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Career-Based Explainable Course Recommendation 18th

Career-Based Explainable Course Recommendation

18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Striebel, Jacob Myers, Rebecca Liu, Xiaozhong Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States

ISBN: (纸本)9783031280313

The type and quality of education that a student receives can have a profound impact on their career. In contrast to education that is not intentionally organized to help students achieve specific career objectives, career-based education seeks to provide students with the skills that are required to achieve specific career goals. In this work, we propose a course recommendation framework that is designed to recommend courses based directly on their ability to teach skills relevant to a user-specified career goal. Within our framework, course recommendations are generated in a transparent manner, using skills to bridge between jobs and courses in a knowledge-based inference procedure. Due to the procedure’s transparency, our system is able to provide faithful template-based explanations detailing why each recommended course was chosen for recommendation. Our framework contrasts with other course recommendation systems in the literature that lack the ability to explain their choices and therefore may lack trustworthiness from the perspective of a user. The proposed framework has several applications, including assisting students with course planning, as well as aiding with curriculum evaluation and development by providing insight into the usefulness of specific courses to specific careers. We conduct a preliminary evaluation of our system, and its performance is competitive against two baselines. We provide all the resources needed to reproduce our results. (http://***/striebel/cbecr ) © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Developing Engagement in the Learning Management System Supported by Learning Analytics

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Computer Systems Science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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Bayesian estimation-based sentiment word embedding model for sentiment analysis

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 144-155页

作者： Jingyao Tang Yun Xue Ziwen Wang Shaoyang Hu Tao Gong Yinong Chen Haoliang Zhao Luwei Xiao Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Physics and Telecommunication EngineeringSouth China Normal UniversityGuangzhouChina College of Mathematics and Informatics&College of Software Engineering South China Agricultural UniversityGuangzhouChina School of Foreign Languages Zhejiang University of Finance&EconomicsHangzhouZhejiangChina Educational Testing Service PrincetonNew JerseyUSA School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Sentiment word embedding has been extensively studied and used in sentiment analysis ***,most existing models have failed to differentiate high-frequency and low-frequency ***,the sentiment information of low-frequency words is insufficiently captured,thus resulting in inaccurate sentiment word embedding and degradation of overall performance of sentiment analysis.A Bayesian estimation-based sentiment word embedding(BESWE)model,which aims to precisely extract the sentiment information of low-frequency words,has been *** the model,a Bayesian estimator is constructed based on the co-occurrence probabilities and sentiment proba-bilities of words,and a novel loss function is defined for sentiment word embedding *** experimental results based on the sentiment lexicons and Movie Review dataset show that BESWE outperforms many state-of-the-art methods,for example,C&W,CBOW,GloVe,SE-HyRank and DLJT1,in sentiment analysis tasks,which demonstrate that Bayesian estimation can effectively capture the sentiment information of low-frequency words and integrate the sentiment information into the word embedding through the loss *** addition,replacing the embedding of low-frequency words in the state-of-the-art methods with BESWE can significantly improve the performance of those methods in sentiment analysis tasks.

关键词： function embedding estimation

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An Explainable Machine Learning-Based Phishing Website Detection using Gradient Boosting

An Explainable Machine Learning-Based Phishing Website Detec...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Prayogo, Rizal Dwi Alfisyahrin, Alvin Rizqi Gambetta, Windy Karimah, Siti Amatullah Nambo, Hidetaka Kanazawa University Division of Electrical Engineering and Computer Science Graduate School of Natural Science and Technology Kanazawa Japan Telkom University School of Computing Bandung Indonesia Kanazawa University Faculty of Transdisciplinary Sciences Institute of Philosophy in Interdisciplinary Sciences Kanazawa Japan Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350376210

