检索结果-内蒙古大学图书馆

8th International Conference on Electrical, Telecommunication and Computer Engineering, ELTICOM 2024

作者： Hulu, Delisman Lydia, Maya Silvi Budiman, Mohammad Andri Universitas Sumatera Utara Master of Informatics Program Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331531249

Symmetric algorithms offer speed but have weaknesses in key distribution, while asymmetric algorithms are secure but less efficient for encrypting and decrypting large text messages. This research aims to secure data and digital documents through the implementation of a hybrid cryptosystem that combines the Cramer-Shoup algorithm and Spritz. This research also analyzes and evaluates the effectiveness and speed of encryption and decryption, as well as the efficiency of the system in protecting various types of data, including docx and pdf document files. The methodology of this research includes a literature review, analysis of the encryption-decryption process, and performance testing of the system using the Python programming language. The speed test results show that this hybrid cryptosystem provides fast encryption-decryption times, with an encryption time of 0.002607 milliseconds for 400 characters and a decryption time of 0.001537 milliseconds, while for digital documents of the pdf file type, the encryption time is 0.424478 milliseconds and the decryption time is 0.404343 milliseconds. In addition, the use of the SHA-3 hash function has proven effective in maintaining data integrity. In conclusion, this hybrid cryptosystem offers an efficient and secure solution for protecting various types of digital data, including text and documents. © 2024 IEEE.

关键词： Cramer-Rao bounds

来源：评论

学校读者我要写书评

暂无评论

SYSTEMATIC REVIEW: OPPORTUNITIES AND CHALLENGES OF MACHINE LEARNING TECHNIQUES FOR CARDIOVASCULAR DISEASE PREDICTION

引用

Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University 2024年第2期59卷 97-127页

作者： Qtaishat, Ahmed Wan Awang, Wan Suryani General Foundation Program Department of Information Technology Sohar University Sohar Oman Faculty of Informatics and Computing University Sultan Zainal Abdlidin Besut Campus Besut Terengganu 22200 Malaysia

Access to healthcare is a fundamental pillar of human well-being, yet cardiovascular diseases (CVD) persist as leading contributors to global mortality. This study explores the transformative potential of Machine Learning (ML) and Deep Learning (DL) algorithms in predicting a spectrum of CVDs, encompassing Heart Failure (HF), Arrhythmia, and coronary artery disease (CAD). With a focus on HF identification and associated risk factors, our primary aim is to rigorously evaluate the efficacy of diverse ML and DL models for CVD diagnosis. Through meticulous analysis of varied datasets, we examine different ML and DL methodologies to determine their predictive capabilities in the realm of CVD. Our findings illuminate promising advancements in CVD prediction accuracy, particularly in heart failure identification. Moreover, we elucidated the intricate interplay between cardiac and pulmonary functions in the context of heart disease, shedding light on disease mechanisms and novel diagnostic avenues. The novelty of our research lies in its comprehensive evaluation of ML and DL algorithms across heterogeneous datasets, fostering the refinement of CVD prediction strategies. By elucidating the effectiveness of various approaches, our study offers invaluable insights for healthcare practitioners and researchers striving to optimize CVD diagnosis and management. Ultimately, the integration of advanced computational techniques holds immense promise for bolstering cardiovascular healthcare outcomes and mitigating the global burden of CVD. © 2024 by the authors.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Systematic Analysis of Student Academic Performance Prediction: Prediction Model, Optimization, Data Augmentation, and Educational Policy Implications

Systematic Analysis of Student Academic Performance Predicti...

引用

2024 International Conference on information technology Systems and Innovation, ICITSI 2024

作者： Setiawan, Ismail Gernowo, Rahmat Warsito, Budi Universitas Diponegoro Department of Information System Semarang Indonesia Universitas 'Aisyiyah Surakarta Bachelor of Information Systems and Technology Program Faculty of Science and Technology Surakarta Indonesia Universitas Diponegoro Department of Statistics Semarang Indonesia

ISBN: (纸本)9798331511470

Predicting student academic performance is a critical challenge in higher education that aims to improve the quality of learning and optimize support for students. The main issues faced are how to choose the right prediction model, utilize optimization methods, and improve prediction accuracy. This paper presents a systematic review of the literature related to various prediction models, ranging from traditional statistical models to machine learning and ensemble/hybrid models, and discusses optimization techniques such as evolutionary algorithms. In addition, determinants of academic performance such as motivation, student engagement, and institutional support are identified as important variables in predicting learning outcomes. The datasets used, model evaluation methods, and technologies and tools that support the analysis process are also discussed in detail. The results of this review show that a hybrid approach that combines machine learning and optimization techniques is able to provide more accurate predictions compared to traditional models. This paper also highlights existing limitations, including challenges in model generalization, and provides policy recommendations to improve the effectiveness of academic performance prediction and its implementation in educational practice. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification

Performance Evaluation of Oversampling Methods on Deep Learn...

