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Sentiment analysis is an essential task in natural language processing that involves identifying a text's polarity, whether it expresses positive, negative, or neutral sentiments. With the growth of social media and the Internet, sentiment analysis has become increasingly important in various fields, such as marketing, politics, and customer service. However, sentiment analysis becomes challenging when dealing with foreign languages, particularly without labelled data for training models. In this study, we propose an ensemble model of transformers and a large language model (LLM) that leverages sentiment analysis of foreign languages by translating them into a base language, English. We used four languages, Arabic, Chinese, French, and Italian, and translated them using two neural machine translation models: LibreTranslate and Google Translate. Sentences were then analyzed for sentiment using an ensemble of pre-trained sentiment analysis models: Twitter-Roberta-Base-Sentiment-Latest, bert-base-multilingual-uncased-sentiment, and GPT-3, which is an LLM from OpenAI. Our experimental results showed that the accuracy of sentiment analysis on translated sentences was over 86% using the proposed model, indicating that foreign language sentiment analysis is possible through translation to English, and the proposed ensemble model works better than the independent pre-trained models and LLM. [ABSTRACT FROM AUTHOR]

Contrails are line-shaped clouds formed in the exhaust of aircraft engines that significantly contribute to global warming. This paper confidently proposes integrating advanced image segmentation techniques to identify and monitor aircraft contrails to address the challenges associated with climate change. We propose the SegX-Net architecture, a highly efficient and lightweight model that combines the DeepLabV3+, upgraded, and ResNet-101 architectures to achieve superior segmentation accuracy. We evaluated the performance of our model on a comprehensive dataset from Google research and rigorously measured its efficacy with metrics such as IoU, F1 score, Sensitivity and Dice Coefficient. Our results demonstrate that our enhancements have significantly improved the efficacy of the SegX-Net model, with an outstanding IoU score of 98.86% and an impressive F1 score of 99.47%. These results unequivocally demonstrate the potential of image segmentation methods to effectively address and mitigate the impact of air conflict on global warming. Using our proposed SegX-Net architecture, stakeholders in the aviation industry can confidently monitor and mitigate the impact of aircraft shrinkage on the environment, significantly contributing to the global fight against climate change. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence refers to developing artificial intelligence models and systems that can provide clear, understandable, and transparent explanations for their decisions and predictions. In deep learning, where complex neural networks often operate as "black boxes", the importance of explainable AI lies in enhancing trust, accountability, and interoperability. For further advancement of explainable artificial intelligence in deep learning, gaining a deep understanding of its applications, approaches, evaluation metrics, current advancements, and challenges is imperative. Therefore, in this article, we began exploring the vast array of applications of explainable AI in different deep learning models, scrutinizing them within the context of existing research. We then explored explainable AI approaches used in Deep Learning models and discussed prevalent evaluation metrics used in evaluating a model's explainability. Subsequently, we precisely reviewed the experimental results and advancements of recent state-of-the-art experiments related to explainable AI in deep learning. Finally, we discussed the diverse challenges encountered in sentiment analysis and proposed future research directions to mitigate these concerns. This extensive review provides a complete understanding of explainable AI in deep learning, covering its applications, approaches, experimental analysis, challenges, and research directions. [ABSTRACT FROM AUTHOR]