Smog poses a direct threat to human health and the environment. Addressing this issue requires understanding how smog is formed. While major contributors include industries, fossil fuels, crop burning, and ammonia from fertilizers, vehicles play a significant role. Individually, a vehicle’s contribution to smog may be small, but collectively, the vast number of vehicles has a substantial impact. Manually assessing the contribution of each vehicle to smog is impractical. However, advancements in machine learning make it possible to quantify this contribution. By creating a dataset with features such as vehicle model, year, fuel consumption (city), and fuel type, a predictive model can classify vehicles based on their smog impact, rating them on a scale from 1 (poor) to 8 (excellent). This study proposes a novel approach using Random Forest and Explainable Boosting Classifier models, along with SMOTE (Synthetic Minority Oversampling Technique), to predict the smog contribution of individual vehicles. The results outperform previous studies, with the proposed model achieving an accuracy of 86%. Key performance metrics include a Mean Squared Error of 0.2269, R-Squared (R²) of 0.9624, Mean Absolute Error of 0.2104, Explained Variance Score of 0.9625, and a Max Error of 4.3500. These results incorporate explainable AI techniques, using both agnostic and specific models, to provide clear and actionable insights. This work represents a significant step forward, as the dataset was last updated only five months ago, underscoring the timeliness and relevance of the research.

Deep learning has transformed image classification tasks across many domains, including medical diagnostics. Medicine wrappers, boxes, and strips often contain valuable but complex information that can be difficult to read and comprehend manually. This complexity drives users to seek additional knowledge online. However, traditional search engines often present a large volume of results, requiring users to manually filter through multiple links to find relevant information, which can be time-consuming. To address this issue, we propose a lightweight pipeline that leverages MobileNetV2 for image classification and Optical Character Recognition (OCR) for extracting text content from medicine packaging. The extracted text is processed using the RAKE algorithm to identify significant keywords, which are then matched with relevant URLs through a Google Search API. To ensure relevance, retrieved links are ranked using ROUGE scores. Performance metrics demonstrate the model's efficiency, with ROUGE-1 achieving 90% Recall, 95% F1-Score, and 90% Accuracy, and ROUGE-L achieving 83% Recall, 91% F1-Score, and 83% Accuracy. The pipeline was trained and validated on a curated dataset of 3,000 real-world medicine packaging images, publicly available on GitHub. These results highlight the novelty and practicality of our solution for automating medical information retrieval from packaging, using an interpretable and scalable deep learning-driven approach.

Companies with diverse product offerings rely on customer reviews to gauge product reception. Following a purchase, customers often share their opinions on the website. Prospective buyers, prior to deciding, typically peruse these reviews to inform their choices. Analysing such feedback, whether positive or negative, holds paramount importance for companies seeking to improve product quality. Researchers are actively exploring methods to categorize comments based on sentiment scores. Notably, customers may express their reviews in Arabic text. Despite challenges such as the structure and morphology of Arabic text, a scarcity of machine-readable Arabic dictionaries, and limited tools for handling Arabic text, minimal progress has been made in the analysis of Arabic reviews. While some attempts have been undertaken, they have achieved suboptimal accuracy. In response, the authors propose a hybrid deep learning model comprising a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with GlobalMaxPooling. Through multiple iterations, the authors fine-tuned the proposed model and applied it to the publicly available Arabic Reviews dataset, achieving a notable 95% accuracy, precision, recall, and F1 score. The results indicate that, when compared to alternative models, the proposed model exhibits superior accuracy.

Hasan J. Alyamani, Shakeel Ahmad, Asif Hassan Syed, Sheikh Muhammad Saqib, and Yasser D. Al-Otaibi. International Journal of Fuzzy Logic and Intelligent Systems 2021;21:409-22. https://doi.org/10.5391/IJFIS.2021.21.4.409

Due to the increasing popularity of posting evaluations, sentiment analysis has grown to be a crucial area of study. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and GRU (Gated Recurrent Unit). Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. To discover the optimal deep learning methodology for the given data, authors here proposed many deep learning methodologies for text data on sentiment analysis. A publicly available dataset including both positive and negative reviews on LSTM, CNN, RNN, and GRU was used in the experiments, and the findings showed that CNN had the highest accuracy compared to the other models. Based on the experimental results of CNN, it was found that prediction from the proposed work exhibited a significant improvement over existing work.