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Journal articles on the topic 'Predictive behaviour'
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1
Vora, Deepali, and Kamatchi Iyer. "Evaluating the Effectiveness of Machine Learning Algorithms in Predictive Modelling." International Journal of Engineering & Technology 7, no. 3.4 (2018): 197. http://dx.doi.org/10.14419/ijet.v7i3.4.16773.
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Predictive modelling is a statistical technique to predict future behaviour. Machine learning is one of the most popular methods for predicting the future behaviour. From the plethora of algorithms available it is always interesting to find out which algorithm or technique is most suitable for data under consideration. Educational Data Mining is the area of research where predictive modelling is most useful. Predicting the grades of the undergraduate students accurately can help students as well as educators in many ways. Early prediction can help motivating students in better ways to select their future endeavour. This paper presents the results of various machine learning algorithms applied to the data collected from undergraduate studies. It evaluates the effectiveness of various machine learning algorithms when applied to data collected from undergraduate studies. Two major challenges are addressed as: choosing the right features and choosing the right algorithm for prediction.
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Cubitt, Timothy, Ken Wooden, Erin Kruger, and Michael Kennedy. "A predictive model for serious police misconduct by variation of the theory of planned behaviour." Journal of Forensic Practice 22, no. 4 (2020): 251–63. http://dx.doi.org/10.1108/jfp-08-2020-0033.
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Purpose Misconduct and deviance amongst police officers are substantial issues in policing around the world. This study aims to propose a prediction model for serious police misconduct by variation of the theory of planned behaviour. Design/methodology/approach Using two data sets, one quantitative and one qualitative, provided by an Australian policing agency, a random forest analysis and a qualitative content analysis was performed. Results were used to inform and extend the framework of the theory of planned behaviour. The traditional and extended theory of planned behaviour models were then tested for predictive utility. Findings Each model demonstrated noteworthy predictive power, however, the extended model performed particularly well. Prior instances of minor misconduct amongst officers appeared important in this rate of prediction, suggesting that remediation of problematic behaviour was a substantial issue amongst misconduct prone officers. Practical implications It is an important implication for policing agencies that prior misconduct was predictive of further misconduct. A robust complaint investigation and remediation process are pivotal to anticipating, remediating and limiting police misconduct, however, early intervention models should not be viewed as the panacea for police misconduct. Originality/value This research constitutes the first behavioural model for police misconduct produced in Australia. This research seeks to contribute to the field of behavioural prediction amongst deviant police officers, and offer an alternative methodology for understanding these behaviours.
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Lipman, Ellen L., Kathryn J. Bennett, Yvonne A. Racine, Rupak Mazumdar, and David R. Offord. "What Does Early Antisocial Behaviour Predict? A Follow-up of 4- and 5-Year-Olds from the Ontario Child Health Study." Canadian Journal of Psychiatry 43, no. 6 (1998): 605–13. http://dx.doi.org/10.1177/070674379804300609.
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Objective: To examine the predictive accuracy of antisocial behaviours among 4- and 5-year-old children for problem behaviours 4 years later (ages 8 and 9 years). Method: Data from the Ontario Child Health Study (1983) and Follow-Up (1987) are used. Predictive accuracy is conceptualized using positive predictive value (PPV) and sensitivity. The predictive accuracy of early antisocial behaviours for the 1987 outcomes is examined overall, by gender, by variable thresholds of predictor and outcome by gender, and by using contextual variables alone or in combination with antisocial behaviour recorded in 1983. Results: The predictive accuracy of 1983 antisocial behaviour for 1987 outcome is generally modest and differs by gender (better for boys for externalizing disorder [PPV = 41%, sensitivity = 57%]; better for girls for internalizing disorder [PPV = 13%, sensitivity = 80%]; better for boys for conduct problems [PPV = 54%, sensitivity = 21%]). Using either gender-specific thresholds or gender-neutral thresholds does not alter predictive accuracy in a consistent way, nor does the use of a single contextual variable. Use of a cumulative risk index increases PPV but decreases sensitivity. Conclusions: The predictive accuracy of antisocial behaviour in 4-and 5-year-old children over 4 years in a nonclinical community population is limited. The clinical, research, and policy implications of this work are discussed.
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Wen, Zhanming, Weizhen Lin, and Hongwei Liu. "Machine-Learning-Based Approach for Anonymous Online Customer Purchase Intentions Using Clickstream Data." Systems 11, no. 5 (2023): 255. http://dx.doi.org/10.3390/systems11050255.
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Since online shopping has become an important way for consumers to make purchases, consumers have signed up to e-commerce platforms to shop online. However, retailers are beginning to realise the critical role of predicting anonymous consumer purchase intent to improve purchase conversion rates and store profitability. Therefore, this study aims to investigate the prediction of anonymous consumer purchase intent. This research presents a machine learning model (MBT-POP) for predicting customer purchase behaviour based on multi-behavioural trendiness (MBT) and product popularity (POP) using 33,339,730 clicks generated from 445,336 sessions of real e-commerce customers. The results show that the MBT-POP model can effectively predict the purchase behaviour of anonymous customers (F1 = 0.9031), and it achieves the best prediction result with a sliding window of 2 days. Compared to existing studies, the MBT-POP model not only improves the model performance, but also compresses the number of days required for accurate prediction. The present research has argued that product trendiness and popularity can significantly improve the predictive performance of the customer purchase behaviour model and can play an important role in predicting the purchase behaviour of anonymous customers.
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Sthienrapapayut, Thuckavadee, George P. Moschis, and Anil Mathur. "Using gerontographics to explain consumer behaviour in later life: evidence from a Thai study." Journal of Consumer Marketing 35, no. 3 (2018): 317–27. http://dx.doi.org/10.1108/jcm-02-2017-2083.
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Purpose The purpose of this study is to test the efficiency of the “gerontographics” model in predicting selected consumer behaviours in older adults. Design/methodology/approach Through regression analysis, a large sample of older Thai adults is used to test the predictive power of gerontographics against predictive measures based on both chronological age and cognitive age. Findings The findings of the study show that the gerontographics model is more powerful in predicting certain types of older adults’ consumer behaviour than chronological age or cognitive age. Originality/value These findings have implications for marketing and business strategies because they suggest that gerontographics segmentation may be a more useful and effective way to segment older consumers.
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Calvo, Paco, and Karl Friston. "Predicting green: really radical (plant) predictive processing." Journal of The Royal Society Interface 14, no. 131 (2017): 20170096. http://dx.doi.org/10.1098/rsif.2017.0096.
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In this article we account for the way plants respond to salient features of their environment under the free-energy principle for biological systems. Biological self-organization amounts to the minimization of surprise over time. We posit that any self-organizing system must embody a generative model whose predictions ensure that (expected) free energy is minimized through action. Plants respond in a fast, and yet coordinated manner, to environmental contingencies. They pro-actively sample their local environment to elicit information with an adaptive value. Our main thesis is that plant behaviour takes place by way of a process (active inference) that predicts the environmental sources of sensory stimulation. This principle, we argue, endows plants with a form of perception that underwrites purposeful, anticipatory behaviour. The aim of the article is to assess the prospects of a radical predictive processing story that would follow naturally from the free-energy principle for biological systems; an approach that may ultimately bear upon our understanding of life and cognition more broadly.
