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An Overview of Recent Research on IoT-Based Energy Management System in Smart Homes 1

18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Increasing load demand and energy crisis issues are becoming more and more concerned. Nowadays, the demand for electricity in household life is rising sharply and the structure of electricity consumption is gradually becoming complex. With the development of the Internet of Things technology, it provides a better platform for the improvement of the energy management system. The home energy management system (HEMS) based on the Internet of Things comes into being, which can integrate the management of all home power loads and distributed energy, realize the optimal configuration of energy, and help users save electricity. This paper firstly describes the basic concept of IoT smart home energy management system, then describes the framework of HEMS, and finally reviews the current research status in this field from three modules, namely, data collection module, communication module, and management and optimization module, and discusses its technical challenges and future research directions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy management systems

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Research on Text Generation of Medical Intelligent Question and Answer Based on Bi-LSTM and Neural Network technology 22

Research on Text Generation of Medical Intelligent Question ...

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22nd IEEE/ACIS International Conference on Computer and Information science, ICIS 2022

作者： Huang, Keyi Ji, Fenda Lu, Wei Xiao, Yue Zhuhai College of Science and Technology Alibaba Cloud Big Data Application Academy Zhuhai China

ISBN: (数字)9781665494632

ISBN: (纸本)9781665494632

Text generation is a popular research direction in the field of natural language processing as well as artificial intelligence, especially in the medical field. Text generation technology plays an extremely important role in medical intelligent question and answer systems. In previous Q&A systems, the display of generated dialogues is usually limited to Q&A and is difficult to adapt to vertical domains with strong characteristics. In this paper, to solve the task of specialized Q&A in medical field and provide users with comprehensive answers. We propose a TensorFlow architecture-based approach to medical text generation that uses sequential models in Keras to transform the task into a classification problem by treating the text generation problem as a prediction problem. By training and experimenting with the model on a large-scale Chinese medical question-and-answer dataset, the results show that our model has a good fit with applications in this specific domain. The model can aggregate medical domain knowledge, extract useful treatment information and generate medical knowledge text. At the same time, through research, it is found that splitting questions into phrases and then inputting them into the model can effectively solve the problem of repeated sentences in the generated answers. Since the model we proposed can answer patients' questions well and accurately in most cases, this research provides an improvement direction for exploring medical intelligent question answering and text generation. © 2022 IEEE.

关键词： Large dataset

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Attention Based data Augmentation for Knowledge Distillation with Few data 2

Attention Based Data Augmentation for Knowledge Distillation...

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2nd International Conference on Computer, big data and Artificial Intelligence, ICCBDAI 2021

作者： Tian, Shengzhao Chen, Duanbing Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Chengdu Union Big Data Tech. Inc. Chengdu610041 China

Knowledge distillation has attracted great attentions from computer vision researchers in recent years. However, the performance of student model will suffer from the absence of the complete dataset, which is used to train the teacher model. Especially for conducting knowledge distillation between heterogeneous models, it is difficult for student model to learn and receive guidance with few data. In this paper, a data augmentation method is proposed based on the attentional response of teacher model. The proposed method utilizes the knowledge in teacher model without requiring homogeneous architecture between teacher model and student model. Experimental results demonstrate that combining the proposed data augmentation method with different knowledge distillation methods, the performance of student model can be improved in knowledge distillation with few data. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Distillation

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Current Status of Objectification of Four Diagnostic Methods on Constitution Recognition of Chinese Medicine

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Chinese Journal of Integrative Medicine 2022年第12期28卷 1137-1146页

作者： LI Cong-cong YAN Xin-sheng LIU Ming-hao TENG Gui-fa School of Information Science and Technology Hebei Agricultural UniversityBaodingHebei Province 071001China Hebei Key Laboratory of Agricultural Big Data BaodingHebei Province 071001China

Chinese medicine(CM)has thousands of years of experience in prevention of *** for CM,people's constitution is closely related to their health status,thus recognition of CM constitution is the fundamental and core contentof research on constitution *** development of technologies such as sensors,arificial intelligence and big data,objectification of the four diagnostic methods of CM has gradually matured,bringing changes in the mindset and innovations in technical means for recognition of CM *** paper presents a systematic review of the latest research trends in constitution recognition based on objectification of diagnostic methods in CM.

