Although predictive power and explanatory insight are both desiderata of scientific models, these features are often in tension with each other and cannot be simultaneously maximized. In such situations, scientists may adopt what I term a ‘division of cognitive labor’ among models, using different models for the purposes of explanation and prediction, respectively, even for the exact same phenomenon being investigated. Adopting this strategy raises a number of issues, however, which have received inadequate philosophical attention. More specifically, while one (...) implication may be that it is inappropriate to judge explanatory models by the same standards of quantitative accuracy as predictive models, there still needs to be some way of either confirming or rejecting these model explanations. Here I argue that robustness analyses have a central role to play in testing highly idealized explanatory models. I illustrate these points with two examples of explanatory models from the field of geomorphology. (shrink)

PurposeAlthough an increasing body of literature suggests a relationship between brain irradiation and deterioration of neurocognitive function, it remains as the standard therapeutic and prophylactic modality in patients with brain tumors. This review was aimed to abstract and evaluate the prediction models for radiation-induced neurocognitive decline in patients with primary or secondary brain tumors.MethodsMEDLINE was searched on October 31, 2021 for publications containing relevant truncation and MeSH terms related to “radiotherapy,” “brain,” “prediction model,” and “neurocognitive impairments.” Risk (...) of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool.ResultsOf 3,580 studies reviewed, 23 prediction models were identified. Age, tumor location, education level, baseline neurocognitive score, and radiation dose to the hippocampus were the most common predictors in the models. The Hopkins verbal learning and the trail making tests were the most frequent outcome assessment tools. All studies used regression as machine learning method. All models were judged to have a high risk of bias mainly due to issues in the analysis.ConclusionExisting models have limited quality and are at high risk of bias. Following recommendations are outlined in this review to improve future models: developing cognitive assessment instruments taking into account the peculiar traits of the different brain tumors and radiation modalities; adherence to model development and validation guidelines; careful choice of candidate predictors according to the literature and domain expert consensus; and considering radiation dose to brain substructures as they can provide important information on specific neurocognitive impairments. (shrink)

This paper describes in some detail a pattern of justification which seems to be part of common sense logic and also part of the logic of scientific investigations. Calling this pattern “abduction,” the paper lays out an “abduction-prediction” model of scientific inference as an update to the traditional hypothetico-deductive model. According to this newer model, scientific theories receive their claims for acceptance and belief from the abductive arguments that support them, and the processes of scientific discovery (...) aim to develop theories with strong abductive support. It is suggested that the study of diagnosis presents a good opportunity for studying abduction under somewhat simpler and more reproducible conditions than occur in scientific discovery. A computer-based diagnostic system is described which provides a small-scale validation of the abduc-tion-prediction model by showing that a version of it can be made precise enough to be implemented and to perform correctly for diagnosis. (shrink)

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)

An implicit ambiguity in the field of prediction-based decision-making concerns the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply refers to ‘fair prediction’. In this paper, we point out that a differentiation of these concepts is helpful when trying to implement algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, (...) what is more properly called ‘fair’ or ‘unfair’ is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. In this paper, we clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. As well as discussing this relationship both from a conceptual and a practical point of view, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the ‘prediction-modeler’ and the ‘decision-maker,’ and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we offer a new perspective shifting the focus from an abstract concept of algorithmic fairness to the concrete context-dependent nature of algorithmic decision-making, where different actors exist, can have different goals, and may act independently. In addition, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios. (shrink)

A link prediction model for weighted networks based on Dempster–Shafer evidence theory and the influence of common neighbours is proposed in this paper. First, three types of future common neighbours and their topological structures are proposed. Second, the concepts of endpoint weight influence, link weight influence, and high-strength node influence are introduced. Then, the similarity based on the impacts of current common neighbours and FCNs is defined, respectively. Finally, the two similarity indices are fused by the DS evidence (...) theory. This model effectively integrates multisource information and completely exploits the influence of all CCNs and FCNs on similarity. Experiments are performed on 9 real and 40 simulation-weighted datasets, and these findings are compared with several classic algorithms. Results show that the proposed method has higher precision than other methods, which can achieve good performance in link prediction in weighted networks. (shrink)