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Meyer, Johanna M., Alex Kirk, Joanna J. Arch, Peter J. Kelly, and Brett J. Deacon. "Beliefs about safety behaviours in the prediction of safety behaviour use." Behavioural and Cognitive Psychotherapy 47, no. 6 (2019): 631–44. http://dx.doi.org/10.1017/s1352465819000298.
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AbstractBackground: Safety behaviours are ubiquitous across anxiety disorders and are associated with the aetiology, maintenance and exacerbation of anxiety. Cognitive behavioural models posit that beliefs about safety behaviours directly influence their use. Therefore, beliefs about safety behaviours may be an important component in decreasing safety behaviour use. Unfortunately, little empirical research has evaluated this theorized relationship.Aims: The present study aimed to examine the predictive relationship between beliefs about safety behaviours and safety behaviour use while controlling for anxiety severity.Method: Adults with clinically elevated levels of social anxiety (n = 145) and anxiety sensitivity (n = 109) completed an online survey that included established measures of safety behaviour use, quality of life, and anxiety severity. Participants also completed the Safety Behaviour Scale (SBS), a measure created for the current study which includes a transdiagnostic checklist of safety behaviours, as well as questions related to safety behaviour use and beliefs about safety behaviours.Results: Within both the social anxiety and anxiety sensitivity groups, positive beliefs about safety behaviours predicted greater safety behaviour use, even when controlling for anxiety severity. Certain beliefs were particularly relevant in predicting safety behaviour use within each of the clinical analogue groups.Conclusions: Findings suggest that efforts to decrease safety behaviour use during anxiety treatment may benefit from identifying and modifying positive beliefs about safety behaviours.
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López Larrosa, Silvia, and José Luis Rodríguez-Arias Palomo. "Risk and protective factors for drug use and antisocial behavior in Spanish adolescents and young people." International Journal of Psychological Research 5, no. 1 (2012): 25–33. http://dx.doi.org/10.21500/20112084.746.
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In this study, 2440 adolescents and young people of different localities in the Northwestern Spanish coast participated. The Communities that Care Youth Survey (CTCYS) (Arthur, Hawkins, Pollard, Catalano y Baglioni, 2002; Glaser, Van Horn, Arthur, Hawkins y Catalano, 2005) was used to identify drug use and antisocial behavior, and their risk and protective factors in the family, the school, the community and the individual/peers. The objective of the research was to identify the predictive role of age and sex in the drug use and the antisocial behaviour, as well as the identification of common predictive risk and protective factors. Data show that age predicts drug use but not the antisocial behaviour. The common predictive risk factors are school failure, gang involvement, attitudes favourable to and early start of antisocial behaviour. The common protective factors are social skills and moral order beliefs. Results show the relevance of considering risk and protection, confirm the existence of common predictive factors and the reciprocal influence of drug use and antisocial behaviour.
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Hodzik, Ena. "Predictive processes in interpreters." Translation, Cognition & Behavior 6, no. 2 (2023): 141–63. http://dx.doi.org/10.1075/tcb.00083.hod.
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Abstract This paper discusses predictive processes in simultaneous interpreting. A review of experimental findings in the interpreting process literature is carried out on prediction and anticipation – both instances of predictive processes – addressing two questions: (i) what causes prediction and anticipation?, and (ii) is there an experience-related advantage for prediction and anticipation in interpreters? A few gaps are identified in the literature, namely, the need for investigating language-specific cues to prediction and the need for investigating predictive behaviour in both source language processing and target language production. The review is followed by a discussion of future directions in addressing the identified gaps by interpreting process studies, as well as a discussion of how current and future experimental findings can inform our understanding of the processes underlying prediction during simultaneous interpreting.
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Wylęgły, Kamila. "Predictive Model of Risk Behaviour in the University Student Community." Journal of Education Culture and Society 15, no. 2 (2024): 97–113. http://dx.doi.org/10.15503/jecs2024.2.97.113.
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Aim. The study aimed to attempt to explain the probable causality of undertaken risk behaviours in a group of university students, from the perspective of various predictors (which included low individual and social resources, as well as other risk behaviours). The choice of predictors was dictated, among other things, by the assumptions of risk behaviour theory and gateway theory. Methods. The research was conducted among university students from many cities in Poland. Empirical data were collected using the survey method. The tool was a survey questionnaire, constructed from the author's scale and the Resilience Scale by Gail Wagnild in the Polish adaptation by Janusz Surzykiewicz and Karol Konaszewski (2019). The data were statistically analysed. The target procedure became path analysis. Results. The study revealed several plausible predictive factors for specific risk behaviours of students. Analyses included behavioural categories such as the use of psychoactive substances and behaviour associated with them, risky sexual contact, low physical and psychosocial health care, dangerous behaviour in daily life, and disclosure of images and private data on the Internet. Each form of risky behaviour was situated in the model. The predictive factors in the path structure were identified as, among others, low social and cultural resources and low levels of resilience. It also appeared that some risk behaviours may be predictors for others. Conclusion. The study reveals that not only syndromes of risk behaviours, but even entire risk pathways creating the possibility to infer probable causality, are noticeable in the group of university students.
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Srivastava, Neeraj. "Development of a Machine Learning Model for Predicting Fracture Behaviour of Materials Using AI." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 2 (2018): 621–31. http://dx.doi.org/10.17762/turcomat.v9i2.13865.
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Fracture behaviour prediction of materials is a critical aspect in various industries, including aerospace, automotive, and manufacturing. Accurate prediction of fracture behaviour can aid in designing robust materials and structures, enhancing safety, and optimizing performance. In this study, we propose the development of a machine learning model for predicting fracture behaviour of materials using artificial intelligence (AI) techniques. The methodology involves the collection and pre-processing of a comprehensive dataset comprising material properties, structural characteristics, and fracture behaviour observations. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, are employed to train and optimize the predictive model. Feature engineering techniques are utilized to extract relevant features and reduce dimensionality. The model's performance is evaluated using appropriate metrics, including accuracy, precision, recall, and F1-score.
 The significance lies in the potential to provide accurate and efficient predictions of fracture behaviour, thereby enabling informed decision-making in material selection, design, and performance optimization. By leveraging AI techniques, we aim to overcome the limitations of traditional fracture prediction methods that rely on empirical models or complex numerical simulations. Developing a machine learning model for fracture behaviour prediction, evaluating the performance of different algorithms and feature engineering techniques, and assessing the practical implications and benefits of the developed model in real-world applications. Introducing a novel approach to predicting fracture behaviour using AI techniques. The results of this study have the potential to enhance the understanding of material fracture mechanisms and pave the way for improved material design and performance optimization.
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Smit, Selmar, Bob van der Vecht, and Layla Lebesque. "Predictive Mapping of Anti-Social Behaviour." European Journal on Criminal Policy and Research 21, no. 4 (2014): 509–21. http://dx.doi.org/10.1007/s10610-014-9259-1.
