Multi-dimensional Bayesian network classifiers: A survey

A structured framework for emotion-cognition dynamics: Implications for assessment and intervention

This paper presents a novel framework for understanding the interaction between cognitive and emotional processes, recognizing the complex and dynamic relationship between these two constructs. The framework categorizes cognitive functions into four distinct categories: Cold Cognition, Hot Cognition, Warm Cognition, and Cool Cognition. By distinguishing between the style of processing (intuitive vs. analytical) and the content of information (emotional vs. non-emotional), the framework provides a model for both assessment and intervention. For assessment, it helps categorize cognitive and emotional processes, enabling targeted evaluations based on specific processing styles and content. For interventions, it supports the development of training programs that address processing styles in relation to the target function, improving the effectiveness of therapeutic and developmental strategies. Overall, this framework has the potential to advance both theoretical understanding and practical applications in cognitive and emotional assessment and training.

Machine learning screening of biomass precursors to prepare biomass carbon for organic wastewater purification: A review

In the past decades, the amount of biomass waste has continuously increased in human living environments, and it has attracted more and more attention. Biomass is regarded as the most high-quality and cost-effective precursor material for the preparation carbon of adsorbents and catalysts. The application of biomass carbon has extensively explored. The efficient application of biomass carbon in organic wastewater purification were reviewed. With briefly introducing biomass types, the latest progress of Machine learning in guiding the preparation and application of biomass carbon was emphasized. The key factors in constructing efficient biomass carbon for adsorption and catalytic applications were discussed. Based on the functional groups, rich pore structure and active site of biomass carbon, it exhibits high efficiency in water purification performance in the fields of adsorption and catalysis. In addition, out of a firm belief in the enormous potential of biomass carbon, the remaining challenges and future research directions were discussed.

Multi-dimensional Classification via Selective Feature Augmentation

In multi-dimensional classification (MDC), the semantics of objects are characterized by multiple class spaces from different dimensions. Most MDC approaches try to explicitly model the dependencies among class spaces in output space. In contrast, the recently proposed feature augmentation strategy, which aims at manipulating feature space, has also been shown to be an effective solution for MDC. However, existing feature augmentation approaches only focus on designing holistic augmented features to be appended with the original features, while better generalization performance could be achieved by exploiting multiple kinds of augmented features. In this paper, we propose the selective feature augmentation strategy that focuses on synergizing multiple kinds of augmented features. Specifically, by assuming that only part of the augmented features is pertinent and useful for each dimension’s model induction, we derive a classification model which can fully utilize the original features while conduct feature selection for the augmented features. To validate the effectiveness of the proposed strategy, we generate three kinds of simple augmented features based on standard kNN, weighted kNN, and maximum margin techniques, respectively. Comparative studies show that the proposed strategy achieves superior performance against both state-of-the-art MDC approaches and its degenerated versions with either kind of augmented features.

Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network

Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller's role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native-Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.