检索结果-内蒙古大学图书馆

Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”

IEEE Access 2025年 13卷 5436-5436页

作者： M. Poongodi Mohit Malviya Mounir Hamdi Hafiz Tayyab Rauf Seifedine Kadry Orawit Thinnukool College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Department of CTO 5G Wipro Ltd. Bengaluru India Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent U.K. Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Research Group of Embedded Systems and Mobile Application in Health Science College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

Presents corrections to the paper, (Corrections to “The Recent Technologies to Curb the Second-Wave of COVID-19 Pandemic”).

关键词： COVID-19 Pandemics

来源：评论

学校读者我要写书评

暂无评论

Preface

Advanced Structured Materials

引用

Advanced Structured Materials 2025年 228卷 v页

作者： Singh, Deepak Kumar Sambhav, Saurabh Pandey, Ashok Kumar Ismail, Azman Zulkipli, Fatin Nur Öchsner, Andreas Amity University Patna Bihar Patna India Employees Selection Board Bhopal Directorate of Technical Education RGPV Madhya Pradesh Bhopal India Maritime Engineering Technology and Centre for Women Advancement and Leadership Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Perak Lumut Malaysia Information Science Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Kelantan Machang Malaysia Faculty of Mechanical Engineering Esslingen University of Applied Sciences Baden-Württemberg Esslingen am Neckar Germany

来源：评论

学校读者我要写书评

暂无评论

A Comparative Analysis of Credit Card Detection Models 8th

A Comparative Analysis of Credit Card Detection Models

引用

8th International Conference on the Applications of Science and Mathematics, SCIEMATHIC 2022

作者： Kim, Kimberly Chan Li Mustapha, Aida Palaniappan, Vaashini Mun, Woon Kah Kasinathan, Vinothini Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia KM1 Jalan Pagoh Pagoh Johor 84600 Malaysia School of Computing and Technology Asia Pacific University of Technology and Innovation Taman Teknologi Malaysia Jalan Teknologi 5 Kuala Lumpur57000 Malaysia

ISBN: (纸本)9789819928491

Data mining and machine learning are gaining popularity for fraud detection due to their effective results to cater the exponentially growing card transactions that comes with the fast-growing frauds. The aim of this paper is to explore which of the many techniques are capable to detect fraudulent transactions the best. Methods such as logistic regression, decision tree, support vector machine (SVM), Naive Bayes, and random forest are evaluated on their performance based on factors such as accuracy, precision, recall, and F1-score. The results showed that the random forest performed the best among the five methods investigated for credit card fraud detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

An Efficient Technique to Prevent Data Misuse with Matrix Cipher Encryption Algorithms

引用

Computers, Materials & Continua 2023年第2期74卷 4059-4079页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Moteeb Al Moteri Sultan Almotairi Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Management Information Systems Business Administration CollegeKing Saud UniversityRiyadh11451Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Many symmetric and asymmetric encryption algorithms have been developed in cloud computing to transmit data in a secure *** cryptography is a data encryption mechanism that consists of different steps and prevents the attacker from misusing the *** paper has developed an efficient algorithm to protect the data from invaders and secure the data from *** this algorithm is applied to the cloud network,the attacker will not be able to access the *** encrypt the data,the values of the bytes have been obtained by converting the plain text to ASCII.A key has been generated using the Non-Deterministic Bit Generator(NRBG)mechanism,and the key is XNORed with plain text bits,and then Bit toggling has been *** that,an efficient matrix cipher encryption algorithm has been developed,and this algorithm has been applied to this *** capability of this algorithm is that with its help,a key has been obtained from the plain text,and only by using this key can the data be decrypted in the first steps.A plain text key will never be used for another plain *** data has been secured by implementing different mechanisms in both stages,and after that,a ciphertext has been *** the end of the article,the latest technique will be compared with different *** will be a discussion on how the present technique is better than all the other techniques;then,the conclusion will be drawn based on comparative analysis.

关键词： Symmetric cryptography ciphertext encryption decryption cloud security matrix cipher

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases 2

Machine Learning Models for Brain Signal Classification: A F...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Almomani, Ammar Sakhnini, Mahmoud Al Aldweesh, Amjad AlAhliyya Amman University Communications And Computer Engineering Department Faculty Of Engineering Amman19328 Jordan Cybersecurity Department Faculty Of Information Technology Amman11831 Jordan Zarqa University Computer Science Department Faculty Of Information Technology Zarqa13110 Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan Shaqra University College Of Computing And It Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities. © 2024 IEEE.

