Autonomous driving has reached significant milestones in research and development over the last few decades. There is growing attention in the area as the utilization of autonomous vehicles (AVs) assurances safer and more environmentally-friendly transportation systems. Adverse environmental conditions like dust or sandstorms, fog, heavy snow, and rain conditions are unsafe limitations on the function of the cameras by decreasing visibility and influencing driving safety. These limitations affect the performance of tracking and detection methods applied in traffic surveillance systems and autonomous driving applications. Using the fast development in mathematically powerful artificial intelligence (AI) approaches, AVs could sense their atmosphere using higher accuracy, make safer real-world decisions, and work consistently without user intervention. Therefore, this paper proposes a Complex Data Analysis for Adverse Weather Detection in Autonomous Vehicles Using Explainable Artificial Intelligence (CDAAWD-AVXAI) approach. The CDAAWD-AVXAI approach improves the safety and reliability of AVs by ensuring robust weather detection. Initially, the presented CDAAWD-AVXAI approach applies image pre-processing by utilizing the median filter (MF) model to reduce noise and enhance the quality of input images. The CapsNet model is employed for feature extraction to recognize spatial hierarchies and capture intricate patterns from complex visual data. Moreover, the temporal convolutional network (TCN) model detects adverse weather conditions. To further enhance performance, the hyperparameter tuning of the TCN model is performed by implementing the improved dung beetle optimization (IDBO) model. Finally, the XAI using LIME provides transparency and interpretability, allowing stakeholders to understand the decision-making process behind weather condition predictions. The stimulation study of the CDAAWD-AVXAI technique is performed under the DAWN dataset. The experimental validation of the CDAAWD-AVXAI technique portrayed a superior accuracy value of 98.83% over existing methods.

Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classification using a metaheuristic Genetic Algorithm (GA) and ensemble learning. The approach explores the effect of Arabic text representation on classification performance using both Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Moreover, it compares the performance of ensemble learning methods such as the Extra Trees Classifier (ETC) and Random Forest Classifier (RFC) against a Logistic Regression Classifier (LRC) as a single and ensemble classifier. We evaluate the approach on a new public dataset, namely, the MAWQIF dataset. The MAWQIF is the first multilabel Arabic dataset for target-specific stance detection. The experimental results demonstrate that the proposed approach outperforms the related work on the same dataset, achieving 80.88% for sentiment classification and 68.76% for multilabel tasks in terms of the F1-score metric. In addition, the data augmentation with feature selection improves the F1-score result of the ETC from 65.62% to 68.80%. The study shows the ability of the GA-based feature selection with ensemble learning to improve the classification of multilabel Arabic text.

We introduce a novel extension of conventional fuzzy sets in this paper: called trimorphic fuzzy sets. As per our research, trimorphic fuzzy sets which exhibit greater capability than intuitionistic fuzzy sets, picture fuzzy sets and bipolar fuzzy sets, present a viable approach to address ambiguity and uncertainty in decision-making scenarios. We present a complete characterization of trimorphic fuzzy sets, discuss their properties, and consider applications to real-world decision-making scenarios. We also present a case study to further highlight the practical applications of trimorphic fuzzy sets. We look into a few aggregation strategies for trimorphic fuzzy data in this work. We create the MCDM method using trimorphic fuzzy aggregation operators to help people with disabilities choose AI-Powered Assistive Technologies. We have also presented the extended TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method with trimorphic fuzzy numbers. A numerical example for selection of AI-Powered Assistive Technologies using TOPSIS method is also provided.

Sustainable agriculture is of utmost importance in Saudi Arabia to resolve problems like environmental degradation and water scarcity. The country has made considerable investments in modern agricultural systems such as vertical farming and hydroponics to maximize crop yields and water efficiency. The most direct manifestation of earlier crop growth problems is Pepper leaf disease. Rapid and accurate detection of pepper leaf disease is crucial to immediately detect growth issues and enable accurate control and preventive measures. The traditional method based on human experience and visual inspection to recognize pepper leaves is costly, subjective and laborious. Hence, it is essential to develop fast, convenient, and precise techniques for identifying pepper leaf disease. The Q-neutrosophic soft relation is a generalization that integrates the concepts of soft set and neutrosophic set, enabling for truth, indeterminacy, and false degree in the membership of element with respect to a relation in a soft computing framework. Therefore, this study introduces a new Q Neutrosophic Soft Relation with Deep Learning based Pepper Leaf Disease Recognition (QNSRDL-PLDR) technique for Sustainable Agriculture in KSA. The proposed QNSRDL-PLDR method leverages DenseNet for feature extraction, the model uses the Adam optimizer for effective parameter optimization. Unique to this framework is the combination of a Q-neutrosophic soft relation classifier, allowing nuanced classification considering truth, indeterminacy, and falsity degrees in disease presence assessment. A comprehensive set of simulations is conducted to demonstrate the better efficiency of the QNSRDL-PLDR technique. This technique aims to improve reliability and accuracy in detecting Pepper Leaf Diseases, critical for crop management and sustainable agricultural practices