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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Jia, Zihe Xue, Peng Dai, Zhiqiang Gao, Qian Zhang, Xiaomeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350330717

The development of the Internet has made people more closely related and has put forward higher requirements for recommendation models. Most recommendation models are studied only for the long-term interests of users. In this paper, the interaction time between the user and the item is introduced as auxiliary information in the model construction. Interaction time is used to determine users' long-term preferences and short-term preferences. In this paper, temporal features are extracted by building a convolutional gated recurrent unit with attention neural network (CNN-GRU-Attention). Firstly, for the problem of accurate feature extraction, CNN are constructed to extract higher-level and more abstract features of themselves and transform high-dimensional data into low-dimensional data;secondly, for the problem of social temporality, GRU are used to not only extract temporal information, but also effectively reduce gradient dispersion, making model convergence and training easier;finally, Graph Attention networks are used to aggregate the social relationship information of users and items respectively, which constitute the final feature representation of users and items respectively. In particular, a modified cosine similarity is used to reduce the error caused by data insensitivity when constructing the social information of the item. In this study, simulation experiments are conducted on two publicly available datasets (Epinions and Ciao), and the experimental results show that the proposed recommended model performs better than other social recommendation models, improving the evaluation metrics of MAE and RMSE by 1.06%-1.33% and 1.19%-1.37%, respectively. The effectiveness of the model innovation is proved. © 2023 IEEE.

关键词： convolutional gated recurrent unit graph attention networks neighborhood recommendation systems social networks

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT 2

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Usharani, R. Sivagami, V.M. Saravanan, K. Pushparani, S. Rekha, K. Sashi Department of Computational Intelligence School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Care College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations. © 2024 IEEE.

关键词： Predictive maintenance

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Estimating Imagined Images from Brain Activities via Visual Question Answering 10

Estimating Imagined Images from Brain Activities via Visual ...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Takada, Saya Togo, Ren Ogawa, Takahiro Haseyama, Miki Hokkaido University Graduate School of Information Science And Technology Japan Hokkaido University Education And Research Center For Mathematical And Data Science Japan Hokkaido University Faculty of Information Science And Technology Japan

ISBN: (纸本)9781665436762

Investigating human mental contents has been a topic for many years, but its ambiguous property has made the analysis difficult. We propose a neural decoding method via a machine learning model that predicts the imagined content based on measuring brain activity in this paper. This technique uses brain activity and computer vision models to discover the association between human functional magnetic resonance imaging (fMRI) activity and imagined contents. Decoding models based on neural networks learned using stimulus-induced brain activity in the visual cortex region showed an accurate estimation of the content. We provide a means of revealing subjective mental content by analysis with visual question answering. This result shows that a mental experience relates to brain activity patterns. © 2021 IEEE.

关键词： Magnetic resonance imaging

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QoE-oriented Soft Caching with Content Recommendation for Edge Computing Networks

QoE-oriented Soft Caching with Content Recommendation for Ed...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Chenyang Chen, Yan Sun, Chuan Jia, Bosen Wang, Xiaofei Taleb, Tarik Leung, Victor C. M. Shenzhen University College of Computer Science & Software Engineering Shenzhen China Shenzhen518132 China Ruhr University Bochum Faculty of Electrical Engineering and Information Technology Bochum Germany Nanyang Technological University College of Computing and Data Science Singapore China Unicom Digital Technology Co. Ltd Beijing China Tianjin University College of Intelligence and Computing Tianjin China University of British Columbia Vancouver Canada

ISBN: (纸本)9798350351255

Mobile Edge Caching (MEC) can potentially alleviate Internet transmission congestion by delivering content at the network edge. However, current MEC solutions suffer from low resource utilization efficiency and often fail to meet user Quality of Experience (QoE), primarily due to dynamic user requests and obsessive pursuit of direct caching hits. Given the prevalence of recommendation systems, users often lack precise requests when using recommendation-based applications like TikTok and Taobao, insted passively enjoying recommended content. In this paper, we introduce a recommendation-enabled MEC architecture to enhance resource utilization and QoE. We develop a recommendation-enabled soft caching model and formulate the optimization problem as maximizing joint system revenue. To address this, we propose an attention-assisted federated learning deep Q-network algorithm. We conduct the simulations by using the real-world MIND dataset. The results demonstrate that our proposed algorithm outperforms existing baselines, demonstrating its effectiveness in improving resource utilization and QoE. © 2024 IEEE.

关键词： Mobile edge computing

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Remote Patient Monitoring Systems Network Adaptation Scenarios

Remote Patient Monitoring Systems Network Adaptation Scenari...

