检索结果-内蒙古大学图书馆

24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Mohta, Bhavicka Oudah, Mai New York University Abu Dhabi Computer Science Program Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331518622

Multiple Sclerosis is a chronic disease of the central nervous system that affects millions worldwide, and early detection is crucial for better treatment outcomes. Current detection methods are expensive and invasive, leading to a need for a cheaper and non-invasive screening tool. We utilized the gut microbiome and machine learning to develop a reliable screening tool for Multiple Sclerosis that can recommend further clinical examination. We identified optimal combinations of feature selection and machine learning algorithms as well as the associated biomarkers for Multiple Sclerosis in the gut microbiome. Our evaluation revealed that among various classifiers, Logistic Regression and Naive Bayes, particularly when trained on features filtered through Correlation-based Feature Selection and Information Gain methods, exhibited exceptional performance in terms of precision, recall, and F-measure consistently higher than 0.95 and high AUC values. Furthermore, our analysis of the biomarkers identified distinct microbial signatures between healthy and Multiple Sclerosis patients, notably the prevalence of certain taxa, such as genera Streptococcus and Dorea in Multiple Sclerosis patients, and phylum Bacteroides and species uniformis in healthy cases. © 2024 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Hybrid Cryptosystem with Cramer-Shoup and Spritz Algorithms for Securing Digital Documents 8

Hybrid Cryptosystem with Cramer-Shoup and Spritz Algorithms ...

引用

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

Machine Learning and Gut Microbiome for Breast Cancer Screening 21

Machine Learning and Gut Microbiome for Breast Cancer Screen...

引用

21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Daga, Priyamvada Oudah, Mai New York University Abu Dhabi Computer Science Program Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350356632

The gut microbiome can provide key information regarding host health and disruptions to the microbiome have been associated with various diseases including breast cancer. A critical factor in BC survival is early detection, when spread is limited. Consequently, analyzing the gut to screen for BC provides an opportunity to introduce a non-invasive, low cost yet reliable screening tool. In this study, we identify key microbial biomarkers from fecal samples and leverage supervised learning techniques to develop a model for BC detection. We conduct experiments across nine algorithms and four feature selection and engineering techniques on 3 splits of data: Invasive-Control, Benign-Control, and Invasive-Benign. Analyzing the results, we highlight the most informative biomarkers for each category. Our best performing models achieve Area under Curve (AUC) score of 0.829, 0.943 and 0.920 for Invasive-Control, Benign-Control and Invasive-Benign respectively. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying Datasets, Labelling, and Alertness Level Classification 1

引用

International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Ariansyah, Dedy Rahutomo, Reza Elwirehardja, Gregorius Natanael Asadi, Faisal Pardamean, Bens Bioinformatics and Data Science Research Centre Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Reduced alertness because of fatigue or drowsiness accounts for a major cause of road accidents globally. To minimize the likelihood of alertness reduction-related crashes, a video-based detection emerges as a non-intrusive method and can offer high accuracy for the countermeasure development. Many Artificial Intelligence (AI) techniques have been developed to aid in video analysis for fatigue detection. However, the issues in AI-based video analysis for fatigue countermeasure development are rarely discussed. This paper reviews current video-based countermeasure development in the literature and highlights some reliability issues of AI-based fatigue countermeasure development in terms of underlying dataset, labelling method, and alertness level classification. The result serves as insights and consideration to academics and industrialists in developing a high reliable AI-based video analysis for driver fatigue countermeasure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Fairness Analysis of Human and AI-Generated Student Reflection Summaries 5

A Fairness Analysis of Human and AI-Generated Student Reflec...

