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Online Collaborative learning Grouping Method Based on Immune Genetic Algorithm 11

Online Collaborative Learning Grouping Method Based on Immun...

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11th international conference on Educational and Information Technology (ICEIT)

作者： Chen, Yingzhi Zhang, Lichen Ma, Hailong Guo, Longjiang Shaanxi Normal Univ Minist Educ Key Lab Modern Teaching Technol Xian Peoples R China Shaanxi Normal Univ Engn Lab Teaching Informat Technol Shaanxi Prov Xian Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China

ISBN: (纸本)9781665445467

Online learning platforms such as MOOCs have been widely applied, on which students can learn online courses anytime and anywhere, and can also be divided into groups to conduct a learning task. through team collaboration, students' comprehensive abilities can be improved, including learning, organization, communication, teamwork ability, etc. Reasonable grouping is the basis and focus of efficient collaborative learning. the existing intelligent optimization algorithms used to solve the combinatorial optimization problem of student grouping still have the limitation of being easy to fall into the local optimum and blind search. In response to this problem, we study an efficient student grouping algorithm for online collaborative learning in this paper. Firstly, we integrate an immune strategy into the Genetic Algorithm to form a new algorithm called Immune Genetic Algorithm (IGA). Secondly, we design a fitness function according to the grouping goal of "Heterogeneity within a group, homogeneity between groups". Finally, we evaluate the performances of the algorithms through experiments based on a real data set. the grouping results show that compared with the Genetic Algorithm, the proposed Immune Genetic Algorithm improves the search efficiency and stability, and can get grouping results with better fitness value.

关键词： online collaborative learning immune genetic algorithm immune strategy encoding method optimized grouping

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Cultural Heritage Meets AI: Advanced Text-to-Image Models for Digital Reconstruction and Preservation 6

Cultural Heritage Meets AI: Advanced Text-to-Image Models fo...

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6th international conference on Control and Robotics, ICCR 2024

作者： Hsieh, Kunta Tsaur, Tsen-Shu Sam Chao, Min Li, I-Cheng Huang, Pin-Chia Lu, Mei-Jou The School of Big Data Fuzhou University of International Studies and Trade Fujian Fuzhou350202 China School of Finance & Accounting Fuzhou University of International Studies and Trade Fujian Fuzhou350202 China Cheng Shiu University Department of Civil Engineering and Geomatics Kaohsiung Taiwan Cheng Shiu University Director of the Conservation and Research Center Kaohsiung Taiwan Cheng Shiu University Postdoctoral Research Fellow of Conservation and Research Center Kaohsiung Taiwan Cheng Shiu University Department of Long-term Care and Health Management Kaohsiung Taiwan

ISBN: (纸本)9798331518158

this paper reviews the applications and development of web crawling and text-image matching technologies in the field of information processing, with an emphasis on the CLIP model and its adaptation for Chinese contexts. Web crawling technology utilizes automated programs to simulate browser behavior, efficiently accessing and extracting webpage information, while text-image matching technology combines computer vision and natural language processing to enable computers to understand textual descriptions and retrieve relevant images. Notably, the CLIP model, a deep learning-based text-image matching technology, has garnered attention for its high accuracy, generalization ability, and unsupervised learning, which does not require large amounts of labeled data. the paper also introduces a major application in the realm of art and cultural heritage restoration, where these technologies play a pivotal role in the digital reconstruction, preservation, and virtual presentation of artworks and artifacts. Additionally, the importance of data ethics, privacy protection, algorithmic fairness, and preventing technological misuse are emphasized. the paper concludes with future trends pointing towards more intelligent web crawlers, more precise image semantic understanding, and more user-friendly experiences. © 2024 IEEE.

关键词： Web crawler

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Hybrid Deep Transfer learning Framework for Humerus Fracture Detection and Classification from X-ray Images 4

Hybrid Deep Transfer Learning Framework for Humerus Fracture...

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4th international conference on intelligent Technologies, CONIT 2024

作者： Dey, Puja Mahmud, Tanjim Foysol, Khan Md. Sharmen, Nahed Hossain, Mohammad Shahadat Andersson, Karl University of Chittagong Department of Cse Chittagong4331 Bangladesh Rangamati Science and Technology University Department of Cse Rangamati4500 Bangladesh Textile Engineering College Noakhali Dept. of Eee Chowmuhani3821 Bangladesh Kitami Institute of Technology Dept. of Applied Microbiology Hokkaido090-0015 Japan Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350349900

the detection and classification of humerus fractures from X-ray images are crucial for effective medical diagnosis and treatment planning. Manual assessment of such fractures is time-consuming and prone to errors, emphasizing the need for automated systems. In this study, we propose a Hybrid Deep Transfer learning Framework for Humerus Fracture Detection and Classification from X-ray Images. Leveraging deep learning techniques, we amassed a dataset of 1266 radiographic images from the publicly available MURA dataset, encompassing both negative (non-fractured) and positive (fractured) cases. Preprocessing techniques were employed to enhance image quality, followed by data augmentation to mitigate overfitting and bolster system accuracy. Subsequently, a hybrid model comprising ResNet50 and DenseNet121 architectures was utilized for feature extraction and classification. through experimentation with various optimizers, we achieved the highest accuracy of 93.41% using the Adam optimizer. Additionally, precision, recall, and F1-score metrics were computed to evaluate model performance comprehensively. Comparative analyses were conducted with other pre-trained models, showcasing the effectiveness of our proposed framework. Our results highlight the deep transfer learning's effectiveness in humerus fracture detection, providing a promising path forward for the development of medical imaging technologies. © 2024 IEEE.

