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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3121 条记录，以下是181-190 订阅

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Implementation of Extreme learning machine and Software as a Service for recognition of Tamil Handwritten words 3

Implementation of Extreme Learning Machine and Software as a...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Naveen, P. Nithyasai, S. Nataraj, B. Sri Eshwar College of Engineering Ece Department Coimbatore India Kathir College of Engineering Ece Department Coimbatore India Sri Ramakrishna College of Engg Ece Department Coimbatore India

ISBN: (纸本)9798350300710

The conventional cloud computing paradigm has spurred the development of innovative architectures for next-generation cloud computing. These novel designs can effectively manage vast amounts of data, which was previously beyond the capabilities of conventional architectures. To tackle this challenge, feed forward neural network algorithms have emerged as a cutting-edge solution for processing massive data sets. As a result, they are now widely employed for solving various tasks, particularly in the pitch of recognition. One promising research application in the domain of postal automation is the recognition of handwritten town names. To address this challenge, we have employed an Extreme learning machine to enhance the concert of feed forward neural networks. Our approach has yielded superior results compared to state-of-the-art algorithms. © 2023 IEEE.

关键词： Network architecture

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Identification Method of Dress pattern Drawing based on machine Vision Algorithm 3

Identification Method of Dress Pattern Drawing based on Mach...

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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： Lyu, Ke Yan, Haizhang Zhejiang Sci-Tech University School of International Education Zhejiang310000 China Xidian University School of Computer Science and Technology Xi'an71000 China

ISBN: (纸本)9781665459112

This paper uses the machine vision method to identify the skirt module. We have constructed three kinds of machine recognition models of skirt profile processing, structure analysis of style drawing, and size estimation. The author constructs a relatively complete image recognition system for dress pattern drawing. In addition, we conducted an effect evaluation with a certain number of samples at the later stage of the experiment. This study has A good effect in distinguishing an A-type skirt from an H-type skirt, identifying the reasonable degree and length of the skirt, and determining the quantity statistics of each component element in the skirt pattern diagram. © 2022 IEEE.

关键词： Image recognition

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machine learning Strategies in Quantum-Resistant Network Security Protocols 3

Machine Learning Strategies in Quantum-Resistant Network Sec...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Dhote, Vijay Sadim, Mohd Tanna, Paresh Tiwari, Annapurna Nand Ies College of Technology Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Ies University Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Computer Engineering Rk University India Ies College of Technology Department of Electronics & Communication Engineering Madhya Pradesh Bhopal462044 India

ISBN: (纸本)9798350343274

The cryptographic techniques that underpin current network security standards run the risk of becoming outdated due to the advancement of quantum computing. Researchers and industry professionals are working to create novel strategies for protecting the privacy of confidential data in the post-quantum age. In this study, we explore the possibility that a mix of quantum-resistant network security protocols and machine learning techniques might serve as a potent countermeasure against the growing threat posed by quantum computers. This is the latest addition to the way we've been talking about it. By employing machine learning, we can enhance the effectiveness and adaptability of encryption systems, strengthening network security, therefore. Furthermore, we discuss the potential contributions of several machine learning methods, including neural network-based approaches, pattern recognition, and anomaly detection, to the field of quantum-resistant security protocols. Considering the emergence of quantum computing, combining machine learning with quantum-resistant encryption has become a crucial tactic for protecting sensitive data. This cooperative endeavor, which will be advantageous to both sides, clarifies a crucial tactic. Our results demonstrate that machine learning offers the potential to be a powerful partner in the never-ending fight to protect digital assets. The understanding that our results provide is crucial for building safe networks in the era of quantum computing. © 2023 IEEE.

关键词： Intrusion detection

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Predicting Course Grades for Students Using machine learning: Determine Early Prediction Time 3rd

Predicting Course Grades for Students Using Machine Learning...

