检索结果-内蒙古大学图书馆

A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

computers, Materials & Continua 2023年第10期77卷 781-797页

作者： Yazeed Yasin Ghadi Mohammed SAlshehri Sultan Almakdi Oumaima Saidani Nazik Alturki Fawad Masood Muhammad Shahbaz Khan Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Institute of Space TechnologyIslamabad4400Pakistan School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK

Securing digital image data is a key concern in today’s information-driven *** encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption *** study introduces an innovative approach to creating S-boxes for encryption *** proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption *** nonlinearity measure of the proposed S-boxes is *** qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data ***,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption *** validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been *** addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are *** results indicate a 99.71%NPCR and 33.51%*** results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

关键词： S-box chaos artificial bee colony image encryption

来源：评论

学校读者我要写书评

暂无评论

The Analysis for the Traffic Risks in Barcelona before and after the COVID-19 Based on Deep Learning 2

The Analysis for the Traffic Risks in Barcelona before and a...

引用

2nd International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2021

作者： Shao, Yinghui Zhang, Enkai Han, Ziheng School of Computing Wuhun China Guizhou University College of Large Data and Information Engineering Guizhou China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9781665412964

Big Traffic data [1] is cross-border multi-source data for multiple industries, but traffic roads have brought significant economic and social benefits, the number of traffic accidents and casualties is on the rise. Among them, traffic accidents are related to many factors, such as weather and population density. The data set used in this article is open source in Barcelona. The Random Forest algorithm is used to screen essential risk factors, establish a traffic risk prediction model, and compare traffic risks before and after COVID-19. It is concluded that the outbreak of the new crown virus -19-19 has a great impact on people's travel and transportation. Finally, the R square of the model established by Random Forest is 0.9. The K-means clustering algorithm is used to determine the location of the accident handling centre. Moreover, the scope of each accident risk management centre can cover more than 85 percent of traffic accident sites from 2016 to 2020. © 2021 IEEE.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

引用

21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

来源：评论

学校读者我要写书评

暂无评论

Exploring Machine Learning Models for Duplicate Question Detection in Online Communities

Exploring Machine Learning Models for Duplicate Question Det...

引用

International Conference on Advances in Electronics, computers and Communications (ICAECC)

作者： Bharathi Mohan G R Prasanna Kumar Elakkiya R V Druva Veer Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai Computer Science and Engineering Birla Institute of Technology and Science School of Computing Dubai UAE

ISBN: (数字)9798350305449

ISBN: (纸本)9798350305456

Duplicate question detection is a crucial task in question-and-answer platforms, aiming to improve user experience and minimize redundancy. This project utilizes NLP techniques and machine learning models to predict whether a pair of questions are duplicates accurately. The project involves comprehensive data pre-processing, including tokenization, stopword removal, and lemmatization, to prepare the textual data for analysis. Here we are using various feature engineering techniques, such as bag-of-words, TF-IDF, and word embedding, are applied to extract meaningful representations of the questions. Multiple machine learning models, including Logistic Regression, Random Forest, SVM, XGBoost, and a Combined model, are trained on labeled data. Evaluation metrics focus on precision and recall minimizing misclassification costs. New sample data is used to validate the models. Predictions are made to classify question pairs as duplicates or non-duplicates, showcasing the model's generalization capabilities. The results highlight the potential of NLP techniques and machine learning models in accurately identifying duplicate question pairs, improving knowledge sharing, and reducing redundancy. This project provides valuable insights and methodologies for future research in duplicate question detection, contributing to the field of NLP and enhancing user experiences on question-and-answer platforms.

关键词： Support vector machines Costs Redundancy Predictive models Feature extraction Data models User experience

来源：评论

学校读者我要写书评

暂无评论

Fuzzy-Based Weighted Federated Machine Learning Approach for Sustainable Energy Management with IoE Integration

Fuzzy-Based Weighted Federated Machine Learning Approach for...

