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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Electronic Music Type Identification Based on Multimodal learning Optimized Residual Neural Networks

Electronic Music Type Identification Based on Multimodal Lea...

引用

Cloud Computing, Big data Applications and Software Engineering (CBASE), international conference on

作者： Jingying Guo Peng Wang School of Media and Performing Arts Coventry UniversityCoventry CV1 5RW UK Beijing City University Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

The classification effect of electronic music may be low due to the complexity of its type branches. Therefore, a research on electronic music type identification method based on multimodal learning optimization residual neural network is proposed. This paper analyzes the characteristics of electronic music from three perspectives of short-time energy, time-domain distribution and frequency-domain distribution, divides the original continuous music signal into several short segments (frames), and extracts the corresponding short-time energy, time-domain variance and frequency-domain variance information. In the stage of classification and recognition, the residual module is introduced into the neural network, and three characteristic modes are fitted to determine the final classification results. In the test results, the AUC of the recognition results of rhythmic electronic music and atmospheric electronic music is basically above 0.7, and the minimum value is also above 0.68. On the overall perspective, the AUG mean is 0.0783 and 0.0615 lower than that of the control group, respectively.

关键词： Training Accuracy Frequency-domain analysis Feature extraction User experience Electronic music data mining Time-domain analysis Residual neural networks Software engineering

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Emotion Representation in Whole-brain Functional Connectivity patterns: An fMRI Study

Emotion Representation in Whole-brain Functional Connectivit...

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2017 3rd international conference on Artificial Intelligence and Industrial Engineering (AIIE2017)

作者： Yin LIANG School of Computer Science and Technology Tianjin Key Laboratory of Cognitive Computing and ApplicationTianjin University

The representation of emotion in human brain is an important question in the cognitive neuroscience. However, it was remained unclear to what extent the connections between different brain regions contribute to the emotion recognition. The present study mainly focused on the emotion decoding based on the functional connectivity patterns. We designed the experiment and collected the neural activities while participants viewed emotion stimuli using the functional magnetic resonance imaging(f MRI) technology. We constructed the whole-brain functional connectivity patterns for each emotion, and performed emotion classification using multivariate pattern analysis combined with machine learning algorithms. We found that emotions could be successfully decoded from the whole-brain functional connectivity patterns. These results provide new evidence that large-scale functional connectivity patterns contain rich emotion information and contribute to the emotion recognition. Our study extends exist f MRI studies on emotion perception and may further our understanding of how human beings achieve easy and quick recognition of emotions.

关键词： Emotion Functional magnetic resonance imaging Functional connectivity Multivariate pattern analysis machine learning

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Design and Application of Personalized Recommendation Algorithm for Electricity Marketing

Design and Application of Personalized Recommendation Algori...

引用

data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yu Yu Lin Bai Xinpo Zhu Hua Liu Tao Zhou Jianbo Wang Beijing China-Power Information Technology Co. Ltd. Beijing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the rapid development of the current economy and technology, the electricity consumption behavior of power users has shown an unprecedented trend of diversification, with diverse and rapidly changing demand patterns. To effectively respond to this market change, power companies urgently need to transform and upgrade, and use innovative marketing strategies to accurately meet user needs, improve service quality, and enhance market competitiveness. In response to the significant drawbacks of traditional power marketing methods, such as rigid strategies, difficulty in accurately matching personalized user needs, uneven distribution of power resources, and serious waste, this article explores and proposes a personalized recommendation algorithm for power marketing based on big data analysis and artificial intelligence (AI) technology. This algorithm utilizes advanced machine learning (ML) models to achieve precise prediction of users' electricity habits, preferences, and future demand through deep mining of massive user electricity data. The experimental results show that the algorithm proposed in this paper performs well in improving user satisfaction and enhancing user stickiness, while significantly reducing the operating costs of power enterprises and reducing resource waste, providing strong support for the sustainable development of the power industry.

关键词： data privacy machine learning algorithms Costs Electricity Biological system modeling Big data Prediction algorithms Power industry data models Sustainable development

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A Novel Rule-based Intrusion Detection System Using data mining

A Novel Rule-based Intrusion Detection System Using Data Min...

