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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Hu, Pengyun Tang, Xianpiao Yang, Liu Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

By recognizing students' facial expressions in a classroom, it is possible to determine in real-time whether students are confused, dissatisfied or happy. Understanding students' emotional states helps teachers adjust teaching strategies and methods, thus improving the quality and effect of teaching. However, classroom scenarios are often complex, and most previous expression recognition methods suffer from the problems of missing local features and low accuracy. Additionally, the imbalanced distribution of datasets has an impact on the recognition accuracy during model training. Therefore, in this paper, based on deep learning theory, an LCANet model, which incorporates an attention mechanism, is proposed to realize the recognition and analysis of students' expressions during classroom teaching. To optimize the global feature extraction capability of the network, the model uses ConvNeXt V2 as the backbone. Moreover, to enable the model to better focus on the important parts of facial expressions, we add an improved channel space attention (CSA) module to extract local features. In addition, we introduce the LF loss function to alleviate the class imbalance problem in the facial expression dataset. The experimental results show that our LCANet model achieves good recognition rate on the public FERPlus dataset with 91.43% accuracy. Therefore, the proposed LCANet model has good robustness and expression recognition performance. © 2023 IEEE.

关键词： Students

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Foundation models in bioinformatics

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National science Review 2025年第4期12卷 400-418页

作者： Fei Guo Renchu Guan Yaohang Li Qi Liu Xiaowo Wang Can Yang Jianxin Wang Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Xiangjiang Laboratory Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Department of Computer Science Old Dominion University School of Life Sciences and Technology Tongji University Department of Automation Tsinghua University Department of Mathematics State Key Laboratory of Molecular Neuroscience and Big Data Bio-Intelligence Lab The Hong Kong University of Science and Technology

With the adoption of foundation models(FMs),artificial intelligence(AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges,such as pre-training frameworks,model evaluation and *** demonstrate notable proficiency in managing large-scale,unlabeled datasets,because experimental procedures are costly and labor *** various downstream tasks,FMs have consistently achieved noteworthy results,demonstrating high levels of accuracy in representing biological entities.A new era in computational biology has been ushered in by the application of FMs,focusing on both general and specific biological *** this review,we introduce recent advancements in bioinformatics FMs employed in a variety of downstream tasks,including genomics,transcriptomics,proteomics,drug discovery and single-cell *** aim is to assist scientists in selecting appropriate FMs in bioinformatics,according to four model types:language FMs,vision FMs,graph FMs and multimodal *** addition to understanding molecular landscapes,AI technology can establish the theoretical and practical foundation for continued innovation in molecular biology.

关键词： foundation model bioinformatics genomics transcriptomics proteomics drug discovery single-cell analysis

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Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model 2

Blockchain based Security Management of Internet of Medical ...

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

作者： Santiago, S. Prasanna, B.T. Qureshi, Kashif Subash, K. Trichy India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India Department of Data Science Ai India Department of Data Science Trichy India

ISBN: (纸本)9798350384277

The introduction of the Internet of Medical Things (IoMT), has drastically transformed the worldwide landscape of healthcare delivery. The proliferation of IoMT devices in healthcare systems brings new issues in guaranteeing the security and privacy of sensitive patient data. We must secure a substantial amount of sensitive data from modification since IoMT is a high-volume, continually developing data domain. Since then, blockchain (BC) technology has been recognised as a distributed ledger that can manage a huge number of intelligent transactions and increase data security at the same time. A comprehensive resource management system may be offered by Blockchain-based security mechanisms with specific customisation and additional levels of authentication and verification. It has functioned brilliantly as a crucial component of the Bitcoin cryptocurrency. This work creates a blockchain-based Internet of Medical Things security management system using homomorphic encryption (HE). Key generation shows how the DOA may enhance cryptographic system security and efficacy. The IoMT-based HE-DOA system provided in this study employs blockchain to secure data exchange and diagnostic models. HE-DOA allows safe data transmission and diagnostic models with BC aid in IoMT. The recommended technique includes data collecting, encryption, and categorization. Blockchains (BCs) securely store data, and IoMT devices are crucial to early data collection. For data encryption, the dingo optimisation algorithm (DOA) may generate the key. Performance experiments and comparison with similar protocols show that the recommended procedure may enhance PSNR, accuracy, and computational time by 19.34%, 95.78%, and 1.887ms, respectively. Comparing HE-DOA to prior studies shows its scalability and durability. © 2024 IEEE.

关键词： Distributed ledger

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Customized Movie Recommendation for Enhanced Entertainment

Customized Movie Recommendation for Enhanced Entertainment

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Minu, M.S. Talapalli, Srikar Chevuri, Saahithi Medhasri, T.S. SRM Institute of Science and Technology Department of Computer Science Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science Engineering with Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331542344

The surging popularity of online movie databases has created a challenge for viewers: choosing a film from a massive library can be overwhelming. In this paper, it proposes to design a new hybrid movie recommendation powered by machine learning techniques as a solution to improve user experience. Among those techniques, two are combined into this paper: content-based filtering, using the real features of a movie such as genre and a director, and collaborative filtering, which uses the rating provided by users. This hybrid approach thus improves the intrinsic weaknesses that reside in both these methods individually. On the other hand, content-based filtering tends to reduce exploration to movies of the exact type one is doing well at, whereas, inversely, collaborative filtering settings often mean that a user will be exposed only to films within his or her already-recognized taste profile. It is expected that the proposed hybrid system will solve both problems by balancing this line between exploring familiar cinematic territory and making new recommendations based on both user preferences and the entire collective intelligence of the user base. The approach would aim to enrich user satisfaction by facilitating the selection of movies and providing a richer and more diversified movie-viewing experience. © 2024 IEEE.

