Relevant bibliographies by topics / Predictive behaviour

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predictive behaviour'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Predictive behaviour"

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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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Dissertations / Theses on the topic "Predictive behaviour"

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Hillebrandt, H. F. "Bayesian hierarchical predictive coding of human social behaviour." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1435549/.

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‘Bayesian hierarchical predictive coding of human social behaviour.’ Biological agents are the most complex systems humans encounter in their natural environment and it is critical to model other’s mental states correctly to predict their behaviour. To do this one has to generate a mental representation based on an internal neural model of the other agent (Chapter 1). Here we show, in a series experiments, that people use and update their Bayesian priors in social situations and explain how they create mental representations of others to guide action selection. We investigate the neural mechanisms and the brain connectivity that underlie these social processes and how they develop with age. In chapter 2, we show how experimentally induced prior experience with other people (here social inclusion or exclusion) influences the level of trust towards those people. In chapter 3, we describe an fMRI study using a social perspective-taking task that examines the developmental differences between adolescents and adults in the control of action selection by social information. Using the same task, in chapter 4, we investigate the effective connectivity between the activated regions with Dynamic causal modelling. In Chapter 5, we explore effective connectivity of fMRI data from the Human connectome project (Van Essen et al., 2012). During the task participants viewed animations of triangles moving either randomly or so that they evoke mental state attribution (Castelli et al., 2000). Chapter 6 concludes with a summary of the experiments and integrate them into existing research, as well as provide a critical synthesis of the findings in order to suggest future research directions. We interpret our findings in a hierarchical predictive coding framework, where agents try to create a neural model of the external world to minimize prediction errors, Bayesian surprise and free energy.
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Zoli, Matteo <1983&gt. "Predictive Factors of Biological Behaviour in Pituitary Adenoma." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amsdottorato.unibo.it/8761/1/Zoli_Matteo_Tesi.pdf.

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Background: Predicting biological behavior of pituitary adenomas is a complex but highly desirable issue for both scientific and practical porpoises. Recently, a clinico-pathological grading based on histopathological and neuroradiological features has been proposed, stratifying the risk of progression/recurrence in 5 classes. Aim of our study is to perform an independent external validation of this score and identify other potential predictor of aggressive behaviour. 
Results: Five hundred sixty-six patients with pituitary adenomas were included in this study (253 FSH/LH, 147 GH, 85 PRL, 72 ACTH and 9 TSH tumors). In 437 cases, pituitary adenomas were non-invasive, with low (grade 1a: 378 cases) or high (grade 1b: 59 cases) proliferative activity. In 129 cases, tumors were invasive, with low (grade 2a: 87 cases) or high (grade 2b: 42 cases) proliferative activity. During the follow-up (mean: 5.8 years), 60 patients developed disease recurrence or progression, with a total of 130 patients with pituitary disease at last follow-up. Univariate analysis demonstrated a significantly higher risk of disease persistence and recurrence/progression in patients with PRL, ACTH and FSH/LH tumors as compared to those with somatotroph tumors, and in those with high proliferative activity (grade 1b and 2b) or >1 cm diameter. Multivariate analysis confirmed that tumor type and grade are independent predictors of disease free-survival and progression free-survival. Tumor grading resulted the first parameter emerging in the decision tree analyses with CHAID growing process to stratify patients according to the risk of recurrence/progression. Conclusions: Our data confirmed the validity of Trouillas’ score, being tumor type and grade independent predictors of disease evolution. Therefore, we recommend to always consider both features, together with tumor histological subtype, in the clinical setting to early identify patients at higher risk of an aggressive behaviour.
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Wang, Jinhuo. "Predictive modelling and experimental measurement of composite forming behaviour." Thesis, University of Nottingham, 2008. http://eprints.nottingham.ac.uk/10602/.

