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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Savita Raghavendra, R. Bhatt, Rahul Yamanappa Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The utility of artificial intelligence (AI) and gadget getting to know (ML) algorithms to medical diagnostic processes has been developing in significance in recent years. AI/ML models were evolved to help clinical professionals in imparting correct and green diagnostic approaches. Those models are capable of providing precise and correct diagnoses at the same time as reducing the complexity of conventional decision making. AI/ML algorithms are getting used to discover styles in medical statistics that could in any other case be hard for human beings to come across or to offer contextual information to refine and enhance the accuracy of diagnoses. The benefits of using AI/ML algorithms for enhancing precision in medical diagnostic tactics consist of rapid and accurate prognosis, extended performance, and better clinical consequences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Application of Waning Immunity Index Model using Spiking Neural Networks for COVID-19 Pandemic in the geographic context of India 2

Application of Waning Immunity Index Model using Spiking Neu...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Senthil Velan, S. Rubini, P. Sivaranjani, S. Cmr Institute of Technology Information Science and Engineering Bengaluru India Cmr University Computer Science and Engineering Soet Bengaluru India Computer Science and Engineering Mvj College of Engineering Bengaluru India

ISBN: (纸本)9798350315905

Spiking Neural Networks (SNN) are biologically inspired networks working on the principle of communication triggered while crossing of threshold potentials. During the COVID-19 pandemic, immunity has been acquired by the population in a geographical location by infections and immunizations. The Waning Immunity Model (WII) has been used to apply the method of SNNs so that the results of the model provide a better way of understanding its effects. The dataset considered in this research is for a time period of six months during the years of 2021 and 2022 focusing on the geographical location of India. Based on the proposed new model, the spike in the WII index is clearly evident in the first half of the time period under consideration, This model will help the healthcare and governments officials to plan for the booster doses to be administered to the human population for reinvigorating the antibodies effectively fighting the COVID-19 virus. © 2023 IEEE.

关键词： COVID-19

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Quantum Error Correction Based Entanglement Routing in Socially-Aware Quantum Networks

Quantum Error Correction Based Entanglement Routing in Socia...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Huang, Shao-Min Chien, Ming-Huang Wen, Ting-Yuan Wang, Qian-Jing Kuo, Jian-Jhih National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei City Taiwan

ISBN: (纸本)9798350351255

Quantum teleportation enables high-security communications via quantum entanglement. However, decoherence, signal decay, and environmental interference may cause imperfect entangled pairs with low fidelity. Such entangled pairs may easily generate errors when used to teleport data qubits and cause fatal computation errors on quantum computers. Fortunately, data qubits can be encoded via quantum error correction (QEC) code to recover the detected error to some extent. Nevertheless, letting any repeaters process data qubits is dangerous because malicious repeaters may peep at, destroy, or fake the data qubits. Thus, in this paper, we propose a novel QEC-enabled routing framework MOON facilitated with the concept of social networks (SNs) for quantum networks (QNs). MOON selects only trusted repeaters to process data qubits and exhibits elastic routing with QEC to maximize the throughput without errors. Last, simulation results show that our framework can outperform existing approaches by up to 34% on network throughput without errors. © 2024 IEEE.

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Multimodal Fake News Detection Based on Multi-source Heterogeneous Data Fusion 14

Multimodal Fake News Detection Based on Multi-source Heterog...

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14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Yu, Yongxin Ji, Ke Gao, Yuan Chen, Zhenxiang Ma, Kun Wu, Jun University of Jinan School of Information Science and Engineering Jinan China University of Beijing Jiaotong School of Computer and Information Technology Beijing China

ISBN: (纸本)9798350371024

In the era of information explosion, people are constantly exposed to a wealth of unreliable information. Nevertheless, societal stability and public trust may be seriously threatened by such unverified or even fabricated false information. In order to discriminate the fake news, several fake news detection approaches have been proposed. However, most of the current multimodal fake news detection methods only rely on news text and images from a single data source. Owing to the scarcity of multimodal information and the limited volume of data, the effectiveness of the majority of these methods is low. As a result, we embrace the approach of amalgamating multi-source heterogeneous data and propose a new network structure FusionNet. In this network structure, we creatively use web crawler technology to expand the data. We achieve double amplification of text and image as opposed to the prior single-mode amplification of text, and by data screening and cleaning, we create multi-source and multimodal datasets for various viewpoints on the same event. We also propose a novel modal fusion approach that leverages the inherent information in news content, integrates an attention mechanism, and explores the shared relevant features between original news and multi-source news. Through empirical results and analysis, our approach is superior to other methods. © 2023 IEEE.

关键词： Web crawler

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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Traceability and Prevention System Construction of Network Attacks Driven by Big Data

Traceability and Prevention System Construction of Network A...

