Results for ' Predictive Modeling'

982 found
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  1. Predictive Modeling of Obesity and Cardiovascular Disease Risk: A Random Forest Approach.Mohammed S. Abu Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 7 (12):26-38.
    Abstract: This research employs a Random Forest classification model to predict and assess obesity and cardiovascular disease (CVD) risk based on a comprehensive dataset collected from individuals in Mexico, Peru, and Colombia. The dataset comprises 17 attributes, including information on eating habits, physical condition, gender, age, height, and weight. The study focuses on classifying individuals into different health risk categories using machine learning algorithms. Our Random Forest model achieved remarkable performance with an accuracy, F1-score, recall, and precision all reaching 97.23%. (...)
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  2. 50 Years of Successful Predictive Modeling Should Be Enough: Lessons for Philosophy of Science.Michael A. Bishop & J. D. Trout - 2002 - Philosophy of Science 69 (S3):S197-S208.
    Our aim in this paper is to bring the woefully neglected literature on predictive modeling to bear on some central questions in the philosophy of science. The lesson of this literature is straightforward: For a very wide range of prediction problems, statistical prediction rules (SPRs), often rules that are very easy to implement, make predictions than are as reliable as, and typically more reliable than, human experts. We will argue that the success of SPRs forces us to reconsider (...)
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  3. 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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  4. Predictive Modeling of Smoke Potential Using Neural Networks and Environmental Data.Abu Al-Reesh Kamal Ali, Al-Safadi Muhammad Nidal, Al-Tanani Waleed Sami & Samy S. Abu-Naser - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (9):38-46.
    This study presents a neural network-based model for predicting smoke potential in a specific area using a Kaggle-derived dataset with 15 environmental features and 62,631 samples. Our five-layer neural network achieved an accuracy of 89.14% and an average error of 0.000715, demonstrating its effectiveness. Key influential features, including temperature, humidity, crude ethanol, pressure, NC1.0, NC2.5, SCNT, and PM2.5, were identified, providing insights into smoke occurrence. This research aids in proactive smoke mitigation and public health protection. The model's accuracy and feature (...)
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  5. Modeling the Past: Using History of Science to predict alternative scenarios on science-based legislation.José Ferraz-Caetano - 2021 - Hypothesis Historia Periodical 1 (1):60-70.
    In an ever-changing world, when we search for answers on our present challenges, it can be tricky to extrapolate past realities when concerning science-based issues. Climate change, public health or artificial intelligence embody issues on how scientific evidence is often challenged, as false beliefs could drive the design of public policies and legislation. Therefore , how can we foresee if science can tip the scales of political legislation? In this article, we outline how models of historical cases can be used (...)
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  6. The Predictive Turn in Neuroscience.Daniel A. Weiskopf - 2022 - Philosophy of Science 89 (5):1213-1222.
    Neuroscientists have in recent years turned to building models that aim to generate predictions rather than explanations. This “predictive turn” has swept across domains including law, marketing, and neuropsychiatry. Yet the norms of prediction remain undertheorized relative to those of explanation. I examine two styles of predictive modeling and show how they exemplify the normative dynamics at work in prediction. I propose an account of how predictive models, conceived of as technological devices for aiding decision-making, can (...)
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  7. 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, achieving an impressive (...)
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  8. Prediction of Used Car Prices Using Artificial Neural Networks and Machine Learning.Sathishkumar A. - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-20.
    This project aims to develop a robust system capable of predicting the prices of used cars based on various factors such as make, model, year, mileage, location, and condition. The rising demand for second-hand vehicles has led to the need for accurate pricing models, and this project utilizes machine learning techniques, particularly Artificial Neural Networks (ANNs), to address this challenge. The system is trained on a comprehensive dataset of used car listings, incorporating key features that impact car prices. Various machine (...)
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    A Deep Prediction of Chronic Kidney Disease by Employing Machine Learning Method.R. Senthilkumar - 2025 - Journal of Science Technology and Research (JSTAR) 6 (1):1-20.
    Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision (...)
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  10. Mathematical Modeling in Biology: Philosophy and Pragmatics.Rasmus Grønfeldt Winther - 2012 - Frontiers in Plant Evolution and Development 2012:1-3.
