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

共 1863 条记录，以下是171-180 订阅

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machine learning algorithms are used to detect and track objects on video images 3

Machine learning algorithms are used to detect and track obj...

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3rd international conference on Optics, computer applications, and Materials Science, CMSD 2023

作者： Beknazarova, S.S. Latipova, N.M. Maxmudova, M.J. Alekseeva, V.S. Turakulova, A.S. Tashkent University of Information Technologies Named by Muhammad Al-Khwarizmi 108 A. Timur Tashkent 100096 Uzbekistan National University of Uzbekistan Named after Mirzo Ulugbek Tashkent Uzbekistan

ISBN: (纸本)9781510674486

In recent years, the integration of machine learning algorithms has significantly advanced the field of computer vision, particularly in the domain of object detection and tracking in video images. This article explores the application of machine learning techniques to enhance the accuracy and efficiency of object detection and tracking systems. The article begins by providing an overview of the challenges associated with traditional methods of object detection and tracking, highlighting the limitations in handling complex scenarios and diverse object types. Subsequently, it delves into the methodology of employing machine learning algorithms, emphasizing their adaptability and capability to discern patterns and features crucial for accurate object recognition. Various machine learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are discussed in detail, elucidating their roles in extracting meaningful representations from video frames. The training process is explored, encompassing the use of labeled datasets to enable the algorithms to generalize and make informed predictions in real-world scenarios. Furthermore, the article investigates the integration of deep learning techniques, exploring the advantages of transfer learning and fine-Tuning pre-Trained models to optimize performance. The role of neural networks in handling object occlusion, scale variations, and pose changes is emphasized, showcasing the adaptability of machine learning algorithms to dynamic and unpredictable environments. The practical implementation of these algorithms in object detection and tracking systems is presented, highlighting real-world applications across industries such as surveillance, autonomous vehicles, and industrial automation. The article concludes by discussing ongoing research and potential future developments, addressing the evolving landscape of machine learning in computer vision and its implications for advancing object det

关键词： Feature extraction

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BIFT: A federated learning System for Connected and Autonomous Vehicles Based on Blockchain 3

BIFT: A federated learning System for Connected and Autonomo...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Yadav, Aarti Kaur, Amandeep Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Department of Master of Computer Application Jhanjeri India

ISBN: (纸本)9798350399264

Algorithms for machine learning(ML) are essential for autonomous driving. The vast majority of history's automated and connected cars send a significant quantity of movement data collected from many automobiles to a central server for regular training. Nonetheless, the info process has made the safety and confidentiality of information essential. Nowadays, the development of federated learning (FL) for data security has the potential to potential to enhance distributed machine learning's data privacy. The malicious attackers might still target the training process, though. FL is extremely vulnerable because of its total dependency on the central server. We suggest BIFT as a solution to the aforementioned issue, which consists of two parts: 1) a completely decentralized 2) A blockchain-based machine learning system for CAVs that guarantees privacy. With the use of their own driving data, BIFT's scattered CAVs may train local machine learning models, which they can subsequently upload to improve the global model. In order to fend off potential attackers, BIFT offers a consensus mechanism called Proof of Federated learning. We assess BIFT's performance and demonstrate that it is scalable, reliable, and capable of fending off harmful assaults. © 2023 IEEE.

关键词： Autonomous vehicles

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Forecasting of Cardiovascular Disease Using machine learning 3

Forecasting of Cardiovascular Disease Using Machine Learning

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Azarudeen, A. Hemavathy, P. Jabbar, Abeer Mohsen Abed, Sarah Jassim Fhadil, Fay Ali, Aqeel Hindustan Institute of Technology and Science Department of Computer Application Chennai India Hindustan Institute of Technology and Science Department of It Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

heart disease or cardiovascular disease includes several circumstances that affect the heart and is one among major reason of putting to demise globally. Here, we employ machine learning to predict cardiovascular disease using a data set containing 14 components. computers are taught to learn knowledge on their own using machine learning, a form of artificial intelligence. machine learning in healthcare is increasingly being used and helps victims and clinicians in a variety of ways. The automated identification and diagnosis system's performance is comparable to that of a skilled radiologist. This article presents multiple heart disease-related characteristics, and includes models constructed using supervised learning techniques such as the Random Forest and K-Nearest Neighbor algorithms. It also focuses on which patients, given certain medical characteristics, are more likely to suffer cardiovascular-disease. © 2023 IEEE.

