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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Selvi, K. Dilip, Golda SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Vadapalani Campus Chennai India

ISBN: (纸本)9798350367171

Cyber-Physical Systems (CPS) combine physical and computational elements to produce intelligent systems communicating with their surroundings. By integrating digital and physical processes, CPS provides increased functionality in various industries, including industry, transportation, and healthcare. However, there are security, data integrity, and privacy issues brought on by the extensive integration of CPS across several domains. Therefore, addressing these issues is significant for an effective and reliable CPS model. This study presents a comprehensive review exploring the synergistic relationship between two cutting-edge technologies, Deep Learning (DL) and Blockchain, deployed to resolve these issues in CPS. This study explores the efficiency of leveraging DL and blockchain to enhance the reliability and effectiveness of the CPS models. Also, this study examines the challenges and limitations faced by those DL and blockchain approaches during their implementation across different CPS domains. This comprehensive review provides promising directions for future research in this dynamic and interdisciplinary field for mitigating the challenges in CPS models. It also aids researchers, practitioners, and policymakers by providing a deeper understanding of the possible effects, difficulties, and opportunities related to utilizing DL and Blockchain technologies to improve the security and functionality of CPS. © 2024 IEEE.

关键词： Blockchain technologies Cyber-Physical Systems Deep Learning Internet of Things

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Leveraging Large Language Models to Identify the Values Behind Arguments 2nd

Leveraging Large Language Models to Identify the Values Be...

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2nd International Workshop on Value Engineering in Artificial Intelligence, VALE 2024

作者： Senthilkumar, Rithik Appachi Homayounirad, Amir Siebert, Luciano Cavalcante Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031854620

Human values capture what people and societies perceive as desirable, transcend specific situations and serve as guiding principles for action. People’s value systems motivate their positions on issues concerning the economy, society and politics among others, influencing the arguments they make. Identifying the values behind arguments can therefore help us find common ground in discourse and uncover the core reasons behind disagreements. Transformer-based large language models (LLMs) have exhibited remarkable performance across language generation and analysis. However, leveraging LLMs in sociotechnical systems that assist with discourse and argumentation necessitates systematically evaluating their ability to analyse and identify the values behind arguments, an under-explored research direction. Using a multi-level human value taxonomy inspired by the Schwartz Theory of Basic Human Values, we present a systematic and critical evaluation of GPT-3.5-turbo in human value identification from a dataset of multi-cultural arguments, across the zero-shot, few-shot and chain-of-thought prompting strategies, carrying forward from prior research on this task which leveraged a fine-tuned BERT model. We observe that prompting strategies exhibit performance levels close to, but still behind fine-tuning for value classification. We also detail some challenges associated with value classification with LLMs, offering potential directions for future research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Human Values Large Language Models Prompting

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Enhancing Sleep Quality Through Digital Olfaction: Exploring the Efficacy of Aroma Therapy in Sleep Improvement

Enhancing Sleep Quality Through Digital Olfaction: Exploring...

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Xu, Ziyu Gao, Yuan Ying, Fangtian Faculty of Humanities and Arts Macau University of Science and Technology China College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9781643685069

This study investigates the efficacy of digital olfaction aroma therapy in enhancing sleep quality. It explores the impact of specific olfactory stimuli on sleep patterns, focusing on deep sleep, light sleep, REM sleep, wakefulness duration, and frequency of awakenings. The findings indicate a significant improvement in sleep quality, with increased deep sleep and light sleep durations and reduced REM sleep, wakefulness, and awakenings. The study highlights the potential of digital olfaction in sleep therapy, though it acknowledges limitations and the need for further research into its mechanisms, long-term effects, and individual variations. © 2024 The Authors.