    Philosophy can shed light on mathematical modeling and the juxtaposition of modeling and empirical data. This paper explores three philosophical traditions of the structure of scientific theory—Syntactic, Semantic, and Pragmatic—to show that each illuminates mathematical modeling. The Pragmatic View identifies four critical functions of mathematical modeling: (1) unification of both models and data, (2) model fitting to data, (3) mechanism identification accounting for observation, and (4) prediction of future observations. Such facets are explored using a recent (...)
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  11. 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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  12. Testable or bust: theoretical lessons for predictive processing.Marcin Miłkowski & Piotr Litwin - 2022 - Synthese 200 (6):1-18.
    The predictive processing account of action, cognition, and perception is one of the most influential approaches to unifying research in cognitive science. However, its promises of grand unification will remain unfulfilled unless the account becomes theoretically robust. In this paper, we focus on empirical commitments of PP, since they are necessary both for its theoretical status to be established and for explanations of individual phenomena to be falsifiable. First, we argue that PP is a varied research tradition, which may (...)
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  13. Metacognitive Awareness as a Predictor of Mathematical Modeling Competency among Preservice Elementary Teachers.John Rey Oficiar, Edwin Ibañez & Jupeth Pentang - 2024 - International Journal of Educational Methodology 10 (2):1079-1092.
    Mathematical modeling offers a promising approach to improving mathematics education. This study aims to determine if the concept of metacognitive awareness in the learning process is associated with mathematical modeling. This study also considers the interaction effect of sex and academic year level on both variables. Focusing the study on preservice elementary teachers might address potential issues and targeted intervention in their preparation program concerning their ability to teach and guide young learners in modeling activities. The research (...)
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  14. The future of climate modeling.Joel Katzav & Wendy S. Parker - 2015 - Climatic Change 132:475-487.
    Recently a number of scientists have proposed substantial changes to the practice of climate modeling, though they disagree over what those changes should be. We provide an overview and critical examination of three leading proposals: the unified approach, the hierarchy approach and the pluralist approach. The unified approach calls for an accelerated development of high-resolution models within a seamless prediction framework. The hierarchy approach calls for more attention to the development and systematic study of hierarchies of related models, with (...)
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  15. The Cost of Prediction.Johannes Lenhard, Simon Stephan & Hans Hasse - manuscript
    This paper examines a looming reproducibility crisis in the core of the hard sciences. Namely, it concentrates on molecular modeling and simulation (MMS), a family of methods that predict properties of substances through computing interactions on a molecular level and that is widely popular in physics, chemistry, materials science, and engineering. The paper argues that in order to make quantitative predictions, sophisticated models are needed which have to be evaluated with complex simulation procedures that amalgamate theoretical, technological, and social (...)
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  16. Predicting Islamic ethical work behavior using the theory of planned behavior and religiosity in Brunei.Nur Amali Aminnuddin - 2019 - Journal of Behavioral Science 14 (1):1-13.
    The objective of this study was to employ the theory of planned behavior in examining the inclusion of Islamic religiosity in predicting Islamic ethical work behavior. Islamic religiosity was included as Islam plays a dominant role in Brunei’s society. Participants consisted of 370 Malay Muslim teachers. Structural equation modeling was used to test three proposed models. While Model 1 was based on the theory of planned behavior, it does not take into consideration the distinctive Islamic context of the Bruneian (...)
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  17. Predicitive modeling, empowering women, and COVID-19 in South Sumatra, Indonesia.Yeni Yeni, Najmah Najmah & Davies Sharyn Graham - 2020 - ASEAN Journal of Community Engagement 4 (1):104-133.
