检索结果-内蒙古大学图书馆

5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Cheng, Shiying Lin, Yuxuan Tang, Bin Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798331506612

In large-scale distributed systems, the performance of computation tasks is often significantly degraded by straggling nodes. Recently, coded computation has emerged as a promising approach to mitigate the effect of stragglers. However, the performance of coded computation is still significantly affected by node workloads, particularly in heterogeneous clusters. Some load allocation schemes have been proposed to minimize the expected computation latency, but for time-sensitive tasks with strict deadlines, they often result in high failure probabilities. In this paper, we examine the fundamental matrix-vector multiplication tasks and focus on the deadline-aware load allocation problem under two typical runtime models, aiming to minimize the task failure probability. We firstly propose a simple yet effective normal approximation method based on the central limit theorem to approximate the failure probability, transforming the problem into a non-convex multivariate optimization, and then present an efficient iterative load allocation algorithm. Extensive simulations demonstrate the effectiveness of our proposed scheme. © 2024 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Reducing Age of Collection with Dynamic-Frame Time Division Multiple Access 100

Reducing Age of Collection with Dynamic-Frame Time Division ...

引用

100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Lai, Yurong Han, Xinhui Wang, Xueer Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798331517786

This paper introduces a dynamic-frame time division multiple access (DF-TDMA) scheme aimed at decreasing the age of collection (AoC) in collaborative monitoring scenarios. Unlike the conventional age of information (AoI) metric, AoC decreases only when partial information from multiple sources is aggregated to form a complete observation. Previous studies on AoC predominantly assumed that complete observations are derived from aggregating information from all sources. However, this assumption does not hold in scenarios where sources are correlated, and a complete observation can be achieved with partial information from only a subset of sources. To address this, new channel access protocols are necessary to achieve a low network-wide average peak AoC. DF-TDMA is proposed as a solution, featuring dynamically adjustable TDMA frame sizes to minimize time wastage and thereby reduce AoC. While the dynamic frame size adjustment complicates AoC analysis, we theoretically derive the average peak AoC of DF-TDMA. Simulations show that DF-TDMA maintains a stable average peak AoC across varying numbers of sources N, and notably reduces the average peak AoC compared to a fixed-frame TDMA scheme, particularly under conditions with a large N. © 2024 IEEE.

关键词： Time division multiple access

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition

引用

computers, Materials & Continua 2024年第2期78卷 1957-1975页

作者： Liya Yue Pei Hu Shu-Chuan Chu Jeng-Shyang Pan Fanli Business School Nanyang Institute of TechnologyNanyang473004China School of Computer and Software Nanyang Institute of TechnologyNanyang473004China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung413310Taiwan

Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by *** number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition *** proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum ***,we use the information gain and Fisher Score to sort the features extracted from ***,we employ a multi-objective ranking method to evaluate these features and assign different importance to *** with high rankings have a large probability of being ***,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local *** random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification *** results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER.

关键词： Speech emotion recognition filter-wrapper high-dimensional feature selection equilibrium optimizer multi-objective

来源：评论

学校读者我要写书评

暂无评论

A Review of Image Steganography Based on Multiple Hashing Algorithm

引用

computers, Materials & Continua 2024年第8期80卷 2463-2494页

作者： Abdullah Alenizi Mohammad Sajid Mohammadi Ahmad A.Al-Hajji Arshiya Sajid Ansari Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim56312Saudi Arabia

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover *** ancient times to the present,the security of secret or vital information has always been a significant *** development of secure communication methods that keep recipient-only data transmissions secret has always been an area of ***,several approaches,including steganography,have been developed by researchers over time to enable safe data *** this review,we have discussed image steganography based on Discrete Cosine Transform(DCT)algorithm,*** have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman(RSA)method,the Blowfish technique,and the hash-least significant bit(LSB)*** this review,a novel method of hiding information in images has been developed with minimal variance in image bits,making our method secure and effective.A cryptography mechanism was also used in this *** encoding the data and embedding it into a carry image,this review verifies that it has been ***,embedded text in photos conveys crucial signals about the *** review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data *** the message is ever intercepted by a third party,there are several ways to stop this operation.A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.

关键词： Image steganography multiple hashing algorithms Hash-LSB approach RSA algorithm discrete cosine transform(DCT)algorithm blowfish algorithm

来源：评论

学校读者我要写书评

暂无评论

Early-stage stroke prediction based on Parkinson and wrinkles using deep learning

引用

Neural Computing and Applications 2024年第30期36卷 18781-18805页

作者： Haritha, T. Babu, A. V. Santhosh Department of Information Technology Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India

