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. Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving an (...) impressive accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

Abstract: 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 (...) an impressive accuracy of 100% and an average error of 0.000015, indicating its robust predictive capability. Moreover, we conducted feature importance analysis to identify the most influential features for predicting audit risk. The key features found to be critical for classifying fraudulent activities in audit risk prediction are Sector_score, PARA_A, SCORE_A, PARA_B, SCORE_B, TOTAL, numbers, marks, Money_Value, District, Loss, Loss_SCORE, History, and History_score. This research contributes to the field of audit risk prediction by demonstrating the effectiveness of a neural network-based approach and highlighting the importance of specific features in detecting fraudulent activities. The findings have significant implications for auditors and organizations seeking to enhance their audit risk assessment processes, ultimately leading to improved financial transparency and fraud detection. (shrink)

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. A neural network model with four layers (1 input, 2 hidden, 1 output) was trained on the dataset and achieved an accuracy of 88% and an average error of 0078296. The most influential factors in heart failure prediction were found to be oldpeak, ST_Slope, sex, fastingBS, chestPainType, exerciseAngina, cholesterol, restingBP, maxHR, restingBP, and age. This study provides a valuable tool for early detection and intervention of heart failure, thereby contributing to the field of health and medicine. (shrink)

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

Hans Reichenbach was a formidable figure in early-twentieth-century philosophy of science. Educated in Germany, he was influential in establishing the so-called Berlin Circle, a companion group to the Vienna Circle founded by his colleague Rudolph Carnap. The movement they founded—usually known as "logical positivism," although it is more precisely known as "scientific philosophy" or "logical empiricism"—was a form of epistemology that privileged scientific over metaphysical truths. Reichenbach, like other young philosophers of the exact sciences of his generation, was deeply impressed (...) by the far-reaching changes in physics brought about by Einstein's special and general theories of relativity. Reichenbach responded to scientific advances by doing fundamental work in space-time theories, in quantum mechanics, in statistical mechanics, and in the development of probability theory—making him the most important philosopher of physics in the first generation of logical empiricism. Forced from his academic position by the Nazi race laws in 1933, Reichenbach wrote _Experience and Prediction_ at the University of Istanbul, where had had fled, expressly to introduce logical positivism to English speakers. In the two decades following World War II, during the explosion of scientific advances in North America, logical positivism was the reigning theory of the philosophy of science and Reichenbach was at the peak of his career. But, inevitably, support for logical positivism began to wane as it became obvious that the justification of scientific theories could not be entirely resolved by relying on strictly formal, technical processes. The growth of the discipline of the history of philosophy of science, which has created an audience of scholars eager for seminal classics in scientific philosophy, and the evidence supporting a historicist paradigm within logical positivism are two important reasons to make _Experience and Prediction_ available once again. "Hans Reichenbach's_ Experience and Prediction_ is one of the most important books in twentieth-century philosophy of science. Its author was, along with Rudolf Carnap, one of the two principal ambassadors to North America of the exciting new European philosophical movement known here under the names 'Logical Positivism' and 'Scientific Philosophy.' In 1938, when the book was published, Reichenbach was an exile from his native Germany, teaching in Istanbul, Turkey, and about to emigrate to the United States to take up a prestigious position at UCLA. He wrote_ Experience and Prediction_ in English as his calling card to his new American colleagues. More than any other single book,_ Experience and Prediction_ set the agenda for the new discipline of the philosophy of science that was to emerge after World War II as, perhaps, the most exciting new area in North American philosophy. Many of the problems still at the focus of discussion were given their classic formulations in this book. Long out of print,_ Experience and Prediction_ appears here in a new edition accompanied by a splendid historical introduction by the noted young philosopher and historian of the philosophy of science, Alan Richardson. A jewel of a book may once again be appreciated in its proper setting." —Don A. Howard, University of Notre Dame. (shrink)

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 model generalizability. (shrink)

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 (...) identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

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. (...) The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

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 (...) based on these features (date, opening price, highest price, lowest price, volume), which were used as input variables and (closing price) as output variable for our ANN model. Our model was created, trained, and validated using data set in JNN environment, which its title is “Google-Stock-Prices”. The model evaluation showed that the ANN model is able to predict correctly 99.48% of the validation samples. (shrink)

