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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： J. Relin Francis Raj M. Saravana Karthikeyan G. Vinoth Rajkumar S. Vijay Shankar R. Augustian Isaac S. Thanga Anushya Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering J. P. College of Engineering Tenkasi India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Thandalam Chennai India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

In the rapidly evolving OTT platforms, providing highly personalized and relevant film recommendations has become a critical challenge. Traditional recommendation systems primarily rely on user ratings and watch history, neglecting the vast potential of other data sources such as social media interactions and search engine queries. This research presents a multi-source film recommendation framework, integrating data from social media posts, search behaviour, and OTT streaming history to deliver enhanced personalization. A novel algorithm, CoFiSent (Collaborative Filtering with Sentiment Analysis), is proposed, combining collaborative filtering techniques with sentiment analysis of user-generated content from social media. By analysing user sentiments and preferences, the algorithm refines recommendations to match users' dynamic interests more effectively. The performance of CoFiSent is evaluated against existing algorithms, including standard collaborative filtering and content-based filtering models. Simulations are conducted using real-world datasets, and the system's effectiveness is measured through precision, recall, F1-score, and mean reciprocal rank (MRR). Results demonstrate that CoFiSent outperforms traditional methods, particularly in capturing emerging trends and contextual preferences reflected in social media activities. The integration of multiple data sources enhances the overall accuracy and relevance of recommendations, offering a significant improvement in user satisfaction. The research findings suggest that incorporating social media sentiment and search behaviour into film recommendation systems represents a promising approach for next-generation entertainment platforms.

关键词： Over-the-top media services Sentiment analysis Social networking (online) Heuristic algorithms Collaborative filtering Soft sensors User-generated content Motion pictures History Recommender systems

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Securing Fog Computing-based Industry 4.0 Communication Using Authenticated Key Agreement Scheme

Securing Fog Computing-based Industry 4.0 Communication Usin...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Amit Kumar Mishra Mohammad Wazidy D. P. Singhy Ashok Kumar Das Mohsen Guizani Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence(MBZUAI) Abu Dhabi UAE

Internet of Things (IoT)-based smart factories offer the manufacturing sectors a great opportunity to embrace the fourth industrial revolution (Industry 4.0). The real-time monitoring of manufacturing operations in an Industry 4.0 needs to be ensured by the deployed technologies, like artificial intelligence (AI) and Big Data analytics. The overall purpose is to improve the outcomes of the production process. However, Industry 4.0 becomes vulnerable to different potential attacks as the communication takes place via public environments. In this article, an authentication and key agreement method has been suggested to secure the communication that can occur in a Fog-based Industry 4.0 environment. The security proposal provides secure mutual authentication along with key establishment between various smart industrial devices and fog servers, as well as between fog servers and cloud servers. The security analysis and comparative study reveal that the proposed method can mitigate various potential attacks, and it also offers important security and functionality attributes as compared to those for other competing schemes.

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Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model

arXiv

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arXiv 2023年

作者： Qiao, Jie Chen, Zhengming Yu, Jianhua Cai, Ruichu Hao, Zhifeng School of Computer Science Guangdong University of Technology Guangzhou510006 China Peng Cheng Laboratory Shenzhen518066 China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates College of Science Shantou University Shantou515063 China

Missing data are an unavoidable complication frequently encountered in many causal discovery tasks. When a missing process depends on the missing values themselves (known as self-masking missingness), the recovery of the joint distribution becomes unattainable, and detecting the presence of such self-masking missingness remains a perplexing challenge. Consequently, due to the inability to reconstruct the original distribution and to discern the underlying missingness mechanism, simply applying existing causal discovery methods would lead to wrong conclusions. In this work, we found that the recent advances additive noise model has the potential for learning causal structure under the existence of the self-masking missingness. With this observation, we aim to investigate the identification problem of learning causal structure from missing data under an additive noise model with different missingness mechanisms, where the 'no self-masking missingness' assumption can be eliminated appropriately. Specifically, we first elegantly extend the scope of identifiability of causal skeleton to the case with weak self-masking missingness (i.e., no other variable could be the cause of self-masking indicators except itself). We further provide the sufficient and necessary identification conditions of the causal direction under additive noise model and show that the causal structure can be identified up to an IN-equivalent pattern. We finally propose a practical algorithm based on the above theoretical results on learning the causal skeleton and causal direction. Extensive experiments on synthetic and real data demonstrate the efficiency and effectiveness of the proposed algorithms. © 2023, CC BY.

