The 4.0 industry revolution and the prevailing technological advancements have made industrial units more intricate. These complex electro-mechanical units now aim to improve efficiency and increase reliability. Downtime of such essential units in the current competitive age is unaffordable. The paradigm of fault diagnostics is being shifted from conventional to proactive predictive approaches. As a result, Condition-based Monitoring and prognostics are now essential components of complex industrial systems. This research is focused on developing a fault prognostic system using Long Short-Term Memory for rolling element bearings because they are a critical component of industrial systems and have one of the highest fault frequencies. Compared to other research, feature engineering is minimized by using raw time series sensor data as an input to the model. Our model achieved the lowest root mean square error and outperformed similar research models where time domain, frequency domain, or time-frequency domain features were used as input to the model. Furthermore, using raw vibration data also enabled better generalization of the model. This has been confirmed by evaluating the performance of the developed model against vibration data generated by distinct sources, including hydro and wind power turbines.

Hydroelectricity, being a renewable source of energy, globally fulfills the electricity demand. Hence, Hydropower Plants (HPPs) have always been in the limelight of research. The fast-paced technological advancement is enabling us to develop state-of-the-art power generation machines. This has not only resulted in improved turbine efficiency but has also increased the complexity of these systems. In lieu thereof, efficient Operation & Maintenance (O&M) of such intricate power generation systems has become a more challenging task. Therefore, there has been a shift from conventional reactive approaches to more intelligent predictive approaches in maintaining the HPPs. The research is therefore targeted at developing an artificially intelligent fault prognostics system for the turbine bearings of an HPP. The proposed method utilizes the Long Short-Term Memory (LSTM) algorithm in developing the model. Initially, the model is trained and tested by bearing vibration data from a test rig. Subsequently, it is further trained and tested with realistic bearing vibration data obtained from an HPP operating in Pakistan via the Supervisory Control and Data Acquisition (SCADA) system. The model demonstrates highly effective predictions of bearing vibration values, achieving a remarkably low RMSE.

Protection of medical images over the internet is a challenging task regarding its communication and usefulness. This work proposes a novel mechanism for the protection of DICOM images. The technique provides the facility to embed the iris (image) as a watermark to protect its integrity. After retrieval of the watermark, the mechanism is so robust that it recognizes the iris efficiently and correctly. Similarly, the system protects the Region of Interest (ROI) before the extraction of watermark as it is quite difficult to find volume, visualize or analyze the ROI. The technique not only protects the watermark but also gives the protection to ROI, which may be different in various medical images. Experimental results provide a deep view about the robustness of the technique in real time against both the intentional and unintentional attacks. The empirical results also show that after extraction of the watermark, the system has the capability to accurately find the volume, visualize and analyze the ROI.

Speech Emotion Recognition (SER) plays a crucial role in Human–Computer Interaction (HCI) by enabling systems to interpret and respond to human emotions through speech analysis. This paper presents a Transformer-based SER framework that leverages the Wav2Vec2 model for self-supervised representation learning. Unlike conventional approaches relying on handcrafted acoustic features or shallow learning, our approach employs transfer learning to extract high-level contextual embeddings from raw audio. We integrate two benchmark datasets, RAVDESS and TESS, to improve generalization across diverse speakers and emotions, and further analyze system robustness by introducing varying levels of environmental noise. The proposed model achieves an accuracy of 79.01%, with balanced precision, recall, and F1-scores, demonstrating competitive performance compared with recent state-of-the-art SER models. The main contributions of this work are threefold: (i) a novel evaluation of Wav2Vec2 embeddings on combined RAVDESS–TESS data, (ii) a systematic assessment of noise robustness in Transformer-based SER, and (iii) a comprehensive benchmark that highlights the strengths and limitations of transfer learning in practical emotion recognition scenarios. These findings suggest broad applicability in voice assistants, call-center analytics, and mental health monitoring, while future extensions may incorporate multimodal data and advanced fine-tuning strategies to further enhance performance.