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A personalized test paper generation model for English examination based on knowledge tracking 5

A personalized test paper generation model for English exami...

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5th international conference on Artificial Intelligence and computer Science, AICS 2023

作者： Zhou, Ya Shao, Xiuli Huang, Guimin Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

In recent years, personalized paper grouping is a hot research topic in the field of intelligence education. In this paper, we propose an optimized deep knowledge tracking model Mul-MAKT combined with a genetic algorithm for personalized paper grouping with English practice as the target. Firstly, we use students’ records (exercise labels, response results, learning behaviors, exercise attributes) as input to the knowledge tracking model to predict students’ performance at the next moment and also to obtain students’ mastery level of knowledge points. Based on the students’ mastery levels, we adjust the students’ ability in each type of English exercise and calculate the students’ weaknesses in each type of question, so as to extract the questions from the test bank with the difficulty matching the students’ ability and containing the students’ weaknesses. © 2023 SPIE.

关键词： Students

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Optimizing Network Management and Virtualization Using Machine Learning Approach: Network Slice Prediction

Optimizing Network Management and Virtualization Using Machi...

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2024 international conference on Science, Engineering and Business for Driving Sustainable development Goals, SEB4SDG 2024

作者： Akinyemi, Lateef Adesola Oshinuga, Olamide Peter Oladejo, Sunday Oladayo Ekwe, Stephen Obono Sumbwanyanbe, Mbuyu Mnkandla, Ernest Shoewu, Oluwagbemiga Omotayo University of South Africa Departments of Computer Science and Elect. Engineering CSET Johannesburg South Africa Lagos State University Department of Electronic and Computer Engineering Lagos Nigeria Stellenbosch University School for Data Science and Computational Thinking Stellenbosch South Africa Cape Peninsula University of Technology Department of Elect. Electronic and Computer Engineering Cape Town South Africa University of South Africa Department of Electrical Engineering CSET Johannesburg South Africa University of South Africa Department for Computer Science CSET Johannesburg South Africa

ISBN: (纸本)9798350358155

This study demonstrates the utilization of Machine Learning (ML) for network slice prediction, enabling the optimization of resources for diverse network slices. Traditional methods for network slice prediction often lack efficiency and result in inaccuracies. By leveraging ML algorithms such as Naive Bayes and Random Forest, an intelligent framework that automates network slice prediction is developed. This framework enhances network virtualization and management, facilitating resource allocation. The ML algorithms take real-time network conditions and usages, such as packet delay and smart city, as input and output for selecting the most suitable network slice. Data analysis is further conducted to reveal the connections between the input parameters and how these parameters influence the selection of the accurate network slice. Network slicing plays a crucial role as it enables the customization of services and facilitates efficient scaling to meet the specific needs of different applications and industries. The accuracy scores of the employed ML algorithms were generally perfect, except for the KNN and SVM classifiers, which achieved an accuracy of 94.30% and 92.16%, respectively, for the prediction of network slices based on incoming network connections and usages. © 2024 IEEE.

关键词： Data Analysis Machine Learning Network Management Network Slicing Network Virtualization

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A New Classification Approach of Cultural Heritage: Deep Learning Based Image Recognition

A New Classification Approach of Cultural Heritage: Deep Lea...

引用

2023 international conference on Sustaining Heritage: Innovative and Digital Approaches, ICSH 2023

作者： Atas, Serhat Karakose, Mehmet Computer Engineering Department Elazig Turkey

ISBN: (纸本)9798350398281

With the development of technology, artificial intelligence is becoming gigantic and entering many areas. This development of artificial intelligence has also revealed beautiful studies by making contributions to the cultural heritage. These studies have shown that our cultural self and technology are actually not far from each other and are instrumental in useful studies. Many platforms have contributed to the development of various studies as a front for combining cultural heritage with developing artificial intelligence. In these various studies, not only artificial intelligence, but many different technologies have been used and tried to contribute to the cultural heritage. We have brought together deep learning and cultural heritage by deconstructing a study in this field. In our study, together with the special data set we have created, we have carried out a study that tries to recognize cultural heritage structures with deep learning by choosing a suitable model for our study, and we have described the contributions we have made in this study in this article. © 2023 IEEE.

关键词： Image classification

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development of 2D Animation for Diarrhea Disease Based on computer Generated Imagery (CGI) 8

Development of 2D Animation for Diarrhea Disease Based on Co...