关键词： Additives

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Metaverse for Wireless Systems: Vision, Enablers, Architecture, and Future Directions

arXiv

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arXiv 2022年

作者： Khan, Latif U. Han, Zhu Niyato, Dusit Guizani, Mohsen Hong, Choong Seon The Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates The Department of Computer Science & Engineering Kyung Hee University Yongin-si17104 Korea Republic of The Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States The School of Computer Science and Engineering Nanyang Technological University Singapore

Recently, significant research efforts have been initiated to enable the next-generation, namely, the sixth-generation (6G) wireless systems. In this article, we present a vision of the metaverse toward effectively enabling the development of 6G wireless systems. A metaverse uses virtual representation (e.g., digital twin), digital avatars, and interactive experience technologies (e.g., extended reality) to assist analyses, optimizations, and operations of various wireless applications. Specifically, the metaverse can offer virtual wireless system operations through the digital twin that allows network designers, mobile developers, and telecommunications engineers to monitor, observe, analyze, and simulate their solutions collaboratively and virtually. We first introduce a general architecture of metaverse for wireless systems. We discuss key driving applications, design trends, and key enablers of the metaverse for wireless systems. Finally, we present several open challenges and their potential solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Augmented reality

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

arXiv

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arXiv 2024年

作者： Handa, Palak Mahbod, Amirreza Schwarzhans, Florian Woitek, Ramona Goel, Nidhi Dhir, Manas Chhabra, Deepti Jha, Shreshtha Sharma, Pallavi Thakur, Vijay Chawla, Simarpreet Singh Gunjan, Deepak Kakarla, Jagadeesh Raman, Balasubramanian Research Center for Medical Image Analysis and Artificial Intelligence Department of Medicine Danube Private University Krems Austria Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Data Sciences Indira Gandhi Delhi Technical University for Women Delhi India Department of Artificial Intelligence and Machine Learning University School of Automation and Robotics Guru Gobind Singh Indraprastha University Delhi India Department of Electronics and Communication Engineering Delhi Technological University Delhi India Columbia University New YorkNY United States Department of Gastroenterology and HNU All India Institute of Medical Sciences Delhi India Chennai Kancheepuram India Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Image Analysis and artificial intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria in collaboration with the 9th International Conference on computer Vision & Image Processing (CVIP 2024) being organized by the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Kancheepuram, Chennai, India. This document provides an overview of the challenge, including the registration process, rules, submission format, description of the datasets used, qualified team rankings, all team descriptions, and the benchmarking results reported by the organizers. © 2024, CC BY.

关键词： Endoscopy

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B-Spine: learning B-Spline Curve Representation for Robust and Interpretable Spinal Curvature Estimation

arXiv

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arXiv 2023年

作者： Wang, Hao Song, Qiang Yin, Ruofeng Ma, Rui Yu, Yizhou Chang, Yi School of Artificial Intelligence Jilin University Changchun130012 China China-Japan Union Hospital Jilin University Changchun130031 China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China Department of Computer Science University of Hong Kong Pokfulam Hong Kong

Spinal curvature estimation is important to the diagnosis and treatment of the scoliosis. Existing methods face several issues such as the need of expensive annotations on the vertebral landmarks and being sensitive to the image quality. It is challenging to achieve robust estimation and obtain interpretable results, especially for low-quality images which are blurry and hazy. In this paper, we propose B-Spine, a novel deep learning pipeline to learn B-spline curve representation of the spine and estimate the Cobb angles for spinal curvature estimation from low-quality X-ray images. Given a low-quality input, a novel SegRefine network which employs the unpaired image-to-image translation is proposed to generate a high quality spine mask from the initial segmentation result. Next, a novel mask-based B-spline prediction model is proposed to predict the B-spline curve for the spine centerline. Finally, the Cobb angles are estimated by a hybrid approach which combines the curve slope analysis and a curve-based regression model. We conduct quantitative and qualitative comparisons with the representative and SOTA learning-based methods on the public AASCE2019 dataset and our new proposed CJUH-JLU dataset which contains more challenging low-quality images. The superior performance on both datasets shows our method can achieve both robustness and interpretability for spinal curvature estimation.. Copyright © 2023, The Authors. All rights reserved.

关键词： Curve fitting

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MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions 9

MD-Unet: Used for the Segmentation of Tobacco Leaf Lesions

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Chen, Zili Peng, Yilong Yang, Yaokai Jiao, Jiadong Wang, Laigang Wang, Aiguo Liu, Jianjun Lin, Wei Guo, Yan School of Tourism Henan Normal University Xinxiang China Institute of Agricultural Information Technology Henan Academy of Agricultural Sciences Zhengzhou China Zhengzhou Tobacco Research Institute of Cntc Zhengzhou China Henan Provincial Tobacco Company of Cntc Zhengzhou China College of Computer and Information Engineering Henan Normal University Xinxiang China Henan Provincial Key Laboratory of Educational Artificial Intelligence and Personalized Learning Xinxiang China Ministry of Agriculture and Rural Affairs Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology Zhengzhou China