引用

3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, Thi Hong Bui, Quang Vinh Nguyen, Viet Anh Do, Trung Kien Hanoi National University of Education Hanoi Viet Nam VNU University of Engineering and Technology Hanoi Viet Nam

ISBN: (纸本)9783031809422

Predicting learning outcomes (LO) is an important problem in educational data mining (EDM). Despite extensive research, there remains a gap in determining the optimal timing for predicting early LO with acceptable accuracy. This study proposes a methodology for developing a predictive model using machine learning techniques to identify the appropriate time to forecast students’ course scores in a blended learning environment. The scope of the research includes students participating in the subject ‘Introduction to Computer Science’ at Hanoi National University of Education (HNUE). The experimental data includes data from the learning management system (LMS) and the face-to-face classroom. The research model is built based on machine learning algorithms, including Linear Regression (LN), Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The research results show that the attributes most strongly influencing course scores are average practice score, midterm score, and major. The ANN model provides the highest efficiency with R2 = 0.6033. Early prediction of final exam scores can be made from week 7. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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The Design and Implementation of AI Based Smart Notice Board Using Advanced machine learning and Image Processing Techniques 3

The Design and Implementation of AI Based Smart Notice Board...

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3rd international conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Islam, Md. Muhaiminul Hossain, Md. Rakib Akter, Farzana Barman, Bhakti Kusum Malakar, Shuvo Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

In contemporary society, conventional notice boards encounter challenges in terms of accessibility and engagement, especially within educational institutions where effective information dissemination is vital. By implementing a Smart Notice Board that is powered by cutting-edge technologies like artificial intelligence (AI), machine learning (ML), image processing, and effective frontend-backend communication, this research offers a game-changing solution. The system surpasses traditional analog methods by integrating digital capabilities, allowing for real-time updates and efficient communication within a global network. AI-based facial recognition is used by the Smart Notice Board to improve user experience and deliver tailored information to individuals or particular groups. When a person appears in front of the camera, the system recognizes them and pulls up relevant notifications based on their profile, which reduces the amount of work teachers, staff, and students have to do to stay informed. The system includes advanced image processing techniques to identify individuals, so notice delivery accuracy is guaranteed. In addition, the integration of socket technology guarantees smooth communication between the backend server and frontend user interface, facilitating effective data storage and transfer. By eliminating the time constraints linked to conventional notice boards, this method enables administrators to remotely oversee and distribute content. Additionally, the server stores individual images for facial recognition and customized notice delivery. By combining AI for facial recognition, ML and image processing for visual data analysis, and an advanced frontend-backend technology for system architecture, this smart notice board project not only streamlines the distribution of information but also greatly improves communication in educational setting as well as lessens burdens and produce a flexible platform that can adapt to changing user requirements. © 2

关键词： machine learning

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Attack Detection System for Cloud-Based Wireless Sensor Networks Using the Proposed Fast Hyper Deep learning Model 3rd

Attack Detection System for Cloud-Based Wireless Sensor Netw...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Saleh, Hadeel M. Marouane, Hend Fakhfakh, Ahmed NTS’COM Laboratory Safax University Sfax Tunisia Continuing Education Center University of Anbar Ramadi Iraq University of Sfax Sfax Tunisia

ISBN: (纸本)9789819624676

Cyber security requires detecting and stopping wireless sensor network attacks. We introduce a fast super deep learning model for cloud-based WSN attack detection in this research. Our offline approach divides data into training and testing sets, preprocesses it, then reduces features using Singular Value Decomposition (SVD) and Principal Component Analysis. We then extract features using our ultrafast deep neural network and put them into a hyper deep-machine learning model. We evaluate our strategy using data mining metrics and preserve the model for further use. We also compare our model to other data mining and machine learning methods using numerous datasets to ensure accuracy and efficiency. We focus on a few key topics. First, we address generalization, complexity, and execution computation time. Second, many datasets increase model generalization. Third, our fast and accurate deep learning approach improves machine learning results. Our Oracle and Java cloud infrastructure lets us design an injection tool to produce and transmit packets to the intrusion detection model. Model predictions stored on an Oracle database offer complete attack information. Convolutional Neural Networks (CNN) and machine learning algorithms like Complement Naive Bayes (CNB) and Stochastic Gradient Descent are among our methods. We also study combining PCA and SVD with machine learning to improve results. Finally, deep feature selection improves the previously reported methods. Overall, our proposed technique for cloud-based WSN attack detection has high generalization, efficiency, and accuracy while addressing major cyber security challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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EngageDat-vL: A Multimodal Engagement dataset Comprising of Emotional, Cognitive, and Behavioral Cues in Virtual learning Environment 10th

EngageDat-vL: A Multimodal Engagement Dataset Comprising of ...

