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Journal of Network Intelligence 2023年第3期8卷 895-912页

作者： Lu, Jiang-Tao Lin, Kai-Biao Chen, Xin Chen, Jin-Po Department of Computer Science and Technology Xiamen University of Technology Xiamen361024 China

Healthcare fraud is a major problem that affects the quality of healthcare ser-vices. Traditional machine learning methods to address healthcare fraud typically focus on only characteristic patient attributes and ignore the underlying behavioral patterns implicit in a patient’s treatment history. To mine behavioral information implied by healthcare fraudsters, we propose a multibehavioral pattern-based graph neural network (MBGNN) to detect healthcare fraud. The proposed MBGNN first mines behavioral attributes in healthcare heterogeneous graphs by multibehavior pattern decomposition. Here, three sampling strategies are employed to capture groups with the same behavioral pat-terns. Then, a two-step attention mechanism is employed to aggregate the compound semantic information of patients with different behavior patterns. Finally, the learned embeddings by combining semantic information with topological information are used to detect patients with abnormal behaviors. Extensive experiments were conducted on two real-world datasets, and the proposed MBGNN exhibited superior performance compared to state-of-the-art baseline methods. In the anomaly detection task, the proposed MBGNN scored high on F1 and precision metrics. We found that the proposed MBGNN outper-formed existing methods in terms of accuracy. The multiple sampling strategies employed in the proposed method reveal the hidden behavior patterns of healthcare fraudsters. The results obtained by the proposed method are effective and explainable;thus, we believe the proposed can be used to detect healthcare fraud in practical applications. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Topology

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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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Descriptor-augmented machine learning for enzyme-chemical interaction predictions

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Synthetic and Systems Biotechnology 2024年第2期9卷 259-268页

作者： Yilei Han Haoye Zhang Zheni Zeng Zhiyuan Liu Diannan Lu Zheng Liu Department of Chemical Engineering Tsinghua UniversityBeijing100084China Department of Computer Science and Technology Tsinghua UniversityBeijing100084China

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals,as they could characterize enzymes and chemicals from the physicochemical and evolutionary *** study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships *** curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold *** influence of protein and chemical descriptors was assessed in three scenarios,which were predicting the activity of unknown relations between known enzymes and known chemicals(new relationship evaluation),predicting the activity of novel enzymes on known chemicals(new enzyme evaluation),and predicting the activity of new chemicals on known enzymes(new chemical evaluation).The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes,whereas chemical descriptors appear no effect.A variety of sequence-based and structure-based protein descriptors were constructed,among which the esm-2 descriptor achieved the best *** enzyme families as labels showed that descriptors could cluster proteins well,which could explain the contributions of descriptors to the machine learning *** a counterpart,in the new chemical evaluation,chemical descriptors made significant improvement in four out of the seven datasets,while protein descriptors appear no *** attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the *** results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein

关键词： Enzyme-substrate interaction Enzyme design Machine learning Descriptor

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Improved security with novel M-Log chaos steganography algorithm for huffman compressed english text

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Multimedia Tools and Applications 2025年第2期84卷 665-688页

作者： Raghuvanshi, Devvrat Joshi, Kamaldeep Nandal, Rainu Sehrawat, Harkesh Singh, Surender Singh, Srishti Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Rohtak Haryana India

This paper addresses the underexplored landscape of chaotic functions in steganography, existing literature when examined under PRISMA-ScR framework it was realized that most of the studies predominantly focuses on utilizing chaotic functions for either concealing or diffusing messages within cover images. This study explored a different direction of using chaotic function as guards for concealed data by proposing the M-Log chaos algorithm which employs logarithmic chaotic function to drive a unique bomb generator equation, strategically placing bomb bits in the image. These bombs act as sentinels, safeguarding the concealed message. Successful retrieval involves diffusing the bombs before extracting data from the rest of the image. Failure to defuse these bombs prior to access results in irreversible damage to concealed information, thwarting unauthorized access. Our proposed method demonstrates notable advancements across quantitative performance metrics, including a 40% increase in payload capacity, a mean PSNR of 45.8981, and a mean SSIM of 0.9931. Future avenues include exploring multi-dimensional chaotic functions and employing nature-based searching algorithms such as genetic algorithms or particle swarm optimization for optimal chaos bomb placement. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Steganography

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An efficient sentiment analysis technique based on fine-tuned EdBERT for virtual learning environments

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Multimedia Tools and Applications 2025年第18期84卷 20161-20175页

作者： Srivastav, Gaurav Kant, Shri Srivastava, Durgesh Sharma, Neha Hu, Yu-Chen Department of Computer Science and Engineering Sharda University Gautam Budh Nagar India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab Rajpura India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung City407224 Taiwan

With artificial intelligence propelling rapid technological advances, many tools and frameworks have surfaced to assist virtual learning settings. They all make unique claims about how best to facilitate distance education, particularly in light of the pandemic. This paper presents a new model called "EdBERT," which is created explicitly for sentiment analysis of evaluations written by virtual learners with unmatched categorization accuracy. With Google BERT, EdBERT improves sentiment analysis specifically for educational feedback datasets. Using a comparison analysis and the "fine-tuned Google BERT," taught at several levels to understand language, context, and emotions, the methodology accurately classifies learners' feedback. The discussion includes thorough testing and analysis of the accuracy of the model, as well as information on its effectiveness and operation. The model and its operation are described in the discussion together with reliable evidence from analyses and accuracy tests. EdBERT is a cutting-edge model in AIEd sentiment analysis, boasting the best evaluation matrix to date with accuracy rates of 87.89%, F1-score of 88%, precision of 89%, and recall of 88%. These evaluation matrix values outperform all other current models covered in the article. In an effort to increase the productivity of students, teachers, and the learning environment, academic academics and scientists are delving into the relatively young field of AIEd. Trustworthy sentiment analysis may be produced using three fundamental sentiment classes: deep learning and natural language processing models. Plutchik's wheel of emotion, which will aid in more accurately recording feelings using artificial intelligence and deep learning, can be used to further this study. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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Computation of Graph Fourier Transform Centrality Using Graph Filter

