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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

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Understanding Cyberterrorism: Exploring Threats, Tools, and Statistical Trends

Understanding Cyberterrorism: Exploring Threats, Tools, and ...

引用

2023 IEEE international conference on Dependable, Autonomic and Secure Computing, 2023 international conference on Pervasive Intelligence and Computing, 2023 international conference on Cloud and Big Data Computing, 2023 international conference on Cyber science and technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Alnuaimi, Rahaf Adam Alawida, Moatsum College of Engineering Abu Dhabi University Department of Computer Science and It Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350304602

Since cyber terrorists are skilled at using a variety of tools and platforms, including radio, television, transportation, the internet, and social media, to carry out their attacks, cyberterrorism poses a serious danger to both national security and the digital sphere. The attacks cover a broad range, have the ability to affect any country, and range from data theft and destruction to message manipulation, network intrusion, and ransom demands. Employing known and new computer security theories that are designed expressly to handle this threat will be necessary to combat the threat of cyberterrorism effectively. This study undertakes a thorough analysis of the statistical patterns of cyberterrorism as well as an in-depth evaluation of terrorists' use of cyberattacks. Our objective is to determine the overall occurrence and effect of these attacks while gaining a comprehensive knowledge of the strategies used to breach digital systems. © 2023 IEEE.

关键词： Terrorism

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Neural Network-Based Handwritten Character Detection

Neural Network-Based Handwritten Character Detection

引用

2023 international conference on System, Computation, automation and Networking, ICSCAN 2023

作者： Chithambaramani, R. Sankar, M. Sivaprakash, P. Marichamy, D. Yazhinian, S. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798350315127

Due to increased utilization of digital technologies in most day to day activities to keep and pass information, Hand- written characters recognition has become a well- liked subject of investigation because people still want to have Handwritten copies converted into electronic copies that can be kept electronically. Detection of hand- written character has got to become computer's ability to recognize and interpret accessible Hand- written input from the sources like Images, records, touchscreens. Understanding the Handwritten characters is complex because handwriting differs from person to person. The goal of this project is to create an artificial neural network-based model for handwritten character detection that can recognize certain typeface characters in part. Being a relatively young discipline, neural computing's style components are less fully defined than those of other architectures. Data parallelism was carried out via neural computers. Neural computers operate in a completely different manner than standard computers. Given a specific initial state (data input), a neural computer is prepared (not programmed) to either classify the input file into one of the many classes or to cause the original data to develop in such a manner that a specific desirable attribute is optimized. The experimental outcomes demonstrate that our suggested approach succeeds in classification accuracy on the test set of 99.3, exceeding the most recent techniques. This study offers a precise answer for real-world applications including postal automation, bank cheque processing, and signature verification while demonstrating the efficiency of utilizing neural networks for handwritten character detection. The field of computer vision and pattern recognition includes hand-written character recognition as a significant research subject. Due to its capacity to learn robust features directly from raw picture input, convolutional neural net-works (CNNs) have recently attained state-of-t

关键词： Feature extraction

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Self-calibrated stacked hourglass for human pose estimation 5

Self-calibrated stacked hourglass for human pose estimation

引用

5th international conference on Frontiers technology of Information and computer, ICFTIC 2023

作者： Tang, Tiannan Tan, Guangxing Yi, Tong Guangxi University of Science and Technology Liuzhou Key Laboratory of Intelligent Sensing and Control College of Automation Liuzhou China

ISBN: (纸本)9798350309034

In recent years, with the rapid development of deep learning algorithms such as CNN and RNN, human key point detection has been paid more and more attention. Human key point detection is always a research difficulty in computer vision because it is easily affected by complex joint occlusion, body deformation or lighting environment changes. With the proposed stacked hourglass network, it has become the mainstream method in the field of key point detection in recent years because of its repeatable upsampling and downsampling, and the addition of intermediate supervision model. Based on the stacked hourglass network, this paper introduces the self-calibrated convolutional network in SCNet network and optimates the structure of the original stacked hourglass network, and proposes a new human key point detection method named Self-calibrated stacked hourglass (SCHNs). This method effectively reduces the problem of inaccurate local feature description in the existing stacked hourglass network, and weakens the interference of useless signals in the global information to local information. Our method is trained and tested on the MPII human pose estimation dataset, and the case studys on the MSCOCO dataset. its detection results can effectively solve the problems existing in the existing stacked hourglass network, and it is significantly improved compared with other network models. © 2023 IEEE.