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8th international conference on Information technology and Digital Applications, ICITDA 2023

作者： Marcheta, Noorlela Matin, Iik Muhamad Malik Nazruli, Rifqi Choiru Sonjaya, Iwan Rosalina, Mira Politeknik Negeri Jakarta Computer and Informatics Engineering Depok Indonesia

ISBN: (纸本)9798350344691

Diarrhea is one of the biggest diseases in the world that can cause death. Diarrhea can be caused by poor environmental sanitation or poor lifestyle habits. Prevention can be done early, such as education at the primary education level to reduce the high number of diarrheal diseases. However, the education provided is still minimal. This is caused by the educational media which still uses books and blackboards, thus affecting students' understanding of diarrheal disease so that it is less than optimal in preventing diarrheal disease. Animation technology is one example of learning media development. Through animation, students become more interested and teachers become more helpful in communicating diarrheal disease material. In this paper, animation development is carried out. We develop 2D video animation using computer Generated Imagery techniques. The results of beta testing via a questionnaire administered to respondents showed that 98.43% of respondents agreed that animated videos could be used as a learning medium for diarrheal disease material. © 2023 IEEE.

关键词： Animation

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Automated Sleep Staging System Using EEG Signal Feature-Based Classification by Machine Learning Techniques 8

Automated Sleep Staging System Using EEG Signal Feature-Base...

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8th IEEE international conference on Information and Communication technology, CICT 2024

作者： Rathod, Tanmay Satapathy, Santosh Kumar Das, Nibedita Mohapatra, Rajesh Kumar Sahu, Suren Kumar Shah, Vaishvi R Pandit Deendayal Energy University Information and Communication Technology Gujarat Gandhinagar India Institute of Advanced Research Department of Computer Science and Technology Gujarat Gandhinagar India Sri Sri University Faculty of Emerging Technology Cuttack India

ISBN: (纸本)9798331531539

Advancements in electrical engineering and signal processing can enable the development of automated sleep data analysis systems that embody significantly more advantages than their traditional manual counterparts. Recent approaches to automated sleep stage classification are considered from the PICO framework (Population, Intervention, Comparison, Outcome). Traditionally, PSG/EEG-based sleep studies were a laborious and time-consuming process. Despite this, significant promise is being offered from promising stride development in machine learning (ML) and deep learning (DL) technologies for full automation of sleep stage scoring. study are applied on data available from the SleepEDF public PSG Hypnogram dataset that combines EOG signal processing along with other physiological signals to classify sleep into five stages. Segmented the raw signals into epochs and passed through four different machine learning models: Random Forest, Gradient Boosting, Bagging Classifier, and Ensemble Learning. Those accuracies were classified using criteria such as accuracy percentages of 78% for Random Forest, 79% for Gradient Boosting, 75% for Bagging Classifier, and 85% for Ensemble Learning. The comparison has demonstrated Ensemble Learning as the most accurate model and, hence, has shown prospects for being implemented into consumer-grade sleep monitoring devices. The results of the experiment, besides highlighting the advantages of automated systems in classification tasks of sleep stages, suggest possible applications in real-world sleep tracking. © 2024 IEEE.

关键词： Sleep research

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development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments 5

Development of Convolutional Neural Network-Based AI-Dermato...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Kahatapitiya, Nipun Shantha Wijethunge, Akila Edirisinghe, Sajith Silva, Bhagya Nathali Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga University of Sri Jayewardenepura Faculty of Technology Dept. of Materials and Mechanical Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Medical Sciences Dept. of Anatomy Gangodawila Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Dept. of Computer Engineering Nugegoda Sri Lanka School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Buk-gu Daegu Korea Republic of Sri Lanka Institute of Information Technology Faculty of Engineering Dept. of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients. © 2023 IEEE.

关键词： computer vision

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Applications of Deep Learning algorithms for retinal diseases diagnosis based on Optical Coherence Tomography imaging 24

Applications of Deep Learning algorithms for retinal disease...

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24th international conference on Control Systems and computer Science, CSCS 2023

作者： Elena-Anca, Paraschiv National Institute for Research and Development in Informatics Communication Digital Applications and Systems Department Bucharest Romania University Politehnica of Bucharest Doctoral School of Electronics Telecommunications and Information Technology Bucharest Romania

ISBN: (纸本)9798350313390

The accurate and early detection of retinal diseases is critical for effective treatment and prevention of vision loss. Optical coherence tomography (OCT) imaging has become an essential tool for non-invasive diagnosis of retinal diseases. In this paper, a deep learning approach for the automated diagnosis of retinal diseases using OCT images is presented. Specifically, five different deep learning models were evaluated, including DenseNet121, DenseNet169, DenseNet201, InceptionResNet, and a 12 Convolutional layers-based model, and compare their performance in terms of accuracy, AUC, f1-score, and loss. Our results demonstrate that the DenseNetl69 model achieved the highest accuracy (97%), AUC (0.997), f1-score (0.97) and a loss of 0.1 among the models evaluated. These results indicate the potential of deep learning models for the automated diagnosis of retinal diseases based on OCT images, and highlight the importance of continued research and development in this field to improve patient outcomes. © 2023 IEEE.

