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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Hamid, Taezeen Nand, Parma Chhabra, Megha School of Engineering and Technology Sharda University Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9789380544472

Parkinson's disease which is a neurodegenerative disease tends to deteriorate muscle control steadily. Although Parkinson's disease primarily affects elderly people, but adults can also be affected by this disease. The symptoms are caused by the gradual nerve cell loss in the area of the brain that regulates movement. Patients' quality of life is greatly reduced because it shows motor, cognitive, and other sorts of symptoms. Parkinson's disease is usually diagnosed based on a patient's medical history and physical symptoms. However, it has a subjective nature and a poor prognosis. In the diagnosis of Parkinson's disease, artificial intelligence (AI) has shown promising results. Yet because of the small sample size, poor validation and lack of big data design, it introduces bias. This paper outlines the state-of-the-art methods that have been used to detect Parkinson's disease. The findings of the literature, as well as frequent methodological flaws that may skew results, are identified. The question is can AI alone be successful in clinical settings for Parkinson's disease identification? By incorporating AI algorithms with Internet of Things (IoT) treatment can be improved effectively while reducing related healthcare expenditures. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Decision trees

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Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference

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Journal of Communications and Information Networks 2024年第4期9卷 330-339页

作者： Chao Gong Ping Li Panlong Yang Zhao Lv School of Computer Science and Technology Anhui UniversityHefei 230000China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of TechnologyMa’anshan 243032China School of Computer science School of Cyber Science and EngineeringNanjing University of Information Science and TechnologyNanjing 210000China

In the realm of the Internet of things(IoT),radio frequency identification(RFID)technology is essential for linking physical objects to digital *** introduction of harmonic technology has expanded RFID’s applications by improving sensitivity and enabling communication with wireless fidelity(Wi-Fi)***,this trend faces challenges,notably interference from strong Wi-Fi signals,which impacts RFID-based sensing *** paper proposes the WiFID(Wi-Fi and RFID)algorithm,which enables Wi-Fi devices to sense weak RFID harmonic *** subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic *** validation on the universal software radio peripheral(USRP)platform demonstrates that WiFID successfully detects 90% of RFID harmonics at−30 dBm,with only a minimal 4%decrease in Wi-Fi throughput.

关键词： RFID harmonic wireless sensing Wi-Fi interference avoidence

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Performance Analysis of Cooperative VLC with L-PPM Modulation and Diversity Combining Strategies under Perfect and Imperfect CSI for IoT Devices

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IEEE Sensors Journal 2025年第12期25卷 23028-23036页

作者： Dwivedy, Prashant Dixit, Vipul Kumar, Atul PDPM-Indian Institute of Information Technology Design and Manufacturing M.P. Jabalpur482005 India Bennett University School of Computer Science Engineering and Technology Greater Noida India Maulana Azad National Institute of Technology Department of Electronics and Communication Engineering Madhya Pradesh Bhopal462003 India

The rise of digital transformation is driven by the convenience of internet of things (IoT) wireless connectivity, facilitating the development of smarter, more interconnected, and efficient solutions across diverse sectors such as smart homes, cities, industrial automation, healthcare, transportation, agriculture, and environmental monitoring. Visible Light Communication (VLC) emerges as a promising wireless data transmission technique utilizing the visible light spectrum. Integrating VLC with IoT devices (IDs) offers myriad benefits and potential *** a practical scenario, this study presents a novel approach for cooperative VLC (C-VLC) systems that use L-ary pulse position modulation (L-PPM) to establish long-range indoor communication across IDs, even in the presence of noisy channel state information (CSI). Three diversity combining procedures, including equal gain combining (EGC), maximum ratio combining (MRC) and selection-based combining (SBC), are employed and compared in the study. The study examines the bit error probability (BEP) of a C-VLC system using L-PPM modulation for IDs under perfect and imperfect CSI conditions for a M-IDs scenario. The BEP of all three diversity combining strategies is computed using extensive simulations and compared to the performance of direct transmission and on-off keying (OOK) C-VLC systems. These comparisons provide empirical support for the analytical BEP findings. © 2001-2012 IEEE.

关键词： Error statistics

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SELF-SUPERVISED HETEROGENEOUS GRAPH LEARNING: A HOMOPHILY AND HETEROGENEITY VIEW 12

SELF-SUPERVISED HETEROGENEOUS GRAPH LEARNING: A HOMOPHILY AN...

