Narrow Results

Distant learning revealed the requirement for evolution in education to adapt to social distinctions and modern technology. Teachers are assessed on their ability to teach as well as motivated and their students are influenced by a variety of factors. Enhancing comprehension and boosting student education and educational success are receiving more attention. To strengthen the processes of decision-making by streamlining data mining tasks, this research proposes an algorithmic-driven decision support system (Algo-DSS) technique. Initially, we gathered a graduate-level academic sample with a variety of attributes. Uniform Manifold Approximation and Projection (UMAP) and Principal Component analysis (PCA) techniques are used to extract the particular characteristics regarding dimensionality reduction. Moreover, the suggested structure for the feature extraction procedures encompasses the clustering methodology. This paper presents the unique swarm-inspired dynamic catboost (SIDCatBoost) approach for enhanced classification performance. The effective feature subsets are chosen using the swarm approach which is known as rock hyraxes optimization (RHO) and then fed into the catboost technique. Employing the suggested framework, the issue of mining academic information has been carefully investigated with the aim of evaluating student performance. In addition to demonstrating a significant boost in performance through the use of a technique for choosing discriminative data attributes, the task of classification was completed with excellent results. Algo-DSS has the potential to be a helpful tool in educational settings, especially for enhancing processes of decision-making, as demonstrated by the conducted study.

This research uses cognitive analytics on a distributed computer system to investigate how students’ decision-making behaviors relate to their personality traits. The goal of the research is to improve our knowledge of the variables affecting students’ professional choices and to offer a solid, expandable framework for individualized career counseling. The investigation examines a range of psychological characteristics, such as personality characteristics, cognitive capacities, passions, and socioeconomic status, by utilizing cognitive algorithms. Massive databases may be processed more easily thanks to the distributed system, which also ensures effective computing and analyses in real time. The approach entails gathering information from a heterogeneous student body, then analyzing it and extracting features to find meaningful trends. Neural networks and combination techniques are examples of sophisticated algorithms for machine learning that are used to create models of prediction that help with career suggestions. The results show how well cognitive computers capture the intricate interactions between variables that influence career choices. The flexibility of the distributed platform guarantees the system’s suitability for big colleges and universities, offering students individualized career counseling on a broad scale. This study offers a fresh method for comprehending and assisting students in their professional choices, which makes a significant contribution to the fields of data mining for education and career planning.

Functional magnetic resonance imaging (fMRI) has become one of the most important tools for decoding the human brain. Due to the characteristics of the functional MRI samples with high-dimensional features and few samples, there are some difficulties in classifying fMRI data. In this paper, a new algorithm based on transfer learning and the state space model to overcome a few samples is proposed for fMRI classification. First, the fMRI samples need to be preprocessed for data input. Second, the Pre-trainable Vision Transformer (Vit) and Mamba (Previt-Mamba) fusion models are used for feature extraction. Finally, the high-accuracy classification of the fMRI samples is realized by the classifier. The testing results show that Previt-Mamba achieved an average classification accuracy of 80.91% after conducting five-fold cross-validation. At the same time, based on this algorithm model, the mask matrix method is used for reverse research to find human brain region clusters related to human brain change decisions. Through the comparison of classification experiments, it is found that the parietal lobe and frontal lobe of the human brain may have a significant impact on the brain’s decision-making. These research results will help people explore the mysteries of the human brain and reveal the operating mechanism of the human brain.

The proliferation of technology has facilitated data accessibility, leading to an expansion in the range of criteria employed in decision problem design. This situation offers an advantage for making precise and rational decisions, but when it comes to managing spending, it becomes a disadvantage. Specifically, the expense of acquiring expert views utilized in the computation of criteria weights by subjective approaches experiences a substantial rise. Hence, decision-makers may employ objective methodologies to determine criterion weights. Nevertheless, objective methods provide a more limited range of choices compared to subjective methods. The study aims to utilize two widely recognized fundamental statistical approaches in order to enhance the capabilities of objective methods. One of the suggested approaches is the dissimilarity-based weighting method, which calculates the differentiation of values within the criteria. Another approach is the weighting method, which relies on the interquartile range. The methods were adapted as means of weighting criteria. Explanatory examples were provided, simulation-based comparisons were conducted, and ultimately applied to an actual data set. The data from each scenario were compared using the factorial analysis of variance method. The findings produced demonstrate that the proposed methods align with other objective methodologies. Furthermore, the proposed approaches were observed to take more time to finish the procedure compared to the Entropy and Standard Deviation methods, but less time compared to the Critic and Merec methods. Consequently, the suggested techniques are introduced as alternative approaches derived from established fundamental statistical procedures, which are straightforward to comprehend and valuable for professionals.

