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Feature selection and prediction of small-for-gestational-age infants

Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 1881-1895页

作者： Li, Jianqiang Liu, Lu Zhou, MengChu Yang, Ji-Jiang Chen, Shi Liu, HuiTing Wang, Qing Pan, Hui Sun, ZhiHua Tan, Feng School of Software Engineering Beijing University of Technology Beijing China Department of Electronical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States Tsinghua University Beijing China Department of Endocrinology Peking Union Medical College Hospital Beijing China Department of Internal Medicine Peking Union Medical College Hospital Beijing China Beijing Chaoyang District Maternal and Child Healthcare Hospital Beijing China Chinese Center For Disease Control And Prevention Beijing China

The small-for-gestational-age (SGA) condition often causes serious problems. Therefore, identifying the risk factors for SGA is important. Traditional statistical methods such as stepwise logistic regression (LR) have been widely utilized to discover possible risk factors. However, other feature selection methods from machine learning field have rarely been employed for the task. In this paper, a comparison of five feature selection methods from both fields for SGA risk factors analysis is conducted for the first time. To evaluate their performance, four classification algorithms are used to construct SGA prediction models. The evaluation criteria are precision and the area under the receiver operator characteristic curve. Stepwise LR achieves the best performance among the five feature selection methods, because it conducts both a univariate significance test and a model significance test, which make it more suitable for handling the complex relations among features. The top 20 features selected by each feature selection method and the 27 features selected by four or five of them could assist physicians to revise traditional SGA evaluation models. Ensemble method is also exploited to build effective SGA prediction models based on the feature subsets, which is indeed superior compared with the individual ones shown in the results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Feature Selection

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Intelligent Vehicle Auxiliary Handling System Based on the Internet of Things Technology

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Journal of Electronic Research and Application 2024年第3期8卷 198-206页

作者： Chenxiao Wu Jue Wang Han Sui Jingru Li Zihang Wang School of Civil Engineering and Transportation Northeast Forestry UniversityHarbin 150040China Aulin College Northeast Forestry UniversityHarbin 150040China College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040China

This paper proposes an intelligent vehicle auxiliary handling system based on Internet of Things(IoT)technology,featuring an innovative holding mechanism design that adjusts to the length and width of various *** system combines precise positioning using satellite tracking technology,intelligent recognition via OpenCV,and the interconnectivity of *** intelligent vehicle auxiliary handling system can independently identify vehicle positions and plan optimal handling paths,eliminating the traditional reliance on manual *** offers efficient and accurate handling,setting a new trend in the handling ***,the system integrates seamlessly with parking lot management systems,providing real-time updates on vehicle locations and *** allows managers to monitor the parking lot operations clearly and *** intelligent coordination greatly enhances overall work efficiency and streamlines parking ***,the innovative design of the intelligent vehicle auxiliary handling system represents a significant breakthrough in both function and performance,gaining user favor with its smooth *** ahead,continued technological advancements and the expansion of application fields will bring even more vitality and intelligence to societal development.

关键词： Vehicle handling Intelligent network OpenCV Image recognition

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Research on the Assessment of Dairy Cow Dry Matter Intake Using Itso-Optimizedstacking Ensemble Learning

SSRN

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SSRN 2025年

作者： Wang, Shuairan Long, Ting Wei, Xiaoli Guo, Qinzu Guo, Hongrui Shen, Weizheng Gu, Zhixin College of Computer and Control Engineering Northeast Forestry University Harbin150040 China College of Electric and Information Northeast Agricultural University Harbin150030 China

Dry matter intake (DMI) in dairy cows is a critical indicator of nutrient intake from feed, serving as the cornerstone of precision feeding practices, playing a critical role in improving production efficiency and enhancing the quality of dairy products. To address the high costs of traditional measurement methods and the structural complexity and large parameter counts of neural network models, this study proposes a Stacking ensemble learning model to assess DMI, with model parameters optimized using the Tuna Swarm Optimization (TSO) algorithm to enhance assessment accuracy, taking cow body weight, lying duration, lying times, rumination duration, foraging duration, walking steps, and the concentrate-to-roughage feed ratio as input variables. To further improve TSO's search efficiency and spatial exploration, this study introduces Sine-Logistic chaotic mapping, Levy flight, and Gaussian random walk strategy to optimize the TSO algorithm, developing the improved Tuna Swarm Optimization (ITSO). ITSO-optimized Stacking model achieved superior performance in DMI assessment, with an accuracy of 95.84%, significantly outperforming SVR, RF, DT, GBR, ETR, and AdaBoost models. This study provides a robust tool for precision feeding, contributing to optimizing cow feeding strategies, improving farm efficiency, and supporting sustainable dairy farming practices. © 2025, The Authors. All rights reserved.

