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computer, Communication and Control (IC4), International Conference on

作者： R. Sivasankari T Veeramakali S M Mustafa Nawaz P. Prakash P Umadevi A Alvin Ancy Dept. of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Sri Sai Ram Engineering College Department of S & H KPR Institute of Engineering and Technology Coimbatore Dept of Electronics and Communication Engineering Anna University Chennai

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types; a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text.

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APADGCN: Adaptive Partial Attention Diffusion Graph Convolutional Network for Traffic Flow Forecasting 4th

APADGCN: Adaptive Partial Attention Diffusion Graph Convolut...

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4th International Conference on Spatial data and Intelligence, SpatialDI 2023

作者： Zhang, Bowen Li, Bohan Wei, Jinzhan Wen, Hao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China School of Electronic Information and Automation Guilin University of Aerospace Technology Guilin541010 China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China

ISBN: (纸本)9783031329098

Traffic flow forecasting is a core task of urban governance and plays a vital role in the development of ITS. Because of the complexity and uncertainty of traffic patterns, it is of great challenge to capture spatial-temporal correlations. Recent researches mainly focus on the pre-defined adjacency matrix based on prior knowledge as the basis of spatial-temporal correlation modeling, but the fixed graph structure cannot adequately describe the dependency between traffic sensors. To tackle this issue, a novel deep learning model framework is proposed in this paper: Adaptive Partial Attention Diffusion Graph Convolutional Network(APADGCN), which consists of three main parts: 1) the Multi-Component module that divides the historical traffic flow into recent, daily-periodic, and weekly-periodic, to capture the traffic patterns of different periodic;2) the spatial correlation modeling which can dynamically capture node relationships and model spatial dependency, and enhance the aggregation ability of low-order information;3) the temporal correlation modeling which models long-term time dependencies using convolution and gating. The final result is obtained by the weighted fusion of the results of the multi-components. We compared our APADGCN with various baseline models in the four real datasets from the Caltrans Performance Measurement System (PeMS). The experimental results show that the prediction accuracy of APADGCN is better than that of the baseline models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation

arXiv

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arXiv 2024年

作者： Lu, Yang Li, Yuhang Zhang, Ruichen Chen, Wei Ai, Bo Niyato, Dusit School of Computer and Technology China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs representing the wireless networks to adapt to the time-varying channel state information and dynamics of network topology. This article aims to provide a comprehensive overview of applying GNNs to optimize wireless networks via answering three fundamental questions, i.e., how to input the system parameters of wireless networks into GNNs, how to improve the expressive performance of GNNs, and how to evaluate GNNs. Particularly, two graph representations are given to transform wireless network parameters into graph-structured data. Then, we focus on the architecture design of the GNN-based models via introducing the basic message passing as well as model improvement methods including multi-head attention mechanism and residual structure. At last, we give task-oriented evaluation metrics for DL-enabled wireless resource allocation schemes. We also highlight certain challenges and potential research directions for the application of GNNs in wireless networks. © 2024, CC BY.

关键词： Graph neural networks

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Internet of Things- Cloud Security Automation Technology Based on Artificial Intelligence 1

Internet of Things- Cloud Security Automation Technology Bas...

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Hussain, Karrar Vanathi, D. Jose, Bibin K Kavitha, S. Rane, Bhuvaneshwari Yogesh Kaur, Harpreet Sandhya, C. Cvr College of Engineering Ibrahimpatnam Department of Ece Telangana India Nandha Engineering College Department of Artificial Intelligence and Data Science Erode India Department of Mathematics Kerala India Saveetha College of Liberal Arts and Science Saveetha Ins Titute of Medical and Technical Sciences Department of Computer Science Chennai India KYDSCT's College of Pharmacy Pharmaceutical Chemistry Department India Lovely Professional University School of Computer Science & Engineering Punjab Phagwara India S.A.Engineering College Management of Science Chennai India

ISBN: (纸本)9781665497107

The development of industrial robots, as a carrier of artificial intelligence, has played an important role in promoting the popularisation of artificial intelligence super automation technology. The paper introduces the system structure, hardware structure, and software system of the mobile robot climber based on computer big data technology, based on this research background. At the same time, the paper focuses on the climber robot's mechanism compound method and obstacle avoidance control algorithm. Smart home computing focuses on 'home' and brings together related peripheral industries to promote smart home services such as smart appliances, home entertainment, home health care, and security monitoring in order to create a safe, secure, energy-efficient, sustainable, and comfortable residential living environment. It's been twenty years. There is still no clear definition of 'intelligence at home,' according to Philips Inc., a leading consumer electronics manufacturer, which once stated that intelligence should comprise sensing, connectedness, learning, adaption, and ease of interaction. S mart applications and services are still in the early stages of development, and not all of them can yet exhibit these five intelligent traits. © 2022 IEEE.

