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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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A Fault Tracing Method based on Knowledge Graph and Weighted Path Ranking Algorithm 15

A Fault Tracing Method based on Knowledge Graph and Weighted...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Wang, Zibo Li, Honghui Zhang, Junwen Yang, Xiaorui Shen, Nahao School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway of MOE Beijing China

ISBN: (纸本)9798350354010

The structure of railway systems is complex, and accurately and rapidly identifying the root cause of faults post-occurrence is an emerging challenge in the industry. However, railway fault tracing mainly relies on manual experience and traditional data analysis techniques, with most tracing methods suffering from low efficiency and insufficient accuracy. To address these issues, this paper proposes a railway safety equipment fault tracing method based on knowledge graphs and weighted path ranking algorithm. The approach integrates knowledge graphs with Bayesian networks, attention mechanisms, and graph computation, utilizing a weighted calculation to run the weighted path ranking algorithm. Experimental results demonstrate that the proposed method achieves excellent tracing performance in the field of railway equipment. © 2024 IEEE.

关键词： Knowledge graph

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Performance Analysis of VADER and RoBERTa Methods for Smart Retail Customer Sentiment on Amazon Go Store

Performance Analysis of VADER and RoBERTa Methods for Smart ...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Rahmania, Rissa Pohan, Husni Iskandar Arrahmah, Annisa Istiqomah Wibowo, Suryo Adhi School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Electrical Engineering Telkom University Ai Center Bandung40257 Indonesia

ISBN: (纸本)9798350369359

The implementation of intelligent systems in the retail industry is still limited in various regions of the world. Amazon Go currently has four stores, located in the Seattle area, and has received customer reviews based on ratings that can be seen in the Google Maps feature. This paper aims to analyze Amazon Go customer sentiment through reviews and star ratings obtained from Google Maps reviews. First, we collect and prepare data from Google Maps Review. Second, we perform experiments and analysis of the VADER and Robustly Optimized BERT Pretraining Approach (RoBERTa) and compare both methods, which are represented by graphs. Lastly, both methods are evaluated in terms of precision, recall, F1-Score, and accuracy. Sentiment analysis from Amazon Go shows that most customers give it a rating of 5.0. Supported by text data from customer comments containing positive, neutral, and negative sentiments showing the relation between the rating given and the sentiment of the comment by the compound score from the results of the VADER method. Sentiment analysis on smart retail from the Amazon Go Store has provided insight and future challenges in the scope of smart retail implementation. © 2023 IEEE.

关键词： Sentiment analysis

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Verifiable and Forward-Secure Multi-Keyword Query in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第13期12卷 23809-23822页

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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Automated heart disease prediction using improved explainable learning-based technique

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Neural Computing and Applications 2024年第26期36卷 16289-16318页

作者： Bizimana, Pierre Claver Zhang, Zuping Hounye, Alphonse Houssou Asim, Muhammad Hammad, Mohamed El-Latif, Ahmed A. Abd School of Computer Science and Engineering Central South University Changsha410083 China School of Mathematics and Statistics Central South University Changsha410008 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt Information Technology Department Faculty of Computers and Information Menoufia University Shebin El-Koom Egypt

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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PCA Dimensionality Reduction Algorithm Based on Ordered Pair of Normalized Real Numbers 17

PCA Dimensionality Reduction Algorithm Based on Ordered Pair...

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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Zhao, Xiang Zheng, Yi Pan, Xiaoqin Zhou, Lei School of Computer Science and Technology Southwest University of Science and Technology China Engineering Technology Center Southwest University of Science and Technology China School of Mathematics and Physics Southwest University of Science and Technology Sichuan621010 China

ISBN: (纸本)9798331507398

This paper presents a PCA (Principal Component Analysis) data dimensionality reduction algorithm based on OPNs (Ordered Pair of Normalized Real Numbers), referred to as OPNs-PCA. This algorithm aims to improve the dimensionality reduction performance of data. Especially, it shows significant advantages in removing feature redundancy and noise, providing a clearer and more effective data basis for subsequent machine learning or data mining tasks. The algorithm proposed in this paper introduces the concept of OPNs into the PCA algorithm and performs PCA dimensionality reduction operations under the framework of OPNs. Through the characteristic of OPNs' operation in processing feature relationships in non-real number spaces, it can effectively reduce data noise, enhance the processing ability of nonlinear data, and improve the performance of subsequent classification algorithms. We have conducted detailed experimental verification on multiple public data sets. The results show that unlike classic PCA, LLE, KPCA, SPCA and other algorithms that require more principal components to retain sufficient information, OPNs-PCA can achieve a similar or even better level of information retention with fewer principal components, and has a significant improvement in the processing ability of nonlinear data. © 2024 IEEE.