关键词： Discrete wavelet transforms

来源：评论

学校读者我要写书评

暂无评论

Exploring Cultural Learning with Vertical Chatbot: Korda 8th

Exploring Cultural Learning with Vertical Chatbot: Korda

引用

8th International Conference on the Applications of Science and Mathematics, SCIEMATHIC 2022

作者： Kasinathan, Vinothini Mustapha, Aida Sewnundun, Neekita Bing, Diong Jien Fang, Lee Khoon Yi, Cindy Koh Xin School of Computing and Technology Asia Pacific University Taman Teknologi Malaysia Jalan Teknologi 5 Kuala Lumpur57000 Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Malaysia KM1 Jalan Pagoh Pagoh Johor 84600 Malaysia

ISBN: (纸本)9789819928491

Cultural heritage is the tangible and tacit legacy of attributes of society inherited from past generations. Its preservation relies on the memory of communities and individuals, hence limiting it from widespread access. To address this issue, this paper proposes the use of a vertical chatbot that serves as the conversational interface between the general public and the expert knowledge. The chatbot is called Korda that focuses on the lost heritage of a traditional dance in Malaysia;the Kuda Kepang. The prototype of Korda is developed on the Chatfuel framework and published as a Messager bot. User acceptance testing of Korda indicated positive feedback due to lack of prior knowledge in the cultural dance. Korda is hoped to promote access to and enjoyment of cultural diversity in Johor, Malaysia, in particular the Kuda Kepang. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Acceptance tests

来源：评论

学校读者我要写书评

暂无评论

Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

引用

Computer Systems Science & Engineering 2023年第3期44卷 2309-2322页

作者： Mahmoud Ragab Jaber Alyami Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Diagnostic Radiology Faculty of Applied Medical SciencesKing AbdulazizJeddah21589Saudi Arabia Imaging Unit King Fahd Medical Research CentreKing AbdulazizJeddah21589Saudi Arabia

Liver cancer is one of the major diseases with increased mortality in recent years,across the *** detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its ***,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response ***,there is a need exists to develop automated tools for liver cancer detection in a precise *** this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)*** proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation ***,Kapur’s entropy-based segmentation technique is used to identify the affected regions in ***,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature *** last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer *** order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different *** comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.

关键词： Liver cancer image segmentation artificial intelligence deep learning CT images parameter tuning

来源：评论

学校读者我要写书评

暂无评论

Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

引用

Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

来源：评论

学校读者我要写书评

暂无评论

Pneumonia Detection in Chest X-ray using InceptionV3 and Multi-Class Classification 3

Pneumonia Detection in Chest X-ray using InceptionV3 and Mul...

引用

3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Pandeeswaran, C. Abed Mohammed, Mazin Glan Devadhas, G. Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq Vimal Jyothi Engineering College Department of Electronics and Instrumentation Engineering Chemperi670632 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The lung is a imperative internal organs in human physiology. The abnormality in the lung will cause severe respiratory problems. Pneumonia is a severe lung infection, and early screening and treatment are essential to heal the illness. This research aims to implement a pre-trained InceptionV3 scheme to detect pneumonia in chest X-ray pictures. This scheme consists of the following phases;(i) Image collection and resizing, (ii) Deep-features extraction using InceptionV3, (iii) Feature reduction with firefly algorithm, (iv) Multi-class classification, and (v) Validation. A four-class classifier is employed in the proposed scheme to classify the X-ray into normal, mild, moderate, and severe classes using 5-fold cross-validation. The experimental outcome of the K-Nearest Neighbor (KNN) classifier confirms that this scheme offered a classification accuracy of 85.18% on the considered image database. © 2022 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Designing virtual environments to support cooperation in the real world

引用

Virtual Reality 2002年第2期6.0卷 63-74页

作者： Crabtree, A. Rodden, T. Mariani, J. School of Computer Science and Information Technology University of Nottingham Nottingham United Kingdom Computing Department Faculty of Applied Sciences Lancaster University Lancaster United Kingdom School of Computer Science and Information Technology University of Nottingham Nottingham NG8 1BB Wollaton Road United Kingdom

Much of the current research in the design of virtual environments is centred on laboratory prototypes and emphasis is placed on supporting cooperation within the virtual world itself. By way of contrast, this paper places an emphasis upon the development of virtual enivronments that support cooperation within the real world. This external research focus situates virtual environments within the bricolage of material artefacts that people use in coordinating and accomplishing ordinary jobs of work. We elaborate the shift to the external environment of ordinary human jobs and users through an ethnographic study of searching for information in the library, and the design and evaluation of a prototype supporting the real world cooperation involved in getting the job of searching done. © Springer-Verlag London Ltd.

关键词： Cooperative work Design Ethnography Evaluation Material affordances Virtual environments

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：