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Unsupervised spectral feature selection algorithms for high dimensional data

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Frontiers of Computer Science 2023年第5期17卷 27-40页

作者： Mingzhao WANG Henry HAN Zhao HUANG Juanying XIE School of Computer Science Shaanxi Normal UniversityXi’an 710119China Department of Computer Science School of Engineering&Computer ScienceBaylor UniversityWacoTX 76798USA

It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of *** selection especially the unsupervised ones are the right way to deal with this challenge and realize the ***,two unsupervised spectral feature selection algorithms are proposed in this *** group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation,so as to detect the global optimal feature clusters as far as *** two feature ranking techniques,including cosine-similarity-based feature ranking and entropy-based feature ranking,are proposed,so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be *** effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods,and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer *** extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison,especially the one based on cosine similarity feature ranking *** statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs *** detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data,such that the AI system built on them would be reliable and *** is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.

关键词： feature selection spectral clustering feature ranking techniques entropy cosine similarity

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Underwater acoustic signal denoising model based on secondary variational mode decomposition

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Defence Technology（防务技术） 2023年第10期28卷 87-110页

作者： Hong Yang Wen-shuai Shi Guo-hui Li School of Electronic Engineering Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China

Due to the complexity of marine environment,underwater acoustic signal will be affected by complex background noise during *** acoustic signal denoising is always a difficult problem in underwater acoustic signal *** obtain a better denoising effect,a new denoising method of underwater acoustic signal based on optimized variational mode decomposition by black widow optimization algorithm(BVMD),fluctuation-based dispersion entropy threshold improved by Otsu method(OFDE),cosine similarity stationary threshold(CSST),BVMD,fluctuation-based dispersion entropy(FDE),named BVMD-OFDE-CSST-BVMD-FDE,is *** the first place,decompose the original signal into a series of intrinsic mode functions(IMFs)by ***,distinguish pure IMFs,mixed IMFs and noise IMFs by OFDE and CSST,and reconstruct pure IMFs and mixed IMFs to obtain primary denoised *** the end,decompose primary denoising signal into IMFs by BVMD again,use the FDE value to distinguish noise IMFs and pure IMFs,and reconstruct pure IMFs to obtain the final denoised *** proposed mothod has three advantages:(i)BVMD can adaptively select the decomposition layer and penalty factor of VMD.(ii)FDE and CS are used as double criteria to distinguish noise IMFs from useful IMFs,and Otsu algorithm and CSST algorithm can effectively avoid the error caused by manually selecting thresholds.(iii)Secondary decomposition can make up for the deficiency of primary decomposition and further remove a small amount of *** chaotic signal and real ship signal are *** experiment result shows that the proposed method can effectively *** improves the denoising effect after primary decomposition,and has good practical value.

关键词： Underwater acoustic signal Denoising Variational mode decomposition Secondary decomposition Fluctuation-based dispersion entropy cosine similarity

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A label noise filtering and label missing supplement framework based on game theory

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Digital Communications and Networks 2023年第4期9卷 887-895页

作者： Yuwen Liu Rongju Yao Song Jia Fan Wang Ruili Wang Rui Ma Lianyong Qi College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266580China Weifang Key Laboratory of Blockchain on Agricultural Vegetables Weifang University of Science and TechnologyShouguangChina China Unicom Taian Branch China School of Natural and Computational Sciences Massey UniversityNew Zealand General Education Department Shandong First Medical UniversityShandong Academy of Medical SciencesTai'an271000China College of Computer Science and Technology Zhejiang UniversityHangzhouChina

Labeled data is widely used in various classification ***,there is a huge challenge that labels are often added *** labels added by malicious users will affect the training effect of the *** unreliability of labeled data has hindered the *** order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our *** the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most *** use cosine similarity to judge and select the most representative *** the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing *** optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the ***,a case study is given to illustrate the effectiveness and reliability of LNFS.

关键词： Label noise FastText cosine similarity Game theory LSTM

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Deep Learning Based Face Detection and Identification of Criminal Suspects

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Computers, Materials & Continua 2023年第2期74卷 2331-2343页

作者： S.Sandhya A.Balasundaram Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past *** of the most tedious tasks is to track a suspect once a crime is *** most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their ***,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been *** detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the *** comparison is performed by calculating the similarity value between each pair of images that are obtained by using the cosine similarity *** plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.

