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arXiv 2024年

作者： Wang, Zhonghao Yuan, Ru Fu, Jiaye Wong, Ka-Chun Peng, Chengbin College of Information Science and Engineering Ningbo University Ningbo315200 China Key Laboratory of Mobile Network Application Technology of Zhejiang Province Ningbo315200 China Department of Computer Science City University of Hong Kong 999077 Hong Kong School of Management Xi’an Polytechnic University Xi’an710048 China

Core-periphery structure is an essential mesoscale feature in complex networks. Previous researches mostly focus on discriminative approaches while in this work, we propose a generative model called masked Bayesian non-negative matrix factorization. We build the model using two pair affiliation matrices to indicate core-periphery pair associattions and using a mask matrix to highlight connections to core nodes. We propose an approach to infer the model parameters, and prove the convergence of variables with our approach. Besides the abilities as traditional approaches, it is able to identify core scores with overlapping core-periphery pairs. We verify the effectiveness of our method using randomly generated networks and real-world networks. Experimental results demonstrate that the proposed method outperforms traditional approaches. © 2024, CC BY.

关键词： Complex networks

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An Ensemble-based Neural Network Model for Natural Disaster in 2019

An Ensemble-based Neural Network Model for Natural Disaster ...

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ACS/IEEE International Conference on computer Systems and applications

作者： Vartika Bhadana Pooja Pathak Anand Singh Jalal Ashish Sharma Bhisham Sharma Imed Ben Dhaou Department of Computer Application and Engineering GLA University Mathura Department of Computer Engineering and Applications GLA University Mathura UP Department of Computer Science & Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

Recently, all have witnessed a rapid growth of COVID-19 coronavirus worldwide. The calculation of COVID-19 time-series prediction is done using many techniques like compartment models, machine learning models (ML), and deep learning models. Therefore, in this paper, the authors have proposed an ensemble-based neural network model. Neural networks (NN), along with non-linear autoregressive (NAR) functions, fitting neural networks (FITNET), or fuzzy Systems, are widely used in time-series forecasting. The responses of NAR, FITNET predictor modules are aggregated using fuzzy logic, which improves the final prediction by intelligently integrating outputs of different modules. The whole model was put to the test in terms of forecasting the coronavirus time series in India, at 13 States. In the validation data set, results of ensemble NN models with fuzzy response integration demonstrate extremely better-predicted values. Overall, the results reveal that a modular neural network with fuzzy (MNNF) beats all other approaches in performance metrics, like Root Mean Squared Error (RMSE). Prediction errors of ensemble NN i.e., MNNF are much smaller than those of classic monolithic neural networks, showing advantages of the method proposed. The model provides the prediction for the upcoming 8 days.

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Comparison of Machine Learning Techniques Based on Feature Reduction to Detect Diabetic Retinopathy

Comparison of Machine Learning Techniques Based on Feature R...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Devendra Singh Deepika Joshi Dinesh C. Dobhal Janmejay Pant Himanshu Pant Computer Science and Engineering Department Graphic Era Hill University Nainital India Faculty of Technology and Computer Application Amrapali Group of Institutes Haldwani India Computer Application Department Graphic Era Deemed to Be University Dehradun Uttarakhad India School of Computing Department Graphic Era Hill University Nainital India

Diabetic retinopathy is considered as an eye disorder condition that can affect persons who are diagnosed positive against diabetes. This condition causes damage to the retina's blood vessels. Diabetic retinopathy detection at an early stage may help patients protect their vision. This disease is often asymptomatic in the early stages but has a significant impact in the latter stages. The timely identification and intervention of diabetic retinopathy (DR) can greatly mitigate the potential for visual impairment. It takes time, effort, and money for ophthalmologists to manually diagnose DR retina fundus images. So there is a need for a screening system that can detect the disease in its early stages. There are various machine learning-based approaches available to detect diabetic retinopathy. In our proposed work first, we have to select the optimal features then we perform the comparative analysis with the help of various machine learning algorithms. We have used Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machine (SVM), Naïve Bayes, and AdaBoost. In our experiment, we observed that Logistic Regression achieved the highest accuracy of 74.2 with optimal features.

关键词： Support vector machines Machine learning algorithms Communication systems Visual impairment Forestry Retina Feature extraction

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Microgrid Recent Innovations & Uses

Microgrid Recent Innovations & Uses

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International Conference on Computing and Networking technology (ICCNT)

作者： Neeraj Kaushik Shubhashis Goswami Keerti Sachan V.J. Vijayalakshmi P. Gajendran V.A. Mishra Department of ECE TMU School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand School of computer science and application IIMT University meerut Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Computer Engg Vishwakarma Institute of Information Technology Pune

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Concerns about global warming brought on by increasing carbon dioxide levels in the environment have sped up technological development in the sustainable energy sector over the past few years. The advancement in science while the shift to less polluting materials sources possesses increased the appeal for DG, subsequent generations distributed generation, using renewable supplies. Nevertheless, it has become widely recognized that increasing DG entry can negatively affect the matrix’s layout and operations. A suggestion made to reduce the effect about DG while improve the suitability of traditional grids over massive DG installation involves the Located concept. encompassing a variety of electrical devices’, the elements and Micro-grids and related power gadgets fields are now very well-liked research areas. This essay examines the history and theory behind microgrids, as well as the present situation regarding the writing, active research initiatives, and pertinent requirements. It additionally provides a more thorough analysis of the numerous Located pilot initiatives which have been implemented surrounding the globe and addresses possible directions for future study.

