https://ojs.wiserpub.com/index.php/CCDS <p>Cloud Computing and Data Science(CCDS) is an international, open-access, and peer-reviewed journal dedicated to advancing research in cloud computing and data science. The topics of strong interest to our readership span the exploration of established and rapidly emerging topics, which include but are not limited to: green cloud computing, edge computing, big data, and data mining, <a href="http://ojs.wiserpub.com/index.php/CCDS/about"><u>click here to see more...</u></a></p> <p> </p> Universal Wiser Publiser en-US Cloud Computing and Data Science 2737-4106 https://ojs.wiserpub.com/index.php/CCDS/article/view/6292 <p>In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. To address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we come up with attribute atomic matching smart contracts that are based on precise data property alignment. The goal is to get trading parties to match data attributes in a very specific way. During the trading execution phase, we use lightweight cryptographic algorithms based on Elliptic Curve Cryptography (ECC) and non-interactive zero-knowledge proofs to encrypt trading data twice and make attribute proof contracts. This keeps the data safe and private. The results of experiments conducted on the Ethereum platform in an industrial Internet of Things (IoT) scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection. Especially in battery industrial manufacturing, the application of distributed computing is in huge demand and essential to maintaining a healthier technology integration among various systems and technological nodes to perform the better management of energy cells within the battery management system.</p> Biswaranjan Senapati Bharat S Rawal Copyright (c) 2025 Biswaranjan Senapati, Bharat S Rawal https://creativecommons.org/licenses/by/4.0 2025-03-10 2025-03-10 136 165 10.37256/ccds.6220256292 https://ojs.wiserpub.com/index.php/CCDS/article/view/6316 <p>Sentiment analysis, an essential task in Natural Language Processing (NLP), determines the sentiment expressed in texts. This paper compares six different sentiment analysis models, categorized into three groups based on their underlying techniques: lexicon-based, machine learning-based, and zero-shot learning. The models are evaluated on four publicly available datasets (Movie Reviews, Amazon, Yelp, and Financial), each varying in complexity. The main objective is to assess the efficiency of these models in both binary (positive and negative) and ternary (positive, neutral, and negative) sentiment classification scenarios. Our results indicate that for binary classification, pre-trained large-scale NLP state-of-the-art models outperform other approaches, demonstrating superior results across all evaluated metrics. On average, across all datasets, these models achieved 94% accuracy, 96% precision, 94% recall, and 94% F1-score. However, these pre-trained NLP models face significant challenges in three-class classification tasks, where their performance noticeably declines. Achieving on average, across datasets, 60% accuracy, 66% precision, 60% recall, and 56% F1-score. This study highlights the limitations of current state-of-the-art models in handling more subtle sentiment distinctions. It emphasizes the need for further advancements in sentiment analysis techniques to effectively manage multi-class sentiment categorization that captures and interprets specialized jargon, technical terminology, and nuanced language.</p> Edgar Ceh-Varela Sarbagya Ratna Shakya Essa Imhmed Copyright (c) 2025 Edgar Ceh-Varela, Sarbagya Ratna Shakya, Essa Imhmed https://creativecommons.org/licenses/by/4.0 2025-03-11 2025-03-11 115 135 10.37256/ccds.6220256316