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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Haoyu Li, Leixiao Lin, Hao Chang, Xiangyang Wan, Jianxiong Li, Jie Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Reg. Eng. and Technol. Research Center of Big Data Based Software Service China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

P2P Botnet is famous for the resilience against termination. However, its dependence on Neighbor List (NL) makes it susceptible to infiltration and poison, also leading to a dearth of adequate protection of Botmaster's identity. So as to overcome these weaknesses, the novel botnet infrastructure combined with blockchain emerges. But due to some flaws such like high latency and expensive transactions fees on blockchain, these new schemes can't satisfy the need of constructing hyper large-scale botnet in practice. We propose ZombieCoin3.0, a Botnet underlying overlay leveraging IoT devices to be maintainers and integrated with Distributed Ledger Technology (DLT), which is able to deploy bots fully on blockchain. And it is versatile for file transfer, data feedback and other command and control scenarios that many contemporaries are impotent to cope with. Our proposal eliminates the P2P botnets' dependence on NL and the bottlenecks that other blockchain combined botnets have. Experiments' results authenticated that the effectiveness of our proposal as well as low latency of communication and nearly zero overhead of cost other comparatives unable to attain are achieved. © 2021 IEEE.

关键词： Costs Distributed ledger Botnet Blockchains Proposals Internet of Things Low latency communication

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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Smart Educational Ecosystems: Tailoring Employee Training With Ai and Iot in the Industry 5.0 Landscape

Smart Educational Ecosystems: Tailoring Employee Training Wi...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Arnima Pathak Mani Shivhare Durgesh Kumar Information and Communication Technology Tecnia Institute of Advanced Studies Delhi India Computer Science and Engineering with Data Science G.L. Bajaj Institute of Technology and Management Noida India Computer Science and Engineering Sharda University Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This study investigates the transformational potential of combining the Internet of Things with artificial intelligence to build intelligent educational ecosystems for Industry 5.0 worker training. Seeing the need for tailored learning experiences to match changing skill requirements, the research looks into how AI and IoT may transform workforce education. Using a mixed-methods approach that includes expert interviews, case study analysis, and literature evaluation, the research identifies and evaluates the essential elements of AI- and IoT-driven training systems. The results show that real-time data gathering and feedback enabled by IoT in conjunction with AI-powered tailored learning greatly enhances organizational flexibility, employee engagement, and learning outcomes. The report specifically notes gains in information retention, skill development speed, and training cost savings. The study also tackles issues which include reluctance to technology adoption and worries about data privacy. It emphasizes a human-centric strategy that strikes a balance between technology improvements and ethical issues for enterprises making the transition to Industry 5.0. It also provides useful insights and suggests an implementation structure.

关键词： Training Industries Ethics Ecosystems Transforms Real-time systems Internet of Things Fifth Industrial Revolution Artificial intelligence Interviews

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Multi-action self-healing coatings with simultaneous recovery of corrosion resistance and adhesion strength

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Journal of Materials science & Technology 2022年第6期101卷 18-27页

作者： Chenhao Ren Yao Huang Wenkui Hao Dawei Zhang Xiejing Luo Lingwei Ma Jinke Wang Thee Chowwanonthapunya Chaofang Dong Xiaogang Li Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China National Materials Corrosion and Protection Data Center University of Science and Technology BeijingBeijing 100083China State Key Laboratory of Advanced Power Transmission Technology(Global Energy Interconnection Research Institute Co.Ltd.) Beijing 100083China Faculty of International Maritime Studies Kasetsart UniversitySriracha Campus199 TungsuklaSrirachaChonburi 20230Thailand BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan 528300China

In this study, a new self-healing strategy that can simultaneously recover the corrosion resistance and the adhesion strength of coatings was introduced. The coating was based on a shape memory epoxy resin containing ethylene vinyl acetate(EVA) microspheres loaded with Ce(NO_(3))_(3)inhibitors, and was cured at a relatively high temperature to facilitate the fusion of adjacent microspheres for a strengthened self-healing effect. The electrochemical impedance spectroscopy(EIS) and scanning electrochemical microscopy(SECM) results demonstrated that the shape memory effect of epoxy matrix, the filling of molten EVA microspheres as well as the release of Ce(NO_(3))_(3)inhibitors contributed synergistically to suppress the corrosion reaction at the coating damage. After healing, the low frequency impedance modulus of the coatings containing Ce(NO_(3))_(3)-EVA microspheres was three orders of magnitude higher than that of the blank epoxy coating. The adhesion strength of the coatings containing Ce(NO_(3))_(3)-EVA microspheres on the metal substrate was also largely repaired thanks to the strong bonding effect of the EVA microspheres.