关键词： Chinese medicine constitution recognition four diagnostic methods objectification machine learning deep learning

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Identification of Offensive Language in Social Media Using Prompt Learning 11

Identification of Offensive Language in Social Media Using P...

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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Su, Leilei Wang, Zezheng Peng, Yifan Sun, Cong Hainan University Department of Mathematics Haikou China Hainan University Department of Data Science and Big Data Technology Haikou China Weill Cornell Medicine Department of Population Health Sciences New York United States

ISBN: (纸本)9798350302639

Offensive language refers to the use of language in a manner that may offend or harm others who are within earshot or view in a public place. Given the importance of identifying such language in social media for promoting emotional well-being, we propose a prompt learning method and compare its performance with fine-tuning on two widely used datasets, HatEval and OffensEval. Experimental results demonstrate that prompt learning can achieve a performance improvement over fine-tuning in a fully supervised setting. © 2023 IEEE.

关键词： Learning systems

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THE GEOMETRICAL-CONVEXITY OF A FUNCTION RELATED TO THE MINC-SATHRE RATIO

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2024年第4期86卷 3-10页

作者： Mortici, Cristinel Postolache, Mihai Valahia University of Târgovişte Târgovişte Romania National University of Science & Technology Politehnica Bucharest Department of Mathematics and Informatics Bucharest060042 Romania Academy of Romanian Scientists 3 Ilfov Bucharest050044 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province and Health Big Data Research Institute North Minzu University Yinchuan750021 China

In this paper we give a new proof for the fact that the function Γ (x)1 x−1 is geometrically convex. As an application, we give an improvement of Minc-Sathre inequality. © 2024, Politechnica University of Buchar... 详细信息

关键词： Choquet integral

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

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RespoBot: Chatbot used for the prediction of diseases using Machine Learning and Deep Learning with respect to Covid-19 2

RespoBot: Chatbot used for the prediction of diseases using ...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Sreedevi, B. Rosewelt, L. Antony Ramesh, Neela Sri Sai Ram Institute of Technology Computer Science and Engineering Chennai India Sri Sai Ram Institute of Technology Big Data Analytics Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

Resembling man's evolution over time the life threatening diseases have also evolved with him a disease is basically a disorder in structure or functioning of an organism's body. There are a several kinds of diseases but the most dangerous ones are the infectious diseases. Infectious diseases occur when a foreign body such as bacteria, fungi, and virus enter the body and attack the immune system. That being the smaller problem of infectious diseases as the main problem it's a communicable disease that spreads when a uninfected person comes in contact with an infected person they are highly likely to contract the same. The prediction is performed using Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Stochastic Gradient Descent SGD), Gradient Boosting (GD), Decision Trees (DT), Naive Bayes Classifier (NBC) and Voting Classifier (VC) ensemble technique. After the prediction a comparative analysis in terms of accuracy is performed. A Chatbot using Natural Learning Processing on a Neural Network is employed. The user is asked to upload a Chest X-ray if in possession as a second step confirmation for Tuberculosis, Pneumonia, Covid-19 and Healthy lung. In terms of highest accuracy for Covid-19 the GBC, VC, 56 tops the charts and for other diseases also GBC, VC, ANN top the charts. © 2023 IEEE.

关键词： Diseases

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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

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Digital Communications and Networks 2025年第2期11卷 442-454页

作者： Zhou Zhou Youliang Tian Jinbo Xiong Changgen Peng Jing Li Nan Yang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou 350117China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin 541004China

Federated learning combines with fog computing to transform data sharing into model sharing,which solves the issues of data isolation and privacy disclosure in fog ***,existing studies focus on centralized single-layer aggregation federated learning architecture,which lack the consideration of cross-domain and asynchronous robustness of federated learning,and rarely integrate verification mechanisms from the perspective of *** address the above challenges,we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning(BSAFL)framework based on dual aggregation for cross-domain *** particular,we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between ***,we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models'availability of intra-domain ***,we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using ***,security analysis demonstrates the security and privacy effectiveness of BSAFL,and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.

关键词： Blockchain Signcryption Federated learning Asynchronous Fog computing

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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Computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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