关键词： Medical imaging

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A Comparative Study of Machine learning Algorithms for Predicting Project Outcomes 11

A Comparative Study of Machine Learning Algorithms for Predi...

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11th international conference on Electrical engineering, Computer Science and Informatics, EECSI 2024

作者： Pacot, Mark Phil B. Daguil, Rolyn C. Pitogo, Vicente A. College of Computing & Information Sciences Caraga State University Butuan City Philippines

ISBN: (纸本)9798350355314

Crowdfunding platforms are online services that allow individuals and organizations raise capital for their various projects or initiatives. these platforms transform the way people can fund their ideas, products or activities. Among these platforms, Kickstarter is one of the most well-known and successful. It has facilitated the launch of thousands of projects, aiding creators bring their ideas to life with the support of generous backers, however, determining the outcomes of the project is a crucial decision-making for both creators and funders. Machine learning algorithms offer powerful tools for analyzing large amounts of data and identifying themes and patterns that can predict whether a project will be successful, fail, be canceled, remain live, or be suspended. By employing machine learning algorithms such as K-nearest Neighbors (K-NN), Decision Trees (DT), and Neural Networks (NN), three distinct models were developed to predict the status of Kickstarter projects. the results indicated that the Decision Tree algorithm outperformed both K-NN and NN in terms of accuracy, achieving a performance rate of 89.02%. this study highlights the significant potential of machine learning algorithms to predict outcomes that are essential for decision-making for creators and backers alike in crowdfunding-related activities. © 2024 IEEE.

关键词： Contrastive learning

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Network Information Big data Analysis Platform Based on Deep learning Technology 11th

Network Information Big Data Analysis Platform Based on Deep...

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11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Yan, Kai Gao, Hao Department of Information and Control Engineering Shenyang Institute of Science and Technology Liaoning Shenyang110000 China

With the rapid development of Internet technology, the amount of information and data is constantly increasing, leading to higher and higher demands for network big data analysis platforms. this paper primarily delves into the application of deep learning technology in digital text classification. In this paper, a deep learning-based algorithm is introduced in detail. It outlines the method of extracting specific numerical eigenvectors from large-scale non-negative eigenattribute samples and converting them into machine-readable code. ased on the aforementioned methodology, an analysis platform is designed, and its basic performance is simulated and tested. the test results indicate that the platform's ability to process abnormal data in Round A is 80, Round B is 90, Round C is 70, Round D is 85, and Round E is 75. the exception handling capacity of each category is represented visually through a bar chart, where the height of the bar signifies the size of the exception handling capacity for that specific category. © 2024 the Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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Spatio-Temporal Missing data Reconstruction by Using Deep Neural Networks in Agricultural Monitoring Systems 11

Spatio-Temporal Missing Data Reconstruction by Using Deep Ne...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Sentop, Mehmet Selahaddin Yucel, Meric Ustundag, Burak Berk Istanbul Technical University Computer Engineering Istanbul Turkey Istanbul Technical University National Software Certification Research Center Istanbul Turkey

ISBN: (纸本)9798350303513

this study addresses the issue of missing, distorted, or inaccurate data in agricultural monitoring systems. Such data issues can lead to errors in prediction and management models, affecting various applications in agricultural meteorology and remote sensing. Conventional missing data completion algorithms often fail to effectively leverage the inherent relationships between temporal and spatial data in agricultural observation systems. Machine learning techniques, specifically deep learning, offer a promising solution by considering factors such as time windows, seasons, and plant characteristics to fill missing data. However, managing the non-linear nature of agricultural monitoring within a machine learning framework poses a challenge. this study proposes a new deep learning approach called Predictive Error Compensated Network that addresses missing data reconstruction while mitigating overfitting. Predictive Error Compensated Network utilizes feature extraction networks and Discrete Wavelet Transform to incorporate different data types and time windows, improving performance. Evaluation against traditional methods demonstrated superior results with Predictive Error Compensated Network, achieving a significant reduction in reconstruction Root Mean Squared Error across different time windows. © 2023 IEEE.

关键词： Discrete wavelet transforms

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Energy Efficient Offloading Mechanism for Blocks in Blockchain-Assist IoT System 11

Energy Efficient Offloading Mechanism for Blocks in Blockcha...

