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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Haewon Byeon Rajit Nair V. Mahalakshmi Mohammed I. Khalaf Bharat Kaushik Mohammad Shabaz Department of AI and Software Inje University Gimhae Republic of Korea School of Computing Science & Engineering VIT Bhopal University Bhopal India Department of Computer Science College of Engineering and computer science Jazan University Jazan Saudi Arabia Department of computer science Al Maarif University College Al Anbar Iraq Masters University of Northern Iowa Lowa USA Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques.

关键词： Fluctuations Reviews Hospitals Measurement uncertainty Surgery Medical services Predictive models

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DCNet: Weakly Supervised Saliency Guided Dual Coding Network for Visual Sentiment Recognition 26

DCNet: Weakly Supervised Saliency Guided Dual Coding Network...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Zhang, Xinyue Xiang, Jing Zhang, Hanxiu Wu, Chunwei Wang, Hailing Cao, Guitao School of Computer Science and Technology East China Normal University Shanghai China Shanghai Institute of Ai for Education East China Normal University Shanghai China MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781643684369

Visual sentiment recognition is a challenging task with scientific significance in probing vision-processing mechanisms. Recent approaches mainly focused on using overall images or precise annotations to learn emotional representations, yet neglected to capture abstract semantics from regional information, or led to a heavy annotation burden. In this paper, we propose an end-to-end weakly supervised framework, called Dual Coding Network (DCNet), which models a dual coding process for both shallow features and high-level regional information. On the one hand, with the help of the fine-grained module (FG), visual features (e.g. texture features) are utilized to enhance the learning of distinguished representation. On the other hand, the DCNet innovatively leverages saliency information to imitate the neural decoding of perceived visual sentiment contents in human brain activity. Specifically, the saliency information guides the generation of sentiment-specific pseudo affective maps (SAMG), which serve as weak annotations. Then the DCNet couples fine-grained features with pseudo affective maps, and obtains semantic vectors for final sentiment prediction. Extensive experiments show that the proposed DCNet outperforms the state-of-the-art performance on five benchmark datasets. © 2023 The Authors.

关键词： Network coding

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Long and Recent Preference Learning with Recent-k Items Distribution for Recommender System

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IEEE Transactions on Multimedia 2025年

作者： Gao, Yongbiao Niu, Sijie Lv, Guohua Ling, Miaogen Geng, Xin Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Southeast University Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing School of Information Science and Engineering China Nanjing University of Information Science and Technology School of Computer Science China Southeast University School of Computer Science and Engineering China

Reinforcement learning (RL) aims to formulate the recommendation task as a Markov decision process (MDP) and trains an agent to automatically learn the optimal recommendation policy from interaction trajectories through trial-and-error and reward mechanisms. However, most existing RL-based approaches overlook the correlation between items and the dynamics of user interests implied in temporally close interactions. Therefore, in this paper, we propose a reinforcement learning method that incorporates a "recent-k items"distribution to capture users' local preferences. Specifically, we model the output layer as two distinct branches. The "recent-k items"branch, formulated with a Kullback-Leibler divergence loss, learns the recent interests of users, whereas the other branch utilizes a one-step temporal difference error to capture long-term preferences. The proposed structure is integrated into deep Q-learning and actor-critics, resulting in two enhanced methods named R k Q and R k AC, respectively. Furthermore, a novel soft inter-reward is carefully designed to enhance the proposed method, and we theoretically prove the convergence of the proposed algorithm. We perform extensive experiments on two large real-world datasets and conduct further analysis of the influences of different action sequences, time intervals, and enhancement capabilities for state-of-the-art models. The experimental results demonstrate the efficacy of our proposed methods. © 1999-2012 IEEE.

关键词： Deep reinforcement learning

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A Generalized Subspace Distribution Adaptation Framework for Cross-Corpus Speech Emotion Recognition 48

A Generalized Subspace Distribution Adaptation Framework for...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Li, Shaokai Song, Peng Ji, Liang Jin, Yun Zheng, Wenming Yantai University School of Computer and Control Engineering China Jiangsu Normal University School of Physics and Electronic Engineering China Research Center for Learning Science Southeast University Key Laboratory of Child Development and Learning Science Southeast University Ministry of Education China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9781728163277

In this paper, we propose a novel transfer learning framework, named generalized subspace distribution adaptation (GSDA), to tackle the challenging cross-corpus speech emotion recognition problem. First, we learn a common low-dimensional feature subspace by utilizing a generalized subspace learning method. Second, we develop a novel distance metric to reduce the divergence between the source and target corpora, which can efficiently explore the similarity and dissimilarity information in the process of knowledge transfer. Third, to demonstrate the effectiveness of our framework, we apply GSDA to the traditional subspace learning algorithms. Finally, we conduct extensive experiments by using the low-level features and deep features on three popular emotional databases, i.e., Berlin, IEMOCAP, and CVE. The results demonstrate that the proposed framework can achieve better performance than several state-of-the-art transfer learning approaches. © 2023 IEEE.