关键词： Forecasting

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Privacy Preservation in Federated learning: its Attacks and Defenses 3

Privacy Preservation in Federated Learning: its Attacks and ...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Maurya, Jyoti Prakash, Shiva Madan Mohan Malaviya University of Technology Department of Information Technology and Computer Application Gorakhpur273010 India

ISBN: (纸本)9798350322842

In the context of artificial intelligence, the traditional centralized server-based machine learning model training approaches have significant security and privacy issues. The federated learning (FL) approach was created to address these problems. FL uses a privacy-by-design architecture, in which several devices work together to develop any machine learning system that does not reveal users' personal information under the control of a single server. In federated learning, privacy preservation is achieved through several techniques that involve adding noise, secure aggregation, local training, and model compression. These techniques aid in making sure that raw data is not transmitted over the network and that only the required information is shared to train a global model. This research study has discussed about the federated learning-related privacy threats and defenses. The existing research works are divided into two types as attack methods and corresponding defense mechanism and further analyzed the relevant research papers from the last few years, and then further discussed the research challenges, and finally concluded the research study. © 2023 IEEE.

关键词： machine learning

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A Comprehensive Review on Areas and applications of Artificial Intelligence, machine learning, Deep learning, and Data Science 3

A Comprehensive Review on Areas and Applications of Artifici...

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3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Mokkapati, Ragini Dasari, Venkata Lakshmi School of Computer Science and Engineering VIT-AP Amaravati India

ISBN: (纸本)9798350343632

Digital Transformation and Data Science (DS) are used in tandem with Artificial Intelligence (AI), machine learning (ML), and Deep learning (DL). learning is a characteristic human way of behaving which has been made a fundamental part of the machines also. AI, encompassing ML, Neural Networks, and DL, aims to replicate human decision-making and perspectives. ML is regarded as a subset of AI and is frequently employed for the implementation of AI. DL represents a progression beyond ML, characterized by its utilization of specialized algorithms known as deep neural network models. DS is an extensive interaction that includes different strides for dissecting information and creating bits of knowledge. As companies journey toward digital transformation and the era of Industry 4.0/Pharma 4.0, an increasing number of them are integrating DS, Advanced Data Analytics, and AI into their development and production workflows. This integration is aimed at enhancing efficiency, minimizing errors, and maintaining competitiveness.. This paper provides an overview of the historical and forthcoming application domains, sub-domains, and practical uses of AI, ML, DL, and DS. © 2023 IEEE.

关键词： Artificial intelligence Deep learning and Data Science machine learning

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Auto Parts Defect Detection Based on Few-shot learning 3

Auto Parts Defect Detection Based on Few-shot Learning

引用

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

作者： Xu, Jiancheng Ma, Jialei Changchun University of Science and Technology College of Science and Technology Jilin Changchun China

ISBN: (纸本)9781665459112

In the process of auto parts products from manufacturing to quality inspection, it is inevitable that there will be defects on the surface due to aging of machine tools or human factors. How to better apply computer vision technology to defect detection of auto parts is still an issue discussed by the industry and academia. For the condition that sufficient labeled samples is usually not available in the industrial field, this paper uses an improved few-shot metric learning model to identify auto parts samples. According to the traditional few-shot learning models lack of the considerations of channel level and internal relations of support set, this paper adds ECA module and Transformer module to few-shot learning. Add more attention to the key information of support set and query set samples at channel level while increasing the attention to the intrinsic feature of support set, to improve the classification effect. © 2022 IEEE.

关键词： machine tools

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Face Classification & Recognition Using Stacking Ensemble learning with Principal Component Analysis Approach 3

Face Classification & Recognition Using Stacking Ensemble Le...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Devi, Shivali Saini, Jyoti Kaur, Harjinder Kaur, Harmanpreet Chandigarh University Computer Science & Engineering Punjab India Ssiet Dinanagar Computer Science & Engineering Punjab India Rayat Bahra University Computer Application Department Punjab India

