Can purely predictive models be useful in investigating causal systems? I argue ‘yes’. Moreover, in many cases not only are they useful, they are essential. The alternative is to stick to models or mechanisms drawn from well-understood theory. But a necessary condition for explanation is empirical success, and in many cases in social and field sciences such success can only be achieved by purely predictive models, not by ones drawn from theory. Alas, the attempt to (...) use theory to achieve explanation or insight without empirical success therefore fails, leaving us with the worst of both worlds – neither prediction nor explanation. Best go with empirical success by any means necessary. I support these methodological claims via case studies of two impressive feats of predictive modelling: opinion polling of political elections, and weather forecasting. (shrink)

The Korea National Health and Nutrition Examination Survey (2020) reported that the prevalence of obesity (≥19 years old) was 31.4% in 2011, but it increased to 33.8% in 2019 and 38.3% in 2020, which confirmed that it increased rapidly after the outbreak of COVID-19. Obesity increases not only the risk of infection with COVID-19 but also severity and fatality rate after being infected with COVID-19 compared to people with normal weight or underweight. Therefore, identifying the difference in potential factors for (...) obesity before and after the pandemic is an important issue in health science. This study identified the keywords and topics that were formed before and after the COVID-19 pandemic in the South Korean society and how they had been changing by conducting a web crawling of South Korea's news big data using “obesity” as a keyword. (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)

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue (...) that topological predictions can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

The exploration of the literature indicated that much studies abound in related areas. Much seems yet to be known about the nature of the relationship that exists between managerial variables and the sustainability of graduate programmes. To bridge this gap, we utilized a standardised multiple regression approach to build up linear models that examine three managerial processes (strategic planning, staff and information/communication management) and how they affect three proxies of the sustainability of graduate programmes (availability of funds and facilities, (...) as well as supervision) respectively and cumulatively. Quantitative data were obtained from an entire population of 149 managers (Head of Departments and Deans) in the University of Calabar, Calabar, Nigeria using a questionnaire. Findings emerged, among others, a significant relationship between each of the managerial processes to the sustainability of graduate programmes generally and particularly in terms of availability of funds, facilities and supervision; the three predictor variables partially and jointly accounted for significant proportions in the sustainability of graduate programmes generally and specifically in terms of the various dimensions. Based on this evidence, one general and three partial predictive linear equations, as well as models were derived, while relevant action-based implications for effective management and practice were discussed. (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)

There is a growing body of literature investigating the impact of retraining and motivation on employee work efficiency. However, little seems to be understood about the effects of employee placement on the commitment of teachers to their jobs. To the best of the researcher's awareness, the partial and composite impact of staff placement, retraining, and motivation on the three aspects of job commitment (affective, continuance and normative) among secondary educators have scarcely been examined. This research was intended to fill this (...) vacuum by using a predictive path modelling approach to analyse the association between these endogenous and exogenous variables. A random sample of 500 secondary school principals was surveyed using two forms of questionnaires. Collected data were analysed and formulated hypotheses tested using Path and multiple linear regression analyses, with the aid of Amos and SPSS packages. Findings indicated that staff placement and motivation were highly predictive of instructors' commitment (at the affective and continuance dimension), but not at the normative dimension; Employee dedication in the three dimensions was not predicted by personnel retraining, but it did result in employee attrition; workers retraining only increased teachers' work commitment when it was augmented with placement and motivation. The combined effect of staff placement, retraining, and motivation was statistically significant in two dimensions of teachers’ job commitment (affective and continuance), but not on the normative dimension. Based on these findings, policy and theoretical ramifications for effective instructional management, assessment, classroom practice, and future study are discussed. (shrink)

The college hopes that every semester students are able to pay tuition properly and smoothly. The hope is that the institution will be able to maintain monthly cash flow so that its operational and maintenance costs can be met. Therefore, this study was conducted to predict and fulfill the institution's cash-in from the method of paying tuition fees either by cash, installments, or sometimes late payments every semester. In predicting the method of paying tuition fees, using student profile data (name, (...) name, study program) and achievement index every semester for 5 semesters passed and the method of payment (cash, installments, and late--cash or installments). Using the Naive Bayes (NB) method, Support Vector Machine (SVM), and Deep Learning, this study aims to forecast tuition costs. The Classification Prediction Model with Naive Bayes, SVM, and Deep Learning produces Confusion Matrix Performance NB with an Accuracy of 91.49%, Confusion Matrix Performance SVM with an Accuracy of 85.11%, and Confusion Matrix Performance Deep Learning with an Accuracy of 89.36%, according to the research findings. (shrink)