The development of Information and Communication Technologies has favored access to technological resources in adolescents. These tools provide access to information that can promote learning. However, they can also have a negative effect against people, as they can be used with other functionality, in which cyberbullying situations are caused during the interactions that arise when using social networks. The objective of this study was to determine the predictive value of the role of cyberbullying victims based on variables related to other (...) roles involved in cyberbullying and bullying, as well as personal characteristics, contextual characteristics and positive teamwork habits.. Information was collected from 227 students of the educational stages of Primary Education and Secondary Education, aged between 11 and 15 years, in a city with a high index of cultural diversity. The step-by-step technique was used to build the regression model. The results indicate that the model has a good goodness of fit coefficient. The role of cyberbully is the most important predictive variable of the role of the victim in cyberbullying and, to a lesser extent, the role of the witness in cyberbullying, the role of the witness in bullying, and the role of the victim of bullying. The role of the bullying aggressor and the variables sex, age, type of educational center, and teamwork habits are excluded in the predictive model. (shrink)

This study aimed at determining B.S. Biology students’ comprehension of the Bioethics principles and conviction schemas towards bioethical issues; as well as at identifying predictors for comprehension of bioethical principles and convictions and creating model constructs of predictors which are fit for the data. One-hundred sixteen Filipino Biology majors were pre and post-tested for comprehension of bioethics principles and convictions towards bioethical issues. Predictors for comprehension and convictions among personal and family background factors, global and primary personality factors, and (...) mental ability were identified through step-wise multiple regression. Constructed path models were tested for goodness of fit using Structural Equation Modeling. Results showed that case study discussion and the bioethics course had positive effects on comprehension of the bioethics principles and on convictions towards bioethical issues. Predictors for comprehension of Bioethics principles before intervention were verbal ability, warmth, and exposure to media. Sex and openness to change were predictors for pre-convictions. Post-conviction predictors were anxiety, self-control, extraversion, liveliness, and perfectionism. Four out of five model constructs were accepted based on model fit criteria. In conclusion, personal factors, i.e., sex and personality factors are contributors in bioethical convictions; not cognitive factors like mental ability or comprehension of Bioethics principles; implying that bioethical convictions are already pre-conceived and that empathy-based experiences may strengthen convictions towards bioethical issues. (shrink)

An integrated hog futures price forecasting model based on whale optimization algorithm, LightGBM, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise is proposed to overcome the limitations of a single machine learning model with low prediction accuracy and insufficient model stability. The simulation process begins with a grey correlation analysis of the hog futures price index system in order to identify influencing factors; after that, the WOA-LightGBM model is developed, and the WOA algorithm is (...) used to optimize the LightGBM model parameters; and, finally, the residual sequence is decomposed and corrected by using the CEEMDAN method to build a combined WOA-LightGBM-CEEMDAN model. Furthermore, it is used for comparison experiments to check the validity of the model by using data from CSI 300 stock index futures. Based on all experimental results, the proposed combined model shows the highest prediction accuracy, surpassing the comparative model. The model proposed in this study is accurate enough to meet the forecasting accuracy requirements and provides an effective method for forecasting future prices. (shrink)

