AUTOMATIC CONTROL AND COMPUTER SCIENCES最新文献_第2页

A Novel Stack Ensemble Learning Techniques for Sentiment Classification in Climate Change Discourse 一种新的用于气候变化语篇情感分类的堆栈集成学习技术

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700555

Vipin Jain, Shilpa Agnihotri Pandey, Ankit Chakrawarti, Rahul Sharma, Vinod Patidar

{"title":"A Novel Stack Ensemble Learning Techniques for Sentiment Classification in Climate Change Discourse","authors":"Vipin Jain, Shilpa Agnihotri Pandey, Ankit Chakrawarti, Rahul Sharma, Vinod Patidar","doi":"10.3103/S0146411625700555","DOIUrl":"10.3103/S0146411625700555","url":null,"abstract":"Primary reason of climate reason is release of greenhouse gases into the biosphere which warms the earth’s atmosphere. Climate change is serious issue that affects public sentiment significantly. In this paper, stack ensemble learning based framework is proposed to analyze public sentiment regarding climate change by using text datasets. First step of proposed framework is text preprocessing which includes tokenization, stemming, and stop-word removal to create TF-IDF representations. Next, stack ensemble model is developed for sentiment classification as positive, neutral, or negative. This model utilizes four machine such as support vector machine (SVM), decision tree (DT), random forests (RF), and gradient boosting classifier (GDB) as base models. The predictions given by all four models are combined using XGBoost as a meta-classifier. The four dataset related to climate change are used for experimental analysis. Results show that the proposed model achieves 89.72% precision, 91.65% recall, 90.23% F1-score, and 91.47% accuracy. These results suggest that the stack ensemble learning approach is effective for analyzing public sentiment about climate change.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 3","pages":"389 - 401"},"PeriodicalIF":0.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Evaluation of Various HEVC Implementations in Error-Prone Environments 在易出错环境中各种HEVC实现的比较评估

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-09-08 DOI: 10.3103/S0146411625700518

Islem Mansri, Nasreddine Kouadria, Ahcen Aliouat, Noureddine Doghmane

引用次数: 0

Utilizing Artificial Intelligence to Detect Fraudulent Manipulation in Recommender Systems 利用人工智能检测推荐系统中的欺诈操作

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-07-04 DOI: 10.3103/S0146411625700178

Kulvinder Singh, Sanjeev Dhawan, Sarika Gambhir

{"title":"Utilizing Artificial Intelligence to Detect Fraudulent Manipulation in Recommender Systems","authors":"Kulvinder Singh, Sanjeev Dhawan, Sarika Gambhir","doi":"10.3103/S0146411625700178","DOIUrl":"10.3103/S0146411625700178","url":null,"abstract":"The recommender system (RS) utilizes collective user opinions to forecast customer preferences. RS can be vulnerable to malicious information attacks. Introducing deceitful “shilling” profiles, such as push and nuke attacks, into the RS can promote inappropriate products. The introduction of these attacks results in inaccurate product recommendations. Because of their openness, Recommender systems are vulnerable to the injection of several fraudulent profiles, which might manipulate their predictions. Conventional detection methods rely on artificial characteristics derived from a single category of user-generated data. These methods need to comprehensively capture detailed user-item interactions, resulting in decreased accuracy when detecting diverse attacks. This study utilizes the modified density peak clustering algorithm to create a well-defined cluster using customer review information. The integration of collaborative filtering and content-based filtering methods has enabled a more advanced recurrent neural network algorithm to more precisely identify these hazards within hybrid recommendation systems. Characteristics obtained through varying degrees of corruption are ultimately incorporated into feeble classifiers capable of detecting attacks. Numerous assaults can be identified using the proposed method, as demonstrated by experimental testing on the Amazon dataset. In the realm of electronic commerce platforms, the recommended approach proves to be more effective for those experiencing rapid expansion in both product offerings and consumer base.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 2","pages":"206 - 218"},"PeriodicalIF":0.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Modified Subtraction Average-Based Optimizer for Solving Optimization Problems 求解优化问题的改进减法平均优化器

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-07-04 DOI: 10.3103/S0146411625700129

Cuicui Cai, Maosheng Fu, Fenghui Zhang, Mingjing Pei, Jing Zhang

{"title":"A Modified Subtraction Average-Based Optimizer for Solving Optimization Problems","authors":"Cuicui Cai, Maosheng Fu, Fenghui Zhang, Mingjing Pei, Jing Zhang","doi":"10.3103/S0146411625700129","DOIUrl":"10.3103/S0146411625700129","url":null,"abstract":"Subtractive average-based optimizer (SABO) is extensively employed to resolve global optimization problems. However, similar to other metaheuristic algorithms, the SABO algorithm also suffers from the problem of insufficient global search ability. To overcome the problems, a novel modified SABO (MSABO) is proposed, which consists of a refracted opposition-based learning (ROBL) mechanism, piecewise mapping randomized operator selection, and a Golden Sine algorithm (GSA) mechanism. The MSABO obtains high-quality populations by the ROBL mechanism, increases the diversity of the populations, and enhances the algorithm’s global searching ability. Then, the piecewise mapping operator is employed for randomized operator selection, and the GSA mechanism is utilized to optimize the searching optimum position, which makes the algorithm easily escape from the local optimum and further elevates the convergence accuracy. To prove the performance of the MSABO, the proposed algorithm and other algorithms are thoroughly analyzed with benchmark functions. Furthermore, to assess the capability of the MSABO, the proposed algorithm is utilized to address two real engineering design problems. The results indicate that the MSABO obtains better results than other state-of-the-art algorithms and is an effective approach to solving design problems.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 2","pages":"138 - 149"},"PeriodicalIF":0.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Anomalies in Docker-Based Containers through Monitoring 通过监控检测基于docker的容器中的异常