This paper addresses the recent rise of the use of alternative medicine in Western countries. It offers a novel explanation of that phenomenon in terms of cognitive and economic factors related to the free-market and patient-centric approach to medicine that is currently in place in those countries, in contrast to some alternative explanations of this phenomenon. Moreover, the paper addresses this troubling trend in terms of the serious harms associated with the use of alternative medical modalities. The explanatory theory defended (...) here is then predicated on the idea that an extreme patient-centric model of medical practice that treats largely ignorant patients as consumers of medical products and services endowed with an essentially unrestricted power of freedom to choose treatments predictably leads serious and avoidable harms. Some important moral and epistemological consequences of this model are then articulated and corrective measures are suggested. (shrink)

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)

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)

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 STARS (...) and this have been done through consulting internet search in which secondary data were extracted. It was observed that most literatures does not quantify the knowledge on how hydrates production prediction with CMG STARS can be done. It was recommended that in the future, the research work on hydrate production prediction with CMG needs to be done by performing sensitivity analysis of different reservoir parameters which affect production process and commences the simulation model for the production prediction. (shrink)

Stanford’s argument against scientific realism focuses on theories, just as many earlier arguments from inconceivability have. However, there are possible arguments against scientific realism involving unconceived (or inconceivable) entities of different types: observations, models, predictions, explanations, methods, instruments, experiments, and values. This paper charts such arguments. In combination, they present the strongest challenge yet to scientific realism.

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of (...) sorts, in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system. (shrink)

Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...) it was evaluated using the testing dataset. The result of testing, the area under receiver operating characteristic curve (AUROC) of the model was 0.99, precision, recall, f1-score and accuracy were 99%, precision, recall, f1-score and accuracy were 96.83%, 96.83%, 96.82% and 96.83% respectively.. Clinicians are presently unaware of dissimilar retinal feature variants between females and males, stressing the importance of model explain ability for the prediction of gender from retinal fundus images. The proposed deep learning may enable clinician-driven automated discovery of novel visions and disease biomarkers. (shrink)

In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The evaluation (...) of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (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)

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 (...) factors – leading to problems with reproducibility. Thus, for methodological reasons, the predictive success causes a reproducibility problem. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used artificial neural networks to (...) predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

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 (...) employ various kinds of scientific representations, differing in the scope of empirical commitments they entail. Two major perspectives on PP qua cognitive theory may then be distinguished: generalized vs. hierarchical. The first one fails to provide empirical detail, and the latter constrains possible physical implementations. However, we show that even hierarchical PP is insufficiently restrictive to disallow incorrect models and may be adjusted to explain any neurocognitive phenomenon–including non-existent or impossible ones–through flexible adjustments. This renders PP a universal modeling tool with an unrestricted number of degrees of freedom. Therefore, in contrast with declarations of its proponents, it should not be understood as a unifying theoretical perspective, but as a computational framework, possibly informing further theory development in cognitive science. (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)

Predictive processing models of perception take issue with standard models of perception as hierarchical bottom-up processing modulated by memory and attention. The predictive framework posits that the brain generates predictions about stimuli, which are matched to the incoming signal. Mismatches between predictions and the incoming signal – so-called prediction errors – are then used to generate new and better predictions until the prediction errors have been minimized, at which point a perception arises. Predictive models (...) hold that all bottom-up processes are signals conveying prediction errors to higher areas, which respond by updating their predictions. We take issue with this claim and argue that object recognition requires bottom-up processing that cannot be understood in terms of prediction errors. Along the way, we expand on previous work casting doubt on the framework's ability to account for attention. Specifically, we argue that the type of attention that allows us to rapidly extract the gist of an object or scene presents an additional challenge to the predictive approach. We conclude by considering how the framework may be augmented to avoid these problems. (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)

Abstract: This research paper presents the development and evaluation of a neural network-based model for predicting fire alarms in smoke detection systems. Using a dataset from Kaggle containing 15 features and 3487 samples, we trained and validated a neural network with a three-layer architecture. The model achieved an accuracy of 100% and an average error of 0.0000003. Additionally, we identified the most influential features in predicting fire alarms.