    The Coronavirus disease (COVID-19) has spread to almost all provinces in Indonesia, including South Sumatra. Epidemiological models are required to provide evidence for public health policymakers to mitigate the virus. The aim of this study is: 1) to create a prediction model for COVID-19 cases in South Sumatra to help inform about public health policy and 2) to reflect on women’s experiences to provide solutions for mitigating the impact of COVID-19. This study uses quantitative and qualitative methods. A quantitative (...) approach called Susceptible–Infected–Recovered (SIR) model is used to predict COVID-19 cases in South Sumatra. The assumption used is that every four days, a doubling of COVID-19 cases is observed, with an average of 15 days for recovery. The sources of data are reports from the South Sumatra Provincial Government and the Ministry of Health of the Republic of Indonesia (MOH RI). Qualitative data are obtained through a feminist participatory action research project, which is focused on children’s experiences of COVID-19. Reflective analysis is conducted to develop insights into how to empower women with respect to mitigating COVID-19. Results show that COVID-19 cases in South Sumatra are still underreported, with only 5%–10% of the total estimated COVID-19 cases being reported. Modeling indicates that over 1,000 people had COVID-19 by the end of April, reaching over 150,000 by the end of May, and over a third of South Sumatra’s population is likely to be infected by the end of June. Multiple interventions are needed to reduce cases and flatten the curve. Women are key to flattening this curve and must be empowered to undertake actions from a familial base. (shrink)
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  18. Effective energy consumption parameters in residential buildings using Building Information Modeling.Nima Amani & Abdulamir Rezasoroush - 2020 - Global Journal of Environmental Science and Management (Gjesm) 6 (4):467–480.
    Building information modeling can help in predicting the energy efficiency in future based on dynamic patterns obtained by visualization of data. The aim of this study was to investigate the effective parameters of energy consumption using BIM technology which can evaluate the buildings energy performance. First, three forms of general states in the building were modeled to evaluate the proposed designs in Autodesk Revit Software. Then, the main building form for energy modeling and analysis was selected. Autodesk Revit (...)
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  19. Environmental Impact of Sustainability Dispersion of Chlorine Releases in Coastal Zone of Alexandra: Spatial-Ecological Modeling.Mohammed El Raey & Moustafa Osman Mohammed - 2024 - International Journal of Environmental and Ecological Engineering 18 (1):21-28.
    The spatial-ecological modeling is relating sustainable dispersions with social development. Sustainability with spatial-ecological model gives attention to urban environments in the design review management to comply with Earth’s system. Naturally exchanged patterns of ecosystems have consistent and periodic cycles to preserve energy flows and materials in Earth’s system. The Probabilistic Risk Assessment (PRA) technique is utilized to assess the safety of an industrial complex. The other analytical approach is the Failure-Safe Mode and Effect Analysis (FMEA) for critical components. The (...)
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  20. Discourseology of Linguistic Consciousness: Neural Network Modeling of Some Structural and Semantic Relationships.Vitalii Shymko - 2021 - Psycholinguistics 29 (1):193-207.
    Objective. Study of the validity and reliability of the discourse approach for the psycholinguistic understanding of the nature, structure, and features of the linguistic consciousness functioning. -/- Materials & Methods. This paper analyzes artificial neural network models built on the corpus of texts, which were obtained in the process of experimental research of the coronavirus quarantine concept as a new category of linguistic consciousness. The methodology of feedforward artificial neural networks (multilayer perceptron) was used in order to assess the possibility (...)
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  21. Modeling migration changing according to alternative scenarios in the context of the global COVID-19 pandemic: the example of Ukraine.Natalia Maslii, Maryna Demianchuk, Igor Britchenko & Maksym Bezpartochnyi - 2022 - Списание «Икономически Изследвания (Economic Studies)» 1 (1):58 - 71.
    Global processes significantly affect the mobility of the population. In the context of geopolitical transformation, globalization and quarantine restrictions of Covid-19, it is important to predict the development of the migration movement of countries that are developing. Therefore, the article is aimed at modelling migration changes according to alternative scenarios using the example of Ukraine. The theoretical and methodological basis of the research is formed by a number of scientific works of leading scientists from different countries, statistical information on migration (...)
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  22. Mechanizmy predykcyjne i ich normatywność [Predictive mechanisms and their normativity].Michał Piekarski - 2020 - Warszawa, Polska: Liberi Libri.
    The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by (...)
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  23. Hydrates Production Prediction With Computer Modelling Group (CMG) Stars. A Comprehensive Review.Daudi Matungwa Katabaro & Wang Jinjie - 2018 - International Journal of Academic Multidisciplinary Research (IJAMR) 2 (11):24-30.
    Abstract: Hydrates are an enormous energy resource with global circulation in the permafrost and in the oceans. Even if conventional estimates are deliberated and only a small fraction is recoverable, the pure size of the resource is so huge that it demands assessment as a potential energy source. In this research work, we discuss the hydrate production prediction with Computer Modeling Group STARS (CMG STARS). In this paper different literatures reviews have been visited concerning hydrate production prediction with CMG (...)
