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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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A Novel Method to Solve the Separation Problem of LDA 11th

A Novel Method to Solve the Separation Problem of LDA

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11th IFIP TC 12 international conference on intelligent Information Processing, IIP 2020

作者： Zhang, Meng Li, Wei Zhang, Bo School of Mechatronic Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China

ISBN: (纸本)9783030469306

Linear discriminant analysis (LDA) is one of the most classical linear projection techniques for feature extraction, widely used in kinds of fields. Classical LDA is contributed to finding an optimal projection subspace that can maximize the between-class scatter and minimize the average within-class scatter of each class. However, the class separation problem always exists and classical LDA can not guarantee that the within-class scatter of each class get its minimum. In this paper, we proposed the k-classifiers method, which can reduce every within-class scatter of classes respectively and alleviate the class separation problem. this method will be applied in LDA and Norm LDA and achieve significant improvement. Extensive experiments performed on MNIST data sets demonstrate the effectiveness of k-classifiers. © 2020, IFIP international Federation for Information Processing.

关键词： Discriminant analysis

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Predicting protein-protein interactions from amino acid sequences using SaE-ELM combined with continuous wavelet descriptor and PseAA composition 11th

Predicting protein-protein interactions from amino acid sequ...

引用

11th international conference on intelligent Computing, ICIC 2015

作者： Huang, Yu-An You, Zhu-Hong Li, Jianqiang Wong, Leon Cai, Shubin College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong518060 China

ISBN: (纸本)9783319221854

Protein-protein interactions (PPIs) are known for its crucial role in almost all cellular processes. Although many innovative techniques for detecting PPIs have been developed, these methods are still both time-consuming and costly. therefore, it is significant to develop computational approaches for predicting PPIs. In this paper, we propose a novel method to identify new PPIs in ways of self-adaptive evolutionary extreme learning machine (SaE-ELM) combined with a novel representation using continuous wavelet (CW) transform and Chou’s pseudo amino acid feature vector. We apply Meyer continuous wavelet transform to extracting wavelet power spectrums from a protein sequence representing a protein as an image, which allows us to use well-known image texture descriptors for extracting protein features. Chou’s pseudoamino- acid composition (PseAAC) expands the simple amino-acid composition (AAC) by retaining information embedded in protein sequence. SaE-ELM, a variant of extreme learning machine (ELM), optimizes the single hidden layer feedforward network (SLFN) hidden node parameters using self-adaptive different evolution algorithms. When performed on the PPI data of yeast, the proposed method achieved 87.87 % prediction accuracy with 91.19 % sensitivity at the precision of 82.62 %. Extensive experiments are performed to compare our method with the method base on state-of-the-art classifier, support vector machine (SVM). It is observed from the achieved results that the proposed method is very promising for predicting PPI. © Springer international Publishing Switzerland 2015.

关键词： Amino acids

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Geomancy: automated Performance Enhancement through data Layout Optimization 36

Geomancy: Automated Performance Enhancement through Data Lay...

引用

36th international conference on Massive Storage Systems and Technology, MSST 2020

作者： Bel, Oceane Chang, Kenneth Tallent, Nathan R. Duellmann, Dirk Miller, Ethan L. Nawab, Faisal Long, Darrell D.E. University of California Santa Cruz United States Pure storage United States Pacific Northwest National Labs United States CERN

Large distributed storage systems such as high-performance computing (HPC) systems used by national or international laboratories require sufficient performance and scale for demanding scientific workloads and must handle shifting workloads with ease. Ideally, data is placed in locations to optimize performance, but the size and complexity of large storage systems inhibit rapid effective restructuring of data layouts to maintain performance as workloads shift. To address these issues, we have developed Geomancy, a tool that models the placement of data within a distributed storage system and reacts to drops in performance. Using a combination of machine learning techniques suitable for temporal modeling, Geomancy determines when and where a bottleneck may happen due to changing workloads and suggests changes in the layout that mitigate or prevent them. Our approach to optimizing throughput offers benefits for storage systems such as avoiding potential bottlenecks and increasing overall I/O throughput from 11% to 30%. © 2020 36th international conference on Massive Storage Systems and Technology, MSST 2020. All Rights Reserved.

关键词： Digital storage

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Wind power intra-day multi-step predictions using pde sum models of polynomial networks based on the pde conversion and substitution with the l-transformation 11th

Wind power intra-day multi-step predictions using pde sum mo...

