检索结果-内蒙古大学图书馆

HighTech and Innovation Journal 2023年第4期4卷 811-820页

作者： Hasibuan, M.S. Abdul Aziz, R.Z. Dewi, Deshinta Arrova Kurniawan, Tri Basuki Syafira, Nasywa Aliyah Faculty Computer Science Institute Informatics and Business Darmajaya Bandar Lampung35136 Indonesia Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Faculty of Technology and Information Science University Kebangsaan Malaysia Malaysia Faculty of Education Yogyakarta State University Yogyakarta Indonesia

The biggest obstacle that students have when participating in a virtual learning environment (e-learning) is discovering a platform that has functionalities that can be customized to fit their needs. This is usually accomplished in several ways using educational resources such as learning materials and virtual classroom design elements. Our research has tried to meet this demand by suggesting an extra element in the virtual classroom design, i.e., classifying the students’ learning styles through machine-learning techniques based on information gathered from questionnaires. This feature allows teachers or instructors to modify their lesson plans to better suit the learning preferences of their students. Additionally, this feature aids in the creation of a learning path that serves as a guide for students as they choose their course materials. In this study, we have selected the Felder-Silverman Learning Style Model (FSLSM) in the questionnaire design, which focuses on identifying the students' learning styles. After that, we employ several machine learning algorithms to create a prediction model for the students’ learning styles. The algorithms include Decision Tree, Support Vector Machines, K-Nearest Neighbors, Naïve Bayes, Linear Discriminant Analysis, Random Forest, and Logistic Regression. The best prediction model from this exercise contributes to the recommendation model that was created using a collaborative filtering algorithm. We have carried out a pre-test and post-test method to evaluate our suggestions. There were 138 learners who were following a learning path and participated in this study. The findings of the pretest and post-test indicated a notable increase in students' motivation to study. This is confirmed by the fact that learners' satisfaction with online learning climbed to 87% when the learning style was considered, from 60% when it wasn't. © 2023, Ital Publication. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights 6

Empowering Air Travelers: A Chatbot for Canadian Air Passeng...

引用

6th Natural Legal Language Processing Workshop 2024, NLLP 2024, co-located with the 2024 Conference on Empirical Methods in Natural Language Processing

作者： Taranukhin, Maksym Ravi, Sahithya Lukács, Gábor Milios, Evangelos Shwartz, Vered Faculty of Computer Science Dalhousie University Canada Department of Computer Science University of British Columbia Canada Vector Institute for AI Canada Air Passenger Rights Canada

ISBN: (纸本)9798891761834

The Canadian air travel sector has seen a significant increase in flight delays, cancellations, and other issues concerning passenger rights. Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights. Our system breaks a complex user input into simple queries which are used to retrieve information from a collection of documents detailing air travel regulations. The most relevant passages from these documents are presented along with links to the original documents and the generated queries, enabling users to dissect and leverage the information for their unique circumstances. The system successfully overcomes two predominant challenges: understanding complex user inputs, and delivering accurate answers, free of hallucinations, that passengers can rely on for making informed decisions. A user study comparing the chatbot to a Google search demonstrated the chatbot's usefulness and ease of use. Beyond the primary goal of providing accurate and timely information to air passengers regarding their rights, we hope that this system will also enable further research exploring the tradeoff between the user-friendly conversational interface of chatbots and the accuracy of retrieval systems. ©2024 Association for Computational Linguistics.

关键词： Air transportation

来源：评论

学校读者我要写书评

暂无评论

Software Defect Prediction Using Support Vector Machine

International Journal of Systematic Innovation

引用

International Journal of Systematic Innovation 2022年第2期7卷 37-47页

作者： Alhija, Haneen Abu Azzeh, Mohammad Almasalha, Fadi Department of Computer Science Applied Science Private University Amman Jordan Department of Data Science Princess Sumaya University for Technology Amman Jordan

Software defect prediction is an essential task during the software development Lifecycle as it can help managers to identify the most defect-proneness modules. Thus, it can reduce the test cost and assign testing resources efficiently. Many classification methods can be used to determine if the software is defective or not. Support Vector Machine (SVM) has not been used extensively for such problems because of its instability when applied on different datasets and parameter settings. The main parameter that influences the accuracy is the choice of the kernel function. The use of kernel functions has not been studied thoroughly in previous papers. Therefore, this research examines the performance and accuracy of SVM with six different kernel functions. Various public datasets from the PROMISE project empirically validate our hypothesis. The results demonstrate that no kernel function can give stable performance across different experimental settings. In addition, the use of PCA as a feature reduction algorithm shows slight accuracy improvement over some datasets. © 2022. All Rights Reserved.

