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MaLTESQuE 2021 - Proceedings of the 5th international Workshop on Machine Learning Techniques for Software Quality Evolution, co-located with ESEC/FSE 2021

MaLTESQuE 2021 - Proceedings of the 5th International Worksh...

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5th international Workshop on Machine Learning Techniques for Software Quality Evolution, MaLTESQuE 2021, co-located with the ACM Joint European Software Engineering conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2021

ISBN: (纸本)9781450386258

the proceedings contain 6 papers. the topics discussed include: comparing within- and cross-project machine learning algorithms for code smell detection;unsupervised learning of general-purpose embeddings for code changes;VaryMinions: leveraging RNNs to identify variants in event logs;toward static test flakiness prediction: a feasibility study;building a bot for automatic expert retrieval on discord;and metrics selection for load monitoring of service-oriented system.

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Solving Imbalanced Data in Credit Risk Prediction: A Comparison of Resampling Strategies for Different Machine Learning Classification algorithms, Taking threshold Tuning into Account 5

Solving Imbalanced Data in Credit Risk Prediction: A Compari...

引用

5th international conference on Machine Learning and Machine Intelligence, MLMI 2022

作者： Yang, Chenyu Dong, Yanjie Lu, Jiachen Peng, Zherui HKU Business School The University of Hong Kong Hong Kong999077 Hong Kong Shanghai United International School Shanghai201108 China Culver Academy CulverIN46511 United States Wuhan Britain China School Wuhan430070 China

ISBN: (纸本)9781450397551

Effective credit risk prediction is critical for commercial banks to actively manage their lending book and reduce negative impact from potential credit losses. In a benign credit markets where default rates are low, the datasets for customers' credit default status prediction are often imbalanced. While many studies have been conducted to address the class imbalance problem in the machine learning field under various contexts, our research focuses on the credit risk sector. We experiment five resampling strategies (SMOTE, Borderline-SMOTE, Random Undersampling, NearMiss, SMOTE + Tomek Links) and a thresholding technique (Optimal G-Mean) on eight different classifiers separately. We also explore whether the combination of resampling and thresholding can enhance the overall classifier performance. the performance of models is evaluated on both threshold metrics (G-Mean, F1-score) and ranking metrics (area under the receiver operating characteristic (ROC) curve). Our findings suggest that pure threshold tuning can often outperforms resampling methods whereas the effect of thresholding on the basis of resampled dataset is minor. © 2022 ACM.

关键词： Forecasting

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A Deep Learning Approach for Face Detection using Max Pooling 5

A Deep Learning Approach for Face Detection using Max Poolin...

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5th international conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Mehedi Shamrat, F.M. Javed Jubair, Md. Al Billah, Md. Masum Chakraborty, Sovon Alauddin, Md. Ranjan, Rumesh Daffodil International University Department of Software Engineering Dhaka Bangladesh European University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Department of Plant Breeding and Genetics Punjab Agriculture University Punjab India

ISBN: (纸本)9781665415712

Deep learning is a trendy term these days, and it refers to a modern age in machine learning in which algorithms are taught to identify patterns in vast amounts of data. It mostly refers to studying various layers of representation, which assists in the understanding of data that includes text, sound, and pictures. To interact with the objects in a video series, many researchers use a form of deep learning called a CNN. Face detection involves several face-related technologies, such as face authentication, facial recognition, and face clustering. For identification and understanding, effective preparation must be carried out. the standard technique did not produce a positive outcome in terms of face recognition precision. the objectives of this research are by using a deep learning model to enhance the accuracy of face detection. For recognizing faces from datasets, the proposed model utilizes a deep learning technique named convolutional neural networks. the proposed work is applied using Max Pooling, a well-known deep learning process. Our model is trained and validated using the LFW dataset, which includes 13000 photos collected from Kaggle. the training accuracy of the model was 95.72% percent, and the validation accuracy was 96.27%. © 2021 IEEE.

关键词： Face recognition

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ICVARS 2021 - Proceedings of the 2021 5th international conference on Virtual and Augmented Reality Simulations

ICVARS 2021 - Proceedings of the 2021 5th International Conf...

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5th international conference on Virtual and Augmented Reality Simulations, ICVARS 2021

ISBN: (纸本)9781450389327

the proceedings contain 11 papers. the topics discussed include: towards learning geometric transformations through play: an AR-powered approach;image-based localization for augmented reality application: a review;learning internet of things by using augmented reality;augmented reality technology for people living with dementia and their care partners;a comparative study of smartphone, desktop, and CAVE systems for visualizing a math simulation;game architecture based obstacle avoidance control of electric surface vessel;using machine learning algorithms to predict diabetes mellitus based on PIMA Indians diabetes dataset;comparing the accuracy and precision of SteamVR tracking 2.0 and oculus quest 2 in a room scale setup;and knowledge network and visual analysis of knowledge graph research.

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ICAAI 2021 - 2021 5th international conference on Advances in Artificial Intelligence

ICAAI 2021 - 2021 5th International Conference on Advances i...

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5th international conference on Advances in Artificial Intelligence, ICAAI 2021

ISBN: (纸本)9781450390699

the proceedings contain 29 papers. the topics discussed include: a deep feedforward neural network and shallow architectures effectiveness comparison: flight delays classification perspective;dynamic guarantee network model and risk spill-over effect;prediction and sensitivity analysis of shear strength of reinforced concrete beams with deformed hook steel fiber using backpropagation neural network coupled with Garson’s algorithm;towards quantification of explainability algorithms;deep learning-based EEG detection of mental alertness states from drivers under ethical aspects;efficient fruit and vegetable classification and counting for retail applications using deep learning;stochastic neural variational learning of noisy-or bayesian networks for images;and an interactive system to improve cognitive abilities using electromyographic signals.

