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Semantic Word Segmenting Technique Based on Artificial Bee Colony Algorithm 6th

Semantic Word Segmenting Technique Based on Artificial Bee C...

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Yang, Haiyan Xiang, Yunjie Hu, Rong Hu, Xian School of information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian350118 China

ISBN: (纸本)9783030697167

Because of the complexity and uncertainty of sentences, ambiguous information often appears in sentence segmentation. To the sentence ambiguity division problem, we proposed a method that can solve the semantic information in the sentence and can select the breakpoint of word dynamically. This study proposed and designed a single-objective discrete optimization model based on the ontology technology of lexeme semantic information. This model analyzed the semantic information of the sentences through the ontology data analysis, combined the case data to calculate the relevant semantic word frequency information;then proposed an optimized artificial bee colony algorithm to solve the model, by introduced an adaptive competition strategy in the selection and elimination of the population to accelerate the convergence speed of the algorithm. Finally, this study experimented on the word segmentation module of the library automatic question-answer consultation system. The experimental results showed that this method can meet the user’s requirements, improve the question segmentation accuracy, accelerate the convergence speed of the bee colony, which verified the effectiveness of the proposed method. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Educational Robotics and Computer Programming in Early Childhood Education: A Conceptual Framework for Assessing Elementary School Students' Computational Thinking for Designing Powerful Educational Scenarios 1

Educational Robotics and Computer Programming in Early Child...

引用

international conference on Smart technologies and Systems for Next Generation computing (ICSTSN)

作者： Alam, Ashraf Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9781665421119;9781665421126

The researcher sought to synthesize data from studies on robotics programming experiences for children in the age bracket zero to eight, as well as for pre- and in-service early childhood educators, to identify possible contributions that robotics programming may make to teaching and learning of STEM (sciences, technology, engineering, and mathematics) subjects. The investigation was initiated with inclusion of research articles as a result of search and assessment processes. All of these studies were selected such that they had the application of educational robotics in common. The synthesis of preliminary findings, study subjects, and potential advantages of educational robots in STEM education, especially in engineering and technology are discussed and dealt with in great detail. Additionally, the implications of educational robotics and robotics programming for educational institutions, curricular materials, and pedagogical techniques are incisively addressed. This review revealed that employing robots to teach computer programming may be a promising teaching tool for early childhood STEM education.

关键词： Engineering Robotics Pedagogical science e-Learning Computer Programming Educational Technology STEM Education Early Childhood Education

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An industrial equipment maintenance support system for networked collaborative evolution in the smart product service ecosystem

An industrial equipment maintenance support system for netwo...

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Digital Twins and Parallel Intelligence (DTPI), IEEE international conference on

作者： Yu-Guang Bao Zhao-Hui Sun Xiao-Qiang Liao Yuan Chang Siqi Qiu Xinguo Ming School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

The traditional model of industrial equipment operation and maintenance has changed with the development of industrial internet technologies such as edge computing, cloud-edge collaboration, big data, artificial intelligence, digital twins, etc. Meanwhile, researchers are beginning to focus on the range of influence of equipment asset reliability, which is shifting from local performance to organizational goals. The networked collaborative evolution of equipment assets and value chain management is becoming the core value proposition of industrial internet platforms. From the perspective of system architecture, this paper discusses a novel industrial equipment maintenance support system based on the industrial internet platform, detailing the design of the resource and edge layer, cloud platform layer, big data governance and service layer, modeling and simulation layer, information model and collaboration layer, and organization, business, and service layer. The proposed system architecture and solution are expected to provide possible guidance for group-type enterprises and cross-industry service providers to help them innovate equipment asset management solutions based on the industrial internet platform.

关键词： Cloud computing Collaboration Systems architecture Organizations Maintenance engineering Big data data models

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The Research on Student data Analysis System Based on data Mining

The Research on Student Data Analysis System Based on Data M...

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international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT 2020

作者： Li, Guozhang Wang, Xue Hainan Vocational University of Science and Technology HaikouHainan China

ISBN: (纸本)9783030627423

Due to the advanced modern computers and storage technologies, more and more data are generated and recorded every day. These complex data cover all aspects of student learning, life and management, and are a hidden resource for the school. However, due to the mass data and the intricate information channels, a large amount of data is not taken seriously, which is regarded as "junk" and ignored. The expansion of the scale of universities and the development of information technology pose new challenges to student management, classroom teaching and employment. Using big data in the education field to predict and judge student behavior in a timely manner can provide decision-making assistance for universities in mental health analysis, teaching quality evaluation, and student employment. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Expression of Concern for: Acne and Rosacea Detection from Images using Deep CNN's EfficientNet

Expression of Concern for: Acne and Rosacea Detection from I...