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Schaefer, Rebecca S., Katie Overy, and Peter Nelson. "Affect and non-uniform characteristics of predictive processing in musical behaviour." Behavioral and Brain Sciences 36, no. 3 (2013): 226–27. http://dx.doi.org/10.1017/s0140525x12002373.
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AbstractThe important roles of prediction and prior experience are well established in music research and fit well with Clark's concept of unified perception, cognition, and action arising from hierarchical, bidirectional predictive processing. However, in order to fully account for human musical intelligence, Clark needs to further consider the powerful and variable role of affect in relation to prediction error.
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Nurazlina Abdul Rashid and Mohd Tahir Ismail. "A Review: Predictive Models and Behaviour of Cryptocurrencies Price." Journal of Advanced Research in Applied Sciences and Engineering Technology 48, no. 2 (2024): 148–67. http://dx.doi.org/10.37934/araset.48.2.148167.
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This study aims to assess the knowledge flow within the research field and provide recommendations for further investigation. Specifically, this study conducts a thematic analysis of articles published in peer-reviewed journals between 2014 and 2022. Two primary themes emerge from the co-occurring keywords: (1) cryptocurrency behaviour and (2) cryptocurrency price prediction models. The findings reveal the use of various methods for predicting cryptocurrency prices, including econometric and statistical approaches, machine learning (ML), deep learning (DL), and hybrid models. The overarching objective of all these models is to achieve optimal results in addressing the various challenges associated with predicting cryptocurrency prices. However, it is important to note that no single model can effectively address all the behavioural nuances within cryptocurrency price prediction datasets. To bridge this gap, we recommend that future researchers explore the development of a hybrid model that combines a statistical model with deep learning. Such a hybrid model has the potential to accurately address the behavioural challenges encountered in cryptocurrency price prediction data series.
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Gitonga, Elizabeth Ndichu, Peter Wang’ombe Kariuki, and Samuel Nduati Kariuki. "Fintech Predictive Modeling and Performance of Investment Firms in Kenya." Webology 18, Special Issue 04 (2021): 1202–12. http://dx.doi.org/10.14704/web/v18si04/web18192.
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Predictive analytics is concerned with the prediction of future trends and outcomes. The approaches used to conduct predictive analytics can be classified into machine learning techniques and regression techniques. This study dteremined the influence of fintech predictive modeling on performance of investment firms in Kenya. The study population was 57 investment firms. The study employed mixed method research design by incorporating descriptive and explanatory research designs. Data was collected using questionnaires and an in-depth interview guide. Coefficient of fintech predictive modeling has a positive and significant effect on performance of investment firms. The study concluded that fintech predictive modeling allows investment firms to forecast business growth and customer behaviour chnages. It is important for an investment firm to be able to understand business growth by accurately forecasting future growth and survival. Moreover, it is of vital necessity to understand changes in customer buying/consumption behavior so as to develop products and services that suit their needs and preferences. As a result, predictive modeling is required to project future business growth and changes in customer consumption pattern.
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Thielmann, Isabel, Johannes Zimmermann, Daniel Leising, Benjamin E. Hilbig, and Mitja Back. "Seeing is Knowing: On the Predictive Accuracy of Self– and Informant Reports for Prosocial and Moral Behaviours." European Journal of Personality 31, no. 4 (2017): 404–18. http://dx.doi.org/10.1002/per.2112.
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Under certain circumstances, well–known others (so–called informants) may possess unique insights into targets’ personality traits beyond the targets’ self–views. Specifically, as proposed by the self–other knowledge asymmetry model, an incremental predictive ability of informants is most likely for traits and corresponding behaviours that are clearly visible to others and highly evaluative in nature. In two studies, we provide an empirical test of this proposition and extend prior research to one of the most important domains of interpersonal interaction: prosocial and moral behaviours. Specifically, we investigate the unique predictive power of informant reports in trait Honesty–Humility for fairness in the dictator game and dishonesty in a cheating paradigm. Importantly, while both these classes of behaviour are highly evaluative in nature, only fairness is clearly visible to others. Correspondingly, in line with the self–other knowledge asymmetry model, our results reveal unique predictive accuracy of informant reports for fairness. For dishonesty, by contrast, there was no conclusive evidence for incremental predictive power of informant reports. This implies that informants may indeed provide valuable information beyond targets’ self–reports on trait aspects driving fair behaviour, but that targets themselves are their own best experts when it comes to judging trait aspects driving dishonest behaviour. Copyright © 2017 European Association of Personality Psychology
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Semarco, Stanley KM, and Seokhee Cho. "The predictive influence of headteachers’ task-oriented managerial leadership behaviours on teachers’ retention intentions in Ghana." Educational Management Administration & Leadership 46, no. 6 (2017): 908–25. http://dx.doi.org/10.1177/1741143217714252.
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The study examined if significant dynamic and reciprocal relationships exist among the task-oriented managerial behaviours of headteachers, and how these behaviours specifically and jointly influence teachers’ retention intention. Out of the multistage sampled 350 schools, suitable questionnaires from 279 schools representing 558 questionnaires filled by teachers provided data on 279 headteachers. The analysis showed that headteachers’ problem-solving behaviour, clarifying behaviour and monitoring operations behaviour influenced their planning activities as leaders. The planning behaviour significantly predicted retention intention and had a significant mediating effect on the relationships between clarifying, monitoring, and problem- solving behaviours on one hand and teachers’ retention intention on the other. The implications of the study’s findings and future research directions are discussed.
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Sharp, Carla, Tim J. Croudace, Ian M. Goodyer, and Dagmar Amtmann. "The Strength and Difficulties Questionnaire: Predictive validity of parent and teacher ratings for help-seeking behaviour over one year." Educational and Child Psychology 22, no. 3 (2005): 28–44. http://dx.doi.org/10.53841/bpsecp.2005.22.3.28.
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Latent growth modelling was applied to investigate the predictive validity of the subscale and total difficulties scores from the Strengths and Difficulties Questionnaire (SDQ). Prospective data were collected on a community sample ofn= 659 children (aged 7 to 11 years) over a one-year period. Outcomes at one year after baseline were defined in terms of parental help-seeking behaviour and worry. Teacher-reported SDQ summary scores were more predictive of help-seeking behaviour from both the general practitioner and the school than parent-reported SDQ scores. Changes (increases) in SDQ scores proved more useful in predicting help-seeking than initial scores. Gender, age and socioeconomic status were not related to help-seeking behaviour, but the parents of children with higher IQ scores were more likely to seek help. The usefulness of the SDQ to predict help-seeking behaviour for emotional-behaviour difficulties in community settings and its implications for service-use issues in the UK context are discussed.
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Zheng, Zhizhe, Zhichao Zhou, Yilin Wang, and Yikun Su. "The Prediction of Evacuation Efficiency on Metro Platforms Based on Passengers’ Decision-Making Capability." Applied Sciences 12, no. 18 (2022): 8992. http://dx.doi.org/10.3390/app12188992.
