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Overview of anomaly detection techniques in machine learning 4

Overview of anomaly detection techniques in machine learning

引用

4th international conference on IoT in Social, Mobile, Analytics and Cloud, ISMAC 2020

作者： Toshniwal, Akanksha Mahesh, Kavi Jayashree, R. Computer Science PES University Bangalore India IIIT Dharwad India

ISBN: (纸本)9781728154640

In any dataset, events which deviate from the majority of regular patterns are called as rare events. these events can be any unusual activity, fraud, intrusion or suspicious abnormal event which may be harmful or helpful for the domain application which remains unidentified with a very large amount of data. these activities are called anomalies and are very important to detect because such undetected events might be any kind of attack in the network, sudden drops/increase in sales, the spread of disease, terrorist attacks. these anomalies can be identified using the techniques of anomaly detection (AD). there are many ways to detect anomalies like classification, nearest neighbor, clustering, statistical, spectral, information-theoretic and graph. this paper provides an overview survey of these different Anomaly Detection Techniques (ADT). Real-life data is not available as expected, so choosing the suitable AD algorithm depends on factors like input data, type of anomalies, output data, domain knowledge. © 2020 IEEE.

关键词： Anomaly detection

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proceedings of the 4th international conference on computer Science and application Engineering, CSAE 2020

Proceedings of the 4th International Conference on Computer ...

引用

4th international conference on computer Science and application Engineering, CSAE 2020

ISBN: (纸本)9781450377720

the proceedings contain 179 papers. the topics discussed include: human behavior recognition based on the genetic algorithm feature selection and sensor data;nonlinear function optimization based on adaptive genetic algorithm;research on spatial lighting based on particle swarm optimization algorithm;QoE evaluation method based on integration of ant colony algorithm and genetic algorithm;a hybrid forecasting model for stock price prediction;optimal scheduling of microgrid based on improved biogeography-based optimization algorithm;a multiple linear regression model for maintainability analysis considering maintainability design characteristics;broad learning system based on nonlinear transformation and feedback adjustment;improved particle swarm optimization based on gradient descent method;and algorithm of shape morphing based on bivariate non-linear interpolation.

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application of Agents to the Recognition of Mathematical Expressions from Noisy Images 2021

Application of Agents to the Recognition of Mathematical Exp...

引用

2021 the 4th international conference on Computational Intelligence and Intelligent Systems

作者： Daniel Ogwok Elizabeth M. Ehlers Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781450385930

Research in image and pattern recognition has been going on for a long time. State of art solutions have been presented, and this has only got better with the introduction of convolutional neural networks. these networks have proven to work well, especially with high dimensional data. Mathematical expressions are written and used on a daily basis; however, it becomes a challenge when they have to be digitalized from images, especially when there is noise in the image. Noise presents an additional layer of difficulty in recognition of the expressions. this paper presents a system that uses agents that implement various segmentation and machine learning methods to recognize mathematical expressions from noisy images taken in a classroom setting.

关键词： Image Recognition Support Vector machines Agents K-Nearest Neighbours Convolutional Neural Networks Connected Components Labelling

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A Motor Fault Detection Method Based on Optimized Extreme learning machine

A Motor Fault Detection Method Based on Optimized Extreme Le...

引用

4th international conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2020 held at the Mechanical Engineering Institute of the Russian Academy of Sciences, 2020

作者： Wei, Siwei Ma, Ning Su, Jun Deng, Wenhui CCCC Second Highway Consultants Co. Ltd. Wuhan China Wuhan Electronic Information Institute Wuhan430019 China School of Computer Science Hubei University of Technology Wuhan China

ISBN: (纸本)9783030671327

To solve the problems that the motor fault detection algorithm based on mathematical model is hard to be adapted to those categories with complex non-linear fault. the accuracy of existing fault detection algorithms is not satisfied. In this paper, two methods of feature selection and parameter optimization of simulated annealing-based whale optimization algorithm with (SAWOA) optimized extreme learning machine (ELM) are proposed. SAWOA is utilized to optimize the selection of network input variables and to determine parameters of hidden layer nodes of ELM. In order to verify proposed method, a real motor fault detection case is studied in the paper. Several methods are compared under the same conditions to find out the best method with satisfied accuracy and reliability. the experiment results show that proposed method is capable to improve the classification accuracy and reliability, effectiveness and application value are introduced in the article. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fault detection

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Energy Consumption Analysis of R-Based machine learning Algorithms for Pandemic Predictions 4th

Energy Consumption Analysis of R-Based Machine Learning Algo...

