Digital transformation in energy sector organizations has huge benefits but also exposes them to cybersecurity challenges. In this paper, we carried out a systematic literature review on cybersecurity challenges and issues in the energy domain. Energy-associated assets are very critical for any nation and cyber-attacks on these critical infrastructures can result in strategic, financial, and human losses. We investigated research papers published between 2019 and 2024 and categorized our work into three domains: oil and gas sector, the electricity sector, and the nuclear energy sector. Our study highlights that there is a need for more research in this important area to improve the security of critical infrastructures in the energy sector. We have outlined research directions for the scientific community to further strengthen the body of knowledge. This work is important for researchers to identify key areas to explore as well as for policymakers in energy sector organizations to improve their security operations by understanding the associated implications of cybersecurity.

Neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) pose a significant global healthcare burden due to challenges in early diagnosis. This study investigates the clinical generalizability of machine learning (ML) and deep learning (DL) models for early AD and PD classification across multiple data modalities. A dual-dataset framework was employed, combining global benchmarks (ADNI, PPMI, OASIS, and UCI voice) with local clinical data from King Fahd Hospital of the University (KFHU) in Saudi Arabia. We evaluated ensemble methods, SVMs, neural networks, CNNs, and LSTMs. On structured global data, tree-based ensembles achieved the best performance, with Random Forest reaching 88.24% accuracy for PD and Gradient Boosting achieving 94.44% for AD. For neuroimaging, an LSTM on CNN features attained 98.68% accuracy on a curated MRI dataset. A critical finding was a substantial generalization gap: models excelling on global data showed markedly reduced performance on local KFHU data, with AUC values between 0.50 and 0.77. This degradation is attributed to real-world clinical challenges including severe class imbalance, diagnostic uncertainty in EHRs, and heterogeneous feature representations. The results underscore that data quality and modality are often more consequential than algorithmic complexity. This study provides a reproducible validation framework, highlights the necessity of institution-specific evaluation, establishes performance benchmarks for Saudi healthcare (with the caveat that local sample sizes remain small and the results should be interpreted as exploratory), and demonstrates the use of explainable AI to validate clinical relevance.