关键词： Deep learning opencv deep neural network single shot multi-box detector auto-encoder cosine similarity

SwCS: Section-Wise Content similarity Approach to Exploit Scientific Big Data

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Computers, Materials & Continua 2021年第4期67卷 877-894页

作者： Kashif Irshad Muhammad Tanvir Afzal Sanam Shahla Rizvi Abdul Shahid Rabia Riaz Tae-Sun Chung Department of Computer Science Capital University of Science and TechnologyIslamabadPakistan Department of Computer Science NAMAL InstituteMianwali42250Pakistan Raptor Interactive(Pty)Ltd. Eco BoulevardWitch Hazel AveCenturion0157South Africa Institute of Computing Kohat University of Science and TechnologyPakistan Department of CS&IT University of Azad Jammu and KashmirMuzaffarabad13100Pakistan Department of Artificial Intelligence Ajou UniversityKorea

The growing collection of scientific data in various web repositories is referred to as Scientific Big Data,as it fulfills the four“V’s”of Big Data—volume,variety,velocity,and *** phenomenon has created new opportunities for startups;for instance,the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and ***,the content of the papers are compared to list the relevant papers from a *** conventional method results in a long list of papers that is often impossible to interpret ***,the need for a novel approach that intelligently utilizes the available data is ***,the primary element of the scientific knowledge base is a research article,which consists of various logical sections such as the Abstract,Introduction,Related Work,Methodology,Results,and ***,this study utilizes these logical sections of research articles,because they hold significant potential in finding relevant *** this study,comprehensive experiments were performed to determine the role of the logical sections-based terms indexing method in improving the quality of results(i.e.,retrieving relevant papers).Therefore,we proposed,implemented,and evaluated the logical sections-based content comparisons method to address the research objective with a standard method of indexing *** section-based approach outperformed the standard content-based approach in identifying relevant documents from all classified topics of computer ***,the proposed approach extracted 14%more relevant results from the entire *** the experimental results suggested that employing a finer content similarity technique improved the quality of results,the proposed approach has led the foundation of knowledge-based startups.

关键词： Scientific big data ACM classification term indexing content similarity cosine similarity

Impact of Large Language Models on Scholarly Publication Titles and Abstracts:A Comparative Analysis

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Journal of Social Computing 2024年第2期5卷 105-121页

作者： Phoey Lee Teh Chukwudi Festus Uwasomba the Department of Cyber and Computing Wrexham UniversityWrexhamLL112AWUK the School of Computing and Computing STEM FacultyThe Open UniversityMilton KeynesMK76AAUK

Artificial Intelligence(AI)tools become essential across industries,distinguishing AI-generated from human-authored text is increasingly *** study assesses the coherence of AI-generated titles and corresponding abstracts in anticipation of rising AI-assisted document *** main goal is to examine the correlation between original and AI-generated titles,emphasizing semantic depth and similarity measures,particularly in the context of Large Language Models(LLMs).We argue that LLMs have transformed research focus,dissemination,and citation patterns across five selected knowledge areas:Business Administration and Management(BAM),Computer Science and Information Technology(CS),Engineering and Material Science(EMS),Medicine and Healthcare(MH),and Psychology and Behavioral Sciences(PBS).We collected 15000 titles and abstracts,narrowing the selection to 2000 through a rigorous multi-stage screening process adhering to our study’s *** shows that there is insufficient evidence to suggest that LLM outperforms human authors in article title generation or articles from the LLM era demonstrates a marked difference in semantic richness and readability compared to those from the ***,it asserts that LLM is a valuable tool and can assist researchers in generating *** LLM’s assistance,the researcher ensures that the content is reflective of the finalized abstract and core research themes,potentially increasing the impact and accessibility and readability of the academic work.