This research focuses on developing a new decision-making model to evaluate school reopening strategies during the COVID-19 pandemic. The model integrates deep learning and factor analysis to address the urgent need to restart educational services without worsening the health crisis. It starts by gathering time series data from various districts to apply deep learning for predicting virus dynamics, emphasizing feature extraction and hyperparameter optimization. The subsequent phase involves factor analysis to discover key factors influencing virus spread, using outputs from the deep learning step. Based on these factors, clustering methods then sort districts into controllable or vulnerable groups. The final stage combines these analyzes into a deterministic decision model aiding policymakers in crafting school reopening guidelines. The model identifies three primary controllable factors: infection growth rate, reduction in active cases, and lowered mortality rates. Clustering then reveals that three groups are controllable, enabling specific interventions. This model is noteworthy for considering causal links between pandemic metrics and its adaptability to diverse datasets across districts/subdistricts, offering a scalable solution for decision-makers. The results highlight the importance of local infection trends and tailored data in shaping policies, showing that strong predictive analytics and insight into significant factors are crucial for developing effective, safe school reopening plans.

In the domain of association rule mining, evaluating the interestingness of discovered rules plays a crucial role in extracting meaningful patterns. However, the context of interestingness poses challenges that call for improvements in rule evaluation. This study focuses on addressing this problem by enhancing the evaluation of interestingness in ontology-based association rules. In this study, we present the effective rule evaluation using the ontology (EREO) model, which aims to evaluate the interestingness of ontology-based association rules in the context of US birth data. The EREO model incorporates three levels of rule evaluation: the utilization of proposed effective measures, consultation with domain experts, and the utilization of AI-based methods. To indirectly evaluate the interestingness of ontology-based rules, we propose two effective measures: Ontology-based rule specificity (ORS) and ontology-based rule complexity (ORC). Rule evaluation is further facilitated by domain experts and AI-based methods, employing an interestingness measurement scale (IMS). Furthermore, we compare the average interestingness scores obtained from ORS, ORC, and the EREO model with those derived from traditional interestingness measures. Our findings demonstrate that the proposed interestingness measures consistently outperform the traditional ones, as indicated by higher average scores. Additionally, we observe a positive relationship between the interestingness scores obtained using the three levels of the EREO model. Overall, this study effectively showcases the efficacy of ontology-based association rule evaluation in improving the quality of discovered rules and supporting informed decision-making processes.

This study explored the electroencephalography (EEG) dynamics during a chemistry-related decision-making task and further examined whether the correctness of the decision-making performance could be reflected by EEG activity. A total of 66 undergraduate students’ EEG were collected while they participated in a chemistry-related decision-making task in which they had to retrieve the relevant chemistry concepts in order to make correct decisions for each task item. The results showed that it was only in the anterior cingulate cortex (ACC) cluster that distinct patterns in EEG dynamics were displayed for the correct and incorrect responses. The logistic regression results indicated that ACC theta power from 300ms to 250ms before stimulus onset was the most informative factor for estimating the likelihood of making correct decisions in the chemistry-related decision-making task, while it was the ACC low beta power from 150ms to 250ms after stimulus onset. The results suggested that the ACC theta augmentation before the stimulus onset serves to actively maintain the relevant information for retrieval from long-term memory, while the ACC low beta augmentation after the stimulus onset may serve the function of mapping the encoded stimulus onto the relevant criteria that the given participant has held within his or her mind to guide the decision-making responses.

To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain–computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier. Then we proposed a new method to set the two constrained thresholds in the sequential probability ratio test (SPRT) based on the cumulative curve and a desired expected stopping time. As a result, it balanced the decision time of each class, and we term it balanced threshold SPRT (BTSPRT). The properties of the method were illustrated on 14 subjects’ recordings from offline and online tests. Results showed the average maximum accuracy of the proposed method to be 83.4% and the average decision time of 2.77s, when compared with 79.2% accuracy and a decision time of 3.01s for the sequential Bayesian (SB) method. The BTSPRT method not only improves the classification accuracy and decision speed comparing with the other nonsequential or SB methods, but also provides an explicit relationship between stopping time, thresholds and error, which is important for balancing the speed-accuracy tradeoff. These results suggest that BTSPRT would be useful in explicitly adjusting the tradeoff between rapid decision-making and error-free device control.

The price risk of fresh agricultural products has been a significant topic in recent years. Taking the two-level fresh agricultural product supply chain as the research object, this paper studies the optimal ordering and coordination of supply chain based on two-period price, wholesale price and option contract. The optimal order decision of the retailer at the single period price and the optimal decision corresponding to the supplier are obtained when the output of the supplier is uncertain under decentralized decision-making. The range of penalty cost parameter that avoids supplier default is also obtained. The effect of two-period price on the optimal order decision and supply chain profits is discussed when the production yield of the supplier is fixed. Cost-sharing contract is introduced to increase the order quantity and achieve coordination because the option contract cannot completely make the supply chain coordination with two-period price. This paper provides a low-cost approach that can be applied in fresh agricultural supply chain to solve financing and order problems.

