The analysis of complex building time-series for actionable insights and recommendations remains challenging due to the nonlinear and multi-scale characteristics of energy data. To address this, we propose a framework that fine-tunes visual language large models (VLLMs) on 3D graphical representations of the data. The approach converts 1D time-series into 3D representations using continuous wavelet transforms (CWTs) and recurrence plots (RPs), which capture temporal dynamics and localize frequency anomalies. These 3D encodings enable VLLMs to visually interpret energy-consumption patterns, detect anomalies, and provide recommendations for energy efficiency. We demonstrate the framework on real-world building-energy datasets, where fine-tuned VLLMs successfully monitor building states, identify recurring anomalies, and generate optimization recommendations. Quantitatively, the Idefics-7B VLLM achieves validation losses of 0.0952 with CWTs and 0.1064 with RPs on the University of Sharjah energy dataset, outperforming direct fine-tuning on raw time-series data (0.1176) for anomaly detection. This work bridges time-series analysis and visualization, providing a scalable and interpretable framework for energy analytics.

Large Language Models (LLMs) have been utilized extensively for cancer detection and diagnosis, benefiting from the vast textual data available in the medical field. However, these models often lack specific training on cancer-related data, which can limit their effectiveness in specialized medical contexts. Traditional methods typically deploy LLMs directly for diagnosis without incorporating domain-specific expertise, potentially compromising outcome reliability. This paper presents a comparative study focusing on the application of Low-Rank Adaptation (LoRA) to fine-tune LLMs for cancer-related tasks. LoRA modifies the self-attention and feed-forward layers of transformer architectures with low-rank matrices, allowing for specialized adaptation with fewer parameters. A general LLM was fine-tuned using LoRA on a dataset derived from four annotated books on breast cancer. The performance of this LoRA-enhanced model was compared against several baseline LLMs fine-tuned through traditional methods. It was found that the LoRA-fine-tuned Biomstral-7B demonstrated the best training loss of 0.91025 and validation loss of 0.912722 scores, indicating enhanced integration of domain-specific knowledge. The potential of adaptive fine-tuning techniques like LoRA in specialized applications is highlighted, suggesting further exploration into their effectiveness across various complex domains requiring expert knowledge. Further research is encouraged to assess such approaches’ broader applicability and impact in diverse AI applications.

Malicious Software (Malware) is a primary element of many cyber crimes and attacks, causing massive damage and financial losses to organizations. Accordingly, malware detection and classification has become a crucial security field resulting in various attempts from researchers to develop solutions including signature-based approaches to Artificial Intelligence (AI) models showing their efficacy in detecting malware. Yet, users still have reservations about AI models due to the ambiguity and mysteriousness of their decisions resulting from their black-box nature. To address this problem, this paper proposes to develop explainable AI models to classify the malware families robustly. The proposed method uses text classification of API call sequences generated by these families by considering two datasets. A weighted training methodology is used to solve the dataset imbalance problem. Subsequently, the method presents an eXplainable AI (XAI) approach to establish an understandable and interpretable relationship between the API call sequences and the Bidirectional Encoder Representations from Transformers (BERT) model decisions, which enhance the model accountability and usability by employing the Local Interpretable Model-Agnostic Explanation (LIME) and the Shapley Additive Explanations (SHAP) platforms. The results reveal that the BERT model outperforms its counterparts considering F1 score, Balanced Accuracy (BA), and Matthews correlation coefficient (MCC).

Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first investigated, including data collection devices and public data sets. Next, event-based and non-event-based NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves outperformance for appliance identification in comparison with other recent techniques. Accordingly, high identification accuracies are attained on the GREEND, UK-DALE, and WHITED data sets, where 99.54%, 98.78%, and 100% rates have been achieved, respectively, using the proposed 2D-PEP descriptor.

Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end-users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one-class support vector machine, namely UAD-OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD-M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule-based algorithm is introduced to extract the micromoments representing the intent-rich moments, in which the end-users make decisions to consume energy; and (iii) an improved K-nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD-M2 achieves both a highest abnormality detection performance and real-time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real-world data set collected at the Qatar University energy lab.

The recent advances in artificial intelligence namely in machine learning and deep learning, have boosted the performance of intelligent systems in several ways. This gave rise to human expectations, but also created the need for a deeper understanding of how intelligent systems think and decide. The concept of explainability appeared, in the extent of explaining the internal system mechanics in human terms. Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable to increase user trust and improve the acceptance of recommendations. In this study, we focus on a context-aware recommendation system for energy efficiency and develop a mechanism for explainable and persuasive recommendations, which are personalized to user preferences and habits. The persuasive facts either emphasize on the economical saving prospects (Econ) or on a positive ecological impact (Eco) and explanations provide the reason for recommending an energy saving action. Based on a study conducted using a Telegram bot, different scenarios have been validated with actual data and human feedback. Current results show a total increase of 19% on the recommendation acceptance ratio when both economical and ecological persuasive facts are employed. This revolutionary approach on recommendation systems, demonstrates how intelligent recommendations can effectively encourage energy saving behavior.