引用

2024 Systems and Information Engineering Design Symposium, SIEDS 2024

作者： Ghazal, Taher M. Iqbal Janjua, Jamshaid Abbas, Sagheer Fatima, Areej Saleem, Muhammad Khan, Muhammad Adnan Alqarafi, Abdulrahman Khalifa University Centre for Cyber Physical Systems Computer Science Department Abu Dhabi United Arab Emirates Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Selangor Malaysia National College of Business Administration and Economics School of Computer Sciences Lahore Pakistan Al-Khawarizmi Institute of Computer Science Lahore Pakistan Bahria University Department of Computer Science Lahore Pakistan LGU Department of Computer Science Lahore Pakistan Minhaj University School of Computer Science Lahore Pakistan Skyline University College School of Computing Sharjah United Arab Emirates Riphah International University Riphah School of Computing and Innovation Lahore Pakistan Taibah University College of Computer Science and Engineering Medina42353 Saudi Arabia

ISBN: (纸本)9798350385144

The considerable surge in energy consumption due to population growth and the use of new technology has posed significant challenges to energy security and the environment. Managing energy consumption on the consumer side is crucial as the demand for energy continues to increase. Consumers need to monitor their daily usage and understand consumption standards to prepare themselves for energy efficiency and cost savings. To address these challenges, a Smart Energy Management System (SEMS) based on the Internet of Energy (IoE) has been developed. This system uses day-Ahead planning and accurate energy availability forecasting to manage energy demand in a smart environment with high sustainable energy penetration. The Fuzzy based Weighted Federated Learning (FWFML) approach is integrated into the system to optimize energy management. The SEMS system monitors energy usage at the user level, and a review of management plans has been introduced to improve energy efficiency in terms of 93.5% accuracy, and 6.5% miss-rate. This research work also highlights future challenges in developing SEMS to increase energy efficiency in a more robust manner. © 2024 IEEE.

关键词： Energy management systems

来源：评论

学校读者我要写书评

暂无评论

Systematic Literature Review on Test Case Quality Characteristics and Metrics 3

Systematic Literature Review on Test Case Quality Characteri...

引用

3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Obaid Barraood, Samera Mohd, Haslina Baharom, Fauziah Almogahed, Abdullah College of Computers and Information Technology Hadhramout University Department of Computer Science Hadhramout Yemen School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Parit Raja Malaysia

ISBN: (纸本)9798350305333

Companies are constantly optimizing their processes to increase the efficiency of software development, which heavily depends on software testing to fulfil stringent business requirements and with high-quality software released. The detection of software defects and the assurance of software quality are facilitated by high-quality test cases. However, the current metrics used to measure test case quality (TCQ) lack clarity. Therefore, this study aims to address this gap by identifying metrics and the corresponding characteristics that measure the TCQ. This paper presents a systematic literature review (SLR) focused on TCQ characteristics and metrics. By analyzing empirical papers published between 2010 and June 2018, a total of 23 papers were identified for inclusion in the review. Through this process, 16 characteristics and 50 metrics were identified. These characteristics and metrics were further analyzed, categorized, and finalized as the evaluation metrics for TCQ characteristics. The outcomes of this study contribute to the development of a comprehensive model for assessing TCQ. © 2023 IEEE.

关键词： Software testing

来源：评论

学校读者我要写书评

暂无评论

Twitter Sentiment Analysis in the Crisis between Russia and Ukraine Using the Bert and LSTM Model 4

Twitter Sentiment Analysis in the Crisis between Russia and ...

引用

4th IEEE International Conference on computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Maheshwari, Himani Chandra, Umesh Yadav, Dharmendra Gupta, Ashulekha School of Computing Graphic Era Hill University Uttarakhand Dehradun India Banda University of Agriculture and Technology Department of Statistics &computer Science India Glocal University Department of Computer Science and Technology UP Saharanpur India Graphic Era Deemed to Be University Department of Management Studies India

ISBN: (纸本)9798350306118

Sentiment analysis is a method for determining the attitude or viewpoint of individuals towards any trending topic. The most appropriate platform for sharing opinions on any topic is social media. Twitter is a social networking site where users may express their opinions using text messages called tweets. Originally, tweets could only be 140 characters long, but they are now up to 280 characters. Sentiment analysis is a component of NLP that aids in determining the polarity of a text or tweet. Using various keywords like 'Ukraine war', 'Ukraine and Russia war', 'Ukraine', and 'NATO in Ukraine,' we are gathering tweets about the conflict between Ukraine and Russia. There were 121156 tweets collected in total, however only 31584 were left for sentiment analysis after cleaning, lemmatization stemming, and deleting duplicate tweets. The 'Text Blob' package of Python was used to determine the polarity of the tweets. The present study used the BERT, LSTM, and LSTM with glove methods of machine learning, which produced the best results in comparison to earlier studies with accuracy levels of 97 to 99%. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Extension and application of Protocol Composition Logic

