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Application of Artificial Intelligence Technology in Big data Nining 6th

Application of Artificial Intelligence Technology in Big Dat...

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6th international conference on Innovative Computing, IC 2023

作者： Zhou, Xueyun Qi, Yongjun Tang, Hailin Department of Computer Science and Engineering Guangzhou College of Technology and Business Guangdong510850 China Faculty of Metadata and Computing Guangdong Baiyun University Guangdong510450 China Mongolian University of Science and Technology Bayanzurkh District Ulaanbaatar13341 Mongolia

ISBN: (纸本)9789819920914

data mining is the process of expressing knowledge through data collection and processing. this is an important technique for efficiently extracting batch data. Artificial intelligence (AI) is a simulation technology of the human brain. It makes full use of computer technology and other subject technologies for intelligent learning and machine control. Both are advanced data management technologies, and both have broad application prospects. this article mainly introduces the clustering algorithm and TextRank algorithm. this paper uses AI technology to conduct research and application in big data mining (BDM), and establishes a mathematical model of the underlying TextRank algorithm. the model is solved by the TextRank algorithm, and the status of research and application of AI technology in BDM is assessed,. Experimental results show that the algorithm improves the research and application of embedded big data intelligent technology by 55% and reduces false positive rate and false positive percentage. Finally, by comparing the research and application analysis of artificial intelligence technology in large data storage. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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A Comprehensive Analysis on Predictor Models for Intrusion Detection using mining And learning Approaches 6

A Comprehensive Analysis on Predictor Models for Intrusion D...

引用

6th international conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Kumar, Yadala. Prabhu Babu, Burra. Vijaya Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram520002 India

ISBN: (纸本)9781665410359

Network security has emerged as a critical research subject due to the increasing importance of networks in modern life. An Intrusion Detection System (IDS) keeps tabs on the state of the network's software and hardware through the use of intrusion detection. In spite of decades of research, existing IDSs still confront difficulties in enhancing detection accuracy, decreasing false alarms, and detecting unknown assaults. Many academics have been working on IDSs that use machine learning and deep learning models to address the security issues. With the help of learning approaches, it is possible to automatically identify the key differences between normal and aberrant data. It is also possible to detect unknown threats using learning approaches because of their excellent generalizability. As a result of its impressive performance, learning researchers have turned their attention to the field of deep learning. An IDS taxonomy based on data objects is analysed in this study to identify and describe IDS literature based on machine learning and deep learning models. In comparison to regular traffic, data-driven network attack detection tends to focus on attack types that are more rare. Simulated systems rather than real-world systems are used to collect many datasets. these difficulties reduce the effectiveness of machine learning models for intrusion detection by training them on fictitious "sandbox"datasets. One such instrument is an IDS, which inspects network traffic in order to ensure the network's confidentiality, integrity, and availability in the case of an intrusion. An in-depth analysis of current NIDS-based models using different learning approaches is examined and presented in this study. this study is useful in identification of numerous threats occurring the network. © 2022 IEEE.

关键词： Network security

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Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining data 4th

Construction of Security Risk Prediction Model for Wireless ...

引用

4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Zhao, Guoqiang Zhang, Meitao Wu, Yingying Shandong Vocational College of Science and Technology Weifang261053 China Weichai Heavy Machinery Co. Ltd. Weifang261108 China Weichai Power Co. Ltd. Weifang261061 China

ISBN: (纸本)9783031200953

In multi axis NC machining, there is a certain security risk in wireless data transmission. A security risk prediction model for wireless data transmission in multi axis NC machining is designed. through frequent pattern mining, multi axis NC machining data wireless transmission security risk data mining is implemented. Based on machine learning, the security risk prediction model of data wireless transmission is constructed. the model is mainly divided into four parts: data collection and processing module, grey synthesis and Markov prediction module, Bayesian network and evidence theory module and risk quantification calculation module. AHP method is used to analyze the weight of model indexes. Test the prediction performance of the model. the test results show that the accuracy of risk prediction of the model in different scenarios is high, higher than 88%, and the prediction time is short, less than 6 s, which proves the prediction performance of the model. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Optimized design of Raman fiber amplifier based on improving extreme learning machine 6

Optimized design of Raman fiber amplifier based on improving...