引用

4th international conference on Advanced Informatics for Computing Research, ICAICR 2020

作者： Benedict, Shajulin Agrawal, Prateek Prodan, Radu Indian Institute of Information Technology Kottayam Kottayam Kerala686635 India Lovely Professional University Phagwara Punjab India University of Klagenfurt Klagenfurt Austria

ISBN: (纸本)9789811636592

the push for agile pandemic analytic solutions has attained development-stage software modules of applications instead of functioning as full-fledged production-stage applications – i.e., performance, scalability, and energy-related concerns are not optimized for the underlying computing domains. And while the research continues to support the idea that reducing the energy consumption of algorithms improves the lifetime of battery-operated machines, advisable tools in almost any developer setting, an energy analysis report for R-based analytic programs is indeed a valuable suggestion. this article proposes an energy analysis framework for R-programs that enables data analytic developers, including pandemic-related application developers, to analyze the programs. It reveals an energy analysis report for R programs written to predict the new cases of 215 countries using random forest variants. Experiments were carried out at the IoT cloud research lab and the energy efficiency aspects were discussed in the article. In the experiments, ranger-based prediction program consumed 95.8 J. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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Frozen Language Models are Gradient Coherence Rectifiers in Vision Transformers 39

Frozen Language Models are Gradient Coherence Rectifiers in ...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Bai, Lichen Xiong, Zixuan Lin, Hai Xu, Guangwei Xie, Xiangjin Guo, Ruijie Kang, Zhanhui Zheng, Hai-Tao Kim, Hong-Gee Shenzhen International Graduate School Tsinghua University China Pengcheng Laboratory China Alibaba Cloud Computing China Machine Learning Platform Department Tencent China Seoul National University Korea Republic of

ISBN: (纸本)157735897X

Large language models (LLMs) have demonstrated remarkable performance in multimodal tasks even with frozen LLM Block and only a few trainable parameters. However, the underlying mechanisms of how LLMs enhance multimodal performance remains unclear. In this work, we focus on the phenomenon that "Merely concatenating a frozen LLM block to the Vision Transformer (ViT) encoder can yield significant performance enhancements. Moreover, the choice of LLM block and insertion position can have a substantial impact, leading to varying degrees of improvement". We analyze the optimization of the training process from the perspective of gradient dynamics and find that frozen LLM blocks act as gradient coherence rectifiers, aligning the gradients of different samples more closely during training. Furthermore, we demonstrate that the representation similarity between the inserted LLM block and the adjacent ViT block influences performance, with greater similarity tending to yield larger positive gains. through these findings, we can justify the selection of suitable LLM blocks to be inserted at appropriate positions, and introduce additional gradient backpropagation paths by incorporating LLM blocks, could improve the performance of vanilla ViT through the rectification effect of gradient consistency during the training process, without the need to add LLM blocks during inference. Our experiments demonstrate the effectiveness of this strategy, making the practical application of the gradient rectification effect feasible. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electric rectifiers

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4th international conference on Data Mining, Communications and Information Technology, DMCIT 2020

4th International Conference on Data Mining, Communications ...

引用

4th international conference on Data Mining, Communications and Information Technology, DMCIT 2020

the proceedings contain 74 papers. the topics discussed include: a hybrid machine learning model with cost-function based outlier removal and its application on credit rating;the learning context analysis system for digital textbook service on learning cloud;enterprise investment value analysis based on machine learning model of rapidminer;research on android ransomware protection technology;breast cancer classification using deep convolutional neural network;research on application of improved random forest in medical ultrasound image classification;tobacco-disease image recognition via multiple-attention classification network;application design and implementation for smart kindergarten;the design of improved neighborhood rough set algorithm and its application in diabetes prediction research;research on effectiveness evaluation method of product quality improvement plan based on actor analysis method;research on decision-making method for allocation of product maintenance spare parts;studies on data quality evaluation index system for internet plus government services in big data era;study on consumer product supplier evaluation method based on actor analysis thinking;and stochastic power flow calculation of high-density correlation input power grid based on hybrid Gaussian model.

