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共 3332 条记录，以下是281-290 订阅

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Image recognition Using machine learning for Classifying Finger Print recognition using direction filtering 4

Image Recognition Using Machine Learning for Classifying Fin...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Bhargava, Neeraj Bhargava, Ritu Taterh, Swapnesh Rathore, Pramod Singh Bhowmick, Apoorva Mds University Ajmer India Ajmer India Amity University Rajasthan India Manipal University Jaipur Jaipur India United Nations Secretariate New YorkNY10017 United States United Nations Secretariate Baghdad Iraq

ISBN: (纸本)9798350393248

Image processing is the new trend of implementing the image classification and many kinds of researchers are being working on this kind of implementation to process the data in the form of images. the current paper is focusing on identifying the images and processing the fingerprints which hold the secret information. We learn about steganography preparation, that involves the images which consists of secret information, and we need to decrypt that kind of information with some tools and techniques. Here in this article, we are focusing on the new concept of image processing which can consider the fingerprint of the human to hide secret information. this kind of information is very sensitive and can be shared with anyone and the concept behind photo-response non- uniformity is the way of linking pictures to the camera sensors which are having well established finger printing information. the information we transform from one user to the other user in the government organizations must be confidential and we are performing better PRNU with machine learning and the implementation consists of different neural network approaches and these approaches are more likely to work with existing approaches of PRNU. machine learning and image processing will lead the concept of highest secured data transmission with the data encrypted in the form of fingerprint. © 2023 IEEE.

关键词： Cryptography

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Intelligent Bone Fracture recognition Framework: Convolutional Neural Network(CNN): Note: Sub-titles are not captured in Xplore and should not be used 4

Intelligent Bone Fracture Recognition Framework: Convolution...

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4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Mishra, Debani Prasad Bhal, Arpita Kumar, Ayush Pradhan, Anirudh International Institute of Information Technology Bhubaneswar India International Institute of Information Technology Bhubaneswar India

ISBN: (纸本)9798331528171

In this study, we introduce a method to diagnose bone disease using advances in machine learning (ML) and deep learning (DL) technologies. Our scheme combines several different technologies with convolutional neural networks (CNN) to distinguish healthy bones. this new hybrid model represents a breakthrough in bone diagnosis and provides a significant performance in computation time compared to existing deep CNN models. the main innovation is that we use an improved sharp-edge algorithm to accurately identify cracks in X-ray images. then the grayscale image and the corresponding image are input into the algorithm for training and evaluation. We perform a performance test evaluating our method against a state-of-the-art deep CNN model using skeletal muscle data. Our results showed the best performance in fracture diagnosis, achieving irregularity, F1 score, and recall of 96.12%, 99%, and 98%, respectively. this demonstrates the effectiveness of combining algorithms to improve CNN performance, demonstrating the potential of our method to revolutionize bone diagnosis in clinical practice. © 2024 IEEE.

关键词： Convolutional neural networks

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Failure analysis of hydraulic system of armored assault vehicle based on Apriori algorithm 4

Failure analysis of hydraulic system of armored assault vehi...

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

作者： Kong, Zining Zhang, Xiaowei Dong, Wentao Dong, Yucai The Fifteenth Research Institute China Electronics Technology Group Corporation Beijing China

ISBN: (纸本)9798400709449

Extract the maintenance data of hydraulic system of armored assault vehicle, use Apriori algorithm, carry out the association rule mining, get the typical association rules, mine all kinds of faults occurring in the geographical area, motorcycle hours and other factors of the correlation, for the development of maintenance plan, maintenance equipment procurement and storage to provide decision-making support for improving the effectiveness of the equipment protection to provide technical support. © 2023 ACM.

关键词： data mining

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Application of Deep learning Neural Networks in Sports Shoe Brand Classification Task 4

Application of Deep Learning Neural Networks in Sports Shoe ...