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)

Recent years, active suspension system has been widely used in automobiles to improve the road holding ability and the riding comfort. This study presents a new fuzzy model predictive control for a nonlinear quarter car active suspension system. A nonlinear dynamical model of active suspension is established, where the nonlinear dynamical characteristic of the spring and damper are considered. Based on the proposed fuzzy model predictive control method is presented to stabilize the displacement of the active suspension in (...) the presence of different road profiles. Parameters of the model predictive and fuzzy logic control laws are designed to estimate the (Bump and Sinusoidal)road profile input in the active suspension. At last, the reliability of the fuzzy model predictive control method is evaluated by the MATLAB simulation tool. Simulation result shows that the fuzzy model predictive control method obtained the satisfactory control performance for the active suspension system. (shrink)

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 come to be adequate for purposes that are defined by both their guiding research questions and their larger social context of application. (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)

In this paper an Artificial Neural Network (ANN) model, for predicting the category of a tumor was developed and tested. Taking patients’ tests, a number of information gained that influence the classification of the tumor. Such information as age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron Topology was established and trained using (...) data set which its title is “primary tumor” and was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia Test data evaluation shows that the ANN model is able to correctly predict the tumor category with 76.67 % accuracy. (shrink)

Abstract: 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)

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)

Magnetic levitation system is operated primarily based at the principle of magnetic attraction and repulsion to levitate the passengers and the train. However, magnetic levitation trains are rather nonlinear and open loop unstable which makes it hard to govern. In this paper, investigation, design and control of a nonlinear Maglev train based on NARMA-L2, model reference and predictive controllers. The response of the Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been (...) as compared for a step input signal. The simulation consequences prove that the Maglev teach system with NARMA-L2 controller suggests the quality performance in adjusting the precise function of the system and the device improves the experience consolation and street managing criteria. (shrink)

Electromagnetic Suspension System (EMS) is mostly used in the field of high-speed vehicle. In this study, a space exploring vehicle quarter electromagnetic suspension system is modelled, designed and simulated using Neural network-based control problem. NARMA-L2, Model reference and predictive controllers are designed to improve the body travel of the vehicle using bump road profile. Comparison between the proposed controllers is done and a promising simulation result have been analyzed.

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, (...) and action control comprising levels of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture. (shrink)

Should we insist on prediction, i.e. on correctly forecasting the future? Or can we rest content with accommodation, i.e. empirical success only with respect to the past? I apply general considerations about this issue to the case of economics. In particular, I examine various ways in which mere accommodation can be sufficient, in order to see whether those ways apply to economics. Two conclusions result. First, an entanglement thesis: the need for prediction is entangled with the methodological role of orthodox (...) economic theory. Second, a conditional predictivism: if we are not committed to orthodox economic theory, then we should demand prediction rather than accommodation – against most current practice. (shrink)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a (...) dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.

This study examined the degree to which students indulge in six prominent misconducts in Distance Education Institutions (DEIs). The study also quantified how class size, instructional delivery and institutional policies predict students’ indulgence in sharp practices using a general linear modelling approach. A sample of 871 participants was drawn from 1,742 final-year students across two DEIs in Nigeria. A structured questionnaire was used for data collection. The questionnaire had acceptable psychometric estimates of dimensionality, content and construct validity, as well as (...) reliability. Sharp practices such as cheating, plagiarism, falsification, impersonation, and arm-twisting were more prevalent in large classes, whereas only inducement was higher in small classes. Class size influenced students’ indulgence in sharp practices in DEIs. Instructional delivery and institutional policies negatively predicted students’ indulgence in the six sharp practices. Almost all the six sharp practices correlated positively and significantly, except for impersonation and inducement. Therefore, distance education students who cheat, plagiarize, arm-twist, falsify records, induce lecturers, and promote impersonation are more likely to indulge in other forms of sharp practices. It was concluded that large class sizes, poor instructional delivery, and poorly implemented institutional policies promote sharp practices among distance education students. Based on this conclusion, key policy and research implications are discussed. (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)

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)

Abstract Heart Attack is the Cardiovascular Disease (CVD) which causes the most deaths among CVDs. We collected a dataset from Kaggle website. In this paper, we propose an ANN model for the predicting whether a patient has a heart attack or not that. The dataset set consists of 9 features with 1000 samples. We split the dataset into training, validation, and testing. After training and validating the proposed model, we tested it with testing dataset. The proposed model reached an accuracy (...) of 98.01% on the Heart Disease Dataset. (shrink)

This investigation focused on relationships among sexual assault, self-blame, and sexual revictimization. Among a female undergraduate sample of adolescent sexual assault victims, those endorsing greater self-blame following sexual assault were at increased risk for sexual revictimization during a 4.2-month follow-up period. Moreover, to the extent that sexual assault victims perceived nonconsensual sex is permitted by law, they were more likely to blame themselves for their own assaults. Discussion focuses on situating victim-based risk factors within sociocultural context.

