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The Relationships between the Short Video Addiction,Self-Regulated Learning,and Learning Well-Being of Chinese Undergraduate Students
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作者 Jian-Hong Ye Yuting Cui +1 位作者 Li Wang Jhen-Ni Ye 《International Journal of Mental Health Promotion》 2024年第10期805-815,共11页
Background:With the global popularity of short videos,particularly among young people,short video addiction has become a worrying phenomenon that poses significant risks to individual health and adaptability.Self-regu... Background:With the global popularity of short videos,particularly among young people,short video addiction has become a worrying phenomenon that poses significant risks to individual health and adaptability.Self-regulated learning(SRL)strategies are key factors in predicting learning outcomes.This study,based on the SRL theory,uses short video addiction as the independent variable,SRL strategies as the mediating variable,and learning well-being as the outcome variable,aiming to reveal the relationships among short video addiction,self-regulated learning,and learning well-being among Chinese college students.Methods:Using a cross-sectional study design and applying the snowball sampling technique,an online survey was administered to Chinese undergraduate students.A total of 706 valid questionnaires were collected,with an effective response rate of 85.7%.The average age of the participants was 20.5 years.Results:The results of structural equation modeling indicate that 7 hypotheses were supported.Short video addiction was negatively correlated with self-regulated learning strategies(preparatory,performance,and appraisal strategy),while SRL strategies were positively correlated with learning well-being.Additionally,short video addiction had a mediating effect on learning well-being through the three types of SRL strategies.The three types of SRL strategies explained 39%of the variance in learning well-being.Conclusion:Previous research has typically focused on the impact of self-regulated learning strategies on media addiction or problematic media use.This study,based on the SRL model,highlights the negative issues caused by short video addiction and emphasizes the importance of cultivating self-regulation abilities and media literacy.Short video addiction stems from failures in trait self-regulation,which naturally impairs the ability to effectively engage in self-regulation during the learning process.This study confirms and underscores that the SRL model can serve as an effective theoretical framework for helping students prevent short video addiction,engage in high-quality learning,and consequently enhance their learning well-being. 展开更多
关键词 Appraisal strategy learning well-being performance strategy preparatory strategy self-regulated learning strategies short videos
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A Fusion Model for Personalized Adaptive Multi-Product Recommendation System Using Transfer Learning and Bi-GRU
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作者 Buchi Reddy Ramakantha Reddy Ramasamy Lokesh Kumar 《Computers, Materials & Continua》 SCIE EI 2024年第12期4081-4107,共27页
Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive... Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications. 展开更多
关键词 personalized recommendation systems transfer learning bidirectional gated recurrent units(Bi-GRU) performance metrics adaptive systems product reviews
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The Nature and Use of Technology-Based Self-Regulated Learning Strategies Among EFL Students
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作者 AN Zhujun 《Sino-US English Teaching》 2024年第11期506-514,共9页
This study explored the nature and use of technology-based self-regulated learning(SRL)strategies among the Chinese university students.A total of 20 undergraduate students in China's Mainland were invited to part... This study explored the nature and use of technology-based self-regulated learning(SRL)strategies among the Chinese university students.A total of 20 undergraduate students in China's Mainland were invited to participate in a focus group interview.The students reported using four types of technology-based SRL strategies including cognitive,meta-cognitive,social behavioral,and motivational regulation strategies.Among the strategies,technology-based vocabulary learning was reported to be a dominant strategy by the students.This study opens a new window to understanding how English as a foreign language(EFL)students utilize different strategies to learn English in technology-based learning context. 展开更多
关键词 self-regulated learning technology-based SRL strategies EFL students language learning
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Pedagogical Alignment of Large Language Models (LLM) for Personalized Learning: A Survey, Trends and Challenges
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作者 Mahefa Abel Razafinirina William Germain Dimbisoa Thomas Mahatody 《Journal of Intelligent Learning Systems and Applications》 2024年第4期448-480,共33页