关键词： Speech recognition

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Research on Rice Disease Identification Based on Improved ShuffleNet V2 Network

Research on Rice Disease Identification Based on Improved Sh...

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computer Technology and Information science (ISCTIS), International Symposium on

作者： Zhihuan Chen Shenming Gu Minghui Yu Zhaoquan Huang Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan China School of Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Mathematics and Computer Science Zhejiang Normal University Jinhua China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

How to efficiently and accurately identify rice diseases plays a crucial role in rice planting and production. Deploying large convolutional neural networks in practical applications is challenging due to their complex model parameters, while lightweight networks often compromise accuracy to reduce model size. In this study, we introduce a lightweight and precise network called GM-ShuffleNet for rice disease recognition. Building upon the ShuffleNet V2 network, we integrated the Ghost module and MCA attention mechanism, adjusted the network structure and number of channels, and replaced the ReLU activation function with Mish. Through comparative experiments, the accuracy of GM-ShuffleNet algorithm proposed in this paper reaches 96.20%, which is superior to ShuffleNet V2 and other excellent lightweight networks. In addition, in terms of model complexity comparison, the number of model parameters and FLOPs decreased by 82.54% and 77.44% respectively compared with the original network.

关键词： Information science Accuracy Attention mechanisms Computational modeling Buildings Production Complexity theory Convolutional neural networks Diseases

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Blockchain-IoT Layered Architecture, Current Trends, Challenges, and applications

Blockchain-IoT Layered Architecture, Current Trends, Challen...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Mukesh Kumar Nidhi Shweta Tiwari Surinder Kaur School of computer Application Lovely Professional University Phagwara India Maharaja Agrasen University Kallujhanda Solan Himachal Pradesh India Computer Science and Engineering Chandigarh University Mohali Punjab India

The term “Internet of Things” (IoT) refers to the potential global connectivity of billions of individually networked devices that can collect and share data. This connectivity might occur everywhere on the globe. By combining IoT with Blockchain (BC), a robust and secure system might be created to store information gathered by intelligent gadgets. Internet-connected IoT devices can upload transaction data to private BC networks. As a result, all transactions in such networks are recorded in immutable ledgers. In order to comprehend how devices may connect with one another, this study examines the interplay between BC technology and IoTs. In this article, a BC layered architecture as a practical framework for integrating BC technology with IoT by making use of state-of-the-art tools and methods is proposed. Current trends and difficulties, as well as potential application domains, are discussed which is in relation to the layered architecture of Blockchain technology.

关键词： Privacy Electric potential Distributed ledger Information sharing Oral communication Market research Blockchains

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Ma-Capsnet-Da: Speech Emotion Recognition Based on Ma-Capsnet Using Data Augmentation

SSRN

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SSRN 2023年

作者： Zhang, Huiyun Huang, Heming Han, Henry School of Computer Science Qinghai Normal University Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China Department of Computer Science School of Engineering and Computer Science Baylor University One bear Place 97141 Waco76798 United States

Speech emotion recognition (SER) plays a crucial role in fluent human-computer interaction (HCI) applications. However, it has two challenges: the lack of effectiveness of deep learning models and data scarcity issues. As a result, the deep learning models used in SER would suffer from overfitting seriously. In this study, a novel SER model called Max-avg-pooling capsule network (MA-CapsNet) is proposed and it is an improved capsule network customized for SER. We also present novel data augmentation (DA) techniques to tackle the data scarcity issue. The proposed MA-CapsNet model consists of five sequential modules: conv-max-pooling, conv-avg-pooling, convolution, primary capsule, and digital capsule module. The DA techniques include Multi-domain integration (MDI), Noisy data augmentation (NDA), and Multiple SNRs consolidations (MSC) to overcome the data scarcity issue from different perspectives. Furthermore, a new evaluation metric called expected accuracy index (EAI) is presented to evaluate the model performance effectively. The proposed method demonstrates strong advantages over its peer models in SER under different data partitions, especially for augmented datasets. Experimental results also show that the proposed model has the following advantages: good interpretability in comparison to the peer methods for its less complicated learning topology, relatively smaller parameter sets, and fewer input features. © 2023, The Authors. All rights reserved.