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machine learning Technique for Energy, Performance and Cost-Effective Resource Management in Multi-Access Edge Computing 22

Machine Learning Technique for Energy, Performance and Cost-...

引用

proceedings of the 4th international conference on Information Management & machine Intelligence

作者： Vanjipriya V Suresh Annamalai Department of Networking and Communications SRM Institute of Science and Technology India

ISBN: (纸本)9781450399937

Modern cloud interconnects on efficient resource allocation and provisioning to reduce their energy footprint. Resource management is complicated by factors such as data centre energy utilisation, virtual machine migration, operating expense, and overhead. Researchers have been using virtualized technologies and methods as optimal-Multi-objective particle swarm optimization, Dynamic Power Saving Resource Allocation (DPRA), Least Squares Regression, etc. to improve the management of their study. Accurately allocating resources to cloud users to meet their requests and offer QoS is a difficult task because of the preceding steps. Allocating cloud infrastructure's resources in the most efficient way possible benefits both users and service providers. the difficulties of resource management are tackled in this study by employing novel approaches, heuristics, authentication, and virtualization. In order to distribute workloads over several physical nodes, cloud computing relies on dynamic scheduling with load balancing. Using the help of host load prediction and a Markov chain model with Particle Swarm Optimization (PSO), VM resources are dynamically allocated to appropriate input requests. High quality of service (QoS) for cloud applications is achieved by SLA-based resource optimization with deadline, cost, storage, and bandwidth targets. Compliance with Service Level Agreements (SLAs), efficient use of resources, and low energy consumption are all achieved using a prioritisation technique based on SLAs. Scheduled users can receive resources in a predetermined order thanks to queuing. We developed the M/M/c/K queuing paradigm for numerous users per server to lessen the burden on data centres. Hardware resource models, such as CPU, I/O, and memory use, reveal VM resource allocation. Information gathering enhances resource utilisation and reduces energy consumption.

关键词： Dynamic Power Saving Resource Allocation

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Two-stage Multi-task learning for Automatic Sleep Staging Method 21

Two-stage Multi-task Learning for Automatic Sleep Staging Me...

引用

proceedings of the 2021 4th international conference on Artificial Intelligence and Pattern Recognition

作者： Haole Xi Yagang Wang Shuiping Cui Rui Niu Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Sleep staging is an important preliminary examination for the diagnosis of sleep disorders. Proper identification of sleep stages is important for the diagnosis and treatment of sleep disorders. It is a common method to classify sleep stages through Polysomnography (PSG) signals, but artificial classification of sleep stages is complicated and prone to errors. To solve the above problems, a two-stage multi-task learning for automatic sleep staging method (Feature Engineering and LightGBM, FLNet) is proposed. In the first stage, sleep representation and event mapping are combined with Feature Engineering. the intrinsic correlation of EEG data can be better mined by selecting the specific frequency bands that affect sleep staging and normalizing the processed EEG data and analyzing the power spectral density. In the second stage, the machine learning model LightGBM is used to fully learn the preprocessed EEG data. On the basis of ensuring the improvement of various indicators and the robustness of the network, the memory consumption is reduced and the sleep stage speed is improved. Using PhysioNet's sleep-EDFX signal database, the accuracy of multi-classification and micro F1-score were 87.32% and 86.73%, respectively. the results show that the proposed method is superior to the existing machine learning methods, and has a significant improvement in the performance of automatic sleep staging, which is more suitable for clinical application.

关键词： EEG Feature engineering LightGBM Sleep staging

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Practical application of pedestrian intelligent detection technology based on computer vision

Practical application of pedestrian intelligent detection te...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Mingjing Liu School of Management Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665428408

Pedestrian detection based on computer vision technology uses camera equipment mounted in vehicles to obtain driving image data, and then calculates whether pedestrians exist from the image or sequence and gives accurate positioning. It improves the safety of driverless vehicles to a large extent and greatly reduces the probability of accidents Pedestrian detection is the use of computer vision technology to determine whether there is a pedestrian in the image or sequence, and at the same time to locate the pedestrian technology. In this paper, first of all, the threshold method, image segmentation technology is described, and then the feature extraction method is divided into two different categories of bottom features and machine learning to make an introduction, and then the classifier construction method is summarized. Finally, the future research of pedestrian detection technology is prospected through the existing problems.

关键词： Deep learning Electrical engineering computer vision Image segmentation Target recognition Feature extraction Video surveillance

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