检索结果-内蒙古大学图书馆

New Modified Controlled Bat Algorithm for Numerical Optimization Problem

computers, Materials & Continua 2022年第2期70卷 2241-2259页

作者： Waqas Haider Bangyal Abdul Hameed Jamil Ahmad Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Joel J.P.C.Rodrigues M.Adil Khan Danda B.Rawat Richard Etengu Department of Computing and Technology Iqra University IslamabadPakistan Computer Science Kohat University of Science and Technology(KUST)KohatPakistan Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Federal University of Piauí(UFPI) Teresina-PLBrazil Instituto de Telecomunicacoes Covilh?6201-001Portugal Department Computer Science and Technology Chang’an UniversityXi’anChina Department of Electrical Engineering and Computer Science Howard UniversityWashingtonDCUSA

Bat algorithm(BA)is an eminent meta-heuristic algorithm that has been widely used to solve diverse kinds of optimization *** leverages the echolocation feature of bats produced by imitating the bats’searching *** faces premature convergence due to its local search *** of using the standard uniform walk,the Torus walk is viewed as a promising alternative to improve the local search *** this work,we proposed an improved variation of BA by applying torus walk to improve diversity and *** *** computerized Bat Algorithm(MCBA)approach has been examined for fifteen well-known benchmark test *** finding of our technique shows promising performance as compared to the standard PSO and standard *** proposed MCBA,BPA,Standard PSO,and Standard BA have been examined for well-known benchmark test problems and training of the artificial neural network(ANN).We have performed experiments using eight benchmark datasets applied from the worldwide famous machine-learning(ML)repository of *** results have shown that the training of an ANN with MCBA-NN algorithm tops the list considering exactness,with more superiority compared to the traditional *** MCBA-NN algorithm may be used effectively for data classification and statistical problems in the future.

关键词： Bat algorithm MCBA ANN ML Torus walk

来源：评论

学校读者我要写书评

暂无评论

Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

引用

8th International Conference on computer Technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

来源：评论

学校读者我要写书评

暂无评论

Adoption of Various Topic Modelling Algorithm to Analysis Indonesian Tourism Customer Feed Back

Adoption of Various Topic Modelling Algorithm to Analysis In...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Muhammad Hanafi Icha Nura Nugraha Sumarni Adi Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

The creative economy and tourism industry are significant sources of foreign exchange and major contributors to a country's gross domestic product (GDP). This study evaluates trends in the creative economy and tourism industry using topic modeling approaches, namely Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and BERTopic. Data were collected from the Twitter platform using API and analyzed with preprocessing steps including tokenization, stemming, and lemmatization. Model evaluation was performed based on topic coherence metrics (UMass, UCI, and Cv) and processing time The results show that LDA and LSA have high stability and consistency in generating coherent topics, while BERTopic has great potential but requires further calibration to achieve comparable stability. This study provides insights into the most effective topic models for analyzing trends in the creative economy and tourism industry, as well as providing recommendations for future research.

关键词： Analytical models Fluctuations Economic indicators Tourism industry Semantics Coherence Market research Stability analysis Calibration Resource management

来源：评论

学校读者我要写书评

暂无评论

Adoption Tea Leaf Disease Detection Using ResNet50 and Advance Hyperparameter

Adoption Tea Leaf Disease Detection Using ResNet50 and Advan...

引用

International Conference on Information and Communications Technology (ICOIACT)

作者： Muh Hanafi Sumarni Adi Magister of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798331536206

ISBN: (纸本)9798331536213

Advanced technology has become a key factor in managing plant diseases. One effective approach to diagnosing plant diseases is the use of deep learning models within the field of image processing. Tea leaf disease is one such condition that demands rapid and efficient intervention. This study employs a Convolutional Neural Network (CNN) architecture, specifically ResNet-50 and Inception V3, to classify six different diseases affecting tea leaves. To enhance the performance of ResNet-50 and Inception V3, data augmentation techniques were incorporated into the training process to increase data variability, and hyperparameters were fine-tuned to improve model effectiveness. The research includes four experimental scenarios aimed at achieving optimal outcomes. Three of these scenarios involve adjusting hyperparameters such as the optimizer, learning rate, and batch size. Classification was performed using three sets of data augmentation methods: zoom, rotation, and flip; zoom, rotation, and shear; and zoom, rotation, and shift. Fine-tuning hyperparameters and applying the right data augmentation techniques significantly improved ResNet-50's performance. The final results revealed that ResNet-50 outperformed the other models in classifying tea leaf diseases, achieving an average accuracy of 98.30% with the Adam optimizer, a learning rate of 0.0001, a batch size of 16, and the augmentation techniques of rotation, zoom, and flip.

