Background: To offer a transparent decision support system able of classifying tweets' sentiment into positive, neutral, and negative sentiment and explains the prediction result by XAI techniques Methods: We started by data preprocessing phase. For data representation, we used TF-IDF, and we applied four machine-learning algorithms including Naive Bayes, random forest, logistic regression, and support vector machine, as well as four deep learning RNN, LSTM, GRU, and Bi-directional RNN. To raise model trust, we used LIME and SHAP to improve model explainability. Findings: The empirical findings show that the Logistic Regression model and SVM model using the TF-IDF feature extraction approach have the best performance when compared to the other models, with an average accuracy of 84% and 86% respectively. The data balancing step pushed the accuracy of the Random Forest model from 47% to 73%, other models slightly changed. The performance of deep learning models was better than traditional machine learning models, LSTM and GRU achieve approximately 78%, and Bi-directional RNN achieve 79% for dataset 2. Novelty and applications: we propose a highly accurate approach for SA which has been tested on two datasets. Also, to increase trust in model prediction, we explain the predicted sentiment.

Leukemia cancer poses a risk to life as acute or chronic leukemia can manifest themselves more severe symptoms. The most frequent type of leukemia cancer is acute lymphoblastic leukemia (ALL). ALL affects about 20% of adult leukemias and presents in 80% of childhood leukemias. ALL diagnosing is very complex that requires labor-intensive, sophisticated procedures. One of the most important criteria of a healthcare system is to give the patient the best possible care based on an examination of their medical history, lifestyle choices, and any molecular trait variability. Several intelligent technologies that are based on machine learning and data-driven methods have been developed to address these problems. this paper examines statistical and machine learning methods. We also provide a trustworthy cloud-based data storage paradigm and a safe Android-based architecture for gathering patient data. The paper introduces the Leu-Life, a m-health android application that uses machine learning methods to detect leukemia cancer along with providing a set of features that helps in managing and facilitating life of leukemia cancer patients. The discussion will conclude with a predictive algorithm that may categorize leukemia cancer based an input of a blood file.

No abstract

Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second.

No abstract