引用

11th international conference on Soft Computing and Pattern Recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired Computing, NaBIC 2019

作者： Zjavka, Ladislav Snášel, Václav Abraham, Ajith Department of Computer Science Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic AuburnWA98071 United States

ISBN: (纸本)9783030493448

Precise forecasts of wind power are required as they allow full integration of wind farms into the electrical grid and their active operation. their daily base poses a challenge due to the chaotic nature of global atmospheric dynamical processes resulting in local wind fluctuations and waves. Surface wind forecasts of NWP models are not fully adapted to local anomalies which can influence significantly in addition its temporal-flow. AI methods using historical observations can convert or refine forecasts in consideration of wind farm location, topography and hub positions. their independent intra-day wind-power predictions are more precise then those based on NWP data as these are usually produced every 6 h with a delay. the designed AI method combines structures of polynomial networks with some mathematic techniques to decompose and substitute for the n-variable linear Partial Differential Equation, which allow complex representation of unknown dynamic systems. Particular 2-variable PDEs, produced in network nodes, are converted using the Laplace transformed derivatives. the inverse L-transformation is applied to the resulting pure rational terms to obtain the originals of unknown node functions, whose sum is the composite PDE model. Statistical models are developed with data samples from estimated periods of the last days which optimally represent spatial patterns in the current weather. they process the latest available data to predict wind power in the next 1–12 h according to the trained data inputs → output time-shift. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Wind power

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Visualization and Analysis of Mapping Knowledge Domains for AI Education System Studies

Visualization and Analysis of Mapping Knowledge Domains for ...

引用

Information and Education Technology (ICIET), international conference on

作者： Yakun Wang Fuxiang Yu College of Digital Economics Nanning University Nanning China Business School Hangzhou City University Hangzhou China

the development of Artificial Intelligence (AI) technology brings opportunities and challenges to the education system. It is changing the way educational information is disseminated and at the same time triggering a change in the educational model. Based on the database of CNKI, this paper uses the visualization tool CiteSpace software to map the knowledge structure of AI education system research and conducts a bibliometric analysis of the relevant literature collected in this field. the results showed that 1) In the past 20 years, the emphasis of studies AI education system in China has shifted from a singular artificial intelligence to smart technology, decision-making systems, learning analysis, big data, and knowledge engineering. the qualities, such as increasing study horizon and growing research scope, are highlighted. 2) AI helps to upgrade the elements of education, intelligent education for large-scale, personalized education, and the development path of AI education technology are research hotspots. 3) Smart education, Artificial intelligence, Talent training, Emotional computing, and Smart classroom are the strongest bursting keywords in the last two years, indicating the recent research frontiers.

关键词： Training Knowledge engineering Education Decision making learning (artificial intelligence) Educational technology Big data

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Cost-Sensitive Universum-SVM

Cost-Sensitive Universum-SVM

引用

international conference on Machine learning and Applications (ICMLA)

作者： Sauptik Dhar Vladimir Cherkassky Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA

Many applications of machine learning involve analysis of sparse high-dimensional data, where the number of input features is larger than the number of data samples. Standard classification methods may not be sufficient for such data, and this provides motivation for non-standard learning settings. One such new learning methodology is called learning through Contradictions or Universum support vector machine (U-SVM) [1, 2]. Recent studies [2-10] have shown U-SVM to be quite effective for such sparse high-dimensional data settings. However, these studies use balanced data sets with equal misclassification costs. this paper extends the U-SVM for problems with different misclassification costs, and presents practical conditions for the effectiveness of the cost sensitive U-SVM. Finally, several empirical comparisons are presented to illustrate the utility of the proposed approach.

关键词： Support vector machines Standards Training Histograms Training data Vectors Machine learning

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Real Time Detection of Learner Fatigue in E-learning Environments through the Combination of Eye and Mouth Features

Real Time Detection of Learner Fatigue in E-Learning Environ...

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international conference on E-learning and E-Teaching (ICELET)

作者： Alireza Abdolhosseinzadeh Gholam Ali Montazer Dept. Information Technology Engineering Tarbiat Modares University Tehran Iran

one of the features of intelligent e-learning environments is the presence of a personalized mechanism to assist the effective learning for each learner. Exploiting personalized tutoring models, learners can experience successful and satisfying learning outcomes due to their learning objectives. In this way, the learner (student) feels that there is always an aware and insightful tutor (teacher) alongside, providing private guidance on the learning path. While personalizing the learning environment is highly desirable, the continuous monitoring of the virtual teacher may lead to fatigue (and even disillusionment) for the learner, potentially reducing the efficiency of the e-learning environment. Considering the above points, in this article an intelligent system has been designed to identify fatigue in learners by examining changes in their facial expressions. To achieve this, a combination of eye and mouth features of the learner is utilized, and with the help of a deep neural network, the level of learner fatigue is detected. the proposed system employs the Media‌Pipe algorithm for face and eye-mouth feature identification, and for training the neural network model, the Efficient‌Net neural network has been utilized. In this system, databases NITYMED and Drowsiness‌Detection have been utilized for training the network. Additionally, 80% of dataset has been used for training and the remaining 20% for testing. It is worth mentioning that given the balanced distribution of samples for each class, accuracy is employed as the evaluation metric, considering the objective of minimizing false positives. the experimental results show the accuracy and precision of 99.73% and 99.72% for the eye model, respectively, and the same measures for the mouth model are 99.85% and 99.95%, respectively. the standard deviation value for the eye model is 0.4997, and for the mouth model, it is 0.4998, indicating that most of the data points are close to the mean. Additionally, the mean va