关键词： human keypoints detection self-calibrating convolution Stacked hourglass networks

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Research on Recommendation Algorithm Based on TrustSVD++ and Deep Matrix Decomposition 24

Research on Recommendation Algorithm Based on TrustSVD++ and...

引用

8th international conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Chen, Gongjin Kong, Yuting He, Yantao Department of Computer Science Guangdong University of Science and Technology Guangdong Dongguan China

ISBN: (纸本)9798400710094

In the recommendation algorithm, due to the incompleteness of the interaction between users and items, there are often a large number of missing data in the interaction matrix, which affects the quality of recommendation. To address this challenge, researchers have introduced matrix factorization techniques combined with machine learning algorithms to fill in missing data. However, the bilinear mapping technology adopted by the traditional matrix factorization method cannot fully capture the nonlinear relationship between users and items. To this end, this study proposes a recommendation algorithm combining TrustSVD++ and deep matrix factorization. Firstly, the explicit and implicit trust information between users is integrated into the SVD++ model to fill the interaction matrix more accurately. Then, in order to preserve the linear relationship and prevent network degradation, the residual network was used as the core module, and the parameter adjustment was optimized by adding channels directly connecting the input and output. Experimental results on public datasets show that compared with DMF1, DMF2, MC+DMF and BDMF models, the proposed TDMF model has the hit rate (HR@10) and normalized discounted cumulative gain (NDCG@10) increased by 2.1%and 3.7%respectively, which proves the effectiveness and practicability of the proposed model. © 2024 Copyright held by the owner/author(s).

关键词： Matrix factorization

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Research on relay protection inspection method based on operation information

Research on relay protection inspection method based on oper...

引用

2022 international conference on computer Application and Information Security, ICCAIS 2022

作者： Bai, Xu Ji, Haiming Dong, Xinghua Guo, Jianfeng Yin, Huan Chengde Power Supply Company State Grid Jibei Electric Power Supply Co. Ltd. Hebei Chengde067000 China Beijing Sifang Automation Co. Ltd. Beijing100085 China

ISBN: (纸本)9781510663459

Through data analysis and method research on online detection of relay protection, the decision tree is used to carry out fault diagnosis on the alarm information of secondary equipment, and based on the operation information, the evaluation of relay protection status and auxiliary maintenance decision are realized. For items that cannot be inspected during the inspection cycle and items that are found abnormal, a complete detection system is formed by means of continuous power transmission. © 2023 SPIE.

关键词： Decision trees

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Overcome STSF phenomenon in catastrophic forgetting

Overcome STSF phenomenon in catastrophic forgetting

引用

2022 international conference on computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2022

作者： Chang, Yifan Zhao, Qifan Department of Automation University of Science and Technology of China Hefei230027 China Software Technology Company Limited Suzhou215163 China

ISBN: (纸本)9781510663350

Catastrophic forgetting is an undesirable phenomenon in convolution neural networks and iCarl is an effective algorithm for preventing catastrophic forgetting. However, a potential defect that more similar tasks result in severer catastrophic forgetting (STSF) in iCarl is explored in this work. The reason of STSF is that similar tasks are prone to occupy similar feature channels and similar feature representations, thus they can be replaced by each other easily. Based on these findings, a novel method Similar Margin Loss (SML) is proposed. SML aims to make feature representations of samples from the same task compact while making feature representations from the different tasks differentiable in the feature space. Experiment results show that SML is effective in alleviating STSF. © 2023 SPIE.

关键词： Convolution

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Handling Non-IID Data in Federated Learning: An Experimental Evaluation Towards Unified Metrics

Handling Non-IID Data in Federated Learning: An Experimental...

引用

作者： Haller, Marc Lenz, Christian Nachtigall, Robin Awayshehl, Feras M. Alawadi, Sadi Karlsruhe University of Applied Sciences Karlsruhe Germany Institute of Computer Science Delta The University of Tartu Tartu Estonia Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden

ISBN: (纸本)9798350304602

Recent research has demonstrated that Non-Identically Distributed (Non-IID) data can negatively impact the performance of global models constructed in federated learning. To address this concern, multiple approaches have been developed. Nonetheless, previous research lacks a cohesive overview and fails to uniformly assess these strategies, resulting in challenges when comparing and choosing relevant options for real-world scenarios. This study presents a structured survey of cutting-edge techniques for handling the Non-IID data, accompanied by proposing a metric to develop a standardized approach for assessing data skew and its harmony with the appropriate approach. The findings affirm the metric's suitability as a heuristic for assessing data skew in distributed datasets without having insight into client data, serving both scientific and practical purposes and thus supporting the selection of handling strategies. This preliminary research establishes the foundation for discussing standardizing methodologies for evaluating data heterogeneity in federated learning. © 2023 IEEE.

