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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Raj, Rakesh S. Kusuma, M. Dayananada Sagar Academy of Technology & Management Department of Computer Science & Engineering Bengaluru India Dayananada Sagar Academy of Technology & Management Department of Information Science & Engineering Bengaluru India

ISBN: (纸本)9798350313413

In recent times, individuals have been confronted with a multitude of health diseases as a result of their living environments and the prevailing environmental conditions. A disease that lasts for over three months is categorized as a chronic disease, and it is projected to become the primary global cause of mortality.A chronic ailment progresses slowly, leading to damage in the body. The circumstances could potentially respond to medical treatment, or they might not. The chronic disease can be managed, but neither medicines nor vaccines can cure it. Chronic disease is mainly divided into various types such as cancer, heart disease, diabetes, asthma, Parkinson's disease, arthritis, rheumatoid, stroke, and kidney disease. Identifying and diagnosing chronic diseases in their early stages is of paramount importance, yet it remains a challenging task for doctors to accurately detect and treat these conditions. Thegoalof this research is to create a model with high interpretability for predicting and diagnosing chronic diseases. The analytics of big data plays a significant role in treating and predicting chronic diseases. This analytics enables higher accuracy results, effectiveness, and less computational and time complexity, as demonstrated in this survey. The goal of this study is to create a model with high interpretability for predicting future diagnosis. © 2023 IEEE.

关键词： Arthritis Big Data Analytics Cancer Chronic Diseases Diabetes Heart Diseases

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Multimodal Data Trajectory Prediction: A Review 10

Multimodal Data Trajectory Prediction: A Review

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wang, Xiaoliang Yue, Hao Yang, Qing Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou China

ISBN: (纸本)9798350312461

Trajectory prediction refers to predicting the future movement of an object, person, or vehicle based on past motion trajectory information and other relevant environmental information. Traditional trajectory prediction solutions are suitable for simple driving scenarios and only applicable for short-term predictions. With the improvement of computing power and data processing speed, people can analyze and utilize large-scale datasets faster, use more data and more complex algorithms to build models, and therefore better predict future trends and behaviors. In addition, different solutions focus on how to efficiently extract features from different types of information and how to more accurately predict the future trajectory of the target object. Our goal is to analyze the impact of different information on prediction, and to classify and compare deep learning-based trajectory prediction methods for vehicles or surrounding pedestrians based on the same data. According to the type and quantity of inputs, they are divided into five categories, and the use and prediction effect of different information solutions are elaborated in detail. © 2023 IEEE.

关键词： Deep neural networks

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Human Gait Recognition Algorithm Based on MobileNetV1 with Attention Mechanism 3

Human Gait Recognition Algorithm Based on MobileNetV1 with A...

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3rd International Conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Zhang, Jinsha Zhang, Xuedong School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China

ISBN: (纸本)9781510663473

For embedded modern equipment, the current gait recognition algorithm model is difficult to deploy on it due to a large amount of gait frame image data, slow network processing speed, complex structure and low computational efficiency. In this paper, a lightweight convolutional network model integrating the attention mechanism is proposed. The algorithm first performs morphological processing on the image, extracts the gait contour image, and calculates the gait energy image;Integrates the attention mechanism with MobileNetV1;The feature information of the image is effectively extracted, and the parameters of the network are reduced. A number of body method validation experiments are conducted in the CAISIAB gait database of the Chinese Academy of sciences, and the experimental results are significantly improved with other deep learning models. © 2023 SPIE.