关键词： Industrial robots

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Spatio-Temporal Aware Knowledge Graph Embedding for Recommender Systems

Spatio-Temporal Aware Knowledge Graph Embedding for Recommen...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Liu Yang Xin Yin Jun Long Tingxuan Chen Jie Zhao Wenti Huang School of Computer Science and Engineering Central South University Changsha China School of Software Technology Zhejiang University Zhejiang China Big Data Institute Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

Knowledge Graphs (KGs) have been incorporated into recommender systems as side information to solve the clas-sical data-sparsity and cold-start problems, with explanations for recommended items. Traditional embedding-based recommender systems generally utilize abundant information from KGs directly to enrich the representation of items or users, but the influences of spatial and temporal dependencies are usually ignored among them. In this paper, we propose a Spatio-Temporal Aware Knowledge Graph Embedding (STAKGE) for recommender systems, which incorporates spatio-temporal information with bias when propagating potential preferences of users in knowl-edge graph embedding. Moreover, we construct a multi-source KG-based recommender dataset - YelpST, containing spatio-temporal information. The experiments on YelpST dataset show that our proposed approach can capture comprehensive spatio-temporal correlations and improve the prediction performance as compared to various state-of-the-art baselines.

关键词： Correlation Knowledge graphs Recommender systems

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Apache Kafka on Big data Event Streaming for Enhanced data Flows

Apache Kafka on Big Data Event Streaming for Enhanced Data F...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： K Padmanaban T. R. Ganesh Babu K. Karthika Balachandra Pattanaik Dhanabhavithra K C. Srinivasan Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Electronics and Communication Engineering KCG College of Technology Chennai Tamil Nadu India Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Department of Artificial Intelligence and Data Science Agni College of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Apache Kafka's distributed architecture and message queuing capabilities offer significant improvements in real-time and batch data processing efficiency and reliability. This research aims to optimize Kafka setups, data partitioning, and Kafka Connect integration to create a robust and scalable data streaming infrastructure. The study focuses on enhancing data input, processing, and dissemination across systems and applications. By optimizing Kafka configurations and leveraging its capabilities, the research seeks to achieve significant improvements in data processing speed, real-time analytics, and scalable data pipelines. The evaluation of Kafka Event Stream Throughput Over Time and Latency Distribution Across Brokers demonstrates the system's performance and efficiency. The results indicate a throughput of 750-1340 events per hour and a latency distribution of 6-15 milliseconds. Additionally, Consumer Lag Over Time analysis reveals consistent performance with values ranging from 70-140. This research contributes to the advancement of big data processing by demonstrating the effectiveness of Apache Kafka in creating a robust and efficient data streaming infrastructure. The findings provide valuable insights for organizations seeking to optimize their data pipelines and leverage the power of real-time analytics.

关键词： System performance Pipelines Semantics Refining Distributed databases Transforms Big data data processing Throughput Real-time systems

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Online Q&A for Machine Learning Courses Based on Formula Recognition

Online Q&A for Machine Learning Courses Based on Formula Rec...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaoke Zhu Xiaopan Chen Minghao Li Tianxiang Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Mathematical formulas are ubiquitous in courses such as Machine Learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. This paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection.

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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Random Forests for Predicting Diabetes Progression and Complications

Random Forests for Predicting Diabetes Progression and Compl...

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Advancement in Technology (ICONAT), International Conference for

作者： Chitra Sabapathy Ranganathan R Nandhini K Nisha G. Sumathi G. Sudhakar C. Srinivasan Mphasis Corporation Chennai Tamil Nadu India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Computer Science and Engineering Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

The main objective of the presented work in this paper is to use Random Forests as an alternative biomarker for predicting the development of diabetes. Identifying high-risk patients is crucial for improving patient outcomes and reducing healthcare expenses. The proposed system improves diabetes treatment via the use of predictive analytics, which promotes preventative and early intervention-focused healthcare practices. It helps manage the risks associated with diabetes by using machine learning, which is part of a larger movement towards precision medicine. This method shows promise to enhance long-term health outcomes, optimize resource allocation, and provide personalized treatment to patients. Findings from the Pima Indians Diabetes database quantify the risks of diabetes and patient characteristics. Blood pressure, glucose, insulin, and body mass index are all taken from each patient. The dangers of retinopathy, neuropathy, and nephropathy are also numerically encoded, with 0 indicating minimal risk, 1 medium risk, and 2 high risks. HbA1c is also included. The blood pressure ranges from 115/78 to $130 / 85 \mathrm{~mm}$ HgA1c levels, the body mass index (BMI) from 26 to 32 mm, and the glucose level from 110 to 140 mm Hg.

关键词： Body mass index Accuracy Retinopathy databases Blood pressure Diabetes Glucose Biomedical monitoring Insulin Random forests

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