检索结果-内蒙古大学图书馆

7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Awotunde, Joseph Bamidele Sur, Samarendra Nath Imoize, Agbotiname Lucky Rodríguez, Demóstenes Zegarra Akanji, Boluwatife Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos 100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Computer Science Federal University of Lavras Minas Gerais Lavras Brazil

ISBN: (纸本)9789819764648

Keyloggers are malicious software programs that record keystrokes of users without their consent or knowledge. They can steal sensitive information like credit card numbers and passwords. They pose a significant threat to users’ privacy and security since they capture keystroke data. Keylogger detection systems play a vital role in safeguarding users from cyberattacks and mitigating the potential harm caused by keyloggers. Keylogger detection systems employing deep learning have been widely used for identifying and mitigating cyber threats associated with keyloggers. However, they struggle to identify new or unfamiliar keylogger samples. This study aims to explore deep learning techniques with feature selection model to detect keylogger. This study employs Recurrent Neural Networks (RNNs) using a collection of known keylogger dataset. Furthermore, a correlation-based feature extraction method was applied to identify the most relevant features for the model, highlighting the importance of specific features, such as URG flag Count, ACK flag count, and Idle Mean, in differentiating between benign and keylog classes. The proposed model demonstrates superior accuracy of 0.8763 and precision of 0.8569 compared to a baseline model using Logistic Regression (LR) 0.7721 and 0.7407, respectively, indicating a better balance between accurate classification and minimizing false negatives and false positives. The findings from this study help to improve keylogger detection methods, making them more powerful and efficient. These results suggest that a keylogger-based detection system employing a deep learning approach can be a valuable tool for addressing the complex and evolving landscape of cyber threats related to keylogging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

An energy-aware routing scheme based on a virtual relay tunnel in flying ad hoc networks

引用

Alexandria engineering Journal 2024年 91卷 249-260页

作者： Hosseinzadeh, Mehdi Ali, Saqib Mohammed, Adil Hussein Lansky, Jan Mildeova, Stanislava Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Hassan Ahmed, Omed Rahmani, Amir Masoud Mehmood, Asif Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Engineering Gachon University Gyeonggi-do Seongnam-si13120 Korea Republic of

Flying ad hoc networks (FANETs) tackle diverse challenges, for example, dynamic topological structure, high mobility of nodes, low density, and energy restrictions. These challenges make problems in designing reliable and stable routing in FANETs. Hence, the design of a routing algorithm is essential for transferring data between drones. In this paper, an energy-aware routing scheme based on a virtual relay tunnel (EARVRT) is proposed in flying ad hoc networks. EARVRT includes a virtual relay tunnel (VRT) that manages relay nodes in the route discovery process and lowers network overhead. Moreover, three metrics, namely route energy, hop counts, and a new metric called path correlation are regarded in the path selection operation. Note that correlation is defined with regard to two scales, including the cosine similarity of two nodes and their distance. EARVRT is evaluated according to delay, network longevity, consumed energy, and packet delivery rate. Then, our scheme is compared with three methods, namely LoCaL, O-LAR, and PSO-GLFR. These results show the superiority and success of EARVRT in comparison with other algorithms. © 2024 The Author(s)

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Prediction Using Machine Learning 2

Heart Disease Prediction Using Machine Learning

引用

2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Kamal, Haram Bakhtawar Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Mustafa, Muzzamil Yaqub, Muhammad Atif Umar, Hooria Fatima, Hoor Nasir, Usama Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Department of Computer Science Lahore Campus Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan University of Management & Technology Department of Artificial Intelligence Lahore Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Pakistan Bahria University Pakistan