引用

10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Akre, Sarthak Palandurkar, Nilesh Iyengar, Akshat Chayande, Gourav Kumar, Praveen Visvesvaraya Natl Inst Technol South Ambazari Rd Nagpur 440010 Maharashtra India

ISBN: (纸本)9783031451690;9783031451706

To assess student engagement in an e-learning environment, this work proposes a novel learning analytics dataset collection that combines emotional, cognitive, and behavioral data. The dataset includes facial expressions recorded by a webcam during e-learning sessions and log data such as play, pause, seek, course views, course material views, lecture views, quiz responses, etc. The procedure for gathering the data and the difficulties encountered are covered in the study. Combining emotional, cognitive, and behavioral cues for engagement detection in e-learning settings is made possible by this dataset, which represents a significant advancement in the field of learning analytics.

关键词： learning analytics Facial emotion recognition Engagement detection

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Research and implementation of fingerprint erasure technology for videos based on Google MediaPipe 3

Research and implementation of fingerprint erasure technolog...

引用

3rd international conference on Computer Vision and data mining, ICCVDM 2022

作者： Zhang, Tao Liu, Hongying Nanjing University of Science & Technology Zijin College Wenlan Rd. 89 Nanjing210033 China

ISBN: (纸本)9781510661363

Fingerprints are used in many fields as important biological data of people. As one of people's important personal privacy, fingerprints are also prone to leakage. Images and videos posted on social media are invitations to this problem. Research in the direction of protection of fingerprint privacy is nearly vacant. Therefore, this paper presents a technical implementation for locating and erasure human fingerprints for videos. This implementation adopted Google's open-source MediaPipe machine learning framework to identify the hand in the frame and obtain crucial landmark information. The fingerprint core range and finger state are deduced from landmark points' positional relationship and distance. This method can efficiently erase as many as 88% of frames with one hand presenting. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Palmprint recognition

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Integrating IoT and machine learning for Enhanced Forest Fire Detection and Temperature Monitoring 3

Integrating IoT and Machine Learning for Enhanced Forest Fir...

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

作者： Varun, M. Kesavraj, K. Suman, S. Raj, X Suman Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350343632

Forest fires have become more frequent and intense in recent years, which has caused serious problems for public safety and environmental preservation. In the situation, combining Internet of Things (IoT) devices with cutting-edge machine learning algorithms offers a viable approach for early temperature and forest fire detection. In order to identify forest fires and track temperature swings in real-time, an intelligent system that integrates IoT sensors and machine learning algorithms is designed, developed, and evaluated in detail in it. The proposed methodology improves the effectiveness of early fire detection while also offering insightful information for efficient wildfire management and prevention methods. The report illustrates the system's efficacy and dependability via thorough experimentation and research, demonstrating its potential for wider use in wooded regions across the world. The paper also analyzes implementation difficulties, such as data privacy and system scalability, and offers suggestions for resolving these problems. The findings in the work add to the body of research on IoT-based environmental monitoring systems and provide new avenues for investigation and advancement in the field of intelligent forest fire detection and prevention. © 2023 IEEE.

关键词： early warning system environmental monitoring forest fire detection Internet of Things (IoT) pattern recognition remote areas

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A Review on EMG-based pattern Identification Methods for Effective Controlling of Hand Prostheses 3

A Review on EMG-based Pattern Identification Methods for Eff...

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

作者： Ramkumar, S. Rema, Dhanusha Devi, T. Archana Elavarasi, K. Selvaganapathi, T. Gokila, S. Department of Computer Science Bangalore India Karpagam Academy of Higher Education Department of Food Technology Coimbatore India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Coimbatore India Karpagam Institute of Technology Department of CSE Coimbatore India

ISBN: (纸本)9798350343632

The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion. © 2023 IEEE.

关键词： Classification Algorithms Deep learning Electromyography Hand Prosthesis recognition Accuracy

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