关键词： Kernel functions Software Defect Prediction Support Vector Machine

来源：评论

学校读者我要写书评

暂无评论

Comparison of Blockchain Ecosystem for Identity Management

Comparison of Blockchain Ecosystem for Identity Management

引用

International Conference on Next Generation Systems and Networks, BITS EEE CON 2022

作者： Suganya, K.S. Nedunchezhian, R. Department of Computer Science and Engineering KCG College of Technology Chennai India Department of Computer Science and Engineering Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9789819904822

In recent years, blockchain technology is found to be the most significant discovery in this digital era, after the discovery of the Internet and Cloud Computing. Blockchain is a simple, distributed public ledger that contains all the user's transaction details in a block. The global copy of the block is then shared among all its peer-to-peer network users after validation by the blockchain miners. Once a block is validated and accepted, it cannot be altered by any users making it a trust-free transaction. It also resolves the problem of double-spending by using traditional cryptographic methods. Since the advent of Bitcoin, blockchain is the backbone for all its transactions. But in recent years, it has found its roots and uses in many fields like smart contracts, smart city management, healthcare, etc. Identity management against digital identity theft had become a major concern among financial and other organizations. To solve this digital identity theft, blockchain technology can be employed with existing identity management systems which maintain a distributed public ledger containing details of an individual's identity containing information such as digital birth certificates, citizenship number, bank details, voter details, and driving license in the form of blocks verified on the blockchain becomes time-stamped, unforgeable and publicly visible for any legitimate users. The main challenge in using blockchain technology for preventing digital identity theft is ensuring the pseudo-anonymity and privacy of the users. This survey paper will exert to study the blockchain concepts, consensus protocols, and various blockchain-based digital identity management systems with their research scope. This paper also discusses the role of blockchain in COVID-19 pandemic management through self-sovereign identity and supply chain management. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this work, we leverage state-of-the-art machine learning algorithms to predict Channel State Information (CSI), for enhancing performance of wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of-the-art performance with minimal training data for difficult problems. These results show the potential of AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Emo-Spots: Detection and Analysis of Emotional Attributes Through Bio-Inspired Facial Landmarks

Emo-Spots: Detection and Analysis of Emotional Attributes Th...

引用

International Conference on IoT, Intelligent Computing and Security, IICS 2021

作者： Bakkialakshmi, V.S. Sudalaimuthu, T. Umamaheswari, B. Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Engineering Bengaluru India

ISBN: (纸本)9789811981357

Modern engineering society requires qualified solutions to many practical problems, to meet accurate outcomes. Emotion detection through face landmarks has unique coordination toward improving the feedback system. Bio-inspired learning methods are an interesting area of research. Face expressions are detected through various methodologies. Bio-inspired techniques are used to extract facial landmarks to detect facial expressions even better. The selection of face attributes is crucial for the prediction of deeper emotions. The proposed system is focused on implementing a bio-inspired facial landmark detection system for emotion extraction. The dataset used for the analysis is taken from the (JAFEE) Japanese female face expressions dataset. It consists of a collection of unique expressions and recorded images. Human face attributes are extracted from the Haar cascade model. The proposed novel methodology is derived from the Robust Emo-Spot Extracting technique (RESET) using point mapping model-based landmark mapping for pre-trained images and labeling spots. The face images are correlated with databases and classified the type using a deep convolutional neural network, in which appropriate Emo-spots are mapped using RESET. The adjustable Emo-Spots are randomly tuned using the randomly looped K-nearest neighbor algorithm. The novel algorithm checks for the correlation ratio (CR) 20. The proposed system is compared with the state-of-art approach in terms of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Beyond Traditional Boundaries: A Survey of Security Mechanisms in Software-Defined Networks 2

Beyond Traditional Boundaries: A Survey of Security Mechanis...

引用

2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Ganeshan, Vaishnavi Manoj, B.S. College of Engineering Department of Computer Science & Engineering Kerala Trivandrum India Indian Institute of Space Science and Technology Department of Avionics Kerala Trivandrum India

ISBN: (纸本)9798350328202

The advent of Software-Defined Networking has revolutionized network management by decoupling the control and data planes, catering to diverse network requirements across various domains. SDNs may seem more secure than traditional networks, but they still have weaknesses and challenges in deployment. Due to its inherent flexibility and centralized control, this model change poses a risk to network integrity, confidentiality, and other security factors by introducing new vulnerabilities like packet spoofing and unauthorized access. In order to address some of the most significant security challenges with SDN implementations, we presented a thorough analysis in this paper that examined the risk associated with different SDN planes and APIs. A systematic review of the literature was carried out in order to explain the problem of potential attack situations and weaknesses. The various security suggestions that have been proposed or implemented for SDN are examined as well in this study. We also identify important research gaps based on the review of previous work that may facilitate further studies in this field. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Human Activity Recognition Using Vision Based Method 1

A Novel Approach for Human Activity Recognition Using Vision...