关键词： Technological innovation Layout Hydroelectric power generation Microgrids Carbon dioxide Production Writing Inverters Research initiatives Standards

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Multi-Temporal Relationship Inference in Urban Areas

arXiv

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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

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A Novel based Hybrid smart System for detecting the cyber-attacks on IOT’s

A Novel based Hybrid smart System for detecting the cyber-at...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： K. Juliana Gnanaselvi Surendra Kumar Shukla Keerthipati Kumar Anita Gehlot Tarun Jaiswal B. T. Geetha Department of Computer Science Rathinam College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Technology University of Technology and Applied Science- Al Mussnna Oman Uttaranchal Institute of Technology Uttaranchal University India Department of Computer Application National Institute of Technology Raipur (C.G.) Department of ECE Saveetha School of Engineering SIMATS Tamil Nadu India

The Iot technology (IoT) is rapidly advancing sufficiently that it might have a larger effect on huge industrial infrastructure and everyday routines. However, this became the focus of fraudsters, whom made IoT into such a prey for their misbehaviour, thereby potentially opening the door for an assault on the computer systems. Have used a traditional intrusion detection device to manage the Iot network is problematic because of the enormous variety and quantity of IoT devices. A revolutionary aggregation Interdisciplinary Detection System (HIDS) that merges a C5 predictor with the a One Category Classification Algorithm is built to guard IoT devices. It project's goal is to find threats that can be inhibited with high classification certainty and low completely bogus rates, for both storing and accessing and negligible vulnerabilities. The User database, consisting includes include legal IoT data stream and other varieties of assaults, is employed to assess the specified HIDS. Studies show that when combined with SIDS and Assistance methodologies, the proposed hybrid IDS provides a higher accuracy results and a smaller percentage of false positives.

关键词： Fault diagnosis Law Databases Fault detection Stacking Ecosystems Intrusion detection

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Finite Buffer Queueing System Performance Study with Multi Heterogeneous Fog Architecture 19

Finite Buffer Queueing System Performance Study with Multi H...

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19th Orissa Information technology Society International Conference on Information technology, OCIT 2021

作者： Dutta, Arijit Pani, Luina Misra, Chinmaya Roy, Ruben School of Computer Science Engineering KIIT Deemed To Be University Bhubaneswar India School of Computer Application KIIT Deemed To Be University Bhubaneswar India Government College of Engineering And Leather Technology Department of IT Kolkata India

ISBN: (纸本)9781665416641

When it comes to new and developing applications that require high computation and low latency, fog computing is an attractive option to consider for implementation. The peripheral network devices are able to share idle resources and work together to complete fog computing tasks. It's up to the task publishers to decide how to distribute computer jobs that take into account transmission line quality and energy utilization. To urge these gadgets to engage in computational offloading, effective stimuli approaches are needed to stimulate them. Three heterogeneous servers with varying service rates are investigated using a finite constant buffer queue. Depending on a Poisson distribution with a mean and service times, the jobs are exponentially spread by distinct mean values There is a limit to how many jobs can be held in a queue while the servers are busy. It's assumed that Because of this, jobs are considered lost if they arrive after a buffer has reached capacity. If there is no client in queue, the next customer will be presumed to be in the first server if there is no customer in line. In the case of routers, this can be seen in their different speeds when serving packets on the network. © 2021 IEEE.

关键词： Fog computing

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Child-Centred AI Design: Definition, Operation, and Considerations

Child-Centred AI Design: Definition, Operation, and Consider...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Wang, Ge Sun, Kaiwen Atabey, Ayça Pothong, Kruakae Lin, Grace C. Zhao, Jun Yip, Jason Department of Computer Science University of Oxford United Kingdom School of Information University of Michigan United States School of Law Centre for Data Culture & Society University of Edinburgh United Kingdom Department of Media and Communications The London School of Economics and Political Science 5Rights Foundation United Kingdom Scheller Teacher Education Program The Education Arcade The Massachusetts Institute of Technology United States Information School University of Washington United States

ISBN: (纸本)9781450394222

AI systems and related algorithms are starting to play a variety of roles in the digital ecosystems of children - being embedded in the connected toys, smart home IoT technologies, apps, and services they interact with on a daily basis. Going forward, AI systems will, in all likelihood, become even more pervasive in children's applications simply due to their sheer usefulness in creating compelling, adaptive, and personal user experiences. Yet, understanding the ways that AI-driven systems used by children operate, and how AI could be designed to better anticipate and respond to children's diverse requirements is still a new and emerging area of investigation. Our goals of this workshop are to (1) extend the current critically constructive dialogue around the meaning of child-centred AI design and (2) explore ways to operationalise such child-centred AI design in practice, and finally (3) further expand and foster a community for those who are interested in designing and developing child-centred AI systems. © 2023 Owner/Author.

关键词： Automation

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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Improving Connectivity of IoT-Enabled Networks Through Big Data Analytics

Improving Connectivity of IoT-Enabled Networks Through Big D...

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International Conference on Computing and Networking technology (ICCNT)

作者： K S Kiran Sumanta Sen Sunil Sharma School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The net of things (IoT) has quick grow to be the backbone of many corporations, taking into account real-time statistics collection and automatic tasks. However, the connectivity of these IoT-enabled networks can become without difficulty congested when huge amounts of data are generated in a brief time frame. To be able to hold a high stage of overall performance, big facts analytics can be used to research the massive volumes of generated statistics, provide insights and locate anomalies to improve community connectivity. Massive statistics analytics will permit higher records processing and evaluation competencies, become aware of regions of improvement and optimize network operations. Additionally, predictive analytics techniques can be used to forecast upcoming community congestion and take movement that could enhance network performance in real time. This, but, requires green facts collection, garage and control gear to ensure that the big records analytics answer is effective and efficient. This paper investigates how massive records analytics may be used to improve the connectivity of IoT-enabled networks through leveraging the energy of predictive analytics.

关键词： Performance evaluation Gears Scalability Big Data Real-time systems User experience Safety Reliability Internet of Things Predictive analytics

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