ISBN: (纸本)9798331528713

The heart plays a pivotal role in the functioning of living organisms, making its diagnosis and prediction of related diseases a matter of utmost importance. Approximately 17.9 million individuals succumb to cardiovascular disease, accounting for 32% of worldwide fatalities. This global concern highlights the importance of early detection, as timely treatment can significantly reduce heart disease-related mortality. Errors in diagnosis can lead to severe consequences, including fatigue or even death. The increasing prevalence of heart-related diseases necessitates the development of precise prediction systems to enhance awareness. Machine learning, a subset of Artificial Intelligence (AI), offers robust tools for predicting various events based on patterns learned from natural occurrences. This research paper evaluates the accuracy of machine learning algorithms, specifically k- nearest neighbor, decision tree, linear regression, and support vector machine (SVM), in predicting heart disease. We utilize the UCI repository dataset for training and testing and employ the Python programming language through the Anaconda Jupyter notebook for implementation and the experimental findings indicate an accuracy rate of 95.7% using the heart disease prediction model, leveraging its extensive libraries and header files to ensure accuracy and precision in our analysis. © 2024 IEEE.

关键词： Random forests

来源：评论

学校读者我要写书评

暂无评论

Energy-Saving VM in SDN-Driven Cloud Data Center with the Operational Vacation Policy 3

Energy-Saving VM in SDN-Driven Cloud Data Center with the Op...

引用

3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Barik, Lalbihari Ghosh, Akash Pandey, Trilok Nath Gourisaria, Mahendra Kumar KIIT Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

The increasing use of the Internet of Things (IoT), which has resulted in an exponential growth in network traffic, has serious implications on energy consumption and network performance. To reduce power consumption in cloud data centers, a server vacation-based energy-efficient traffic management solution is recommended. The primary idea is that using software-defined network (SDN) technologies, cloud system virtual machines (VMs) can take vacations. The goal of the energy-saving optimization task is to reduce the number of virtual machines (VMs) in use while maintaining service quality standards. This research study focuses on impatient client behaviour, several vacations, limited buffer and queuing system with many servers. Impatient customers may be kept in the cloud system by implementing certain customer retention strategies. The steady-state probability for the proposed SDN-assisted cloud system have been determined using recurrences. To determine the efficiency of the proposed system, several performance studies are conducted. © 2023 IEEE.

关键词： Cloud data center Energy optimization Performance measurement Queueing model Software Defined Network Vacation policy

来源：评论

学校读者我要写书评

暂无评论

An efficient astronomical image processing technique using advance dynamic workflow scheduler in cloud environment

引用

International Journal of information technology (Singapore) 2022年第6期14卷 2779-2791页

作者： Ahmad, Faiyaz Ahmad, Wakar Faculty of Engineering and Technology Department of Computer Engineering Jamia Millia Islamia New Delhi 110025 India Department of Information Technology Meerut Institute of Engineering and Technology Meerut 250005 India

The Montage project of NASA introduces an efficient astronomical image mosaic processing service with the help of Montage workflow applications. These workflow applications contain a set of input astronomical images that are able to produce custom mosaics of the sky to understand the universe’s activities. In the last 2 decades, extensive research has been done for processing Montage application in the various distributed computing paradigms such as Grids and Clusters but fails to achieve the required performance in terms of makespan. Recently, Cloud computing is the latest computing paradigm that provides online computing resources for efficiently processing these applications. In this article, we introduce an efficient astronomical image processing technique (EAIPT) for Montage workflow application in the cloud environment that shows notable improvements in terms of minimization of makespan without increasing the time complexity with the help of an advance dynamic scheduler. The proposed dynamic scheduler tries to schedule the current workflow task on the virtual machine that already processed its last parent task based on proposed scheduling constraints. In this way, it bypasses the communication time among different VMs. The performance of the proposed algorithm is compared with the existing state of art algorithms. The experimental results prove that our proposed technique has significant performance over compared algorithms in terms of schedule length ratio (SLR), the percentage of best results, and average running time metrics. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Cloud computing Image processing Montage application Workflow scheduling

来源：评论

学校读者我要写书评

暂无评论

Investigating Privacy and Security Concerns in a Running eHealth information System 3

Investigating Privacy and Security Concerns in a Running eHe...