引用

5th Workshop on Gender Bias in Natural Language Processing, GeBNLP 2024, held in conjunction with the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Baghel, Bhiman Kumar Narayanan, Arun Balajiee Lekshmi Yoder, Michael Miller Department of Computer Science United States Intelligent Systems Program University of Pittsburgh PA United States

ISBN: (纸本)9798891761377

This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students. We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others. Our analysis reveals that though human-generated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students. Pedagogical themes are overrepresented from male reflections in these summaries, while concept-specific topics are underrepresented from female reflections. This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases. ©2024 Association for Computational Linguistics.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Beyond the Gut Feeling: Machine-Learning Methods for Autism Screening 24

Beyond the Gut Feeling: Machine-Learning Methods for Autism ...

引用

24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Zhusubalieva, Aigerim Oudah, Mai New York University Abu Dhabi Computer Science Program Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331518622

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that manifests itself as speech impairment and difficulties in social and cognitive skills. The onset of ASD is typically before the age of three, but many children are not diagnosed until later in life due to the current diagnosis being based on behavioral assessments. However, early intervention is crucial for minimizing the impact of ASD on both the child and their caregivers, and for developing an effective treatment plan for symptoms. We propose a gut microbiome-based screening tool for ASD utilizing feature selection, feature engineering, and machine learning algorithms. Our approach significantly reduces the feature space to 1-2% of its original size, emphasizing computational efficiency and informative attribute focus. Training and testing on two diverse datasets revealed minimal common features, underscoring the impact of regional differences on taxonomic behavior. Biomarker analysis confirmed known associations and unveiled novel connections, contributing valuable insights to the ongoing debate on ASD biomarkers. Notably, the genus Oscillospira has been identified by our Attribute Selection tools as being increased in ASD. Model performance, measured by Area Under the Curve, showcases the Bayes-Net algorithm on the Hierarchical Feature Engineering subset as the most optimal for ASD prediction. Our findings surpass existing literature, demonstrating the effectiveness of our approach in decoding the complex interplay between gut microbiome and ASD. © 2024 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Neural controller design for a class of nonlinear systems with time-varying delay: A bibliometric analysis-based short literature review

Neural controller design for a class of nonlinear systems wi...

引用

2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Wijaya, Santo Gaol, Ford Lumban Wibowo, Antoni Zarlis, Muhammad Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information System Management Information System Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

Real-world practical systems inherently exhibit non-linearities in their dynamics. Also, it is known that a time-varying delay exists in the system state or input-output. Combined, it affects the stability of the closed-loop system. It also increases the complexity of the controller design. In-depth controller design research on the class of Nonlinear Systems with Time-Varying Delay (NSTVD) has been the focus of the control community for many years. However, there is a lack of Systematic Literature Review (SLR) and classifications of the papers on this topic. This paper aims to review controller design utilizing a neural network model for the class of NSTVD systems. The study employs Kitchenham's SLR method to gather, analyze and synthesize published papers from reliable databases between 2017 and 2021. The bibliometric analysis for the selected 38 papers reveals the prolific authors, countries, affiliations, publishers, co-authorship network, co-occurrences of keywords, and ten most-cited papers. Finally, this paper developed a conceptual map outlining six multi-layered findings: the addressed problem, control design method, nonlinear system properties, time-varying delay properties, system constraint properties, and actuator limit properties. A brief qualitative analysis of the ten most-cited papers is performed based on the map. The findings highlighted that the proposed methods have shown encouraging results in the simulation domain and can be used as a source of inspiration for future studies and implementation of the neural controller design of the NSTVD system. © 2023 IEEE.

关键词： Nonlinear systems

来源：评论

学校读者我要写书评

暂无评论

Random Forest Based QSAR Model Analysis for Predicting Drug Effectiveness Against Mycobacterium Tuberculosis Bacteria 11

Random Forest Based QSAR Model Analysis for Predicting Drug ...