关键词： Sleep research

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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Bitcoin Price Prediction Using Deep Learning Models 4

Bitcoin Price Prediction Using Deep Learning Models

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

作者： Hajare, Rasika Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Forecasting the price of bitcoins is significant in contemporary research, given the fact that the digital currency is relatively unpredictable and highly integrated in global securities markets. This paper compares the use of three deep learning models, namely recurrent neural networks (RNN), gated recurrent unit (GRU), and a convolutional neural network-long short-term memory (CNN-LSTM) for Bitcoin price prediction. Historical Bitcoin price data is used in the dataset, and every one of the models is trained and tested based on its performance to predict future prices based on past data. The proposed models include the RNN and GRU, which incorporate sequential dependency mappings to learn complex sequences, and the CNN-LSTM structures combine convolution layers for feature extraction and LSTM layers for sequencing. Different models' competency is discussed using the averaged Mean Squared Error (MSE) and graphs of the model's prediction against actual prices. The experimental results reveal that the proposed CNN-LSTM model has better prediction accuracy than RNN and GRU in time-series forecasting tasks, indicating that more complex architecture hybrid models might provide better forecasting performance. In analyzing and analyzing cryptocurrency price dynamics, this investigation gives information about the opportunities and challenges of these models to enhance future research. © 2025 IEEE.

关键词： Prediction models

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Comparative Analysis and Evaluation of Well-being Activity-Infused Fine-Tuned Language Models with Benchmark Models

Comparative Analysis and Evaluation of Well-being Activity-I...

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2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Mohammed, Hamza Haruna Kiss, Gabriel Serrano, J. Artur Lindseth, Frank Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway Department of Neuromedicine and Movement Science Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9798331508333

Large language models (LLMs) have significantly impacted natural language processing (NLP), healthcare, and beyond fields. LLMs have demonstrated unprecedented abilities to understand and generate human-like text, making them highly valuable for various tasks, including information retrieval, question-answering, and conversational AI. However, as these models are integrated into real-world applications, especially in sensitive domains such as healthcare, it becomes essential to evaluate their performance comprehensively. We present a process for fine-tuning LLMs to incorporate cardiac exercise and corpus of well-being activity, aiming to promote activity health support through NLP-driven applications. Using QLoRA, you can ensure that the model size remains small and reduce overhead while maintaining the high performance of the fine-tuned model. Subsequently, this paper presents a comparative analysis benchmark pipeline to evaluate fine-tuned models with base models on standard benchmark datasets from the healthcare domain. The aim is to evaluate the performance, robustness and accuracy of these models, showing that the fine-tuning process does not degrade the model performance, but improves the performance to some extent in some benchmarks. © 2025 IEEE.

关键词： Nutrition

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Data Importance Measurement Based on Sampling Region Information for Oversampling

Data Importance Measurement Based on Sampling Region Informa...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Xuanxuan Guo, Li Chen, Long College of Computer Science & Technology Qingdao University Qing-dao China University of Macau Faculty of Science and Technology China

ISBN: (纸本)9781665410205

Sampling is commonly employed to tackle the classification of imbalanced data, with the Synthetic Minority Oversampling Technique (SMOTE) being the most widely used sampling method. In recent years, multiple variations of SMOTE, including the clustering & SMOTE based oversampling methods are proposed. Nevertheless, these methods often neglect the calculation of importance measurement for minority class samples while generating noise, boundary, or overlapping samples in some cases. To solve such problems, we propose a novel importance measurement based on sampling region information (SRI) in minority class samples by using clustering. The measurement focuses more on important samples. Based on the measurement, we also propose an oversampling method called KRISMOTE. The method not only reduces noise, but also clarifies the classification boundaries, effectively enhancing the classification performance of imbalanced data. Experimental results over the publicly available KEEL dataset demonstrate that the proposed KRISMOTE method outperforms other popular oversampling algorithms. © 2024 IEEE.