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Optimised design of textile composite structures based on computer simulation techniques requires an understanding of the deformation behaviour during forming of 3-dimensional double-curvature components. Purely predictive material models are highly desirable to facilitate an optimised design scheme and to significantly reduce time and cost at the design stage, such as experimental characterisation. In-plane shear and out-of-plane bending are usually thought to be the key forming mechanisms. Therefore, this thesis is concerned with studies of the shear and bending behaviour by experimental characterisation and theoretical modelling. Micromechanical interaction between fibre and matrix offers fundamental understanding of deformation mechanisms at the micro-scale level, leading to development of composite viscosity models, as input to shear and bending models. The composite viscosity models were developed based on rheological behaviour during movement of fibres, and validation was performed using experimental results collected from the literature. A novel characterisation method for measuring the bending behaviour, by means of a large-displacement buckling test, was attempted due to some significant advantages over other methods. Development of a bending model was also undertaken for unidirectional composites but experimental validation suggests further study may be required for woven composites. The shear behaviour was characterised using a picture frame test for viscous polymer composites. To obtain reliable experimental data, some efforts of improving the characterisation method were made. The experimental results were then used to validate a shear model, suggesting that further improvement is required, in terms of weave patterns, rate and temperature dependence.
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Keen, Steven Dale. "Modeling driver steering behaviour using multiple-model predictive control." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611428.

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Swan, J. "The role of predictive accuracy in Pavlovian conditioning." Thesis, Bucks New University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378415.

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Stephens, Philip Andrew. "Behaviour based models population dynamics and the conservation of social mammals." Thesis, University of East Anglia, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251708.

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Fisher, Jacinda. "A comparison of predictive models of adherence behaviour in hypertensive patients /." Title page, abstract and contents only, 1998. http://web4.library.adelaide.edu.au/theses/09SPS/09spsf534.pdf.

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Villavicencio, Rojas Maria Daniela. "Predictive modelling of the tribological behaviour of self-lubricating composite materials." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI040.