As the stock market is an important part of the national economy, more and more investors have begun to pay attention to the methods to improve the return on investment and effectively avoid certain risks. Many factors affect the trend of the stock market, and the relevant information has the nature of time series. This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory improved on bidirectional long short-term memory (...) adds 1 − tanh function in the output gate which makes the model better predict the stock price. The model extracts advanced features that influence stock price through convolutional neural network, and predicts the stock closing price through BiSLSTM after the data processed by CNN. To verify the effectiveness of the model, the historical data of the Shenzhen Component Index from July 1, 1991, to October 30, 2020, are used to train and test the CNN-BiSLSTM. CNN-BiSLSTM is compared with multilayer perceptron, recurrent neural network, long short-term memory, BiLSTM, CNN-LSTM, and CNN-BiLSTM. The experimental results show that the mean absolute error, root-mean-squared error, and R-square evaluation indicators of the CNN-BiSLSTM are all optimal. Therefore, CNN-BiSLSTM can accurately predict the closing price of the Shenzhen Component Index of the next trading day, which can be used as a reference for the majority of investors to effectively avoid certain risks. (shrink)

User location prediction in location-based social networks can predict the density of people flow well in terms of intelligent transportation, which can make corresponding adjustments in time to make traffic smooth, reduce fuel consumption, reduce greenhouse gas emissions, and help build a green cycle low-carbon transportation green system. This paper proposes a Markov chain position prediction model based on multidimensional correction. Firstly, extract corresponding information from the user’s historical check-in position sequence as a position-position conversion map. Secondly, (...) the influence of check-in period, space distance, and other factors on the position prediction is linearly weighted and merged with the position prediction of the n-order Markov chain to construct MDC-MCM. Finally, we conduct a comprehensive performance evaluation of MDC-MCM using the dataset collected from Brightkite. Experimental results show that compared with other advanced location prediction technologies, MDC-MCM achieves better location prediction results. (shrink)

Facing an increasingly competitive market, enterprises need correct decisions to solve operational problems in a timely manner to maintain their competitive advantages. In this context, insufficient information may lead to an overfitting phenomenon in general mathematical modeling methods, making it difficult to ensure good analytical performance. Therefore, it is important for enterprises to be able to effectively analyze and make predictions using small data sets. Although various approaches have been developed to solve the problem of prediction, their application is (...) often limited by insufficient observations. To further enforce the effectiveness of data uncertainty processing, this study proposed an aggregating prediction model for management decision analysis using small data sets. Compared with six popular approaches, the results from the experiments show that the proposed method can effectively deal with the small data set prediction problem and is thus an appropriate decision analysis tool for managers. (shrink)

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but (...) also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models. (shrink)

Intellectual humility (IH) is often conceived as the recognition of, and appropriate response to, your own intellectual limitations. As far as we are aware, only a handful of studies look at interventions to increase IH – e.g. through journalling – and no study so far explores the extent to which having high or low IH can be predicted. This paper uses machine learning and natural language processing techniques to develop a predictive model for IH and identify top terms and (...) features that indicate degrees of IH. We trained our classifier on the dataset from an existing psychological study on IH, where participants were asked to journal their experiences with handling social conflicts over 30 days. We used Logistic Regression (LR) to train a classifier and the Linguistic Inquiry and Word Count (LIWC) dictionaries for feature selection, picking out a range of word categories relevant to interpersonal relationships. Our results show that people who differ on IH do in fact systematically express themselves in different ways, including through expression of emotions (i.e., positive, negative, and specifically anger, anxiety, sadness, as well as the use of swear words), use of pronouns (i.e., first person, second person, and third person) and time orientation (i.e., past, present, and future tenses). We discuss the importance of these findings for IH and the value of using such techniques for similar psychological studies, as well as some ethical concerns and limitations with the use of such semi-automated classifications. (shrink)