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12th International Conference on Learning Representations, ICLR 2024

作者： Mo, Yujie Nie, Feiping Hu, Ping Shen, Heng Tao Zhang, Zheng Wang, Xinchao Zhu, Xiaofeng School of Computer Science and Engineering University of Electronic Science and Technology of China China National University of Singapore Singapore Northwestern Polytechnical University United States School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

Self-supervised heterogeneous graph learning has achieved promising results in various real applications, but it still suffers from the following issues: (i) meta-paths can be employed to capture the homophily in the heterogeneous graph, but meta-paths are human-defined, requiring substantial expert knowledge and computational costs;and (ii) the heterogeneity in the heterogeneous graph is usually underutilized, leading to the loss of task-related information. To solve these issues, this paper proposes to capture both homophily and heterogeneity in the heterogeneous graph without pre-defined meta-paths. Specifically, we propose to learn a self-expressive matrix to capture the homophily from the subspace and nearby neighbors. Meanwhile, we propose to capture the heterogeneity by aggregating the information of nodes from different types. We further design a consistency loss and a specificity loss, respectively, to extract the consistent information between homophily and heterogeneity and to preserve their specific task-related information. We theoretically analyze that the learned homophilous representations exhibit the grouping effect to capture the homophily, and considering both homophily and heterogeneity introduces more task-related information. Extensive experimental results verify the superiority of the proposed method on different downstream tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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Elevating Cystic Fibrosis Detection in Lungs using HRCT Images with a Cutting-Edge CNN-Based Approach 2

Elevating Cystic Fibrosis Detection in Lungs using HRCT Imag...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Ganjam, Amrutha Sriya Reddy C, Kishor Kumar Radhika, D. Ganjam, Karthik Singh.t, Monika Akuthota, Keerthi Sai Stanley College of Engineering and Technology for Women abids Department of Computer Science Hyderabad India University of Edinburgh Department School of Informatics MSc Data Science Edinburgh United Kingdom Stanley College of Engineering and Technology for Women Deptartment. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331519056

The creation of an algorithm for recognizing pathological abnormalities in cystic fibrosis is investigated in this paper using the CNN model with a modified psp-net. Currently, Decision Trees, Random Forests, PSP Nets, and Neural Networks are utilized in the diagnosis of cystic fibrosis. Since convolutional neural networks (CNNs) can process complicated picture data rapidly and efficiently, the goal of this study is to use CNNs for the detection of anomalies associated with cystic fibrosis. The method groups distinct annotated images into a simple and efficient structure, runs the set of images through a multiscale CNN procedure, and precisely locates the lung region affected by cystic fibrosis. The result of this paper demonstrated that differences in the training dataset can impact performance, but annotating CT images and categorizing them in terms of similar pathologies can improve the accuracy of the model. The proposed CNN model achieved an Accuracy of 84 %, Precision of 74%, Recall of 79%, F1-Score of 72%, error rate of 16% Which is better when compared with existing approaches. © 2024 IEEE.

关键词： Convolutional neural networks

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Decoding OTT: Uncovering Global Insights for Film Industry Advancement and Cultural Exchange 2

Decoding OTT: Uncovering Global Insights for Film Industry A...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Arvind, Vidisha Kumar, Abhay Kumar, Ayush Pal, Yogesh School of Computer Science Engineering & Technology Bennett University U.P. Greater Noida India

ISBN: (纸本)9798350327533

This study explores the effects of over-the-top content by examining data from popular streaming services such as Netflix, Hotstar Disney Plus, and Amazon Prime in order to learn more about consumer preferences, industry trends, and cross-cultural film exchange. To improve user experience, the study makes use of techniques including textual reviews analysis and machine learning methods (K-Means Clustering, Linear Regression, and Support Vector Machine Regression). Issues with OTT platforms, like the churn issue and biased content recommendations, are discussed. The aim is to suggest methods for impartial advice, alleviating the cold start issue, handling sparse data, and preventing customer churn. The goal of the study is to promote a positive interaction between platforms and their audience while advancing personalized suggestions, enhanced content production, and market expansion in the over-the-top (OTT) business. © 2024 IEEE.

关键词： Clustering Content analysis OTT(Over-the-top) Recommendation

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Multi-RIS-Aided Secure MIMO Dual-Function Radar-Communication System Based on Signal-to-Interference-plus-Noise Ratio 7

Multi-RIS-Aided Secure MIMO Dual-Function Radar-Communicatio...

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7th IEEE Advanced Information technology, Electronic and Automation Control Conference, IAEAC 2024

作者： Elhag, Nihad A. A. Liu, Liang Wei, Ping Babikir, Sharief F. Gao, Lin University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China University of Tennessee College of Engineering and Computer Science Chattanooga United States

ISBN: (纸本)9798350339161

In this paper, we introduce multiple Reconfigurable Intelligent Surfaces (RISs) aided secure MIMO Dual-Function Radar-Communication (DFRC) system for enhanced dual-functionality performance. We aim to optimize the Signal-to-Interference-plus-Noise Ratio (SINR) as a performance metric for radar, while simultaneously meeting the secrecy rate requirements for the communication link. Major effort is given to optimize multiple RISs configurations that counteract the presence of eavesdroppers and actively manage the propagation environment to benefit legitimate user. The optimization problem is solved iteratively by using CVX and the majorization-minimization (MM) algorithm. Numerical results demonstrate that the proposed method with multiple RISs can improve SINR for radar target detection and also secure communication link, which in turn enhances system performance. The proposed model is used to determine the optimum directions for RISs deployment. © 2024 IEEE.