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2022 IEEE International Conference on Current Development in Engineering and technology, CCET 2022

作者： Khujamatov, Halimjon Akhmedov, Nurshod Lazarev, Amir Arsheen, Sobiya Tashkent University of Information Technologies Dean of the Faculty of Computer Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Data Transmission Networks and Systems Department Tashkent Uzbekistan Maulana Azad National Urdu University Department of Computer Science and Information Technology India

ISBN: (纸本)9781665454155

This article is devoted to the actual topic of studying scenarios for adapting a network infrastructure to a network of remote patient monitoring systems (PCMS). The paper presents an overview and analysis of scenarios for adapting the network infrastructure of monitoring systems. The stages of scenario execution have been studied and simulation models of the RPMS network infrastructure have been developed that implement various adaptation scenarios. A comparative analysis of adaptation scenarios was carried out according to several criteria, such as the speed of request delivery, delivery time, and network load. Simulation models built in the Anylogic environment, a powerful simulator of mass service system, showed the effectiveness of the mesh topology in relation to the ad-hoc topology according to several criteria. The theoretical significance of the work lies in carrying out analytical work on scenarios for adapting the network infrastructure of the RPMS, as well as in studying the network architecture. The practical significance of the work lies in the development of a simulation model that allows you to explore scenarios for adapting the network architecture. © 2022 IEEE.

关键词： Network architecture

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ForumPFN: Online Forum Post Fusion Network for Volatility Index Movement Prediction

ForumPFN: Online Forum Post Fusion Network for Volatility In...

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IEEE International Conference on Big data

作者： Kentaro Ueda Hirohiko Suwa Eiichi Umehara Yuki Ogawa Tatsuo Yamashita Kota Tsubouchi Keiichi Yasumoto Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Department of Information Systems Niigata University of International and Information Studies Niigata Japan Faculty of Design and Data Science Tokyo City University Yokohama Japan LY Corporation Tokyo Japan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In risk management and investment strategy formulation within financial markets, successfully predicting the Volatility Index (VIX) is crucial. While prediction methods leveraging social media texts have been considered promising, they predominantly rely on Twitter data, leaving the potential of online forums underexplored. However, online forums are rich sources of investor discussions and can provide valuable information for VIX prediction. In this study, we propose a deep learning architecture called ForumPFN that effectively captures the complex discussion structures and contextual dependencies unique to online forums, thereby enhancing financial market predictions. At its core, the Discussion Aggregator Module comprises two main components: a Topic Align Algorithm that classifies and reorganizes posts by topic, and a Multi-Scale 1D-Convolutional Path that integrates features at different scales. This design allows for precise modeling of the discussion flows and dynamics specific to Online Forum, maximizing the utilization of information obtained from online forums. We conduct experiments on directional prediction of the Nikkei 225 Volatility Index (Nikkei 225VI)—a representative VIX of Japanese stocks—using data from Yahoo Finance Message Boards, Japan’s largest online forum. The experimental results confirm that ForumPFN outperforms traditional baseline methods. Furthermore, through ablation studies, we demonstrate the effectiveness of each module in detail and explain the module’s operation via visualization of the attention matrix.

关键词： Social networking (online) Heuristic algorithms Finance Deep architecture data visualization Prediction methods Prediction algorithms Indexes Risk management Investment

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Phytochemical composition and toxicity assessment of Ammi majus L.

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Asian Pacific Journal of Tropical Biomedicine 2023年第4期13卷 165-175页

作者： Otman El-guourrami Najoua Salhi Fatima Zahra Benkhouili Gokhan Zengin Mustafa Abdullah Yilmaz Mouna Ameggouz Ahmed Zahidi Lamiaa Rouas Abdelhakim Bouyahya Khang Wen Goh Toong Hai Sam Long Chiau Ming Anass Doukkali Hanane Benzeid Laboratory of Analytical Chemistry Faculty of Medicine and PharmacyMohammed Ⅴ UniversityRabatMorocco Laboratory of Pharmacology and Toxicology Faculty of Medicine and Pharmacy Mohammed Ⅴ UniversityRabatMorocco Laboratory of Medicinal Chemistry Faculty of Medicine and PharmacyMohammed Ⅴ UniversityRabatMorocco Department of Biology Faculty of ScienceSelcuk UniversityCampus/KonyaTurkey Dicle University Science and Technology Research and Application Center 21280DiyarbakirTurkey Dicle University Faculty of PharmacyDepartment of Analytical Chemistry21280DiyarbakirTurkey Center Hospitalo-Universitaire Ibn Sina Laboratory of AnatomopathologyRabatMorocco Laboratory of Human Pathologies Biology Department of BiologyFaculty of SciencesGenomic Center of Human PathologiesMohammed Ⅴ University in RabatMorocco Faculty of Data Science and Information Technology INTI International UniversityNilai 71800Malaysia Faculty of Business and Communications INTI International UniversityNilai 71800Malaysia School of Medical and Life Sciences Sunway UniversitySunway City 47500Malaysia