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4th international conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2023

作者： Li, Biao Sun, Guang Sheng, Haoyu Liu, Junjun Hunan University of Finance and Economics School of Information Technology and Management Changsha China Central South University School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350373233

In recent years, deep learning has gained widespread attention and importance due to its outstanding performance in image feature learning. the rapid development of machine learning and deep learning has provided a powerful tool for handling complex classification problems and has achieved significant results. the development of neural network algorithms has also benefited from the progress in deep learning research. Deep learning is a machine learning method based on multi-layer neural network structures, which improves the modeling capability for complex data and tasks by increasing the depth and complexity of the network. For the task of classifying shoe brands, deep learning can help neural network models better understand features such as appearance, texture, and brand logos of shoes, as well as identify other everyday items to assist in their classification. After identifying the items, using different optimizers can improve the accuracy of classification, enabling more effective classification decisions. © 2023 IEEE.

关键词： Image recognition

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A Residual Neural Network-Based Incremental learning Model for Driver State Perception 4

A Residual Neural Network-Based Incremental Learning Model f...

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4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Cai, Jie Zhang, Jingming Sun, Erxin Cai, Jianjun Company Ltd Research and Development Department Tianjin China

ISBN: (纸本)9798350314496

this paper presents a model for implementing active machine sensing of fatigue states in an intelligent cockpit. To make the model more robust and achieve higher accuracy, the model introduces residual modules in the convolutional neural network and incorporates input primitive attributes and contour information in the learning. In addition, new learning branches are added to the network. By learning the user's personalized performance data in the new branch, the model is more adaptable to different drivers. With reference to the user state by the model perceived, the cockpit switches between different cloud scenario modes. Finally, in comparative experiments, our model shows advantages over other models in driver state recognition. © 2023 IEEE.

关键词： learning systems

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

4th International Conference on Innovations in Computational...

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

ISBN: (纸本)9789819769919

the proceedings contain 40 papers. the special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. the topics include: dataset Balancing Techniques and Supervised learning Algorithms for Predictive Analysis of Rice and Corn Yields;fish Blood Cell as Biological Dosimeter: In Between Measurements, Radiomics, Preprocessing, and Artificial Intelligence;predictive Analysis for Early Detection of Breast Cancer through Artificial Intelligence Algorithms;boosting Security: An Effective Approach to Intrusion Detection in Wireless Sensor Networks with AdaBoost Classifiers;Chat2Fluency: Enhancing Language learning through Conversational AI;REED-NET: Residual Enhanced Encoder-Decoder Network for Low-Dose CT Reconstruction;comparative Analysis of Large Language Models;Visualizing Insights to Empower HR Decision-Making: A data-Driven Approach;handling Uncertainty in Parkinson’s Disease Voice data Using Intuitionistic Fuzzy Entropy Measure;Exploring Shopping Opportunities and Elevating Customer Experiences through AI-Powered E-Commerce Strategies;ioT-Based Vehicle Class Detection for Smart Traffic Control;Domain Adaptation for NER Using mBERT;SQL Query Recommendation Based on Matrix Factorization;retenSure: Ensemble learning for Managing Employee Attrition;Subject–Verb Agreement Error Handling Using RNN Architectures;ad-Spend Analytics;an Efficient Real-Time Word-Level recognition of Indian Sign Language;innovating Drug Design for Alzheimer’s Disease via Reinforcement learning for Enhanced Molecular Generation;an Efficient Real-Time recognition of Static Kannada Sign Language;unveiling Diagnostic Clarity: A machine learning Approach to Distinguish Borderline Personality Disorder and Bipolar Disorder for Enhanced Mental Health Diagnostics;DLSTM with Adam Waterwheel Optimization for Groundwater Level Prediction in India;PhishGuard: machine learning Model for Real-Time URL Detection;Improved Grasshopper Optimization with Squeezenet (IGO-SNet)

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dataset Bias in Deception Detection 26

Dataset Bias in Deception Detection

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Mambreyan, Ara Punskaya, Elena Gunes, Hatice Univ Cambridge Dept Engn Cambridge England Univ Cambridge Dept Comp Sci & Technol Cambridge England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

