Data-driven electrical energy efficiency management is the emerging trend in electrical energy forecasting and management. This fusion of data science, artificial intelligence, and electrical energy management has turned out to be the most precise and robust energy management solution. The Smart Energy Informatics Lab (SEIL) of the Indian Institute of Technology (IIT) conducted an experimental study in 2019 to collect massive data on university campus energy consumption. The comprehensive comparative study preparatory to the recommendation of the best candidate out of 24 machine learning algorithms on the SEIL dataset is presented in this work. In this research work, an exhaustive parametric and empirical comparative study is conducted on the SEIL dataset for the recommendation of the optimal machine learning algorithm. The simulation results established the findings that Bagged Trees, Fine Trees, and Medium Trees are, respectively, the best-, second-best-, and third-best-performing algorithms in terms of efficacy. On the contrary, a reverse ranking is observed in terms of efficiency. This is grounded in the fact that Bagged Trees is most effective algorithm for the said application and Medium Trees is the most efficient one. Likewise, Fine Trees has the optimum tradeoff between efficacy and efficiency.

In the face of rapidly evolving cyber threats, traditional security systems have proven inadequate for protecting critical infrastructure environments such as Industrial Control Systems (ICS). This study investigates the integration of machine learning (ML) techniques and cybersecurity knowledge graphs (CKGs) to enhance threat detection and situational awareness in complex cyber-physical ecosystems. By evaluating advanced context-aware detection models, including LSTM-Autoencoders and hybrid SVM-based systems, the research demonstrates significant improvements in anomaly detection accuracy and interpretability. Furthermore, the study explores how knowledge graphs—constructed from heterogeneous data sources—facilitate semantic reasoning and automate threat intelligence processing. Empirical evidence highlights the value of combining behavioral attributes with semantic context to identify and mitigate sophisticated attacks that evade conventional intrusion detection systems (IDS). Despite these advancements, the research also identifies operational challenges, including system integration, computational overhead, and limited scalability of AI models in real-world deployments. Recommendations are made for future implementation strategies that prioritize interoperability, explainability, and collaboration among interdisciplinary teams. Finally, potential directions for further research are proposed, such as the adoption of blockchain for secure data provenance and the expansion of CKG frameworks into domains like finance and smart healthcare. This work contributes to the growing body of knowledge advocating for intelligent, adaptive, and semantically enriched cybersecurity systems capable of responding proactively to dynamic threat landscapes.

Sepsis is responsible for approximately 270,000 deaths annually in the United States. Conventional scoring systems, such as SOFA and qSOFA, are largely reactive and do not effectively leverage longitudinal ICU data for early prediction. This study aims to develop a deep learning framework capable of predicting sepsis onset up to 6 h before Sepsis-3 criteria are met while also providing clinically interpretable temporal explanations. The PhysioNet/CinC 2019 Challenge dataset, comprising 1,552,210 patient hours from 40,336 ICU patients, was utilized. A temporal transformer encoder (TTE) was trained using 12-h look-back windows with 92 engineered features. Severe class imbalance (2.6% positive rate) was addressed through weighted random sampling and focal loss. Fivefold patient-level cross-validation was employed to prevent temporal leakage. Platt scaling was applied for probability calibration. Grad-CAM was adapted for temporal explainability, while SHAP was used for feature-level attribution. BiLSTM-Attention and XGBoost models served as baseline comparators. The TTE model achieved a cross-validated AUROC of $0.8320\pm 0.0032$ and an AUPRC of $0.1505\pm 0.0148$, significantly outperforming BiLSTM-Attention (AUROC: 0.7859) and XGBoost (AUROC: 0.7731; DeLong $p<0.0001$). Platt scaling reduced the expected calibration error from 0.3154 to 0.0017. The median alert lead time was 46.5 h (IQR: 21–84 h), with 95.3% of septic patients receiving alerts at least 3 h before onset. Grad-CAM analysis identified time steps $t-10$ and $t-9$ as the most predictive. However, high-severity patients (SOFA proxy $\ge 3$) demonstrated substantially reduced performance (AUROC: 0.257). The proposed TTE framework demonstrated strong and well-calibrated early sepsis prediction with substantial clinical lead time. The concentration of predictive signals 10–11 h prior to alert generation supports the feasibility of continuous automated ICU monitoring from admission onward. Reduced performance in high-severity patients highlights the need for severity-stratified modelling in future research.

Background/Objectives: Sepsis is responsible for approximately 270,000 deaths annually in the United States. Conventional scoring systems, such as SOFA and qSOFA, are largely reactive and do not effectively leverage longitudinal ICU data for early prediction. This study aimed to develop a deep learning framework capable of predicting sepsis onset up to 6 hours before Sepsis-3 criteria are met, while also providing clinically interpretable temporal explanations. Methods: The PhysioNet/CinC 2019 Challenge dataset, comprising 1,552,210 patient-hours from 40,336 ICU patients, was utilized. A Temporal Transformer Encoder (TTE) was trained using 12-hour look-back windows with 92 engineered features. Severe class imbalance (2.6% positive rate) was addressed through weighted random sampling and focal loss. Five-fold patient-level cross-validation was employed to prevent temporal leakage. Platt scaling was applied for probability calibration. Grad-CAM was adapted for temporal explainability, while SHAP was used for feature-level attribution. BiLSTM-Attention and XGBoost models served as baseline comparators. Results: The TTE model achieved a cross-validated AUROC of 0.8320±0.0032 and an AUPRC of 0.1505±0.0148, significantly outperforming BiLSTM Attention (AUROC: 0.7859) and XGBoost (AUROC: 0.7731; DeLong p < 0.0001). Platt scaling reduced the Expected Calibration Error from 0.3154 to 0.0017. The median alert lead time was 46.5 hours (IQR: 21–84 h), with 95.3% of septic patients receiving alerts at least 3 hours before onset. Grad-CAM analysis identified timesteps t − 10 and t −9 as the most predictive. However, high-severity patients (SOFA proxy ≥ 3)demonstrated substantially reduced performance (AUROC: 0.257). Conclusions: The proposed TTE framework demonstrates strong and well-calibrated early sepsis prediction with substantial clinical lead time. The concentration of predictive signals 10–11 hours prior to alert generation supports the feasibility of continuous automated ICU monitoring from admission onward. Reduced performance in high-severity patients highlights the need for severity-stratified modelling in future research.