引用

6th international conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Gong, Jiamin Wei, Xumeng Jin, Ku School of Modern Postal Xi'an University of Posts and Telecommunications China China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

An efficient method to design the broadband gain-flattened Raman fiber amplifier(RFA) with multiple pumps is proposed based on a Extreme learning machine optimized by the salp swarm optimization algorithm(SSA-ELM), and uses this method to optimize the parameter configuration of the multi-pump RFA. A multi-input multi-output SSA-ELM model is introduced to replace the complicated solving process of the nonlinear coupled Raman amplification equation. the differences between the above models and BP neural networks, traditional ELM model and GRNN model in the five evaluation indicators are analyzed. As a result, the model prediction performance proposed in this article is better. In order to improve the flatness of gain, the pump parameter is optimized by using the adaptive differential evolution algorithm to obtain the best parameter configuration. the simulation results show that the RFA designed by this method has achieved the expected effect, and the maximum error of the target gain and the predictive gain does not exceed 0.8dB. this method provides a new idea for the design of the Raman fiber amplifier in the future. © 2023 ACM.

关键词： Nonlinear equations

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Deep Active learning Image Classification Algorithm Based on Class-Wise Self-Knowledge Distillation 6

Deep Active Learning Image Classification Algorithm Based on...

引用

6th international conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Pang, Yuliang Liu, Ying Hao, Yu Gong, Yanchao Li, Daxiang Xu, Zhijie Xi'an University of Posts and Telecommunications Center for Image and Information Processing China Xi'an University of Posts and Telecommunications Center for Image and Information Processing International Joint-Research Center for Wireless Communication and Information Processing China University of Huddersfield International Joint-Research Center for Wireless Communication and Information Processing United Kingdom

ISBN: (纸本)9798400707674

the success of image classification techniques based on deep learning relies heavily on a large amount of labeled data. However, the cost of data annotation is often expensive. this paper investigates active learning algorithms for image classification to reduce the cost of data annotation. However, traditional active learning algorithms for image classification suffer from overfitting issues due to training deep neural networks with millions of parameters using a small amount of labeled data. this makes it difficult for the model to effectively assess the information richness of unknown unlabeled samples and is detrimental to the selection of high-value samples. To alleviate these issues, this paper proposes a deep active learning-based image classification algorithm with class-wise self-knowledge distillation. this algorithm reduces overfitting and class-wise variations by matching or distilling the predictive distribution between different samples with the same label during training, thereby enabling a more accurate evaluation of the informativeness of unlabeled data by the active learning algorithm and improving the performance of the classification model. Additionally, an efficient Shuffle Attention mechanism is introduced to improve the sample selection strategy by combining spatial and channel feature information of the images. the proposed algorithm is compared with five active learning baselines on CIFAR10, CIFAR100, SVHN, and FashionMNIST datasets. Experimental results demonstrate the proposed algorithm exhibits superior classification performance. © 2023 ACM.

关键词： Image classification

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Neural Topic Model with Distance Awareness 27th

Neural Topic Model with Distance Awareness

引用

27th international conference on pattern recognition, ICPR 2024

作者： Chen, Shangyu Zhao, He Huynh, Viet Phung, Dinh Cai, Jianfei FIT-DSAI Monash University Exhibition Walk ClaytonVIC3168 Australia CSIRO’s Data61 Garden St EveleighNSW2015 Australia School of Science Edith Cowan University 270 Joondalup Dr JoondalupWA6027 Australia

ISBN: (纸本)9783031781889

Neural topic models (NTMs) have shown their success in topic modeling with a wide range of applications in text analysis. NTMs based on generative models prioritize document representations with good reconstruction capabilities, but they are they are insufficient in preserving distances between documents in the topic space. To bridge this gap, inspired by manifold learning, we propose a neural topic model that enables the reflection of word-to-word relationships onto topic-to-topic associations. this is achieved by approximating the distances between documents in the word space within the topic space. Extensive experiments demonstrate that the proposed model outperforms state-of-the-art NTMs in improving the quality of learned topics, as evidenced by metrics such as purity, diversity, coherence. Beyond that, the model can provide more interpretable low dimensional visualizations of documents. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Customer Lifetime Value Analysis Based on machine learning 22

Customer Lifetime Value Analysis Based on Machine Learning

引用

6th international conference on Information System and data mining, ICISDM 2022

作者： Dai, Xinqian University of Massachusetts Amherst United States

ISBN: (纸本)9781450396257

Customer lifetime value (CLV) is a powerful tool to determine the value of customers and filter customers most likely to attrite or most likely to make their first purchase, especially for e-commerce companies. this article reviewed machine learning models in analyzing CLV and prospected some potential directions for future research. data of 8099 samples were collected and analyzed through four kinds of machine learning methods: Linear Regression, Support Vector machine, Random Forest, Neural Network. the correlations between features showed that CLV are generally affected by monthly premium auto, total claim amount, and coverage. Analysis through machine learning models has high precision and Random Forest performs best. CLV prediction and customer segmentation are vital in business field today. Marketers could take advantage of the huge amount of data and machine learning models to portrait customer behaviors. Collecting browsing and purchase histories is also beneficial for providing best offers to individual customers. © 2022 ACM.

