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

共 1897 条记录，以下是861-870 订阅

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Towards Practical Secure Privacy-Preserving machine (Deep) learning with Distributed Data 6th

Towards Practical Secure Privacy-Preserving Machine (Deep) L...

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33rd international conference on Database and Expert Systems applications (DEXA)

作者： Kumar, Mohit Moser, Bernhard Fischer, Lukas Freudenthaler, Bernhard Software Competence Ctr Hagenberg GmbH A-4232 Hagenberg Austria Univ Rostock Fac Comp Sci & Elect Engn Inst Automat Rostock Germany

ISBN: (纸本)9783031143434;9783031143427

A methodology for practical secure privacy-preserving distributed machine (deep) learning is proposed via addressing the core issues of fully homomorphic encryption, differential privacy, and scalable fast machine learning. Considering that private data is distributed and the training data may contain directly or indirectly an information about private data, an architecture and a methodology are suggested for 1. mitigating the impracticality issue of fully homomorphic encryption (arising from large computational overhead) via very fast gate-by-gate bootstrapping and introducing a learning scheme that requires homomorphic computation of only efficient-to-evaluate functions;2. addressing the privacy-accuracy tradeoff issue of differential privacy via optimizing the noise adding mechanism;3. defining an information theoretic measure of privacy-leakage for the design and analysis of privacy-preserving schemes;and 4. addressing the optimal model size determination issue and computationally fast training issue of scalable and fast machine (deep) learning with an alternative approach based on variational learning. A biomedical application example is provided to demonstrate the application potential of the proposed methodology.

关键词： Privacy Homomorphic encryption machine learning Differential privacy Membership-mappings

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Examining Driver Sleepiness Detection Based on Visual Actions Using Haar Cascading and CNN 3

Examining Driver Sleepiness Detection Based on Visual Action...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Rawat, Vandana Manral, Pratibha Gupta, Lakshya Joshi, Nidhi Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Department of Computer Application Dehradun India Graphic Era Hill University Department of Computer Application Dehradun India

ISBN: (纸本)9798331519582

Every hour, there are numerous road accidents all over the world. Some of these accidents are caused by drunk driving, lack of sleep, inattention behind the wheel, and a variety of other factors that can be dangerous for both passengers and other road users. The most frequent circumstance is sleeping deprivation, which can cause the driver to be reckless while operating a motor vehicle. Driver drowsiness detection systems are particularly effective in identifying drowsiness by measuring the size of the driver's eyes and the rate at which they blink using a camera and software that is connected to it. The driver fatigue detection system works utterly offline, leveraging the Haar Cascade and Perclos machine learning methodologies, and can warn the driver via alarm if he or she is feeling drowsy. © 2025 IEEE.

关键词： Time division multiple access

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Evaluating Fake News Detection Models and Associated Biases in IoT 3

Evaluating Fake News Detection Models and Associated Biases ...

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3rd international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2022

作者： Qian, Long Qian, Hao Chen, Yangyu School of Economics Xihua University Chengdu610039 China Nantong Vocational College of Science and Technology Nantong226007 China

ISBN: (纸本)9781510657281

Fake news has always been a problem for online communities. It can build mistrust, create boundaries between people, and lower productivity in many government departments. Distinguishing fake news by human is slow and expensive, therefore, machines were proposed by many scholars to do the job. There are mature machine learning algorithms available to use for distinguishing fake news and real news. Despite the high accuracy of most well designed machine learning models, they can still make mistakes on some news. In this paper, we compared three machine learning models for news classification and evaluate biases in these models. The first one is Embedding Bag, second one is CNN network, and then the Naive Bayes Classifier. After tidying and vectorizing the data, they were feed to these classifiers to get the model. As a result, all of the models achieved very high accuracy. All of them had an accuracy of higher than 90 percent. The biases for all of the approaches are primarily sampling biases. The less represented words have higher chances to be mis classified than those more represented words in the database. © 2022 SPIE.

关键词： machine learning

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An Intelligent machine learning Approach for Prediction of Road Accident Severity 3

An Intelligent Machine Learning Approach for Prediction of R...

