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Student Behavior Modeling for an E-learning System Offering Personalized learning Experiences

Computer Systems Science & Engineering 2022年第3期40卷 1127-1144页

作者： K.Abhirami M.K.Kavitha Devi Department of Computer Science and Engineering Kings College of EngineeringPudukkottai613303India Department of Computer Science and Engineering Thiagarajar College of EngineeringMadurai625015India

With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and ***-based learning promotes active and independent *** scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning *** digital learning improves the quality of teaching and also promotes educational ***,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning *** quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional *** is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning *** is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest *** this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective *** to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning *** adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.

关键词： Learner behavior modeling e-learning intelligent learning system machine learning algorithm

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Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence

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NUTRITION IN CLINICAL PRACTICE 2024年第4期39卷 736-742页

作者： Atwal, Kiranjit Kings Coll London Dept Nutr Sci London England Univ Plymouth Sch Hlth Profess Plymouth England

The rapid surge in artificial intelligence (AI) has dominated technological innovation in today's society. As experts begin to understand the potential, a spectrum of opportunities could yield a remarkable revolution. The upsurge in healthcare could transform clinical interventions and outcomes, but it risks dehumanization and increased unethical practices. The field of clinical nutrition and dietetics is no exception. This article finds a multitude of developments underway, which include the use of AI for malnutrition screening;predicting clinical outcomes, such as disease onset, and clinical risks, such as drug interactions;aiding interventions, such as estimating nutrient intake;applying precision nutrition, such as measuring postprandial glycemic response;and supporting workflow through chatbots trained on natural language models. Although the opportunity and scalability of AI is incalculably attractive, especially in the face of poor healthcare resources, the threat cannot be ignored. The risk of malpractice and lack of accountability are some of the main concerns. As such, the healthcare professional's responsibility remains paramount. The data used to train AI models could be biased, which could risk the quality of care to vulnerable or minority patient groups. Standardized AI-development protocols, benchmarked to care recommendations, with rigorous large-scale validation are required to maximize application among different settings. AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics.

关键词： artificial intelligence clinical nutrition dietetics machine learning algorithm

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Prediction improvement of compressive strength and strain of directionally solidified TiAl alloy based on training data size adjustment

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2024年 30卷 5017-5027页

作者： Kwak, Seungmi Kim, Jaehwang Ding, Hongsheng Liang, He Chen, Ruirun Guo, Jingjie Fu, Hengzhi Harbin Inst Technol Sch Mat Sci & Engn Natl Key Lab Precis Hot Proc Met Harbin 150001 Peoples R China Korea Inst Ind Technol Carbon Mat Applicat R&D Grp Jeonju 54853 South Korea 92 Xidazhi St Harbin 150001 Heilongjiang Peoples R China 222 Palbok Ro Jeonju 54853 South Korea

Compressive strength and compressive strain, which are important mechanical properties of directionally solidified TiAl alloy, were predicted using machine learning algorithms, specifically Multiple Linear Regression (MLR), and Random Forest Regression (RFR). The input variables for the machine learning model were designated as the composition of the directionally solidified TiAl alloy, experimental parameters (pulling velocity, input power), and compression test parameters (strain rate, compression temperature). Compressive strength and compressive strain were designated as the output variables. Although the typical ratio of training and test data is 8:2, this study used different ratios of 9:1, 7:3, and 6:4 for machine learning, and excellent R2 values were obtained for all ratios. The feature importance, which can identify the factor that has the most influence on the output variables, was obtained through the RFR algorithm. According to the feature importance, temperature was found to have the greatest influence on compressive strength, while the Erbium (Er) element had the most significant influence on compressive strain. Through the results of feature importance, it was possible to quantitatively investigate the relationship between the Er element, a microalloying element that affects the microstructure of TiAl alloy, and the compressive properties. Furthermore, the study was conducted on which data ratio between training and test data is most suitable for predicting the compressive strength and strain of TiAl alloy.