Intelligent algorithms have shown promise in supporting marketing strategy decisions through data mining. However, existing methods have primarily relied on expertise, lacking autonomous decision-making abilities. Consequently, a marketing strategy decision model based on particle swarm optimization and multi-objective programming is proposed. This study first explores the potential for integrating particle optimization and multi-objective programming models partially, and then assesses the overall effectiveness of each marketing strategy by defining a fitness function. Subsequently, the particle swarm optimization algorithm is employed to search for and optimize decision variables to identify the optimal combination of marketing strategies. Finally, several simulation experiments are conducted using external real data. The research findings indicate that the algorithm’s error rate in this study was initially 0.23. However, after 500 training sessions, it decreased to 0.08 and maintained a relatively low level. The proportion of marketing strategy revenue increased by 15.2 percentage points between 0 and 100 training sessions, then remained relatively stable at over 30%. Its revenue proportion continued to rise during the training process, significantly surpassing that of other algorithms.

Symmetries in the external world constrain the evolution of neuronal circuits that allow organisms to sense the environment and act within it. Many small “modular” circuits can be viewed as approximate discretizations of the relevant symmetries, relating their forms to the functions they perform. The recent development of a formal theory of dynamics and bifurcations of networks of coupled differential equations permits the analysis of some aspects of network behavior without invoking specific model equations or numerical simulations. We review basic features of this theory, compare it to equivariant dynamics, and examine the subtle effects of symmetry when combined with network structure. We illustrate the relation between form and function through examples drawn from neurobiology, including locomotion, peristalsis, visual perception, balance, hearing, location detection, decision-making, and the connectome of the nematode Caenorhabditis elegans.

Biomedical research becomes increasingly multidisciplinary and collaborative in nature. At the same time, it has recently seen a vast growth in publicly and instantly available information. As the available resources become more specialized, there is a growing need for multidisciplinary collaborations between biomedical researchers to address complex research questions. We present an application of a data mining algorithm to genomic data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and interpreting biomedical data. Through the proposed approach, researchers can easily decide about which data repositories should be considered, analyze the algorithmic results, discuss the weaknesses of the patterns identified, and set up new iterations of the data mining algorithm by defining other descriptive attributes or integrating other relevant data. Evaluation results show that the proposed approach facilitates users to set their research objectives and better understand the data and methodologies used in their research.

In this paper we present a new neuroeconomics model for decision-making applied to the Attention-Deficit/Hyperactivity Disorder (ADHD). The model is based on the hypothesis that decision-making is dependent on the evaluation of expected rewards and risks assessed simultaneously in two decision spaces: the personal (PDS) and the interpersonal emotional spaces (IDS). Motivation to act is triggered by necessities identified in PDS or IDS. The adequacy of an action in fulfilling a given necessity is assumed to be dependent on the expected reward and risk evaluated in the decision spaces. Conflict generated by expected reward and risk influences the easiness (cognitive effort) and the future perspective of the decision-making. Finally, the willingness (not) to act is proposed to be a function of the expected reward (or risk), adequacy, easiness and future perspective. The two most frequent clinical forms are ADHD hyperactive(AD/HDhyp) and ADHD inattentive(AD/HDdin). AD/HDhyp behavior is hypothesized to be a consequence of experiencing high rewarding expectancies for short periods of time, low risk evaluation, and short future perspective for decision-making. AD/HDin is hypothesized to be a consequence of experiencing high rewarding expectancies for long periods of time, low risk evaluation, and long future perspective for decision-making.

In group decision making (GDM) processes, prior to the selection of the best alternative(s), it would be desirable that experts achieve a high degree of consensus or agreement between them. Due to the complexity of most decision making problems, individuals' preferences may not satisfy formal properties. ‘Consistency’ is one of such properties, and it is associated with the transitivity property. Obviously, when carrying out a rational decision making, consistent information, i.e. information which does not imply any kind of contradiction, is more appropriate than information containing some contradictions. Therefore, in a GDM process, consistency should also be sought after. In this paper we present a consensus model for GDM problems that proceeds from consistency to consensus. This model integrates a novel consistency reaching module based on consistency measures. In particular, the model generates advice on how experts should change their preferences in order to increase their consistency. Also, the consensus model is considered adaptive because the search for consensus is adapted to the level of agreement achieved at each consensus round.