Extension and application of Protocol Composition Logic

引用

2010 2nd International Conference on computer Engineering and technology, ICCET 2010

作者： You, Ziyi Li, JunTao Xie, Xiaoyao School of Computer Science and Technology Guizhou University Province Guiyang China Departments of Physics and Electronic Science Guizhou Normal University Province Guiyang China Key Laboratory of Information and Computing Science of Guizhou Province Guiyang China

ISBN: (纸本)9781424463503

Protocol Composition Logic (PCL) is a logic for proving security properties of network protocols that use public and symmetric key cryptography. Because the PCL composition theorems support compositional reasoning about security protocols, including parallel composition of different protocols, and sequential composition of protocol steps, it is particularly useful in carrying out larger-scale security protocol studies. In this paper, PCL is extened by strand space model (SSM) and temporal logic (TL) to better describe and verify complex protocols Based on improved PCL, the TLS protocol is verified as an example and proved to satisfy authentication property. © 2010 IEEE.

关键词： Temporal logic

来源：评论

学校读者我要写书评

暂无评论

A Machine Learning-based SDN Controller Framework for Drone Management

A Machine Learning-based SDN Controller Framework for Drone ...

引用

2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Yazdinejad, Abbas Rabieinejad, Elnaz Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam University of Guelph Cyber Science Lab School of Computer Science ON Canada K.N.Toosi University of Technology Information Technology Department Tehran Iran Kennesaw State University College of Computing and Software Engineering GA United States Brandon University Department of Mathematics and Computer Science MB Canada

ISBN: (纸本)9781665423908

With the advancement of information and communication technology, Unmanned Aerial Vehicles (UAV), popularly known as drones, have also increased. The drones have been noted for their wide range of applications such as military, search and rescue operation, disaster detection and monitoring, agriculture, and delivery. Each type of drone has different characteristics and functionality based on its application, making them a security threat for some city zone. Therefore, there is an essential need for efficient drone management based on their type and application in different zones. To do this, we proposed a Machine learning (ML) based Software Defined Network (SDN) drone management framework. In this framework, the SDN controller uses ML with the drone's radio frequency feature to detect its type and application and, according to its application, authenticate it and assign communication rules. SDN controller records authentication information in a DAG-based Distributed Ledger technology (DLT) available for other SDN controllers. When a drone desires to migrate to another zone, the destination SDN controller can achieve authentication information by referring to DAG-based DLT, and there is no need for re-authentication. The experimental result shows authentication delay reduction in our proposed framework. Moreover, we adopted ML algorithms includes Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), to evaluate our proposed framework in drone's type classification. The result shows that the RF algorithm shows the best performance with 92.81% accuracy in the classification of the drone's type. © 2021 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

A Study of Word Embedding Models for Machine Translation of North Eastern Languages 5th

A Study of Word Embedding Models for Machine Translation o...

引用

5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Nath, Basab Sarkar, Sunita C. Debnath, Narayan Department of Computer Science and Engineering Assam University Silchar India School of Computing and Information Technology Eastern International University Binh Duong Thu Dau Mot Viet Nam

ISBN: (纸本)9783031488788

Neural Machine Translation (NMT) has experienced significant growth in recent years and is now a well-established field. Despite being the most popular machine translation solution, NMT’s performance on low-resource language pairs is inferior to that of high-resource language pairs because of the lack of large parallel corpora for these *** paper explores how pre-trained word embedding models, specifically FastText and Word2Vec, can enhance the translation performance of NMT (neural machine translation) systems for low resource English-Assamese, English-Manipuri, and English-Nepali language pairs. We have compared the performance of NMT systems trained with the use of these pre-trained word embedding models and evaluated the results on test sets of English-Assamese, English-Manipuri, and English-Nepali translations. The resukts indicate that the use of FastText significantly improves the translation performance of NMT systems for both language pairs, with an average increase in BLEU score of 5.6, 7.3, and 4.4 points for English-Assamese, English-Manipuri, and English-Nepali, respectively. We have also discussed the potential challenges and limitations of using pre-trained word embedding models for NMT in low-resource languages and suggested directions for future work in this area. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural machine translation

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：