关键词： Signal to noise ratio

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A Multi-Vehicle Cooperative Localization Method Based on Belief Propagation in Satellite Denied Environment

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Journal of Beijing Institute of technology 2022年第5期31卷 464-472页

作者： Jiaqi Wang Lina Wang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

The global navigation satellite system(GNSS)is currently being used extensively in the navigation system of ***,the GNSS signal will be faded or blocked in complex road environments,which will lead to a decrease in positioning *** to the higher-precision synchronization provided in the sixth generation(6G)network,the errors of ranging-based positioning technologies can be effectively *** the same time,the use of terahertz in 6G allows excellent resolution of range and angle,which offers unique opportunities for multi-vehicle cooperative localization in a GNSS denied *** paper introduces a multi-vehicle cooperative localization *** the proposed method,the location estimations of vehicles are derived by utilizing inertial measurement and then corrected by exchanging the beliefs with adjacent vehicles and roadside *** multi-vehicle cooperative localization problem is represented using a factor *** iterative algorithm based on belief propagation is applied to perform the inference over the factor *** results demonstrate that our proposed method can offer a considerable capability enhancement on localization accuracy.

关键词： cooperative localization belief propagation factor graph inertial navigation system internet of vehicles

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Diabetic Retinopathy Detection using Deep Learning Framework and Explainable Artificial Intelligence Technique 14

Diabetic Retinopathy Detection using Deep Learning Framework...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Posham, Uppamma Bhattacharya, Sweta School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350344837

Diabetic retinopathy (DR) is a serious eye condition induced by diabetes that can result in eyesight. In order to overcome the vision impairment of diabetes mellitus patients, it is essential for early age prevention by medical practitioners. In the traditional approach, ophthalmologists conduct various screening tests to detect DR but fail to achieve accurate and conclusive diagnosis due to the associated time consuming processes. In order to eliminate such burden on the ophthalmologists, deep learning and machine learning techniques have evolved rapidly playing a significant role in the classification of diabetic and non diabetic patients. In this paper, the proposed framework implements a publicly available Benchmark APTOS 2019 Gaussian-filtered DR image dataset using a customized CNN model, yielding an enhanced accuracy of 98 %. In addition, the framework also employs a LIME explainer visualization model to provide enhanced transparency and interpretability to the generated predictions. This eliminates the 'blackbox' -ed nature of the predicted results from the traditional ML process and enables healthcare providers to take clinical decisions with confidence providing visual representations of the significant features that contribute towards an outcome. © 2024 IEEE.

关键词： Diagnosis

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Thermo-hydro-poro-mechanical responses of a reservoir-induced landslide tracked by high-resolution fiber optic sensing nerves

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Journal of Rock Mechanics and Geotechnical engineering 2024年第3期16卷 1018-1032页

作者： Xiao Ye Hong-Hu Zhu Gang Cheng Hua-Fu Pei Bin Shi Luca Schenato Alessandro Pasuto School of Earth Sciences and Engineering Nanjing UniversityNanjing210023China School of Computer Science North China Institute of Science and Technology(National Safety Training Center of Coal Mines)Beijing101601China State Key Lab of Coastal and Offshore Engineering Department of Geotechnical EngineeringDalian University of TechnologyDalian116024China National Research Council-Research Institute for Geo-Hydrological Protection(CNR-IRPI) Padova35127Italy Department of Information Engineering University of PadovaPadova35131Italy

Thermo-poro-mechanical responses along sliding zone/surface have been extensively ***,it has not been recognized that the potential contribution of other crucial engineering geological interfaces beyond the slip surface to progressive ***,we aim to investigate the subsurface multiphysics of reservoir landslides under two extreme hydrologic conditions(*** and dry),particularly within sliding *** on ultra-weak fiber Bragg grating(UWFBG)technology,we employ specialpurpose fiber optic sensing cables that can be implanted into boreholes as“nerves of the Earth”to collect data on soil temperature,water content,pore water pressure,and *** Xinpu landslide in the middle reach of the Three Gorges Reservoir Area in China was selected as a case study to establish a paradigm for in situ thermo-hydro-poro-mechanical *** UWFBG-based sensing cables were vertically buried in a 31 m-deep borehole at the foot of the landslide,with a resolution of 1 m except for the pressure *** reported field measurements covering the period 2021 and 2022 and produced the spatiotemporal profiles throughout the *** show that wet years are more likely to motivate landslide motions than dry *** annual thermally active layer of the landslide has a critical depth of roughly 9 m and might move downward in warmer *** dynamic groundwater table is located at depths of 9e15 m,where the peaked strain undergoes a periodical response of leap and withdrawal to annual hydrometeorological *** interface behaviors may support the interpretation of the contribution of reservoir regulation to slope stability,allowing us to correlate them to local damage events and potential global *** paper also offers a natural framework for interpreting thermo-hydro-poro-mechanical signatures from creeping reservoir bank slopes,which may form the basis for a landslide monitoring and early warning system.

关键词： Reservoir landslide Thermo-hydro-poro-mechanical response Ultra-weak fiber bragg grating(UWFBG) subsurface evolution engineering geological interface Geotechnical monitoring

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