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  24. Building Energy Efficiency Using Building Information Modeling (BIM).Abdulamir Rezasoroush - 2019 - Dissertation, Department of Civil Engineering, Faculty of Technical and Engineering, Islamic Azad University, Chalous Branch, Iran
    Buildings are the largest energy consumer worldwide, according to the United Nations Environment Programme (UNEP). Most of the building’s energy consumption is in the building’s life cycle stage. Therefore, achieving sustainable development at the national level requires minimizing the building’s effects on the environment via reducing energy consumption by buildings. The building’s energy performance will be predicted and evaluated by the energy simulation. Using BIM in EPAs significantly reduces time and costs. This study aimed to optimize energy consumption in buildings (...)
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  25. Handbook of Energy Analysis Using Building Information Modeling (BIM).Nima Amani, Abdulamir Rezasoroush & Mohsen Tahernezhad - 2024 - University Jihad Publishing Organization, Mazandaran branch, Iran.
    According to the United Nations Environment Programme (UNEP), buildings are the largest worldwide consumers of energy. Most of the energy used by any building is consumed during the usage (or operational) stage of the building’s life-cycle. Achieving sustainable development at the national level will require minimizing the effects of buildings on the environment with the low energy consumed by buildings. The energy performance of a given building is predicted and assessed by conducting an energy simulation. Using BIM in EPAs greatly (...)
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  26. Detection and Mathematical Modeling of Anxiety Disorder Based on Socioeconomic Factors Using Machine Learning Techniques.Razan Ibrahim Alsuwailem & Surbhi Bhatia - 2022 - Human-Centric Computing and Information Sciences 12:52.
    The mental risk poses a high threat to the individuals, especially overseas demographic, including expatriates in comparison to the general Arab demographic. Since Arab countries are renowned for their multicultural environment with half of the population of students and faculties being international, this paper focuses on a comprehensive analysis of mental health problems such as depression, stress, anxiety, isolation, and other unfortunate conditions. The dataset is developed from a web-based survey. The detailed exploratory data analysis is conducted on the dataset (...)
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  27. Societal-Level Versus Individual-Level Predictions of Ethical Behavior: A 48-Society Study of Collectivism and Individualism.David A. Ralston, Carolyn P. Egri, Olivier Furrer, Min-Hsun Kuo, Yongjuan Li, Florian Wangenheim, Marina Dabic, Irina Naoumova, Katsuhiko Shimizu, María Teresa Garza Carranza, Ping Ping Fu, Vojko V. Potocan, Andre Pekerti, Tomasz Lenartowicz, Narasimhan Srinivasan, Tania Casado, Ana Maria Rossi, Erna Szabo, Arif Butt, Ian Palmer, Prem Ramburuth, David M. Brock, Jane Terpstra-Tong, Ilya Grison, Emmanuelle Reynaud, Malika Richards, Philip Hallinger, Francisco B. Castro, Jaime Ruiz-Gutiérrez, Laurie Milton, Mahfooz Ansari, Arunas Starkus, Audra Mockaitis, Tevfik Dalgic, Fidel León-Darder, Hung Vu Thanh, Yong-lin Moon, Mario Molteni, Yongqing Fang, Jose Pla-Barber, Ruth Alas, Isabelle Maignan, Jorge C. Jesuino, Chay-Hoon Lee, Joel D. Nicholson, Ho-Beng Chia, Wade Danis, Ajantha S. Dharmasiri & Mark Weber - 2014 - Journal of Business Ethics 122 (2):283–306.
    Is the societal-level of analysis sufficient today to understand the values of those in the global workforce? Or are individual-level analyses more appropriate for assessing the influence of values on ethical behaviors across country workforces? Using multi-level analyses for a 48-society sample, we test the utility of both the societal-level and individual-level dimensions of collectivism and individualism values for predicting ethical behaviors of business professionals. Our values-based behavioral analysis indicates that values at the individual-level make a more significant contribution to (...)
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  28. Using Computer Simulations for Hypothesis-Testing and Prediction: Epistemological Strategies.Tan Nguyen - manuscript
    This paper explores the epistemological challenges in using computer simulations for two distinct goals: explanation via hypothesis-testing and prediction. It argues that each goal requires different strategies for justifying inferences drawn from simulation results due to different practical and conceptual constraints. The paper identifies unique and shared strategies researchers employ to increase confidence in their inferences for each goal. For explanation via hypothesis-testing, researchers need to address the underdetermination, interpretability, and attribution challenges. In prediction, the emphasis is on the model's (...)