In e-commerce applications, the magnitude of products and the diversity of venders cause confusion and difficulty for the common consumer to choose the right product from a trustworthy vender. Although people have recognized the importance of feedback and reputation for the trustworthiness of individual venders and products, they still have difficulty when they have to make a shopping decision from a massive number of options. This paper introduces fuzzy logic into rule definition for users' preferences and designs a novel agent-based decision system using fuzzy rules. This system can help users to find the right product recommendation from a trustworthy vender following users' own preferences.

In this paper, a descriptive decision-making model under uncertainty is proposed which incorporates two types of decision attitudes for uncertainty; one is an attitude about ignorance (optimism/pessimism) and the other one is about risk (risk-seeking and risk-aversion). At first, Evidential Decision Making Problem (EDMP) has been defined where Dempster-Shafer Theory (DST) has been used to represent uncertainty. Then probability approximation approach of solving EDMP is shown. For deciding the decision weights in different attitudes of decision maker, Ordered Weighted Averaging (OWA) operator has been used. Later on, Prospect Theory has been applied to accomplish a descriptive decision-making model. To show the effectiveness of our approach, a real life decision problem of travelers' route choice from a set of alternatives has also been provided.

Uncertainty and its imposed risk have significant impacts on decision-making. However, both are disregarded in many trust-based applications. In this paper, we propose a risk-aware approach to explicitly take uncertainty of trust and its effects into account. Our approach consists of a trust, a confidence, and a risk model. We do not prescribe a specific trust model, and any probabilistic trust model can be empowered by our approach. The confidence model calculates the uncertainty of the trust model in the form of a confidence interval, and is independent of the inner-workings of the trust model. This interval is used by the utility-based risk model which assesses the effects of uncertainty on trust-based decisions. We evaluated our approach by a four-state HMM-based simulated trustee, and employed the Beta, HMM and evidence-based trust models. We proposed and compared different methods for calculating confidence intervals, as well as methods for determining the risk and opportunity of a trust-based interaction. The results demonstrate how our approach should be used to improve the correctness of decision-making in trust-based applications. According to the statistical analysis of the simulation results, confidence intervals can properly represent the trust value and its uncertainty, and strongly improve trust-based decisions.

This article uses theoretical approaches from cognitive psychology to examine the basis for entrepreneurial alertness and to connect it to existing theories of attention in strategic management and decision-making. It thereby provides a theoretical basis for understanding how entrepreneurial alertness leads the individual to pay attention to new opportunities. A model is developed to show how attention and entrepreneurial alertness work together to support the recognition or creation of opportunities. Entrepreneurial alertness is believed to be a manifestation of differences in the schemata and cognitive frameworks that individuals use to make sense of changes in the environment. This suggests that entrepreneurial alertness mediates the impact of observed phenomena upon the situated attention of individual decision-makers.

Entrepreneurs are a product of their social environment. The manner by which they perceive opportunities; access or process information; and make decisions is, influenced by both social interaction, and their social background. Using insights from Socially Situated Cognition (SSC) theory, that posits one’s social environment can have a normative or informative effect on decision-making process we consider proximal social factors influencing the decision-making processes of student entrepreneurs. We propose that entrepreneurial education, networking, and incubation spaces provide direct information to students to aid entrepreneurial decision-making, and indirect informational cues that are situational, synergistic and omnipresent. Noting the multi-faceted and dynamic nature of the entrepreneurial journey of the student, we explore the potential effect of each of these factors on the student decision-making process. We discuss the implications of this inquiry from a researcher and educator perspective, and note the current challenges faced by student entrepreneurs in a socially distanced educational and entrepreneurial context. It is envisaged that this paper will serve as the basis for further thought and empiricism.

Downtime of rotating equipment in large petrochemical plants often led to serious or even disastrous safety and environmental accidents, which generally stem from inadequate maintenance or incapability of failure prediction. In order to allocate maintenance resources rationally and improve the reliability, availability and safety of equipment, a kind of risk- and condition-based maintenance decision-making and task optimizing system for rotating equipment in large petrochemical plants is established in this paper. Using real-time database, web service and service-oriented architecture (SOA), a risk- and condition-based maintenance decision-making system architecture is developed to provide a unified data structure and man–machine interface, which integrates reliability-centered maintenance (RCM), condition monitoring system (CMS) and manufacturing executive system (MES) together. Risk assessment and condition monitoring technology is applied to form maintenance decision making, such as to determine the priority maintenance level, to optimize maintenance content, and to determine the right maintenance time. Based on the decision-making system, the risk rank and degradation trend of failure characteristics are used to support the decision making and to optimize maintenance tasks. The result of an engineering case shows that the maintenance decision-making based on the risk assessment and condition monitoring can lower the operational risk while enhancing the reliability, availability and safety.