Abstract: 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. The prediction is based on these features (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which were used as input variables and (average_rating) as output variable for our ANN model. Our model were created, trained, and validated using data set in JNN environment, which its title is “Goodreads-books”. Model evaluation showed that the ANN model is able to predict correctly 99.78% of the validation samples. (shrink)

The rapid advancement of intelligent computing has revolutionized the field of economic forecasting, providing unprecedented capabilities for developing, implementing, and analyzing advanced prediction models. This paper explores the comprehensive process of harnessing intelligent computing for economic forecasting, emphasizing the critical stages of model development, integration, and evaluation. Initially, it discusses data collection and preprocessing techniques essential for building robust models, followed by the selection of suitable statistical, machine learning, and deep learning algorithms. The paper then outlines the practical aspects of (...) model implementation, including real-time data integration, deployment strategies, and scalability considerations. Further, it delves into the continuous monitoring and maintenance required to ensure model accuracy and reliability over time. The analysis section evaluates model performance using various accuracy metrics, scenario analyses, and visualization techniques to provide actionable insights. Through case studies across the financial sector, government policy, and business planning, the paper illustrates the transformative impact of intelligent computing on economic forecasting. The findings underscore the potential of advanced prediction models to enhance decision-making, anticipate economic trends, and respond proactively to dynamic economic environments. (shrink)

Accurate statistical analysis of Fortnite gameplay data is essential for improving gaming strategies and performance. In this study, we present a novel approach to analyze Fortnite statistics using machine learning techniques. Our dataset comprises a wide range of gameplay metrics, including eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. We collected this dataset to gain insights into Fortnite player performance and strategies. The proposed model employs (...) advanced machine learning algorithms and data preprocessing techniques. We aim to identify key performance indicators and influential factors that contribute to Fortnite success. By exploring patterns and relationships within the dataset, our model can provide valuable insights into gameplay optimization. Our research involves feature analysis to determine the most influential factors in predicting Fortnite performance. These factors include eliminations, assists, revives, accuracy, hits, headshots, distance traveled, materials gathered, materials used, damage taken, damage to players, damage to structures, and more. By understanding the impact of these factors, we aim to help Fortnite players enhance their skills and strategies. Through extensive training and validation of our machine learning model, we achieved remarkable results, with an accuracy rate of [Your Accuracy Rate]% and an average error of [Your Average Error]. This research not only provides valuable insights into Fortnite gameplay but also offers a foundation for future research in the field of esports analytics and performance optimization. (shrink)

In the last decade, the cases of breast and cervical cancer have been positioned at the top rank of cancer statistics worldwide. Consequently, many husbands become family caregivers (FCGs) and get the burden of cancer caregiving. Being blind and incompetent, they need supportive care from healthcare professionals (HCPs). To support them, HCPs provide various healthcare information to meet their needs. Further, their demand for a specific type of healthcare information may reflect their need for emotional support from the HCPs to (...) reduce the burden. This study aims to examine the predictors of the need for emotional support among FCGs of female cancer patients by analyzing the demanded healthcare information. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 husbands and 12 significant others in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, the demanded cancer-specific information and information on cancer physical needs significantly predict the need for emotional support among respondents. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need emotional support. Other types of information have ambiguous effects on the need for emotional support. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, and information on cancer physical needs must be prioritized in information disclosure to meet the needs for emotional support among FCGs of female cancer patients. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive (...) 99.27% and an average error of 0.0034, underscoring the potency of deep learning in tackling complex, real-world challenges. Furthermore, our study delved into feature importance analysis, identifying critical determinants such as HIV/AIDS prevalence, income composition of resources, and socioeconomic status that significantly influence life expectancy. These findings provide actionable insights for healthcare policymakers and practitioners, emphasizing the importance of addressing health disparities, promoting economic development, and implementing targeted interventions. This research bridges the gap between data-driven methodologies and global health, offering a robust predictive tool and enriching our understanding of the multifaceted dynamics that shape life expectancy across nations. (shrink)

This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented (...) as states of a hierarchy of dynamical systems. Phenomenologically, they suggest a more fine-grained analysis of the perceptual contents of the specious present, in terms of a hierarchy of temporal wholes. Visual perception of static scenes not only contains perceived objects and regions but also spatial gist; similarly, auditory perception of temporal sequences, such as melodies, involves not only perceiving individual notes but also slightly more abstract features (temporal gist), which have longer temporal durations (e.g., emotional character or rhythm). Further investigations into these elusive contents of conscious perception may be facilitated by findings regarding its neural underpinnings. Predictive processing models suggest that sensorimotor areas may influence these contents. (shrink)