The disambiguation of a syntactically ambiguous sentence in favor of a less preferred parse can lead to slower reading at the disambiguation point. This phenomenon, referred to as a garden‐path effect, has motivated models in which readers initially maintain only a subset of the possible parses of the sentence, and subsequently require time‐consuming reanalysis to reconstruct a discarded parse. A more recent proposal argues that the garden‐path effect can be reduced to surprisal arising in a fully parallel parser: words consistent (...) with the initially dispreferred but ultimately correct parse are simply less predictable than those consistent with the incorrect parse. Since predictability has pervasive effects in reading far beyond garden‐path sentences, this account, which dispenses with reanalysis mechanisms, is more parsimonious. Crucially, it predicts a linear effect of surprisal: the garden‐path effect is expected to be proportional to the difference in word surprisal between the ultimately correct and ultimately incorrect interpretations. To test this prediction, we used recurrent neural network language models to estimate word‐by‐word surprisal for three temporarily ambiguous constructions. We then estimated the slowdown attributed to each bit of surprisal from human self‐paced reading times, and used that quantity to predict syntactic disambiguation difficulty. Surprisal successfully predicted the existence of garden‐path effects, but drastically underpredicted their magnitude, and failed to predict their relative severity across constructions. We conclude that a full explanation of syntactic disambiguation difficulty may require recovery mechanisms beyond predictability. (shrink)

With the rapid development of emerging technologies such as electric vehicles and high-speed railways, the insulated gate bipolar transistor is becoming increasingly important as the core of the power electronic devices. Therefore, it is imperative to maintain the stability and reliability of IGBT under different circumstances. By predicting the junction temperature of IGBT, the operating condition and aging degree can be roughly evaluated. However, the current predicting approaches such as optical, physical, and electrical methods have various shortcomings. Hence, the backpropagation (...) neural network can be applied to avoid the difficulties encountered by conventional approaches. In this article, an advanced prediction model is proposed to obtain accurate IGBT junction temperature. This method can be divided into three phases, BP neural network estimation, interpolation, and Kalman filter prediction. First, the validities of the BP neural network and Kalman filter are verified, respectively. Then, the performances of them are compared, and the superiority of the Kalman filter is proved. In the future, the application of neural networks or deep learning in power electronics will create more possibilities. (shrink)

To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based (...) on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy. (shrink)

Systemic lupus erythematosus is an inflammatory autoimmune disease with depression as one of its most common symptoms. The aim of this study is to establish a nomogram prediction model to assess the occurrence of depression in patients with SLE. Based on the Hospital Anxiety and Depression Scale cutoff of 8, 341 patients with SLE, recruited between June 2017 and December 2019, were divided into depressive and non-depressive groups. Data on socio-demographic characteristics, medical history, sociopsychological factors, and other risk (...) factors were collected. Between-group differences in clinical characteristics were assessed with depression as the dependent variable and the variables selected by logistic multiple regression as predictors. The model was established using R language. Marital status, education, social support, coping, and anxiety predicted depression. The nomogram prediction model showed that the risk rate was from 0.01 to 0.80, and the receiver operating characteristic curve analysis showed that the area under the curve was 0.891. The calibration curve can intuitively show that the probability of depression predicted by the nomogram model is consistent with the actual comparison. The designed nomogram provides a highly predictive assessment of depression in patients with SLE, facilitating more comprehensive depression evaluation in usual clinical care. (shrink)