关键词： Computational efficiency

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Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies

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computers, Materials & Continua 2022年第2期70卷 2209-2224页

作者： Nasir Mahmud Khokhar Muhammad Nadeem Majeed Syed Muslim Shah Department of Computer Engineering University of Engineering and TechnologyTaxila47050Pakistan Punjab University College of Information Technology University of PunjabLahore54000Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan

In this study,it is proposed that the diffusion least mean square(LMS)algorithm can be improved by applying the fractional order signal processing *** of Caputo’s fractional derivatives are considered in the optimization of cost *** is suggested to derive a fractional order variant of the diffusion LMS *** applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless *** topology of the network is selected such that a smaller number of nodes are *** the network,a random sleep strategy is followed to conserve the transmission power at the *** proposed fractional ordermodified diffusionLMS algorithms are applied in the two configurations of combine-then-adapt and *** average squared error performance of the proposed algorithms along with its traditional counterparts are evaluated for the estimation of the Rayleigh channel *** proof of convergence is provided showing that the addition of the nonlinear term resulting from fractional derivatives helps adjusts the autocorrelation matrix in such a way that the spread of its eigenvalues *** increases the convergence as well as the steady state response even for the larger step *** results are shown for different number of nodes and fractional *** simulation results establish that the accuracy of the proposed scheme is far better than its classical counterparts,therefore,helps better solves the channel gains estimation problem in a distributed wireless *** algorithm has the potential to be applied in other applications related to learning and adaptation.

关键词： Diffusion strategies distributed channel gains fractional derivatives mean squared error

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Implementation of IoT Enabled RFID Driven Human Life Rescuer in Railways 2

Implementation of IoT Enabled RFID Driven Human Life Rescuer...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Kalaiselvi, V.K.G. Swathi, K. Sri Aishwarya, W. Rakshana, S. Shanmugasundaram, Hariharan Kukreja, Vinay Sri Sai Ram Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9781665456302

As per the Data Compiled by the Ministry of Indian Railways, there are nearly 5413 people who lost their lives in Railway accidents from 2015-2020, most of which are due to careless and rushed boarding & de-boarding. Thus, a proposed model called 'RFID enabled Railway Rescuer' is introduced to minimize these railway accidents. The proposed Internet of Things (IoT) system consists of a Radio Frequency Identification (RFID) tag, that detects the passenger in ZOR (Zone of Risk - The Door Edge) which in turn triggers the RFID reader fixed in the compartment. The Activated RFID reader will sense all the RFID tags that are present in the ZOR by providing alerts in the form of sound beeps or lights on the Door Edge. Or in other cases, it sends quick notifications to the passengers who are holding respective RFID tags. These notifications alert the passenger to change their standing position. By this, the proposed model tends to minimize railway accidents & thus save many innocent lives. © 2023 IEEE.

关键词： Internet of things

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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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LASKV: Lightweight Authenticated Session Key Agreement for Video Surveillance System 5th

LASKV: Lightweight Authenticated Session Key Agreement for V...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Kumar, Pramod Pal, Arup Kumar Islam, SK Hafizul Jharkhand Dhanbad India Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani West Bengal Kanchrapara India

ISBN: (纸本)9789819795147

The video surveillance system (VSS) is an integral part of the modern society. The surveillance camera captures the video of its designated areas and subsequently stores the video data in the server for future needs. In this situation, the VSS should send the captured video to the server securely;later, according to the demand, legitimate users can access the video data securely. This chapter suggests a secure VSS where the camera transmits the surveillance video data (SVD) to the server once both entities are mutually authenticated and generates the session key. Further, a legitimate user can access the SVD from the server once both parties are mutually authenticated and ensure a session key for secure data transmission. This chapter proposes a lightweight authenticated session key agreement for VSS based on the ASCON, strengthening access control and safeguarding private data. We call the proposed scheme LASKV. The LASKV scheme is lightweight and upholds security against various cryptographic attacks. In this regard, the informal security analysis and the Scyther simulation endorse the security of the LASKV scheme. Overall, the LASKV scheme effectively stores and accesses the SVD safely within an organization. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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Urban green space extraction from BJ-2 remote sensing image based SegFormer semantic segmentation model (vol 51,2439835 ,2025)

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CANADIAN JOURNAL OF REMOTE SENSING 2025年第1期51卷