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  29. The Epistemic Virtue of Robustness in Climate Modeling (MA Dissertation).Parjanya Joshi - 2019 - Dissertation, Tata Institute of Social Sciences
    The aim of this dissertation is to comprehensively study various robustness arguments proposed in the literature from Levins to Lloyd as well as the opposition offered to them and pose enquiry into the degree of epistemic virtue that they provide to the model prediction results with respect to climate science and modeling. Another critical issue that this dissertation strives to examine is that of the actual epistemic notion that is operational when scientists and philosophers appeal to robustness. In attempting (...)
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  30. Activity in early visual areas predicts interindividual differences in binocular rivalry dynamics.Hiroyuki Yamashiro, Hiroki Yamamoto, Hiroaki Mano, Masahiro Umeda, Toshihiro Higuchi & Jun Saiki - 2014 - Journal of Neurophysiology 111:1190-1202.
    When dissimilar images are presented to the two eyes, binocular rivalry (BR) occurs, and perception alternates spontaneously between the images. Although neural correlates of the oscillating perception during BR have been found in multiple sites along the visual pathway, the source of BR dynamics is unclear. Psychophysical and modeling studies suggest that both low- and high-level cortical processes underlie BR dynamics. Previous neuroimaging studies have demonstrated the involvement of high-level regions by showing that frontal and parietal cortices responded time (...)
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  31. Physical Methodology for Economic Systems Modeling.I. G. Tuluzov & S. I. Melnyk - 2010 - Electronic Journal of Theoretical Physics (EJTP) 7 (24):57-78.
    The paper discusses the possibility of constructing economic models using the methodology of model construction in classical mechanics. At the same time, unlike the "econophysical" approach, the properties of economic models are derived without involvement of any equivalent physical properties, but with account of the types of symmetry existing in the economic system. It has been shown that at this approach practically all known mechanical variables have their "economic twins". The variational principle is formulated on the basis of formal mathematical (...)
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  32. The Problem of Differential Importability and Scientific Modeling.Anish Seal - 2024 - Philosophies 9 (6):164.
    The practice of science appears to involve “model-talk”. Scientists, one thinks, are in the business of giving accounts of reality. Scientists, in the process of furnishing such accounts, talk about what they call “models”. Philosophers of science have inspected what this talk of models suggests about how scientific theories manage to represent reality. There are, it seems, at least three distinct philosophical views on the role of scientific models in science’s portrayal of reality: the abstractionist view, the indirect fictionalist view, (...)
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  33. Transforming Consumer Behavior Analysis with Cutting-Edge Machine Learning.M. Arul Selvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):360-368.
    The research outlines a workflow that incorporates data collection, preprocessing, model training, and optimization. Real-world datasets from retail and e-commerce sectors are utilized to validate the proposed methodology, showcasing substantial improvements in model performance. The results indicate that optimized models not only provide better predictions of consumer behaviour but also enhance customer segmentation and targeting strategies. The study concludes with recommendations for future research, including the exploration of hybrid optimization techniques and the application of these methods in real-time analytics.
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  34. How Does Hands-On Making Attitude Predict Epistemic Curiosity and Science, Technology, Engineering, and Mathematics Career Interests? Evidence From an International Exhibition of Young Inventors.Yuting Cui, Jon-Chao Hong, Chi-Ruei Tsai & Jian-Hong Ye - 2022 - Frontiers in Psychology 13:859179.
    Whether the hands-on experience of creating inventions can promote Students’ interest in pursuing a science, technology, engineering, and mathematics (STEM) career has not been extensively studied. In a quantitative study, we drew on the attitude-behavior-outcome framework to explore the correlates between hands-on making attitude, epistemic curiosities, and career interest. This study targeted students who joined the selection competition for participating in the International Exhibition of Young Inventors (IEYI) in Taiwan. The objective of the invention exhibition is to encourage young students (...)
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  35. OPTIMIZING CONSUMER BEHAVIOUR ANALYTICS THROUGH ADVANCED MACHINE LEARNING ALGORITHMS.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):360-368.
    Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine learning algorithms. By (...)