This survey paper investigates how personalized learning offered by Large Language Models (LLMs) could transform educational experiences. We explore Knowledge Editing Techniques (KME), which guarantee that LLMs mainta... This survey paper investigates how personalized learning offered by Large Language Models (LLMs) could transform educational experiences. We explore Knowledge Editing Techniques (KME), which guarantee that LLMs maintain current knowledge and are essential for providing accurate and up-to-date information. The datasets analyzed in this article are intended to evaluate LLM performance on educational tasks, such as error correction and question answering. We acknowledge the limitations of LLMs while highlighting their fundamental educational capabilities in writing, math, programming, and reasoning. We also explore two promising system architectures: a Mixture-of-Experts (MoE) framework and a unified LLM approach, for LLM-based education. The MoE approach makes use of specialized LLMs under the direction of a central controller for various subjects. We also discuss the use of LLMs for individualized feedback and their possibility in content creation, including the creation of videos, quizzes, and plans. In our final section, we discuss the difficulties and potential solutions for incorporating LLMs into educational systems, highlighting the importance of factual accuracy, reducing bias, and fostering critical thinking abilities. The purpose of this survey is to show the promise of LLMs as well as the issues that still need to be resolved in order to facilitate their responsible and successful integration into the educational ecosystem. 展开更多
关键词 Chain of Thought Education IA LLM Machine learning NLP personalized learning Prompt Optimization Video Generation
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Revolutionizing diabetic retinopathy screening and management:The role of artificial intelligence and machine learning
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作者 Mona Mohamed Ibrahim Abdalla Jaiprakash Mohanraj 《World Journal of Clinical Cases》 SCIE 2025年第5期1-12,共12页
Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transforma... Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare. 展开更多
关键词 Diabetic retinopathy Artificial intelligence Machine learning SCREENING MANAGEMENT Predictive analytics personalized medicine
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The Effectiveness of Self-regulated Learning Strategies on Chinese College Students' English Learning
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作者 张晓雁 李安玲 《海外英语》 2011年第10X期127-128,共2页
The purpose of this paper is to argue the effectiveness of self-regulated learning in English education in Chinese college classroom instruction. A study is given to show whether the introduction of self-regulated lea... The purpose of this paper is to argue the effectiveness of self-regulated learning in English education in Chinese college classroom instruction. A study is given to show whether the introduction of self-regulated learning can help improve Chinese college students' English learning, and help them perform better in the National English test-CET-4 (College English Test Level-4,). 展开更多
关键词 self-regulated learning GOAL-SETTING self-instructional strategies motivation SELF-EFFICACY EXPERIENTIAL GROUP and control GROUP
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Exploring Frontier Technologies in Video-Based Person Re-Identification:A Survey on Deep Learning Approach
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作者 Jiahe Wang Xizhan Gao +1 位作者 Fa Zhu Xingchi Chen 《Computers, Materials & Continua》 SCIE EI 2024年第10期25-51,共27页
Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence uncertainties.Recent adva... Video-based person re-identification(Re-ID),a subset of retrieval tasks,faces challenges like uncoordinated sample capturing,viewpoint variations,occlusions,cluttered backgrounds,and sequence uncertainties.Recent advancements in deep learning have significantly improved video-based person Re-ID,laying a solid foundation for further progress in the field.In order to enrich researchers’insights into the latest research findings and prospective developments,we offer an extensive overview and meticulous analysis of contemporary video-based person ReID methodologies,with a specific emphasis on network architecture design and loss function design.Firstly,we introduce methods based on network architecture design and loss function design from multiple perspectives,and analyzes the advantages and disadvantages of these methods.Furthermore,we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance evaluation.Lastly,through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets,we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person Re-ID.This comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID,fostering continued progress and breakthroughs in this challenging yet promising research domain. 展开更多
关键词 Video-based person Re-ID deep learning survey of video Re-ID loss function
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Personality Trait Detection via Transfer Learning
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作者 Bashar Alshouha Jesus Serrano-Guerrero +2 位作者 Francisco Chiclana Francisco P.Romero Jose A.Olivas 《Computers, Materials & Continua》 SCIE EI 2024年第2期1933-1956,共24页
Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-... Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications. 展开更多
关键词 personality trait detection pre-trained language model big five model transfer learning
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Learning Activity Sequencing in Personalized Education System
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作者 ZHU Fan CAO Jiaheng 《Wuhan University Journal of Natural Sciences》 CAS 2008年第4期461-465,共5页