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advances in computing, Communication and Applied Informatics (ACCAI), international conference on

作者： Akibur Rahman Prodeep Rafiul Araf Prosenijit Ray Md. Shabbir Ajam Ulubbi Syma Nusrat Ananna M. F. Mridha Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9781665495295

ISBN: (纸本)9781665495301

Detecting skin diseases is dire and perilous as per the perspective of contamination of everything, like diseases, much of the time spread in human skin. Regardless, it is testing, routinely common, and drawn-out to see skin infections with manual strategies. In this manner, motorized acknowledgment of skin diseases is outstandingly needed in the field of dermatological mechanization. The capacity to recognize ailments utilizing acne or rosacea pictures of skin has been shown because of ongoing advances in computer vision. In light of everything, the massive piece of the proposed models can break down illnesses quickly. Thus, in this segment, we investigate an ideal acne and rosacea unmistakable confirmation model combining skin assurance. Regardless of multi-class grouping, the model utilizes a multilabel format system to recognize both acne and rosacea infection. For the examination and evaluation, we have blended two data sets from various internet-based sources that included skin pictures of skin inflammation and rosacea. In our study, we complete different well-known convolutional neural organization (CNN) plans. The exploratory results endorse that the EfficientNet perform better in multilabel skin diseases classification undertakings. Furthermore, we have found that a CNN design, skip affiliations, spatial convolutions, and more restricted mystery layer network sway better results in skin inflammation and rosacea recognizable proof portrayal.

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Authentication Scheme Based on Non-Interactive Zero-Knowledge Proof for Mobile Health

Authentication Scheme Based on Non-Interactive Zero-Knowledg...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Edward Mensah Acheampong Shijie Zhou Yongjian Liao Emmanuel Antwi-Boasiako Isaac Amankona Obiri School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science University of Electronic Science and Technology of China Chengdu China

Digital technologies, such as wireless body area networks (WBANs) for mobile health (mHealth) applications, are expected to enhance the quality of the public health care system. Although mHealth can improve patients' quality of health by offering outpatient real-time health monitoring systems instead of being stuck in the hospital all the time to monitor chronic diseases. The major challenge in adopting mHealth is data security and privacy. The health data routed on the internet from the patient monitoring device to the health center for remote monitoring is vulnerable to confidentiality attacks. To handle this issue, we present an authentication scheme based on non-interactive zero-knowledge proof (NIZKP), which issues certificates and authenticates monitoring devices each time performing transactions without revealing sensitive information. Our authentication scheme provides a high level of security with a low computational cost, which is lightweight for WBANs.

关键词： Performance evaluation Wireless communication Wireless sensor networks Patient monitoring Hospitals Authentication Real-time systems

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EdgeMan: Ensuring Real-Time Service for Containerized Edge Systems

EdgeMan: Ensuring Real-Time Service for Containerized Edge S...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Wenzhao Zhang Wei Dong Geng Ren Yi Gao Zhejiang Key Lab of Service Robot College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Institute of Frontier Technologies Alibaba Group

Containers have emerged as a popular technology for edge computing platforms. Although there are varieties of container orchestration frameworks, e.g., Kubernetes to provide high-reliable services for cloud infrastructure, ensuring realtime service at the containerized edge systems (CESs) remains a challenge. In this paper, we propose Edgeman,a holistic edge service management framework for CESs, which consists of (1) a model-assisted event-driven lightweight online scheduling algorithm to provide request-level execution plans; (2) a bottleneck-metric-aware progressive resource allocation mechanism to improve resource efficiency. We then build a testbed that installed three containerized services with different latency sensitivities for concrete evaluation. Besides, we adopt real-world data traces from Alibaba and Twitter for large-scale emulations. Extensive experiments demonstrate that the deadline miss ratio of Edgemanis reduced 85.9% on average compared with existing methods in both industry and academia.

关键词： Industries Sensitivity Job shop scheduling Scheduling algorithms Social networking (online) Emulation Containers

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Starting Behavior Analysis of Pure Electric Bus Based on K-means Clustering 6th

Starting Behavior Analysis of Pure Electric Bus Based on K-m...

