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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1866 条记录，以下是131-140 订阅

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Automatic Deep learning for Automatic Target Detection of Sonar Images 3

Automatic Deep Learning for Automatic Target Detection of So...

引用

3rd international conference on machine learning and computer application, icmlca 2022

作者： Zhang, Peng Zhang, Pengjv Wu, Haoran Tang, Jinsong Naval University of Engineering 1 Jiefang Avenue No.717 Wuhan430000 China Anhui Agricultural University 1 Changjiang West Road No.130 Hefei230000 China

ISBN: (纸本)9781510664814

The typical deep learning detection workflow of sonar images uses a transfer-learning paradigm, i.e., to select an external optical image pre-trained classification network as the backbone for feature extraction, and then to fine-tune the entire R-CNN network on detection datasets. Though transfer learning can effectively avoid overfitting and accelerate network training, transferred models are usually too redundant and cannot achieve the optimal generalization. This paper uses Automatic Deep learning (AutoDL) to improve the automatic target detection performance of sonar images. An improved neural architecture search algorithm is applied to automatically design the architectures of R-CNN detectors. The automatically designed architectures don't require external optical images to pre-train and have lower requirements for R-CNN parameters and computations. Experiments on an actual measured sonar image dataset composed of small targets show that, the automatic detectors designed by the neural architecture search algorithm are better than the transfer learning detectors in terms of recall, detection precision, computations, and parameters. © 2023 SPIE.

关键词： Feature extraction

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Competing Texture Directions and Continuous Surface Types For 3D Palmprint Recognition 3

Competing Texture Directions and Continuous Surface Types Fo...

引用

3rd international conference on machine learning and computer application, icmlca 2022

作者： Lin, Sen Shang, Peng School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9781510664814

Three dimensional(3D) palmprint recognition is a prospective biometric recognition technology, and texture direction is one of its main features. Firstly, the texture direction extracted by the Shape Index(SI) is susceptible to noise interference, and to solve this problem, the noise is eliminated by convolution operation, meanwhile the texture direction is extracted using competitive coding. Secondly, the traditional Surface Type(ST) tends to ignore the small curvature feature information, while the palmprint adjacent points have the same curvature, so a Continuous Surface Type(CST) is proposed to obtain surface consistency. Finally, the method of Collaborative Representation(CR) is used to combine features to complete palmprint recognition in classification recognition. The results of the experiment on the 3D palmprint library of the Hong Kong Polytechnic University show that the average recognition rate obtained can reach 99.92%, and the average recognition time is 0.032s compared with other classification methods. The proposed method can improve the recognition accuracy of 3D palmprint while maintaining low recognition time, and the feature extraction ability and classification recognition effect are of practical value. © 2023 SPIE.

关键词： Palmprint recognition

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A Survey on Artificial Intelligence Models for Endometrial Tumor Detection, Classification and Diagnosis 3rd

A Survey on Artificial Intelligence Models for Endometrial T...

引用

3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Karthick, N. Nithya, P. Department of Computer Applications Sri Krishna Arts and Science Tamil Nadu Coimbatore641041 India Department of Networking and Mobile Application PSG College of Arts and Science Tamil Nadu Coimbatore641041 India

ISBN: (纸本)9783031862922

An Endometrial Tumor (ET) is a common uterine disease that continues to play a significant role in the mortality linked to a tumor. Advanced ET diagnosis exhibits worse diagnostic accuracy. The medically used ET screening methods require a significant amount of time and money, and not all patients can easily access them. So, oncologists have focused huge attention on molecular modeling as it has grown quickly, which has sped up the invention of computer-aided tumor detection systems. Artificial Intelligence (AI) models offer possibilities for molecular diagnostics, early tumor classification, efficient diagnosis, and diagnosis modality selection. It may be especially pertinent to use AI models in ET detection, classification, and diagnosis. From this perspective, researchers have been actively developing and implementing AI models to create effective ET diagnostic systems. This article studies the background of ET characteristics and earlier diagnosis techniques to stimulate further research in this field. The review is planned to investigate AI models, such as machine learning (ML) and Deep learning (DL) algorithms, for ET detection, classification, and diagnosis. After that, the positives and negatives of each classification model, such as Logistic Regression (LR), Support Vector machine (SVM), Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) networks, are compared. Amongst them, a multi-modal CNN-LSTM model achieves 93% accuracy on the TCGA-UCEC dataset, compared to the other models. At last, promising prospects are emphasized to achieve greater efficiency in classifying and diagnosing ET. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Transformative Influence: AI's application in Improving Risk Prevention and Management in Banking Institutions 3rd

Transformative Influence: AI's Application in Improving Risk...

