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Adaptive Nonlinear Dimensionality Reduction with a Local Metric 23

Adaptive Nonlinear Dimensionality Reduction with a Local Met...

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12th international Symposium on Information and Communication Technology, SOICT 2023

作者： Yao, Jun Chen, Huanhuan University of Science and Technology of China Anhui Hefei China

ISBN: (纸本)9798400708916

Understanding the structure of multidimensional patterns, especially in unsupervised cases, is of fundamental importance in data mining, pattern recognition and machine learning. Several algorithms have been proposed to analyze the structure of high dimensional data based on the notion of manifold learning. these algorithms have been used to extract the intrinsic characteristics of different types of high dimensional data by performing nonlinear dimensionality reduction. Most of these algorithms rely on the Euclidean metric and manually choosing a neighborhood size. they cannot recover the intrinsic geometry of data manifold automatically and accurately for some data sets. In this paper, we propose an adaptive version of ISOMAP that integrates the advantages of local and global manifold learning algorithms. Faster convergence rate of this algorithm is obtained by replacing the Euclidean metric with the arc length metric as the sum of second-order approximation to the geodesic distance. Our experiments on synthetic data as well as real world images demonstrate that our proposed algorithm can achieve better performance than that of ISOMAP and solve some harder data sets that are impossible for existing global methods. © 2023 ACM.

关键词： data mining

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A Comparative Analysis of Convolutional Neural Networks, Bi-Directional Gated Recurrent Units, and Bi-Directional Long Short-Term Memory for Sequential data Processing Using Human Emotions dataset 4

A Comparative Analysis of Convolutional Neural Networks, Bi-...

引用

4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Vambe, William Tichaona Garidzira, Tinashe Crispen Walter Sisulu University Institute for Theoretical and Computational Sciences Mthatha5099 South Africa Independent Research East London South Africa

ISBN: (纸本)9798350387988

Emotion classification is an important field that can help healthcare professionals or researchers in their day-to-day activities when dealing with the emotional diagnoses of people. Not having an accurate classification might hinder the correct diagnosis of what a person is feeling, which also affects the work of healthcare professionals or researchers. therefore, this study performed a comprehensive comparative analysis of three leading deep learning architectures: Convolutional Neural Networks (CNNs), Bidirectional Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) to know their effectiveness in sequential data processing using human emotion dataset guided by the CRoss Industry Standard Process for data mining (CRISP-DM) methodology. the models were evaluated using the accuracy, computational efficiency, and generalization ability matrix. Our results gave valuable insights into the optimal selection of neural network models for sequential data analysis, for emotion recognition. the GRU model had the highest accuracy of 92.90%, followed by the LSTM model with 92.70%, and lastly, the CNN with 91.95%. this analysis aims to guide researchers and practitioners in the effective application of DL techniques to improve the accuracy and reliability of emotion-based systems based on sequential data. © 2024 IEEE.

关键词： Convolutional neural networks

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Chess Piece Image recognition Using Transfer learning, Simple Neural Network, and Convolutional Neural Network 4

Chess Piece Image Recognition Using Transfer Learning, Simpl...

引用

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

作者： Yohanes, Gabriel Nursalim, Mario Nicholas Kurniadi, Felix Indra School of Computer Science Bina Nusantara University Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

Online chess has opened up a way for players to sharpen their skills through move analytics. Based on this feature, a support system called chess advisor can be utilized to assist players in a real-time match. However such system doesn't exist within the website itself, rather an involvement of 3rd party software is required. these kinds of software usually need assistance for input. this paper aims to create a system that is able to recognize chessboard state in real-time. We have proposed a chess piece image recognition system using transfer learning using ResNet50, a simple neural network, and a convolutional neural network (CNN). We collected our own 693 2D chess piece images dataset and performed data preprocessing to standardize the image dimensions. the proposed system detects and converts individual positions of each piece into a computer-readable output. this output can be used to facilitate the chess advisor in determining the best move. the transfer learning approach utilizing the ResNet50 model exhibited superior performance with an accuracy of 95.65%. this outperformed alternative methods such as the simple neural network and the CNN. the ResNet50 model's advantage stemmed from its pre-training on the ImageNet dataset, enabling effective pattern generalization. In contrast, the simple neural network and CNN achieved lower accuracies of 73.4% and 85%. these models encountered difficulties due to limited dataset quantity and quality, as well as the absence of regularization techniques. Nevertheless, the CNN displayed better consistency in generalizing patterns compared to the simple neural network. © 2023 IEEE.

关键词： Image recognition

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AIChainDNS: A Framework for Optimizing DNS Security through Blockchain and machine learning 4th

AIChainDNS: A Framework for Optimizing DNS Security Through ...