引用

2023 International Seminar on Application for technology of information and Communication, iSemantic 2023

作者： Supriyanto, Catur Salam, Abu Zeniarja, Junta Wijaya, Adi Universitas Dian Nuswantoro Bachelor Program in Informatics Engineering Faculty of Computer Science Semarang Indonesia Universitas Indonesia Maju Department of Health Information Management Jakarta Indonesia

ISBN: (纸本)9798350339215

The field of dermatology faces considerable challenges when it comes to early detection of skin cancer. Our study focused on using different datasets, including original data, augmented data, and SMOTE oversampled data, to identify skin cancer. Our dataset consisted of images of skin lesions from the MNIST Skin Cancer dataset (HAM 10000), including samples of both cancerous and benign cases in the dataset. We employed data augmentation to expand the dataset's size and increase the diversity of skin lesion features. Furthermore, to tackle class imbalance in the dataset, we applied the SMOTE oversampling technique to generate synthetic samples for the under-represented group. With the original, augmented, and SMOTE oversampled datasets, we trained a Convolutional Neural Network (CNN) model. The performance of the model was evaluated using accuracy, recall, precision, and F1-score. The comparison between the results obtained from the original data, augmented data, and SMOTE oversampling data clearly revealed distinctions in performance. Our findings clearly demonstrate that employing data augmentation and SMOTE oversampling can significantly enhance the efficacy of skin cancer detection. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

引用

Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

arXiv

引用

arXiv 2024年

作者： Toba, Hapnes Hernita, Yolanda Trixie Ayub, Mewati Wijanto, Maresha Caroline Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom's epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom's epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis. © 2024, CC BY-SA.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Incorporating teacher intelligence into digital games: An expert system-guided self-regulated learning approach to promoting EFL students' performance in digital gaming contexts

引用

British Journal of Educational technology 2023年第2期54卷 534-553页

作者： Chu, Shih-Ting Hwang, Gwo-Jen Chien, Shu-Yun Chang, Shao-Chen Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan International Bachelor Program in Informatics Department of Information Communication Yuan Ze University Taoyuan Taiwan

In recent years, several studies have reported the potential of employing digital games in EFL (English as Foreign Language) courses to promote students' learning motivation. However, scholars have pointed out that students generally lack self-learning ability, which is the key to the success of learning a foreign language. Therefore, it is crucial to foster students' self-learning ability during the game-based learning process. In this study, an expert system was developed to facilitate self-regulated learning in digital game-based learning contexts. To evaluate the effectiveness of the proposed approach, a quasi-experimental design was employed in a university English course. The experimental group students learned with the self-regulated English vocabulary game (SR-EVG) approach, while the control group students learned with the conventional English vocabulary game (C-EVG) approach. The experimental results indicated that the use of the SR-EVG approach could improve learners' English vocabulary achievement and self-regulation compared with the C-EVG approach without increasing students' English learning anxiety. Moreover, through qualitative interviews, it was found that students who used the SR-EVG approach would focus on their learning due to goal-setting in the game and would pay more attention to the learning strategies they used. Practitioner notes: What is already known about this topic Digital game-based learning situates students in a realistic situational environment, enabling students to learn by experiencing and interacting with the situations. Without appropriate scaffolding during digital game-based learning, students may become overly focused on the game, or perform many non-learning behaviors Self-regulated learning refers to students’ ability to learn on their own via goal setting, strategic planning, self-monitoring, and self-adjustment. What this paper adds An expert system-guided self-regulated learning approach was proposed to facilitate EFL s

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Effects of incorporating a two-tier assessment-based guiding mechanism into contextual games on students’ learning achievement and behaviors in horticultural education

引用

Interactive Learning Environments 2024年第10期32卷 6845-6867页

作者： Hwang, Gwo-Jen Lin, An-Chi Chang, Shao-Chen Graduate Institute of Educational information and Measurement National Taichung University of Education Taichung Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei Taiwan Yuan Ze University Taoyuan Taiwan International bachelor program in informatics department of information communication Yuan Ze University Taoyuan Taiwan