关键词： Importance sampling

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Sentimental Analysis for Trump Tweets Using R 8th

Sentimental Analysis for Trump Tweets Using R

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Nishok, V.G. Bharathi, N. Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Campus Chennai India

ISBN: (纸本)9789819786657

Twitter is the best source for sentiment analysis, product reviews, current issues, etc. Sentiment analysis extracts positive and negative opinions from the Twitter data set, and R studio provides the best environment for this Twitter sentiment analysis. Find if Trump’s phone Android is fake or not by comparing his iPhone and Android tweets using R with datasets. Detailed analysis is not there. The Twitter data collected might not be representative of the entire population. By using filtering of tweets as we need Android and iPhone tweets here and usage of tweets per day, no of tweets with and without quotes and pictures and links, taking common words from Android and iPhone and trying with log ratio that is Android /iPhone and with log ratio and common word find sentiment. We got good results by the usage of Twitter of each device, no of tweets quoted and non-quoted, no picture/link and picture/link, most common words, words compare with Android and iPhone and log ratio, finding sentiment of it which type of word it is. My work helps his account is a trump or not for Android as it contains negative tweets by common words, Android and iPhone words and log odd ratio with that it detects sentiment as it is a positive, negative or neutral by showing anger, sad, happy, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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Channel Estimation Model and Their Role in Optimizing Customer Lifetime Value with Robust Optimization 24

Channel Estimation Model and Their Role in Optimizing Custom...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Elveny, Marischa Nasution, Mahyuddin K.M. Purnamasari, Fanindia Wook, Tengku Siti Meriam Tengku Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (纸本)9798400717833

In a dynamic and competitive business world, optimizing customer lifetime value (CLV) is an important goal for companies to maximize the effectiveness of their marketing strategies. Utilizing effective channel estimation techniques and adapting robust statistical methodologies is critical to accurately understanding and improving CLV. The study aims to investigate the part that channel estimation plays in CLV optimization by using the robust M-estimation and iteratively reweighted least squares (IRLS) approaches. This study specifically integrates RFM (recency, frequency, and monetary) analysis to measure and improve customer value predictions. We integrate the RFM variable as the main predictor in the regression model to estimate CLV based on different channel interactions. The research results show an R-square value of 0.94, which means 94% of data variability shows very good agreement between models. An RMSE value of 0.20 means that the average model prediction deviates from the actual value by around 0.20 units. A low RMSE indicates high prediction accuracy. Channel estimation techniques, combined with RFM and robust methods, provide significant insight into the factors influencing CLV. Increased email interactions and social media engagement are associated with higher CLV values, indicating that businesses should invest more in digital marketing strategies. We have proven that the frequency of current customer interactions and engagement through optimal channels has the most significant positive impact on increasing CLV. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Utilizing Clustering Analysis and Machine Learning Techniques for Providing Student Learning Recommendations 24

Utilizing Clustering Analysis and Machine Learning Technique...

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2024 10th International Conference on Education and Training Technologies, ICETT 2024

作者： Woo, Bary Cheong, Ngai Peng, Li Liu, Gaojian Faculty of Applied Sciences Macao Polytechnic University China Computer Science and Technology Hunan University of Science and Technology China

ISBN: (纸本)9798400717895

This research endeavors to scrutinize the influence of courses on students' final year project (FYP) scores and prognosticate FYP scores by applying methodologies such as clustering analysis, decision trees, logistic regression, and support vector machines. By scrutinizing the correlation between students' course grades and FYP scores, the objective is to gain deeper insights into the impact of diverse courses on FYP performance and offer tailored learning recommendations. Initially, clustering analysis is employed to categorize students into distinct groups, discerning the potential impact of specific course combinations on FYP outcomes. Subsequently, machine learning algorithms, including decision trees, logistic regression, and support vector machines, are leveraged to forecast FYP scores based on students' course grades. Through these analyses, customized learning recommendations and guidance can be provided to augment students' FYP performance, fostering their academic advancement and maturation. The findings of this research are pivotal for educational institutions and individual students, contributing to the enhancement of curriculum design and pedagogical methodologies, thereby elevating the standard of education and enriching students' learning encounters. Furthermore, drawing upon assessment data spanning over 16 years and encompassing more than 2,000 non-CS majors at MPU, we delve into the factors influencing student learning quality and proffer timely learning recommendations based on amassed student subject grades and FYP scores. © 2024 ACM.

关键词： Contrastive Learning

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