Crime prediction has emerged as a critical application of machine learning (ML) and deep learning (DL) techniques, aimed at assisting law enforcement agencies in reducing criminal activities and improving public safety. This project focuses on developing a robust crime prediction system that leverages the power of both ML and DL algorithms to analyze historical crime data and predict potential future incidents. By integrating a combination of classification and clustering techniques, our system identifies crime-prone areas, trends, and patterns. Key parameters such (...) as time, location, type of crime, and socio-economic factors are considered to build a comprehensive predictive model. Machine learning algorithms like Decision Trees, Random Forest, and Support Vector Machines are used for efficient data preprocessing and pattern recognition. Deep learning models, including Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), enable advanced feature extraction and temporal analysis for enhanced prediction accuracy. The system's performance is evaluated using metrics such as precision, recall, F1-score, and accuracy, demonstrating its reliability and scalability for real-world applications. Furthermore, the use of visualization tools allows stakeholders to comprehend crime patterns effectively, supporting proactive policing strategies. This data-driven approach not only improves crime deterrence but also aids policymakers in resource allocation and urban planning. The proposed system has the potential to revolutionize crime management by transforming traditional reactive measures into predictive, preventive, and intelligent solutions. (shrink)

Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the (...) nuanced dynamics of student performance. The key influential features identified in this study offer valuable insights into the complex factors shaping student achievement. These insights are vital for educators, policymakers, and institutions seeking to enhance educational outcomes and promote equitable access to quality education. This research provides a data-driven foundation for proactive interventions, personalized learning strategies, and support systems, ultimately contributing to improved student performance and academic success. The high accuracy of the predictive model and the feature analysis it provides empower decision-makers in the education sector. This model holds significant potential for applications in student performance monitoring, early intervention, and tailored educational strategies. By adopting a data-driven approach, this work advances the field of educational analytics and contributes to the goal of fostering student success and educational equity. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with the (...) goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Testimony about the future dangerousness of a person has become a central staple of many judicial processes. In settings such as bail, sentencing, and parole decisions, in rulings about the civil confinement of the mentally ill, and in custody decisions in a context of domestic violence, the assessment of a person’s propensity towards physical or sexual violence is regarded as a deciding factor. These assessments can be based on two forms of expert testimony: actuarial or clinical. The purpose of this (...) paper is to examine the scientific and epistemological basis of both methods of prediction or risk assessment. My analysis will reveal that this kind of expert testimony is scientifically baseless. The problems I will discuss will generate a dilemma for factfinders: on the one hand, given the weak predictive abilities of the branches of science involved, they should not admit expert clinical or actuarial testimony as evidence; on the other hand, there is a very strong tradition and a vast jurisprudence that supports the continued use of this kind of expert testimony. It is a clear case of the not so uncommon conflict between science and legal tradition. (shrink)

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 (...) presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

This paper concerns how extant theorists of predictive coding conceptualize and explain possible instances of cognitive penetration. §I offers brief clarification of the predictive coding framework and relevant mechanisms, and a brief characterization of cognitive penetration and some challenges that come with defining it. §II develops more precise ways that the predictive coding framework can explain, and of course thereby allow for, genuine top-down causal effects on perceptual experience, of the kind discussed in the context of cognitive (...) penetration. §III develops these insights further with an eye towards tracking one extant criterion for cognitive penetration, namely, that the relevant cognitive effects on perception must be sufficiently direct. Throughout these discussions, we extend the analyses of the predictive coding models, as we know them. So one open question that surfaces is how much of the extended analyses are genuinely just part of the predictive coding models, or something that must be added to them in order to generate these additional explanatory benefits. In §IV, we analyze and criticize a claim made by some theorists of predictive coding, namely, that (interesting) instances of cognitive penetration tend to occur in perceptual circumstances involving substantial noise or uncertainty. It is here that our analysis is most critical. We argue that, when applied, the claim fails to explain (or perhaps even be consistent with) a large range of important and uncontroversially interesting possible cases of cognitive penetration. We conclude with a general speculation about how the recent work on the predictive mind may influence the current dialectic concerning top-down effects on perception. (shrink)