关键词： Standardization

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ICFusion: An Infrared and Visible Image Fusion Network Based on Illumination Aware and Convolutional Attention

ICFusion: An Infrared and Visible Image Fusion Network Based...

引用

2023 international conference on Artificial Intelligence and automation Control, AIAC 2023

作者： Cao, Zhiyuan Liu, Hu Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

ISBN: (纸本)9798350383805

Infrared and visible image fusion plays a pivotal role in image enhancement, aiming to merge the unique attributes of both imaging modalities: the comprehensive scene understanding of infrared image and the detailed visual cues of visible image. However, many current fusion techniques are designed for single-illumination settings, overlooking the challenges of diverse illuminance conditions and the intricacies of modality fusion. In this paper, we present ICFusion, a cutting-edge image fusion network anchored by illumination aware and convolutional attention mechanisms. This framework integrates two key modules: the Illumination Probability Computation Module (IPCM) to gauge scene illuminance and the Convolutional Attention Fusion Module (CAFM) to discern latent features and reduce redundancy. By capitalizing on illumination aware, our approach deftly combines information from both imaging modes. Experimental outcomes underscore ICFusion's edge over contemporary methods, especially in retaining fine textural nuances. © 2023 IEEE.

关键词： Lighting

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Applications of Artificial Intelligence in Public Sector and its Challenges 4

Applications of Artificial Intelligence in Public Sector and...

引用

4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Krishna, Somanchi Hari Aljohani, Nasser Mishra, Deepti Garg, Neha Verma, Vibha Malathy, V. Vignana Bharathi Institute of Technology Department of Business Management Ghatkesar Mandal Malkangiri Medchal Dist Telangana Aushapur501301 India Islamic University of Madinah Information Systems Department Madinah Saudi Arabia G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Law College Dehradun Uttaranchal University Uttarkhand Dehradun India School of Engineering Sr University Department of Ece Telangana Warangal India

ISBN: (纸本)9798350393248

A series of technologies collectively referred to as artificial intelligence (AI) 'give intelligent computers the ability to learn, enhance and make reasoned judgements in ways that allow them to perform tasks that are usually considered to demand only human expertise, creativity, and ingenuity.' Significant advancements in hardware and software have accelerated the development of AI and increased the possibility that it will have a beneficial social impact, according to the 2018 UN E-Government Survey. The learning process of computers is being accelerated by increased data utilisation, which is also enhancing the effectiveness of AI systems that largely rely on data. On the one hand, AI has the potential to spur prosperity and assist developing nations in overcoming some enduring challenges. The aforementioned concerns are analysed from the perspective of the public sector in this working paper, taking into account public managers in particular those involved in the development of digital government. The results of the 2018 United Nations E-Government Survey are also taken into consideration in the study. It is anticipated that the working paper will be actively debated at the sessions held to prepare for the 2019 United Nations E-Government Survey. © 2023 IEEE.

关键词： Artificial intelligence

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Fish Farm Monitoring System Using IoT

Fish Farm Monitoring System Using IoT

引用

2023 international conference on System, Computation, automation and Networking, ICSCAN 2023

作者： Kishore, K.V. Dhinakaran, D. Kumar, N. Jagadish Sankar, S. M. Udhaya Chandu, K. Visuwasam, L. Maria Michael Velammal Institute of Technology Department of Information Technology Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Csbs Sri Sairam Engineering College Chennai India R.M.K College of Engineering and Technology Chennai India Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Chennai India

ISBN: (纸本)9798350315127

Fish aquaculture is an essential sector of the economy;it has seen rapid expansion in recent years. It can be challenging to manage a fish farm, though, as it demands continual attention to environmental parameters like water quality and temperature. In this study, we offer a monitoring system for fish farms that leverages IoT technology to gather and process data from sensors placed inside a fish farm. When any parameters are outside the specified range, the system notifies the farmer and provides real-time monitoring of water quality, temperature, and other environmental conditions. The method comprises a network of wireless sensors connected to a central computer and installed across the fish farm. The server gathers and analyses the sensor data before giving the farmer real-time access via a web-based interface. The fish farmer can utilize this interface to keep an eye on the fish farm's water quality and temperature as well as to change the feeding schedule and other environmental factors as needed. The system has undergone testing in a commercial fish farm, and the outcomes indicate that it successfully keeps track of the environmental conditions there. The technology can increase fish farming's productivity and profitability while minimizing its adverse environmental effects. © 2023 IEEE.

关键词： Water quality

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