引用

4th international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024

作者： Kong, Lingshan Ma, Runsi Jia, Hongfeng Wang, He China Mobile Xicheng Beijing 100000 China

ISBN: (纸本)9789819653171

As a critical element of the internet’s framework, the Domain Name System (DNS) consistently faces security vulnerabilities. this paper proposes the AIChainDNS framework, which integrates the decentralization and consensus mechanisms of blockchain with the advanced pattern recognition capabilities of deep learning to enhance DNS security. the framework’s efficacy is validated through extensive tests, where AIChainDNS exhibits superior performance: utilizing the NSL-KDD dataset, the model demonstrates an anomaly detection precision of 98.26% on the Bytedance DNS dataset (BDD), it attains a 94.56% accuracy rate. these results demonstrate AIChainDNS’s potential to better satisfy the security demands of elite internet content providers in real-world scenarios. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Domain Name System Internet Security machine learning

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A Review on Skin Pigment and Vein pattern Detection Techniques 4th

A Review on Skin Pigment and Vein Pattern Detection Techniqu...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Kharakwal, Komal Raiwani, Y. P. Verma, Rohan HNB Garhwal Univ Dept Comp Sci & Engn Srinagar Uttarakhand India

ISBN: (纸本)9783031243660;9783031243677

Over the past few years, image processing has dominated work in suspect detection, medical science, and meteorology. Facial recognition, finger knuckles, Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM), vein patterns, and biometrics are all included in this review paper. Unlike birthmarks, RPPVSM is inherited, whereas birthmarks are acquired. the two primary elements of RPPVSM are matching and detection. Consequently, it is improbable that redundant evidence could produce a vein pattern. Several cases involving child sexual assault or disharmony have been resolved by using RPPVSMs as a biometric attribute for recognition. In this paper, we also give a review on findings and results of various authors.

关键词： RPPVSM Vein Criminal Identification Biometric

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Network intrusion detection model based on K-means algorithm and Multi-CNNs 4

Network intrusion detection model based on K-means algorithm...

引用

4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Zhou, Yang Zhang, Wei Zeng, Haoyang Zheng, Zhourong China Electronic Technology Cyber Security Co. Ltd. Chengdu610041 China The 30th Research Institute of CETC Chengdu610041 China Army Unit 95486 Chengdu610041 China

ISBN: (纸本)9781510686106

Network intrusion detection has always been a pivotal area in the field of network security. this paper proposes a novel network intrusion detection model that integrates the K-means algorithm with multiple convolutional neural networks (Multi-CNNs). this model comprises two main modules: network attack learning and network attack recognition. the network attack learning module focuses on learning known traffic features. Initially, the K-means algorithm is utilized to cluster the traffic features. Subsequently, Multi-CNNs are constructed, each tailored to extract the spatial features of the data within its respective cluster. through supervised learning, a corresponding relationship is established between the inputs and the attack patterns. the output of this module includes the K-means clustering centers and the trained CNN models, which serve as the foundation for network attack recognition. the network attack recognition module is responsible for detecting unknown traffic. Firstly, the unknown traffic features are assigned to their respective clusters according to the distance from them to the K-means clustering centers. then, Multi-CNNs are employed to identify the attack pattern for each clustered feature independently. Finally, the detection outcomes from the Multi-CNNs are fused to achieve intrusion detection. the experimental results demonstrate that the integration of K-means clustering and Multi-CNNs can significantly enhance the performance of network intrusion detection. Specifically, for the UNSWNB15 and CICIDS2017 datasets, the proposed model achieves an improvement in F1-score by 11.11% and 0.27%, respectively, compared to the baseline model. © 2024 SPIE.

关键词： Network intrusion

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Research on YOLOv4 Object Detection Based on K-means Algorithm and Fusion Attention Mechanism 4

Research on YOLOv4 Object Detection Based on K-means Algorit...

引用

4th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Peng, Chufei Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798350308082

Object detection technology is a machine learning application based on deep learning and computer vision technology, which can replace error-prone manual inspections in various scenarios, such as mask recognition applications. In view of the problems of easy omission and difficult error detection in traditional object detection algorithms, this paper proposes a research on YOLOv4 object detection based on K-means and fusion attention mechanism, with mask recognition as the research object. Firstly, the preprocessing of the dataset is carried out. the K-means algorithm is used to cluster and analyze the face mask dataset, generate more suitable anchor box prior boxes, and use the Mosaic method for data augmentation. then, the SE module and SPP module of the channel attention mechanism are introduced to improve the basic YOLOv4 model, and the SE-SPP3-YOLOv4 detection model is constructed. the experiment shows that the proposed improved model has significantly improved the detection rate and accuracy of both face and mask targets, and can perform accurate recognition of the wearing of masks on faces in specific application scenarios. © 2023 IEEE.