Digital games have been used in various disciplines to enhance students’ learning interest and effectiveness through the gaming contexts. However, most of the digital educational games use multiple-choice questions to confirm students’ learning status, implying the challenge of understanding the actual learning status of students. Also, the gaming system cannot analyze the possible reasons for students’ wrong answers and so provide suggestions. Therefore, this study proposed a two-tier assessment-based contextual gaming (TTA-CG) approach to confirm whether students correctly answer the questions based on the right reasons as well as identifying the reasons for students’ wrong answers. To evaluate the effectiveness of the proposed approach, a quasi-experiment was conducted in a horticulture course. Two classes of first-year vocational high school students were recruited as the participants. One class with 35 students was the experimental group learning with the TTA-CG approach;the other class with 35 students was the control group adopting the conventional contextual gaming (C-CG) approach. The experimental results showed that the TTA-CG approach was more effective than the C-CG approach in terms of enhancing students’ learning achievement, motivation, and self-efficacy. In addition, there was no significant difference between the two groups in terms of cognitive load. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Contextual game-based learning learning achievement learning behavior analysis plant identification two-tier assessment

来源：评论

学校读者我要写书评

暂无评论

Aircraft Flight Schedule Announcement at Pionir Indonesian Airport Based on SMS Broadcast 1

Aircraft Flight Schedule Announcement at Pionir Indonesian A...

引用

1st International Conference of Adisutjipto on Aerospace Electrical Engineering and informatics, ICAAEEI 2024

作者： Kusumaningrum, Anggraini Wahyu, Septian Sahal, Ahmad Pujiastuti, Asih Ayuningtyas, Astika Pratiwi, Elizabeth Anna Adisutjipto Institute of Aerospace Technology Informatics Study Program Yogyakarta Indonesia Adisutjipto College of Technology Informatic Engineering Department Yogyakarta Indonesia Respati University D3 Information Technology Study Program Faculty of Science and Technology Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Engineering Study Program Yogyakarta Indonesia

ISBN: (纸本)9798331507930

The design and construction of delivering announcements using a web-based SMS gateway aims to help overcome problems that often occur at pioneer airports;this is because when passengers want to travel, many passengers still arrive late to check-in, even though the passenger ticket has a notification for checking. -in 1 hour before Departure. If a flight's Departure is delayed or accelerated, passengers will only receive this information when they arrive at the airport, whereas if at the time the departure notification is delayed or accelerated, the passenger is still outside the airport or still on their way to the airport, as a result, they will definitely not receive the information. Testing voice announcements on parking, arrival, departure, delay, check-in, and waiting room services, as well as testing SMS broadcasts (30 user numbers and one airline), shows that the announcement application provides information through voice data recording and broadcasts SMS to passengers and families. Testing was carried out at Indonesian Pioner airport for 30 passengers for a week. The percentage of Ease of obtaining test voice information was 81.98%, and the percentage of Ease of obtaining broadcast SMS information was 83.1%. Implementing the announcement is feasible at pioneer airports with a percentage of 82.54%. © 2024 IEEE.

关键词： Text messaging

来源：评论

学校读者我要写书评

暂无评论

Long Short Term Memory (LSTM) Improvement Accuracy Using FastText and Glove for Language Translation Batak - Indonesian 2

Long Short Term Memory (LSTM) Improvement Accuracy Using Fas...

引用

2nd International Conference on technology Innovation and Its Applications, ICTIIA 2024

作者： Mandasari, Atikah Rahayu Nababan, Erna Budhiarti Mawengkang, Herman Informatics Program Universitas Sumatera Utara Medan Indonesia Universitas Sumatera Utara Faculty Of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Faculty of Mathematics and Natural Sciences Medan Indonesia

ISBN: (纸本)9798350351613

This research discusses the improvement of Long Short-Term Memory (LSTM) model accuracy in translating Batak to Indonesian by utilizing FastText and GloVe embeddings. Traditional LSTM models often struggle with the complexity and richness of underrepresented languages like Batak. By integrating FastText and GloVe, which provide word vectors with deeper semantic meanings, the translation quality can be significantly enhanced. Experimental results show that this approach offers a substantial performance improvement compared to conventional LSTM models, producing more accurate and contextual translations. These findings have the potential to be applied in broader multilingual translation systems and support the preservation and digital accessibility of minority languages. © 2024 IEEE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：