This study conducts a bibliometric analysis of data mining publications in the Scopus database, examining the evolution of the field from 2015 to 2024. The study examines the bibliometric structure of data mining in management. Analyzing 2,942 publications, the research identifies significant growth in data mining studies. It reveals gaps in integrating data mining with decision-making, artificial intelligence, forecasting, and sentiment analysis. Despite a large number of publications, interdisciplinary applications of data mining are limited. The scientific publication on data mining and its relationship with decision-making, artificial intelligence, forecasting, and sentiment analysis is found to be weak, showing significant research gaps in these areas. China and the USA are prominent contributors, indicating geographical concentration. The study highlights the need for broader interdisciplinary exploration in data mining beyond traditional areas, urging global researchers to diversify contributions. The analysis focuses solely on publications indexed in Scopus, potentially excluding relevant studies from other databases or sources. This study provides insights into the evolution of data mining research and identifies areas for further interdisciplinary exploration, contributing to the advancement of the field's boundaries.

This article aims to study technological intelligence in innovative startups in Algeria using Kerr’s model. Technological intelligence consists of four main dimensions: intelligent systems, competitive intelligence, market intelligence, and intelligent processes. To collect data, a questionnaire was distributed to a sample of 255 innovative startups in Algeria, and the data were analyzed using confirmatory factor analysis (CFA) with Smart PLS software. The results indicated that the two-dimensional model combining intelligent systems and competitive intelligence provided the best fit, with a relationship value of 0.605 between these two dimensions. On the other hand, the relationship between market intelligence and competitive intelligence was weak, with a value of 0.281, reflecting the limited use of analytical methods by startups to monitor competitors. Based on these findings, the study recommends that innovative startups in Algeria enhance their use of competitive intelligence and intelligent systems to improve decision-making processes. Additionally, these startups should make better use of available market technologies to develop their products and services, while focusing on continuous competitor analysis and identifying opportunities. In conclusion, technological intelligence is a strategic element for startups, helping them improve their performance and achieve a competitive edge in the changing business environment in Algeria.

Cyber-attacks become manifested as a series of behavioral patterns, but not as an event, and many current security regimes stay based upon a static detection and fixed trust implementation. Such incongruence restricts their capability to act in a dependable manner in fluctuating and unpredictable threat situations. The existing artificial intelligence-based cybersecurity products mainly focus on the detection performance. Due to this, such systems will still be vulnerable to false positives, erratic reactions, and degradation of performance over time. This paper proposes a behavior-sensitive cybersecurity model that brings together sequential behavioral modelling, risk-adaptive cryptography implementation, and integrity-guaranteed learning in an architecture with closed loops. The temporally structured patterns of activity are considered as behavioral risk, which allows making proportional, not binary, trust decisions. Cryptographic policies are adaptively changed based on the inferenced risk, whereas learning updates are explicitly secured to maintain the model reliability as time goes by. The experimental findings indicate that the proposed framework can obtain a detection accuracy of 96.7% and F 1-score of 96.0, as well as a false positive rate decreased to 3.1%. Moreover, the adaptive response latency is also decreased by a factor of about 20-30% relative to the representative baselines and also enhanced stability in response to adversarial noise. These results indicate behavior-based intelligence.

The recent explosion of cyber threats in big data ecosystems has exposed the vulnerability of black-box machine learning models that prioritize accuracy over explainability. Traditional cybersecurity mechanisms do not create a sense as to why a prediction is provided and the analysts are left with doubts and the response mechanisms are slow. This is not very transparent and therefore compromises the operational trust and the traceability of high-risk decisions in real-time defense infrastructures. Current explainable AI (XAI) methods, despite their usefulness, are mostly stagnant, not connected to the changing situation of network behavior and human monitoring. They seldom inculcate the feedback mechanisms that can adjust the model explanations with new threat patterns. The research paper introduces an Explainable Artificial Intelligence and Data-Driven Security Analytics Framework that is hybrid and serves to combine global interpretability with SHAP, local reasoning with LIME, and adaptive refinement with an analyst-in-loop feedback layer. The architecture converts the threat detection to a self-fixing transparent process in which the analytical reasoning is dynamically developed with the data flow. In experimental tests of less than 10k concurrent traffic conditions, experimental results indicate that the system had a detection accuracy of 98.4 and Spearman correlation (0.91) between predicted risk scores and real levels of severity with a quantifiable 3.9% stability improvement over the similar XAI-based systems of intrusion detection. The combination of explainability with real-time analytics will further enhance the accuracy of detecting cyber threats and its interpretability reliability as well. The findings indicate the essential change to credible, self-explanatory, and adaptive security intelligence of the next-generation data networks.