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A Review on Predicting Autism Spectrum Disorder(ASD) meltdown using Machine Learning algorithms 5

A Review on Predicting Autism Spectrum Disorder(ASD) meltdow...

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5th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2021

作者： Karim, Sara Akter, Nazina Patwary, Muhammed J. A. Islam, Md. Rashedul International Islamic University Dept. of CSE Chittagong Bangladesh

ISBN: (纸本)9781665495226

Autism Spectrum Disorder (ASD) is a well-known mental disorders that prevails in the ability of a person's social communication. the significance of early diagnosing drew the attention of researchers to use different machine learning-based procedures. Many analyses are done with the help of machine learning (ML) techniques to foresee meltdowns of autism together with Support Vector Machines, Random Forests, Naive Bayes, K-nearest Neighbors and many more. this paper gives a wide-spread review of papers applying machine learning in predicting ASD, along with algorithms for data analysis and classification. More than 80 research papers are considered, and the articles are assembled from the internet. Finally 48 research articles are coped up with the prerequisites in this study. the main goal of this review is to distinguish and mark out the machine learning trends in ASD literature and show the way to researchers curious in expanding the core of predicting ASD data and observe momentous research patterns in the field of ML. this paper will be a guideline to future researchers who are willing to work in the field of predicting ASD meltdown. © 2021 IEEE.

关键词： Support vector machines

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

ISBN: (纸本)9789811978418

the proceedings contain 23 papers. the special focus in this conference is on New Approaches for Multidimensional Signal Processing. the topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm Applied in Electrical Engineering Automation;Truss Structure Optimization Design Based on FE-PSO-SQP Algorithm;practice System of Ant Colony Optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain Technology;a Comprehensive Study of 5th Generation Scheduling algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big Data;video Tracing of Moving Objects by Fusing three-Term Decompositions;SAAS Application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with Internet of things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep Learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, Based on 3D FO-AHKLT;electromyography Signal Acquisition, Processing, Optimization and Its Applications;research on the Radar Signal Classification Method Based on the Deep Faith Network Model;measuring Machine Intelligence Using Black-Box-Based Universal Intelligence Metrics;COVID Detection Using ECG Image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

5th International Conference on Innovative Intelligent Indus...

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

ISBN: (纸本)9783031807596

the proceedings contain 61 papers. the special focus in this conference is on Innovative Intelligent Industrial Production and Logistics. the topics include: An Out-of-Sample Clustering Ensemble Method for Defect Detection and Classification in Metal Additive Manufacturing;Advanced Process Monitoring and OEE Metrics: Leveraging AASs for Efficiency;obsolescence Forecasting of Intel Processors: A Transformers Approach;machine Learning Tool for Yield Maximization in Cream Cheese Production;comparative Evaluation of Irregular Shape Strip-Packing algorithms;an Inventory Management Support Tool through Indirect Q-Value Estimation: A Combined Optimization and Forecasting Approach;multiscale Clustering to Improve Anomaly Detection in Nuclear Equipments;clustering Analysis for Forecasting Medicine Consumption;a Decision Support System of the Configuration of a Supermarket in a Components Company for the Automotive Industry;a Distributed Framework for Cooperative Scheduling of Production, Transportation, and Maintenance Using Multi-Agent Systems;the Role of Actor’s Creative Self-efficacy in AI Enabled Value Creation;building Realistic Environment from Computer Vision Approach Applied to Manufacturing Simulation in the Digital Twin Context;towards a Process-Based Industry 5.0 Maturity Model: A Feasibility Study in Supply Chain;a Multi-objective Genetic Algorithm Approach for Multi-component Products Recovery and Remanufacturing Planning;reinforcement Learning for Optimizing Routing in the Production Supply of Matrix Production Systems;an Innovative Fault Detection Robotic Tool for Overhead Cranes in Industries: Magnetic Wheel Modelling and Experimental Validation;current Trends and Future Challenges to Put Circular Manufacturing in Practice;enhancing Human – Robot Collaboration in the Industry 5.0 Framework with Physics-Informed Neural Networks: Application to Collision Detection;AI-Driven Smart Air Conditioning System for a Sustainable and Energy-Efficient Industrial Future.

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

5th International Conference on Innovative Intelligent Indus...

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

ISBN: (纸本)9783031807749

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Online forecast of ultra-short term wind power based on GWO-OL-OR-ELM

Online forecast of ultra-short term wind power based on GWO-...

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Computers and Artificial Intelligence Technologies (CAIT), international conference on

作者： Xiaojing Wen Yongsheng Wang Guangwen Liu Chunlei Liu Haotian Chen Jiaxin Liu College of Data Science and Application Inner Mongolia University of Technology Hohhot China College of International Information and Sofiware Dalian University of Technology Dalian China

ISBN: (数字)9798331530891

ISBN: (纸本)9798331530907

Continuously increasing installed capacity and large fluctuations in wind power output have a negative impact on the energy supply. However, the limitations of static models lead to their limited generalization ability and robustness in ultra-short term wind power prediction. In this paper, we propose an online prediction based on gray wolf optimization algorithm (GWO), online adaptive learning rate (OL), and online recurrent extreme learning machine (OR-ELM) for online prediction of ultra-short term wind power. First, a multi-layer self-coding online sequential extreme learning machine is used to build a static pre-training model. then, GWO and OL algorithms are introduced to balance the convergence speed and generalization ability of the model. Finally, the incremental updating of output weights is realized by establishing an online wind power prediction method based on GWO-OL-OR-ELM with incremental updating mechanism. the results show that the method performs better than the traditional method and effectively mitigates the effects of wind power output fluctuations.

关键词： Degradation Adaptation models Fluctuations Extreme learning machines Production Wind power generation Predictive models Prediction algorithms Data models Wind forecasting

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