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2023 IEEE Global Conference on information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Bhusan Dash, Bibhuti Barik, Lalbihari De, Utpal Chandra Tripathy, Abinash Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350308167

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models. © 2023 IEEE.

关键词： AdaBoost Ensemble model Heart Disease LightGBM Machine Learning Multilayer Perceptron XGBoost

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Diagnosis of Various Skin Cancer Lesions Based on Fine-Tuned ResNet50 Deep Network

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computers, Materials & Continua 2021年第7期68卷 117-135页

作者： Sameh Abd ElGhany Mai Ramadan Ibraheem Madallah Alruwaili Mohammed Elmogy Department of Information Systems College of Computers and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Information Systems Faculty of Computers and InformationMansoura UniversityEgypt Department of Information Technology Faculty of Computers and InformationKafrelsheikh UniversityEgypt Department of Computer Engineering and Networks College of Computers and Information SciencesJouf UniversitySaudi Arabia Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

With the massive success of deep networks,there have been signi-cant efforts to analyze cancer diseases,especially skin *** this purpose,this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion *** paper aims to develop and ne-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic ***-tuning is a powerful method to obtain enhanced classication results by the customized pre-trained ***,batch normalization,and hyperparameter optimization are performed for ne-tuning the proposed deep *** proposed ne-tuned ResNet50 model successfully classied 7-respective classes of dermoscopic lesions using the publicly available HAM10000 *** developed deep model was compared against two powerful models,i.e.,InceptionV3 and VGG16,using the Dice similarity coefcient(DSC)and the area under the curve(AUC).The evaluation results show that the proposed model achieved higher results than some recent and robust models.

关键词： Deep learning model multiclass diagnosis dermatoscopic images analysis ResNet50 network

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, information and Communication Engineers.

关键词： Deep learning

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A new online field feature selection algorithm based on streaming data

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1365-1377页

作者： Zhang, Zhenjiang Song, Fuxing Zhang, Peng Chao, Han-Chieh Zhao, Yingsi School of Software Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Economics and Management Beijing Jiaotong University Beijing100044 China

The rapid development of Internet technology derived out a massive network text data. Therefore, how to classify the massive text data efficiently has important theoretical significance and application value. In order to acquire accurate classification results, the process has been divided into two parts. In terms of text representation, this paper proposes an online field feature selection algorithm (OFFS algorithm) based on streaming data, which solves the problems of low efficiency and memory consumption of traditional feature selection algorithms. With improvements in the vector space model, the new algorithm can select the real-time feature of the data and quickly generate text vector. In the aspect of classifier design, an OFFS-BP neural network text classifier based on BP neutral network and OFFS algorithm is designed. It adapts to the distributed parallel computing, reduces the training time and balances the computation efficiency and classification accuracy. Finally based on the Spark platform, the OFFS-BP neural network classifier is implemented. The experimental results show that the OFFS-BP neural network classifier is more suitable for big data environment with less computation time and higher classification efficiency. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Neural networks

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Wind Driven Optimization-Based Medical Image Encryption for Blockchain-Enabled Internet of Things Environment

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computers, Materials & Continua 2022年第11期73卷 3219-3233页

作者： C.S.S.Anupama Raed Alsini N.Supriya E.Laxmi Lydia Seifedine Kadry Sang-Soo Yeo Yongsung Kim Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Malla Reddy Engineering College(A)Hyderabad500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University College4612KristiansandNorway Department of Computer Engineering Mokwon UniversityDaejeon35349Korea Department of Technology Education Chungnam National University 99Daehak-roYuseong-guDaejeon34134Korea

Internet of Things(IoT)and blockchain receive significant interest owing to their applicability in different application areas such as healthcare,finance,transportation,*** image security and privacy become a critical part of the healthcare sector where digital images and related patient details are communicated over the public *** paper presents a new wind driven optimization algorithm based medical image encryption(WDOA-MIE)technique for blockchain enabled IoT *** WDOA-MIE model involves three major processes namely data collection,image encryption,optimal key generation,and data ***,the medical images were captured from the patient using IoT ***,the captured images are encrypted using signcryption *** addition,for improving the performance of the signcryption technique,the optimal key generation procedure was applied by WDOA *** goal of the WDOA-MIE algorithm is to derive a fitness function dependent upon peak signal to noise ratio(PSNR).Upon successful encryption of images,the IoT devices transmit to the closest server for storing it in the blockchain *** performance of the presented method was analyzed utilizing the benchmark medical image *** security and the performance analysis determine that the presented technique offers better security with maximum PSNR of 60.7036 dB.

关键词： Internet of things image security medical images encryption optimal key generation blockchain

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An Ensemble Framework for Dropout Prediction in Online Learning

An Ensemble Framework for Dropout Prediction in Online Learn...

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2022 IEEE International Conference on Knowledge engineering and Communication systems, ICKES 2022

作者： Srinivasan, Sruthi Dewan, M. Ali Akber School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computing and Information Systems Athabasca University Faculty of Sceinece and Technology AB Canada

ISBN: (纸本)9781665456371

Online learning has gained traction over recent years, especially since online education has become more widespread. However, it comes with its own set of challenges of which high dropout is still a major one. Identifying at-risk learners at an early stage is pivotal to offering personalized attention that can potentially prevent them from dropping out from the online courses. This work proposes two methods to analyze students' progress in an online course and subsequently identify dropout prone students. The first method performs fusion of course activity features by concatenating previous weeks' features before training. The second method extracts course activity features from the start date of the courses to a current week instead of concatenating as the first method does. A set of machine learning models and an ensemble framework were trained and tested on these two types of feature sets. On evaluating the models, the benchmark dataset KDDCup15 has been used, where the first method yielded an F1-score 91% while the second method yielded a score 92%. It was observed that both feature fusion methods produce comparable results although we expected that the concatenation of the features over time would produce better results. We also found that using features over a longer duration of time can help in achieving better performance. Further, ensemble model consistently outperformed the base classifiers. © 2022 IEEE.

关键词： Students

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Simulation analysis of erosion characteristics of slurry pipelines based on CFD-DDPM pipeline parameters 43

Simulation analysis of erosion characteristics of slurry pip...

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43rd Chinese Control Conference, CCC 2024

作者： Yuan, Haoran Wu, Jiande Xiong, Xin Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Yunnan Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Yunnan Kunming650500 China

ISBN: (纸本)9789887581581

In order to explore the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion of slurry pipe, a numerical simulation method approach grounded in Computational Fluid Dynamics - Dense Discrete Phase Model (CFD-DDPM) is proposed. Firstly, the geometric models of bends with different pipe diameters, bend-diameter ratios, and bend angles are constructed;secondly, the slurry pipe erosion model based on Dense Discrete Phase Model (DDPM) is constructed;then, model validation is conducted on the slurry pipe erosion model to affirm the accuracy of the model;finally, the computational fluid dynamics method is employed to simulate the erosion and wear behavior of the pipeline, to investigate the impact of pipe diameter, bend-diameter ratio and bend angle on the erosion and wear of the slurry pipeline. At the same time, gray correlation analysis was also used to analyze the significant relationship between the three pipeline factors and erosion and wear. The results show that the main erosion area is not determined, with the change of the pipeline parameters, the most serious erosion area will move;the effect of different pipeline parameter variations on the erosion rate is different, in which the pipe diameter of the largest impact, the second largest bend-diameter ratio, the smallest impact of the bend angle. Therefore, in the design of slurry pipeline, should first consider the influence of pipe diameter factors. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Slurry pipelines

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in engineering & Sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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