关键词： Directionally solidified TiAl alloy Compressive strength Interlamellar space Prediction machine learning algorithm Training data size

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IDS-based machine learning Model Specific for Secure Data Transmission within the Frame of IoT Network Infrastructure 17

IDS-based Machine Learning Model Specific for Secure Data Tr...

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17th IEEE International Scientific Conference on Informatics, INFORMATICS 2024

作者： Gladic, Dejana Petrovacki, Jelena Sladojevic, Srdan Arsenovic, Marko Ristic, Sonja University of Novi Sad Faculty of Technical Sciences Novi Sad Serbia

ISBN: (纸本)9798350387674

Industry 4.0 represents a constant source of data originating directly from industrial plants. Data is transported via the communication network from the device that generates the data to the place of its permanent storage. In the process of transport, hacker attacks occur on the actors of communication - the sender, the recipient and the message itself. In the paper we propose a machine learning model aimed to analyze network traffic patterns and predict potential attacks, which is specialized for data generated within IoT devices network. For the development of the model, a publicly available dataset is used, which describes normal and problematic communication within the network of connected Internet of Things (IoT) devices. The developed and tested machine learning models are then compared and the algorithm with the best results is accepted as the final result. The comparative analysis of model performance is reflected in the comparison of the metrics of precision, accuracy, false positive rate, false negative rate, as well as the time required to execute different stages in the decision-making process. Summarizing all the results, the XGBoost model stood out as the model with the best performance. © 2024 IEEE.

关键词： classification Industry 4.0 Internet of Things Intrusion Detection System machine learning algorithm

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Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model

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JOURNAL OF ADVANCED RESEARCH 2025年 69卷 61-74页

作者： Yuan, Quan Yao, Lin-Fei Tang, Jia-Wei Ma, Zhang-Wen Mou, Jing-Yi Wen, Xin-Ru Usman, Muhammad Wu, Xiang Wang, Liang Southern Med Univ Guangdong Prov Peoples Hosp Guangdong Acad Med Sci Lab Med Guangzhou 510080 Guangdong Peoples R China Xuzhou Med Univ Sch Med Informat & Engn Xuzhou 210000 Jiangsu Peoples R China Xuzhou Med Univ Sch Pharm Dept Pharmaceut Anal Xuzhou 210000 Jiangsu Peoples R China Xuzhou Med Univ Sch Clin Med 1 Dept Clin Med Xuzhou 210000 Jiangsu Peoples R China

Introduction: Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contamination in water, implementing universal and rapid antibiotic residue detection technology is critical to maintaining antibiotic safety in aquatic environments. Surface-enhanced Raman spectroscopy (SERS) provides a powerful tool for identifying small molecular components with high sensitivity and selectivity. However, it remains a challenge to identify pure antibiotics from SERS spectra due to coexisting components in the mixture. Objectives: In this study, an intelligent analysis model for the SERS spectrum based on a deep learning algorithm was proposed for rapid identification of the antibiotic components in the mixture and quantitative determination of the ratios of these components. Methods: We established a water environment system containing three antibiotic residues of ciprofloxacin, doxycycline, and levofloxacin. To facilitate qualitative and quantitative analysis of the SERS spectra antibiotic mixture datasets, we developed a computational framework integrating a convolutional neural network (CNN) and a non-negative elastic network (NN-EN) method. Results: The experimental results demonstrate that the CNN model has a recognition accuracy of 98.68%, and the interpretation analysis of Shapley Additive exPlanations (SHAP) shows that our model can specifically focus on the characteristic peak distribution. In contrast, the NN-EN model can accurately quantify each component's ratio in the mixture. Conclusion: Integrating the SERS technique assisted by the CNN combined with the NN-EN model exhibits great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments. (c) 2024 The Authors. Published by Elsevier B.V. on behalf of Cairo University. This is an open access

关键词： Surface-enhanced Raman spectroscopy machine learning algorithm Convolutional neural network Aqueous solution Mixed antibiotics