Objective:To assess the acute and subacute toxicity as well as the phytochemical composition of two extracts and three fractions of Ammi majus ***:The aqueous extracts were prepared separately by maceration for 48 h and by infusion for 1 h,while the fractions were prepared by the Soxhlet extractor,successively employing cyclohexane,ethyl acetate,and *** acute toxicity study was carried out in accordance with the OECD N°423 guideline at a single dose(2000 mg/kg)in mice for 14 *** subacute toxicity study was performed by a daily oral administration of 250 mg/kg 2 for 10 d and 100 mg/kg doses for 28 *** screening was performed using staining and precipitation reactions,while the chemical characterization of some analytes was detected by HPLC-MS/MS ***:In the acute toxicity study,no signs of toxicity such as convulsion,salivation,diarrhea,sleep and coma were observed during 30 minutes and 14 days,so the lethal dose was higher than 2000 mg/kg for each extract and *** subacute toxicity results showed that at a dose of 250 mg/kg,61.10%of the animals died and the rest developed *** the other hand,at a dose of 100 mg/kg,all the animals were still alive after 28 days,with no morbidity and the biochemical parameters were normal with no abnormalities in the liver,kidneys and *** screening indicated the presence of flavonoids,tannins,coumarins,and free quinones and the absence of alkaloids and ***:The extracts and fractions of Ammi majus *** not toxic in the short and long term with a varied chemical *** tests on animals other than rodents and in the long term(more than 28 days)are needed to further confirm the safety of Ammi majus extracts.

关键词： Ammi majus L. Phytochemical composition Acute toxicity Subacute toxicity Flavonoids

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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Exploring the Benefits of DNS-Based Authentication for Wireless Network Security 3

Exploring the Benefits of DNS-Based Authentication for Wirel...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Swarup, Lakshya Jayadurga, R. Borade, Monali R. Ramachandran, T. Singh, Akhilendra Pratap Prabha, Savita Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Wi-wireless networks are becoming more pervasive and are frequently the primary goal of malicious attackers. Therefore, it is miles crucial that wireless networks are nicely secured to defend their users from malicious moves. DNS-based total authentication is a promising answer for attaining ease of authentication of wi-wireless customers in an open Wi-Fi community. This paper surveys numerous current works that discover the wi-fits wireless of DNS-based total authentication for Wi-Fi wireless community safety. especially this paper opinions three one-of-a-kindwireless strategies of using DNS for authentication purposes: making use of DNS's cache for identifying windows-primarily based Wi-Fi wireless clients;Open Mobile Alliance (OMA)'s tool control (DM) projects to relaxed get right of entry to;and a modern scheme for protecting the use of the DNS-based techniques. Wi-finally, this paper discusses a few safety-demanding situations and future studies directions. © 2023 IEEE.

关键词： authentication initiatives management protecting secured

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GAF-GAN: Gated Attention Feature Fusion Image Inpainting Network Based on Generative Adversarial Network 29

GAF-GAN: Gated Attention Feature Fusion Image Inpainting Net...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qiu, Ran Liang, Hu Liu, Yuchen Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

Image inpainting, which aims to reconstruct reasonably clear and realistic images from known pixel information, is one of the core problems in computer vision. However, due to the complexity and variability of the underwater environment, the inability to extract valid pixel points and insufficient correlation between feature information in existing image inpainting techniques lead to blurring in the generated images. Therefore, a novel gated attention feature fusion image inpainting network based on generative adversarial networks (GAF-GAN) is proposed. The accuracy of feature similarity matching depends heavily on the validity of the information contained in the features. On the one hand, gating values are dynamically generated by gated convolution to reduce the interference of invalid information. On the other hand, semantic information at distant locations in an image is accurately acquired by the attention mechanism. For these reasons, we designed an improved gated attention mechanism. Gated attention mechanism make the network focus on effective information such as high-frequency texture and color fidelity of restored images. In addition, the dense feature fusion module is added to expand the overall receptive field of the network to fully learn the image features. Experimental results show that the proposed method can effectively repair defective images with complex texture structures and improve the reality and integrity of image details and structures. © 2023 IEEE.

关键词： Dense feature fusion module Gated attention mechanism Image inpainting Underwater environment

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