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Deep learning based ensemble approach to predict student academic performance: Case study 3

Deep learning based ensemble approach to predict student aca...

引用

3rd International conference on intelligent Sustainable Systems, ICISS 2020

作者： Salal, Y.K. Abdullaev, S.M. South Ural State University Department of System Programming Chelyabinsk Russia

ISBN: (纸本)9781728170893

Traditional practices of education, medicine, and economics often use collective expert methods to find acceptable solutions to complex issues. The computer model of such methods is an ensemble classification which combines prediction of a number base classifier trained by various machine learning techniques. The predictive sample in the educational process is always not balanced-it leads to a shift in estimates. The basic idea of this work is that the binary forecasting on unbalanced datasets is carried out by a Meta-classifier-an ensemble of different classification algorithm (Decision tree J48, Naive base NB, Multilayer perception MLP, k-Neighbour Nearest k-NN and Support Vector Machine SVM) which trained by stacking, bagging and boosting procedures on SMOTE, RUS, ROS balanced samples. The experiments conducted on moderately unbalanced dataset of 350 tuples of student data with using simple voting Meta-ensemble models, show that the best overall accuracy A and F1 measure of minority class were achieved by the Meta-ensembles containing 5-15 members classifiers trained on samples prepared by *** is also proposed that these of Meta-ensembles can be used to resolve quantification learning problem-novel task to predict collective performance. As in our recent works the deterministic outputs of Meta-ensemble members can be transformed to probabilistic outputs and then to prediction of class frequency. After analyzing the results of quantification, it becomes obvious that the most optimal design of the meta-ensembles is to collect pre-boosted classifiers trained on the SMOTE sample. © 2020 IEEE.

关键词： Forecasting

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KIRC: Kyutech intelligent robot club 3rd

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3rd Robot World Cup Soccer Games and conference

作者： Ohashi, T Fukuda, M Enokida, S Yoshida, T Ejima, T Kyushu Inst Technol Fukuoka Japan

ISBN: (纸本)3540410430

Autonomous soccer robots should recognize the environment from the captured image from a video camera and plan to proper behavior. Furthermore, when some robots play cooperatively, communication system between robots is important inputs. We choose simple vision and actuator system, then the gap between the real world and simulation environment are small. Our research target is to accomplish multiagent system using reinforcement learning.

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A Study on the Development of Wordle Based on Machine learning 3

A Study on the Development of Wordle Based on Machine Learni...

引用

2023 IEEE 3rd International conference on Data Science and Computer Application, ICDSCA 2023

作者： Jin, Junzhe Yin, Yitian Northwestern Polytechnical University School of Software Xi'an China Civil Engineering and Architecture Northwestern Polytechnical University School of Mechanics Xi'an China

ISBN: (纸本)9798350341546

This paper builds a Wordle Word Difficulty Assessment Model based on Machine learning which achieves input 5-letter English words, can output the predicted the percentage of different tries times on the specified date, and the word difficulty level (difficult, average or easy). First, the Regressor Chain is applied to correlate a set of independent variables (percentage of different tries) and dependent variables (frequency of each letter, word frequency, lexical properties, number of common letter combinations, number of repeated letters, date) in a certain order. Then, based on the Decision Tree, build Wordle Word n-tries percentage Prediction Model. The final implementation: input 5-letter word, output n tries percentage. Second, Using the optimized KNN clustering algorithm with percentage of n tries as a variable, three difficulty levels -'difficult', 'medium' and 'average' were classified. Next, the KNN Regression Model was optimized using the Particle Swarm optimization (PSO) to build Wordle Word Difficulty Assessment Model. This model can classify the difficulty level of any 5-letter English word. By this model, it can also provide a solution to better enhance the user experience and increase the popularity of the game. In a way, this scientific analysis of Wordle historical data, which can better guide the future innovation and ideas of this popular game. © 2023 IEEE.

关键词： Decision Tree KNN PSO Regressor Chain Wordle

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intelligent Multi-Physics Design of 3-D Leadframe-based SiC Power Module with Die Stacking for Transportation Electrification 73

Intelligent Multi-Physics Design of 3-D Leadframe-based SiC ...