Personalized education provides an open learning environment which enriches the advanced technologies to establish a paradigm shift, active and dynamic teaching and learning patterns. E-learning has a various establis... Personalized education provides an open learning environment which enriches the advanced technologies to establish a paradigm shift, active and dynamic teaching and learning patterns. E-learning has a various established approaches to the creation and sequencing of content-based, single learner, and self-paced learning objects. However, there is little understanding of how to create sequences of learning activities which involve groups of learners interacting within a structured set of collaborative environments. In this paper, we present an approach for learning activity sequencing based on ontology and activity graph in personalized education system. Modeling and management of learning activity and learner are depicted, and an algorithm is proposed to realize learning activity sequencing and learner ontology dynamically updating. 展开更多
关键词 learning activity sequencing ONTOLOGY personalized education
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PERCEPOLIS: Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support
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作者 Ali R. Hurson Sahra Sedigh 《Intelligent Information Management》 2010年第10期586-596,共11页
This paper describes PERCEPOLIS, an educational platform that leverages technological advances, in particular in pervasive computing, to facilitate personalized learning in higher education, while supporting a network... This paper describes PERCEPOLIS, an educational platform that leverages technological advances, in particular in pervasive computing, to facilitate personalized learning in higher education, while supporting a networked curricular model. Fundamental to PERCEPOLIS is the modular approach to course development. Blended instruction, where students are responsible for perusing certain learning objects outside of class, used in conjunction with the cyberinfrastructure will allow the focus of face-to-face meetings to shift from lecture to active learning, interactive problem-solving, and reflective instructional tasks. The novelty of PERCEPOLIS lies in its ability to leverage pervasive and ubiquitous computing and communication through the use of intelligent software agents that use a student’s academic profile and interests, as well as supplemental information such as his or her learning style, to customize course content. Assessments that gauge the student’s mastery of concepts are used to allow self-paced progression through the course. Furthermore, the cyberinfrastructure facilitates the collection of data on student performance and learning at a resolution that far exceeds what is currently available. We believe that such an infrastructure will accelerate the acquisition of knowledge and skills critical to professional engineering practice, while facilitating the study of how this acquisition comes about, yielding insights that may lead to significant changes in pedagogy. 展开更多
关键词 learning Technology personalized learning CYBERINFRASTRUCTURE PERVASIVE Computing.
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Personalized Learning Path Recommendations for Software Testing Courses Based on Knowledge Graphs
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作者 Wei Zheng Ruonan Gu +2 位作者 Xiaoxue Wu Lipeng Gao Han Li 《计算机教育》 2023年第12期63-70,共8页
Software testing courses are characterized by strong practicality,comprehensiveness,and diversity.Due to the differences among students and the needs to design personalized solutions for their specific requirements,th... Software testing courses are characterized by strong practicality,comprehensiveness,and diversity.Due to the differences among students and the needs to design personalized solutions for their specific requirements,the design of the existing software testing courses fails to meet the demands for personalized learning.Knowledge graphs,with their rich semantics and good visualization effects,have a wide range of applications in the field of education.In response to the current problem of software testing courses which fails to meet the needs for personalized learning,this paper offers a learning path recommendation based on knowledge graphs to provide personalized learning paths for students. 展开更多
关键词 Knowledge graphs Software testing learning path personalized education
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The Process of Personalized Learning Based on Flipped Classroom
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作者 ZHANG Fan FENG Shu-xiong 《Sino-US English Teaching》 2017年第4期233-238,共6页
The new teaching mode of flipped classroom plays an important role in college English teaching reform in China. Personalized learning can be realized by flipped classroom. Firstly, selection and production of the teac... The new teaching mode of flipped classroom plays an important role in college English teaching reform in China. Personalized learning can be realized by flipped classroom. Firstly, selection and production of the teaching content before class is very important. Secondly, the organization of teaching activities in class should be well prepared. At last, the realization of combining personalized evaluation and integrity evaluation system is a vital issue for teachers to consider. 展开更多
关键词 personalized learning flipped classroom micro-video evaluation system
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The Model of Speaking in Teaching Indonesian to Foreign Speakers Based on Self-Regulated Learning and Anxiety Reduction Approaches
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作者 Endry Boeriswati 《Sino-US English Teaching》 2012年第5期1154-1163,共10页