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In the research, decision-making capabilities are explored in relation to the prediction of evacuation efficiency to improve forecast accuracy on metro platforms. For this purpose, this study reviewed theories related to evacuation behaviours utilising the anomaly-seeking approach and the paradigm of relationship development. The conceptual framework of decision-making capability and evacuation behaviours was explored based on risk perception, level of emergency knowledge, survivability and emotion, and their relationship with the partial least squares equation was constructed. A predictive model of evacuation efficiency and its differential equations incorporating this relationship were also proposed based on the epidemic model. By developing and testing the conceptual framework and model, theoretical support is provided for evacuation behaviour, while assisting emergency management in developing plans and measures to respond to emergencies on metro platforms. This study realises the possibility of predicting evacuation efficiency from a decision-making capability perspective.
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Abubakar, Abdussamad, Garba Uba, and Hadiza Aliyu Biu. "Kinetics Modelling of Pseudomonas stutzeri strain DN2 Growth Behaviour in Tributyltin Chloride." Journal of Environmental Microbiology and Toxicology 9, no. 2 (2021): 13–18. http://dx.doi.org/10.54987/jemat.v9i2.641.
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A predictive model was performed to describe Pseudomonas stutzeri strain DN2 growth behaviour in tributyltin chloride, using primary Modelling and a polynomial model as a secondary predictive model. In this investigation, data predicted using the modified Logistic (ML) was the most accurate. The Bias Factor (Bf) and Accuracy Factor (Af) values for the (ML) model were 1.39 and 1.51, indicating that the predictions were within a reliable range. The low RMSE value of 0.14, R2 and adj R2 (0.99) value closer to 1, showing that modified logistics is better than the other models at describing the growth behaviour of Pseudomonas stutzeri strain DN2 in toxic tributyltin chloride. Both the Aiba and Haldane models on the other hand, among the secondary model best fit the behaviours having low RMSE and MSE values and adjR2 value closer to 1. In this study, the primary and secondary kinetics of Pseudomonas stutzeri strain DN2 growth behaviour in tributyltin chloride was explored and it was shown in this study that the modified logistic and the Haldane models better suit the growth behavior of Pseudomonas stutzeri strain DN2 in tributyltin chloride. The parameters obtained from the modelling exercise will be very valuable in transferring the laboratory results to the field.
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Millidge, Beren, Mufeng Tang, Mahyar Osanlouy, Nicol S. Harper, and Rafal Bogacz. "Predictive coding networks for temporal prediction." PLOS Computational Biology 20, no. 4 (2024): e1011183. http://dx.doi.org/10.1371/journal.pcbi.1011183.
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One of the key problems the brain faces is inferring the state of the world from a sequence of dynamically changing stimuli, and it is not yet clear how the sensory system achieves this task. A well-established computational framework for describing perceptual processes in the brain is provided by the theory of predictive coding. Although the original proposals of predictive coding have discussed temporal prediction, later work developing this theory mostly focused on static stimuli, and key questions on neural implementation and computational properties of temporal predictive coding networks remain open. Here, we address these questions and present a formulation of the temporal predictive coding model that can be naturally implemented in recurrent networks, in which activity dynamics rely only on local inputs to the neurons, and learning only utilises local Hebbian plasticity. Additionally, we show that temporal predictive coding networks can approximate the performance of the Kalman filter in predicting behaviour of linear systems, and behave as a variant of a Kalman filter which does not track its own subjective posterior variance. Importantly, temporal predictive coding networks can achieve similar accuracy as the Kalman filter without performing complex mathematical operations, but just employing simple computations that can be implemented by biological networks. Moreover, when trained with natural dynamic inputs, we found that temporal predictive coding can produce Gabor-like, motion-sensitive receptive fields resembling those observed in real neurons in visual areas. In addition, we demonstrate how the model can be effectively generalized to nonlinear systems. Overall, models presented in this paper show how biologically plausible circuits can predict future stimuli and may guide research on understanding specific neural circuits in brain areas involved in temporal prediction.
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Gupta Lakkimsetty, N. V. Rama Sai Chalapathi. "Role of AI in Business Analytics: Predictive Insights for Future Trends." International Journal of Computer Science and Mobile Computing 14, no. 3 (2025): 1–10. https://doi.org/10.47760/ijcsmc.2025.v14i03.001.
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In the current corporate environment, combining Artificial Intelligence (AI) using Cloud corporate Intelligence (CBI) is a revolutionary way to improve data visualisation and predictive analytics. This article examines how cloud-based solutions and AI technologies work together, emphasising how both have an effect on decision-making. The ability to make decisions is significantly enhanced by AI-driven systems, which provide precise real-time insights and predictive analytics. In this article, we look at the many benefits of integrating AI with BI, including improved operational efficiency, personalised customer experiences, and predictive analytics accuracy. This capacity is particularly relevant in the fast-paced market climate of today, when a variety of elements, from social media trends to economic realities, are progressively influencing customer behaviour. The research looks at a number of AI methods, including deep learning, machine learning, and natural language processing, emphasising how they might improve prediction accuracy. For example, complicated and large-scale data may be processed by machine learning algorithms to estimate customer demand and find hidden relationships. Analysis of written content from reviews, social media, and other sources is made possible by natural language processing, which offers a better comprehension of customer sentiment and new trends. With the help of sophisticated neural networks, deep learning models are able to identify complex patterns in data and further improve predictions. Businesses have several challenges, including the requirement for specialised knowledge in data science and artificial intelligence, privacy issues, and data quality and integration. The research highlights how crucial it is to deal with these issues in order to properly use AI's promise in predictive analytics. To sum up, AI-powered predictive analytics provides revolutionary tools for identifying and predicting market trends. It helps companies to remain ahead of their competitors and successfully meet changing market needs by offering accurate and useful insights about customer behaviour. In order to improve the precision and usefulness of AI-driven analytics for prediction in a variety of market situations, the study emphasises the need for ongoing research and development.
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Freeman, Robin, Ben Dean, Holly Kirk, et al. "Predictive ethoinformatics reveals the complex migratory behaviour of a pelagic seabird, the Manx Shearwater." Journal of The Royal Society Interface 10, no. 84 (2013): 20130279. http://dx.doi.org/10.1098/rsif.2013.0279.
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Understanding the behaviour of animals in the wild is fundamental to conservation efforts. Advances in bio-logging technologies have offered insights into the behaviour of animals during foraging, migration and social interaction. However, broader application of these systems has been limited by device mass, cost and longevity. Here, we use information from multiple logger types to predict individual behaviour in a highly pelagic, migratory seabird, the Manx Shearwater ( Puffinus puffinus ). Using behavioural states resolved from GPS tracking of foraging during the breeding season, we demonstrate that individual behaviours can be accurately predicted during multi-year migrations from low cost, lightweight, salt-water immersion devices. This reveals a complex pattern of migratory stopovers: some involving high proportions of foraging, and others of rest behaviour. We use this technique to examine three consecutive years of global migrations, revealing the prominence of foraging behaviour during migration and the importance of highly productive waters during migratory stopover.
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Lana, X., M. D. Martínez, C. Serra, and A. Burgueño. "Complex behaviour and predictability of the European dry spell regimes." Nonlinear Processes in Geophysics 17, no. 5 (2010): 499–512. http://dx.doi.org/10.5194/npg-17-499-2010.