In this study, we predict the different levels of performance in a Nintendo Entertainment System (NES) Tetris session based on the score and the number of matches played by the players. Using the first 45 seconds of gameplay, a Random Forest Classifier was trained on the five keys used in the game obtaining a ROC_AUC score of 0.80. Further analysis revealed that the number of down keys (forced drop) and the number of left keys (left translation) are the most (...) relevant keys in this task, showing that by merely including the data from these two keys our Random Forest Classifier reached a ROC_AUC score of 0.83.We conclude that the keylogger data during the early phases of a game session can be successfully used to predict performance in longer sessions of Tetris. (shrink)

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%. (...) The model's success is attributed to its ability to effectively leverage attributes related to eating habits, physical condition, and demographic factors. Feature importance analysis reveals key factors contributing to accurate predictions. This paper contributes to the field of health analytics by demonstrating the effectiveness of Random Forest in predicting and categorizing health risks. The insights gained from this research have the potential to inform personalized health interventions and contribute to the advancement of precision health strategies. Furthermore, the study highlights the importance of leveraging machine learning techniques for health risk assessment in diverse populations. (shrink)

Abstract: This study has effectively tackled the critical challenge of accurate calorie prediction in dishes by employing a robust neural network-based model. With an outstanding accuracy rate of 99.32% and a remarkably low average error of 0.009, our model has showcased its proficiency in delivering precise calorie estimations. This achievement equips individuals, healthcare practitioners, and the food industry with a powerful tool to promote healthier dietary choices and elevate awareness of nutrition. Furthermore, our in-depth feature importance analysis has shed (...) light on the indispensable role played by specific nutritional attributes in calorie estimation. This unveiling of crucial factors provides valuable insights for further research endeavors and practical applications. Notably, this research extends its impact beyond the immediate context by making substantial contributions to the domains of nutrition science and dietary planning. It underscores the transformative potential of artificial intelligence, demonstrating how it can revolutionize our approach to food, nutrition, and health. As the world grapples with the challenges of diet-related health issues and environmental concerns, the accuracy and precision achieved in calorie prediction through neural networks represent a significant stride towards more informed and conscientious dietary practices. In this era of data-driven decision-making, our research paves the way for healthier lifestyles, heightened nutritional awareness, and a more health-conscious society. (shrink)

Previous research has assessed school facilities, administrative expenditures and curriculum and their relative contributions to students’ cognitive learning outcomes. This suggested the need to investigate further how these predictors may impact students’ affective and psychomotor outcomes. The current research studied the combined and relative prediction of school facilities, administrative expenses and curriculum on students’ overall cognitive, affective and psychomotor learning outcomes in public secondary schools. A cross-sectional research design was employed in this study, involving 87 school administrators and a randomly (...) selected group of 915 senior secondary class II (SS2) students. For data collection, we utilised the School Inputs Questionnaire (SIQ) and Educational Outcomes Questionnaire (EOQ), both developed by the researchers and validated through expert assessments, including content validity, Exploratory Factor Analysis (EFA) for dimensionality, Confirmatory Factor Analysis (CFA) for goodness of fit, and reliability using Cronbach's alpha. The results of these assessments demonstrated acceptable outcomes aligned with international standards. Hierarchical regression analysis was conducted to analyse the collected data. The findings indicated that enhancing the provision of quality school facilities, administrative expenses, and school curricula improved students' overall cognitive, affective, and psychomotor learning outcomes. Specifically, administrative expenses and school curriculum had significant predictive power for students’ overall cognitive, affective, and psychomotor learning outcomes. However, while school facilities significantly predicted students' overall, affective, and psychomotor dimensions, they did not significantly predict the cognitive dimension. These findings offer valuable insights for policymakers and educators aiming to enhance the educational quality in public secondary schools. (shrink)

John N. Williams (1994) and Matthew Weiner (2005) invoke predictions in order to undermine the normative relevance of knowledge for assertions; in particular, Weiner argues, predictions are important counterexamples to the Knowledge Account of Assertion (KAA). I argue here that they are not true counterexamples at all, a point that can be agreed upon even by those who reject KAA.

