Social connections developed within narrow cultural limits, such as physical locations, prior to the invention of information and communication technology (ICT). Social technologies have revolutionised online social networks, user-generated content, and rich human behaviour data. Online social networks (OSN) promote social interaction but also trolling, hate speech, and cyberbullying. NLP-based automatic detection is essential to ending cyberbullying. A deep learning algorithm is suggested to detect cyberbullying aggression in this work automatically. Pre-processing, feature extraction, feature selection, and classification are among the processes included in the suggested workflow. The initial pre-processing steps for the Twitter database include noise removal, tokenisation, and stemming. The features from the pre-processed database have been extracted using the SAE, TF-IDF, and other techniques. To choose the subset of characteristics, the SCOA is next applied. FWLSTM classifier is then given features. The K-nearest neighbour (KNN), ANN, random forest (RF), and EK-SVM classifiers are contrasted with the FWLSTM classifier. Results are evaluated using precision, recall (sensitivity), specificity, false positive, false discovery, miss, and accuracy.

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This study addresses the critical need for enhanced security in wireless sensor networks (WSNs), which are integral to modern infrastructure yet highly vulnerable to security breaches. We introduce a ground-breaking intrusion detection system (IDS) that employs recursive feature elimination (RFE) alongside advanced fuzzy classifiers, providing a robust solution against these security threats. The proposed IDS uniquely integrates RFE with three distinct fuzzy classifiers: the adaptive neuro-fuzzy classifier (ANFC) with a 97% accuracy rate, the fuzzy nearest neighbour classifier (FNNC) with 92% accuracy, and the fuzzy decision tree classifier (FDTC) achieving an exceptional 98% accuracy. This innovative approach leverages the strength of RFE in feature selection and the sophisticated pattern recognition capabilities of fuzzy logic. It significantly enhances the system's ability to accurately differentiate between normal operations and potential security threats in WSNs. The effectiveness of this IDS is highlighted by its remarkable accuracy rates, which are a direct result of the focused analysis of critical attributes facilitated by the RFE process. This research contributes significantly to bolstering the integrity and security of WSNs, presenting a notable methodological advancement in intrusion detection technologies.

This study employs the University of Bonn Dataset to address the importance of frequency information in EEG data and introduces a methodology utilising the short-time Fourier transform. The proposed method transforms conventional 1D EEG signals into informative 2D spectrograms, offering an approach for advancing the detection of neurological diseases. Integrating advanced CNN architectures with the conversion of EEG signals into 2D spectrograms forms the foundation of our proposed methodology. The 1D CNN model utilised in this study demonstrates exceptional performance metrics, achieving a specificity of 0.996, an overall test accuracy of 0.991, a sensitivity of 0.987, and an F1 score of 0.989. Shifting to the 2D approach discloses a slight reduction in accuracy to 0.987, sensitivity of 0.976, specificity of 0.988, and an F1 score of 0.97. This analysis provides nuanced insights into the performance of 1D and 2D CNNs, clarifying respective strengths in the context of neurological disease detection.

Industry 4.0, the new production standard, integrates small to medium-level devices for efficient automation. Secure communication among these devices requires protection from external attacks in IoT applications. The imminent threat of quantum attacks to prevailing public-key approaches necessitates a secure authentication architecture tailored for IoT devices. Furthermore, IoT devices' limited computing, storage, and energy resources demand a lightweight authentication mechanism. In response, hash-based signatures have been proposed as a post-quantum solution for Industry 4.0. Presented approach involves an in-depth analysis of collision-resistant hash functions for faster, memory-optimised authentication. Implementing a hash-based signature scheme through experiments, a 27.18% improvement has been achieved in key generation speed, particularly with Blake2s over popularly used SHA-256. These results affirm the efficiency of the proposed hash-based signature scheme, offering superior performance in time and memory utilisation for Industry 4.0 applications.

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In automated brain-computer interaction (BCI), EEG signals are essential. This research uses AI to detect epileptic seizures, employing data from the BONN dataset (UCI), CHB-MIT dataset (physionet server), and Bangalore EEG Epilepsy Dataset (BEED). The goal is to develop an automated system for accurate seizure detection using adaptive fast Fourier transform with non-uniform sampling (AIFFT-NS) and an improved deep belief network (IDBN) model to enhance classification accuracy. The AIFFT-NS model serves as a channel for transforming spectro-temporal data. Using various EEG datasets, a number of extensive experiments are carried out, resulting in the validation of the efficacy of the proposed approach. High accuracy metrics, with 96.16% for the BEED dataset, 99.41% for the BONN dataset, and 96.31% for the CHB-MIT dataset, represent the evidentiary outcomes. This study emphasises the critical function of AI-facilitated spectro-temporal EEG analysis within the domain of medical diagnostics, going beyond the realm of automated seizure onset classification.

One problem that reduces performance in data analysis is missing data. Improper imputation of missing data may lead to an incorrect prediction. In the internet of medical things (IoMT) era, when a lot of data is created every second and data utilisation is a major issue for healthcare providers, missing values must be managed well. The literature proposes many missing data imputation methods. However, excessive missing value instances diminish the number of complete examples in the collection. Imputing missing data with few complete instances will not improve results. The number of complete instances could be raised by considering the imputed item as a complete object and utilising it alongside the existing complete instances for future imputations. So this work introduces a new dual strategy-based missing data imputation (DS-MDI) approach for IoMT missing completely at random (MCAR) data. The proposed DS-MDI technique uses cube-root-of-cubic-mean and enhanced Levenshtein distance-based clustering (ELDC) with cluster-directed closest neighbour selection (CSNN). This approach imputes more items using imputed objects. The Kaggle Machine Learning Repository's cStick IoMT dataset was processed using the suggested technique. The DS-MDI algorithm outperforms current missing data imputation algorithms in accuracy, precision, recall, and F-measure.