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Abstract. The complex spatial and temporal characteristics of European dry spell lengths, DSL, (sequences of consecutive days with rainfall amount below a certain threshold) and their randomness and predictive instability are analysed from daily pluviometric series recorded at 267 rain gauges along the second half of the 20th century. DSL are obtained by considering four thresholds, R0, of 0.1, 1.0, 5.0 and 10.0 mm/day. A proper quantification of the complexity, randomness and predictive instability of the different DSL regimes in Europe is achieved on the basis of fractal analyses and dynamic system theory, including the reconstruction theorem. First, the concept of lacunarity is applied to the series of daily rainfall, and the lacunarity curves are well fitted to Cantor and random Cantor sets. Second, the rescaled analysis reveals that randomness, persistence and anti-persistence are present on the European DSL series. Third, the complexity of the physical process governing the DSL series is quantified by the minimum number of nonlinear equations determined by the correlation dimension. And fourth, the loss of memory of the physical process, which is one of the reasons for the complex predictability, is characterized by the values of the Kolmogorov entropy, and the predictive instability is directly associated with positive Lyapunov exponents. In this way, new bases for a better prediction of DSLs in Europe, sometimes leading to drought episodes, are established. Concretely, three predictive strategies are proposed in Sect. 5. It is worth mentioning that the spatial distribution of all fractal parameters does not solely depend on latitude and longitude but also reflects the effects of orography, continental climate or vicinity to the Atlantic and Arctic Oceans and Mediterranean Sea.
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Komol, Md Mostafizur Rahman, Mohammed Elhenawy, Jack Pinnow, et al. "Prediction of Drivers’ Red-Light Running Behaviour in Connected Vehicle Environments Using Deep Recurrent Neural Networks." Machine Learning and Knowledge Extraction 6, no. 4 (2024): 2855–75. https://doi.org/10.3390/make6040136.
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Red-light running at signalised intersections poses a significant safety risk, necessitating advanced predictive technologies to predict red-light violation behaviour, especially for advanced red-light warning (ARLW) systems. This research leverages Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to forecast the red-light running and stopping behaviours of drivers in connected vehicles. We utilised data from the Ipswich Connected Vehicle Pilot (ICVP) in Queensland, Australia, which gathered naturalistic driving data from 355 connected vehicles at 29 signalised intersections. These vehicles broadcast Cooperative Awareness Messages (CAM) within the Cooperative Intelligent Transport Systems (C-ITS), providing kinematic inputs such as vehicle speed, speed limits, longitudinal and lateral accelerations, and yaw rate. These variables were monitored at 100-millisecond intervals for durations from 1 to 4 s before reaching various distances from the stop line. Our results indicate that the LSTM model outperforms the GRU in predicting both red-light running and stopping behaviours with high accuracy. However, the pre-trained GRU model performs better in predicting red-light running specifically, making it valuable in applications requiring early violation prediction. Implementing these models can enhance red-light violation countermeasures, such as dynamic all-red extension (DARE), decreasing the likelihood of severe collisions and enhancing road users’ safety.
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Gerritzen, Johannes, Michael Müller-Pabel, Jonas Müller, et al. "Development of a High-Fidelity Framework to Describe the Process-Dependent Viscoelasticity of a Fast-Curing Epoxy Matrix Resin including Testing, Modelling, Calibration and Validation." Polymers 14, no. 17 (2022): 3647. http://dx.doi.org/10.3390/polym14173647.
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Fast-curing epoxy resins enable substantial reduction of cycle times during production of thermoset polymer matrix composites. Due to the snap-cure behaviour, both characterisation and processing of these resins are associated with high complexity which motivates the development of a high-fidelity framework for the prediction of the process-dependent behaviour ranging from experiment to model validation. In order to determine influence of time, temperature, and degree of cure, a multitude of rheometer and dynamic mechanical analysis experiments are conducted and evaluated. Building on the experimental results, a material model based on a generalised Maxwell model is developed. It is calibrated on the results obtained in the tests and shown to describe the material’s behaviour with high accuracy under all investigated conditions. The model’s predictive capabilities are further tested by applying it to a dynamic mechanical analysis, exposing the model to previously unknown loading and temperature conditions. It is demonstrated that the model is capable of predicting such changing boundary conditions with high accuracy.
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Visser, Jeroen Heijmens, Jan Van Der Ende, Hans M. Koot, and Frank C. Verhulst. "Predictors of psychopathology in young adults referred to mental health services in childhood or adolescence." British Journal of Psychiatry 177, no. 1 (2000): 59–65. http://dx.doi.org/10.1192/bjp.177.1.59.
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BackgroundFor children referred to mental health services future functioning may be hampered.AimsTo examine stability and prediction of behavioural and emotional problems from childhood into adulthood.MethodA referred sample (n=789) aged 4–18 years was followed up after a mean of 10.5 years. Scores derived from the Child Behavior Checklist, Youth Self-Report and Teacher Report Form were related to equivalent scores for young adults from the Young Adult Self-Report and Young Adult Behavior Checklist.ResultsCorrelations between first contact (T1) and follow-up (T2) scores were 0.12–0.53. Young adult psychopathology was predicted by corresponding TI problem scores. Social problems and anxious/depressed scores were predictors of general problem behaviour.ConclusionsProblem behaviour of children and adolescents referred to outpatient mental health services is highly predictive of similar problem behaviour at young adulthood. Stability is higher for externalising than for internalising behaviour and for intra-informant than for inter-informant information. Stabilities are similar across gender. To obtain a comprehensive picture of the young adult's functioning, information from related adults may prove valuable.
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Fleischhauer, Monika, Anja Strobel, Sören Enge, and Alexander Strobel. "Assessing Implicit Cognitive Motivation: Developing and Testing An Implicit Association Test to Measure Need for Cognition." European Journal of Personality 27, no. 1 (2013): 15–29. http://dx.doi.org/10.1002/per.1841.
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The personality trait need for cognition (NFC) refers to individual differences in cognitive motivation and has proven to be an extraordinarily useful descriptor and predictor in the context of information processing. So far, NFC has been assessed via self–report. More recent research, however, accentuates the value of indirect measures, as they tap into implicit aspects of the personality self–concept and are assumed to provide incremental validity especially in predicting automatic aspects of behaviour. Therefore, in the present research, different NFC–Implicit Association Tests (IATs) were developed and pretested for psychometric properties. The final version was systematically tested for its predictive validity over and above the direct NFC measure based on a latent variable approach. The results provide evidence for a double dissociation model and suggest the NFC–IAT to exert its predictive value regarding the more spontaneous aspects of NFC–related behaviour, whereas the NFC scale was rather predictive for the more reflective aspects of behaviour. Moreover, the present research contributes to the understanding of construct–unrelated variance in personality IATs and offers valuable information for test development in the realm of personality IATs. Copyright © 2012 John Wiley & Sons, Ltd.
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Motomura, Jun. "Social implementation trial of the ALD/NAFLD predictive model to increase health literacy and promote behaviour change." Impact 2023, no. 3 (2023): 18–20. http://dx.doi.org/10.21820/23987073.2023.3.18.
