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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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A Multi-Objective Evolutionary Algorithm Based on Indicator and Performing Local Searches 4

A Multi-Objective Evolutionary Algorithm Based on Indicator ...

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4th international conference on Information Science and Education, ICISE-IE 2023

作者： Chen, Hanning Zeng, Xiangrui Jia, Kun Hu, Yabao Wang, Guopeng School of Computer Science and Technology Tiangong University Tianjin China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of Education Beijing China School of Artificial Intelligence Tiangong University Tianjin China Jingwei Textile Machinery Co. Ltd Beijing China College of Artificial Intelligence University of Science and Technology Tianjin China The Open University of China China

ISBN: (纸本)9798350394610

the indicator-based multi-objective optimization evolutionary algorithm has gained significant attention for its strong performance across various optimization problems. We draw on the advantages and try to make improvements in the environmental selection process based on different selection criteria. From this, we developed a distinctive multi-objective evolutionary algorithm that based on indicator and performing local searches, namely INLS. the INLS algorithm includes a novel selection mechanism that does not rely on weight vectors, which employs a strategy choosing solutions with relatively poor convergence but good diversity to perturb the decision variables of the selected solutions, aiming to explore and discover solutions that demonstrate enhanced convergence and diversity characteristics. the experimental results obtained from conducting tests on the ZDT and DTLZ test suites provide clear evidence of the remarkable performance exhibited by the proposed INLS in comparison to various other well-known MOEAs. these results serve to underscore the competitiveness of the proposed method. © 2023 IEEE.

关键词： Evolutionary algorithms

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Intelligent machine learning Method for Detecting Suspicious Encoded Messages 2

Intelligent Machine Learning Method for Detecting Suspicious...

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2nd international conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Hussain, Syed Pakkir Mohideen, S. B.S Abdur Rahman Crescent Institute of Science & Technology Computer Science and Engineering Department Tamil Nadu Chennai India B.S Abdur Rahman Crescent Institute of Science & Technology Computer Application Department Tamil Nadu Chennai India

ISBN: (纸本)9781665491990

With the use of an Advanced machine learning (AML) approach for suspicious coded message detection such as cryptanalysis SR4S or any messaging system, a framework to prevent worldwide crime, including terrorist attacks, bomb explosions, drone attacks, and all large and minor assaults. Terrorists communicate with their teammates in different parts of the world and give instructions with resources to attack the government or the public. To maintain secrecy, they use encrypted messages. the proposed framework at the server site will detect encrypted messages and decrypt them using Rail fence Cipher techniques. there is currently no proper solution to prevent a large crime across the globe. Suspicious Message Detection (SMD) is employed when a server receives an encoded message;the proposed system decrypts the message and accelerates the process of predicting the type of crime from microblogs before it is committed by criminals. the security sector will be less tense as a result of the dissemination of information regarding criminals. © 2023 IEEE.

关键词： Cryptography

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Classification and Prediction of Medical Dataset Using Ensemble DeepQ Classifier 4th

Classification and Prediction of Medical Dataset Using Ensem...

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4th international conference on Communication and Intelligent Systems, ICCIS 2022

作者： Salman, Rahama Gupta, Subodhini Jain, Neelesh Department of Computer Science SAM Global University MP Bhopal India Department of Computer Application School of Information Technology SAM Global University MP Bhopal India Department of Computer Science and Engineering SAM Global University MP Bhopal India

ISBN: (纸本)9789819923212

the clinical dataset consists of a huge amount of disease information and which marks negative effects with respect to features. Feature selection is a major key player removing redundant information. It also increases the decision making result in an effective manner. the important step in feature selections is classification and dimensionality reduction. Medical experts and researchers are using various machine learning techniques to predict and analyze the huge volume of data. the large scale volume of medical dataset is having number attributes and dimensions. Dimensions can affect the analytics and diagnose the results. In this paper we evaluate principle component analysis, support vector machine, factor analysis and ranking methods compared with our proposed CNN-based ensemble feature selection using K-nearest classifier. Our proposed method is followed as standard testing features with parameter optimization. Compared with the existing method our method has 94% of accuracy result by using TensorFlow. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

4th International Conference on Computing, Communications, a...