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Dans les matériaux composites autolubrifiants, la génération de particules d’usure est nécessaire pour assurer la lubrification. Dans les roulements à billes, ce type de lubrification est possible grâce au matériau de la cage, composé d’un matériau composite autolubrifiant, tandis que le reste du roulement est fait en métal (AISI 440C). Pour les applications spatiales, le RT/Duroid 5813 est un composite autolubrifiant reconnu pour les cages de ce type de roulements. Ce matériau a été largement utilisé car il a répondu aux besoins de la lubrification sèche dans l’espace. Cependant, la production de ce matériau a été arrêtée dans les années 90. Cette situation a conduit à la recherche d’un « matériau équivalent » , répondant à la fois aux besoins du marché spatial et aux « besoins tribologiques ». Aujourd’hui, le principal inconvénient lié à ces matériaux est le manque de prévisibilité de leur comportement tribologique. Dans ce travail, une approche couplée expérimentale et numérique a été proposée afin de modéliser le comportement tribologique des matériaux composites autolubrifiants. Le modèle numérique proposé a été nourri par des caractérisations expérimentaux (comme la tomographie à rayons X pour la création de la morphologie du matériau numérique, ou la microscopie à force atomique pour informer la valeur de l’adhésion entre les composants). Le but d’une telle démarche numérique est de palier les limites d’une approche entièrement expérimentale qui ne permet pas d’observer in-situ le contact de par son caractère confiné. L’objectif du présent travail est de donner une réponse au problème de compréhension du comportement tribologique de matériaux composites autolubrifiants dans le mécanisme de double transfert. Ceci en vue de contribuer au développement d’un nouveau matériau tribologique répondant aux besoins des applications spatiales. Parmi tous les matériaux autolubrifiants, le PGM-HT a été sélectionné dans cette étude car sa morphologie grossière a permis de construire une version numérique du matériau (avec la résolution du tomographe à rayons X utilisé dans ce travail). Néanmoins, l’approche proposée ici pour construire le modèle numérique peut être étendue à d’autres matériaux composites autolubrifiants. Le modèle numérique proposé dans ces travaux ouvre de nouvelles perspectives en termes de conception de matériaux, car il permet d’étudier directement les scénarios de dégradation et d’usure des matériaux composites. D’un point de vue général, il est à noter que la tribologie numérique est un outil offrant de multiples possibilités pour la compréhension des matériaux autolubrifiants, et permet d’aider dans le processus de prédiction du comportement tribologique des matériaux autolubrifiants<br>In self-lubricating composite materials, the generation of a stable third body layer is necessary to ensure contact lubrication. This is specially true for the contact in which these materials are directly involved, and also in other contacts implicating its counterface. Such type of lubrication is possible in self-lubricating bearings thanks to its cage material, which is made of the self-lubricating composite, while the rest of the bearing is usually made of AISI 440C. For space applications, RT/Duroid 5813 is a recognized self-lubricating composite cage material for this kind of bearings. This material has been widely used not only because of the space heritage, but also because it has satisfied the needs of space dry lubrication. However, the production of this material has been stopped in the 90’s, and it has placed the latter out of the market. This situation has led to the search for an equivalent material, that meets both the needs of the space market and the "tribological needs." Today, the main inconvenient related to these materials is the lack of predictability of their tribological behaviour. In this work, the "making of" a coupled numerical-experimental approach has been proposed in order to carry out the understanding of these materials. The goal of this numerical approach is to let to "complement" the limitations of a fully experimental or a fully numerical approach (the confined nature of the contact does not allow in situ observation). Such numerical approach has been informed with experimental test (as X-ray tomography for the creation of the numerical morphology, or atomic force microscopy to inform the value of adhesion between the components). Among all the self-lubricating materials, PGM-HT has been selected in this study because its coarse morphology let to build a numerical version of the material (with the resolution of the X-ray tomograph used in this work). Nevertheless, the approach that has been proposed here to build the numerical model, can be extended to other self-lubricating composite materials. The numerical model developed in this work opens new perspectives in terms of material design, as it makes it possible to directly study the scenarios of damage and wear of self-lubricating composite materials. From a general point of view, from this work it can be highlighted that numerical tribology is a tool that offers multiple possibilities in the understanding of self-lubricating materials, and that helps in the predictionof the tribological behaviour of self-lubricating materials. This work has then let to advance in the understanding of these materials
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Meinhardt, HR. "Evaluation of predictive models for pesticide behaviour in South African soils / HR Meinhardt." Thesis, North-West University, 2009. http://hdl.handle.net/10394/5092.

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The re-evaluation of pesticide use patterns is a high priority internationally. The process has led to a reduction in the numbers of pesticides allowed for use in many countries. This withdrawal of pesticides is aimed at consumer and environmental protection. Pesticide mobility and persistence is of major importance especially when considering the protection of water sources. In order to evaluate the suitability of a pesticide for use it is essential that its environmental behaviour is understood and predictable. In this thesis several case studies in which damage to crops may have been caused as a result of herbicides migration are described. Pesticide adsorption and persistence determinations showed that the adsorption coefficients do not differ from those published. Pesticide half lives are however likely to be prolonged in South African soils. Field migration studies show that pesticides are vertically and horizontally mobile in South African soils, seemingly independent of soil type. Both migration as well as upward movement was found, due to the mobility of the pesticides in the test soils. Mobility evaluation was identified as an aspect of importance for registration of pesticides under local conditions. To this end a system is proposed whereby migration can be incorporated into the existing pesticide evaluation framework. The system proposed used the migration model PESTAN as a predictive tool for pesticide migration in the evaluation process. The model is used for the evaluation of pesticides, according to Proposed Pesticide Migration Categories (PPLC). Migration evaluation should be conducted in conjunction with GPS systems to aid in determining potential risk areas where certain pesticide should not be used.<br>Thesis (Ph.D. (Zoology))--North-West University, Potchefstroom Campus, 2009.
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Al, Nasseri Alya Ali Mansoor. "The predictive power of stock micro-blogging sentiment in forecasting stock market behaviour." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13575.