引用

1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Parida, Lomashradhha Parida, Bivasa Ranjan Mishra, Manas Ranjan Jayasingh, Suvendra Kumar Samal, Tusharkanta Ray, Sujata Institute of Management and Information Technology Department of Computer Science and Engineering Cuttack India Silicon Institute of Technology Department of Computer Science and Engineering Bhubaneswar India C. V. Raman Global University Department of Computer Science and Engineering Bhubaneswar India Dhaneswar Rath Institute of Engineering and Management Studies Department of Computer Science and Engineering Cuttack India

ISBN: (纸本)9798350315769

Human Activity Recognition (HAR) is a fascinating process that involves identifying and categorizing human activities based on observations of subject behavior and environmental factors. Out of the major phases of HAR, preprocessing and feature extraction, in particular, require a lot of attention because they constitute the foundation for the training phase. The current evaluation effectively examines the trends in vision-based research approaches. Many models based on the artificial intelligence are suggested for the activity recognition;however, they couldn't perform satisfactorily on real world long-term HAR due to their lacuna in the extraction of spatial and temporal data. In light of these drawbacks, we propose a hybrid methodology for the human activity recognition which integrates Convolutional Neural Network (CNN) with the Long Short-Term Memory (LSTM), where CNN works for extracting the spatial characteristics and LSTM is used for the learning of the temporal information. A complete evaluation of the substantial work done in HAR domain and its vision-based techniques would help the readers. © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Cyber Attack Detection System in University Private Cloud Using Machine Learning

Cyber Attack Detection System in University Private Cloud Us...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Chethan, M.S. Channakrishnaraju Rajeswari, R. Selvam, M. Shridevi Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Tumkur India Sri Siddhartha Institute of Technology Department of Computer Science and Engineering Karnataka Tumkur India Alliance University Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350300857

As cloud computing adoption in colleges continues to rise, the security of private cloud systems has become a paramount concern. data breaches resulting from cyber attacks can inflict severe damage to a university's revenue and reputation. This research proposes a novel machine learning-based cyber threat detection system tailored to the university's private cloud environment. The system's main objective is to continuously monitor the cloud infrastructure and employ advanced machine learning algorithms to analyze network traffic, identify and prevent unusual activities that may indicate potential cyber-attacks. Here, the challenges posed on two sides of known possible threats and attack worldwide come across, and administrative defaults leads to security hole. By leveraging the power of machine learning, this innovative system aims to enhance the university's cyber defence capabilities. It considers the dynamic and evolving nature of cyber threats, enabling real-time detection and proactive measures against malicious activities. The integration of cutting-edge machine learning models and feature extraction techniques empowers the system to identify patterns of anomalous behaviour, even in the face of sophisticated attacks. Key components of the proposed system include network traffic analysis, anomaly detection and threat intelligence integration. Through the analysis of network packets and access logs, the system can effectively detect signs of unauthorized access, data exhilaration, and other cyber threats. Additionally, threat intelligence feeds provide the system with up-to-date information on emerging threats, enabling quick responses to potential risks. Moreover, the system's implementation adheres to privacy and data protection regulations, ensuring secure handling of sensitive information within the private cloud environment. Regular updates and adaptive learning capabilities enable the system to evolve with changing cyber threats, ensuring continued robustn

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Bayesian Neural Networks for Satellite Fog Detection: Quantifying Epistemic and Aleatoric Uncertainties

引用

Remote Sensing in Earth Systems sciences 2024年第1期8卷 232-250页

作者： Deshpande, Prasad Tripathi, Shivam Bhattacharya, Arnab Department of Civil Engineering Indian Institute of Technology Kanpur Kanpur India Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

Conventional threshold-based dual-channel methods, as well as recent deep learning-based methods, can deterministically detect fog using satellite observations. However, stochastic processes like fog are best represented by probabilistic models. This study proposes a method for probabilistic fog detection from satellite data using the Bayesian neural network (BNN)-based model. The novelty of the method lies in quantifying fog detection uncertainty and disentangling its epistemic and aleatoric components. The model generates a probability of fog for each input, which can be thresholded into fog/no-fog status, if needed. Using data from the INSAT-3D geostationary satellite over 18 North Indian cities, we demonstrate that the proposed method significantly outperforms the operational fog detection product of the Indian Space Research Organization, INSAT-FOG. The critical success indices (CSI) of the proposed model during testing and the INSAT-FOG are 0.48 and 0.12, respectively, whereas their probabilities of fog detection (PoD) are 0.68 and 0.13, respectively. The diurnal evaluation of the results shows that the model performs better even during dawn and dusk, which is a critical challenge in satellite fog detection. Moreover, the proposed model can detect very dense fog observations (visibility < 50 m) accurately (with CSI 0.93). On average, the detection uncertainty is lower for correct detections than for incorrect detections, suggesting that the model is more confident about correct detections. The uncertainty analysis reveals that the aleatoric uncertainty is usually higher than the epistemic uncertainty, suggesting potential avenues for future improvements in satellite fog detection. It is concluded that the proposed BNN is a promising method for real-time operational fog detection. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Bayesian networks Indo-Gangetic Plains INSAT-3D Reliable AI Smog Visibility

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：