引用

3rd international conference on Advancements in Smart Computing and Information Security, ASCIS 2024

作者： Kashni, Tarun Shukla, Anurag Dadwal, Neeraj Dadhich, Manish Alliance School of Business Alliance University Bengaluru India Departmet of Management and Commerce Arni University Kangra India School of Management Sir Padampat Singhania University Udaipur India

ISBN: (纸本)9783031862922

This paper examines deeper and profound implications of artificial intelligence on risk management practices for the banking and financial services industry. As AI technology continues to penetrate nearly every facet of finance, more banks embrace AI algorithms, including decision trees, random forests, and neural networks, with some related machine learning techniques aimed at improving their risk assessment processes and decision-making processes and mitigation strategies. In this context, the research study orients itself towards the application of AI in risk management and proceeds further to evaluate the efficacy and accuracy of AI models against conventional methods, using statistical tests and confidence intervals in order to justify such differences. Ethical considerations, such as algorithmic bias and data privacy, will be also discussed in depth, including strategies for mitigating such risks. The study of the present article outlines the transformative capacity of AI in banking risk management and its implications for the industry through an inclusive review of current literature and case studies, particularly in cybersecurity and risk assessment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Risk management

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Digital Signature and Hash Algorithms Used in Bitcoin and Ethereum 3

Digital Signature and Hash Algorithms Used in Bitcoin and Et...

引用

3rd international conference on machine learning and computer application, icmlca 2022

作者： Liu, Jiasong Department of Computer Science University of Manitoba WinnipegMBR3T 2N2 Canada

ISBN: (纸本)9781510664814

Digital signature and hash algorithms are essential components of the blockchain. Bitcoin and Ethereum use the same digital signature scheme Elliptic Curve Digital Signature Algorithm (ECDSA). However, they use the different hash algorithms. Bitcoin chooses to use Secure Hash Algorithm (SHA), and Ethereum uses Keccak-256. This paper studies the digital signature ECDSA by looking into its design, implementation and security. ECDSA is a variant of Digital Signature Algorithm (DSA). It requires a shorter key length than Rivest-Shamir-Adleman (RSA), so it was preferred to use in the blockchain. Furthermore, this paper will also explore the design and implementation of SHA-256 and Keccak-256. Bitcoin chose to use SHA-256 since it came out earlier than Keccak-256 with adequate security. Conversely, Keccak-256 is preferred by Ethereum since it has better performance and security compared to SHA-256. The role of SHA-256 and Keccak-256 in Bitcoin and Ethereum are also explored. SHA-256 and Keccak-256 are used in the blockchains' proof-of-work (or proof-of-stake) and merkle tree structure. The paper will also look into their security by analyzing the result of possible attacks against them. In addition, the paper will provide some thoughts on the security of ECDSA, SHA-256 and Keccak-256 by analyzing their designs and possible attacks. © 2023 SPIE.

关键词： Blockchain

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Review on application of machine learning Algorithm for Data Science 3

Review on Application of Machine learning Algorithm for Data...

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3rd international conference on Inventive Computation Technologies (ICICT)

作者： Dalal, Kushal Rashmikant 491 5th Ave Lyndhurst NJ 07071 USA

ISBN: (纸本)9781538649855

The concept of machine learning and data science is no more new buzz words in the area of computer science. People have started using it for their applications. The presented paper presents the tools used to implement machine learning used for data science application. This paper throws light on thorough review on algorithms of machine learning used specifically for data science application, the feasibility and suitability of machine learning techniques for a specific application is discussed.

关键词： Data Science machine learning

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application and Analysis of Fuzzy ARTMAP Network in computer Servo System 3

Application and Analysis of Fuzzy ARTMAP Network in Computer...

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3rd international conference on Mechanical, Control and computer Engineering (ICMCCE)

作者： Yang, Rui 10 Xitucheng Rd Beijing Peoples R China

ISBN: (纸本)9781538684818

From the perspective of operational logic, machine learning essentially uses the way of function, transforms the problem of machine learning into a function problem, and then implements machine learning by solving the objective function. The most important tool in solving the objective function is the optimization algorithm. In order to speed up the training process, it is necessary to design a parallel and distributed optimization algorithm in the context of big data. In recent years, many researchers have carried out extensive research on this issue, and some research results have been widely applied to the practice of machine learning.