Model for spoken is expected to overcome difficulties in teaching and learning Indonesian language for foreign speakers. Language anxiety is the anxiety that arises when a person learns foreign language. Foreign Langu... Model for spoken is expected to overcome difficulties in teaching and learning Indonesian language for foreign speakers. Language anxiety is the anxiety that arises when a person learns foreign language. Foreign Language Anxiety (anxiety to learn a foreign language) is of concern or negative emotional reactions that arise when studying or using foreign language. Self-regulated learning is an active and constructive process undertaken by learners in setting goals for their learning and trying to monitor, regulate, and control of cognition, motivation, and behavior, then everything is directed and driven by purpose and adapted to the context and environment. The research method used is an R and D (research and development) method with a sample of foreign speakers of Chinese. Variables that receive interference are the ability to speak in Indonesian, while the variables used to interfere with the self-regulated learning and language anxiety as a variable controller. Intrapersonal factors become barriers that cause stuttering speech limited due to the mastering subject content. On the basis of that, this speaking model applies the principle of self-regulated learning in the learning process, using a communicative and contextual approach. This model intended for foreign speakers who learn Indonesian language outside of Indonesia, so to bring the atmosphere mandated in sociolinguistic built through media and relevant teaching methods. 展开更多
关键词 Indonesian for Foreign Foreign Language Anxiety self-regulated learning
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Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach
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作者 KEGANG JIA YAWEI WANG +1 位作者 QI CAO YOUYU WANG 《Oncology Research》 SCIE 2024年第2期409-419,共11页
Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse... Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs. 展开更多
关键词 Lung adenocarcinoma Drug resistance Machine learning Molecular features personalized treatment
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Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT
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作者 Renwan Bi Mingfeng Zhao +2 位作者 Zuobin Ying Youliang Tian Jinbo Xiong 《Digital Communications and Networks》 SCIE CSCD 2024年第2期380-388,共9页
With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders... With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm. 展开更多
关键词 Mobile edge crowdsensing Dynamic privacy measurement personalized privacy threshold Privacy protection Reinforcement learning
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From prediction to prevention:Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring
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作者 Mariana Michelle Ramírez-Mejía Nahum Méndez-Sánchez 《World Journal of Gastroenterology》 SCIE CAS 2024年第7期631-635,共5页
In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular ca... In this editorial,we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma.Hepatocellular carcinoma(HCC),which is characterized by high incidence and mortality rates,remains a major global health challenge primarily due to the critical issue of postoperative recurrence.Early recurrence,defined as recurrence that occurs within 2 years posttreatment,is linked to the hidden spread of the primary tumor and significantly impacts patient survival.Traditional predictive factors,including both patient-and treatment-related factors,have limited predictive ability with respect to HCC recurrence.The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research.The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence.Challenges persist,including sample size constraints,issues with handling data,and the need for further validation and interpretability.This study emphasizes the need for collaborative efforts,multicenter studies and comparative analyses to validate and refine the model.Overcoming these challenges and exploring innovative approaches,such as multi-omics integration,will enhance personalized oncology care.This study marks a significant stride toward precise,efficient,and personalized oncology practices,thus offering hope for improved patient outcomes in the field of HCC treatment. 展开更多
关键词 Hepatocellular carcinoma Early recurrence Machine learning XGBoost model Predictive precision medicine Clinical utility personalized interventions
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Enhancing personalized exercise recommendation with student and exercise portraits
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作者 Wei-Wei Gao Hui-Fang Ma +2 位作者 Yan Zhao Jing Wang Quan-Hong Tian 《Journal of Electronic Science and Technology》 EI CAS CSCD 2024年第2期91-109,共19页
The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions gen... The exercise recommendation system is emerging as a promising application in online learning scenarios,providing personalized recommendations to assist students with explicit learning directions.Existing solutions generally follow a collaborative filtering paradigm,while the implicit connections between students(exercises)have been largely ignored.In this study,we aim to propose an exercise recommendation paradigm that can reveal the latent connections between student-student(exercise-exercise).Specifically,a new framework was proposed,namely personalized exercise recommendation with student and exercise portraits(PERP).It consists of three sequential and interdependent modules:Collaborative student exercise graph(CSEG)construction,joint random walk,and recommendation list optimization.Technically,CSEG is created as a unified heterogeneous graph with students’response behaviors and student(exercise)relationships.Then,a joint random walk to take full advantage of the spectral properties of nearly uncoupled Markov chains is performed on CSEG,which allows for full exploration of both similar exercises that students have finished and connections between students(exercises)with similar portraits.Finally,we propose to optimize the recommendation list to obtain different exercise suggestions.After analyses of two public datasets,the results demonstrated that PERP can satisfy novelty,accuracy,and diversity. 展开更多