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11th international conference on intelligent Computing and Wireless Optical Communications, ICWOC 2023

作者： Su, Hang Zhou, Jianhong Lei, Boyi He, Zhongxiu Xihua University School of Computer and Software Engineering Chengdu China

ISBN: (纸本)9798350321791

With the development and large-scale application of the Internet of things (IoT), security issues in IoT systems have received widespread attention. Applying blockchain technology can effectively solve the security risks faced by IoT systems. However, the deployment of blockchain in the IoT system also faces a series of challenges. For example, fast and large-scale data in the IoTs must be stored on the blockchain and maintained by some IoT devices, but constrained IoT devices usually cannot store the complete blockchain. In this regard, we design a strategy for deploying blockchain in the IoT system with limited resources. Specifically, we propose an energy-efficient block offloading strategy to release storage space when the storage resources of IoT devices are exhausted in the long-term dimension. In this process, the offloading of blocks is closely related to the subsequent query process, and a large amount of energy consumption accompanies it. We design a corresponding offloading algorithm by collaboratively considering the block offloading process and the block query process, which can minimize the average energy in the IoT system while satisfying the block storage and data query services. Simulation results show that our proposed offloading algorithm can effectively reduce the total average energy consumption of the system. © 2023 IEEE.

关键词： Energy utilization

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Clustering of Milling Strategies Using Machine Internal data 27

Clustering of Milling Strategies Using Machine Internal Data

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27th IEEE international conference on intelligent engineering Systems, INES 2023

作者： Brecher, Christian Ochel, Janis Ahmed, Muhammad Mehmood Fey, Marcel Aachen Germany

ISBN: (纸本)9798350328516

Increasing the efficiency of milling processes demands the automated evaluation of time series data collected from the machine. Solutions based on Artificial Intelligence are becoming popular for this purpose, which require tailored datasets for model training. the programmed strategies for milling particular features divide the process into subsections reflected by repeating patterns in multiple signals. In this paper, a clustering model is developed, which differentiates similar milling strategies based on these patterns. the model is combined with a classification model, so that recurring milling strategies of future processes are continuously assigned with corresponding cluster labels. Storing these labels in combination with the raw data enables finding customized data in growing data lakes. © 2023 IEEE.

关键词： Machine learning

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learning ON LARGE-SCALE TEXT-ATTRIBUTED GRAPHS VIA VARIATIONAL INFERENCE 11

LEARNING ON LARGE-SCALE TEXT-ATTRIBUTED GRAPHS VIA VARIATION...

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11th international conference on learning Representations, ICLR 2023

作者： Zhao, Jianan Qu, Meng Li, Chaozhuo Yan, Hao Liu, Qian Li, Rui Xie, Xing Tang, Jian Mila - Québec AI Institute Canada Microsoft Research Asia China Université de Montréal Canada Central South University China Sea AI Lab China Dalian University of Technology China HEC Montréal Canada Canada

this paper studies learning on text-attributed graphs (TAGs), where each node is associated with a text description. An ideal solution for such a problem would be integrating both the text and graph structure information with large language models and graph neural networks (GNNs). However, the problem becomes very challenging when graphs are large due to the high computational complexity brought by training large language models and GNNs together. In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM. Instead of simultaneously training large language models and GNNs on big graphs, GLEM proposes to alternatively update the two modules in the E-step and M-step. Such a procedure allows training the two modules separately while simultaneously allowing the two modules to interact and mutually enhance each other. Extensive experiments on multiple data sets demonstrate the efficiency and effectiveness of the proposed approach. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Graphic methods

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Machine learning-Based Attacks Detection in loT Networks Routing Protocols 11

Machine Learning-Based Attacks Detection in loT Networks Rou...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kumar, Vipin Malik, Navneet Lovely Professional University Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350350357

loT Devices and Sensors are increasingly being used in various sectors, including industrial architecture and smart cities. the security of sensor networks has gotten much attention recently. Introducing new loT networks has shifted the paradigm to vast Internet of things (loT) networks. loT networks provide new applications and opportunities for WSN networks and open up new possibilities. However, the security of these networks must be taken carefully, and a new approach to loT network security must be proposed. the new options give way to new types of attacks, and the security of these networks becomes very critical. Routing in loT networks uses a different protocol for communication for low-power and lossy networks (RPL). these loT routing protocols are vulnerable to various attacks, such as rank attacks, hello flood attacks, and wormhole attacks. there are many techniques to prevent these attacks, but implementing security techniques in these networks may degrade the network's performance. Traditional security services such as secrecy, authentication, integrity, authorization, non-reputation, and availability make it harder to provide security in low-energy devices. this paper proposes a new machine-learning model for attack detection in loT networks based on parameters like packet received ratio, number of messages between nodes, and packet length or duration. data is collected by a simulation network and analyzed using the proposed model, which gives good accuracy. © 2024 IEEE.

关键词： Network security

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