The recent increase in the road transportation necessitates scheduling to reduce the adverse impacts of the road transportation and evaluate the effectiveness of previous actions taken in this context. However, it is impossible to undertake the scheduling and evaluation tasks unless previous information are available to predict the future. The grey model requires a limited volume of data for estimating the behavior of an unknown system. It provides high-accuracy predictions based on few data points. Various grey prediction models (...) have been proposed so far, in which three different approaches are followed to increase the accuracy: (1) data preprocessing, (2) improved equation models, and (3) error improvement or error balancing. In this paper, firstly, a theorem is proposed and proved to recognize the parameters affecting two grey models, namely GM(1, 1) and FGM(1, 1). Then, the effective parameters are adjusted through particle swarm optimization (PSO) to formulate two adjusted models, namely IGM(1, 1) and IFGM(1, 1). According to the simulation results of the proposed models, accuracy of the modeling improved by a minimum of 14.24% and a maximum of 82.39%. Finally, the number of users of a public road transportation system was predicted using the proposed models. The results showed enhanced accuracy (by 7.7%) of the proposed models for predicting the number of users of the public road transportation system. (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 COVID-19 global health emergency has greatly impacted the educational field. Faced with unprecedented stress situations, professors, students, and families have employed various coping and resilience strategies throughout the confinement period. High and persistent stress levels are associated with other pathologies; hence, their detection and prevention are needed. Consequently, this study aimed to design a predictive model of stress in the educational field based on artificial intelligence that included certain sociodemographic variables, coping strategies, and resilience capacity, and to study (...) the relationship between them. The non-probabilistic snowball sampling method was used, involving 337 people from the university education community in south-eastern Spain. The Perceived Stress Scale, Stress Management Questionnaire, and Brief Resilience Scale were administered. The Statistical Package for the Social Sciences was used to design the architecture of artificial neural networks. The results found that stress levels could be predicted by the synaptic weights of coping strategies and timing of the epidemic, with a predictive capacity of over 80% found in the neural network model. Additionally, direct and significant associations were identified between the use of certain coping strategies, stress levels, and resilience. The conclusions of this research are essential for effective stress detection, and therefore, early intervention in the field of educational psychology, by discussing the influence of resilience or lack thereof on the prediction of stress levels. Identifying the variables that maintain a greater predictive power in stress levels is an effective strategy to design more adjusted prevention programs and to anticipate the needs of the community. (shrink)

With the continuous improvement of the level of science and technology, the popularization of the Internet and the development of applications, online consumption has become a major force in personal consumption. As a result, digital consumption is born, and digital consumption is not only reflected in transaction consumption at the monetary level. Like some intangible services similar to the use of dating software, it can also become digital consumption. In this environment, a new economic concept, the digital economy, has emerged (...) as the times require. The digital economy helps to achieve the rapid optimal allocation and regeneration of resources and achieve high-quality economic development. Therefore, as a new economic form, the digital economy has penetrated into all fields of human society. The Canopy algorithm is a fast clustering technique that requires only one pass through the data technology to get the results. But it is inaccurate for large-scale data clustering. Therefore, when analyzing the data, it is necessary to use the Canopy algorithm for preliminary clustering, and then combine with other algorithms or model software for refinement. This article introduces the development of the digital economy in the Internet era. It conducts a theoretical discussion on consumer psychology in the context of the digital economy. It introduces the basic calculation formula of the clustering algorithm and the algorithm flow of the Canopy clustering algorithm. It does model optimization for the Canopy clustering algorithm. On this basis, it designs questionnaires for experimental design. The indicators are divided into commodity attributes and consumer psychology. It builds a consumer psychology prediction model and tests the prediction results. The results show that the maximum difference between the prediction results of digital economy consumption psychology based on the Canopy clustering algorithm and the actual results is 0.047. It can be shown that the psychological prediction model of digital economy consumption based on the Canopy clustering algorithm has certain practicability. (shrink)

Physics has for a long time been regarded as the most mature of all sciences due to strict mathematically formulated laws of physics and success of theories in applications, for which it has been taken as the example of scientificity which other sciences should strive towards. Just what aspect of physics it is that is regarded as the cause of its success and hence the yardstick of scientificity – this question has given rise to differing opinions. In his book Making (...) Social Sciences More Scientific. The Need For Predictive Models Rein Taagepera criticises the opinion that physics is a rigorous science ‘merely’ due to the use of mathematical operations and numerical accuracy of results. He shows that the strictness of physics consists instead in its method that allows to set numbers and mathematical formalism into correspondence with real phenomena in a way that enables application (first of all prediction), and to unite physical theories into uniform, integral systems. At the same time he teaches how it would be possible to reach the same in social sciences. In the first part of my review I will give an overview of the book’s chapters, describing in more detail Taagepera’s general understanding of science and scientific method. In the second part of the review I analyse the positions presented in the book from the point of view of philosophy of science (particularly that of constructive realism), providing examples from social sciences. With my critique I show that the society cannot be handled with strict theories similar to those of physics, and that in order to raise the applicational strength of social sciences, other means often suit better than rendering them similar to physics by developing mathematical formalism. (shrink)