关键词 Educational data mining Exercise recommend Joint random walk Nearly uncoupled Markov chains Optimization personalized learning
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Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction 被引量:1
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作者 Ali Safavi Mehrdad H.Zadeh 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2017年第4期704-713,共10页
This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior ... This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces. 展开更多
关键词 Haptic guidance learning from demonstration(LfD) personalized physical human-robot interaction(p2HRI) user performance
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An Analysis and Prediction of Health Insurance Costs Using Machine Learning-Based Regressor Techniques
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作者 Gagan Kumar Patra Chandrababu Kuraku +3 位作者 Siddharth Konkimalla Venkata Nagesh Boddapati Manikanth Sarisa Mohit Surender Reddy 《Journal of Data Analysis and Information Processing》 2024年第4期581-596,共16页
One of the most significant annual expenses that a person has is their health insurance coverage. Health insurance accounts for one-third of GDP, and everyone needs medical treatment to varying degrees. Changes in med... One of the most significant annual expenses that a person has is their health insurance coverage. Health insurance accounts for one-third of GDP, and everyone needs medical treatment to varying degrees. Changes in medicine, pharmaceutical trends, and political factors are only a few of the many factors that cause annual fluctuations in healthcare costs. This paper describes how a system may analyse a person’s medical history to display their insurance plans and make predictions about their health insurance premiums. The performance of four ML models—XGBoost, Lasso, KNN, and Ridge—is evaluated using R2-score and RMSE. The analysis of medical health insurance cost prediction using Lasso regression, Ridge regression, and K-Nearest Neighbours (KNN), and XGBoost (XGB) highlights notable differences in performance. KNN has the lowest R2-score of 55.21 and an RMSE of 4431.1, indicating limited predictive ability. Ridge Regression improves on this by an R2-score of 78.38 but has a higher RMSE of 4652.06. Lasso Regression slightly edges out Ridge with an R2-score of 79.78, yet it suffers from an advanced RMSE of 5671.6. In contrast, XGBoost excels with the highest R2-score of 86.81 and the lowermost RMSE of 4450.4, demonstrating superior predictive accuracy and making it the most effective model for this task. The best method for accurately predicting health insurance premiums was XGBoost Regression. The findings beneficial for policymakers, insurers, and healthcare providers as they can use this information to allocate resources more efficiently and enhance cost-effectiveness in the healthcare industry. 展开更多
关键词 Medical Cost Health Insurance Cost Prediction Medical Cost personal Datasets Machine learning
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Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment 被引量:1
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作者 Baixuan Tang Xiaolong Xu +1 位作者 Fei Dai Song Wang 《Intelligent Automation & Soft Computing》 2023年第10期35-55,共21页
Person re-identification(ReID)aims to recognize the same person in multiple images from different camera views.Training person ReID models are time-consuming and resource-intensive;thus,cloud computing is an appropria... Person re-identification(ReID)aims to recognize the same person in multiple images from different camera views.Training person ReID models are time-consuming and resource-intensive;thus,cloud computing is an appropriate model training solution.However,the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments,leading to significant communication overheads.This paper proposes a federated person ReID method with model-contrastive learning(MOON)in an edge-cloud environment,named FRM.Specifically,based on federated partial averaging,MOON warmup is added to correct the local training of individual edge servers and improve the model’s effectiveness by calculating and back-propagating a model-contrastive loss,which represents the similarity between local and global models.In addition,we propose a lightweight person ReID network,named multi-branch combined depth space network(MB-CDNet),to reduce the computing resource usage of the edge device when training and testing the person ReID model.MB-CDNet is a multi-branch version of combined depth space network(CDNet).We add a part branch and a global branch on the basis of CDNet and introduce an attention pyramid to improve the performance of the model.The experimental results on open-access person ReID datasets demonstrate that FRM achieves better performance than existing baseline. 展开更多
关键词 person re-identification federated learning contrastive learning
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