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Online stock forums have become a vital investing platform on which to publish relevant and valuable user-generated content (UGC) data such as investment recommendations and other stock-related information that allow investors to view the opinions of a large number of users and share-trading ideas. This thesis applies methods from computational linguistics and text-mining techniques to analyse and extract, on a daily basis, sentiments from stock-related micro-blogging messages called “StockTwits”. The primary aim of this research is to provide an understanding of the predictive ability of stock micro-blogging sentiments to forecast future stock price behavioural movements by investigating the various roles played by investor sentiments in determining asset pricing on the stock market. The empirical analysis in this thesis consists of four main parts based on the predictive power and the role of investor sentiment in the stock market. The first part discusses the findings of the text-mining procedure for extracting and predicting sentiments from stock-related micro-blogging data. The purpose is to provide a comparative textual analysis of different machine learning algorithms for the purpose of selecting the most accurate text-mining techniques for predicting sentiment analysis on StockTwits through the provision of two different applications of feature selection, namely filter and wrapper approaches. The second part of the analysis focuses on investigating the predictive correlations between StockTwits features and the stock market indicators. It aims to examine the explanatory power of StockTwits variables in explaining the dynamic nature of different financial market indicators. The third part of the analysis investigates the role played by noise traders in determining asset prices. The aim is to show that stock returns, volatility and trading volumes are affected by investor sentiment; it also seeks to investigate whether changes in sentiment (bullish or bearish) will have different effects on stock market prices. The fourth part offers an in-depth analysis of some tweet-market relationships which represent an open problem in the empirical literature (e.g. sentiment-return relations and volume-disagreement relations). The results suggest that StockTwits sentiments exhibit explanatory power in explaining the dynamics of stock prices in the U.S. market. Taking different approaches by combining text-mining techniques with feature selection methods has proved successful in predicting StockTwits sentiments. The applications of the approach presented in this thesis offer real-time investment ideas that may provide investors and their peers with a decision support mechanism. Investor sentiment plays a critical role in determining asset prices in capital markets. Overall, the findings suggest that investor sentiment among noise traders is a priced factor. The findings confirm the existence of asymmetric spillover effects of bullish and bearish sentiments on the stock market. They also suggest that sentiment is a significant factor in explaining stock price behaviour in the capital market and imply the positive role of the stock market in the formation of investor sentiment in stock markets. Furthermore, the research findings demonstrate that disagreement is not only an important factor in determining trading volumes but it is also considered a very significant factor in influencing asset prices and returns in capital markets. Overall, the findings of the thesis provide empirical evidence that failure to consider the role of investor sentiment in traditional finance theory could lead to an imperfect picture when explaining the behaviour of stock prices in stock markets.
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Books on the topic "Predictive behaviour"

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Bontcheva, Nikolina. Metal behaviour and predictive simulation in thermomechanical processing. Prof. Marin Drinov Academic Publishing House, 2012.

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Brennscheidt, Gunnar. Predictive Behavior. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-95718-5.

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Chen, X. Modelling and predicting textile behaviour. Woodhead Publishing, 2010.

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Nagaraj, T. S. Analysis and prediction of soil behaviour. Wiley Eastern, 1994.

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Lee, Gerry. Predicting behavior in marriage and divorce. Learning Specialists, 1987.

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Ron, Hubbard L. Science of survival: Prediction of human behavior. New Era Publications International Aps, 1993.

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F, Lignelli Alphonse, ed. Fatigue crack growth: Mechanics, behavior, and prediction. Nova Science Publishers, 2009.

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Cooke, LaNina N. Structure matters: Predicting juvenile justice system behavior. LFB Scholarly Publishing, 2015.

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Marder, Eric. The laws of choice: Predicting customer behavior. Free Press, 1997.