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  36.  88
    Innovating Financial and Medical Services: Generative AI’s Impact on Banking and Healthcare.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):610-618.
    Results indicate substantial improvements in efficiency, accuracy, and personalized care, but also highlight the challenges of data privacy, ethical considerations, and system scalability. By providing a structured analysis, this research contributes insights into optimizing generative AI deployments for both banking and healthcare, ensuring a balance between innovation and risk management. The study concludes with recommendations for future research directions, including advanced model training, ethical guidelines, and enhanced privacy measures. These insights aim to inform practitioners on the benefits of generative AI, (...)
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  37. Transforming Industries: The Role of Generative AI in Revolutionizing Banking and Healthcare.M. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):580-600.
    The research evaluates generative AI’s capabilities through a multi-phase framework, addressing how data synthesis, language models, and predictive algorithms contribute to sector-specific applications. In banking, the model assesses the impact of AI-driven chatbot interactions, credit risk assessment, and personalized financial services on customer experience and bank performance. Healthcare applications are explored through image synthesis for diagnostics, predictive modeling in patient care, and drug discovery simulations. The experimental setup is rigorously tested across metrics such as response accuracy, cost-effectiveness, (...)
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  38. Algorithmic Fairness from a Non-ideal Perspective.Sina Fazelpour & Zachary C. Lipton - 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.
    Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world (...)
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  39.  47
    AI-Driven Legislative Simulation and Inclusive Global Governance.Michael Haimes - manuscript
    This argument explores the transformative potential of AI-driven legislative simulations for creating inclusive, equitable, and globally adaptable laws. By using predictive modeling and adaptive frameworks, these simulations can account for diverse cultural, social, and economic contexts. The argument emphasizes the need for universal ethical safeguards, trust-building measures, and phased implementation strategies. Case studies of successful applications in governance and conflict resolution demonstrate the feasibility and efficacy of this approach. The conclusion highlights AI’s role in democratizing governance and ensuring (...)
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  40. Leveraging Artificial Intelligence for Strategic Business Decision-Making: Opportunities and Challenges.Mohammed Hazem M. Hamadaqa, Mohammad Alnajjar, Mohammed N. Ayyad, Mohammed A. Al-Nakhal, Basem S. Abunasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Information Systems Research (IJAISR) 8 (8):16-23.
    Abstract: Artificial Intelligence (AI) has rapidly evolved, offering transformative capabilities for business decision-making. This paper explores how AI can be leveraged to enhance strategic decision-making in business contexts. It examines the integration of AI-driven analytics, predictive modeling, and automation to improve decision accuracy and operational efficiency. By analyzing current applications and case studies, the paper highlights the opportunities AI presents, including enhanced data insights, risk management, and personalized customer experiences. Additionally, it addresses the challenges businesses face in adopting (...)
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  41. Faithfulness, Coordination and Causal Coincidences.Naftali Weinberger - 2018 - Erkenntnis 83 (2):113-133.
    Within the causal modeling literature, debates about the Causal Faithfulness Condition have concerned whether it is probable that the parameters in causal models will have values such that distinct causal paths will cancel. As the parameters in a model are fixed by the probability distribution over its variables, it is initially puzzling what it means to assign probabilities to these parameters. I propose that to assign a probability to a parameter in a model is to treat that parameter as (...)
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  42. The Role of AI in Enhancing Business Decision-Making: Innovations and Implications.Faten Y. A. Abu Samara, Aya Helmi Abu Taha, Nawal Maher Massa, Tanseen N. Abu Jamie, Fadi E. S. Harara, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Pedagogical Research (IJAPR) 8 (9):8-15.
    Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the (...)
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  43. Bayesian Cognitive Science. Routledge Encyclopaedia of Philosophy.Matteo Colombo - 2023 - Routledge Encyclopaedia of Philosophy.
    Bayesian cognitive science is a research programme that relies on modelling resources from Bayesian statistics for studying and understanding mind, brain, and behaviour. Conceiving of mental capacities as computing solutions to inductive problems, Bayesian cognitive scientists develop probabilistic models of mental capacities and evaluate their adequacy based on behavioural and neural data generated by humans (or other cognitive agents) performing a pertinent task. The overarching goal is to identify the mathematical principles, algorithmic procedures, and causal mechanisms that enable cognitive agents (...)