Inventory prediction and management is a key issue in a retail store. There are a number of inventory prediction techniques. However, it is difficult to identify a time series prediction model for inventory forecasting that provides uniformly good results for all the products in a store. This paper uses data from a small retail store to demonstrate the variability of results for different modeling techniques and different products. We demonstrate inadequacy of a generic inventory model. (...) Stability and seasonality analysis of the time series is used to identify groups of products exhibiting similar sales patterns. Different clustering techniques are applied to determine reasonable local groups. With the help of Mean absolute percentage error, the effectiveness of dataset partitioning for better inventory management is demonstrated. Appropriate inventory management strategies are proposed based on the stability and seasonality analysis. (shrink)

In this paper, based on computer reading and processing of music frequency, amplitude, timbre, image pixel, color filling, and so forth, a method of image style transfer guided by music feature data is implemented in real-time playback, using existing music files and image files, processing and trying to reconstruct the fluent relationship between the two in terms of auditory and visual, generating dynamic, musical sound visualization with real-time changes in the visualization. Although recommendation systems have been well developed in real (...) applications, the limitations of CF algorithms are slowly coming to light as the number of people increases day by day, such as the data sparsity problem caused by the scarcity of rated items, the cold start problem caused by new items and new users. The work is dynamic, with real-time changes in music and sound. Taking portraits as an experimental case, but allowing users to customize the input of both music and image files, this new visualization can provide users with a personalized service of mass customization and generate personalized portraits according to personal preferences. At the same time, we take advantage of the BP neural network’s ability to handle complex nonlinear problems and construct a rating prediction model between the user and item attribute features, referred to as the PSO-BP rating prediction model, by combining the features of global optimization of particle swarm optimization algorithm, and make further improvements based on the traditional collaborative filtering algorithm. (shrink)

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, (...) with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy. (shrink)

The purpose of this paper is to identify the factors that affect the intention to use Big Data Applications in companies. Research into Big Data usage intention and adoption is scarce and much less from the perspective of the use of these techniques in companies. That is why this research focuses on analyzing the adoption of Big Data Applications by companies. Further to a review of the literature, it is proposed to use a UTAUT model as a starting (...) class='Hi'>model with the update and incorporation of other variables such as resistance to use and perceived risk, and then to perform a neural network to predict this adoption. With respect to this non-parametric technique, we found that the multilayer perceptron model for the use of Big Data Applications in companies obtains higher AUC values, and a better confusion matrix. This paper is a pioneering study using this hybrid methodology on the intention to use Big Data Applications. The result of this research has important implications for the theory and practice of adopting Big Data Applications. (shrink)

The regulation of chemicals is undergoing drastic changes with the use of computational models to predict environmental toxicity. This particular issue has not attracted much attention, despite its major impacts on the regulation of chemicals. This raises the problem of causality at the crossroads between data and regulatory sciences, particularly in the case models known as quantitative structure–activity relationship models. This paper shows that models establish correlations and not scientific facts, and it engages anew the way regulators deal with uncertainties. (...) It does so by exploring the tension and problems raised by the possibility of causal explanation afforded by quantitative structure–activity relationship models. It argues that the specificity of predictive modelling promotes rethinking of the regulation of chemicals. (shrink)