As the need for achieving optimal performance in prediction models, several recent and complex models have been developed. However, many of these models operate as black boxes, providing little insight into their predictive results. In recent years, three gradient-boosting methods based on Decision Trees have been recommended, i.e., XGBoost, CatBoost, and LightGBM. These methods have proven to deliver competitive performance with fast training times. Nevertheless, in critical domains like security, there is a growing need for increased transparency among stakeholders. One pressing concern in the security field is the proliferation of phishing websites. To address this issue, we propose an explainable machine learning-based approach using gradient-boosting methods with hyperparameter optimization on three phishing website datasets. Our best methods surpass the state-of-the-art phishing website detection methods, achieving accuracy rates of 97.45%, 99.16%, and 97.85% for the UCI (2015), Mendeley (2018), and Mendeley (2020) datasets, respectively. Subsequently, we implement posthoc explainability using SHAP and LIME for the selected dataset. The experimental results indicate that three features, i.e., length_url, directory_length, and time_domain_activation, are consistently identified as the most influential features in the dataset. Moreover, our proposed approach demonstrates promising results for detecting phishing websites with both high accuracy and explainability. © 2024 IEEE.

关键词： Phishing

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Aspect-Based Subjectivity Analysis Using a BERT-based Approach

Aspect-Based Subjectivity Analysis Using a BERT-based Approa...

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2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2024

作者： Ng, Hu Chong, Wing Kin Chia, Yu Zhang Vun Yap, Timothy Tzen Goh, Vik Tor Wong, Lai Kuan Teck Tan, Ian Kim Cher, Dong Theng Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia School of Mathematical & Computer Sciences Heriot-Watt Univeristy Putrajaya Malaysia SIRIM Berhad Shah Alam Malaysia

ISBN: (纸本)9798350353464

Aspect-based subjectivity analysis stands as an important task in natural language processing, seeking to identify the subjectivity of various aspects or features within a text. A new method for aspect-based subjectivity analysis using BERT is introduced in this paper. BERT has demonstrated impressive performance across various NLP tasks, and its capabilities are utilized to accurately ascertain the subjectivity of specific aspects within a given text. The approach involves fine-tuning BERT on a sizable dataset annotated with aspect-level subjectivity labels, enabling the model to grasp the subtleties of aspect-based subjectivity analysis. Extensive experiments on benchmark datasets are conducted to showcase the effectiveness of this approach and compare it with existing methods. The results reveal that this proposed approach surpasses state-of-the-art techniques in aspect-based subjectivity analysis, underscoring the potential of leveraging BERT for such purposes. ©2024 IEEE.

关键词： Aspect-based NLP Subjectivity Analysis

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Recent Advances in Fiber-Optic Sensors for the Detection of Inorganic Acidic Gases

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Photonic Sensors 2024年第4期14卷 74-100页

作者： Lingnan KONG Yuanke GONG Xiaoyu ZHONG Yang LIU Bo WAN Quanhua XIE Yuanyuan HE Xiaoling PENG Haixing CHANG Dengjie ZHONG Huiling LIU Nianbing ZHONG Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection Chongqing Key Laboratory of Modern Photoelectric Detection Technology and InstrumentsChongqing Engineering Research Center of Intelligent Optical Fiber Sensing TechnologyChongqing University of TechnologyChongqing 400054China Meishan San Su Shrine Museum Meishan 620010China School of Chemistry and Chemical Engineering Chongqing University of TechnologyChongqing 400054China College of Computing and Informatics Sungkyunkwan UniversitySeoul 03060South Korea