关键词： semantic alignment cosine similarity patterns large language models technology impact

An Automated System to Predict Popular Cybersecurity News Using Document Embeddings

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Computer Modeling in Engineering & Sciences 2021年第5期127卷 533-547页

作者： Ramsha Saeed Saddaf Rubab Sara Asif Malik M.Khan Saeed Murtaza Seifedine Kadry Yunyoung Nam Muhammad Attique Khan National University of Sciences and Technology IslamabadPakistan Beriut Arab University BeirutLebanon Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Hitec University TaxilaPakistan

The substantial competition among the news industries puts editors under the pressure of posting news articleswhich are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teamsin making decisions about posting a news article. Article similarity extracted from the articles posted within a smallperiod of time is found to be a useful feature in existing popularity prediction approaches. This work proposesa new approach to estimate the popularity of news articles by adding semantics in the article similarity basedapproach of popularity estimation. A semantically enriched model is proposed which estimates news popularity bymeasuring cosine similarity between document embeddings of the news articles. Word2vec model has been used togenerate distributed representations of the news content. In this work, we define popularity as the number of timesa news article is posted on different websites. We collect data from different websites that post news concerning thedomain of cybersecurity and estimate the popularity of cybersecurity news. The proposed approach is comparedwith different models and it is shown that it outperforms the other models.

关键词： Embeddings semantics cosine similarity popularity word2vec

Benchmarking Performance of Document Level Classification and Topic Modeling

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Computers, Materials & Continua 2022年第4期71卷 125-141页

作者： Muhammad Shahid Bhatti Azmat Ullah Rohaya Latip Abid Sohail Anum Riaz Rohail Hassan Department of Computer Science COMSATS University IslamabadLahorePakistan Department of Communication Technology and Network Faculty of Computer ScienceUniversiti Putra MalaysiaSelangor43400Malaysia Othman Yeop Abdullah Graduate School of Business(OYAGSB) Universiti Utara Malaysia(UUM)Kuala Lumpur50300Malaysia

Text classification of low resource language is always a trivial and challenging *** paper discusses the process of Urdu news classification and Urdu documents *** is one of the most famous spoken languages in *** implementation of computational methodologies for text classification has increased over ***,Urdu language has not much experimented with research,it does not have readily available datasets,which turn out to be the primary reason behind limited research and applying the latest methodologies to the *** overcome these obstacles,a mediumsized dataset having six categories is collected from authentic Pakistani news *** is a rich but complex *** processing can be challenging for Urdu due to its complex features as compared to other *** frequency-inverse document frequency(TFIDF)based term weighting scheme for extracting features,chi-2 for selecting essential features,and Linear discriminant analysis(LDA)for dimensionality reduction have been *** matrix and cosine similarity measure have been used to identify similar documents in a collection and find the semantic meaning of words in a document FastText model has been *** training-test split evaluation methodology is used for this experimentation,which includes 70%for training data and 30%for testing ***-of-the-art machine learning and deep dense neural network approaches for Urdu news classification have been ***,we trained Multinomial Naïve Bayes,XGBoost,Bagging,and Deep dense neural *** and deep dense neural network outperformed the other *** experimental results show that deep dense achieves 92.0%mean f1 score,and Bagging 95.0%f1 score.

关键词： Deep neural network machine learning natural language processing TFIDF sparse matrix cosine similarity classification linear discriminant analysis gradient boosting

Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

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Computers, Materials & Continua 2024年第3期78卷 4153-4176页

作者： Yumin Jo Jongho Paik Major in Software Fusion Department of ComputerSeoulWomen’s UniversitySeoul01797South Korea Department of Software Convergence Seoul Women’s UniversitySeoul01797South Korea

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input ***,when the transmission environment is unstable,problems such as reduction in the lifespan of equipment due to frequent switching and interruption,delay,and stoppage of services may ***,applying a machine learning(ML)method,which is possible to automatically judge and classify network-related service anomaly,and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors,is *** this paper,we propose an intelligent packet switching method based on the ML method of classification,which is one of the supervised learning methods,that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on ***,we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update *** obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation *** the broadcasting gateway equipment to which the proposed method is applied,it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.

关键词： Broadcasting and communication convergence multi-stream packet switching advanced television systems committee standard 3.0(ATSC 3.0) data pre-processing machine learning cosine similarity