引用

IEEE 73rd Electronic Components and Technology conference (ECTC)

作者： Torres-Surillo, Emanuel Molina-Mangual, Christian Smet, Vanessa Georgia Inst Technol Sch Mech Engn Atlanta GA 30332 USA

ISBN: (纸本)9798350334982

The increasing demand for more efficient, compact and reliable power conversion in modern electric vehicles has led to the fast adoption of silicon carbide (SiC) inverters. While SiC devices bring dramatic gains in power density and system efficiency compared to their silicon counterparts, the technology is still underperforming due to aggravated chip-package interactions. On the electrical side, greater switching speeds result in increased dv/dts and di/dts, which, when coupled with the package parasitics result in increased overcurrent and voltage spikes and oscillation, aggravated noise and risks of electrical discharge machining failures. To address this challenge, a new leadframe-based package architecture with die stacking is proposed in this paper. While it provides output capacitance decoupling as well as a low-inductive current flow in the power module, die stacking may lead to thermal densification and subsequent reliability concerns that need to be considered in the design phase. To mitigate these multi-physics trade-offs, a novel machine-learning-based algorithm is used to optimize the package layout for best electrical, thermal and reliability performances and power density.

关键词： Silicon-carbide traction inverter electric vehicles 3D package architectures die stacking machine-learning optimization

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Dynamic assembly of personalized learning content on the Semantic Web

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3rd European Semantic Web conference

作者： Jovanovic, Jelena Gasevic, Dragan Devedzic, Vladan Univ Belgrade Sch Business Adm FON Belgrade Serbia

ISBN: (纸本)3540345442

This paper presents an ontology-based approach for automatic decomposition of learning objects (LOs) into reusable content units, and dynamic reassembly of such units into personalized learning content. To test our approach we developed TANGRAM, an integrated learning environment for the domain of intelligent Information Systems. Relying on a number of ontologies, TANGRAM allows decomposition of LOs into smaller content units, which can be later assembled into new LOs personalized to the user's domain knowledge, preferences, and learning styles. The focus of the presentation is on the ontologies themselves, in the context of user modeling and personalization. Furthermore, the paper presents the algorithm we apply to dynamically assemble content units into personalized learning content. We also discuss our experiences with dynamic content generation and point out directions for future work.

关键词： Semantics

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Deep learning Based Leak Detection of Oil Transmission 3

Deep Learning Based Leak Detection of Oil Transmission

引用

3rd International conference on intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Ihsan, Aditya Firman Uttunggadewa, Saladin Himawan, Salamet Nur Pratama, Choiryaldi Setya Darmadi Dasilfa, Rinaldy Telkom University School of Computing Bandung Indonesia Institut Teknologi Bandung Mathematics Department Bandung Indonesia Politeknik Negeri Indramayu Informatics Department Indramayu Indonesia Institut Teknologi Bandung Center of Mathematical Modeling and Simulation Bandung Indonesia

ISBN: (纸本)9798350394559

Leakage of oil pipeline pose significant challenges in the area of petroleum transport and infrastructure management, with far-reaching implications encompassing economic, environmental, and public safety concerns. While machine learning has developed quite rapidly in recent years and has gained prominence in various sectors, its adoption in petroleum engineering domain remains relatively rare. This paper explores the application of machine learning, specifically Artificial Neural Networks (ANN) method, in predicting the location of oil leaks in a pipeline, given the outlet pressure of the pipe and the expected leak rate. Due to very low number of leak occasion in the real practical case, it is quite challenging to obtain enough balanced leak data to train the learning model properly. To manage that issue, we generate the data manually by simulating the leak process using Beggs-Brill pressure drop correlation in various combination of different experimental cases. The generated data incorporates outlet pressure and estimated leak rates, to serve as input features for the ANN model. We fine-tune the ANN architecture to optimize model performance and analyzes its learning capabilities. This paper underscores the potential of ANN technology in addressing the complex issue of oil pipeline leaks and contributes to the ongoing efforts to enhance pipeline safety. © 2023 IEEE.

关键词： Neural networks

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A New Variant of the Minimum-Weight Maximum-Cardinality Clique Problem to Solve Conflicts between Aircraft 3rd

A New Variant of the Minimum-Weight Maximum-Cardinality Cliq...

