检索结果-内蒙古大学图书馆

On a family of q-modified-Laguerre-Appell polynomials

Arab Journal of Basic and Applied Sciences 2024年第1期31卷 165-176页

作者： Fadel, Mohammed Muhyi, Abdulghani Department of Mathematics Lahj University Lahj Yemen Department of Mathematics Hajjah University Hajjah Yemen Department of Mechatronics Engineering Faculty of Engineering and Smart Computing Modern Specialized University Sana’a Yemen

This paper aims to introduce a new class of special polynomials called q-modified Laguerre-Appell polynomials. Some definitions and concepts related to this class of polynomials, including generating function and series definition are explored. Also, certain interesting properties related to this class and some of its members are investigated. Furthermore, the class of 2D q-modified Laguerre-Appell polynomials is introduced. Finally, we present certain graphical representations related to some members of these new classes. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： determinant definition q-Appell polynomials q-modified-Laguerre polynomials

来源：评论

学校读者我要写书评

暂无评论

Insider Threat Identification from Accessed Website Content Optimized by Modified Metaheuristic

Insider Threat Identification from Accessed Website Content ...

引用

2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Jovanovic, Luka Kaljevic, Jelena Zivkovic, Miodrag Bacanin, Nebojsa Antonijevic, Milos Cajic, Miroslav Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Health and Business Studies Belgrade Serbia University 'Union Nikola Tesla' Faculty of Information Technology and Engineering Belgrade Serbia

ISBN: (纸本)9798350365306

In today's tech-driven landscape, safeguarding organizational security is imperative. Threats can arise externally or internally, necessitating preemptive measures. Despite the prevalence of insider risks like data breaches and intellectual property theft, literature lacks exploration of artificial intelligence (AI) potential in detecting malicious insiders. This study proposes leveraging natural language processing (NLP) and powerful classifiers to analyze employee behavior, offering an efficient means to mitigate insider threats. Effective classifiers depend on precise parameter selection, requiring optimization to align algorithms with specific problems. However, hyperparameter tuning faces NP-hard challenges due to extensive search spaces. Specialized algorithms like metaheuristic optimizers are commonly employed but require careful pairing with problems. In order to identify insider risks, this study presents a unique method that combines extreme gradient boosting (XGBoost) with bidirectional encoder representations using transformers (BERT) to analyze personnel HTTP content access. Leveraging NLP enhances robustness against common firewall evasion tactics. Additionally, a modified reptile search algorithm (RSA) algorithm is proposed for hyperparameter selection, achieving accuracy exceeding 97% on simulated security datasets. This framework offers a promising avenue for preempting insider threats and bolstering organizational security. © 2024 IEEE.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Helmet Detection for Motorcycle Ignition: A Computer Vision and Android-Based Approach 1

引用

8th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2023

作者： Hutabarat, Daniel Patricko Saleh, Robby Mae, Johannes Prasad, P.W.C. Computer Engineering Department Faculty of Engineering Bina Nusantara University West Jakarta Indonesia School of Computing Mathematics and Engineering Charles Sturt University Wagga WaggaNSW2678 Australia

ISBN: (数字)9783031717734

ISBN: (纸本)9783031717727

To enhance safety and combat the motorcycle accident fatality rate, this research introduces a motorbike user authorization system that integrates computer vision technology and an Android application. The system is designed to ensure motorcycle ownership verification while also confirming correct helmet usage by riders. Built on the Raspberry Pi Zero platform, it is capable of processing camera images and communicating with the Android application via Bluetooth. Data, including the speed and accuracy of helmet detection, are analyzed, and assessed using a neural network model and an extensive validation dataset. This discovery underscores the significance of helmet usage in maintaining road safety, given the high incidence of fatal motorcycle accidents associated with rider non-compliance with helmet regulations. With an impressive 90% accuracy rate based on a dataset of 48,000 validation images, this research makes a substantial contribution to mitigating this issue and pioneers a novel approach to bolstering rider safety and compliance with regulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motorcycles

来源：评论

学校读者我要写书评

暂无评论

Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

引用

5th International Conference on Advancements in computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

来源：评论

学校读者我要写书评

暂无评论

Non-IT Program Students Learn Basic Web Language;The Preliminary Study of Affective and Cognitive Response on IVML Prototype 17th

Non-IT Program Students Learn Basic Web Language;The Prelimi...