The Austrian School seems to remain outside the debate on the realism of economic models. In principle, given the association of the term “model” with the Chicago School, and also for understanding that Hayek had critized the model of perfect competition as unrealistic. Even though in previous opportunities we showed how the theory of market as a process could be understood as the model of the Austrian School, and that Hayek’s criticism to the model of perfect competition was not (...) so much of unrealistic, but that it was wrongly stated (Zanotti & Borella, 2015), and we developed the ontological foundation of the model in Hayek’s thought (Borella, 2019), in this paper we will show how it is posible to compare pattern predictions with models, and how in this sense, the Austrian School, also seems to get near, even without sufficiently noticing it, to the discussion on models in economics and their realism. Keywords: Realism of models- Hayek- pattern predictions-models . (shrink)

Predictive approaches to the mind claim that perception, cognition, and action can be understood in terms of a single framework: a hierarchy of Bayesian models employing the computational strategy of predictive coding. Proponents of this view disagree, however, over the extent to which perception is direct on the predictive approach. I argue that we can resolve these disagreements by identifying three distinct notions of perceptual directness: psychological, metaphysical, and epistemological. I propose that perception is plausibly construed (...) as psychologically indirect on the predictive approach, in the sense of being constructivist or inferential. It would be wrong to conclude from this, however, that perception is therefore indirect in a metaphysical or epistemological sense on the predictive approach. In the metaphysical case, claims about the inferential properties of constructivist perceptual mechanisms are consistent with both direct and indirect solutions to the metaphysical problem of perception (e.g. naïve realism, representationalism, sense datum theory). In the epistemological case, claims about the inferential properties of constructivist perceptual mechanisms are consistent with both direct and indirect approaches to the justification of perceptual belief. In this paper, I demonstrate how proponents of the predictive approach have conflated these distinct notions of perceptual directness and indirectness, and I propose alternative strategies for developing the philosophical consequences of the approach. (shrink)

Recently, active suspension system will become important to the vehicle industries because of its advantages in improving road managing and ride comfort. This paper offers the development of mathematical modelling and design of a neural network control approach. The paper will begin with a mathematical model designing primarily based at the parameters of the active suspension system. A nonlinear three by four-way valve-piston hydraulic actuator became advanced which will make the suspension system under the active condition. Then, the model can (...) be analyzed thru MATLAB/Simulink software program. Finally, the NARMA-L2, model reference and predictive controllers are designed for the active suspension system. The results are acquired after designing the simulation of the quarter-car nonlinear active suspension system. From the simulation end result using MATLAB/Simulink, the response of the system might be as compared between the nonlinear active suspension system with NARMA-L2, model reference and predictive controllers. Besides that, the evaluation has been made between the proposed controllers thru the characteristics of the manage objectives suspension deflection, body acceleration and body travel of the active suspension system. . As a conclusion, designing a nonlinear active suspension system with a nonlinear hydraulic actuator for quarter car model has improved the car performance by using a NARMA-L2 controller. The improvements in performance will improve road handling and ride comfort performance of the active suspension system. (shrink)

In this paper we describe a technical system for DC motor speed control. The speed of DC motor is controlled using Neural Network Based Model Reference and Predictive controllers with the use of Matlab/Simulink. The analysis of the DC motor is done with and without input side Torque disturbance input and the simulation results obtained by comparing the desired and actual speed of the DC motor using random reference and sinusoidal speed inputs for the DC motor with Model Reference (...) and Predictive controllers. The DC motor with Model Reference controller shows almost the actual speed is the same as the desired speed with a good performance than the DC motor with Predictive controller for the system with and without input side disturbance. Finally the comparative simulation result prove the effectiveness of the DC motor with Model Reference controller. (shrink)

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict Birth Weight. A number of factors were identified that may affect birth weight. Factors such as smoke, race, age, weight (lbs) at last menstrual period, hypertension, uterine irritability, number of physician visits in 1st trimester, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed and trained using the data from some birth cases in hospitals. The (...) evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the birth weight with 100% accuracy. (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)