In air traffic control, the airspace is divided into several smaller sectors for better management of air traffic and air traffic controller workload. Such sectors are usually managed by a team of two air traffic controllers: planning controller and executive controller. D-side controller is responsible for processing flight-plan information to plan and organize the flow of traffic entering the sector. R-side controller deals with ensuring safety of flights in their sector. A better understanding and predictability of D-side controller actions, for (...) a given traffic scenario, may help in automating some of its tasks and hence reduce workload. In this paper, we propose a learning model to predict D-side controller actions. The learning problem is modeled as a supervised learning problem, where the target variables are D-side controller actions and the explanatory variables are the aircraft 4D trajectory features. The model is trained on six months of ADS-B data over an en-route sector, and its generalization performance was assessed, using crossvalidation, on the same sector. Results indicate that the model for vertical maneuver actions provides highest prediction accuracy. Besides, the model for speed change and course change action provides predictability accuracy of 80% and 87%, respectively. The model to predict the set of all the actions for each flight achieves an accuracy of 70% implying for 70% of flights; D-side controller’s action can be predicted from trajectory information at sector entry position. In terms of operational validation, the proposed approach is envisioned as ATCO assisting tool, not an autonomous tool. Thus, there is always ATCO discretion element, and as more ATCO actions are collected, the models can be further trained for better accuracy. For future work, we will consider expanding the feature set by including parameters such as weather and wind. Moreover, human in the loop simulation will be performed to measure the effectiveness of the proposed approach. (shrink)

The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, (...) violent and nonviolent, are created by merging multiple smaller classes of crimes. Exploratory data analysis is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well. (shrink)

IntroductionAccurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance.ObjectiveThe present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies.MethodsThe data, consisting of 16 seasons of world-class women’s all-around speed (...) skating competition results, used in the present study came from the International Skating Union. According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method.ResultsThe results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC. Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals.ConclusionCompared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals. (shrink)

This paper starts with the study of realistic three-dimensional models, from the two aspects of ink art style simulation model and three-dimensional display technology, explores the three-dimensional display model of three-dimensional model ink style, and conducts experiments through the software development platform and auxiliary software. The feasibility of the model is verified. Aiming at the problem of real-time rendering of large-scale 3D scenes in the model, efficient visibility rejection method and a multiresolution fast rendering method (...) were designed to realize the rapid construction and rendering of ink art 3D virtual reality scenes in a big data environment. A two-dimensional cellular automaton is used to simulate a brushstroke model with ink and wash style, and outlines are drawn along the path of the brushstroke to obtain an effect close to the artistic style of ink and wash painting. Set the surface of the model with ink style brushstroke texture patterns, refer to the depth map, normal map, and curvature map information of the model, and simulate the drawing effect of the method by procedural texture mapping. Example verification shows that the rapid visualization analysis model of ink art big data designed in this paper is in line with the prediction requirements of ink art big data three-dimensional display indicators. The fast visibility removal method is used to deal with large-scale three-dimensional ink art in a big data environment. High efficiency is achieved in virtual reality scenes, and the multiresolution fast rendering method better maintains the appearance of the prediction model without major deformation. (shrink)

This paper reports the experience of using the PAELLA algorithm as a helper tool in robust regression instead of as originally intended for outlier identification and removal. This novel usage of the algorithm takes advantage of the occurrence vector calculated by the algorithm in order to strengthen the effect of the more reliable samples and lessen the impact of those that otherwise would be considered outliers. Following that aim, a series of experiments is conducted in order to learn how to (...) better use the information contained in the occurrence vector. Using a contrively difficult artificial data set, a reference predictive model is fit using the whole raw dataset. The second experiment reports the results of fitting a similar predictive model but discarding the samples marked as outliers by PAELLA. The third experiment uses the occurrence vector provided by PAELLA in order to classify the observations in multiple bins and fit every possible model changing which bins are considered for fitting and which are discarded in that particular model. The fourth experiment introduces a sampling process before fitting in which the occurrence vector represents the likelihood of being considered in the training data set. The fifth experiment considers the sampling process as an internal step to be performed interleaved between the training epochs. The last experiment compares our approach using weighted neural networks to a state of the art method. (shrink)

The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict (...) the health status of the power grid sensor network. The LPR-MLP model consists of two parts: local binary pattern, principal component analysis, and ReliefF are used to process image data and meteorological and mechanical data and the multilayer perceptron method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods. (shrink)