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6th international conference on Advanced Machine Learning technologies and Applications, AMLTA 2021

作者： Wang, Tianjie Zou, Fumin Li, Yifan Chang, Kuo-Chi Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian350118 China Fujian Provincial Big Data Research Institute of Intelligent Transportation Fujian University of Technology Fuzhou Fujian350118 China

ISBN: (纸本)9783030697167

Due to the relatively fixed route and obvious difference between different routes, how to reduce the energy consumption level of pure electric buses is a research hotspot at home and abroad. Based on the actual operation of pure electric buses in a city as the research object and through the analysis of its starting data, the K-means clustering method is used to study the influencing factors and differences of energy consumption under different starting modes of electric buses. Then, the evaluation system of drivers’ starting behavior is built. The results show that the energy consumption of pure electric buses varies greatly under different starting modes. The energy consumption of radical starting buses is much higher than that of safe starting buses. The evaluation system can objectively and effectively evaluate drivers’ starting behaviors, provide a sufficient basis for the supervision of operating enterprises, and have certain reference value. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： K-means clustering

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Modelling New Crop Disease Indices for Crop Species Classification using Restructured Convolution Neural Network on hyperspectral Satellite images

Modelling New Crop Disease Indices for Crop Species Classifi...

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Recent advances and Innovations in Engineering (ICRAIE)

作者： O. Vishali Priya R. Sudha Department of Computer Science PSG College of Arts & Science Coimbatore Tamilnadu India

The computing the chlorophyll concentration in the food crops aid to recognize the situation of the crops and classify the crops on basis of its soil characteristics which assist the farmer to increase agriculture growth by protecting the crop against various disease. In this article, a new restructured convolution neural network has constructed as proposed approach to determine the Chlorophyll concentration in the crops on employing the hyperspectral image segmentation mechanism as deep learning architecture, which composed of two primary functions. Initially, a Sparse Multinomial Logistic Regression (SMLR) model has been employed to learn the class posterior probability distributions of the spectral signature of the hyperspectral images to produce the unique crop spectral index to various crop diseases. Next we employed a Markov Random Field to segment the hyperspectral images on basis of the spectral and spatial information of the spectral signature obtained on each end member extracted. Further, Particle Swarm Optimization (PSO) is a Metaheuristics optimization has been performed on the segmented spectral signature to reduce the computation cost and reduce the feature vector of the training data. PSO optimization yields the optimal feature set on the class posterior probability. Optimal feature set is processed using the restructured convolution neural network to generate new disease index for crop on basis of wavelength of the spectral bands for different crops species diseases on interpreting it with other available indices with respect to Chlorophyll and Nitrogen spectral values. Restructured convolution Neural Network maps the features to the classes of the disease on the wavelength of the spectral band of the feature vector. The optimized spectral indices are employed to testing data and disease is classified using Restructured neural Network. The experimental results of the proposed model are evaluated on employing real-time hyperspectral image data sets a

关键词： Image segmentation Convolution Neural networks Crops Training data Indexes Particle swarm optimization

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Smart Farming based on Deep Learning Approaches

Smart Farming based on Deep Learning Approaches

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Electrical Power Engineering, Communication and computing Technology(ODICON), Odisha international conference on

作者： Subhranshu Sekhar Tripathy Niva Tripathy Mamata Rath Abhilash Pati Amrutanshu Panigrahi Manoranjan Dash Dept. of Computer Science and Engg. DRIEMS Autonomous College Cuttack Odisha India Dept. of Computer Science and Engg. Siksha ‘O’ Anusandhan University Bhubaneswar Odisha India Faculty of Management Sciences Siksha ‘O’ Anusandhan University Bhubaneswar Odisha India

ISBN: (纸本)9781665478403

A novel idea called “smart farming” aims to increase the productivity and effectiveness of agriculture by utilizing cutting-edge information technologies. Using the most recent developments in automation, artificial intelligence, and networking, farmers are better able to keep an eye on every step of the process and apply exact treatments selected by machines with superhuman accuracy. With an expanding global population comes a rise in the need for both employment and food. The farmers' traditional practices fell short of meeting these demands. New automated methods were consequently proposed. These innovative techniques satisfied the world's food needs while also creating jobs for billions of people. For effective farm management, a variety of techniques are utilized, including IoT, cloud computing, AI, machine learning, deep learning, big data, etc. Deep learning is one of these, and it is a developing field of study for crop yield forecasting. Engineers, data scientists, and farmers are still developing methods to optimize the amount of human labor needed in agriculture. As key information sources improve, smart farming transforms into a learning system that grows smarter every day. Artificial neural network principles are used in a machine learning method known as deep learning. The depth of deep learning networks, which also allows them to detect latent structures in unlabeled, unstructured data, is what sets them apart from neural networks. Compared to ML techniques, deep learning networks that automatically extract features without human involvement have a considerable advantage. In this research, we suggest a recommendation model and an algorithm to assess crop yield in the upcoming year. We have contrasted the deep learning algorithm with the random forest machine learning algorithm. These algorithms have been the subject of a brief comparison. Python has been used to implement the suggested model.

关键词： Deep learning Smart agriculture Productivity Machine learning algorithms Sociology Crops Transforms

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