关键词： machine learning neural network computer system fuzzy theory

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MaskBEV: Mask of Accurate 2D Object to Optimize Multi-view 3D Object Detection 3

MaskBEV: Mask of Accurate 2D Object to Optimize Multi-view 3...

引用

3rd international conference on machine learning and computer application, icmlca 2022

作者： Hao, Xiaoxing Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9781510664814

As a key part of autonomous driving technology, 3D object detection in Bird-Eye-View(BEV) view is designed to accurately detect road conditions based on multiple sensor inputs. However, current 3D detection methods are usually biased towards the acquisition of depth information. There is a strong correlation between 2D object detection and 3D object detection in BEV, especially the latest 2D detection has high robustness and accuracy in complex environments. To the end, this paper proposes a 3D object detection algorithm with object feature enhencement, which reasonably uses mask generated by 2D object detection as prior information and facilitates object extraction in BEV. In particular, in order to amplify the difference between the target and the background, we first designed the Data Preparation Module(DPM) to convert the 2D detection results into a binary target mask which completely separates the target from the background. Then, an Object Enhencement Module (OEM) is designed to enhence the contrast and texture details between the object and background. Finally, we propose Channel Interaction Module(CIM) considering the correlation between multi-channel features, which fully allocates the weights between channels. In this way, MaskBEV-Tiny scores 34.0% mAP and 41.1% NDS on the nuScenes val set which surpasses BEVDet-Tiny by a largin of 2.9% mAP and 2.0% NDS. © 2023 SPIE.

关键词： Object detection

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A New Dynamic Clustering Scheme for VANETs Driven by Deep Reinforcement learning 3

A New Dynamic Clustering Scheme for VANETs Driven by Deep Re...

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3rd international conference on machine learning and computer application, icmlca 2022

作者： Zhang, Rongbai Wu, Kezhi School of IT Deakin University Melbourne Australia School of IT Southwest University Chongqing China

ISBN: (纸本)9781510664814

Vehicular nodes in Vehicular Ad Hoc Networks (VANETs) are featured with high speed, unpredictable sudden acceleration, and constantly changing positions. Due to the continuous mobility, the topology of VANETs becomes highly dynamic, which causes difficulties in effective data transmissions among V2V and V2I. The difficulties are originated from high communication overheads for exchanging and updating the topology information, transmission delay or interruption caused by high-speed movements. In order to boost up the performance of VANETs, we take the clustering as an efficient method to segment the moving vehicles in the traffic environment into different subgroups. In regards to the service of vehicle network, some tasks like video transmissions with long periods or intensive data interactions require a stable connection within the cluster, the longer lifetime is one of important factors for maintaining the dynamics as well as the cluster stability. To improve the life time of cluster, we develop the reinforcement learning (RL) agents into self-organized VANETs to form stable clusters and therefore increase the average lifetime, which can interact with the dynamic vehicle environment and output the optimal policy about cluster creation. A simulation is conducted to compare our RL-based scheme against other schemes with no RL factors. The results show that our method outperforms other dynamic schemes in the context of a high lifetime in comparison with the fixed policy. The average life time of our scheme has 6.5 % and 13.6 % improvement approximately in the statics of cluster change time and average vehicle's speed. © 2023 SPIE.

关键词： Vehicular ad hoc networks

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A study of a federated learning framework based on the interstellar file system and blockchain: Private Blockchain Federated learning 3

A study of a federated learning framework based on the inter...

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3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Zhang, Peng Liu, Gang Chen, Zhiyu Guo, Jianwei Liu, Peng University of Technology School of Computer Science and Engineering Changchun ChangChun China Jilin Heshun Hengtong Technology Co. Product Department ChangChun China

ISBN: (纸本)9781665459112

Federated learning is a scalable machine learning paradigm in which a large group of individuals collaborates to build a high-quality machine learning model. This paper proposes Private Blockchain Federated learning (PriChainFL). It is a federated learning method that combines blockchain and IPFS, in response to the fact that federated learning is still vulnerable to attacks such as data poisoning and member inference attacks. It also reduces the storage cost of blockchain. © 2022 IEEE.

关键词： Blockchain

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