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L, Quinsey Vernon, ed. The prediction and control of violent behavior. Sage, 1986.

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Book chapters on the topic "Predictive behaviour"

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Munkh-Ulzii, Massoud Moslehpour, and Pham Van Kien. "Empirical Models of Herding Behaviour for Asian Countries with Confucian Culture." In Predictive Econometrics and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_34.

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Francisco, Laura Rojas-De, Carlos Monroy Juan Osorio, and Rodríguez Santiago Cadavid. "Social Complex Networks Analysis as Predictors of Users' Behaviour in the Digital Society." In Predictive Technology in Social Media. CRC Press, 2022. http://dx.doi.org/10.1201/9781003214878-7.

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Spenrath, Yorick, Marwan Hassani, and Boudewijn F. van Dongen. "Online Prediction of Aggregated Retailer Consumer Behaviour." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_16.

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AbstractPredicting the behaviour of consumers provides valuable information for retailers, such as the expected spend of a consumer or the total turnover of the retailer. The ability to make predictions on an individual level is useful, as it allows retailers to accurately perform targeted marketing. However, with the expected large number of consumers and their diverse behaviour, making accurate predictions on an individual consumer level is difficult. In this paper we present a framework that focuses on this trade-off in an online setting. By making predictions on a larger number of consumers at a time, we improve the predictive accuracy but at the cost of usefulness, as we can say less about the individual consumers. The framework is developed in an online setting, where we update the prediction model and make new predictions over time. We show the existence of the trade-off in an experimental evaluation on a real-world dataset consisting of 39 weeks of transaction data.
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Rogić, Sunčica, and Ljiljana Kašćelan. "Decoding Customer Behaviour: Relevance of Web and Purchasing Behaviour in Predictive Response Modeling." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34111-3_31.

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Chaudhary, Roopal, Anchal Garg, and Madhulika Bhadauria. "Predictive Analytics for LAMA and Absconding Behaviour of Patient." In Smart Computing and Informatics. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5547-8_66.

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Iucu, Romiţă, Simona Iftimescu, George Gunnesch-Luca, et al. "Exploring Degrees of Connection: Challenges and Acceptance of the European Degree Label Towards Shaping the Future of Higher Education." In European Higher Education Area 2030: Bridging Realities for Tomorrow’s Higher Education. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75140-0_32.

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Abstract This paper investigates the adoption and implementation of the European Degree Label (EDL) within joint degree programmes, using Ajzen’s Theory of Planned Behaviour (Ajzen, 1991) as a predictive framework. Specifically, the study examines how attitudes, subjective norms, and perceived behavioural control influence key stakeholders' intentions and actual behaviours towards EDL adoption. The Theory of Planned Behaviour (TPB) framework is extended to the context of organisational transformation, highlighting its applicability in navigating resistance to change within educational institutions. Administered to academic stakeholders across Europe, the questionnaire focuses on three TPB dimensions: attitudes towards the EDL, academic community norms, and stakeholders’ self-efficacy and perceived control over EDL implementation. Utilising a mixed-method approach, the study aims to produce nuanced insights into these behavioural predictors. Anticipated results are expected to identify practical strategies for fostering EDL acceptance, including aligning organisational norms with policy objectives and enhancing stakeholders’ perceived behavioural control (PBC) through leadership support and training, thus fostering cooperation throughout the European Higher Education Area and furthering its fundamental values. The results presented in this paper are part of a broader project: SMARTT–Screening, Mapping, Analysing, Recommending, Transferring and Transforming Higher Education international programmes. SMARTT aimed to analyse, test, and pilot the new European Degree label criteria, with the goal of improving the quality and increasing the transferability of future developments of European Degrees across Europe and beyond (SMARTT, 2023).
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Pohlkötter, Fabian J., Dominik Straubinger, Alexander M. Kuhn, Christian Imgrund, and William Tekouo. "Unlocking the Potential of Digital Twins." In Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27933-1_18.