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2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Liu, Benfa School of Computer and Information Engineering Wuhan Railway Vocational College of Technology Wuhan Hubei430205 China

ISBN: (纸本)9798350329773

The traditional mode of defending against DDoS attacks forms a distributed and strongly coupled system by integrating data processing and control logic into network devices. This system structure can improve the reliability of network operation, but it also has some problems, such as overburdened managers, unable to achieve global unified scheduling and consuming huge resources. In this paper, typical network attacks such as SQL(Structured Query Language) and DDOS (Distributed Denial of Service) are taken as research objects, and the characteristics of these two types of attacks in the context of big data are modeled. The packet is tested by NS2 simulation software, and the results show that BP(Backpropagation) algorithm is used to improve the probabilistic packet marking method, which can effectively improve the convergence speed of the tracking algorithm and improve the tracking accuracy. In view of the large number and high complexity of attack sources in big data environment, it is difficult to solve them by traditional methods based on experience or theory. This paper intends to improve the existing traceability methods by using the adaptability and self-learning ability of BP network. When the number of attack sources is 60, the false alarm rate of the traditional packet marking algorithm is 0.34, and the false alarm rate of the improved probabilistic packet marking algorithm is 0.20. © 2023 IEEE.

关键词： Big data

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Analyzing Clinical Data for Improved Diagnosis Using AI/ML Algorithms in Smart Health Care 5th

Analyzing Clinical Data for Improved Diagnosis Using AI/ML A...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Lakshmisha, S.K. Alam, Intekhab Bhuvana, J. Bhatt, Rahul School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

This study analyzes the capability for using AI/ML algorithms to improve diagnostic accuracy in smart health care. AI/ML strategies have revolutionized clinical analysis by way of offering state-of-the-art and dependable techniques for studying clinical facts. Via using superior techniques such as machine learning, deep gaining knowledge of, reinforcement studying, temporal pattern recognition, and superior decision tree algorithms, AI/ML algorithms provide plenty extra accurate predictions of diseases and conditions from affected person records. Further, AI/ML algorithms can discover relationships between different clinical elements that aren't visible to medical doctors and clinicians. Through the use of AI/ML algorithms, doctors will be able to make greater correct diagnoses and provide higher remedies to patients. The consequences of this take a look at imply that the AI/ML algorithms tested own the ability to enhance prognosis in clever healthcare in phrases of accuracy, precision, do not forget, and cost-effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Overview of RGBD semantic segmentation based on deep learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13627-13645页

作者： Zhang, Hongyan Sheng, Victor S. Xi, Xuefeng Cui, Zhiming Rong, Huan School of Electronic and Information Engineering Suzhou University of Science and Technology Jiangsu Suzhou215009 China Department of Computer Science University of Central Arkansas ConwayAR United States School of Artifcial Intelligence Nanjing University of Information Science and Technology Jiangsu Nanjing China

Semantic segmentation is one of the basic tasks in computer vision. Its purpose is to achieve pixel-level scene segmentation. With the popularity of depth sensors, combining depth data with RGB images for semantic segmentation can improve the accuracy of semantic segmentation. First, this paper mainly summarizes the fusion of RGB information and depth information and then describes the RGBD semantic segmentation method, evaluation metrics, data set, and comparison of the results on the two mainstream data sets, and then make a prospect of possible future research directions, and finally, a conclusion is made. This part of the work has a certain guiding significance for future research on RGBD semantic segmentation and lays a foundation for later research. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Semantic Segmentation

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An Extensive Analysis of Green Cloud Computing: Overview, Associated Challenges and Research Directions 3

An Extensive Analysis of Green Cloud Computing: Overview, As...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Suratia, Nishi Thakkar, Priyansh Sheth, Kunal Ramoliya, Dipak Patel, Anand K. Department of Computer Science and Engineering Gujarat Changa India Dharmsinh Desai University Faculty of Technology Department of Information Technology India

ISBN: (纸本)9798350343632

Current questions and challenges for environmental shielding are introduced by Cloud Computing. Reliability, high performance, and scalability are application states that are offered by cloud computing technologies. The Cloud Computing revolution promises environmental protection, modern networking redesign, and economic and technological advantages. Huge data centers are needed for this facility and they are coupled with a system that increases energy consumption and enormous CO2 emissions. Since the prime concern is energy, the problem generates the need for Green Cloud Computing, which can boost the efficiency of energy and to lower down tracks of carbon and electronic waste. © 2023 IEEE.

关键词： Architecture Challenges Green Cloud Computing Green Initiatives Merits and Demerits

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Near-Optimal Swapping and Purifying Strategy for All-Optical-Switching Entanglement Routing

Near-Optimal Swapping and Purifying Strategy for All-Optical...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Huang, Shao-Min Hsu, Tang-Ming Du, Jing-Jhih Kuo, Jian-Jhih Wang, Chih-Yu Academia Sinica Research Center for Information Technology Innovation Taipei City Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350351255

Entangled pairs serve as the cornerstone for secure data transmission. All-optical-switching technology on nodes enables the entangling signals to bypass nodes and build ultra-long entangled pairs. Nevertheless, entangled pairs suffer from decoherence over distance, causing inadequate fidelity and potentially compromising transmission quality. To address the challenges, we employ entanglement purification to enhance fidelity to meet the threshold. However, the purification process consumes additional entangled pairs and may fail. Besides, the purification efficiency would be poor if the input pairs have low fidelity. Thus, it is unavoidable to divide the path into sub-paths with appropriate lengths for better purification efficiency and then merge them into a longer entangled pair by swapping. The novel optimization problem DOSP then emerges: maximizing the probability while adhering to fidelity constraints. To tackle the DOSP efficiently, we propose a (1-δ)-approximation algorithm NSPS to consider the probability and fidelity jointly, where is a positive user-defined constant. Finally, the simulation results manifest that the NSPS can outperform the existing methods by at least 70%. © 2024 IEEE.

关键词： Quantum entanglement

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