引用

11th International Conference on ICT for Smart Society, ICISS 2024

作者： Ekosputri, Alicia Josephine Ardianti, Amanda Anggreainy, Maria Susan Artanto, Brychan Fitriawati, Nora Valentina, Fanny Angelia Santoso, Vincent Kartamulya School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11530 Indonesia Bina Nusantara University Binus Online Learning Information Systems Department Jakarta11530 Indonesia

ISBN: (纸本)9798350389654

This research focuses on the use of machine learning methods, especially Random Forest, in designing tuberculosis drugs more effectively and efficiently. The research process begins with the creation of a QSAR (Quantitative Structure-Activity Relationship) model using a dataset from ChEMBL that includes compounds with activity against Mycobacterium tuberculosis. This model aims to predict the pIC50 value, which is an indicator of a compound's biological activity as a tuberculosis drug. The results of the Random Forest model showed high accuracy, which is 92%. This accuracy indicates that the model has a good ability in predicting the effectiveness of compounds against Mycobacterium tuberculosis. With this result, the developed model can be used as an auxiliary tool in the discovery of tuberculosis drugs and can be further developed to increase the effectiveness in drug research. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and Cost-Effective Inference in Large Language Models 9

Knowledge Graph-Enhanced Semantic Cache for Low-Latency and ...

引用

9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Dominic, Nicholas Pardamean, Bens Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

In organizational knowledge management, Large Language Model (LLM) caches act as a semantic repository gathered from previous LLM responses. Due to intensive calls from multiple users, LLM may suffer from high inference latency. While there are many prior available approaches to solve this problem, most of them are inherently complex. This paper introduced a Knowledge Graph-enhanced Semantic Cache mechanism as an alternative, lightweight technique to boost retrieval for similar prompts. The latest state-of-the-art open-source LLM, named Google's Gemma-2B-it, was used to generate sample prompts and responses as a draft, while a knowledge graph (KG) was built from Wikipedia sentences. To create embeddings of prompts and KG, all-MiniLM-L6-v2 from SentenceTransformer was used. This new cache system resulted in up to 28% improvement over a standard model. In particular, reinforcement with KG cache embeddings yielded more than 85% semantic cache accuracy. To map the next trajectory of this pilot study, an overview of the extended framework for LLM knowledge management was also presented in this paper. The framework includes the new KG- enhanced cache system equipped with scalable security and fallback mechanisms that can promote green technology through substantial improvements in latency, throughput, and overall LLM costs. © 2024 IEEE.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Investigating the potential adverse effects of virtual reality-based learning system usage: from UTAUT2 and PPVITU perspectives

引用

Interactive Learning Environments 2024年第9期32卷 5106-5125页

作者： Wang, Yu-Yin Chuang, Yu-Wei Department of Computer Science and Information Management Providence University Taichung Taiwan Bachelor Program in Business and Management National Yunlin University of Science and Technology Yunlin Taiwan

A review of the literature shows that much academic effort has been expended studying information system usage and information technology adoption. However, these theories/models based on psychological research are not specific to the virtual reality context and may not fully capture the nature of virtual reality-based learning system (VR-BLS) usage behavior in the virtual environment. Given the limited utility of existing theories/models, this study aims to build a theoretical model and to validate the psychological and physiological effects of using VR-BLSs. To explore students’ usage intentions, this study integrates the extended unified theory of acceptance and use of technology (UTAUT2), representing the psychological effects, and the perceived physiological vulnerability to IT usage (PPVITU), representing the physiological effects. We empirically examine the research model using a cross-sectional survey, collecting data from a sample of 352 students, and utilizing partial least squares (PLS) for the analysis. Our findings show that the behavioral intention to use VR-BLSs is determined by performance expectancy, social influence, and facilitating conditions. Moreover, PPVITU negatively moderates the association between social influence and the behavioral intention to use VR-BLSs. The results of this study enhance our understanding of the relationship between students’ VR-BLS usage behavior and VR-based education. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： extended unified theory of acceptance and use of technology (UTAUT2) perceived physiological vulnerability to IT usage (PPVITU) system usage virtual reality-based education Virtual reality-based learning

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：