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AbstractIncreasing competitive pressure is confronting the automotive industry with major challenges. As a result, conventional reactive maintenance is being transformed into predictive maintenance. In this context, wearing and aging effects no longer lead to plant failure since they are predicted at an earlier stage based on comprehensive data analysis.Furthermore, the evolution towards Smart Factory has given rise to virtual commissioning in the planning phase of production plants. In this process, a Hardware-in-the-Loop (HiL) system combines the real controls (e.g., PLC) and a virtual model of the plant. These HiL systems are used to simulate commissioning activities in advance, thus saving time and money during actual commissioning. The resulting complex virtual models are not further used in the series production.This paper builds upon virtual commissioning models to develop a Digital Twin, which provides inputs for predictive maintenance. The resulting approach is a methodology for building a hybrid predictive maintenance system. A hybrid prediction model combines the advantages of data-driven and physical models. Data-driven models analyse and predict wearing patterns based on real machine data. Physical models are used to reproduce the behaviour of a system. From the simulation of the hybrid model, additional insights for the predictions can be derived.The conceptual methodology for a hybrid predictive maintenance system is validated by the successful implementation in a bottleneck process of the electric engine production for an automotive manufacturer. Ultimately, an outlook on further possible applications of the hybrid model is presented.
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Aplin, P. F., and D. D’Angelo. "Predictive Modelling of Long-Term Creep Behaviour of Type 316 Steel." In Materials for Advanced Power Engineering 1994. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1048-8_49.

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Kocańda, Andrzej, and Stanisław Kocańda. "Predictive Analysis of Low Cycle Fatigue of Tool Steel in Warm Working Conditions." In Low Cycle Fatigue and Elasto-Plastic Behaviour of Materials. Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3459-7_19.

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Hardy, John. "The Rise of the Modern Intelligence State." In Advanced Sciences and Technologies for Security Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90221-6_7.

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AbstractThe rise of the formal surveillance state in the early twenty-first century was precipitated by political impetus to empower security and intelligence organisations to perform a broad range of counterterrorism functions. Ethical debates about the implications of the security intelligence reach of modern states have focused on balancing individual rights, liberties, and privacy against the security of the state. Meanwhile, the surveillance state has rapidly evolved into an intelligence state, capable not only of pervasive data collection, but also of analytical modelling which expands existing boundaries of surveillance. Existing concerns about the ethical collection and use of surveillance data are compounded by three emergent capabilities of the modern intelligence state: persistent data surveillance, pattern-of-life analysis, and activity-based intelligence. These intelligence methods provide descriptive and/or predictive models of human behaviour that empower governments to generate intelligence about the actual and the potential subjects of counterterrorism investigations. The ethical implications of counterterrorism intelligence extend beyond the collection and use of data to the application of predictive modelling to dehumanised patterns of behaviour. This process has the potential to redefine the boundaries of the person, particularly by blurring the distinction between thoughts and actions which threaten the state.
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Conference papers on the topic "Predictive behaviour"

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Suresh Dahake, Parihar, and Nihar Suresh Dahake. "Predicting Buyer Behaviour: A Reconnaissance of Retail Predictive Analytics Model." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007411.

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Dahake, Parihar Suresh, and Nidhi Somani. "Harnessing Predictive Analytics for Accurate Consumer Behaviour Forecasting: A Comprehensive Review." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842743.

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Balaji, S., and D. Manikavelan. "Enhancing E-Commerce Predictive Analytics using Ensemble Models for Consumer Behaviour and Market Forecasting." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934325.

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Dahake, Parihar Suresh, Srushtee Parchake, Nidhi Somani, and Ashish R. Ingale. "Predictive Analytics in Automotive Consumer Behaviour: An Insight into Personal use 4-Wheeler Purchase Decisions." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912242.

