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BDLR:lncRNA identification using ensemble learning

BIOCELL 2022年第4期46卷 951-960页

作者： LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO Jiangsu Key Lab of Big Data Security&Intelligent Processing School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210046China Smart Health Big Data Analysis and Location Services Engineering Laboratory of Jiangsu Province Nanjing210046China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325035China College of Computer Science and Technology Nanjing Forestry UniversityNanjing210037China

Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human *** are many limitations in identifying and annotating lncRNAs using traditional biological experimental *** the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning *** on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called *** identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random *** experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other ***,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning.

关键词： lncRNAs High-throughput sequencing Ensemble learning Bagging Decision Tree

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Enhancing Programming Course Quality through Human Posture Recognition and Screen Content Detection

Enhancing Programming Course Quality through Human Posture R...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaopan Chen Haiyang Bai Xiaoke Zhu Lingyuan Dong School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

The evaluation of programming course instruction is a key component of educational management. However, relying solely on supervising teachers for assessment poses challenges in comprehensive evaluation and providing feedback on students' classroom learning status. Additionally, a significant amount of monitoring video data from programming courses in Chinese universities remains underutilized. To address these challenges, this paper combines traditional educational management with artificial intelligence and proposes an intelligent algorithm for detecting students' learning status in programming courses. We first establish a dual YOLO network model to analyze students' postures and screen content in programming classroom monitoring videos, thereby identifying their behaviors. Subsequently, we develop a Programming Classroom Student Behavior Recognition System (PCSBS) that integrates data collection, student behavior detection, and result visualization functionalities. Experimental results demonstrate that the system can accurately provide feedback on students' programming course learning status, offering graded results that assist supervising teachers in their classroom instructional evaluation tasks to enhance educational management efficiency.

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Improving Classroom Quality by Analyzing Student Behaviors Through Computer Vision

Improving Classroom Quality by Analyzing Student Behaviors T...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaopan Chen Boyuan Li Xiaoke Zhu Tao Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

This paper aims to enhance the efficiency of teaching and learning by leveraging computer vision technology for automated analysis of student behavior in the classroom. We propose a feature-enhanced method for recognizing student behavior, which integrates an Upsampling module (UPS) and a convolutional block layer (CBL) module into the YOLOv5 model. This innovative approach significantly improves the detection and feature extraction capabilities for student targets, effectively addressing challenges associated with small-sized targets and lowresolution images. Through algorithm optimization, it achieves a balance in sample diversity, enhancing the model's adaptability and generalizability in complex classroom environments. Building upon these technological advancements, we have developed a Classroom Student Posture Perception System (CSP2S). This system integrates functions for data collection, student behavior detection, and result visualization. The CSP2S operates in realtime by capturing and analyzing students’ behavioral dynamics while providing teachers with immediate feedback. Experimental results demonstrate that this system substantially reduces teachers’ workload in student assessment, offering significant potential to improve teaching effectiveness. These robust technical advancements provide strong support for future applications in real educational environments.

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The Metro Operation Plan with Overtaking and Passenger Travel Options

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第6期52卷 1952-1963页

作者： Xie, Yi Liu, Yan Niu, Ru Dang, Lanxue Cao, Peng Yang, Shuanglei Zou, Yan Zheng, Jiping Qiao, Baojun Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China Zhongke Institute of Spatial Information Langfang065000 China CHN Energy Shendong Coal Group Co. Ltd Eerduosi017000 China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China Henan Key Laboratory of Big Data Analysis and Processing Kaifeng475004 China Co Ltd Zhengzhou450001 China Suzhou Rail Transit Construction Co. Ltd Suzhou215000 China Zhengzhou Transportation Development Investment Group Co. Ltd Zhengzhou450001 China

Metro operation planning stands a pivotal concern in urban rail transit operation, significantly influencing service quality and operational costs. The complexity of planning is further amplified in urban rail transit lines that bridge cities with their suburbs and cater to long-distance passengers. To optimize metro operation planning, this paper introduces a hybrid algorithm that integrates the improved simulated annealing algorithm with the MSA algorithm, and it constructs a comprehensive model that optimizes the combined operation of express and local trains, alongside full-length and short-turn routing. This model closely mirrors real-world scenarios by considering passengers’ optimal route choices and the overtaking of express trains. The algorithm’s primary objective is to minimize both passengers’ travel time and trains’ running time, thereby reducing travel costs for suburban passengers and operational costs for metro companies. The efficacy of this algorithm has been validated through testing on both simulation data and the actual data from Zhengzhou Metro Line 2 in China. © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Urban transportation

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A New Partial Task Offloading Method in a Cooperation Mode under Multi-Constraints for Multi-UE

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Computers, Materials & Continua 2023年第9期76卷 2879-2900页

作者： Shengyao Sun Ying Du Jiajun Chen Xuan Zhang Jiwei Zhang Yiyi Xu School of Information Science and Technology Zhengzhou Normal UniversityZhengzhou450044China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng475004China School of Geography and Tourism Zhengzhou Normal UniversityZhengzhou450044China Science and Engineering College South China University of TechnologyGuangzhou510641China Department of Electrical and Electronic Engineering Luohe Vocational Technology CollegeLuohe462002China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing100876China Cardiff School of Engineering Cardiff UniversityCardiffCF103XQ 15UK

In Multi-access Edge Computing(MEC),to deal with multiple user equipment(UE)’s task offloading problem of parallel relationships under the multi-constraints,this paper proposes a cooperation partial task offloading method(named CPMM),aiming to reduce UE’s energy and computation consumption,while meeting the task completion delay as much as *** first studies the task offloading of single-UE and then considers the task offloading ofmulti-UE based on single-UE task *** the critical path algorithmto divide the modules into key and non-key *** to some constraints of UE-self when offloading tasks,it gives priority to non-key modules for offloading and uses the evaluation decision method to select some appropriate key modules for *** on fully considering the competition between multiple UEs for communication resources and MEC service resources,CPMM uses the weighted queuing method to alleviate the competition for communication resources and uses the branch decision algorithm to determine the location of module offloading by BS according to the MEC servers’*** achieves its goal by selecting reasonable modules to offload and using the cooperation ofUE,MEC,andCloudCenter to determine the execution location of *** experiments demonstrate that CPMM obtains superior performances in task computation consumption reducing around 6%on average,task completion delay reducing around 5%on average,and better task execution success rate than other similar methods.