关键词： Collaborative filtering

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series data

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IEEE Transactions on Services Computing 2025年第3期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of Computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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Dynamic Task Offloading and Service Migration Optimization in Edge Networks

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International Journal of Crowd science 2023年第1期7卷 16-23页

作者： Han, Yibo Li, Xiaocui Zhou, Zhangbing Nanyang Institute of Big Data Research Nanyang Institute of Technology Nanyang473004 China Beijing100083 China Computer Science Department TELECOM SudParis Evry 91000 France

In recent years, edge computing has emerged as a promising paradigm for providing flexible and reliable services for Internet of things (IoT) applications. User requests can be offloaded and processed in real time at the edge of a network. However, considering the limited storage and computing resources of IoT devices, certain services requested by users may not be configured on current edge servers. In this setting, user requests should be offloaded to adjacent edge servers or requested edge servers should be configured by migrating certain services from the former, further reducing the service access delay of user requests and the energy consumption of IoT devices in such networks. To address this issue, in this study, we model this dynamic task offloading and service migration optimization problem as the multiple dimensional Markov decision process and propose a deep q-learning network (DQN) algorithm to achieve fast decision-making, an approximate optimal task offloading, and service migration solution. Experimental results show that our algorithm performs better than existing baseline approaches in terms of reducing the service access delay of user requests and the energy consumption of IoT devices in edge networks. © The author(s) 2023. The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://***/licenses/by/4.0/).

关键词： Reinforcement learning

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Study on human pose estimation based on channel and spatial attention 3

Study on human pose estimation based on channel and spatial ...

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3rd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2023

作者： Liu, Yilong University of Electronic Science and Technology of China Big Data Research Center Chengdu China

ISBN: (纸本)9798350331578

Accurate pose estimation is crucial for understanding human behavior in images or videos. Given an RGB image, we want to be able to accurately locate some important keypoints on the body. Understanding human pose and body structure is important for high-level tasks such as human-computer interaction. Human pose estimation usually has problems such as low discrimination between human body and background, and human pose estimation based on HRnet network does not make full use of important feature information. To solve these problems, a human pose estimation method MCSA-hrnet (Multi-scale Channel and Spatial Attention) based on multi-scale channel and spatial attention is improved by using channel attention mechanism and spatial attention mechanism. Starting from the channel domain and spatial domain, MCSA-HRnet integrates the multi-level attention mechanism into the high-resolution network structure, and designs the channel attention block and spatial attention block. This enables the network to focus on the regions of the image that are highly associated with the human body and not on other regions. MCSA-HRnet uses 1×1 convolutions for information extraction in the core part of the ca block (channel attention block) and parallel 3x3 and 5x5 convolutions in the sa block (spatial attention block). Different sizes of parallel convolutions can derive spatial attention maps of different scales, which makes the ability of the network to distinguish human features from background features more significant. Thus, the human body region and its key points can be accurately located. The improved method is verified on COCO keypoint dataset, and the results show that MCSA-HRnet can effectively improve the accuracy of human pose estimation joint point localization. © 2023 IEEE.

关键词： Convolution

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On PM t chart for monitoring changes in location parameter 3

On PM t chart for monitoring changes in location parameter

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3rd International Conference on Computer Vision and data Mining, ICCVDM 2022

作者： Sajjad, Muhammad Kamran Li, Dequan Amir, M. Wasim Selhaba, Ayesha School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

ISBN: (纸本)9781510661363

The Progressive Mean (PM) control chart is a widely recognized tool to notice the insignificant and standard variations in the process location parameter. There is one deficiency in PM chart, it generates signals which are out of control, and when the standard deviation is processed this deficiency changes the results. To overcome this problem, we proposed a method in case of not stable process stand deviation chart is used, which enables monitoring of process which is more robust in this case. The suggested chart is a participant for and charts. The numerical results concluded that the performance of the proposed chart is superior to detect small-scale and standard changes in the process parameter. To support the study an expressive application is also provided. © COPYRIGHT SPIE.

关键词： Stability criteria

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Research and Application of Music Personalized Recommendation System Based on Random Forest Algorithm 6

Research and Application of Music Personalized Recommendatio...

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6th IEEE Information technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Shu, Ziyan Shen, Qing Zeng, Tianlun Wuhan Business University Department of Information Engineering Hubei China Major in Data Science and Big Data Technology China Major in Internet of Things Engineering China

ISBN: (纸本)9781665460033

In recent years, with the advent of the era of big data and the rise of data mining technology, people used to waste a lot of time looking for advantageous information, which was very inefficient. Music is an indispensable part of everyone's daily life, and major music platforms are gradually emerging. Finding music that users like is the key to attracting users. At present, there are relatively few studies on personalized music recommendation using data mining algorithms. In this paper, the random forest algorithm is used to pre-process the collected data, and then the data is modeled, the model parameters and fitting coefficients are adjusted, the appropriate index is selected as the learning parameter, and the learning curve is obtained at the same time. Finally, the prediction is carried out through examples, and it is found that the prediction accuracy is high, and the WeChat applet is used for development and application. The approach in this article is simple and constructive, and it is dedicated to recommending personalized and exclusive music for particular users, so that you can spend every day happily. © 2023 IEEE.

关键词： Curve fitting

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