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Chhim, Eavly, Takashi Matsumoto, Ko Kakuma, and Osamu Hatakeyama. "Basic Study on Mechanisms of Disintegration of Concrete in Bridge Slabs by Wheel Load Running Test on Simulated Damaged RC Beams." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.3216.

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&lt;p&gt;This study addresses the disintegration of concrete on the top surfaces of reinforced concrete (RC) bridge slabs, where cement matrix and aggregates separate, reducing thickness and load capacity. The mechanisms and timelines of this deterioration are unclear. To develop predictive technology for this deterioration, this research investigates the occurrence and progression of disintegration of concrete in RC beam specimens with cracks induced by expansive fracture agents to simulate frost damage. The specimens are then subjected to wheel load running test to observe their behaviours under the cyclic loading. As a result, the disintegration of concrete is successfully reproduced, and its detailed progression is captured. Results show that disintegration of concrete significantly influences structural behaviour of the specimens. Furthermore, it is identified that the loading level is a critical factor in determining the number of cycles at which disintegration of concrete occurs.&lt;/p&gt;
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Case, Raymundo. "Estimation of the Replacement Time Interval Due to CO2 Corrosion in Oilwells by Random Process Modelling." In CORROSION 2010. NACE International, 2010. https://doi.org/10.5006/c2010-10366.

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Abstract The predictive assessment of the optimum replacement time due to the corrosion damage accumulation allows the control of associated operating expenditures in oilfield operations. To such purpose a model was developed for the assessment of CO2 corrosion related damage accumulation. The model proposed is based on the calculation of the CO2 corrosion damage accumulation and failure probability as a sequence of random processes. The distribution of failure likelihood is then assessed in terms of a Markov random process from both an Ergodic, or repair, and an Absorbent, or unhindered damage accumulation, scenarios. The value of the optimum replacement time estimated by the model proposed was compared with an actual documented case from a mature oilfield development. The modelled results indicate that the mean transition time for failure without mitigation is around 6 years which coincide with the observed behaviour from the field. The modelled optimum replacement time interval for this case was estimated as 5 years of service time.
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Bhattacharya, Sangeeta, Sayani Chandra, and Debasish Dhar. "Customer behaviour forecasting using predictive analysis of sales data." In SECOND INTERNATIONAL CONFERENCE ON INNOVATIONS IN SOFTWARE ARCHITECTURE AND COMPUTATIONAL SYSTEMS (ISACS 2022). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0166610.

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Gupta, Manvi, Jishnu Mittal, and Anuradha Tomar. "Predictive Performance of EV Charging Behaviour in COVID-19." In 2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET). IEEE, 2023. http://dx.doi.org/10.1109/sefet57834.2023.10245140.

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Straton, Nadiya, Raymond Ng, Hyeju Jang, Ravi Vatrapu, and Raghava Rao Mukkamala. "Predictive modelling of stigmatized behaviour in vaccination discussions on Facebook." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983175.

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Tanuwijaya, Suryadi, Andry Alamsyah, and Maya Ariyanti. "Mobile Customer Behaviour Predictive Analysis for Targeting Netflix Potential Customer." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527487.

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Reports on the topic "Predictive behaviour"

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LaRaine Ingram, Keisha. Applied Sales Predictive Analytics for Business Development. Vilnius Business College, 2024. http://dx.doi.org/10.57005/ab.2024.1.2.

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In the dynamic business environment, leveraging predictive analytics for sales optimization and business development has become crucial for achieving sustained growth. As the e-commerce landscape continues to evolve, many e-businesses must harness the power of predictive analytics to anticipate sales trends and optimize business development strategies. This paper explores the application of sales predictive analytics, focusing on its role in forecasting sales, optimizing resource allocation, and enhancing customer relationship management. The application of predictive analytics in sales forecasting in online marketplace platforms is also explored, through the examination of various predictive models using real-world case studies. By exploring various methodologies and tools, the study illustrates how predictive analytics can be integrated into e-businesses' operations to drive growth, and enhance decision-making, highlighting the transformative potential of analytics in making data-driven decisions, ultimately fostering sustainable growth and competitive advantage. Through the analysis of historical sales data, consumer behaviour patterns, and market trends, predictive analytics provides actionable insights that are crucial for strategic planning and operational efficiency. The paper also addresses challenges and best practices for implementing predictive analytics into the business process of e-businesses.
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Mousavi, Seyedmostafa, Behzad Rismanchi, Stefan Brey, and Lu Aye. Experimental evaluation of PCM embedded radiant chilled ceiling for efficient space cooling. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau609928685.