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3rd International conference on Modelling, Computation and optimization in Information Systems and Management Sciences (MCO)

作者： Lehouillier, Thibault Omer, Jeremy Soumis, Francois Desaulniers, Guy Grp Res Decis Anal 3000 Cote St Catherine Rd Montreal PQ H3T 2A7 Canada

ISBN: (纸本)9783319181615;9783319181608

In this article, we formulate a new variant of the problem of finding a maximum clique of minimum weight in a graph applied to the detection and resolution of conflicts between aircraft. The innovation of the model relies on the cost structure: the cost of the vertices cannot be determined a priori, since they depend on the vertices in the clique. We apply this formulation to the resolution of conflicts between aircraft by building a graph whose vertices correpond to a set of maneuvers and whose edges link conflict-free maneuvers. A maximum clique of minimal weight yields a conflict-free situation involving all aircraft and minimizing the costs induced. We solve the problem as a mixed integer linear program. Simulations on a benchmark of complex instances highlight computational times smaller than 20 seconds for situations involving up to 20 aircraft.

关键词： Air Traffic Control Conflict Resolution Maximum Clique Mixed Integer Linear Programming

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An implementation and analysis of deep learning models for the detection of wheat rust disease 3

An implementation and analysis of deep learning models for t...

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3rd International conference on intelligent Sustainable Systems, ICISS 2020

作者： Sood, Shivani Singh, Harjeet Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781728170893

Wheat is one of the essential food crops all over the world. In India, it is the largest growing winter cereal crop that contributes almost 14% to the world's food production. This crop is usually affected by various types of rust diseases. Rust is one kind of fungal disease, which occurred on wheat crop. The rust is characterized as leaf-rust (brown rust), stem-rust (black rust), and yellow rust (strip rust). Due to these types of diseases, a huge amount of loss may occur in the production of wheat crop. Therefore, it is required to early detect this disease during the growing stage. In the present study, we consider two classes of diseased plants and one for a healthy plant. Due to less amount of image dataset, we performed the image augmentation and generated the synthetic data from the original data. Various experiments are performed on different dataset sample sizes i.e., 100, 500, 1000, 1500, and 2000. The weights for building the model have been considered as the same in ResNet50 and VGG16 transfer learning models. Moreover, to improve the accuracy of the model, we tune the hyper-parameters. The hyper-parameters used to train this model are: batch_size = 32, optimizer = "adam", initial learning rate = 0.01 with decay of 0.0001. After performing the experiments, the model produced appreciable results with the VGG16 model. The proposed approach has obtained the highest classification accuracy rate of 99.07% using the VGG16 model. © 2020 IEEE.

关键词： Crops

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Enhancing Banach's contraction method using the particle swarm optimization to solve the system Drinfeld-Sokolov-Wilson 3

Enhancing Banach's contraction method using the particle swa...

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3rd International conference on Mathematics and Applied Science, ICMAS 2022

作者： Raslan, Amina Entesar, Ahmed Department of Mathematics College of Computer Sciences and Mathematics Universityof Mosul Mosul Iraq

In this manuscript, the system (Drinfeld-Sokolov-Wilson) has been solved using the analytical method (Banach contraction) and then using the intelligent method (particle swarm optimization) to improve the results we obtained from the analytical method, the results we obtained from the hybrid method (BCM - PSO) proved the efficiency of this method by finding (MAE and MSE) compared with the exact solution, and also the results of the analytical and hybrid methods were drawn compared with the exact solution. © Published under licence by IOP Publishing Ltd.

关键词： Particle swarm optimization (PSO)

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Integrated Model of Bandwidth optimization and Energy Efficiency in Internet-of-Things 3rd

Integrated Model of Bandwidth Optimization and Energy Effici...

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3rd conference on Computational Methods in Systems and Software (CoMeSySo)

作者： Ambore, Bhagyashree Suresh, L. CiTech Dept Comp Sci & Engn Bengaluru India

ISBN: (纸本)9783030313623;9783030313616

The Internet-of-Things (IoT) is an inevitable technology that is going to be transform the automation process by massively connected communicating devices that has limited resources. However, in order to carry out fault tolerant communication in IoT, it is essential to ensure that each IoT nodes should be able to perform seamless communication in a defined level of restricted bandwidth along with better energy retention capability. Review of existing solution shows that it offers solution to highly narrowed scale of problem. Therefore, the proposed system offers a novel framework which uses scheduling approaches in order to optimize the bandwidth utilization as well as retain maximum amount of residual energy among the communicating IoT nodes. The simulation of the model shows that proposed scheme offers better bandwidth management and efficient energy conservation in contrast to existing system.

关键词： Internet-of-Things Bandwidth management Energy efficiency Scheduling Communication

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