关键词： Dimensionality reduction

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The effect of LPSO phase on the high-temperature oxidation of a stainless Mg-Y-Al alloy

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Journal of Magnesium and Alloys 2024年第10期12卷 4045-4052页

作者： Zhipeng Wang Zhao Shen Yang Liu Yahuan Zhao Qingchun Zhu Yiwen Chen Jingya Wang Yangxin Li Sergio Lozano-Perez Xiaoqin Zeng National Engineering Research Center of Light Alloy Net Forming and State Key Laboratory of Metal Matrix Composites School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai 200240China Department of Materials Science University of OxfordParks RoadOxford OX13PHUK

In this study,we investigated the oxidation of the Mg-11Y-1Al alloy at 500℃in an Ar-20%*** analysis showed the network-like long-period stacking ordered(LPSO)phase transformed into needle-like LPSO and polygonal Mg24Y5 phases,leading to the formation of a high-dense network of needle-like oxides at the oxidation *** oxides grew laterally along the oxide/matrix interfaces,forming a thicker,continuous scale that effectively blocked elemental ***,the preferential oxidation along the needle-like LPSO is believed to accelerate the formation of a thicker and continuous oxide scale,further improving the oxidation resistance of the Mg-11Y-1Al alloy.

关键词： Mg alloy LPSO phase Oxidation TEM TKD.

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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Social engineering attack types and prevention techniques- A survey 1

Social engineering attack types and prevention techniques- A...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatia, Shruti Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

The term "Cyber Security"is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim's social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article. © 2023 IEEE.

关键词： Crime

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QoI-Aware Configuration Adaptation and Heterogeneous Resource Allocation for Edge Video Analytics

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IEEE Internet of Things Journal 2025年第12期12卷 19215-19230页

作者： He, Zixuan Sun, Yanjing Zhang, Beibei Dong, Kaiwen Wang, Bowen Xu, Hongli China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China China University of Mining and Technology IoT Perception Mine Research Center Xuzhou221116 China China University of Mining and Technology School of Information and Control Engineering IoT Perception Mine Research Center Xuzhou221116 China University of Science and Technology of China School of Computer Science and Technology Anhui Hefei230027 China Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou215123 China

Edge video analytics enables agile responses of machine-centric applications by streaming videos from end devices to edge servers (ESs) for resource-intensive deep neural network (DNN) inference. Quality of Inference (QoI), reflected by end-to-end analytics delay and inference accuracy, is crucial for the high-quality real-time decision-making. Due to the limited ES resources and dynamic network conditions, video configuration adaptation and fine-grained resource allocation are necessary to enhance the QoI of video streams, which involves a delicate balance between delay and accuracy. In this article, we propose an edge-enabled multivideo analytics framework, in which performing DNN inference relies on heterogeneous resources, including CPU, GPU, and memory. Considering the different impacts of heterogeneous resources on QoI and double-queue backlogs, we formulate the joint video configuration adaptation, CPU-GPU resource allocation, and batch size selection (JCCGB) problem based on semi-Markov decision process to maximize the long-term average QoI. Then, Lyapunov optimization is applied to transform the original problem into one that minimizes the Lyapunov drift-plus-penalty upper bound, thereby ensuring the stability of both the transmission and computation queues. To tackle the potential multimodality of the optimal configuration adaptation and resource allocation policy, we propose the diffusion-deep-reinforcement-learning-based JCCGB (DD-JCCGB) scheme. This approach effectively enhances the policy performance and accelerates the training process. Experiments driven by real-world network traces demonstrate that the DD-JCCGB algorithm improves the long-term average QoI and outperforms baseline schemes. © 2014 IEEE.

关键词： Markov processes

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