The application of noninvasive methods to enhance healthcare systems has been facilitated by the development of new technology. Among the four major cardiovascular diseases, stroke is one of the deadliest and potentially fatal, but, if detected early enough, a patient's life may be spared. Most stroke research has centered on MRI and CT scans for uncomplicated categorization. This medical approach (imaging) is costly, time-consuming and needs the utilization of complex technology. To make up for these shortcomings, however, there has been a lot of interest in adopting noninvasive, measurable EEGs. Nevertheless, the raw data should be classified before the proper characteristics can be formed, both the forecasting algorithms and the analytical techniques demand time. As a result, this work proposes a deep learning-based model that aims to predict the chance of stroke at an early stage utilizing Parkinson's disease and wrinkles as markers. A patient may have a stroke disease if they are diagnosed with both Parkinson's disease and wrinkles. To the best of our knowledge, this research is the first to use these biomarkers to predict the risk of having a stroke. The proposed model achieves a higher accuracy of 94.7% on the considered dataset. Additionally, the recommended model was evaluated and tested in terms of loss, training time, accuracy, recall, and F1-score versus the other existing models. With less price and pain than present testing approaches, these discoveries are predicted to result in major enhancements in the early detection of strokes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Analysis and Classification of Fake News Using Sequential Pattern Mining

引用

Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

来源：评论

学校读者我要写书评

暂无评论

UAV-Assisted Maritime Data Collection via Optical Communications using Deep Reinforcement Learning 9

UAV-Assisted Maritime Data Collection via Optical Communicat...

引用

9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Ma, Saisai Li, Mengzhen Deng, Tinglong Wang, Jinglong Ruby, Rukhsana Luo, Hanjiang Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350331240

Ocean data collection via unmanned aerial vehicles (UAVs) has received widespread attention due to its flexibility and low cost. To further improve the efficiency of large maritime data collection between the UAVs and the ocean surface buoys, optical wireless transmission is considered as a promising technique because of its low latency and high bandwidth. However, the waves and other disturbances in the complex oceanic environment result in drift and instability of the buoy, which deteriorate and even interrupt the line-of-sight (LOS) optical transmission. To tackle these challenges, in this paper, we propose a reliable data collection scheme based on a deep reinforcement learning (DRL) approach. We first model the optical channel and calculate the maximum transmission range that satisfies a pre-defined bit error rate (BER) to ensure the quality of service (QoS). Then, we formulate the data collection procedure as a Markov decision process (MDP) aiming at maximizing the received signal intensity and minimizing the energy consumption, in which the system uncertainty caused by the oceanic environmental disturbances is taken into account. Finally, we propose a beam pointing adjustment algorithm based on deep deterministic policy gradient (DDPG) approach to alleviate the performance deterioration and maintain a stable LOS communication. Through extensive simulations, the results demonstrate that the proposed scheme is effective and achieves reliable data collection via the optical links. © 2023 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

引用

computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

来源：评论

学校读者我要写书评

暂无评论

Surtify: A Smart Surface Identification System Based on Multi-dimensional Acoustic Dispersion

Surtify: A Smart Surface Identification System Based on Mult...

引用

2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Wang, Yunshu Yuan, Baojie Dong, Feihang Hong, Shicong Zou, Yongpan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798331507886

With the continuous advancement of the smart home market, household items have become more intelligent. By identifying the different material attributes of various household tabletops, we can obtain contextual information and engage in further interactions. This paper explores a phenomenon, acoustic dispersion and applies it to surface identification. Integrating mobile deep learning, we propose a method to recognize solid surface materials through tapping and utilize tapping events as triggers for interaction by identifying surface material categories. To achieve the goal of zero training effort and tiny hardware cost for unseen users, we analyze the propagation patterns of different frequency components in tapping sound signals from both temporal and spatial dimensions. Based on this, we propose a time-frequency data augmentation strategy that combines multiple event points to improve the recognition accuracy. The results show that, using the leave-one-user-out cross validation for 24 volunteers trained with simple guidance on tapping intensity and position, our system achieves a recognition accuracy of 98.6% for 11 common material types. For 11 newly recruited volunteers without specific control over force and position, the accuracy also remains at 93.4%. We also conduct a series of cross-environment experiments and user study, all of which indicate the high potential of our system. © 2024 IEEE.

关键词： Smart homes

来源：评论

学校读者我要写书评

暂无评论

An Efficient Framework for Utilizing Underloaded Servers in Compute Cloud

引用

computer Systems science & engineering 2023年第1期44卷 143-156页

作者： M.Hema S.Kanaga Suba Raja Department of Information Technology Easwari Engineering CollegeChennai600089India Department of Computer Science and Engineering Easwari Engineering CollegeChennai600089India

In cloud data centers,the consolidation of workload is one of the phases during which the available hosts are allocated *** phenomenon ensures that the least possible number of hosts is used without compromise in meeting the Service Level Agreement(SLA).To consolidate the workloads,the hosts are segregated into three categories:normal hosts,under-loaded hosts,and over-loaded hosts based on their *** is to be noted that the identification of an extensively used host or underloaded host is challenging to ***-old values were proposed in the literature to detect this *** current study aims to improve the existing methods that choose the underloaded hosts,get rid of Virtual Machines(VMs)from them,andfinally place them in some other *** researcher proposes a Host Resource Utilization Aware(HRUAA)Algorithm to detect those underloaded and place its virtual machines on different hosts in a vibrant Cloud *** mechanism presented in this study is contrasted with existing mechanisms *** results attained from the study estab-lish that numerous hosts can be shut down,while at the same time,the user's workload requirement can also be *** proposed method is energy-efficient in workload consolidation,saves cost and time,and leverages active hosts.

关键词： Workload consolidation energy consumption under load server resource utilization

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：