ISBN: (纸本)9798350376548

For tobacco leaf disease spots with varying scales and shapes causing difficulties in segmentation and low accuracy, a novel MD-Unet algorithm is proposed for precise segmentation of tobacco leaf disease spots, aiding in scientific prevention and control of tobacco diseases. Innovative design includes Multi-scale Convolution Module (MC) and Dense Residual Dilated Convolution Module (MVCR) to enhance feature extraction of multiscale disease spots and perception of global contextual information. Utilizing Residual Unit Fusion Framework (RUFF) in the upsampling section effectively compensates for spatial details loss and strengthens network representation capability. Additionally, integration of Channel Spatial Attention Mechanism (CBAM) and Attention Gate (AG) further enhances the network's focusing ability on key information and fine-grained feature extraction capability. The MD-Unet algorithm achieves significant improvements in spot CPA, Recall, IoU, F1 metrics with 92.75%, 90.94%,84.93%,91.81% respectively, and an overall Dice score of 94.67%. Compared to mainstream methods like Unet, PSP, DeepLab v3+, FCN, SegNet, UNET++, DoubleU-Net, it shows remarkable enhancements, providing theoretical basis and technical support for precise segmentation of tobacco leaf disease spots and other plant diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparative study for performance efficiency of conventional optical transmitter with spatial optical transmitter through CWDM fiber system

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Journal of Optical Communications 2024年第s1期45卷 s2263-s2271页

作者： Odugu, Venkata Krishna Kulkarni, Anita Ahammad, Shaik Hasane Hossain, Md. Amzad Prabu, Ramachandran Thandaiah Jagan, B. Omkar Lakshmi Hemadan, Ayman Zain CVR College of Engineering Hyderabad India EIE Department VNR Vignana Jyoti Institute of Engineering and Technology Hyderabad India Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electrical and Electronic Engineering Jashore University of Science and Technology Jashore7408 Bangladesh Department of ECE Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Artificial Intelligence and Machine Learning School of Engineering Malla Reddy University Maisammaguda Hyderabad Telangana Dulapally India Ismaillia Institute of Technology Ismailia Egypt

This paper presents the comparative study for performance efficiency of conventional optical transmitter with spatial optical transmitter through coarse wavelength division multiplexed fiber system. The spatial light source mode polar and rectangular configuration are clarified in x directions. The lighted power signal through CWDM fiber system is simulated and measured with the use of conventional and spatial light transmitters. The lighted base band signal through CWDM fiber system is clarified with the use of conventional and spatial light transmitters. The lighted base band signal with spectral wavelength through CWDM fiber system is simulated with the use of conventional and spatial light transmitters. The Q base band signal quality factor and bit error signal rates are studied in the same configuration after receiver photodiode system with the use of conventional and spatial light transmitters. © 2023 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Optical transmitters

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CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations

arXiv

引用

arXiv 2023年

作者： Mai, Gengchen Lao, Ni He, Yutong Song, Jiaming Ermon, Stefano Spatially Explicit Artificial Intelligence Lab Department of Geography University of Georgia United States Department of Computer Science Stanford University United States School of Computing University of Georgia United States Google Inc United States Machine Learning Department Carnegie Mellon University United States

Geo-tagged images are publicly available in large quantities, whereas labels such as object classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has achieved tremendous success in various natural image and language tasks with limited labeled data. However, existing methods fail to fully leverage geospatial information, which can be paramount to distinguishing objects that are visually similar. To directly leverage the abundant geospatial information associated with images in pre-training, fine-tuning, and inference stages, we present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images, which can be transferred to downstream supervised tasks such as image classification. Experiments show that CSP can improve model performance on both iNat2018 and fMoW dataset. Especially, on iNat2018, CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling *** Codes 68T07, 68T45 © 2023, CC0.

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Improving EFL Students' Reading Skills Using Graph Attention Networks and intelligence-Based Tasks

Improving EFL Students' Reading Skills Using Graph Attention...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Radha Ranjan Arpita Goswami Benzigar M Julius Irudayasamy K. Lokeswar Reddy M V Rathnamma Department of Law Amity Law School Amity University Patna India Department of Humanities School of Liberal Study Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Department of Arts and Humanities Christ Academy Institute for Advanced Studies Bangalore India Department of English Language and Literature CAAS Dhofar University Salalah Oman Department of Artificial Intelligence and Machine Learning Annamacharya University Rajampet India Department of Computer Science and Engineering KLM College of Engineering for Women Kadapa India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Reading is an engaging practice that helps language learners understand texts. Develop and prioritize basic skills like reading strategies to improve performance and learn new languages. Reading and reading methods will be defined, distinct reading techniques reviewed, successful reading models established, and essential reading principles investigated. The suggested approach involves preprocessing, feature extraction, and model training. PREP techniques such stop word reduction and Porter stemming address data sparsity. Latent Dirichlet Allocation (LDA) selects features, and Hierarchical Generative Adversarial Network trains models. When compared to BERT and GAN, the recommended model wins. The model's 95.39% accuracy is impressive. This proposed shows that the suggested technique improves reading comprehension and text processing precision. Superior preprocessing, feature selection, and hierarchical GAN-based training make it superior to state-of-the-art methods.

关键词： Training Accuracy Inspection Generative adversarial networks Feature extraction Resource management Proposals Integrated circuit modeling Pupils Text processing

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