With the advances in machine learning, lie detection technology gained significant attention. In recent years, several multi-modal techniques achieved as high as 99% accuracy results using the Real-life Trial dataset with only 121 data points. this led to considerable media hype and research interest in lie detection with machine learning. In this paper, we analyze the effect of dataset bias in deception detection. More specifically, we train a classifier to predict the sex of the identity appearing in the video. On a test data point, we use the sex predictor to predict sex which we use as a proxy for predicting deception, predicting lie for females and truth for males. this lie predictor simulates a classifier that uses nothing but dataset bias. Nevertheless, we find that the performance of this biased classifier is comparable to those of state-of-the-art papers. More specifically, when using IDT features, our biased classifier achieves 64.6% and 59.3% AUC while a classifier trained normally on truth/lie labels achieves 57.4% accuracy and 69.3% AUC. We perform similar experiments on the Bag-of-Lies dataset and show that it too is biased with respect to sex. In addition, we apply the state-of-the-art techniques on an unbiased dataset and show that their performance is no better than chance. Our experiments strongly suggest that the results of recent deception detection techniques can be explained by the bias inherent in the datasets.

关键词： Measurement Industries Ethics machine learning algorithms Government machine learning Detectors

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2023 4th international conference on Advancements in Computational Sciences, ICACS 2023 - Proceedings

2023 4th International Conference on Advancements in Computa...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

ISBN: (纸本)9781665463775

the proceedings contain 30 papers. the topics discussed include: automated white matter segmentation in MR images using residual UNet;open research knowledge graph for structuring scholarly contributions using transformers;a novel framework for human action recognition based on features fusion and decision tree;abnormal action recognition in crowd scenes via deep data mining and random forest;automate appliances via gestures recognition for elderly living assistance;predicting early withdrawal of university students: a comparative study between KNN and decision tree;design and analysis of an improved deep ensemble learning model for melanoma skin cancer classification;detection of tomato leaf disease using deep convolutional neural networks;natural language processing (NLP) based extraction of tacit knowledge from written communication during software development;multipath mitigation for single frequency stand-alone receivers using wavelet denoising;deep activity recognition based on patterns discovery for healthcare monitoring;and artificial neural network for human object interaction system over aerial images.

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Speeding up Heterogeneous Federated learning with Sequentially Trained Superclients 26

Speeding up Heterogeneous Federated Learning with Sequential...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zaccone, Riccardo Rizzardi, Andrea Caldarola, Debora Ciccone, Marco Caputo, Barbara Politecnico Torino Turin Italy

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Federated learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. this approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational heterogeneity. In particular, the presence of highly non-i.i.d. data severely impairs both the performance of the trained neural network and its convergence rate, increasing the number of communication rounds requested to reach a performance comparable to that of the centralized scenario. As a solution, we propose FedSeq, a novel framework leveraging the sequential training of subgroups of heterogeneous clients, Le. superclients, to emulate the centralized paradigm in a privacy-compliant way. Given a fixed budget of communication rounds, we show that FedSeq outperforms or match several state-of-the-art federated algorithms in terms of final performance and speed of convergence. Finally, our method can be easily integrated with other approaches available in the literature. Empirical results show that combining existing algorithms with FedSeq further improves its final performance and convergence speed. We test our method on CIFAR-10 and CIFAR-100 and prove its effectiveness in both i.i.d. and non-i.i.d. scenarios.(1)

关键词： Training Performance evaluation Federated learning Computational modeling Neural networks Statistical distributions data models

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Intelligent financial management system based on data mining Technology 4

Intelligent financial management system based on Data Mining...

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4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Du, Chunyu Chengdu Polytechnic Chengdu610095 China

this article is based on decision tree algorithm and data mining analysis technology to achieve the intelligence of financial management and decision-making in enterprise, providing effective support for financial management. An improved metric based C4.5 decision tree algorithm was proposed, which combines data warehousing, data mining, and analysis techniques, and adopts a data-driven approach to establish intelligent financial management in enterprise. Apply it to early warning of budget execution progress in financial projects, conduct experimental analysis and result research. In this experiment, data mining achieved significant results. From the root node of the decision tree, it can be seen that whether the project budget execution progress at the end of July is greater than 22.79% is of great significance for determining whether the project budget can reach 97% of the year-end budget execution progress. Based on the experimental results, it is concluded that as of July, more attention should be paid to the execution of project budgets, and effective management of data is the primary task and goal of financial institutions in enterprise. © 2024 the Authors.

关键词： Decentralized finance

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