作者： Guo, Yan Shao, QuanSen Liu, FuJiang Lin, Weihua Wang, Mianzhi Li, Zhuowu Chen, Shuo Liu, Hongchen Su, Junshen Wang, Xianbin School of Computer Science China University of Geosciences (Wuhan) Wuhan China School of Geography and Information Engineering China University of Geosciences (Wuhan) Wuhan China China Geological Survey Xining Comprehensive Survey Center of Natural Resources Xining China Piesat Information Technology Co. Ltd Beijing China

Accurate extraction of urban green spaces is of great significance for promoting urban planning and management, and promoting sustainable urban development. Given the characteristic spatial distribution of urban green spaces in high-resolution remote sensing imagery, a framework for automated extraction of urban green spaces using the SegFormer semantic segmentation network is proposed. The framework leverages self-attention mechanisms to capture global information and enhance the extraction of green spaces. We have constructed an urban green space dataset that includes four study areas: Ruyuan, Tongcheng, Jianli, and Hanchuan. Experimental results demonstrate that the proposed architecture achieves superior performance compared with other models, with a mean intersection over union ratio of 89.26% and an accuracy rate of 95.34% (pixel accuracy). The visualization results exhibit exceptional performance in extracting green spaces with clear spatial relationships, such as roadside and community greening while effectively distinguishing between urban green spaces and farmland. Furthermore, we conduct experiments on generalization ability on four different areas. The results show that the generalization ability of SegFormer outperforms other models. Lastly, the experiments verify the impact of different data augmentation multiples on model accuracy, providing valuable insights to guide data augmentation strategies in practical engineering. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Semantic Segmentation

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An Effective Feature Selection Technique for Detecting Parkinson's Disease Using Binary Whale Optimization Algorithm

An Effective Feature Selection Technique for Detecting Parki...

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Chaudhuri, Abhilasha Mohdiwale, Samrudhi Op Jindal University Department of Computer Science and Engineering Raigarh India Siksha O Anusandhan University Department of Computer Science and Information Technology Bhubaneshwar India

ISBN: (纸本)9798350310061

Parkinson's disease is a neurological condition that affects the nerves of the body and results in uncontrollable body movements like shaking, stiffness etc. Parkinson's disease affects around 10 million people worldwide. Early detection of the disease is very important for patients. The easiest way to detect the disease is with the help of the voice recordings i.e., speech signals. As the speech dataset has very large number of features it is important to choose the optimal feature subset for disease classification. This task is known as feature selection. This work proposes a feature selection approach for efficient detection of the Parkinson's disease based on binary whale optimization algorithm. Eight different transfer functions have been tried and the best one is chosen. The result has been evaluated based on classification accuracy, feature selection ratio and the execution time metrics. A significant improvement of 7.7 % have been achieved over the classical method. © 2023 IEEE.

关键词： Feature Selection

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An AAE-based Proactive Content Chunked Caching Scheme with Mobility Prediction in VANETs 23

An AAE-based Proactive Content Chunked Caching Scheme with M...

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Jin, Limei Zhu, Chenchen Yu, Lei Zhang, Benhong School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Medical Information Technology Anhui University of Chinese Medicine Hefei China

ISBN: (纸本)9798400700705

In Vehicular Ad hoc Networks(VANETs), edge caching can meet the increasing needs of users for vehicular applications and reduce the network burden. However, the existing studies rarely consider the differences in popularity caused by different regions and the high mobility of vehicles. To improve the efficiency of edge caching and reduce the content retrieval cost, an AAE-based proactive content chunked caching scheme(APCCMP) with mobility prediction is proposed. Firstly, according to the local historical request matrix and user preference matrix, Adversarial Autoencoder(AAE) is used to predict local content popularity. Then, considering the moving trajectory, RSUs predict the transition probability of vehicles on the Markov model. Finally, according to the prediction results and vehicles' speeds, the content chunks are cached on multiple RSUs. The experimental results show that the proposed scheme is significantly superior to other baseline schemes. © 2023 ACM.

关键词： Vehicular ad hoc networks

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