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machine learning-Assisted Sensor Array Based on Poly(amidoamine) (PAMAM) Dendrimers for Diagnosing Alzheimer?sDisease

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ACS SENSORS 2022年第5期7卷 1315-1322页

作者： Xu, Lian Wang, Hao Xu, Yu Cui, Wenyu Ni, Weiwei Chen, Mingqi Huang, Hui Stewart, Callum Li, Linxian Li, Fei Han, Jinsong China Pharmaceut Univ State Key Lab Nat Med Coll Engn Nanjing 211109 Peoples R China China Pharmaceut Univ Natl R&D Ctr Chinese Herbal Med Proc Coll Engn Dept Food Qual & Safety Nanjing 211109 Peoples R China Karolinska Inst Ming Wai Lau Ctr Reparat Med S-17177 Stockholm Sweden

Alzheimer's disease (AD) is the most prevalentneurodegenerative disorder, and the early diagnosis of AD remainschallenging. Here we have developed afluorescent sensor arraycomposed of three modified polyamidoamine dendrimers. Proteins ofvarious properties were differentiated via this array with 100%accuracy, proving the rationality of the array's design. The mechanismof thefluorescence response was discussed. Furthermore, the robustthree-element array enables parallel detection of multiple A beta 40/A beta 42aggregates (0.5 mu M) in diverse interferents, serum media, andcerebrospinalfluid (CSF) with high accuracy, through machinelearning algorithms, demonstrating the tremendous potential of thesensor array in Alzheimer's disease diagnosis.

关键词： Alzheimer?s disease PAMAM dendrimers sensor array machine learning algorithm linear discriminant analysis amyloid-?protein

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Performance investigation of a hybrid PV/T collector with a novel trapezoidal fluid channel

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ENERGY 2024年 288卷

作者： Dong, Shiqian Long, He Guan, Jingxuan Jiang, Lina Zhuang, Chaoqun Gao, Yafeng Di, Yanqiang Chongqing Univ Joint Int Res Lab Green Bldg & Built Environm Minist Educ Chongqing 400044 Peoples R China China Acad Bldg Res Beijing 100013 Peoples R China Alan Turing Inst Data Centr Engn British Lib 96 Euston Rd London NW1 2DB England BYD AUTO CO LTD Auto Engineer Res Inst Xian 710000 Peoples R China

The photovoltaic-thermal collector (PV/T) is an innovative technology that combines PV cells and solar thermal collectors into a co-generation unit, enabling full-spectrum solar utilization. This research proposes and examines a hybrid PV/T system with novel trapezoidal fluid channels embedded in graphite, arranged in a parallel S-shaped structure under outdoor conditions of Beijing. Parametric discussions and daily performance analysis are conducted to assess its effectiveness and stability. A daily average of 12.7 degrees C reduction in cell temperature compared to PV modules results in a relative 5.20 % improvement in electrical output under the intensity of solar radiation is 597 W/m2. Similarly, the peak temperature of the water tank reaches 56.9 degrees C, and thermal efficiency is enhanced by 7.86 % over conventional sheet-tube PV/T collectors. For further optimization, a constrained adaptive particle swarm optimization algorithm (EPF-APSO) is constructed based on the experimental data, implementing a variable water flow rate (VWV) strategy. Compared to a constant water flow rate (CWV), the overall energy collected varies from 2.755 to 2.828 kWh per day, potentially reducing pump energy consumption by 17.93 %. The contribution of this paper provides references on structural and operational parameters optimization of PV/T collectors applying machine learning algorithms for better comprehensive efficiency.