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, and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

The folk Psychology frames propositional attitudes as fundamental theoretical entities for the construction of a model designed to predict the behavior of a subject. A trivial, such as grasping a pen and writing reveals - something complex - about the behavior. When I take a pen and start writing I do, trivially, because I believe that a certain object in front of me is a pen and who performs a specific function that is, in fact, that of writing. When I (...) believe that the object that stands before me is a pen, I am in relation to "believe" with the propositional content: that in front of me is a pen. Philosophers of the proposition, from Frege onwards, have dedicated their studies to the analysis of what kinds of entities are the propositional attitudes. Jerry Fodor says that now, the proper prediction of the psychology of common sense, can not be questioned and that the propositional attitudes represent the most effective way to describe our behavior. What Fodor says, however, is that propositional attitudes function, but not how they work. Most philosophers interested in the issue, we are dedicated to the search for a theory that can account consistently both a semantics for propositional attitudes, both of these entities that seem to cause the behavior of a rational subject. There are two main paradigms in the theory of the proposition that contributed to the discussion of the propositional attitudes. One is the one that begins with Gottlob Frege, the other with Bertrand Russell. Defenders of Frege argue that the paradigm scrub objects and properties can not be constituents of the propositional content which have a purely conceptual. In other words, the philosophers belonging to the paradigm of Frege, but not all, mean that you can test in a rigorous way the truth conditions of propositional attitudes. Who defends the russellian’s paradigm argues that the propositional content are made by the objects and properties on which propositional attitudes relate. The purpose of this article is not to rebuild - in detail - both paradigms, nor to reconstruct one but, in a sense, my work will be a completely partial objective is to demonstrate how the paradigm is more profitable russell not only to make a coherent semantic theory for propositional attitudes2 but also to predict the behavior of a rational subject thing, completely innovative, given the repeated objections in contemporary literature3. At the end of this paper will be drafted a proposal to build a consistent model to predict the behavior,of a rational agent, based on a referential theory of propositional attitudes. (shrink)

Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with (...) experimental findings and explana- tory capabilities (Edelman, 2012). The point we are making in this note is that a brain with predictive capa- bilities is certainly necessary to endow the agent situated in the environment with forethought or foresight, a crucial issue to outline the unified account advocated by Clark. But the capacity for fore- thought is deeply entangled with the capacity for emotions and when emotions are brought into the game, cogni- tive functions become part of a large-scale functional brain network. However, for such complex networks a consistent view of hierarchical organization in large-scale functional networks has yet to emerge (Bressler and Menon, 2010), whilst heterarchical organization is likely to play a strategic role (Berntson et al., 2012). This raises the necessity of a multilevel approach that embraces causal relations across levels of explanation in either direc- tion (bottom–up or top–down), endorsing mutual calibration of constructs across levels (Berntson et al., 2012). Which, in turn, calls for a revised perspective on Marr’s levels of analysis framework (Marr, 1982). In the following we highlight some drawbacks of Clark’s proposal in address- ing the above issues. (shrink)

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. (...) It involves the collection, analysis, and integration of diverse datasets encompassing epidemiological, clinical, and social factors that influence the virus's dissemination. (shrink)

An increasing number of technology firms are implementing processes to identify and evaluate the ethical risks of their systems and products. A key part of these review processes is to foresee potential impacts of these technologies on different groups of users. In this article, we use the expression Ethical Foresight Analysis to refer to a variety of analytical strategies for anticipating or predicting the ethical issues that new technological artefacts, services, and applications may raise. This article examines several existing (...) EFA methodologies currently in use. It identifies the purposes of ethical foresight, the kinds of methods that current methodologies employ, and the strengths and weaknesses of each of these current approaches. The conclusion is that a new kind of foresight analysis on the ethics of emerging technologies is both feasible and urgently needed. (shrink)

Introduction: There is no study investigating the factors predicting postoperative febrile urinary tract infection (fUTI) in pediatric patients. We aimed to determine the factors predicting postoperative fUTI in pediatric patients who underwent ureterorenoscopic lithotripsy (URS-L). -/- Methods: Pediatric patients who underwent URS-L due to ureter or kidney stones in our clinic between 2012 and 2019 were analyzed retrospectively. The demographic data, stone characteristics, intraoperative and postoperative data of those with and without postoperative fUTI were compared. Univariable and multivariable binary logistic (...) regression analysis was performed to determine predictors of fUTI. -/- Results: A total of 136 patients were included in the study. Postoperative fUTI developed in 17 patients (12.5%). History of stone surgery, history of UTI, presence of nephrostomy/D-J stent, type of surgery, operation time, length of stay, and presence of complication except fUTI were found to be significantly different in the fUTI group. In the univariate analysis, history of stone surgery, history of UTI, presence of nephrostomy/D-J stent, type of surgery, operation time, and presence of complication except fUTI were determined as factors predicting fUTI. In multivariate analysis, only the history of UTI was identified as an independent predictive factor. -/- Conclusion: In pediatric patients, infectious complications constitute the majority of complications after URS-L. History of UTI is the only independent factor that predicts postoperative fUTI after URS-L. (shrink)