关键词： MEC partial task offloading parallel dependencies completion delay

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Online Q&A for Machine Learning Courses Based on Formula Recognition

Online Q&A for Machine Learning Courses Based on Formula Rec...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaoke Zhu Xiaopan Chen Minghao Li Tianxiang Zhang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Mathematical formulas are ubiquitous in courses such as Machine Learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. This paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection.

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Narrow-Band Interference Suppression Method for SAR Based on CEEMD

Narrow-Band Interference Suppression Method for SAR Based on...

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2021 CIE International Conference on Radar, Radar 2021

作者： Zhang, Hengrui Lv, Zongsen Min, Lin Li, Ning College of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Network Information Center Office Henan University Kaifeng China

ISBN: (纸本)9781665498142

Narrow-band interference (NBI) is a common form of interference in SAR systems, severely reducing the quality of SAR images. The NBI suppression method based on empirical mode decomposition (EMD) can adaptively split the raw signal into multiple independent intrinsic modal functions (IMFs), and reconstruct the useful signal after removing the IMFs of the NBI. However, the mode mixing problem it brings will cause part of the useful signal to be removed simultaneously when removing the NBI. A novel NBI suppression method based on complementary ensemble EMD (CEEMD) is proposed in this paper to solve this problem. First, the moment kurtosis coefficient is used to detect the presence of NBI in the raw echo, and CEEMD is used to decompose the NBI-contained raw echo into a series of IMFs. Then, the second-order difference of permutation entropy (PE) of all IMFs is calculated to obtain the global threshold for distinguishing IMFs with NBI and useful signal. Finally, the target IMF is used to reconstruct useful signals to remove the NBI. Experimental results based on real C-band spaceborne SAR data with simulated NBI verify the performance of the proposed method. © 2021 IEEE.

关键词： complementary ensemble empirical mode decomposition (CEEMD) empirical mode decomposition (EMD) Narrow-band interference (NBI) permutation entropy (PE) synthetic aperture radar (SAR)

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Enhancing Online Teaching Effectiveness Through Hand-Raising Detection

Enhancing Online Teaching Effectiveness Through Hand-Raising...

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Information Science and Education (ICISE-IE), International Conference on

作者： Xiaoke Zhu Zhiwei Dong Xiaopan Chen Yufeng Shi Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments.

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A Handover Scheme Based on Mobility Prediction for Autonomous Moving Platforms in 5G Networks

A Handover Scheme Based on Mobility Prediction for Autonomou...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Lei Zhang Xuefei Chen Yuxiang Ma School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

In recent years, Autonomous Moving Platforms (AMP) have gradually become an essential part of industry and life, including a variety of water, land and air mobile platforms. 5G networks provide AMP with low-latency and high-speed communication services which can promote the construction of smart cities. However, the mobility of smart-AMP affects the handover in 5G networks. With the intensive deployment of gNBs, unreasonable handover will increase the power con-sumption of AMP and reduce the quality of service (QoS) of AMP communications. Thus, we propose a 5G handover method based on AMP trajectory prediction in this paper. A CNN-STLSTM neural network which combines a convolutional neural network (CNN) and Spatioal-Timporallong short-term memory (ST- LSTM) is proposed to predict the trajectory of AMP. Then, the gNB is selected to handover through the AMP's trajectory points predicted by the prediction model. The evaluation results show that the proposed mechanism can effectively reduce the number of hand over and improve the efficiency and stability of AMP networks.

关键词： Wireless communication 5G mobile communication Smart cities Neural networks Quality of service Handover Predictive models

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A Systematic Review on Long-Tailed Learning

arXiv

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arXiv 2024年

作者： Zhang, Chongsheng Almpanidis, George Fan, Gaojuan Deng, Binquan Zhang, Yanbo Liu, Ji Kamel, Aouaidjia Soda, Paolo Gama, João Henan University Henan Key Lab of Big Data Analysis and Processing Kaifeng China Baidu Inc. Beijing China University Campus Bio-Medico di Roma Rome Italy The Laboratory of Artificial Intelligence and Decision Support Faculty of Economics University of Porto Porto Portugal

Long-tailed data is a special type of multi-class imbalanced data with a very large amount of minority/tail classes that have a very significant combined influence. Long-tailed learning aims to build high-performance models on datasets with long-tailed distributions, which can identify all the classes with high accuracy, in particular the minority/tail classes. It is a cutting-edge research direction that has attracted a remarkable amount of research effort in the past few years. In this paper, we present a comprehensive survey of latest advances in long-tailed visual learning. We first propose a new taxonomy for long-tailed learning, which consists of eight different dimensions, including data balancing, neural architecture, feature enrichment, logits adjustment, loss function, bells and whistles, network optimization, and post hoc processing techniques. Based on our proposed taxonomy, we present a systematic review of long-tailed learning methods, discussing their commonalities and alignable differences. We also analyze the differences between imbalance learning and long-tailed learning approaches. Finally, we discuss prospects and future directions in this field. © 2024, CC BY.

关键词： Contrastive Learning

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