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Alcoholic liver disease (ALD) and non-alcoholic fatty liver disease (NAFLD) are serious health problems across the globe and research has shown that, within Japan, the situation in Okinawa Prefecture is particularly concerning. Researchers from Meio University, Hirosaki University, and Kyoto University in Japan are collaborating on ‘The Yambaru Health Promotion Project’ (YHPP). This epidemiological survey is designed to collect data on the people of Okinawa Prefecture with the goal of extending life expectancy and healthy life expectancy and improving quality of life. The big data collected by the YHPP will be analysed by the Prediction Application for Liver Disease (PALD) Plan and the PALD Plan will implement a trial of an ALD/NAFLD predictive model in the community to increase health literacy and promote behaviour change. Senior Associate Professor Jun Motomura is part of the team working on this research. He and his collaborators are working to develop a low-cost algorithm for predicting the future risk of developing ALD/NAFLD from health check-up results and a smartphone application that utilises the predictive model. The researchers are hopeful that the PALD Plan means that Okinawans could enter their annual health check-up results into their low-cost ‘ALD/NAFLD Prediction Model’ smartphone app that the team is developing and use the app to view lifestyle improvement advice specific to them.
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Osman, Krešimir, Mario Štorga, Tino Stanković, and Dorian Marjanović. "Behaviour Prediction Framework in System Architecture Development." Applied Mechanics and Materials 104 (September 2011): 3–12. http://dx.doi.org/10.4028/www.scientific.net/amm.104.3.
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This paper proposes a Behaviour Prediction Framework with an objective to help designers tackling the problem of uncertainty emerging from system architecture and the effects of the uncertain operating conditions. The proposed framework combines structural and dynamic system model. The Design Structure Matrix is applied to model structural arrangements and dependencies between the subsystems. The Model Predictive Control is applied to model the system in discrete and continuous dynamic domains. As the result of the proposed framework, stability analysis of subsystems in interaction become possible and feedback on system architecture could be provided. To test validity of the proposed approach, the test case involving climate chamber with heat regeneration is presented.
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ABBOD, M. F., J. W. F. CATTO, M. CHEN, D. A. LINKENS, and F. C. HAMDY. "ARTIFICIAL INTELLIGENCE FOR THE PREDICTION OF BLADDER CANCER." Biomedical Engineering: Applications, Basis and Communications 16, no. 02 (2004): 49–58. http://dx.doi.org/10.4015/s1016237204000098.
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New techniques for the prediction of tumour behaviour are needed as statistical analysis has a poor accuracy and is not applicable to the individual. Artificial intelligence (AI) may provide these suitable methods. We have previously shown that the predictive accuracies of neuro-fuzzy modelling (NFM) and artificial neural networks (ANN), two methods of AI, are superior to traditional statistical methods for the behaviour of bladder cancer (Catto et al, 2003). In this paper, we explain the AI techniques required to produce these predictive models. We used 9 parameters, which were a combination of experimental molecular biomarkers and conventional clinicopathological data, to predict the risk of tumour progression in a population of 109 patients with bladder cancer, NFM, using fuzzy logic to model data, achieved similar or superior predictive accuracy to ANN, which required cross-validation. However, unlike the impenetrable opaque structure of neural networks, the rules of NFM are transparent, enabling validation from clinical knowledge and the manipulation of input variables to allow exploratory predictions.
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Rowsell, Melanie, and Nigel Stennett-Cox. "Suitability of Clinical Outcome in Routine Evaluation as a predictor of frequency of risk behaviours in an inpatient mental health setting." Clinical Psychology Forum 1, no. 195 (2009): 42–48. http://dx.doi.org/10.53841/bpscpf.2009.1.195.42.
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The present study investigated the suitability of CORE as a predictor of frequency of risk behaviours in an inpatient mental health setting. We tested for possible presence and strength of any predictive risk behaviour loading on non-‘Risk’ questions.
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Cruz, Miguel G., Susan Kidnie, Stuart Matthews, et al. "Evaluation of the predictive capacity of dead fuel moisture models for Eastern Australia grasslands." International Journal of Wildland Fire 25, no. 9 (2016): 995. http://dx.doi.org/10.1071/wf16036.
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The moisture content of dead grass fuels is an important input to grassland fire behaviour prediction models. We used standing dead grass moisture observations collected within a large latitudinal spectrum in Eastern Australia to evaluate the predictive capacity of six different fuel moisture prediction models. The best-performing models, which ranged from a simple empirical formulation to a physically based process model, yield mean absolute errors of 2.0% moisture content, corresponding to a 25–30% mean absolute percentage error. These models tended to slightly underpredict the moisture content observations. The results have important implications for the authenticity of fire danger rating and operational fire behaviour prediction, which form the basis of community information and warnings, such as evacuation notices, in Australia.
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P. Karthik, P. Jayanth, K. Tharun Nayak, and K. Anil Kumar. "Crime Prediction Using Machine Learning and Deep Learning." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 08–15. http://dx.doi.org/10.32628/ijsrset241134.
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The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and elements associated with criminal behaviour and underscores the existing deficiencies and prospective avenues for advancing crime prediction precision. This thorough examination of the current research on crime forecasting through machine learning and deep learning serves as an essential resource for scholars in the domain. A more profound comprehension of these predictive methods will empower law enforcement to devise more effective prevention and response strategies against crime.
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Gukiina, Joshua, Joseph Mpeera Ntayi, Waswa Balunywa, and Augustine Ahiauzu. "Intercultural Sensitivity and Organizational Citizenship Behaviour of Uganda Hotels’ Staff." Journal of Intercultural Communication 19, no. 2 (2019): 1–16. http://dx.doi.org/10.36923/jicc.v19i2.782.
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The study examined the extent to which; intercultural sensitivity and organizational citizenship behaviour are constructs of the social exchange theory and intercultural sensitivity explains organizational citizenship behaviour. A mixed research design was applied to determine the predictive relationship intercultural sensitivity and organizational citizenship behaviour. Intercultural sensitivity significantly predicts Organizational citizenship behaviour.The instruments were adapted to the Uganda hotels’ environment. It was cross sectional yet, behaviour unfolds gradually. Limited qualitative inquiry was undertaken because; only explanations for the quantitative results were sought. The paper domesticates intercultural sensitivity and organizational citizenship behaviour within the social exchange theory and directly tested for the predictive relationship between intercultural sensitivity and organizational citizenship behaviour.
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Eshtiaghi, Nicky, Flora Markis, and Paul Slatter. "The laminar/turbulent transition in a sludge pipeline." Water Science and Technology 65, no. 4 (2012): 697–702. http://dx.doi.org/10.2166/wst.2012.893.
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Globally, wastewater treatment plants are under pressure to handle high concentration sludge in a sludge treatment line. Unawareness of the non-Newtonian behaviour of the thickened sludge has the potential to cause unexpected problems when the fluid behaviour changes from turbulent to laminar flow. In this study, sludge apparent viscosity was plotted as a function of total suspended solids concentration (TSS) and shear rate. Then, the transition velocity based on several predictive models in the literature was determined. This analysis provides a practical basis for the prediction of the pipe flow behaviour of thickened sludge in troubleshooting and engineering design.