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3rd IEEE international conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Ragam, Prashanth Rahul, Namana Sri Balaji, Pandi Jyothi, Sankati Danaveni, Pavithra School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India VIT-AP University Software Development Cell Andhra Pradesh Amaravati522237 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798331533205

Road accidents are a primary global concern for public safety, with India having a very high death toll. This study presents an intelligent machine learning approach to predict the severity of road accidents, contributing to the development of more effective and preventive measures. We utilized a comprehensive dataset of road accidents in India, employing advanced machine learning algorithms, including Histogram-based Gradient Boosting (HGB), Random Forest (RF), Support Vector machines (SVM), and K-Nearest Neighbors (KNN). Our methodology involved extensive data pre-processing, feature engineering, and model optimization. The Random Forest algorithm demonstrated superior performance, achieving an accuracy of 94.7% and an F1-score of 0.947. This research provides a robust predictive model and information on the key factors influencing the accident's severity. The novelty of our approach lies in the comprehensive feature engineering tailored to the Indian context and the application of ensemble learning techniques to capture complex, nonlinear relationships in accident data. The findings of this study have significant implications for policymakers, urban planners, and law enforcement agencies in their efforts to improve road safety. © 2025 IEEE.

关键词： Highway accidents

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The World of the Bluetooth 3

The World of the Bluetooth

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3rd international conference on Electronics and Communication;Network and computer Technology (ECNCT)

作者： Shi, Ruotong Ivy Middle Schoole Taiyuan Shanxi Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

At present, some achievements have been made in the field of Bluetooth, but the technical level needs to be improved. Relevant companies summarize the Bluetooth technology in the wireless communication field involved in their products in order to overcome the bottleneck problem of Bluetooth all over the world. The purpose of this paper is to analyze the current situation of Bluetooth technology, such as high power consumption, unstable technology and low security index, so as to broaden its application scope and field, so that it is no longer limited to household appliances, and can be applied to automobiles, information points, aviation, consumer electronics and military in the future. The data of this paper comes from the report of today's Bluetooth market. The conclusion is that Bluetooth will shine in the field of the Internet of things and wearable devices in the future, which is no longer limited to our previous research field.

关键词： The development of Bluetooth the course of Bluetooth the application of Bluetooth the prospect of Bluetooth the advantages and disadvantages of Bluetooth

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EAFNet: Feature Enhancement and Self-Adaptive Guided Feature Fusion for Object Detection in Haze Conditions 3

EAFNet: Feature Enhancement and Self-Adaptive Guided Feature...

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3rd international conference on Electronic Information Engineering and computer, EIECT 2023

作者： Hu, Xinxin Zhou, Yuanbo Kuang, Xu Chen, Hao Lin, Xiaoyong Liu, Fang Liu, Hongjun Wang, Xiaofei Tong, Tong Gao, Qinquan Fuzhou University Fuzhou China Xiamen University of Technology Fujian Provincial Key Laboratory for Network Audiovisual Application Innovationand Fujian China Chongqing Dadukou District Archives Chongqing China Imperial Vision Technology FuzhouFujian China

ISBN: (纸本)9798350357707

Recently, there is a significant success in the current object detection task. However, existing research has demonstrated that detectors trained on regular images experience a significant decrease when facing to wild scences such as haze scence. Although enhancing detection performance is possible through multi-stage adaptive parameter-guided image enhancement, this method fails to consistently enhance downstream object detection performance. To address this issue, we propose an end-To-end object detection framework that utilizes weakly supervised parameter-guided feature fusion to improve detection performance. Specifically, we introduce the Self-Adaptive Feature Fusion Module (SAFFM) that dynamically guides feature fusion in the detection network through the haze image information encoded by the Prior Knowledge (PK) components. By the joint learning of the PK components and the detection network in an end-To-end manner, we ensure that the PK components weakly supervise the feature fusion process to enhance detection performance. Additionally, we propose a Downsampling Feature Enhancement Module (DFEM) that utilizes multibranch down-sampling operators and reparameterization to enhance the feature extraction capability of the network, allowing it to capture more latent information. Comprehensive experiments on synthetic datasets and real datasets, demonstrated that the detection performance of the proposed network significantly outperforms other state-of-The-Art detection methods. © 2023 IEEE.

关键词： Object detection

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application of a Multimodal Model Optimized by Multi-Head-Attention Mechanism in the Classification of Fishery Resource Remote Sensing Data Files 3

Application of a Multimodal Model Optimized by Multi-Head-At...