关键词： Deep learning Training Image quality Plant diseases Image processing Data augmentation Information and communication technology Convolutional neural networks Engines Residual neural networks

来源：评论

学校读者我要写书评

暂无评论

Performance Improvements of Vehicular PKI Protocol for the Security of V2X Communications 46

Performance Improvements of Vehicular PKI Protocol for the S...

引用

46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Farran, Hassan Khoury, David Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services BudapestH-1111 Hungary American University of Science and Technology Department of Computer Science and Information and Communication Technology Beirut Lebanon

ISBN: (纸本)9798350303964

Vehicle-to-Everything (V2X) technology allows vehicles to communicate with numerous transportation system components. To provide safe communication between vehicles and other entities, V2X depends on Public Key Infrastructure (PKI). However, the present PKI employed in V2X has certain drawbacks, including scalability and high computational cost. This article offers an enhancement to the current PKI in V2X that tackles these constraints by managing and distributing keys using blockchain technology. The suggested Public Key Infrastructure enhancement is intended to be secure, efficient, and scalable, providing a more solid and trustworthy foundation for V2Xcommunication. The article explains the suggested PKI enhancement and examines its advantages and disadvantages. Overall, the suggested Public Key Infrastructure enhancement has the potential to increase the security and dependability of Vehicle-to-Everything communication, allowing for a secure and more effective transportation system. © 2023 IEEE.

关键词： Vehicle to Everything

来源：评论

学校读者我要写书评

暂无评论

Vehicle type recognition based on audio data 58

Vehicle type recognition based on audio data

引用

58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of Start-up Success Rate Prediction Using Machine Learning Techniques

Comparative analysis of Start-up Success Rate Prediction Usi...

引用

Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Mohammad Riyaz Belgaum Ediga Kasi Viswanath Pavukolla Chakradhar Ediga Chenna Kesava Goud Kuruva Chandu Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Software startups create innovative products or services using programming, which can lead to a need for products from potential customers. Researchers suggest that trial and error methodology can increase the success rate of these organizations by evaluating assumptions about client needs before fostering a product. However, programming new businesses typically do not require trial and error. The study aims to predict the success of startup companies in their early development stages by analyzing their success based on initial funding. A study of 106 unsuccessful software start-ups was conducted to predict the success rate. The experimentation progression model (X Pro) was developed to show that effective experimentation requires understanding trial and error, setting objectives, conducting tests, analyzing results, and acting based on new knowledge. The findings provide insights into supporting procedures and approaches that enhance trial- and-error reception in developing new business models. Researchers can become aware of potential factors leading to startup disappointment and take precautions to avoid them. The research focuses on using web-based open sources, including social networking services and the web, to model the task. The study uses machine learning techniques like Random Forest, Gradient Boost Classifier, XG Boost, and Ada Boost Classifier. The analysis provides insights into the types of useful signals discovered on the web and market mechanisms underlying the funding process. This approach offers a more comprehensive approach to predicting startup success.

关键词： Analytical models Accuracy Social networking (online) Machine learning Companies Predictive models Programming

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based License Plate Number Recognition for Smart Cities

引用

computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework for Preserving Patient Health Record in IoT Healthcare

A Novel Framework for Preserving Patient Health Record in Io...

引用

Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Mohammad Riyaz Belgaum Priyanka Pothureddy Sai Suraksha Patnam Parimala Deepthi Gadda Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

As artificial intelligence, the Internet of Things (IoT), and next-generation mobile communication continue to evolve at a rapid pace, an increasingly popular application called the Internet of Medical Things (IoT) has arisen to provide ease and practicality in healthcare. Nonetheless, privacy preservation issues are brought up by accessing patient medical information inside the IoT framework, which calls for the creation of safe and private-preserving solutions. To address these issues, we provide in this study an IoT-Framework for preserving patient health record (FFPPHR) based on ELGamal blind signatures. We provide an enhanced privacy protection medical record searching technique (FFPPHR) based on the elliptic curve discrete logarithm problem (ECDLP) and point out security vulnerabilities in the current FFPPHR. Our security research shows that by utilizing the hard problem assumption of ECDLP, the FPMR guarantees identity privacy and accuracy. Furthermore, the use of Blind ECDSA Signatures improves data security. Performance assessments confirm the FFPPHR efficiency, and theoretical findings agree with simulations. The suggested FFPPHR approach yields lower time costs when compared to state-of-the-art techniques, which makes it a potential option for safe and effective medical record searches in IoT contexts.

关键词： Privacy Costs Elliptic curves Data security Internet of Medical Things Search problems Mobile communication

来源：评论

学校读者我要写书评

暂无评论

A Low Complexity ML-Based Methods for Malware Classification

引用

computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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：