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Burgos, Daniel. "A Predictive System Informed by Students’ Similar Behaviour." Sustainability 12, no. 2 (2020): 706. http://dx.doi.org/10.3390/su12020706.
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It is quite complex to adapt instruction to student needs in view of online education owing to the ensuing communication disconnection in such learning environments. Decision support schemes offer assistance by automatically gathering students’ data and forwarding them to the tutor in the appropriate perspective, in order to predict their behaviour and implement some action beforehand to avert or promote the final upshot. This study shows of a decision support scheme known as u-Tutor that is centred on the similarity computation between learners in the past, and how it was used in a real-case scenario. For this case study, this tool has been utilized by two real courses comprising of 392 learners alongside academic faculty, as of 2015 to 2019. The analysis offered focuses on 3 research areas: (1) perceived usefulness, (2) usability of the tool and (3) success rate of classification. From the acquired data, it can be seen that the teaching group managed to offer excellent approximations for those learners who eventually managed to pass the course, whereas u-Tutor seemed to be an early warning for learners at risk, indicating its capacity as a tutors’ supportive tool.
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De La horra, Julian, and Maria Teresa Rodriguez-bernal. "Asymptotic behaviour of the posterior predictive p-value." Communications in Statistics - Theory and Methods 26, no. 11 (1997): 2689–99. http://dx.doi.org/10.1080/03610929708832072.
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Arnold, J. C., and V. E. White. "Predictive models for the creep behaviour of PMMA." Materials Science and Engineering: A 197, no. 2 (1995): 251–60. http://dx.doi.org/10.1016/0921-5093(95)09733-3.
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D. Sandhya rani, Dr.T. Vara Lakshmi, and K. Ashok Kumar. "Predictive Analysis of Customer Behaviour on Mobile Apps." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 05 (2024): 1744–47. http://dx.doi.org/10.47392/irjaem.2024.0256.
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The widespread use of high-tech mobile devices has made them indispensable, driving up the number of users. This surge has intensified the demand for quality mobile apps, putting pressure on developers to swiftly improve user experiences. Our article tackles the challenges faced by both consumers and developers, proposing solutions to bridge the gap. By understanding consumer behavior and empowering developers, we aim to enhance mobile app functionality and satisfaction, fostering a better relationship between users and creators. Customers' preferences for online payments are largely influenced by the current study's research of customer satisfaction with online payment apps. Simple random and sample methods were used to choose 100 respondents. Utilizing statistical tools such as ANOVA and LINEAR REGRESSION, these studies identified the online payment apps behaviors of their clients.
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Florian, M., and J. Kottferová. "Dog Behaviour Prediction Testing." Folia Veterinaria 66, no. 2 (2022): 22–28. http://dx.doi.org/10.2478/fv-2022-0014.
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Abstract Dogs exhibit behavioural heterogeneity as a result of their close proximity to people as pets, working animals, or research animals. This variability stems from their natural talents as well as contextual effects. This document examines the several types of dog behavioural tests, including those that are used to evaluate dogs and others that are used to categorize individual animals. This study revealed a lack of agreement on all of these testing procedures. Individual variations in behaviour, or personality differences, may now be quantified and described in the working dog literature. The predictive association between certain dog behavioural features (if any) and crucial working results is less well-known.
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Clavel San Emeterio, Iván, Jorge García-Unanue, Eliseo Iglesias-Soler, Leonor Gallardo, and José Luis Felipe. "Drop out prediction in sport centres. Definition of models and reproducibility (Predicción de bajas de clients en centros deportivos. Definición de modelos y reproducibilidad)." Retos, no. 37 (July 24, 2019): 54–61. http://dx.doi.org/10.47197/retos.v37i37.71423.
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Abstract. Purpose: One of the main problems detected in the sport centre field is abandonment of physical activity by members. The aim of this estudy is to evaluate the reproducibility of predictive models of abandonment in sports centres, based on information collected through access control and the Customer Relationship Management system. Method: Monthly data corresponding to the actual behaviour of of two sport centres in Spain were recorded over the course of one year (12,545 members). Based on these variables, two predictive models were designed using the absence or permanence in the centre as an independent variable. Subsequently, the regression formula obtained was applied on all members of the same sports centres a year later (12,986 members), checking the effectiveness and reproducibility of the model. Results:. Logistic regression models based on customer historical behaviour were useful to predict abandonment. The effectiveness of the models was around 70% a year after. Conclusions: The segmentation of users based on the likelihood of drop out and the identification of behaviour variables that predict such drop out and the confirmation of the effectiveness of the predictive model, can help to optimize the design of loyalty strategies to prevent the abandonment in sport centres.Resumen. Objetivo: El abandono de la práctica deportiva por parte de clientes en centros deportivos es uno de los principals problemas en el sector. El objetivo de este estudio es evaluar la reproducibilidad de modelos predictivo de abandono en centros deportivos, basados en información recogida a través del control de accesos y el sistema Customer Relationship Management. Método: Se recopiló comportamientos mensuales de los clientes de dos centros deportivos en España durante un año (12,545 members). En base a dichas variables se diseñaron dos modelos predictivos utilizando la baja o permanencia en el centro como variable independiente. Posteriormente, se aplicó la formula de regression obtenida sobre todos los miembros de los mismos centros deportivos un año después (12,986 members), comprobando la efectividad y reproducibilidad del modelo. Resultados: Las estimaciones de regression logística fueron útiles para predecir el abandon en los centros deportivos. Además, reproduciendo el mismo modelos en una muestra diferente se consiguió aproximadamente un 70% de efectividad. Conclusiones: La segmentación de usuarios en base a la probabilidad de baja y la identificación de variables predictivas de dicha baja pueden optimizar el diseño de estrategias de prevención de abanadono en centros deportivos.
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Kicova, Eva, Lucia Svabova, Olga Ponisciakova, and Zuzana Rosnerova. "Forecasting Financial Literacy Levels with Respect to Consumer Shopping Behaviour." International Journal of Financial Studies 13, no. 1 (2025): 26. https://doi.org/10.3390/ijfs13010026.
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Contemporary consumer society is deeply intertwined with the concepts of money and shopping, which are closely related yet often studied separately. Despite their interconnectedness, there is a notable gap in research that simultaneously addresses financial literacy as a tool for effective money management and informed shopping decisions. This study investigates the relationship between these concepts through theoretical analysis and an empirical survey conducted in the Slovak Republic. Given the similarities in business environments across V4 countries, we posit that our findings may have broader applicability. Survey results were analysed using selected statistical methods, including the chi-square test of independence, to test hypotheses related to financial literacy. Where dependencies were identified, contingency coefficients were used to quantify their strength. Additionally, predictive models of financial literacy levels of two types were developed. Firstly, models were created for both predictive and explanatory purposes using the CHAID classification tree and logistic regression demonstrating high performance, clarity, and interpretability. Secondly, several prediction models using advanced machine learning techniques were created for highly precise predictions of financial literacy levels.
