Environmental, Social, and Governance (ESG) indicators have become critical determinants in sustainable investment and regulatory decision-making. However, building models that deliver both high predictive accuracy and interpretability remains a major challenge. This paper presents an explainable ensemble learning framework that enhances the interpretability of ESG score prediction through rule extraction and pruning. The proposed approach applies a structured preprocessing, feature encoding, and Random Forest-based ensemble learning. Further, decision rules are extracted and locally pruned to eliminate redundancy while preserving predictive power. The refined rules are aggregated using a weighted voting mechanism for multi-class ESG classification. Experimental evaluation on firm-level ESG datasets demonstrates that the proposed approach achieves competitive predictive accuracy (AUC $=0.90$, Precision $=0.72$) while producing concise, humaninterpretable rules. enhancing transparency and actionable insights for sustainable finance.
A distribution transformer (DT) is an intrinsic element in the overall power system. With increasing energy demands, the system requires an effective forecasting method at the distribution transformer level. With the recent emergence of deep learning models and their adaptation to this task, forecasting has generally improved; however, when incorporating weather and load features, these models often overlook granular electrical parameters that could enhance prediction reliability. In this paper, this gap has been addressed by incorporating overlooked electrical parameters alongside conventional features. Afterwords, state-of-the-art deep learning models have been deployed, including the Recurrent Neural Network (RNN), which is beneficial for sequence modelling; the Bidirectional LSTM (Bi-LSTM), which can capture dependencies in both forward and backward directions; and the hybrid RNN-Bi-LSTM, which increases computational efficiency by incorporating bi-directional learning. To further extract both spatial and temporal features, the CNN-Bi-LSTM model is utilized, which leverages its deeper architecture to extract features, thereby improving pattern recognition. An extensive evaluation of proposed models was conducted on a dataset of 48,000 instances by MIRAD LESCO using standard performance metrics, including MSE, RMSE, MAE, MAPE, and R2. The results have been compared with other notable studies in the domain and indicate the superiority of the proposed method, specifically RNN-Bi-LSTM, which achieves a MAPE of 9.476% and captures both short- and long-term dependencies, thereby enhancing predictive accuracy for ShortTerm Load Forecasting (STLF) of distribution transformers. The proposed work enhances forecast accuracy at the DT level, enhancing load management and operational efficiency.
Digital twins integrate analytics and human expertise to create models that support informed healthcare decisions. In resource-limited areas, access to medical care is often limited by a lack of facilities, skilled professionals, and transportation. Wireless capsule endoscopy (WCE), being portable, reliable, and easy to use, has emerged as a superior alternative to traditional endoscopy, particularly for patients in remote areas. However, WCE generates large volumes of video data, requiring significant computation to analyze and extract relevant information. To address this challenge, we propose a video summarization scheme that identifies key frames while removing redundant content, preserving essential diagnostic information. Furthermore, we introduce a deep learning-based digital gastroenterologist twin for the automated classification of stomach-related pathological findings. The system reduces storage requirements without compromising critical information, enabling gastroenterologists to provide remote support. Experimental results demonstrate a performance improvement of at least 3% compared to state-of-the-art deep learning techniques, highlighting its potential for intelligent, scalable, and efficient gastroenterology care.
Approximately 1.13 billion websites span diverse domains like music, movies, health, video games, social media, entertainment, and more, making website classification highly valuable yet challenging. This research explores using Artificial Intelligence techniques to categorize websites into appropriate classes. The DMOZ dataset is one of the most reliable data sources for research focused on URLs, containing about 1.5 million URLs manually classified into different categories, though the distribution of URLs across different categories is highly unbalanced. To overcome this limitation, we collected 500,000 URLs from DomCop's Top 10 Million Websites and classified them using Large Language Models (LLMs), specifically LLaMA 3 3B. Numerous machine learning and deep learning models were trained on both datasets, including logistic regression, support vector machine, decision trees, random forest, CatBoost, XgBoost, LSTM, Bi-LSTM, and FastText. Different data handling and pre-processing techniques were also employed, including chunking for balancing and subdomain extraction to increase URLs per category. We report the most successful pipeline using the FastText model, achieving an average accuracy of 82%.