关键词： Deep learning

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machine learning Method Evaluates Upper-Limb Motor Function by fNIRS in Stroke Patients 4

Machine Learning Method Evaluates Upper-Limb Motor Function ...

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Xu, Weiwei Tao, Yi Zhu, Jialin Yuan, Ziwen Guo, Jing Wang, Gang Xi'An Jiaotong University Key Lab. of Biomed. Info. Engineering of Ministry of Education School of Life Science and Technology Xi'an China First Affiliated Hospital of xi'An Jiaotong University Department of Rehabilitation Xi'an China

ISBN: (纸本)9798350324303

the upper limb motor impairment after stroke seriously affects patients' quality of life, and the evaluation of upper limb motor function depends on professionals, which is subjective, complicated, and time-consuming. Neurophysiological information evaluation Fugl-Meyer assessment of the upper limb (FMA-UL) is expected to solve this problem. Our study aims to estimate FMA-UL using the functional connectivity measures and network properties of task state functional near-infrared spectroscopy (fNIRS). FNIRS was recorded when the subjects performed the hand-grasping task, representing the oxyhemoglobin concentration changes. Functional connectivity was gauged via Pearson correlation coefficients, selectively retained within a 10% to 50% threshold. We calculated two macroscale and two mesoscale network properties based on graph theory. then the support vector regression (SVR) was applied to estimate the FMA-UL. Cross-validation with leave-one-out, resulted in an R^{\mathbfth} of 0.49, with a root mean square error of 4.96, 5 of the 15 subjects had absolute errors less than 0.75, when the sparsity of functional connectivity is 40%. Functional connectivity and network properties by fNIRS is a promising method to predict motor impairment. Our study demonstrated that functional connectivity and network properties are correlated with motor impairment and brain reorganization in stroke patients. the method proposed reflects prospects for finding neural biomarkers to predict FMA-UL and is feasible for using fNIRS to predict the motor function of stroke. © 2023 IEEE.

关键词： Graph theory

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Real-time prediction of free lime in cement clinker using support vector machine algorithm 4

Real-time prediction of free lime in cement clinker using su...

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4th international conference on Big data Analytics and Practices, IBDAP 2023

作者： Kannan, Rathimala Ghanim, Ahmad Hanif Ramakrishnan, Kannan More, Satesh Multimedia University Faculty of Management Department of Information Technology Selangor Cyberjaya Malaysia Ipmi International Business School Jakarta Indonesia Multimedia University Faculty of Computing and Informatics Selangor Cyberjaya Malaysia Pt Cemindo Gemilang Tbk Performance Management Technical Directorate Jakarta Indonesia

ISBN: (纸本)9798350300192

Free lime content is an important quality parameter in the production of clinker, targeted between 0.5% to 1.5%. Existing studies have tried to predict absolute free lime content, using soft sensors data, with limited success due to the complexity of the clinker burning process. Subject matter experts believe that, instead of predicting the free lime absolute value, predicting free lime quality as good, over-burn, or under-burn is more practically beneficial. this study aims to predict the free lime clinker quality as good or under-burn by leveraging data mining methods and machine learning techniques. Seven months of hourly data pertinent to rotary kiln feed chemistry and operation parameters were collected from a real operational cement plant. Classification models were built using support vector machine (SVM). the SVM produced a sensitivity value of 0.998 for good clinker class and 0.865 for under-burn class with an accuracy of 96%. the availability of these predictions in real time can help plant operators to avoid under-burning and over-burning. Such insights will assist relevant cement plants to reduce off-specification products, coal usage, production cost, and carbon emissions. © 2023 IEEE.

关键词： Support vector machines

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Research on Financial Fraud Identification Model Based on Privacy-Preserving machine learning 4

Research on Financial Fraud Identification Model Based on Pr...

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4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Jiao, Li Li, Hui Shandong Institute of Commerce and Technology Jinan250103 China

ISBN: (纸本)9798400709449

Financial fraud will not only mislead investors and reduce investor confidence, but also disrupt market order and hinder the orderly development of the capital market. In order to solve the problem of financial fraud, based on the data mining process, a process framework of the financial fraud identification method based on machine learning is proposed, which includes three steps: sample and feature selection, data preparation, and model construction and analysis. Based on the process framework of the financial fraud identification method based on machine learning, GBDT methods are used to build models respectively, and the model effect is evaluated based on indicators such as recall rate. Due to the particularity of financial reporting fraud, the model should aim to identify fraud samples as much as possible, so the recall rate is a major evaluation index in this article. the recall rate of the GBDT model is better when based on fewer indicators. therefore, after comprehensively considering factors such as data acquisition costs, this article recommends using the GBDT model to identify financial fraud. © 2023 ACM.

关键词： machine learning

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