引用

3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Denkovski, Viktor Stojmenovska, Irena Gavrilov, Goce Radevski, Vladimir Trajkovik, Vladimir University American College Skopje School of Computer Science and Information Technology Skopje Macedonia University 'Ss. Cyril and Methodus' Faculty of Computer Science and Engineering Skopje Macedonia

ISBN: (纸本)9798350333732

The healthcare authorities should be seriously dedicated in maintaining a high level of data security and data privacy, due to the highly sensitive nature of patient data The study investigates the results explored via interview and survey examination on the privacy and security concerns related to medical providers' use of eHealth information systems in Macedonia. The purpose is to determine whether medical professionals are familiar with the security measures taken by healthcare authorities and to evaluate their opinions regarding the demand for system upgrades. About a dozen medical providers underwent in-depth email interviews to discover the privacy and security concerns in using the eHealth systems. This was a cornerstone to conduct an online questionnaire in which 370 medical providers including professionals from primary and secondary healthcare responded. The results underline how crucial it is to implement strong security controls and deal with system usability issues to guarantee the confidentiality and integrity of sensitive patient data. The study's conclusions highlight the importance of thorough privacy and security protocols in the context of eHealth technologies. © 2023 IEEE.

关键词： eHealth

来源：评论

学校读者我要写书评

暂无评论

Text Mining for Intelligent information Processing and Analysis 3

Text Mining for Intelligent Information Processing and Analy...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Srivastav, Adarsh Indira, G. Tiwaskar, Shweta A. Garg, Avni Singh, Shweta Das, Sunanda Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

textual content Mining is the process of extracting meaningful information from large volumes of unstructured text. it's far a form of synthetic intelligence used to investigate and interpret files along with consumer feedback, emails, transcripts, net logs, surveys, and on line documents. Its intention is to discover hidden shape, patterns, institutions, and correlations in textual facts in order to draw inferences and broaden new insights. textual content Mining allows a deeper level of intelligence and selection-making by means of uncovering which means from unstructured textual facts. by means of utilising present day statistical strategies and herbal Language Processing (NLP), text Mining can produce automatic fashions that interpret and translate textual content into intelligent information which can be used to make correct predictions and provide actionable insights. textual content Mining is also being used to find out new trends, categorize client sentiment, locate correlations in purchaser comments, and section document collections. eventually, text Mining is being used to assist automate selection-making by automating workflows, processing massive volumes of text in a fragment of time, and providing deep insights into patron conversations. © 2023 IEEE.

关键词： Automating Categorize Collections Conversations Insights

来源：评论

学校读者我要写书评

暂无评论

Exploring the Use of Particle and Kalman Filters for Obstacle Detection in Mobile Robots

Periodica polytechnica Electrical engineering and computer s...

引用

Periodica polytechnica Electrical engineering and computer science 2023年第4期67卷 384-393页

作者： Gyenes, Zoltán Bölöni, Ladislau Szádeczky-Kardoss, Emese Gincsainé Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Muegyetem rkp. 3. BudapestH-1111 Hungary Department of Computer Science University of Central Florida 4328 Scorpius St OrlandoFL32816-2362 United States

The present study aims to explore the adaptation of estimation methodologies, specifically Particle filters and Kalman filters, for the purpose of determining the position and velocity vector of obstacles within the operational workspace of mobile robots. These algorithms are commonly employed in the motion planning tasks of mobile robots for the estimation of their own position. The proposed methodology utilizes LiDAR sensor data to estimate the position vectors and calculate the velocity vectors of obstacles. Additionally, an uncertainty parameter can be determined using the introduced perception method. The performance of the newly adapted algorithms is evaluated through comparison of the absolute error in position and velocity vector estimations. © 2023 Budapest University of technology and Economics. All rights reserved.

关键词： Mobile robots

来源：评论

学校读者我要写书评

暂无评论

Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images 23rd

Robust Multiresolution and Multistain Background Segmentatio...

引用

Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

来源：评论

学校读者我要写书评

暂无评论

A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

引用

computer Modeling in engineering & Sciences 2021年第3期126卷 991-1009页

作者： Sunil Kr.Jha Ivan Uzunov Xiaorui Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Faculty of Computer Science and Engineering University of Information Science and TechnologyOhrid6000Republic of Macedonia

Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural *** external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their *** data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid *** this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth *** PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and *** classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.

关键词： Salamo letnica growth phase modeling PCA SVM

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：