检索结果-内蒙古大学图书馆

2024 International Conference on computer and Applications, ICCA 2024

作者： Hesham, Alaa Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt

ISBN: (纸本)9798350367560

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science. © 2024 IEEE.

关键词： Program processors

来源：评论

学校读者我要写书评

暂无评论

Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data 4

Topic Modeling Using Latent Dirichlet Allocation Method Base...

引用

4th International Conference of Science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Fernandis, Rasio Swanjaya, Daniel Ramadhani, Risky Aswi Kasih, Patmi Sahertian, Julian Universitas Nusantara Pgri Kediri Faculty of Engineering and Computer Science Dept. Informatics Engineering Kediri Indonesia

ISBN: (纸本)9798350376111

Kindergarten education has a very important role in shaping the foundation of child development. For parents, see reports on the achievement of children's development at school through the results of each semester's assessment. The process of preparing teacher assessments uses 4 types of assessments, one of which is anecdotal record assessment. However, teachers experience problems in analyzing the topics in children's anecdotal notes so that the results become less clear. In addition, it is still done manually so it takes a long time to analyze. Assessment of children's anecdotal notes is an assessment in the form of a record of events that occur to children while at school. Anecdotal notes are in the form of text data, therefore this research aims to find out the topics in children's anecdotal notes data by utilizing topic modeling using the Latent Dirichlet Allocation (LDA) method. The children's anecdotal notes data is used as one dataset with a total of 276 collected directly from YBPK Sidorejo Kindergarten, after which data preprocessing is carried out in the form of case folding, remove punctuation, tokenizing, stopwords, and stemming. In the LDA topic modeling research using the Gensim library, 5 experiments were carried out by determining the number of different iterations, and displaying a range of 2-10 topics for each experiment. Analysis and evaluation using coherence score to determine the optimal number of topics by looking at the highest coherence score value in the experiment conducted. The analysis results in 3 topics with a coherence score of 0.4704 and 40 iterations or passes. © 2024 IEEE.

关键词： anecdotal record coherence score kindergarten Latent Dirichlet Allocation topic modeling

来源：评论

学校读者我要写书评

暂无评论

Developing an Anti -Phishing Large Language Model: A Focus Group Study on Human, Technological, and Legal Factors 15

Developing an Anti -Phishing Large Language Model: A Focus G...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Thomopoulos, George A. Kiortsi, Panagiota Sigalas, Damianos Dumi Kosmopoulos, Dimitris Igglezakis, Ioannis Fidas, Christos University of Patras Department of Electrical and Computer Engineering Greece University of Thessaloniki Faculty of Law Aristotle Greece University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350368833

The phishing problem poses a significant threat in modern information systems, putting both individuals and businesses at risk of financial and professional harm. Owing to social media's rapid development and widespread appeal, deception of this sort is hurting millions of people and growing more dangerous. The current work, as part of the AILA (Artificial Intelligence-driven Framework and Legal Advice Tools for Phishing Prevention and Mitigation in Information Systems) project, aims to specify and validate an AI-driven multifactor (human, technology and legal) anti-phishing data model, with the implementation of focus group studies. The findings assist to provide human, technology, and legislative user model endpoints that will be identified and discussed for explicit and implicit user modeling, which will guide the development of the corresponding AI-driven user modeling and profiling mechanisms. To this end a Large Language System is planned to be employed. © 2024 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation 4

Semi-Automated Video Archiving Using Object Detection, Track...

引用

4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350348248

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023 IEEE.

关键词： computer vision

来源：评论

学校读者我要写书评

暂无评论

Deep Transfer Learning Techniques in Intrusion Detection System-Internet of Vehicles: A State-of-the-Art Review

引用

computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal Kandala N.V.P.SRajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih Najibullah Haqjoo Mobeen Nazar Hamed Vahdat-Nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles(IoV)*** functional advantages of IoV include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,IoV technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of Vehicles(IDS-IoV)based on anomaly ***-IoV leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-IoV *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in IoV environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

来源：评论

学校读者我要写书评

暂无评论

End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

引用

HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Brain Waves for EEG-Based Depression Detection in the Prefrontal Cortex Lobe using LSTM 7

Comparative Analysis of Brain Waves for EEG-Based Depression...

引用

7th International Conference on New Media Studies, CONMEDIA 2023

作者： Pratiwi, Monica Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Computer Engineering Tangerang Indonesia

ISBN: (纸本)9798350307504

Global anxiety and depression have become 25% more prevalent, with teenagers and women being the most affected. Approximately 280 million people suffer from depression. Doctors and psychologists are able to diagnose depressive disorders through counselling sessions and ask relevant questions to the subject, despite being vulnerable to mistakes due to the examiner's lack of experience. Therefore, automated depression detection development is necessary to validate doctor and psychiatrist assessment. Electroencephalography (EEG) is considered to be a popular option for the detection and investigation of various mental disorders. In this study, a comparison and analysis of each existing brain wave is carried out, namely Alpha (8-12Hz), Beta (13-30Hz), Theta (4-8 Hz), Delta (0.5-4 Hz) and Gamma (30-50Hz). From each wave, an accuracy testing is carried out for three groups of features: linear features, nonlinear features, and a combination of linear and nonlinear features. The given results demonstrate that the combination of linear and nonlinear data consistently yields the highest accuracy outcomes across all waves. Also, the combination of theta waves and linear nonlinear features contributed the highest accuracy (84%) using LSTM as the classifier. © 2023 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Challenges of Integrating Blockchain Technology into Educational Institutions

Challenges of Integrating Blockchain Technology into Educati...

引用

2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Ali, Abbas Hussein Yavanoǧlu, Uraz Graduate School of Informatics Gazi University Dept. Of Computer Science Ankara Turkey Gazi University Faculty of Engineering Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350364910

The latest advancements in blockchain technology have significantly influenced several sectors, such as banking, healthcare, and supply chain networks. Because of its distinct attributes, like decentralization, trustworthiness, and security, this technology has recently been employed in the field of education. While the blockchain is bright, several problems, such as legal, immutability, and scalability questions, continue to hinder its adoption in education. In this paper, we look at educational application system in terms of blockchain-based solutions that have been created for educational purposes, as well as the potential challenges of blockchain technology in the field of education. © 2024 IEEE.

关键词： Blockchain technology educational application

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：