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  44. A Speculation About Consciousness.Edward A. Francisco - manuscript
    This is a sketch of the basis and role of consciousness and the minimally required elements and constraints of any setting that may produce consciousness. It proposes that consciousness (as we know it) is a biologically-mediated product of evolved recursive and hierarchically nested representational systems that obey information theoretic principles and Bayesian (probabilistic) feedback and feedforward predictive modeling processes.
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  45. Causal graphs and biological mechanisms.Alexander Gebharter & Marie I. Kaiser - 2014 - In Marie I. Kaiser, Oliver R. Scholz, Daniel Plenge & Andreas Hüttemann (eds.), Explanation in the special science: The case of biology and history. Dordrecht: Springer. pp. 55-86.
    Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research (...)
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  46. The Idea of Mismatch in Evolutionary Medicine.Pierrick Bourrat & Paul Griffiths - 2024 - British Journal for the Philosophy of Science 75 (4):921-946.
    Mismatch is a prominent concept in evolutionary medicine, and a number of philosophers have published analyses of this concept. The word ‘mismatch’ has been used in a diversity of ways across a range of sciences, leading these authors to regard it as a vague concept in need of philosophical clarification. Here, in contrast, we concentrate on the use of mismatch in modelling and experimentation in evolutionary medicine. This reveals a rigorous theory of mismatch within which the term ‘mismatch’ is indeed (...)
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  47. A conceptual framework for data-driven sustainable finance in green energy transition.Omotayo Bukola Adeoye, Ani Emmanuel Chigozie, Ninduwesuor-Ehiobu Nwakamma, Jose Montero Danny, Favour Oluwadamilare Usman & Kehinde Andrew Olu-Lawal - 2024 - World Journal of Advanced Research and Reviews 21 (2):1791–1801.
    As the world grapples with the urgent need for sustainable development, the transition towards green energy stands as a critical imperative. Financing this transition poses significant challenges, requiring innovative approaches that align financial objectives with environmental sustainability goals. This review presents a conceptual framework for leveraging data-driven techniques in sustainable finance to facilitate the transition towards green energy. The proposed framework integrates principles of sustainable finance with advanced data analytics to enhance decision-making processes across the financial ecosystem. At its core, (...)
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  48.  61
    Revolutionizing Drug Discovery: The Role of Artificial Intelligence in Accelerating Pharmaceutical Innovation".Alaa Soliman Abu Mettleq, Alaa N. Akkila, Mohammed A. Alkahlout, Suheir H. A. ALmurshidi, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - Information Journal of Academic Engineering Research (Ijaer) 8 (10):45-53.
    Abstract: The integration of artificial intelligence (AI) into drug discovery is revolutionizing the pharmaceutical industry by accelerating the development of novel therapeutics. AI-powered tools enable researchers to process vast datasets, identify drug candidates, and predict their efficacy and safety with unprecedented speed and accuracy. This paper explores the transformative impact of AI on drug discovery, highlighting key advancements in machine learning algorithms, deep learning, and predictive modeling. Additionally, it addresses the challenges associated with AI implementation, including data quality, (...)
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  49.  59
    Automated Dam Operation System.K. Amani - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (2):1-13.
    This project focuses on estimating reservoir inflows by integrating rainfall data, soil moisture levels in the catchment area, and releases from upstream reservoirs, coupled with an automated gate control system to prevent flooding in the basin. Utilizing hydrological models, the methodology predicts runoff from rainfall, adjusted for current soil moisture to enhance accuracy. Real-time data from upstream releases further refines inflow predictions. The automated system leverages predictive analytics and real-time monitoring to optimize gate operations, ensuring moderate water releases to (...)
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  50.  63
    Smart Route Optimization for Emergency Vehicles: Enhancing Ambulance Efficiency through Advanced Algorithms.Vishal Parmar - 2024 - Technosaga 2024 1 (1):1-6.
    Emergency response times play a critical role in saving lives, especially in urban settings where traffic congestion and unpredictable events can delay ambulance arrivals. This paper explores a novel framework for smart route optimization for emergency vehicles, leveraging artificial intelligence (AI), Internet of Things (IoT) technologies, and dynamic traffic analytics. We propose a real-time adaptive routing system that integrates machine learning (ML) for predictive modeling and IoT-enabled communication with traffic infrastructure. The system is evaluated using simulated urban environments, (...)
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