Traditional security methods fail to match the speed of evolving threats because Industrial Internet of Things (IIoT) technologies have become more widely adopted. A lightweight adaptive AI-based intrusion detection system (IDS) for IIoT environments is presented in this paper. The proposed system detects cyber threats in real time through an ensemble of online learning models that also adapt to changing network behavior. The system implements SHAP (SHapley Additive exPlanations) for model prediction explanations to allow human operators to verify and understand alert causes while addressing the essential need for trust and transparency. The system validation was performed using the ToN_IoT and Bot-IoT benchmark datasets. The proposed system detects threats with 96.4% accuracy while producing 2.1% false positives and requiring 35 ms on average for detection on edge devices with limited resources. Security analysts can understand model decisions through SHAP analysis because packet size and protocol type and device activity patterns strongly affect model predictions. The system underwent testing on a Raspberry Pi 5-based IIoT testbed to evaluate its deployability in real-world scenarios through emulation of practical edge environments with constrained computational resources. The research unites real-time adaptability with explainability and low-latency performance in an IDS framework specifically designed for industrial IoT security. The solution provides a scalable method to boost cyber resilience in manufacturing, together with energy and critical infrastructure sectors. By enabling fast, interpretable, and low-latency intrusion detection directly on edge devices, this solution enhances cyber resilience in critical sectors such as manufacturing, energy, and infrastructure, where timely and trustworthy threat responses are essential to maintaining operational continuity and safety.

Clustering is a significant approach in data mining, which seeks to find groups or clusters of data. Both numeric and categorical features are frequently used to define the data in real-world applications. Several different clustering algorithms are proposed for the numerical and categorical datasets. In clustering algorithms, the quality of clustering results is evaluated using cluster validation. This paper proposes an efficient clustering algorithm for mixed numerical and categorical data using re-clustering and cluster validation. Initially, the mixed dataset is clustered with four traditional clustering algorithms like expectation-maximization (EM), hierarchical cluster (HC), k-means (KM), and self-organizing map (SOM). These four algorithms are validated, and the best algorithm is selected for re-clustering. It is an iterative process for improving the quality of cluster results. The incorrectly clustered data is iteratively re-clustered and evaluated based on the cluster validation. The performance of the proposed clustering method is evaluated with a real-time dataset in terms of purity, normalized mutual information, rand index, precision, and recall. The experimental results have shown that the proposed reclust algorithm achieves better performance compared to other clustering algorithms.

Educational data mining is a research field that is used to enhance education system. Research studies using educational data mining are in increase because of the knowledge acquired for decision making to enhance the education process by the information retrieved by machine learning processes. Sentiment analysis is one of the most involved research fields of data mining in natural language processing, web mining, and text mining. It plays a vital role in many areas such as management sciences and social sciences, including education. In education, investigating students' opinions, emotions using techniques of sentiment analysis can understand the students' feelings that students experience in academic, personal, and societal environments. This investigation with sentiment analysis helps the academicians and other stakeholders to understand their motive on education is online. This article intends to explore different theories on education, students' learning process, and to study different approaches of sentiment analysis academics. Request access from your librarian to read this chapter's full text.