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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sathish, Nandigama Jamalpur, Bhavana School of Computer Science and Artificial Intelligence Sr University Telangana Warangal506371 India

ISBN: (纸本)9798331523923

This research study reviews the integration of Machine Learning (ML) techniques with Natural Language Processing (NLP)-based sentiment analysis to improve stock market prediction accuracy by capturing public sentiment from unstructured data sources such as news articles, social media, and financial forums. The study highlights the performance improvements achieved when sentiment data is incorporated into predictive models using ML algorithms, including K-Nearest Neighbors (K-NN), Logistic Regression, Support Vector Machine (SVM), and a hybrid KNN-LR model. A hybrid KNN-LR model combined with sentiment data outperforms individual algorithms, suggesting that combining ML with NLP-based sentiment analysis provides a more nuanced understanding of market behavior. The survey addresses key challenges, such as data quality, processing complexity, and the need for model transparency, while recommending future research on large datasets, advanced NLP techniques, and adaptive machine learning models to enhance accuracy and adaptability in dynamic financial markets. In a rapidly evolving market landscape, sentiment-driven tools will empower investors and analysts in place of theoretical models. © 2025 IEEE.

关键词： Financial data processing

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Weighted Priority Linked Feature Subset Model with Enhanced Logistic Regression for Accurate Heart Stroke Prediction 4

Weighted Priority Linked Feature Subset Model with Enhanced ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Bramaramba, J. Shaik, Mohammed Ali Maram, Balajee School of Computer Science and Artificial Intelligence Sr University Telangana Warangal506371 India

ISBN: (纸本)9798331523923

The implication of heart stroke is also high, as it kills about twenty two million people worldwide every year. This is why diagnosis in the early stage is important, and the patient must be treated as soon as possible in order to avoid death. This paper discusses how Machine learning (ML), which is a computer science subfield of artificial intelligence, is helping healthcare practitioners to detect early signs of a heart stroke. This research applies Logistic Regression (LR) on heart stroke data set available in the public data set for the classification of heart disease having done the feature selection based on target value correlation. The proposed approach improves the traditional LR by the proposed model of Weighted Priority Linked Feature Subset (WPLFS) combined with the Enhanced Logistic Regression (ELR) model. The model is constructed in three stages: In regard to effective methods, there exist the methods of sampling data representation, prediction by means of autoregression remainder, and joining the LR and RP models. As a result, the accuracy of heart stroke prediction for the proposed WPLFS-ELR model is higher than the traditional methods. © 2025 IEEE.

关键词： Logistic regression

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Heterologous Image Matching Based on Salience Region

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 894-900页

作者： Zhai, Jinlong Xu, Yang Yan, Xiaohu Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan114051 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China

To address the matching problem caused by the significant differences in spatial features, spectrum and contrast between heterologous images, a heterologous image matching method based on salience region is proposed in this paper. Firstly, texture features and corner features are detected. Secondly, the epanechnikov kernel function is used to calculate the point with the maximum kernel function value among all feature points, which is then defined as E-point. Thirdly, the salience region is defined and extracted according to the position of the E-point. Finally, six groups of heterologous images are used as data sources, and compared with HAPCG, HOWP, SIFT, and PSO_SIFT algorithms respectively. The results show that the comprehensive matching performance of the proposed method is better than the original algorithm without salience region processing. The root mean square error and the matching time are less than the original method while ensuring a similar number of matching points, which greatly improves the accuracy and speed of heterologous image matching. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Image matching

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Tourist Destination Recommendation System based on Machine Learning 24

Tourist Destination Recommendation System based on Machine L...

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9th International Conference on Big data and Computing, ICBDC 2024

作者： Kongpeng, Sumitra Hanskunatai, Anantaporn Data Science and Computational Intelligence Lab Department of Computer Science School of Science King Mongkut's Institute of Technology Ladkrabang Thailand

ISBN: (纸本)9798400718205

Thailand has a wide variety of tourist attractions, making it difficult for tourist to choose where to go on vacation. The tourist destination recommendation system is a challenge for creating a system to help recommend tourist destinations that are appropriate for personal. Therefore, the principal aims of this research encompass two distinct objectives: firstly, to create a recommendation system for tourist destinations in Thailand by applying machine learning algorithms;and secondly, to analyze factors influencing tourists' choices of destinations. The dataset was gathered from an online survey conducted via Google Forms, comprising responses from 429 tourists in Thailand. In the experiments, three different types of feature selection methods were applied in a data preprocessing step. In the modeling process, four machine learning algorithms, namely Decision Tree, Random Forest, k-Nearest Neighbors (k-NN), and Multi-Layer Perceptron (MLP), were used to construct the model and compare the predictive performance of the recommendation system based on hit rate and NDCG. The experimental results showed that suggesting tourist destinations in the Central region was the most effective, with the highest hit rate and NDCG compared to other regions. The average hit rate and NDCG for the five regions were 0.8 and 0.59, respectively. In addition, there has been an analysis of key factors influencing destination selection, such as activity, travel month, travel budget, and the age of tourists, to understand their impact on travel choices in each region of Thailand. © 2024 Owner/Author.