关键词： Dairy products

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Multi Modal EEG-Based Classification of Alzheimer's Disease and Mild Cognitive Impairment Using Hilbert Huang Transform

Multi Modal EEG-Based Classification of Alzheimer's Disease ...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： G. Gopu S. Sathish T. S. Shanmathan G. Suthakar N. Sivakumaran M S Geetha Devasena Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

This study investigates whether high-resolution brain-functional connectivity, which is measured by using Electroencephalography (EEG) combined with Hilbert Huang Transform (HHT) can be useful in Alzheimer Disease Diagnosis. The major issue with Alzheimer's disease (AD) is that it leads to cognitive decline and affects millions of people worldwide. Early prediction of the disease is mandatory and crucial for timely intervention and effective care. EEG signals, a non-invasive and cost-effective method to assess brain activity, have shown potential in predicting this disease. In this study, we propose an approach that utilizes EEG data combined with Hilbert-Huang Transform (HHT) and machine learning algorithms for classification which is a novelty of the study that enables early prediction of Alzheimer's disease. Our methodology was tested on a dataset comprising EEG signals recorded using 19 electrodes from both healthy individuals and patients with Alzheimer's and the Mild Cognitive Impairment (MCI). The results were promising, demonstrating high accuracy, sensitivity, and specificity. Machine learning techniques can be employed for categorizing EEG signals, but it essential to validate their effectiveness using large and diverse datasets. This research highlights the potential of HHT and EEG data in the prediction of this disease. Principal Component Analysis (PCA) is used for dimensional reduction of the dataset. Notably EEG signals combined with Hilbert Huang Transform and Principal Component Analysis gives better accuracy of 98.6% for KNN. This research highlights the potential of EEG analysis combined with advanced techniques for accurate classification of dementia-related disorders.

关键词： Accuracy Machine learning algorithms Empirical mode decomposition Transforms Machine learning Sensitivity and specificity Electroencephalography

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Student modeling considering learning behavior history with deep factorization machines 27

Student modeling considering learning behavior history with ...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Kakizaki, Toma Oeda, Shinichi Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan

To use an intelligent tutoring system (ITS) in an educational setting effectively, it is necessary to understand the skill status of students and recommend appropriate questions. Existing studies focused on improving the performance of ITS using student modeling to estimate the skill status of students. Knowledge tracing (KT) is the mainstream student modeling method, and deep learning approaches such as deep knowledge tracing and self-attentive knowledge tracing have been studied extensively in recent years. These models take only the questions solved by the student and the correct or incorrect answers to those questions as input;they do not assume the use of other features. In this study, we perform student modeling using DeepFM and FiBiNET that combines factorization machines and deep learning. Our results indicate that these models are more accurate than KT because of their ability to cope with sparse data and consider pairwise feature interactions, and more suited for real-world applications than KT. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： DeepLearning Educational data mining Factorization machines Intelligent tutoring system Knowledge tracing Student modeling

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A student modeling method combining Deep Learning and forgetting models with Knowledge Tracing 27

A student modeling method combining Deep Learning and forget...

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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Oeda, Shinichi Hasegawa, Shunichi Department of Information and Computer Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan Advanced Course of Control and Information Engineering National Institute of Technology Kisarazu College 11-1 Kiyomidaihigashi 2-chome Chiba Kisarazu City Japan

Educational Data Mining (EDM) aims to enhance education by analyzing learners' skills and question difficulty levels using machine learning methods. Knowledge Tracing (KT), a subfield of EDM, utilizes Hidden Markov Models to estimate learners' abilities and predict their performance on unseen questions. While deep learning methods, such as bi-directional RNNs, have improved KT's accuracy, they may lack interpretability from an educational psychology perspective. Item Response Theory (IRT), widely used in educational statistics, offers greater explanatory potential. This study proposes a model that integrates the concept of forgetting into IRT for improved accuracy and explainability in Knowledge Tracing using bi-directional RNNs. The forgetting concept is based on Ebbinghaus' forgetting curve theory. Three experiments were conducted using synthetic data to compare a model from a previous study, a model based on the proposed method, and a model that combines the previous study's model with IRT but excludes the forgetting concept. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Deep Learning Educational Data Mining Intelligent Tutoring System Item Response Theory Knowledge Tracing Student Modeling

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Attention-Based Lightweight Network for PCB Defect Data Augmentation

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Journal of computers (Taiwan) 2024年第6期35卷 185-199页

作者： Zhang, Hong-Yan Wang, Xin Zhao, Jian-Wei College of Electrical and Control Engineering Shenyang Jianzhu University Shenyang110168 China Department of Computer Information Engineering Baoding Vocational and Technical College Baoding071052 China

Performance of deep learning-based PCB (Printed Circuit Board) surface defect detection networks is often limited by the depth of feature extraction networks and the quality of training data. While significantly increasing network parameters can only slightly enhance system performance, optimizing training data can improve network performance without adding computational overhead. Therefore, this paper proposes a data augmentation network to enhance detection accuracy. First, an autoencoder generator is designed to enhance the feature fitting capability of the latent space. Second, a generative adversarial structural loss function is introduced, and an adversarial training method with different learning rates for the generator and discriminator is employed. Finally, experimental results demonstrate that this method enhances the diversity of PCB defect data and effectively improves the detection network’s recognition accuracy. © 2024 Codon Publications. All rights reserved.