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 (...) analysis empower decision-makers, with potential applications in real-time smoke event monitoring and preparedness strategies. This work contributes to the field of air quality forecasting and environmental stewardship, offering a data-driven approach to address smoke-related challenges and enhance community well-being. (shrink)

Despite their success in describing and predicting cognitive behavior, the plausibility of so-called ‘rational explanations’ is often contested on the grounds of computational intractability. Several cognitive scientists have argued that such intractability is an orthogonal pseudoproblem, however, since rational explanations account for the ‘why’ of cognition but are agnostic about the ‘how’. Their central premise is that humans do not actually perform the rational calculations posited by their models, but only act as if they do. Whether or not the problem (...) of intractability is solved by recourse to ‘as if’ explanations critically depends, inter alia, on the semantics of the ‘as if’ connective. We examine the five most sensible explications in the literature, and conclude that none of them circumvents the problem. As a result, rational ‘as if’ explanations must obey the minimal computational constraint of tractability. (shrink)

In this chapter we first elucidate the subjective flow of time particularly as developed by Husserl. We next discuss time and timescales in predictive processing. We then consider how the phenomenological analysis of time can be naturalized within a predictive processing framework. In the final section, we develop an analysis of the temporal disturbances characteristic of depression using the resources of both phenomenology and predictive processing.

This research project utilized advanced computer algorithms to predict the outcomes of Premier League soccer matches. The dataset containing match data and odds from seasons was processed to handle missing information, select features and reduce complexity using Principal Component Analysis. To address imbalances, in the target variable Synthetic Minority Over sampling Technique (SMOTE) was employed. Various machine learning models such as RandomForest, DecisionTree, SVM, XGBoost and LightGBM were evaluated.

Dredging operations are essential for maintaining navigable waterways, but determining the ideal time to dredge requires a multifaceted approach, incorporating both environmental and operational variables. This paper presents the Dredging Analysis and Decision Support System (DADSS), a data-driven solution that employs historical sedimentation data, weather patterns, and water flow statistics to optimize dredging decisions. The system leverages Random Forest Classifier and Regressor models to predict the need for dredging and estimate associated costs. Key inputs include sedimentation depth, water flow (...) rate, sediment type, and precipitation levels. By providing real-time insights and cost-effective guidance without reliance on physical sensors, this solution enhances waterway management efficiency while reducing environmental impact. Results demonstrate a clear optimization in dredging operations with a reduced frequency of unnecessary dredging. (shrink)

Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., Bayesian model (...) exploration, Bayesian model averaging, and elastic-net regression. The study successfully replicated past research supporting the relationship between moral foundations, political orientation, and the moral circle. Additional moral psychological constructs, such as post-conventional moral reasoning and moral identity, significantly predicted the moral circle width. The identified components of the moral circle were conceptually related to phronesis, i.e., practical wisdom. We discussed the educational implications of the findings, particularly those related to moral education focusing on phronesis cultivation, multiculturalism and global citizenship, and climate justice. (shrink)

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 monitoring and decision-making for stakeholders and policymakers. (shrink)

Day 6 of advanced course on "Crosslinguistic compositional semantics" at 2009 LSA Summer Institute at UC Berkeley. Plan for today: (a) Review: scope prediction, Kalaallisut data, (b) Analysis of Kalaallisut data, (c) Questions & discussion.