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

ISBN: (纸本)9789819914784

the proceedings contain 67 papers. the special focus in this conference is on Computing, Communications, and Cyber-Security. the topics include: Resource Sharing in Back Haul Satellite-Based NOMA Network;a Review on Various Deepfakes’ Detection Methods;Proposed Framework for Implementation of Biometrics in Banking KYC;paddy Pro: A MobileNetV3-Based App to Identify Paddy Leaf Diseases;Cryptanalysis of RSEAP2 Authentication Protocol Based on RFID for Vehicular Cloud Computing;a Survey on Code-Mixed Sentiment Analysis Based on Hinglish Dataset;An Alternative to PHP for the Development of Web applications: Java Server Pages Engine;Drone Ecosystem: Architecture for Configuring and Securing UAVs;novel Load Balancing Technique for Microservice-Based Fog Healthcare Environment;Self-improved COOT Algorithm for Resource Allocation in Cloud Data Centers;XGBoost-Based Prediction and Evaluation Model for Enchanting Subscribers in Industrial Sector;cleanO-Renewable Energy-Based Robotic Floor Cleaner;Impact of "COVID-19 Pandemic" on Children Online Education: A Review and Bibliometric Analysis;digital-Based learning in Indian Government’s Higher Education: Initiatives and Insights;Autonomous Vehicles Adoption Classification for Future Mobility in UAE Using machine learning;an Augmented Reality Framework as a Solution to Enhance the Experience of Visiting a Museum;Hybrid Real-Time Implicit Feedback SOM-Based Movie Recommendation Systems;Automatic SMS Spam Filtering for Disaster Response Using Classification Algorithms;an Improved Neural Network-Based Routing Algorithm for Mobile Ad Hoc Networks;Lung Cancer Diagnosis Using X-Ray and CT Scan Images Based on machine learning Approaches;hybrid machine learning Algorithm for Prediction of Malaria;yoga Pose Estimation Using machine learning;COVID Detection from Chest X-Ray Images Using Deep learning Model;preface.

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BERT-BiLSTM Architecture to Modelling Depression Recognition for Indonesian Text from English Social Media 4

BERT-BiLSTM Architecture to Modelling Depression Recognition...

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4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Chowanda, Andry Andangsari, Esther Widhi Yesmaya, Violitta Chen, Tin-Kai Fang, Hsiao-Lin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Humanities Psychology Department Jakarta11480 Indonesia Tainan University of Technology Creative Computing Centre Department of Comic Art Taiwan Taiwan Tainan University of Technology Living Service Industry Department Taiwan Taiwan

ISBN: (纸本)9798350318432

Depression is a common mental health disorder. It can greatly affect our daily lives. Depressed people are generally prone to negative emotions. Hence, recognising a sign of depression is an important task. Several techniques are proposed to model automatic depression recognition from several modalities. However, there is a limited number of datasets and research done in a local language (i.e. Indonesian). Expressing thoughts and feelings are unique based on their backgrounds (e.g. race, religion and culture). Hence, fine-tuning the model to a local language or culture is also important. this research aims to build a model using deep learning to recognise depression signs from the text in the local language (i.e. Indonesian). Seven models are proposed in this research to model depression recognition from social media. the result illustrates that combining Bidirectional Long short-term memory with Bidirectional Encoder Representations from Transformers architecture can improve the performance of the model. © 2023 IEEE.

关键词： Deep learning

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP&DL 2022

4th International Conference on 3D Imaging Technologies- Mul...

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4th international conference on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning, 3D IT-MSP and DL 2022

ISBN: (纸本)9789819911448

the proceedings contain 51 papers. the special focus in this conference is on 3D Imaging Technologies- Multidimensional Signal Processing and Deep learning. the topics include: Research on the application of 3D Modeling Technology in Mechanical Structure Teaching;2D Numerical Model Used to Investigate the Influence of Vegetation on Geomorphological Evolution of Mudflats;design and Experimental Verification of High Functional Density Cubesat System;space Design of Exhibition Hall Based on Virtual Reality;Identification of Expressway Traffic States Based on the Enhanced FCM Algorithm;algorithms Applied in Soft Tissue Deformation Simulation: A Survey;soft Tissue Cutting Based on Position Dynamics;research on application Technology of Multi-source Fusion of 3D Digital Resources in Power Grid;rapid Identification of Herbaceous Biomass Based on Raman Spectrum Analysis;a Knowledge Representation Method for Hierarchical Diagnosis Decision-Making Under Uncertain Conditions;research on Spam Detection with a Hybrid machine learning Model;research on Weld Defect Object Detection Based on Multi-channel Fusion Convolutional Neural Network;Design and Research of Highway Tunnel Construction Positioning System Based on UWB;research on Cross Domain Data Analysis and Data Mining Technology of Power Grid Digital Benefits;graph Convolutional Neural Networks for Drug Target Affinity Prediction in U-Shaped and Skip-Connection Architectures;SOC Estimation of Lithium Titanate Battery Based on Variable Temperature Equivalent Model;research and Implementation of Hybrid Storage Method for Multi-source Heterogeneous Data of Electric Distribution Network;Research on Measuring Method of Ball Indentation Based on MATLAB Image Edge Detection;multi-feature Extraction of Mineral Zone of Tabling through Deep Semantic Segmentation.