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Vehicle detection based on the stereo concept of (axis, width, disparity) symmetry map

Vehicle detection based on the stereo concept of (axis, widt...

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international conference on intelligent Transportation

作者： Yaqian Li Gwenaelle Toulminet Abdelaziz Bensrhair LITIS INSA Rouen France CAOR Ecole des Mines de Paris France

In this paper, we introduced the concept of (axis,width,disparity) symmetry map. It is a stereo extension of the monocular concept of (axis,width) symmetry map. In its principle, this concept can be used for the detection of any symmetric obstacle provided that its approximate width and height are known. In this paper, we used it for vehicle detection. As the presence or absence of road features, and hilly or plan road do not perturb the process, the proposed method is an alternative to v-disparity approach.

关键词： Vehicle detection Testing intelligent transportation systems Layout intelligent vehicles Road safety Automotive engineering data mining Shape Image edge detection

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Internet of things in Agriculture Industry: Implementation, Applications, Challenges and Potential 8th

Internet of Things in Agriculture Industry: Implementation, ...

引用

8th international conference on Microelectronics, Circuits, and Systems, Micro 2021

作者： Singh, Kiran Jot Kapoor, Divneet Singh Sharma, Anshul thakur, Khushal Bajaj, Tanishq Tomar, Ashwin Mittal, Sparsh Singh, Baljap Agarwal, Raghav Embedded Systems and Robotics Research Group Chandigarh University Punjab Mohali140413 India

ISBN: (纸本)9789819904112

the Internet of things (IoT) is allowing agriculture, particularly arable farming, to become more data-driven, resulting in more timely and cost-effective agricultural production and management while reducing environmental impact. In comparison with other agricultural systems, this study would involve an empirical evaluation of current and prospective IoT applications in arable farming where spatial details, vastly diverse landscapes, mission variety, and mobile devices provide unique challenges to overcome. the analysis describes the state-of-the-art in terms of deployed technologies. It discusses present and future applications and provides an overview of current and possible applications, as well as problems, viable alternatives, and implementations. Finally, it explores any potential IoT applications in arable agriculture. Smart phones, intelligent sensor control, middleware platforms, integrated field management systems across the supply chain, and automated vehicles and robots all stand out as present issues preventing arable farming from becoming smart. Interoperability is a key obstacle across all layers of an IoT system’s design, which may be solved through shared standards and protocols, and it arises during implementation. the paper examined has identified and addressed in detail difficulties such as data privacy, cost, power consumption, network latency, data analytics, and security, among others. Different resolutions to all recognized challenges are presented by technologies such as artificial intelligence, intelligent data managing, and further addressed in depth issues such as availability, device power usage, latency of network, big data processing, data protection, and protection, among others. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Applying blockchain technology for improvement of the educational process in terms of data processing 11

Applying blockchain technology for improvement of the educat...

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11th Majorov international conference on Software engineering and Computer Systems, MICSECS 2019

作者： Zimina, Dina Mouromtsev, Dmitry ITMO University Saint-Petersburg197101 Russia

the paper considers some new methods of using blockchain technology as a tool of improving educational process management. Two e-learning problems are described. the first one is low ratings of completing MOOCs. the second one is the need for more detailed and accurate reports. Blockchain technology and its applications in education is considered. Blockchain's ability of documenting events is reviewed particularly. Two cases of using blockchain in this way are performed. the first one is making educational reports more detailed and easier to design. Another one is using blockchain for creating any student model in adaptive education to make electronic educational courses more effective and to increase ratings of completing MOOCs. A blockchain's option of documenting timestamps and users' addresses automatically is specially mentioned. Both cases are described mathematically and united in one model. Using Ethereum blockchain for the evaluation because of its smart-contract's mechanism is justified. Sequence diagram showing application workflow, is presented. Input data from the real e-learning course are processed with using statistics to get the most frequently used system events. Application tools for working with the Ethereum blockchain are presented. the process of developing, deploying and mining smart-contract is shown. Output report is presented. Implementation is described, the ways of future learn are reported. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Smart contract

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