The analysts are in the field of "knowledge". Intelligence refers to knowledge and the types of problems addressed are knowledge problems. So, we need a concept of work based on knowledge. We need a basic understanding of what we know and how we know, what we do not know, and even what can be known and what is not known. The analysis should provide a useful basis for conceptualizing intelligence functions, of which the most important are "estimation" and "prediction". (...) The intelligence itself, in its basic form, has a decision-making function. Intelligence analysis applies individual and collective cognitive methods to assess data and test assumptions in a secret socio-cultural context. DOI: 10.13140/RG.2.2.25298.40646. (shrink)

The art of prediction of stock market volatility has always been a most challenged interdisciplinary research problem among scientist due to its highly non- linear nature of market flow. This paper tries to analysis the historical data of BSE Sensex using extreme volatilities estimators, GARCH, ANN and new proposed Text Mining approach for stock market predictions. Finally experimental results illustrates that the new proposed Text model can able to predict the volatilities of the stock price better than other models.

This paper presents a falsifiable prediction based on A-theories of time, which require both an A-series (future/present/past) and B-series (earlier/simultaneous/later) of time. We make an unusual argument based on the temporal search parameters of YouTube videos, which requires *two* parameters. We make the falsifiable prediction that no interface with just *one* parameter can be made that has the same functionality (as would be asserted in B-theories). This circumstance applies to many areas of human endeavor. We extend this analysis to (...) clocks, showing how sundials, analog clocks, digital clocks, and atomic clocks all fundamentally operate using both temporal series. The progression from hardware to software clocks in computers reveals how both series persist through increasing levels of abstraction. These findings suggest that physics, being fundamentally based on clock measurements, must incorporate both temporal series rather than attempting to reduce one to the other. This approach offers new insights into the relationship between quantum mechanics and relativity, suggesting that quantum mechanics primarily operates in A-series time while relativity operates in B-series time, potentially illuminating why their unification has proven challenging. (shrink)

Prior research found correlations between reflection test performance and philosophical tendencies among laypeople. In two large studies (total N = 1299)—one pre-registered—many of these correlations were replicated in a sample that included both laypeople and philosophers. For example, reflection test performance predicted preferring atheism over theism and instrumental harm over harm avoidance on the trolley problem. However, most reflection-philosophy correlations were undetected when controlling for other factors such as numeracy, preferences for open-minded thinking, personality, philosophical training, age, and gender. Nonetheless, (...) some correlations between reflection and philosophical views survived this multivariate analysis and were only partially confounded with either education or self-reported reasoning preferences. Unreflective thinking still predicted believing in God whereas reflective thinking still predicted believing that (a) proper names like ‘Santa’ do not necessarily refer to entities that actually exist and (b) science does reveal the fundamental nature of the world. So some robust relationships between reflection and philosophical tendencies were detected even among philosophers, and yet there was clearly more to the link between reflection and philosophy. To this end, demographic and metaphilosophical hypotheses are considered. (shrink)

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by communication difficulties and repetitive behaviors. Early diagnosis and intervention are critical to improving outcomes for individuals with autism spectrum disorder. In this context, machine learning techniques, especially deep neural networks (DNN), offer effective solutions for pattern prediction and analysis of ASD. This study presents a fivelayer DNN algorithm for behavioral and treatment-based prediction in ASD. Our model uses the power of deep learning to identify complex patterns from multiple sources, (...) including demographic data, medical history, and behavioral assessments collected from multiple individuals with autism spectrum disorder, both positive and negative. The model consists of an input layer, followed by three hidden layers connected to neurons activated by the power function (RELU), and finally oscillation using the sigmoid activation function. ASD process. The model is trained using the Adam optimizer and binary cross-entropy loss function, with additional measures such as early stopping and hyperparameter tuning to prevent overfitting and increase the accuracy of predictions. Our results demonstrate the effectiveness of the proposed DNN model in accurately diagnosing ASD based on comprehensive data and ultimately determining performance metrics. This enables our models to rapidly detect the disease and ultimately contribute to better disease control. K. (shrink)

In recent years, the prediction of air quality has become a critical task due to its significant impact on human health and the environment. With urbanization and industrial growth, the need for accurate air quality forecasting has become more urgent. Traditional methods for air quality prediction are often based on statistical models or physical simulations, which, while valuable, can struggle to capture the complexity of air pollution dynamics. This study explores the use of deep learning techniques to predict air quality, (...) providing a comparative analysis of different neural network models and their performance. (shrink)

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 explaining variance in ethical behaviors than do values at the societal-level. Implicitly, our findings question the soundness of using societal-level values measures. Implications for international business research are discussed. (shrink)

Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put (...) forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction. (shrink)