ISBN: (纸本)9798350369083

Developing a high-level automated security system for identification/authentication has long been an active research subject in practically all fields. Traditional security systems utilize a key or a password to secure a process/product, whereas biometric security systems use a person's physical or behavioral attributes. Face patterns play a significant part in several potential recognition/authentication applications because of their uniqueness, universality, dependability, and stability. This study provides a face identification system that starts with eye detection and ends with face image recognition. AN ensemble stacking approach for face classification is presented in this study that combines various machine learning (ML) algorithms with and without Principal Component Analysis (PCA). The proposed system is trained and tested on a well-known publically available Olivetti Face Dataset. The dataset is split into 75% train and 25% test dataset which is also transformed using PCA. The results show that the accuracy of each base model having PCA data is on par or better than the one without PCA. The paper also proves that the performance of the new stacking ensemble system results in an average or better performance than the other base models. For Olivetti face dataset, the proposed Stacked Ensemble learning model's accuracy with PCA is 94%, which is on average than the accuracy of the other base models, including Neural Network (NN) (100%), Random Forest (92%), Support Vector machine (SVM)(96%), Gaussian Naives Bayes(81%), K-Nearest Neighbour (KNN) (89%), and Logistic Regression (LR) (97%) respectively. © 2024 IEEE.

关键词： Authentication

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Construction of English Chinese translation units and translation models based on deep learning algorithms 3

Construction of English Chinese translation units and transl...

引用

3rd international conference for Advancement in Technology, ICONAT 2024

作者： Wang, Bing Chengdu Neusoft University Sichuan Chengdu611844 China

ISBN: (纸本)9798350354171

the mainly using computer methods and intelligent methods to make comprehensive judgments about the English translation process and content, so as to achieve better analysis and research. The role of translation units and models in the theoretical system of English Chinese translation is very important, but there are problems of poor timeliness and narrow domain coverage. The traditional shallow learning mode cannot solve the problem of large vocabulary analysis in English-Chinese transliteration, and the application method is unreasonable. The deep learning algorithm to analyze the translation unit system. First, the web crawler technology is used to evaluate the data of English Chinese bilingual news websites and English Chinese translation websites, according to the translation standards It is required to characterize indicators to reduce interference factors in bilingual translations. Then, the machine translation mode evaluates the translation unit system, forms a unity of principle and flexibility in translation, and corrects Comprehensive analysis of the results of the translation unit system. The analysis results show that the method proposed in this paper can improve the accuracy of analysis and calculation, so as to realize the effective and rational judgment of analysis and better analyze the results. Comprehensive utilization facilitates the effective analysis and realization of analysis. MATLAB simulation shows that under certain evaluation criteria, the deep learning algorithm has a translation unit system for the English Chinese translation theory system The accuracy and timeliness are better than the traditional shallow learning mode. © 2024 IEEE.

关键词： Web crawler

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Prevention and Detection of Poisoning Attacks in Medical-Based machine learning Web applications 3rd

Prevention and Detection of Poisoning Attacks in Medical-Bas...

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3rd international conference on Communication, Networks and Computing, CNC 2022

作者： Sheela, T. Gnana Padmesh, C.K. Lokesh, V. Department of Information Technology Sri Sai Ram Engineering College Chennai India

ISBN: (纸本)9783031431395

machine learning concepts and processes are widely used in all the fields for automated, accurate, and immediate decisions. But, hackers have vigorous motives to change the outcomes of the models and applications generated by algorithms and techniques of machine learning. The project revolves around the detection and prevention of poisoning attacks on the medical models of machine learning by either an Anomaly detection method or cryptographically based authentication and provenance. Anomaly detection is an application of cybersecurity where an anomaly detector is built. It identifies the rare occurrence of any unfamiliar data from the model and prevents the models from poisoning attacks. The web application holds cryptographic authentication and provenance through a secure socket layer and by some framework methods, through which the poisoning attacks can be detected and prevented. On this basis, our projects prevent hacks from occurring in the field of medicine. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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Job Recommendation System using machine learning 3

Job Recommendation System using Machine Learning

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Gadegaonkar, Sakshi Lakhwani, Darsh Marwaha, Sahil Salunke, Prof. Abhijeet Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9781665462167

Millions of students graduate from college every year and start looking for jobs, but hunting for a suitable job that pays according to the applicant's skills is not an easy process. On the other hand, recruiters and companies look for graduates to join them according to their requirements. Unfortunately, there is often a gap between the applicants (job seekers) and recruiters (job providers). To bridge this gap and solve this problem, this study has come up with a job recommendation system that will recommend jobs in the IT sector that match the applicants' skills and expectations. This study has built an Android application to recommend jobs to the users based on their interests, databases, frameworks, platforms, and languages comfortable with. The recommendation is done by using a machine learning (ML) model (written in Python), which makes use of the content-based filtering algorithm. This study has built the application using Kotlin, Jetpack Compose, Ktor, and the UI has been designed by using Material 3 Design Principles. © 2023 IEEE.

关键词： Recommender systems

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