关键词： Optical tomography

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Robotics in Healthcare: Artificial Intelligence-Driven Application for Medical Diagnosis and Treatment

Robotics in Healthcare: Artificial Intelligence-Driven Appli...

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2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Dhanya, D. Devendran, A. Balaji, S. Vaishali, M. Sujatha, R. Sreelekha, K.V. Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Kanyakumari629171 India School of Business Woxsen University Telangana Hyderabad502345 India Sri Manakula Vinayagar Engineering College Department of Information Technology Puducherry605107 India Sri Eshwar College of Engineering Deportmet of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India SubbalakshmiLakshmipathy College of Science Department of Computer Science Tamil Nadu Madurai625022 India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore641032 India

ISBN: (纸本)9798331543617

The rapid development of deep learning methods presents a potentially game-changing opportunity in the realm of education, particularly in the promotion of student involvement and the comprehension of the subject matter. To exemplify learning gestures and resolve educational challenges, this exploratory study investigates the operation of deep learning algorithms to determine the interests of students. Our deep learning model can provide direct predictions on the areas of interest for individual students by analyzing enormous volumes of educational data. These data include pupil relations, performance standards, and behavioral patterns of students. By aligning instructional tactics with the preferences of students, this strategy not only makes it easier for students to become accustomed to newly presented educational material but also encourages active learning. The research reveals that deep learninghelps capture the intricacies of student engagement. It also provides preceptors with valuable insights that may be used to cultivate a learning landscape that is more engaging and investigative. The results of our research highlight the possibility that deep learning will be used to change educational procedures to make them more adaptable and sensitive to the various needs of students. In this paper, the practice of using deep learning for interest identification in education is discussed, along with its methodology, perpetrators, and counteraccusations © 2024 IEEE.

关键词： Medical robotics

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Evaluation Dataset for Lexical Translation Consistency in Chinese-to-English Document-level Translation 30

Evaluation Dataset for Lexical Translation Consistency in Ch...

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Joint 30th international conference on Computational Linguistics and 14th international conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Lei, Xiangyu Li, Junhui Tao, Shimin Yang, Hao School of Computer Science and Technology Soochow University Suzhou China Huawei Translation Services Center Beijing China

ISBN: (纸本)9782493814104

Lexical translation consistency is one of the most common discourse phenomena in Chinese-to-English document-level translation. To better evaluate the performance of lexical translation consistency, previous researches assumes that all repeated source words should be translated consistently. However, constraining translations of repeated source words to be consistent will hurt word diversity and human translators tend to use different words in translation. Therefore, in this paper we construct a test set of 310 bilingual news articles to properly evaluate lexical translation consistency. We manually differentiate those repeated source words whose translations are consistent into two types: true consistency and false consistency. Then based on the constructed test set, we evaluate the performance of lexical translation consistency for several typical NMT systems. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Label Distribution Learning Method Based on Deep Forest 23

Label Distribution Learning Method Based on Deep Forest

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23rd international conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wang, Rong-Rong Tsang, Eric C.C. Xu, Wei-Hua School of Computer Science and Engineering Macau University of Science and Technology Taipa China College of Artificial Intelligence Southwest University Chongqing400715 China

ISBN: (纸本)9798331528041

Label Distribution Learning (LDL), as a novel paradigm aimed at resolving label ambiguity, has attracted significant attention in recent years. To further improve the predictive accuracy of LDL, we propose a LDL Method Based on Deep Forest (DFLDL). This method capitalizes on the cascade structure of deep forests, processing input data layer by layer to efficiently capture and model complex feature representations. To mitigate potential degradation in deep models, DFLDL integrates a confidence function that ensures the maximal exploitation of features at each layer, thereby minimizing training errors and enhancing predictive outcomes. Experimental results reveal that DFLDL surpasses other LDL methods across various datasets, demonstrating enhanced capabilities. © 2024 IEEE.

关键词： Adversarial machine learning

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