关键词： Adversarial machine learning

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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A glance at in-context learning

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Frontiers of computer science 2024年第5期18卷 231-232页

作者： Yongliang WU Xu YANG School of Computer Science&Engineering Key Lab of New Generation Artificial Intelligence Technology&Its Interdisciplinary Applications(Ministry of Education)Southeast UniversityNanjing 211189China

The advent of large scale models such as CLIP[1],GPT-2[2],and GPT-3[3]has marked a significant shift in the field of artificial intelligence(AI).Unlike the early days of Al,characterized by the design of distinct models or the utilization of pre-trained models for fine-tuning in specific tasks,these modern models adopt a unified approach.

关键词： such has unified

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Motivating people to adopt electricity management system based on push–pull–mooring model

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Energy and Environment 2024年第3期35卷 1479-1492页

作者： Ho, Chien-Wei School of Management National Taiwan University of Science and Technology Taipei Taiwan Artificial Intelligence for Operations Management Research Center National Taiwan University of Science and Technology Taipei Taiwan

With growing concerns about power and environment issues, smart grid technology, such as meter data management systems, can be viewed as a great solution to handle issues of the environment and energy efficiency. Based on the push–pull–mooring (PPM) model, this research views dissatisfaction, personal innovativeness, information quality, locatability, economic benefits, and perceived value as predictors to examine their impact on switch intention to an electricity management system. The analysis results display that dissatisfaction has no significant effect on switch intention. As a mooring variable, personal innovativeness not only influences switch intention positively, but also moderates the relationship between perceived value and switch intention. Moreover, through perceived value, locatability and economic benefits influence switch intention positively. This research fills the gap in government policies as well as energy companies and provides ponderable suggestions if a government and energy companies would like to change citizens’ behavior from paper-based bill to electricity management system. The government and energy companies should not overlook the effect of personal innovativeness and perceived value and put more effort to publicize the advantages of locatability and economic benefits. © The Author(s) 2022.

关键词： Smart power grids

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GAN-based Fairness-Aware Recommendation for Enhancing the Fairness of dataGAN-based Fairness-Aware RecommendationFairness-Aware Recommendation 24

GAN-based Fairness-Aware Recommendation for Enhancing the Fa...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and artificial intelligence, DEAI 2024

作者： Zhang, Kangzhi Qian, Xuezhong Song, Wei The School of Artificial Intelligence and Computer Science JiangNan University Wuxi214000 China

ISBN: (纸本)9798400717147

Current recommendation algorithms, focusing on maximizing user utility, often overlook significant fairness concerns. Many, while aiming for fairness, ignore inherent fairness information in training datasets, resulting in recommendations that seem fair in isolation but not in entirety. This paper seeks to enhance data fairness by integrating overlooked fairness information into the objective of fair recommendation. Recommendation models utilizing GAN techniques frequently face a sharp decline in effectiveness beyond a specific training point, primarily due to the imbalanced capacities of the discriminator and generator. This paper explores strategies to equalize these capacities, using accuracy metrics to measure the generator's abilities and halting training upon objective achievement. We introduce the PFGAN (Prior Fair GAN) algorithm based on these ideas. Empirical tests on various datasets confirm PFGAN's ability to boost model accuracy while maintaining data fairness. © 2024 ACM.

关键词： Exposure Fairness GAN Recommendation

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Power system log anomaly detection based on spatiotemporal combined GAT-BiGRU 5

Power system log anomaly detection based on spatiotemporal c...

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5th International Symposium on computer Engineering and Intelligent Communications, ISCEIC 2024

作者： Shi, Bozheng Zhao, Congcong Zhang, Yiying Zhang, Yanan School of Artificial Intelligence Tianjin University of Science and Technology Tainjin China

ISBN: (纸本)9798331518677

Because the existing logo detection methods are often only targeted at single features, such as quantitative relationships or sequential relationships and log module characteristics, they often ignore the characteristics and time structure characteristics of the log, which leads to a higher error reporting rate. It is proposed that the GAT-BiGRU based on a logo detection model combined with time and space makes the log abnormal detection more effective. First of all, through the improved word -level two -way door control cycle unit capture the symbol of logic and time structure combination characteristics, the structure is an orderly incremental timing event diagram, and the viewing network model (GAT) is used to extract the time to compose a log time diagram. Structural characteristics to achieve effective integration of time structural relationships and log semantic information. The TGBLog algorithm precision proposed in this paper reaches 95.67%, and the F1 value reaches 96.37%, which is improved. © 2024 IEEE.

关键词： Anomaly detection

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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computer Modeling in Engineering & sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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