Making more accurate marketing decisions by managers requires building effective predictive models. Typically, these models specify the probability of customer belonging to a particular category, group or segment. The analytical CRM categories refer to customers interested in starting cooperation with the company, customers who purchase additional products or customers intending to resign from the cooperation. During building predictive models researchers use analytical tools from various disciplines with an emphasis on their best performance. This article attempts to build a hybrid predictive (...) model combining decision trees and cluster analysis. During experiments five different cluster validity indices and eight datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The authors tried to find a connection between the number of clusters and models' quality. (shrink)

The construction of a financial risk prediction model has become the need of the hour due to long-term and short-term violations in the financial market. To reduce the default risk of peer-to-peer companies and promote the healthy and sustainable development of the P2P industry, this article uses a model based on the LightGBM algorithm to analyze a large number of sample data from Renrendai, which is a representative platform of the P2P industry. This article explores the base (...) LightGBM model along with the integration of linear blending to build an optimal default risk identification model. The proposed approach is applicable for a large number of multi-dimensional data samples. The results show that the prediction accuracy rate of the LightGBM algorithm model on the test set reaches 80.25%, which can accurately identify more than 80% of users, and the model has the best prediction performance in terms of different performance evaluation indicators. The integration of LightGBM and the linear blending approach yield a precision value of 91.36%, a recall of 75.90%, and an accuracy of 84.36%. The established LightGBM algorithm can efficiently identify the default of the loan business on the P2P platform compared to the traditional machine learning models, such as logistic regression and support vector machine. For a large number of multi-dimensional data samples, the LightGBM algorithm can effectively judge the default risk of users on P2P platforms. (shrink)

In this paper, a new FEPA portfolio forecasting model is based on the EMD decomposition method. The model is based on the special empirical modal decomposition of financial time series, principal component analysis, and artificial neural network to model and forecast for nonlinear, nonstationary, multiscale complex financial time series to predict stock market indices and foreign exchange rates and empirically investigate this hot area in financial market research. The combined forecasting model proposed in this paper is (...) based on the idea of decomposition-reconstruction synthesis, which effectively improves the model’s prediction of internal financial time series. In this paper, we select the CSI 300 Index and foreign exchange rate as the empirical market and data and establish seven forecasting models to make predictions about the short-term running trend of the closing price. The interval EMD decomposition algorithm is introduced in this paper, considering both high and low prices to be contained in the input and output. By analyzing the closing price, high and low prices of the stock index at the same time, the volatility of this interval time series of the index and its trend can be better captured. (shrink)

The ability of predicting upcoming events or conditions in advance offers substantial selective advantage to living beings. The most successful systematic tool for fairly reliable prognoses is the use of dynamical causal models in combination with memorised experience. Surprisingly, causality is a fundamental but rather controversially disputed concept. For both models and memory, symbol processing is requisite. Symbols are a necessary and sufficient attribute of life from its very beginning; the process of their evolutionary emergence was discovered by Julian Huxley (...) a century ago. In behavioural biology, this universal symmetry-breaking kinetic phase transition became known as ritualisation. Symbol use for predicting future dynamical processes has culminated in the unprecedented complexity of mental models used in science and technology, coining the historical ascent of modern humans. Observation and measurement transform structural information of physical exchange processes into symbolic information from which state quantities are derived by means of mental models. However, phylogenetically inherited models such as naïve realism do not necessarily explain the sophisticated insights revealed by modern experiments with, say, entangled quantum states. It is suggested to carefully distinguish observed exchange quantities from predicted unobservable state quantities, and physical reality from mental models thereof. (shrink)

Climate models don’t give us probabilistic forecasts. To interpret their results, alternatively, as serious possibilities seems problematic inasmuch as climate models rely on contrary-to-fact assumptions: why should we consider their implications as possible if their assumptions are known to be false? The paper explores a way to address this possibilistic challenge. It introduces the concepts of a perfect and of an imperfect credible world, and discusses whether climate models can be interpreted as imperfect credible worlds. That would allow one to (...) use models for possibilistic prediction and salvage widespread scientific practice. (shrink)