An acidic gas is an important basic chemical raw material used for synthesizing fertilizers,insecticides,explosives,dyes,and ***,inorganic acidic gases that leak into the air have harmful effects on the human health,infrastructure,and cultural ***,the demand for inorganic acidic gas sensors for air quality monitoring and management has continuously increased,enabling the development of various sensing *** them,fiber-optic sensors are promising for acidic gas detection because of their excellent in-situ measurement,resistance to corrosion,anti-electromagnetic interference,long service life,and smart *** particular,fiber-optic sensors have proven to be very useful for the in-situ detection and distributed monitoring of multiple gas ***,the sensitivity,selectivity,repeatability,and limits of detection of these sensors can be improved to achieve acceptable performance levels for practical *** this review,we introduce fiber-optic sensors based on structured optical fibers and fiber gratings for detecting H_(2)S,SO_(2),NO_(2),CO_(2),and N_(2)*** structures of the sensing regions,gas-sensitive materials,and measurement principles of these sensors are *** sensitivity,selectivity,limit of detection,and response time of the sensors are ***,the future of fiber-optic sensors for the detection of inorganic acidic gases is discussed.

关键词： Fiber-optic sensors structured optical fibers fiber gratings inorganic acidic gases measurement principles sensing performance

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Sliding Mode Control Strategy for State-of-Charge (SoC) Balancing in Lithium-ion Battery Packs 13

Sliding Mode Control Strategy for State-of-Charge (SoC) Bala...

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13th International Conference on Control, Automation and Information Sciences, ICCAIS 2024

作者： Feng, Yong Han, Fengling Yang, Rebecca Wang, Yanmin Bai, Fan Truong, Nguyen-Vu School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia School of Science Rmit University Melbourne Australia School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Institute of Applied Mechanics and Informatics Vietnam Academy of Science and Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331542047

This paper proposes a sliding mode control strategy for balancing the State of Charge (SoC) in lithium-ion battery packs. By thoroughly analyzing the battery balancing circuit topology, a bidirectional Cuk circuit balancing topology for a series-connected battery pack is established, and its mathematical model is developed. Utilizing sliding mode control technology, a balancing strategy is designed to equalize the SoC of all cells in a battery pack composed of multiple series-connected battery cells. The effectiveness of the proposed method is verified through experiments, demonstrating that the developed battery balancing system can successfully balance the SoC of each cell in the battery pack. © 2024 IEEE.

关键词： State of charge

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A Survey on Product Promotion via E-commerce Platforms - Case Study in Malaysia

A Survey on Product Promotion via E-commerce Platforms - Cas...

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2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021

作者： Alomari, Mohammad Ahmed Ahmed Al-Qasem Al-Hadi, Ismail Yusoff, M. Hafiz Sulaiman, Ibrahim Mohammed Universiti Tenaga Nasional Institute of Informatics and Computing in Energy Malaysia Faculty of Ocean Engineering Technology Informatics Malaysia School of Informatics and Computing Malaysia Faculty of Informatics and Computing Malaysia

ISBN: (纸本)9781665403436

A survey by Malaysian Communication and Multimedia Commission (MCMC) has shown that Internet users in Malaysia has increased up to 88.7% in year 2020 compared to 76.9% in year 2016 which is quite high increase in percentage. The pervasive use of smartphones and computers nowadays as well as the availability of high-speed Internet access has brought a new way of promoting products. E-commerce is a buy and sell system that can be accessed globally around the world. This system can provide efficient strategies for promoting products and services. It is widely agreed that even small enhancements in promotion techniques can increase the profitability of any e-commerce system. In this paper, a research has been conducted to study methods to enhance product promotion among Malaysians. The study investigates through a survey the factors affecting users' WTP (willing-to-pay) during performing e-commerce transaction as well as factors what attract/repel them more. To achieve the study aim, a group of 385 respondents throughout Malaysia have been involved in this research through questionnaire study. Detailed analysis has been introduced to clarify the results. The study has identified the top e-commerce platforms that are used by Malaysians to execute product promotion. The results show that promotion through e-commerce platforms could increase profitability and help businesses to expand rapidly. This is due to the fast market penetration of online promoting compared to conventional one. © 2021 IEEE.

关键词： Surveys

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes 1

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Yadav, Abhivyakti Divya, S. Oviya, I.R. Raja, Kalpana Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350342796

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization. © 2023 IEEE.

关键词： DNA

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