Cloud Computing and Data Science

Retraction Note to "Evaluating the Effect of Studying Computer Ethics and Computer Ethics Rules and Regulations on Computer Ethics at Work" [Cloud Computing and Data Science, Volume 1 Issue 1 (2020), 23-33]

2025-04-10T11:55:45+08:00

The Editor-in-Chief and the Editorial Office of Cloud Computing and Data Science (CCDS) have retracted the following article:

Alsultanny Y. Evaluating the Effect of Studying Computer Ethics and Computer Ethics Rules and Regulations on Computer Ethics at Work. Cloud Computing and Data Science. 2020; 1(1): 23-33.

This article has been retracted at the request of the Editor-in-Chief.

Following a journal-wide investigation, it was identified that this article falls outside the scope of the journal, and does not align with the thematic focus or subject matter requirements as outlined by the journal's editorial policies.

The retraction is in accordance with the Committee on Publication Ethics (COPE) guidelines, ensuring the integrity of the publication record is maintained. As part of journal's ongoing efforts to improve the quality of publications, the editorial office will continue to rigorously conduct preliminary review and peer review to ensure that only manuscripts that align with the journal's scope and meet the highest academic standards are published.

The editorial office sincerely apologize for any confusion or inconvenience this retraction may have caused.

The author has not responded to the Editor about this retraction notice.

Distributed Hybrid Quantum Computing Applications into Battery Cell Manufacturing Industries as per the Industries 5.0

2025-01-24T08:50:29+08:00

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.

Challenges in Detecting Nuanced Sentiment with Advanced Models

2025-01-22T14:37:52+08:00

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.