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Because of climate change, together with rapid urbanisation and continuous population growth, the global demand for space cooling is increasing dramatically. Under a business-as-usual trajectory, there will be a more than threefold rise in the number of in-use air conditioners worldwide by 2050. A radical shift to innovative space cooling technologies is therefore essential, ones that can sustainably meet the growing requirements. Phase change material embedded radiant chilled ceiling, called PCM-RCC, offers an emerging alternative for more sustainable space cooling provision. This system provides a range of benefits to endusers, in terms of efficiency and indoor environmental quality, together with demand-side flexibility. PCM-RCC, however, is still under development, and further research is needed to realise its full capabilities. The present work experimentally analyses the thermal-energy performance of a PCM-RCC system using a full-scale test cabin equipped with PCM ceiling panels. Here, the transient thermal behaviour of the panels besides the cooling energy delivered in charging-discharging cycles are examined. Additionally, the indoor thermal comfort and peak energy demand reduction enabled by the present PCM-RCC are discussed. Based on the results, typically 4–5 hours of chilled water circulation overnight could sufficiently be able to fully recharge the panels in the morning. Over 80% of the occupancy time was found within Class B thermal comfort defined in ISO 7730. About 70% of the system’s daily electricity usage time was during off-peak hours. The significance of implementing optimal predictive operating schedules was also highlighted to fully utilise PCM-RCC’s potentials.
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Соловйов, В. М., та В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a characteristic way in time periods that characterize the different phases of the behavior and state of the stock market. This fact encouraged to use monitoring and prediction of critical and crisis states in socio-economic systems.
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Tuniki, Himanshu Patel, Gabriel Bekö, and Andrius Jurelionis. Using Adaptive Behaviour Patterns of Open Plan Office Occupants in Energy Consumption Predictions. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541563857.

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One of the factors that affects energy consumption in buildings is the level of control that occupants have over their environment, as well as their adaptive behaviour. The aim of this study was to focus on the adaptive clothing behaviour pattern, and to analyse its impact on energy consumption when integrated into a dynamic energy prediction tool. A questionnaire survey was conducted in an office building to collect the occupant behaviour data. The occupant clothing levels and the window opening behaviour were integrated into the dynamic energy performance prediction software, IDA ICE. The results of the simulations showed that the impact of adaptive clothing behaviour on energy consumption is relatively small, but it can meaningfully improve thermal comfort. Including adaptive behaviour in energy simulations can help in improving the accuracy of the energy performance and comfort predictions.
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Huntley, David, Rob Holman, Huib de Vriend, et al. Intermediate Scale Coastal Behaviour: Measurement, Modelling and Prediction. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada630166.

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Huntley, David, Rob Holman, Huib de Vriend, et al. Intermediate Scale Coastal Behaviour: Measurement, Modelling and Prediction. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada634918.

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Huntley, David, Rob Holman, Huib de Vriend, et al. Intermediate Scale Coastal Behaviour: Measurement, Modelling and Prediction. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada626897.

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Linn, R. R. A transport model for prediction of wildfire behavior. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/505313.

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Kimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0305.

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Johnson, Joseph E., Vladimir Gudkov, Chin-Tser Huang, Cilia Farkas, and Duncan Buell. New Metrics for Characterizing and Predicting Network Behavior. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada462797.

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