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-07-04 DOI: 10.3103/S0146411625700208

G. M. Siddesh, S. R. Mani Sekhar, K. G. Srinivasa

引用次数: 0

DIAT-DSCNN-GRU-HARNet: A Lightweight DCNN for Video Based Classification of Human Activities DIAT-DSCNN-GRU-HARNet：基于视频的人类活动分类的轻量级DCNN

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-07-04 DOI: 10.3103/S014641162570021X

Ajay Waghumbare, Upasna Singh

引用次数: 0

Differential Privacy and Multilayer Grouping Consensus Algorithm for Social Network Privacy and Security Management 社交网络隐私与安全管理的差分隐私与多层分组一致性算法

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-07-04 DOI: 10.3103/S0146411625700166

Hejun Zhou

{"title":"Differential Privacy and Multilayer Grouping Consensus Algorithm for Social Network Privacy and Security Management","authors":"Hejun Zhou","doi":"10.3103/S0146411625700166","DOIUrl":"10.3103/S0146411625700166","url":null,"abstract":"The research aims to propose a social network privacy protection scheme that combines differential privacy and multilayer grouping consensus algorithm to solve the problems of user privacy leakage and data abuse. Firstly, a community discovery data storage mechanism based on regional chains was designed to protect the security and integrity of data. Then, a multilayer grouping consensus algorithm was proposed to improve consensus efficiency through classification and hierarchical consensus. These results confirm that the proposed privacy protection scheme has improved privacy protection by about 75% and increased data availability by about 55% compared to other schemes such as Spctr Switch. When nodes are 200, compared to the traditional Byzantine consensus algorithm, the communication cost based on multilayer grouping consensus algorithm is saved by about 89.9%, and the consensus delay is reduced by about 75.6%. This research plan not only ensures user privacy and security, but also improves data availability, providing an effective method for social network privacy and security management, which helps maintain the stability and security of blockchain networks.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 2","pages":"194 - 205"},"PeriodicalIF":0.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Application of Interactive Drawing Teaching System Based on Artificial Intelligence Technology 基于人工智能技术的交互式绘画教学系统设计与应用

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-07-04 DOI: 10.3103/S0146411625700221

Haifeng Wang

{"title":"Design and Application of Interactive Drawing Teaching System Based on Artificial Intelligence Technology","authors":"Haifeng Wang","doi":"10.3103/S0146411625700221","DOIUrl":"10.3103/S0146411625700221","url":null,"abstract":"With the rapid development and progress of intelligent technology, interactive painting is facing unprecedented opportunities and challenges. The development of intelligent technology in the field of human–computer interaction has broken through the limits, making the interactive process more humanized, intelligent and diversified, but its creation mechanism has not yet been constructed and is in urgent need of theoretical guidance and normative constraints. To improve the adaptive image generation effect of art painting and enhance the efficiency of painting teaching, this paper proposes an interactive painting teaching system based on artificial intelligence technology. The innovation of this article lies in proposing an improved deep learning based image description generator model on the basis of existing image description generation algorithms based on adaptive attention mechanisms. The model is used to design a block sentinel that controls entity block switching and uses a two-layer LSTM with an improved adaptive attention mechanism as the image description generator. This method effectively improves the efficiency of image generation and enhances teaching efficiency. The experimental results show that the method in this paper can generate image contents accurately.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 2","pages":"266 - 277"},"PeriodicalIF":0.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on PID Parameter Tuning Based on SIMCDE-RBF Algorithm 基于SIMCDE-RBF算法的PID参数整定研究

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-07-04 DOI: 10.3103/S0146411625700142

Yueting Liu, Weihua Meng

引用次数: 0

Analysis and Research of Unbalanced Transformer Insulation Oil Monitoring Data Using Machine Learning Methods 不平衡变压器绝缘油监测数据的机器学习分析与研究

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AUTOMATIC CONTROL AND COMPUTER SCIENCES Pub Date : 2025-07-04 DOI: 10.3103/S0146411625700191

Shanghu Zhou, Bingyu Mo, Yanjiao He, Menglong Han, Pengsheng Xie, Peixuan Li

{"title":"Analysis and Research of Unbalanced Transformer Insulation Oil Monitoring Data Using Machine Learning Methods","authors":"Shanghu Zhou, Bingyu Mo, Yanjiao He, Menglong Han, Pengsheng Xie, Peixuan Li","doi":"10.3103/S0146411625700191","DOIUrl":"10.3103/S0146411625700191","url":null,"abstract":"As intelligent transformers continue to advance—a comprehensive preventive maintenance system has been gradually established for transformers. However, the main impediment to effective analysis is the imbalanced distribution of character data and positive anomaly data in the monitoring data of transformer oil, which adversely affects the intelligent evaluation of transformer status. Therefore, in this paper, we proposed analysis and research of unbalanced transformer insulation oil monitoring data using machine learning methods. First, we collected data pertaining to the status of insulation oil in smart transformers. Subsequently, it designed a numerical method based on word vector clustering tailored to the characteristics of insulation oil status data. Furthermore, a novel algorithm named KASMOTE (k nearest neighbor average smote) was introduced to process imbalanced insulation oil data. Finally, the paper validates the efficacy of the implemented dataset by employing seven machine learning algorithms. The experimental results demonstrate that the insulation monitoring dataset, incorporating word vector clustering and the KASMOTE algorithm, is both efficient and challenging, thus enhancing the feasibility of big data analysis.","PeriodicalId":46238,"journal":{"name":"AUTOMATIC CONTROL AND COMPUTER SCIENCES","volume":"59 2","pages":"230 - 243"},"PeriodicalIF":0.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0