IEEE Open Journal of Circuits and Systems

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IEEE Open Journal of Circuits and Systems 2024年 5卷 69-80页

作者： Tseng, Chien-Cheng Lee, Su-Ling National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung824005 Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan711301 Taiwan

In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method;the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter. © 2020 IEEE.

关键词： Complex networks

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Cardiovascular Disease Prediction Using Machine Learning Techniques: A Review

Cardiovascular Disease Prediction Using Machine Learning Tec...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Enihe, Raphael Ozighor Prasad, Rajesh Ogwueleka, Francisca Nonyelum Sanusi, Muhammad Baze University Department of Computer Science Abuja Nigeria African University of Science and Technology Department of Computer Science Abuja Nigeria University of Abuja Department of Computer Science Nigeria

ISBN: (纸本)9798350358155

Cardiovascular Disease (CVD), encompassing various conditions, still stands as the foremost worldwide contributor to mortality, accounting for 17.9 million fatalities in 2019 (comprising 32% of deaths globally). with 85% attributed to heart attacks and strokes. Low- and middleincome countries bear a significant burden, contributing to disability and reduced quality of life. Diabetes, physical inactivity, high blood pressure, obesity, smoking, high cholesterol, and unhealthy eating habits are risk factors that can be changed over time. are Accurate and timely heart disease diagnosis is crucial, with traditional methods often lacking accuracy and efficiency. Advanced diagnostic systems are urgently needed for better detection. Machine learning technology is capable of improving diagnosis and management through accurate data analysis and prediction. This paper provides an overview of advanced machine learning methods suggested by different researchers for predicting cardiovascular disease and offers analytical conclusions. The investigation uncovers that a variety of classification algorithms, such as Logistics Regression, Decision Tree, Naïve Bayes, Artificial Neural Network, Support Vector Machine, and others, have been extensively employed, yielding varying prediction accuracies. Furthermore, it is observed that combining multiple algorithms through ensemble techniques can significantly enhance the prediction accuracy of cardiovascular disease. This paper showcases how cutting-edge machine learning techniques are being harnessed to address real-world challenges in cardiovascular disease prevention and management. © 2024 IEEE.

关键词： Adversarial machine learning

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Embedding Virtual and Augmented Reality in Higher Education in Yemeni Universities 1

Embedding Virtual and Augmented Reality in Higher Education ...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Nagi, Gawed Al-Fuhaidi, Belal Department of Information Technology University of Science and Technology Sana’a Yemen Department of Computer Science University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

Virtual reality (VR) and augmented reality (AR) have increasingly applied to different areas including medicine, engineering, advertising, tourism, entertainment, training, and education. Although VR and AR are still new and not yet used in higher education in Yemen, many colleges and universities worldwide have implemented multimedia and VR/AR technologies as promising learning tools. VR/AR technologies effectively add more engagement, motivation, retention, satisfaction, and enjoyment into learning process when compared to the traditional teaching and learning methods. The aim of the paper is to highlight the uses of VR and AR in different scenarios in many areas and more specifically in higher education and distance learning. In this paper, simple models are proposed and presented to help universities and policymakers in Yemen adopt these innovative technologies and encourage them to invest more resources and training towards implementing those technologies within their curriculum. © 2024 IEEE.

关键词： Virtual environments

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of computer science & technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models 38

PersonalSum: A User-Subjective Guided Personalized Summariza...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Lemei Liu, Peng Henriksboe, Marcus Tiedemann Oekland Lauvrak, Even W. Gulla, Jon Atle Ramampiaro, Heri Department of Computer Science Norwegian University of Science and Technology Norway

With the rapid advancement of Natural Language Processing in recent years, numerous studies have shown that generic summaries generated by Large Language Models (LLMs) can sometimes surpass those annotated by experts, such as journalists, according to human evaluations. However, there is limited research on whether these generic summaries meet the individual needs of ordinary people. The biggest obstacle is the lack of human-annotated datasets from the general public. Existing work on personalized summarization often relies on pseudo datasets created from generic summarization datasets or controllable tasks that focus on specific named entities or other aspects, such as the length and specificity of generated summaries, collected from hypothetical tasks without the annotators' initiative. To bridge this gap, we propose a high-quality, personalized, manually annotated abstractive summarization dataset called PersonalSum. This dataset is the first to investigate whether the focus of public readers differs from the generic summaries generated by LLMs. It includes user profiles, personalized summaries accompanied by source sentences from given articles, and machine-generated generic summaries along with their sources. We investigate several personal signals - entities/topics, plot, and structure of articles-that may affect the generation of personalized summaries using LLMs in a few-shot in-context learning scenario. Our preliminary results and analysis indicate that entities/topics are merely one of the key factors that impact the diverse preferences of users, and personalized summarization remains a significant challenge for existing LLMs. Our dataset and code are available at https://***/SmartmediaAI/PersonalSum. © 2024 Neural information processing systems foundation. All rights reserved.

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