引用

17th International Conference on Information Technology and Applications, ICITA 2023

作者： Naim, Nur Faraha Mohd Ibrahim, Ag. Asri Ag. Baharum, Aslina Moung, Ervin Gubin Salimun, Carolyn Multimedia Technology Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kampus Antarabangsa Labuan Federal Territory of Labuan Malaysia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia User Experience Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia

ISBN: (纸本)9789819983230

This paper examines the response of affective and cognitive processes under human–computer interaction (HCI) toward interactive video mobile learning known as the IVML prototype. The idea is to determine the potential of the interactive elements in the educational video as the key factor that can influence learners’ engagement and comprehension of the presented topic. This paper aims to measure the level of affective and cognitive response of learners toward the subject learning that are not from their field, not exposed to, or not familiar with. The data analyze based on user experience, user interface, and human–computer interaction. The statistical result may produce a significant outcome that could contribute to the hyper video platform. The findings may confirm the significant role of technical aspects and psychological aspects included in video production style, hence heightening the hyper video platform in teaching and learning activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Electrical Load Classification Using Combinative Current-Power Characteristics in Time Domain and Machine-Learning-Based NILM Techniques 3rd

Electrical Load Classification Using Combinative Current-Pow...

引用

3rd International Conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, Thanh Cong Van Pham, Nam Truong, Hai Nam Nguyen, Ngoc Son Cao, Song Toan Nguyen, Huy Tinh Dang, Quoc-Viet Ware, Jonathan Andrew Nguyen, Ngoc An VNU University of Engineering and Technology Hanoi Viet Nam Samueli School of Engineering University of California Irvine United States Faculty of Computing Engineering and Science University of South Wales Wales United Kingdom

ISBN: (纸本)9783031809422

Non-intrusive load monitoring (NILM) is a large research topic dedicated to the detection and management of electrical loads, drawing international interest in the context of a growing global concern on sustainability in the energy sector. However, most current public datasets offer inadequate and non-unified sets of measured features, electrical devices and measurement methods, causing difficulties in method comparison and the application of research in new environments. In addition, previous studies faced challenges in classifying appliances with small and similar operational power or with large disparities in operational power. In this study, we utilized a combination of current-power characteristics in the time domain (e.g. RMS current (Irms), real power (P), power factor (pf), reactive power (Q), apparent power (S)) to monitor electrical appliances using their steady states. The combinative current-power characteristics were used to train machine learning models that can efficiently distinguish between appliances while addressing the posed challenges. Our dataset comprises 128 device combinations sampled at steady-state from 07 common electrical appliances in Vietnam, collected at a high sampling rate of 600 Hz, resulting in 73 million data points. By leveraging the current-power signatures in the time domain, we achieved a classification accuracy of approximately 99.1%, capable of differentiating between combinations containing devices with small and similar operational power and those with substantial disparities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： appliance classification load disaggregation load monitoring machine learning Non-intrusive Load Monitoring (NILM)

来源：评论

学校读者我要写书评

暂无评论

Brain Tumor Detection Using Image Processing 5

Brain Tumor Detection Using Image Processing

引用

5th International Conference on Advancements in computing, ICAC 2023

作者： Wijerathna, Udari Dissanayake, Nimavi Nimasha, Dinithi Senarathne, Udara Weerasinghe, Lokeesha Kahandawaarachchi, Chathurangika Sri Lanka Institute of Information Technology Faculty of Computing Dept of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Dept of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Dept of Computer System Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Abnormal formation of cells in a layer. The brain is known as the brain cancer complex, and a brain cancer detection system is needed to detect cancer in its early stages Phase. As a result, the primary goal of this initiative is to assist existing doctors in their training for the early identification o f brain cancer by utilizing magnetic resonance imaging (MRI), which is the most effective approach to identifying brain cancer. This system is done through four components, and it has made it easier to detect brain tumors. These techniques with different image processing techniques include noise removal, segmentation, classification, a nd u se o fa nalysis segments f or detection. Pre-processing techniques prepare the image for further processing by removing image noise. During segmentation, the brain tumor system determines the size of the brain tumor and estimates its size. Specialists should examine them and classify brain tumors as meningiomas, gliomas, and pituitary tumors. In analysis, this component is expected to be designed so that users or medical experts can know the future through a dashboard. We explored the methodology of using generative adversarial networks (GANs) implemented in TensorFlow to denominate brain tumor images. Analyses the outcomes of various types of established deep learning models on the brain tumor MR image datasets, such as classification methods ResNet-50, VGG 16, CNN, and Inception V3. The validation precision, AUC, recall, and validation loss for CNN are 95.3%, 99.43%, 92.1 %, and 0.360 adjustments respectively. © 2023 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Clustering Analysis Towards Educator’s Readiness to Adopt Augmented Reality as a Teaching Tool