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3rd international conference on Advanced Algorithms and Neural Networks, AANN 2023

作者： Zhang, Fengwei Tao, Wenxin Information Technology Research Center Chinese Academy of Fishery Sciences Beijing China Faculty of Computing Harbin Institute of Technology Harbin China

ISBN: (纸本)9781510668355

Remote sensing data is a complex transformation process from data to information, from acquisition to application. These data come from a variety of sources and have a complex structure. It is extremely difficult for different researchers to access each other, which seriously affects the efficiency of scientific research. How to use artificial intelligence technology to manage these scattered files in a systematic multi-level classification is the key to remote sensing data management. In this paper, we propose a specially optimized file classification method based on deep learning technology, which aims to classify the random heterogeneous data generated by satellite remote sensing and expeditions in the field of fisheries expertise by means of artificial intelligence. The main contributions of this paper are threefold: (1) The binary data itself and its summary information in remote sensing data are input as different modalities for classification. (2) The BiLSTM model and the CNN model are improved for remote sensing data classification scenarios based on the multi-headed attention mechanism. (3) Feature extraction is performed based on the improved model and compared with the model of traditional classification methods.(3) The experimental results show that the improved file classification method has higher accuracy than the traditional machine learning classification methods in classifying fisheries remote sensing data files. © 2023 SPIE.

关键词： Classification (of information)

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Design of a Free Trade Zone Data Management and Analysis Platform Based on computer machine learning Technology

Design of a Free Trade Zone Data Management and Analysis Pla...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Chunyang Liu Yi Liu Shuli Wang School of Economics Wuhan University of Technology Wuhan China

This study is dedicated to architecting a data management and analysis platform, integrating advanced computer technologies and machine learning algorithms. Traditional approaches often fall short in handling and analyzing vast data sets. Thus, this paper proposes an innovative solution: enhancing data processing efficiency and precision through machine learning technologies. The research thoroughly examines the application of machine learning in data management, including the selection of appropriate algorithms, effective model training and optimization, and their integration into a user-friendly platform. The platform's design aims to deliver more accurate data analysis, supporting informed decision-making.

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Research on Cross-project Defect Prediction Based on Instance Migration Method 3

Research on Cross-project Defect Prediction Based on Instanc...

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3rd international conference on computer Science and Management Technology, ICCSMT 2022

作者： Wang, Wennan Li, Yu Song, Shiyang Lu, Jiadong Chen, Baiyu Wang, Baoping Guangdong University of Science and Technology Academy of Management Dongguan China Zhuhai Yingying Technology Co. Ltd Zhuhai China Zhuhai College of Science and Technology Alibaba Cloud Big Data Application College Zhuhai China Zhuhai College of Science and Technology School of Foreign Language Education China

ISBN: (纸本)9798350320404

The field of software engineering data mining has become increasingly interested in cross-project defect prediction (CPDP), which uses defect codes from other projects to develop prediction models, addressing the problem of insufficient data in the model construction process. However, the source and target projects usually have large differences in data distribution, which reduces the prediction performance. Based on the learning idea of instance migration, the distribution of target project features is changed to be close to the distribution of source project features, thus improving the performance of cross-project defect prediction. Specifically, the cross-project software defect prediction is investigated by combining software metric features with instance migration methods. First, we analyze the difficulty of class imbalance in the training set and extract features from the instances in the training set. Then the idea of improved AdaBoost is proposed. Finally, the algorithm based on instance migration is proposed and experimented, and the result analysis shows the effectiveness and feasibility of the method. © 2022 IEEE.

关键词： Adaptive boosting

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How to mimicking the construction of deep learning model for log anomaly detection 3

How to mimicking the construction of deep learning model for...

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3rd international conference on Electronics and Communication;Network and computer Technology (ECNCT)

作者： Sun, Jia Zhang, Jianhui Bu, Youjun Chen, Bo Lu, Xiangyu Zhang, Surong Zhengzhou Univ Sch Cyberspace Secur Zhengzhou 450001 Peoples R China PLA Strateg Support Force Informat Engn Univ Zhengzhou 450001 Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

Log anomaly detection based on deep learning is one of the research hotspots in the field of computer security. It is foreseeable that the mimicry theory proposed by Academician Wu Jiangxing will further improve the detection capabilities of deep learning models, but will also bring high resource consumption and difficulty in application. Therefore, this paper proposes a mimic model construction method that uses the output of complex models as prior knowledge to train lightweight heterogeneous execution bodies and then integrates them. Finally, it is based on DPCNN and TextCNN as complex models and lightweight executions respectively. The experiment of the body structure mimic model proves that while reducing the number of parameters from millions to thousands, its detection accuracy and F1 value are only about 2% and 4% lower than the original model, which greatly retains the original model. The detection capability.

关键词： Log anomaly detection deep learning mimic model lightweight executive body

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