In Industrial Internet of Things (IIoT) networks, securing device connectivity through effective intrusion detection systems is essential for maintaining operational integrity. This paper presents an adaptive hybrid framework for IIoT intrusion detection that combines Artificial Neural Networks (ANNs) with Genetic Algorithms (GA) for feature optimization. This study utilizes the dataset, which is widely recognized benchmark for intrusion detection research. The dataset comprises 625783 network traffic samples, classified into five categories: Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), User-to-Root (U2R), and Normal traffic. Initially, an ANN model was developed, yielding high accuracy but exhibiting signs of overfitting. To enhance generalization, we introduced L2 regularization, adjusted dropout rates, and optimized the learning rate, ultimately achieving a 99.7% validation accuracy with an AUC score of 0.9969. Additionally, Genetic Algorithms were employed to optimize feature selection, further refining the ANN’s input space to improve computational efficiency without sacrificing predictive power. After training over 50 epochs with early stopping, the model demonstrated exceptional robustness, achieving 99.5% accuracy on the test set, with precision and recall values of 0.97 and 0.98, respectively. This combination of ANN and GA yielded a highly efficient and sensitive framework, providing enhanced detection of anomalies in IIoT environments. The proposed hybrid model thus establishes a robust solution for real-time IIoT security, outperforming conventional detection systems through a strategic blend of neural learning and evolutionary optimization.

Depression is a prevalent mental health disorder characterized by persistent sadness, loss of interest, and impaired daily functioning. Wearable monitoring systems have emerged as promising tools for continuous mental health assessment; however, they face challenges such as data privacy concerns, misclassification risks, and limited ability to capture complex emotional states. To address these limitations, this study proposes a Self-Supervision-Enabled Compounded Multi-Modal Feature-Learning Network (S2-CFL) for depressive state classification using wearable sensor data and psychological self-reports. The framework integrates a Twin-Path Encoder–Decoder Network (TP-EDN) for extracting temporal features from raw signals and a Densely Connected Convolution Pyramidal Transformer Network (DC2-PTN) for learning spatial representations from signal-to-image transformations. A fusion mechanism combines multi-modal features to predict depressive states, valence, and arousal levels, while a Fine-Grained Emotion Classification Network (FGECN) is employed to categorize emotional states into multiple classes using supervised learning models. Experimental results demonstrate that the proposed multi-modal approach improves classification performance and provides interpretable insights into emotional and depressive patterns.

Higher education institutes (HEI) effect global sustainability goals in two key dimensions: the first being their own physical and environmental impact (due to factors like campus design, consumption of resources, waste production etc.). Secondly, they are a critical venue to impart education and awareness about sustainability principles that can be studied, discussed and refined to be applied in terms of policies and practice. Clear sustainability goals are the metrics through which progress in attainment of such goals can be assessed and tracked. While a variety of approaches are being employed to introduce sustainability both at an administrative and academic level, they face numerous challenges. Many of these challenges can either be mitigated or better managed with the use of modern technologies. In this article, we present an overview of various pivotal technologies that can used to assist in attaining sustainability goals in HEIs and it then defines a framework consisting of these technological tools that can be used in this context. Several use cases of technologies used to achieve sustainability goals in HEIs have been presented. Finally, several open challenges and future opportunities in the area of technology assisted sustainable higher education have been identified.