Mendel

引用

Mendel 2023年第2期29卷 147-154页

作者： Sangodiah, Anbuselvan Yi, Wei Chooi Ayob, Ayu Norafida Binti Jalil, Norazira Binti A. Subramaniam, Charles Ramendran S P. R. Lirong, Gong School of Computing Faculty of Computing and Engineering Quest International University Malaysia Department of Finance Faculty of Business and Finance Universiti Tunku Abdul Rahman Malaysia Department of Information Systems Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Malaysia Department of Business and Public Administration Faculty of Business and Finance Universiti Tunku Abdul Rahman Malaysia Department of Teaching Quality Monitoring University of Sanya Hainan China

The advanced digital revolution has shifted conventional teaching and learning into digital education. In consistency with digital education, Augmented Reality (AR) applications started to shine in the education industry for their ability to create conducive teaching and learning environments, especially in remote learning during the COVID-19 pandemic. Movement Control Order (MCO) implemented in the year 2020 has led to emergency remote teaching and learning without much prepa-ration for all educators and learners. Throughout these few years, most educators got familiar with digital teaching tools and online teaching platforms. Hence, this study aims to explore educators’ readiness to adopt AR as a teaching tool in their teaching during the endemic period. A quantitative approach via questionnaire has been distributed to the Private Higher Education Institutions (PHEIs) in the states of Selangor and Kuala Lumpur. Machine learning using a clustering tech-nique was used to find patterns between the demographics of educators towards the AR perception of educators. The results revealed that educators’ perceptions of AR technology are influenced by their familiarity with it, their personal beliefs, and their attitudes toward technology. This study provides an insightful overview of the benefits of AR applications in education and the implications of the adoption of AR in Malaysian schools and educational institutions. It also highlights the importance of motivating educators and students to embrace AR as an enhancement learning tool, providing a valuable discussion for the government, learning institutions, and educators on the implementation of AR in Malaysia. © 2023, Brno University of Technology. All rights reserved.

关键词： Augmented reality

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Symptom Checker: Leveraging Big Data Technologies for Real-Time Healthcare Triage and Diagnostics 5

AI-Driven Symptom Checker: Leveraging Big Data Technologies ...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Banerjee, Augnik Baibhav, Piyush Kumar, Nitin Singh, Aniket Gouthaman, P. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331530013

The healthcare industry continues to face significant challenges in delivering timely and accurate diagnoses, particularly in remote and underserved areas where access to medical professionals is limited. The work aims to develop an AI-driven symptom checker that leverages big data technologies to offer preliminary diagnoses and support patient triage, ensuring quicker medical response times. Utilizing Hadoop for scalable data storage, Hive for advanced data analysis, and Spark for real-time distributed data processing, the system is designed to efficiently handle large volumes of patient data. Tableau is integrated for intuitive data visualization, allowing healthcare professionals to gain actionable insights and make informed decisions promptly. The proposed architecture enables the swift analysis of patient symptoms in real-time, significantly reducing diagnostic delays and enhancing the quality of care. By focusing on scalable and efficient data processing, the work addresses the need for a robust healthcare solution capable of operating in environments with limited resources. This system has the potential to transform patient care in remote areas by providing real-time analysis, identifying critical cases, and aiding in effective resource allocation. The solution's emphasis on rapid diagnosis and visualization offers a comprehensive approach to improving healthcare outcomes in underserved regions. © 2024 IEEE.

关键词： AI-Driven Symptom Checker Apache Spark Hadoop Healthcare Triage Hive Tableau

来源：评论

学校读者我要写书评

暂无评论

Predictive Analytics in Hotel Recommendations Using Machine Learning 5

Predictive Analytics in Hotel Recommendations Using Machine ...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Singh, Siddharth Dey, Shayan Sharma, Shreyanshi Prathaban, Banu Priya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

In a world where travellers want the perfect stay, a new system emerges as the one that goes beyond simple hotel recommendations. By analysing details like interaction words and prices, it predicts hotel ratings using a powerful Random Forest Regressor which performs better than methods like Linear Regressor. With careful data processing and encoding, it achieves high accuracy, measured through precise metrics like RMSE, MSE and Max Error. It achieves a great RMSE score of 0.424. This system offers a scalable solution, turning every hotel suggestion into a personalized experience, redefining the way people find their ideal stay. © 2024 IEEE.

关键词： Hotels

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：