereas the latter uses a market-based method to re-distribute the number of MANET nodes based on existing and new events. A detection technique for malicious packet dropping (...)
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  36. Artificial Neural Network for Predicting COVID 19 Using JNN.Walaa Hasan, Mohammed S. Abu Nasser, Mohammed A. Hasaballah & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (10):41-47.
    Abstract: The emergence of the novel coronavirus (COVID-19) in 2019 has presented the world with an unprecedented global health crisis. The rapid and widespread transmission of the virus has strained healthcare systems, disrupted economies, and challenged societies. In response to this monumental challenge, the intersection of technology and healthcare has become a focal point for innovation. This research endeavors to leverage the capabilities of Artificial Neural Networks (ANNs) to develop an advanced predictive model for forecasting the spread of COVID-19. (...)
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  37.  67
    Pistachio Variety Classification using Convolutional Neural Networks.Ahmed S. Sabah & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (4):113-119.
    Abstract: Pistachio nuts are a valuable source of nutrition and are widely cultivated for commercial purposes. The accurate classification of different pistachio varieties is important for quality control and market analysis. In this study, we propose a new model for the classification of different pistachio varieties using Convolutional Neural Networks (CNNs). We collected a dataset of pistachio images form Kaggle depository with two varieties (Kirmizi and Siirt). The images were then preprocessed and used to train a CNN model (...)
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  38. Unlocking Literary Insights: Predicting Book Ratings with Neural Networks.Mahmoud Harara & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (10):22-27.
    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 (...)
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  39. 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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  40. 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. (...)
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  41. 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 (...)
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  42. Artificial Neural Network for Predicting Car Performance Using JNN.Awni Ahmed Al-Mobayed, Youssef Mahmoud Al-Madhoun, Mohammed Nasser Al-Shuwaikh & Samy S. Abu-Naser - 2020 - International Journal of Engineering and Information Systems (IJEAIS) 4 (9):139-145.
    In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Buying, Maint, Doors, Persons, Lug_boot, Safety, and Overall. ANN was used in forecasting car acceptability. The results showed that ANN model was able to predict the car acceptability with 99.12 %. The factor of Safety has the most influence on car acceptability evaluation. Comparative (...)
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  43. 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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  44. Predicting Tumor Category Using Artificial Neural Networks.Ibrahim M. Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (2):1-7.
    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 (...)
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  45. 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. (...)
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  46. 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, (...)
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  47. Predicting Birth Weight Using Artificial Neural Network.Mohammed Al-Shawwa & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (1):9-14.
    In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases (...)
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  48. New Approaches to Evaluating the Performance of Corporate–Community Partnerships: A Case Study from the Minerals Sector. [REVIEW]Ana Maria Esteves & Mary-Anne Barclay - 2011 - Journal of Business Ethics 103 (2):189-202.
    A continuing challenge for researchers and practitioners alike is the lack of data on the effectiveness of corporate–community investment programmes. The focus of this article is on the minerals industry, where companies currently face the challenge of matching corporate drivers for strategic partnership with community needs for programmes that contribute to local and regional sustainability. While many global mining companies advocate a strategic approach to partnerships, there is no evidence currently available that suggests companies are monitoring these partnerships to see (...)
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  49. 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 (...)
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  50. 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 (...)
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