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2010 3rd IEEE international conference on Computer Science and Information Technology—ICCSIT 2010

作者： Lei Li, De-Zhang Yang, Fang-Cheng Shen School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China

Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks. data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Association rules and sequence rules are the main technique of data mining for intrusion detection. Considering the classical Apriori algorithm with bottleneck of frequent itemsets mining, we propose a Length-Decreasing Support to detect intrusion based on data mining, which is an improved Apriori algorithm. Experiment results indicate that the proposed method is efficient.

关键词： Intrusion Detection Rule-based Length-Decreasing Support Association Rules data mining

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Research on Negative learning Emotion recognition in English Classroom using Cross Space based Convolutional Neural Network

Research on Negative Learning Emotion Recognition in English...

引用

Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Xuanxuan Tan Xiaoli Hou Xiaojia Dai Lilanjun Tan Department of International Business and Economics Hunan Sany Polytechnic College Changsha China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In English classroom the teaching behavior, changes according to the student’s facial emotions which are observed by facial expressions and behavioral phases in the class. Nonetheless, in the English classroom, behavior of the students during learning is changeable and complex to understand by teacher’s and it also difficult for understanding the negative emotions such as bored, stressed and not interested in the English classroom. To overcome these limitations, a Cross-Space based Convolutional Neural Network (CS-CNN) model is proposed to recognize the which is observed from students. The proposed method learns multiscale information such as subtle differences between various emotions and complex negative emotion pattern efficiently by cross-space feature fusion. This helps to enhance the recognition performance to assist the teachers to change their teaching strategies to make the students more focused and improve learning environment of students. For extracting significant features from the facial images are preprocessed by histogram equalization and extracted using DeepNet architecture. The experimental results of the proposed CS-CNN method achieved accuracy of 94.33% which is higher when compared to existing recognition approaches such as Support Vector machine (SVM) and Yolo-V3.

关键词： Support vector machines Image quality Emotion recognition Histograms Accuracy Education Computer architecture Feature extraction Convolutional neural networks data mining

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Apple Leaf Disease Identification and Classification using ResNet Models

Apple Leaf Disease Identification and Classification using R...

引用

IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Xin Li Laxmisha Rai College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

With the development and popularization of intelligent agricultural system, more and more research and attention have been paid to the detection and identification of leaf diseases. We used data sets of apple grey-spot disease, black star disease, cedar rust disease and healthy leaves to study the identification and classification of apple leaf diseases. Image segmentation SVM classifier and ResNet and VGG convolutional neural network model were used for comparison and improvement. In the final experiment, ResNet-18 with fewer layers of ResNet obtained an accuracy rate of 98.5% achieving better recognition effects.

关键词： Training Symbiosis Support vector machines Image segmentation machine learning Classification algorithms Diseases

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Secure and Efficient IoT Networks: An AI and ML-based Intrusion Detection System

Secure and Efficient IoT Networks: An AI and ML-based Intrus...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Mohammed A. Mahdi Department of Information and Computer Science College of Computer Science and Engineering University of Hail Hail City Saudi Arabia

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The growing deployment of IoT devices has led to unprecedented interconnection and information sharing. However, it has also presented novel difficulties with security. Using intrusion detection systems (IDS) that are based on artificial intelligence (AI) and machine learning (ML), this research study proposes a unique strategy for addressing security issues in Internet of Things (IoT) networks. This technique seeks to address the challenges that are associated with these IoT networks. The use of intrusion detection systems (IDS) makes this technique feasible. The purpose of this research is to simultaneously improve the present level of security in ecosystems that are connected to the Internet of Things (IoT) while simultaneously ensuring the effectiveness of identifying and mitigating possible threats. The frequency of cyber assaults is directly proportional to the increasing number of people who rely on and utilize the internet. data sent via a network is vulnerable to interception by both internal and external parties. Either a human or an automated system may launch this attack. The intensity and effectiveness of these assaults are continuously rising. The difficulty of avoiding or foiling these types of hackers and attackers has increased. There will occasionally be individuals or businesses offering IDS solutions who have extensive domain expertise. These solutions will be adaptive, unique, and trustworthy. IDS and cryptography are the subjects of this research. There are a number of scholarly articles on IDS. An investigation of some machine learning and deep learning techniques was carried out in this research. To further strengthen security standards, some cryptographic techniques are used. Problems with accuracy and performance were not considered in prior research. Furthermore, further protection is necessary. This means that deep learning can be even more effective and accurate in the future.