Social commerce stands as a pivotal strategy amidst the modern retail environment. However, the distinct cultural and economic landscapes of different nations may lead to variations in understanding shopper behavior. This study endeavors to delve into the efficacy of shopping motivation theory, trust theory, and the information quality model in elucidating purchase intent, all within a comprehensive research framework. Through a survey conducted within the Jordanian context, it was revealed that utilitarian motivations and trust in e-vendors exert a positive influence on customers' inclination to purchase in the context of social commerce. Interestingly, hedonic motivations and the quality of electronic word-of-mouth (eWOM) were found to have no effect on purchase intention. Furthermore, trust in e-vendors was identified to mediate the relationship between eWOM quality and purchasing intention, manifesting as an indirect-only association. The paper concludes with a discussion on the contributions and limitations of the research.

Advancements in technology have had a profound impact on the way we learn, teach, and access knowledge. From online learning platforms to interactive educational games and virtual reality simulations, technology has transformed the traditional classroom into a dynamic, engaging, and inclusive space for education. One of the promising advancements in the field of artificial intelligence technology is ChatGPT which offers personalized and effective learning experiences by providing students with customized feedback and explanation. The effect of ChatGPT must be compared with the effect of Google at the educational level since both present a source of information and explanation. Thus, this study aims at investigating the differences between these two learning sources to measure their effectiveness from different perspectives. The model proposed in this study was evaluated using the PLS-SEM approach, utilizing data collected from 153 university students in the UAE. The results of this evaluation indicate that the GPT (Generative Pre-trained Transformer) has a significant impact on user acceptance, mediated by information quality, system quality, perceived learning value, and perceived satisfaction. These factors play a crucial role in determining users' acceptance of the GPT. However, it is important to note that some aspects of the model were not supported, suggesting that they do not have a significant predictive effect on the use of ChatGPT. Nonetheless, the findings of this study contribute to the existing literature on AI and environmental sustainability, providing valuable insights for practitioners, policymakers, and AI product developers. These insights can help guide the development and implementation of AI technologies in a way that aligns with users' needs and preferences while considering the larger environmental context.

The classification of large-scale textual dataset is associated with a huge number of instances and millions of features which must be discriminated between large numbers of categories. The task requires the utilization of a defined hierarchy structure and tools that automatically classify instances within the hierarchy known as Large Scale Hierarchical Text Classification (LSHTC). Predicting the labels of instances by the employed classifiers is challenging due to the high number of features. Furthermore, the existing Dimensional Reduction (DR) approaches in cooperation with the LSHTC framework are still quite inefficient. In such a problem, an effective Hierarchical Dimensional Reduction approach can be advantageous in improving the performance of the LSHTC. Therefore, in this paper, we enhance the performance of LSHTC by proposing a Multi-stage Hierarchical Dimensional Reduction (MHDR) approach based on Modified Feature Hashing (MFH) and Hierarchical Bi-Filtering (HBF) method. In addition to alleviating bad collision and result discrepancy, experimental results show that the proposed approach has achieve the best performance in terms of micro-f1 and macro-f1 by recording average scores of 58.47% and 54.77% using TD-SVM, and average scores of 51.14% and 48.70% using TD-LR, respectively. The method also achieved 11% speed-up than the approaches compared.