关键词： Adversarial machine learning

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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1176-1193页

作者： Xiaoling Wang Qi Kang MengChu Zhou Qi Deng Zheng Fan Haoyue Liu Department of Control Science and Engineering Tongji University Shanghai China College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

To realize Industry 5.0, manufacturers face various optimization problems that seldom appear in isolation. Evolutionary MultiTasking (EMT) is an effective method to solve multiple related problems by extracting and utilizing common knowledge. Knowledge transfer is the key to the effectiveness of EMT. Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance. There is plentiful knowledge in assistant tasks, and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks. EMT is thus confronted with a challenge to find appropriate knowledge. This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks. During the evolution process, better-performing candidates are supposed to have advantages in exploitation. Therefore, assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge. Specifically, the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population. Considering that target and assistant sub-populations have different characteristics, we use domain adaptation to reduce their distribution discrepancies. In this way, the trained classifier can classify assistant individuals more accurately, and truly useful knowledge can be selected for target tasks. The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems.

关键词： Learning systems Diversity reception Machine learning Learning (artificial intelligence) Multitasking Fifth Industrial Revolution Optimization Knowledge transfer Faces Convergence

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A novel high-through cell micronucleus detection algorithm based on improved YOLOv5 4

A novel high-through cell micronucleus detection algorithm b...

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4th International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Wei, Weiyi Li, Jingyu Wu, Xiaoqin Zhang, Hangjian College of Computer Science and Engineering Northwest Normal University Lanzhou China Radiology Department Gansu Provincial Center for Disease Control and Prevention Lanzhou China Business School Northwest Normal University Lanzhou China

ISBN: (纸本)9798350341690

In response to the challenges associated with cell micronucleus image recognition, namely small target sizes, high class similarity, and limited sample data, A novel cell micronucleus detection algorithm named YOLOv5-ECA is proposed based on YOLOv5. The proposed algorithm gets hyper-accurate micronucleus identification through a series of improvements. To begin with, the anchor frame size is recalibrated based on a meticulously constructed dataset. This process serves to expedite convergence speed while bolstering accuracy. Subsequently, in C3 module, a coordinate attention (CA) is introduced, which enlarging the receptive field and enabling the model to allocate greater focus to crucial elements within processed images. Ultimately, a superficial detection layer is incorporated, thereby enhancing the precision of cell micronucleus identification through multi-scale detection. Comparative analysis with the original YOLOv5 which demonstrates a 2% increase in average precision, accuracy, and a 1.3% enhancement in recall for the YOLOv5-ECA algorithm based on experimental results. © 2023 IEEE.

关键词： Cells

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Evaluating the Performance of Intelligent Energy Management Systems for Hybrid Electric Vehicles Based on Embedded Raspberry Pi Module 30th

Evaluating the Performance of Intelligent Energy Management ...

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Advances in Systems engineering - Proceedings of the 30th International Conference on Systems engineering, ICSEng 2023

作者： Gaber, Mohab Khamis, Ahmed Zydek, Dawid Egyptian Navy R&D Center Alexandria Egypt Guidance and Control Department Military Technical College Cairo Egypt Electrical and Computer Engineering Department University of Nevada Las Vegas United States

ISBN: (纸本)9783031405785

The global development of electrical power and generation systems has been accelerated by regulations to reduce greenhouse gas emissions and improve ecologically friendly energy sources. For the industry, designing and producing these criteria is extremely challenging. Electrification technologies are the best answer that is selected by the designers. They rely on electricity to run all systems;however, the electricity is generated by burning fossil fuels. The method adopted to address it is the diversification of fossil fuels with renewable energy sources. Technologies used to support this approach are utilizing intelligent energy management strategies. This paper proposes a hybrid power system based on the Adaptive Neuro-Fuzzy Interface System (ANFIS) technology and an intelligent Energy Management System (EMS). The performance of ANFIS is simulated by the SIMULINK/MATLAB system. The Raspberry PI embedded system is used as an intelligent energy management control system on a test bed laboratory bench in order to collect data in real-time. To verify the correctness of the EMS, the simulation results are compared with real-time data from the test bed with a Raspberry PI embedded device. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy management systems

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