关键词： Electronic commerce

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Rice Pest and Disease Classification using Transfer learning Inception V3 with data augmentation. 6

Rice Pest and Disease Classification using Transfer Learning...

引用

6th international conference on Information and Communications Technology, ICOIACT 2023

作者： Satoto, Budi Dwi Anamisa, Devie Rosa Yusuf, Mohammad Sophan, Mohammad Kautsar Indriyani, Tutuk Dwi Irmawati, Budi University of Trunojoyo Madura Information System Department East Java Bangkalan Indonesia University of Trunojoyo Madura Informatics Engineering Department East Java Bangkalan Indonesia Institut Teknologi Aditama Information System Department East Java Surabaya Indonesia University of Mataram Informatics Engineering Department West Nusa Tenggara Indonesia

ISBN: (纸本)9798350315639

Rice food security is crucial amidst rapid population growth and global climate instability. Rice is one of the primary food sources for most of the world's population, especially in the Asian region. It is necessary to carry out food security, especially by helping farmers identify pests and diseases. Computers can help identify conditions, one of which is through pattern recognition of pests and diseases. the method that can be used is the Deep learning convolutional neural network (CNN). this research used twelve types of pests and diseases that often attack rice plants: mealybugs, leafhoppers, weeds, stem borers, rats, and birds. the conditions that often attack are leaf blight, brown spot, blast, striped leaves, tungro, and healthy. this research uses data taken from agricultural locations and secondary data. Each class consists of 50-70 images. the proposed method is Inception transfer learning with augmentation. the contribution offered is an architecture that does not require training computation time and has high accuracy. the test scenario results show that the average model accuracy is 97.75, with a computational training time of around 45 minutes. MSE 0.0018, RMSE 0.0426 dan MAE 0.0018. Testing using experimental data takes 1-2 seconds, with confidence accuracy ranging from 95.8%-99.2%. © 2023 IEEE.

关键词： Convolutional neural networks

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machine learning Approaches for Electronic Design Automation in IC Design Flow 6

Machine Learning Approaches for Electronic Design Automation...

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6th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Varghese, M.P. Muthumanickam, T. Vinay Aka Mission's Research Foundation Deemed to Be University Department of Electronics and Communication Engineering Salem India Vinay Aka Mission's Research Foundation Deemed to Be University Vinay Aka Mission's Kirup Ananda Variy Ar Engineering College Department of Electronics and Communication Engineering Salem India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Due to the vast amount of data collected and the very high level of complexity in VLSI design and manufacturing, the implementation using machine learning can be used in physical design has increased significantly. ML can be used to increase the abstraction level that is obtained from complex simulations based on physics models and provide results that represent a significant level of quality. Computer science techniques such as pattern matching and machine learning can reduce the design time of VLSI circuits by working with large datasets. © 2022 IEEE.

关键词： Computer aided design

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MobileViT-based classification of Alzheimer's disease 6

MobileViT-based classification of Alzheimer's disease

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Li, Yanmei Tang, Jinghong Li, Lihong Wang, Xingyu Ding, Weiwu Li, Xiaoshuang Yu, Tao Wu, Xia Chongqing University of Technology School of Artificial Intelligence Chongqing China Chengdu Aerospace Science and Industry Big Data Research Institute Co. Ltd. Chengdu China

ISBN: (纸本)9798350325485

Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the detection and discovery of early AD is still difficult due to the insidious nature of its pathogenesis. With the rapid development of deep learning nowadays, Transformer has been commonly used in the field of computer vision and the results are very good. therefore, in this paper, we propose a MobileViT style model BrMobile for AD diagnosis using 1.5T T1-weighted baseline magnetic resonance images (MRI). the model combines the features of the Transformer and Self-Attention mechanisms and was tested on the ADNI dataset, achieving excellent accuracy. the accuracy of the method was 96.7%, 91.8% and 90.9% for the classification of AD with NC, MCI with NC and AD with MCI, respectively. In addition to these dichotomous tasks we also tested multiclassification AD vs. NC vs. MCI with a classification accuracy of 88.8%. Compared to other state-of-the-Art studies, this method provides an effective and accurate diagnosis in AD classification due to the light weight and efficiency of the model. © 2023 IEEE.

关键词： Neurodegenerative diseases

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