关键词： Self-healing coating Corrosion protection Adhesion strength Shape memory effect Bonding effect

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CNN-LSTM: Development of Offline Signature Authentication

CNN-LSTM: Development of Offline Signature Authentication

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Revathy Ganapathy Lingeshwaran D P. SuthanthiraDevi Harish Kumar M V Department of Computer Science and Engineering Vels Insitute of Science Technology and Advanced Studies Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India

Offline Signature Authentication is a critical task in the field of document authentication, and its accuracy is essential for ensuring security while transactions. This research proposes two approaches: Initially Pre-trained CNN models are used to extract features from signature images, which are then combined with handcrafted features such as HOG and some other geometric features of signature. Such combined features are passed to bidirectional LSTM model in which drop out layer undergoes classification which differentiate real and forgery signature. The proposed system has potential applications in document authentication and security, subsequently combination of CNN models and additional features provides more comprehensive representation of signature images resulting in improved accuracy. Three signature datasets are utilized namely GDPS, CEDAR, and BHSig-Bengali each with varying signature styles and image quality. Our experimental outcomes reveal that Bidirectional Convolutional LSTM along with handcraft features attained maximum accuracy in offline signature verification system.

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of Machine Learning and Deep Learning algorithms

A novel Approach for optimizing the crop yield by considerin...

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International Conference on Computing and Networking Technology (ICCNT)

作者： K. Suganthavalli C. Meenakshi K Parvathy Department of Computer Science Vels Institute of Science Technology and Advanced Studies(VISTAS) Pallavaram Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India’s economy, which covers a significant 60.45% of the country’s land and stands as the world’s second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector Machines (SVM) as an example of Machine Learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep Learning (DL) techniques.

关键词： Deep learning Support vector machines Recurrent neural networks Machine learning algorithms Costs Soil moisture Prediction algorithms Forecasting Long short term memory Crop yield

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A Gated Spatial-Channel Transformer Network for the Prediction of Molecular Properties 15

A Gated Spatial-Channel Transformer Network for the Predicti...

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15th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2022

作者： Shen, Jiahao Tong, Qiang Cui, Zhanqi Dong, Zanqiang Liu, Xiulei Laboratory of Data Science and Information Studies Beijing Information Science and Technology University Beijing100101 China Zhengzhou University of Aeronautics Henan450046 China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Information Science and Technology University Beijing100101 China

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Although spatial features of molecules have been widely used for molecular property prediction, the importance of interactive features is coming to the surface these days. By using molecular voxel-based representation, each voxel contains the distribution of atoms, while each channel corresponds to one atomic type. Thus the interaction between multiple atoms is actually contained in the channel information. In this work, we propose a gated spatial-channel transformer (GatedSCT) network for molecular property prediction. We design a channel transformer to capture interactive features from channels, which indicates the relationship between multiple atoms. Also, a spatial transformer is used to extract spatial features of molecules. We apply a gated mechanism to merge these two parts efficiently. Since our proposed network takes advantage of channel information, the experiments show that it can predict molecular properties more accurately than other networks. © 2022 IEEE.

关键词： Atoms

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Decentralized Federated Graph Learning with Lightweight Zero Trust Architecture for Next-Generation Networking Security

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1908-1922页

作者： Zhou, Xiaokang Liang, Wei Wang, Kevin I-Kai Yada, Katsutoshi Yang, Laurence T. Ma, Jianhua Jin, Qun Kansai University Faculty of Business Data Science Osaka565-0823 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China The University of Auckland Department of Electrical Computer and Software Engineering Auckland1010 New Zealand St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada Hosei University Faculty of Computer and Information Sciences Chiyoda-ku102-8160 Japan Waseda University Faculty of Human Sciences Tokorozawa359-1192 Japan

The rapid development and usage of digital technologies in modern intelligent systems and applications bring critical challenges on data security and privacy. It is essential to allow cross-organizational data sharing to achieve smart service provisioning, while preventing unauthorized access and data leak to ensure end users' efficient and secure collaborations. Federated Learning (FL) offers a promising pathway to enable innovative collaboration across multiple organizations. However, more stringent security policies are needed to ensure authenticity of participating entities, safeguard data during communication, and prevent malicious activities. In this paper, we propose a Decentralized Federated Graph Learning (FGL) with Lightweight Zero Trust Architecture (ZTA) model, named DFGL-LZTA, to provide context-aware security with dynamic defense policy update, while maintaining computational and communication efficiency in resource-constrained environments, for highly distributed and heterogeneous systems in next-generation networking. Specifically, with a re-designed lightweight ZTA, which leverages adaptive privacy preservation and reputation-based aggregation together to tackle multi-level security threats (e.g., data-level, model-level, and identity-level attacks), a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) agent is introduced to enable the real-time and adaptive security policy update and optimization based on contextual features. A hierarchical Graph Attention Network (GAT) mechanism is then improved and applied to facilitate the dynamic subgraph learning in local training with a layer-wise architecture, while a so-called sparse global aggregation scheme is developed to balance the communication efficiency and model robustness in a P2P manner. Experiments and evaluations conducted based on two open-source datasets and one synthetic dataset demonstrate the usefulness of our proposed model in terms of training performance, computa

关键词： Federated learning

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： P. Suthanthiradevi G Revathy K. Rejini Muthu Lakshmi.V Department of Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Department of Computer science and engineering Vels Institute of science Technology and Advanced studies Chennai Department of Computer Science Engineering Amrita college of Engineering and Technology Nagercoil Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Biological system modeling Large language models Text detection Writing Transformers Natural language processing

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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