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Cooke, David J. "Predicting offending in prison: The predictive validity of the Prison Behaviour Rating Scales." Legal and Criminological Psychology 1, no. 1 (1996): 65–82. http://dx.doi.org/10.1111/j.2044-8333.1996.tb00307.x.
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Zheng, Feidong, Qiang Liu, Jinchao Xu, Aqiang Ming, and Jia Dong. "Numerical Simulation of Subaerial Granular Landslide Impulse Waves and Their Behaviour on a Slope Using a Coupled Smoothed Particle Hydrodynamics–Discrete Element Method." Journal of Marine Science and Engineering 12, no. 10 (2024): 1692. http://dx.doi.org/10.3390/jmse12101692.
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Numerical simulations were conducted to investigate the wave features of subaerial granular landslide-generated impulse waves and their impact on slopes. A numerical solution was obtained by coupling smoothed particle hydrodynamics (SPH) and the discrete element method (DEM). Several predictive equations were tested for their applicability in predicting the maximum crest amplitude of impulse waves generated by slides of different shapes. The results indicated that the predictive model developed by Heller and Hager, utilising slide centroid impact velocity, showed favourable prediction accuracy for the maximum crest amplitude, almost independent of the slide shape at impact. Regarding the leading wave, although the wave profile and velocity distribution deviated significantly from a solitary wave of the same wave amplitude, the maximum run-up could be satisfactorily estimated using solitary wave theory. In addition, the increase in the maximum dynamic forces exerted by the impulse waves on the slope followed a power law with the incident wave amplitude.
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Qiu, Chunyan, Changhong Su, Xiaoxiao Liu, and Dian Yu. "A Study of Feature Construction Based on Least Squares and RBF Neural Networks in Sports Training Behaviour Prediction." Computational Intelligence and Neuroscience 2022 (March 7, 2022): 1–7. http://dx.doi.org/10.1155/2022/5034081.
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This paper examines the problem of athletes’ training in sports, exploring the methods and means by which athletes can perform difficult movements in which they normally make minor training errors in order to achieve better competition results and placements. To this end, we test the explanatory and predictive effects of a theoretical model starting with planned behaviour and then use exercise planning, self-efficacy, and support as variables to develop a partial least squares regression model of sports to improve the explanation and prediction of sporting athletes’ intentions and behaviour. An improved RBF network-based method for player behaviour prediction is proposed. On the basis of the RBF analysis, the number of layers and the number of neurons in the hidden layer of the network are adjusted and optimised, respectively, to improve its generalisation and learning abilities, and the athlete behaviour prediction model is given. The results demonstrate the advantages of the improved algorithm, which in turn provides a more scientific approach to the current basketball training.
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Semarco, Stanley K. M. "The Relationship among Communication, Leadership Behaviour and Performance." Journal of Communications, Media And Society (JOCMAS) 1, no. 1 (2009): 2–20. https://doi.org/10.63772/jocmas.v1n1.1.
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The present study explored the relationship among communication, leadership behaviours and performance. It was hypothesised that leader's communication would predict the leader’s exhibition of leadership behaviours and performance. Participants (104 students) were asked to provide communication, leadership behaviour and performance ratings of lecturers within classroom instructional setting. Results indicated that communication was a better predictor of performance and choice of leadership behaviours. The current study provides support in demonstrating the validity and practicability of using communication dimensions as a predictive variable of leadership success. The findings were discussed.
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Chen, Pang, Hui Wang, Shaojun Cao, and Xueyuan Lv. "Prediction of Mechanical Behaviours of FRP-Confined Circular Concrete Columns Using Artificial Neural Network and Support Vector Regression: Modelling and Performance Evaluation." Materials 15, no. 14 (2022): 4971. http://dx.doi.org/10.3390/ma15144971.
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The prediction and control of the mechanical behaviours of fibre-reinforced polymer (FRP)-confined circular concrete columns subjected to axial loading are directly related to the safety of the structures. One challenge in building a mechanical model is understanding the complex relationship between the main parameters affecting the phenomenon. Artificial intelligence (AI) algorithms can overcome this challenge. In this study, 298 test data points were considered for FRP-confined circular concrete columns. Six parameters, such as the diameter-to-fibre thickness ratio (D/t) and the tensile strength of the FRP (ffrp) were set as the input sets. The existing models were compared with the test data. In addition, artificial neural networks (ANNs) and support vector regression (SVR) were used to predict the mechanical behaviour of FRP-confined circular concrete columns. The study showed that the predictive accuracy of the compressive strength in the existing models was higher than the peak compressive strain for the high dispersion of material deformation. The predictive accuracy of the ANN and SVR was higher than that of the existing models. The ANN and SVR can predict the compressive strength and peak compressive strain of FRP-confined circular concrete columns and can be used to predict the mechanical behaviour of FRP-confined circular concrete columns.
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Malinauskas, Romualdas, Audrone Dumciene, Saule Sipaviciene, and Vilija Malinauskiene. "Relationship Between Emotional Intelligence and Health Behaviours among University Students: The Predictive and Moderating Role of Gender." BioMed Research International 2018 (June 4, 2018): 1–11. http://dx.doi.org/10.1155/2018/7058105.
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This study investigated the role of gender as a potential predictor of health behaviour and potential moderator of the relationship between emotional intelligence and health behaviour. This cross-sectional study included 1214 students (597 males and 617 females). Data were collected using the Schutte Self-Report Inventory and the Health Behaviour Checklist. Stepwise multiple regression analysis was executed with the components of health behaviour as the dependent variables to examine the predictive value of the emotional intelligence indicators as the independent variables. Gender predicted all categories of health behaviours. Only one indicator of emotional intelligence, appraisal, predicted the Accident Control and Traffic Risk Taking categories. The emotional intelligence indicator of social skills emerged only as a predictor of Wellness Maintenance and Enhancement in university students. Gender moderates the relationship between all emotional intelligence indicators and health behaviour components except the relationship between Appraisal and Substance Risk Taking and the relationship between Utilization and traffic risk taking.
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Păunescu, Carmen, Alexandra Ioana Onea, Elisabeta Molnar, and Enikő Mátyus. "Well-being and behaviour at work: to what extent are they related?" Journal of East European Management Studies 29, no. 2 (2024): 340–62. http://dx.doi.org/10.5771/0949-6181-2024-2-340.
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The purpose of the article is to examine the extent to which workplace well-being influences individual behaviours at work. Various studies have found that increasing the state of feeling good and seeing life positively influences the way a person responds or behaves. This article explores the causal relationship between the psychological and subjective well-being of individuals and their behaviour at work and/or at school. Additionally, it investigates the extent to which socio-economic status participates in the causal relationship between workplace well-being and behaviour at work and/or at school. To understand the predictive power of independent variables on behaviour in the workplace, we used linear regression models. Mediation tests were also used to quantify the extent to which socio-economic status influenced the confirmed causal relationship. The study reports the results of a sample of 533 Romanian respondents. Data were collected from university business students, based on a questionnaire administered online. The results confirm the prediction role of the psychological and subjective well-being of individuals in their workplace behaviour. Additionally, research confirms the mediation role of socio-economic status in this relationship.