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A Time Series Clustering Method for Network Big Data 29

A Time Series Clustering Method for Network Big Data

引用

29th IEEE international conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhu, Yujia Min, Geyong Wu, Yulei Wang, Haozhe Science and Economy University of Exeter Falcuty of Environment Department of Computer Science ExeterEX4 4QF United Kingdom

ISBN: (纸本)9798350330717

In the era of big data, network data increase rapidly in a distributed manner, giving birth to the network big data. Network big data with the extra features such as distributed and decentralized data collection and storage, distributed and parallel data processing, more complex and evolving relationships among data, and heterogeneous data representation, pose opportunities together with challenges to the traditional network analysis algorithms. A hopeful solution is combining machine learning techniques with network big data analysis. In this paper, we proposed a novel time series clustering method which effectively combines machine learning techniques with network big data analysis for fault diagnosis task based on network logs. Verification experimental results on classic HDFS dataset demonstrate the outstanding performance of the proposed method. © 2023 IEEE.

关键词： deep clustering hidden representations network big data Time series clustering

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Base Station Traffic Prediction based on Feature Selection and Stacking Ensemble learning 23

Base Station Traffic Prediction based on Feature Selection a...

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4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Zhao, Long Huang, Youzhi Wang, Yanyan Xu, Yin Feng, Qiangzhong Chen, Enhong School of Computer Science and Technology University of Science and Technology of China Innovation + Research Institute GuoChuang Cloud Technology LTd Hefei China Innovation + Research Institute GuoChuang Cloud Technology LTd. Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798400700705

Accurately predicting base station network traffic is of great significance to improve network service quality and reduce base station operating costs. Aiming at the problem of low prediction accuracy of single model in the existing base station traffic prediction methods, a multi-model fusion prediction method based on feature selection and stacking ensemble learning is proposed. Firstly, a large number of features are constructed on the historical data, and then feature selection and correlation verification are carried out based on the tree model, and the features with high correlation are retained as the input of the predictive model to improve the performance and interpretability of the model. On this basis, a stacking ensemble learning prediction model with GDBT, XGBoost, LightGBM as the base learner and MLP as the meta-learner is established, and finally experimental verification is carried out on the real 1731 base stations. the results show that the mean squared error (MSE) and mean absolute error (MAE) of this method are reduced by 9.8% and 4.3%, respectively, compared with the single machine learning prediction model, and have better prediction accuracy and generalization ability. © 2023 ACM.

关键词： Base stations

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Design and Fabrication of Adhesive application System for Magnets in Motors 4

Design and Fabrication of Adhesive Application System for Ma...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sanjeev Kumar, S. Shree Arryaman, A.S. Suganthprabhu, R. Pritima, D. Arun Kumar, P. Sri Krishna College of Engineering and Technology Department of Mechatronics Engineering Coimbatore India Mahle Electric Drives India Pvt Ltd Coimbatore India

ISBN: (纸本)9798350300093

Electric Motors requires Permanent Magnets in them to function. the following paper explains the design, construction and critical analysis of an Adhesive applicant machine for magnets in rotor casings [1] [2]. through the integration of both human and mechanical systems, this innovation addresses the problem of work efficacy in an industry. We used a scientific research model to analyze and answer the question. the industry we chose for our model is on a small scale and is very simple to work with and analyze for its full potential. Our main objective is to construct a cartridge-based method to load the workpiece into the machinery and to create the ideal end-effector system for Glue delivery on the specified path [3]. Compared to electromagnets, permanent magnets on a rotor can both simplify the design and improve the efficiency of electric machines. the absence of automated assembly techniques for large machinery is a limitation, too. the strategy for resolving this issue is building a cohesion environment where people and machines work as a unified unit capable of producing more products. © 2023 IEEE.

关键词： Permanent magnets

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application of Vision-Series Transformer in screening for coronary heart diseases using coronary CT angiography 23

Application of Vision-Series Transformer in screening for co...

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4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Wang, Kunlun Meng, Hanyang Wang, Xingjun Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798400700705

With the development of computer vision, more and more researches support the utilization of machine learning (ML) in the diagnosis of coronary heart disease (CHD) using CT angiography. Artificial intelligence shows strength in assisting researchers and clinicians in CT scan information reading. CTA data provides a series of scans of images from patients and thus could be reconstructed as three dimensions (3-D) images, also known as volume rendering images. We use the projection of those 3-D images as a dataset and developed a deep neural network with Transformer to diagnose coronary heart diseases automatically. Compared with several traditional and classic deep learning methods, i.e. simple CNN, VGGNet, and ResNet which show accuracy of 52.5%, 57.82%, and 76.3%, respectively, vision transformer(ViT) improves it to 81.5%. Besides, we take great advantage of the vision transformer and series of CTA. Based on that, we developed a new deep learning model Vision-Series Transformer(ViST). It has better performance in accuracy(83.78%). 5-fold cross-validation was used to determine whether it performs better in other statistical indicators. It shows the same high sensitivity and specificity as ViT, better than that of other models. the results show that both ViT and ViST could be used in screening for CHD, while ViST has better statistical indicators. © 2023 Owner/Author.

关键词： Diseases

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