关键词： Human computer interaction

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Challenges in Metaverse in Problem-Based Learning as a Game-Changing Virtual-physical Environment for Personalized Content Development 6

Challenges in Metaverse in Problem-Based Learning as a Game-...

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6th Smart Cities Symposium, SCS 2022

作者： Dutta, P.K. Bose, Manoswita Sinha, Akash Bhardwaj, Ramakant Ray, Samrat Roy, Sudipta Prakash, Kolla Bhanu School of Engineering and Technology Amity University Kolkata India School of Computer Application Amity University Kolkata India Department of Mathematics Amity University Kolkata India The Institute of Industrial Management Economics and Trade Peter the Great Saint Petersburg Polytechnic University Russia Artificial Intelligence & Data Science Jio Institute India Department of Computer Science and Engineering Konneru Lakshmaih Education Foundation India

ISBN: (纸本)9781839538544

The benefits and drawbacks of using the metaverse in the field of education are discussed, along with the possibilities and potential issues it will open up. Utilizing the idea of personalised knowledge transfer and the use of an immersive environment to match students' learning pace using horizon classroom, the Metaverse environment can add a new dimension to the field of educational technologies. We have also highlighted the virtualization-based redesign of educational environments using a personalised cloud, which is anticipated to improve learning for students by making it more affordable, collaborative, and useful. The goal is to build a metaversity that can serve as a communication tool for teachers and students anywhere in the world by providing each with an individual avatar. This platform will offer a virtual setting for students to socialise with their friends and peers and receive peer-to-peer learning, which is lacking in the current e-learning environment. The students won't need an entrance score card;instead, they will need critical learning skills to advance and mature. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Students

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Using Trust-Based Data Integrity Verification and Energy-Aware Hierarchical Data Aggregation for WSN

Using Trust-Based Data Integrity Verification and Energy-Awa...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Dushyant Singh Gaganpreet Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business

ISBN: (纸本)9781665487351

Because of their numerous uses, wireless sensor networks are currently quite popular. These nodes are typically placed in severe environments and have limited power supplies, making it difficult to replace them. Because of these problems, reducing energy consumption is a top priority to increase network lifespan. To solve the challenging issue of data aggregate, we created a novel combining technique that carries out grouping & trusted computation procedures. This design divides the networks into three categories based on remaining energy: normal networks, advance networks, & super networks. The proposed model utilises a hierarchical structure, and we present a novel method for determining the best number of cluster. the clustered head of choice. After selecting the root node, we employ a confidence calculation strategy that offers sense confidence, connection trust, & nodes trust. The nodes trust is computed using indirect & direct trust. To protect the data integrity, this confidence mechanism is used hop by hopping. The empirical study shows that the proposed approach boosts efficiency while preserving WSN's safeguards.

关键词： Wireless sensor networks Energy consumption Power supplies Data integrity Computational modeling System performance Packet loss

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AI applications and Simulation-Based Learning Integrating Future of Nursing Education

AI Applications and Simulation-Based Learning Integrating Fu...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Suwalka, Deepak Pandita, Deepak Godse, Snehal Patil, Rani Ramrao Salam Khan, Abdul Kumar, Alok Rama University Department of Mental Health Nursing Uttar Pradesh Kanpur India Sate Maval Maharashtra Pune412106 India JSPM's Rajarshi Shahu College of Engineering Department of MCA Tathawade India Dr.D.Y. Patil School of Science and Technology Department of Computer Application Maharashtra Pune India Amity University Amity School of Fine Arts UP Noida India

ISBN: (纸本)9798350390049

Nurse educators are modifying their practices with the help of artificial intelligence. artificial intelligence helps nursing students gain their Goal facility arsenic good arsenic their important Althought skills. The purpose of this research is to investigate the advantages disadvantages and results of using artificial intelligence to power Imitation-based learning for nursing school instructors and students. amp mixed-methods access was old inch rate to analyse the memory attending employment and science control of ii c nursing students. The Information utilized included both quantitative and qualitative information. observations such as arsenic the score percentage gain inch attending rates and the forty-five head v head v percentage gain inch important Althought lots point that thither was amp important advance inch pupil interest atonement and Goal competency. Even if significant advancements have been made there is still a need for expensive equipment and insufficient Teaching for faculty members. it is light from these results that Imitations powerful away ersatz word bear the prospective to translate nursing pedagogy and break school prospective nurses for amp active healthcare surround. © 2024 IEEE.

关键词： Students

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