关键词： PV/T collector Trapezoidal fluid channel Graphite machine learning algorithm APSO

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Explore the AI Anxiety Attitude of E-Commerce Employees

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2024年

作者： Chen, Shuai Zhao, Yang Wuhan Univ Wuhan Peoples R China

With the rapid development and widespread application of artificial intelligence (AI) technology in the e-commerce industry, understanding practitioners' attitudes and psychological reactions to AI has become critical. This study explores the impact of AI on the anxiety of e-commerce professionals, establishes a structural equation model by constructing an integrated Artificial Intelligence Anxiety (AIA) framework, and analyzes the path relationships between various anxiety factors. The results show that learning Anxiety and Job Replacement Anxiety are associated with Existential Risk Anxiety, AI Configuration Anxiety, and Sociotechnical Blindness Anxiety. The study further extends its analysis using a machine learning algorithm to assess the impact of each AIA variable and individual variables on job optimism. The results indicate that among the AIA variables, Job Replacement Anxiety has a significant negative impact on job optimism, while among the individual variables, gender has a significant negative impact. This study provides important insights into understanding the AI-related anxiety of e-commerce practitioners and offers implications for the management and development of AI in practice.

关键词： Artificial intelligence anxiety e-commerce machine learning algorithm

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Wear prediction model of hot rolling backup roll based on FEM plus ML algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第12期131卷 5923-5939页

作者： Lu, Jia Hao, Luhan Wang, Pengfei Huang, Huagui Li, Xu Hua, Changchun Su, Lihong Deng, Guanyu Yanshan Univ Natl Engn Res Ctr Equipment & Technol Cold Strip R Qinhuangdao 066004 Peoples R China Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Univ Wollongong Sch Mech Mat Mechatron & Biomed Engn Wollongong NSW 2522 Australia Univ Queensland Sch Mech & Min Engn St Lucia Qld 4072 Australia

The wear of backup rolls will have a great impact on the quality of the shape of hot rolled strip sheet. In order to overcome the limitations of the finite element method (FEM) in calculating backup roll wear in terms of efficiency and accuracy, this paper proposes a tandem FEM + ML hybrid model to optimise the predictive effect of the finite element method (FEM) on backup roll wear. Firstly, a backup roll wear model based on FEM is established. Secondly, in order to select the optimal machine learning (ML) algorithm as the finite element error compensation model, three types of finite element error compensation models were established based on the random forest (RF) algorithm, the radial basis function (RBF) neural network algorithm, and the particle swarm optimisation support vector machine (PSO-SVM) algorithm. Finally, the three types of finite element error compensation models were connected in series with the FEM model to compare the prediction performance of the three types of FEM + ML models on backup roll wear. The numerical experimental results show that the FEM + PSO-SVM model can better predict the wear of the backup roll, and the PSO-SVM algorithm is the most suitable for building the finite element error compensation model. It is proved that the FEM + ML model proposed in this paper can effectively improve the accuracy and computational efficiency of the FEM model for predicting backup roll wear without adding microelements. In addition, among the hot rolling parameters, the rolling force has the greatest influence on the backup roll wear, and excessive rolling force for a single pass should be avoided to slow down the backup roll wear.

关键词： Roller wear Finite element method machine learning algorithm Rolling steel

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Research on Fault Monitoring Method of Information System Based on machine learning 6

Research on Fault Monitoring Method of Information System Ba...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Guan, Jianchao Lu, Jun Wang, Wei Information and Telecommunication Branch State Grid Anhui Electric Power Co. Ltd. Hefei China

ISBN: (纸本)9798350305623

A monitoring system based on machine learning has been established to achieve real-time early warning and positioning. By using MSP430F149 single chip microcomputer and FPGA, we form a communication cable fault detection system with high speed and variable collection rate. The combination of DTMF FM single-chip microcomputer MT8888 and MSM7512 single-chip microcomputer realizes remote alarm and data transmission. The process of program management and serial communication in Windows system is analyzed. The real-time communication corresponding to the serial port is realized by using low-level software design methods such as process injection and API hooking based on the serial port. Based on the network operation mode under the SNMP mode, the polling and reporting information under the network operation mode is analyzed. An alarm probability model based on SNMP mode is proposed. The actual test proves that this method is practicable, and provides a practicable method for the analysis of communication protocol mechanism and communication fault analysis. © 2023 IEEE.

关键词： communication information failure fault monitoring machine learning algorithm network monitoring

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