关键词： Deep learning Accuracy Simulation Intrusion detection Real-time systems pattern recognition Internet of Things

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Automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks 13th

引用

13th international conference on machine learning and data mining in pattern recognition, MLDM 2017

作者： Antony, Joseph McGuinness, Kevin Moran, Kieran O’Connor, Noel E. Insight Centre for Data Analytics Dublin City University Dublin Ireland School of Health and Human Performance Dublin City University Dublin Ireland

ISBN: (纸本)9783319624150

This paper introduces a new approach to automatically quantify the severity of knee OA using X-ray images. Automatically quantifying knee OA severity involves two steps: first, automatically localizing the knee joints;next, classifying the localized knee joint images. We introduce a new approach to automatically detect the knee joints using a fully convolutional neural network (FCN).We train convolutional neural networks (CNN) from scratch to automatically quantify the knee OA severity optimizing a weighted ratio of two loss functions: categorical cross-entropy and mean-squared loss. This joint training further improves the overall quantification of knee OA severity, with the added benefit of naturally producing simultaneous multi-class classification and regression outputs. Two public datasets are used to evaluate our approach, the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST), with extremely promising results that outperform existing approaches. © Springer international Publishing AG 2017.

关键词： Convolution

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The Cryptographic Algorithm Identification: Using Deep learning to Empower Smart Grids

The Cryptographic Algorithm Identification: Using Deep Learn...

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Networking Systems of AI (INSAI), international conference on

作者： Mingliang Chen Yayun Zhu Lei Mei Xiaojuan Zhang Sheng Yuan Baiji Hu State Grid Jiangxi Electric Power Co. Ltd. Nanchang China China Electric Power Research Institute Co. Ltd. Beijing China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

The smart grid has now been fully implemented with a robust defense system that is built on the primary security principles of "security zoning, network specialization, horizontal isolation, and vertical authentication." The secure cryptographic module plays a pivotal role as one of the key components in the power system, facilitating horizontal isolation and vertical authentication. Nevertheless, assessing secure cryptographic modules within power systems presents several challenges. These include low recognition efficiency of cryptographic algorithms, the difficulty in identifying the characteristics of encryption algorithm variants, and challenges specific to special business scenarios. To address these issues, this paper introduces an automatic cryptographic algorithm recognition method. Leveraging a deep learning framework in conjunction with modules such as data encryption, data enhancement, feature extraction, model construction, and five-fold cross-validation, this method achieves automated recognition of cryptographic algorithms, enhancing the testing capabilities of secure cryptographic modules. The experiments reveal that cryptographic algorithm detection using deep learning offers significant advantages, including a high recognition success rate, strong adaptability, and a high degree of automation. This method proves beneficial in significantly enhancing the efficiency of security evaluation and modification, effectively detecting the ciphertext features of commercial codes, and providing vital support for the secure operation of the smart grid.

关键词： Deep learning machine learning algorithms Passwords Manuals Feature extraction Smart grids Encryption Classification algorithms Cryptography Security

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Deep learning-based Functional Prediction Model for Genome Sequence

Deep Learning-based Functional Prediction Model for Genome S...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Bo Lan United World College of South East Asia East Singapore Singapore

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

This study proposes a deep learning-based genome sequence function prediction model and systematically evaluates its performance. The study begins with an overview of the importance of genome sequence function prediction and its applications in biomedicine, followed by a detailed description of the main current research methods and advances. We use Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Transformer model for capturing local features, sequence dependencies and long-range dependencies, respectively. Through the combined application of multiple feature extraction methods, the model demonstrates excellent prediction performance on different genomic datasets. The experimental results show that the deep learning model can effectively improve the accuracy of genomic sequence function prediction, especially in complex feature extraction and pattern recognition. Convolutional neural networks have obvious advantages in local feature extraction, recurrent neural networks excel in dealing with the time dependence of sequence data, and the Transformer model demonstrates its superiority in dealing with long-distance dependencies and parallel computation.

关键词： Deep learning Recurrent neural networks Computational modeling Biological system modeling Genomics Predictive models Feature extraction Transformers Convolutional neural networks Bioinformatics

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