检索结果-内蒙古大学图书馆

Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

引用

Computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

来源：评论

学校读者我要写书评

暂无评论

Translating Words to Pixels: An Effective Text-To-Image System Utilizing Upsampling and CLIP Models

Translating Words to Pixels: An Effective Text-To-Image Syst...

引用

2024 International Conference on engineering and Computing, ICECT 2024

作者： Saeed, Nuaima Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Department of Artificial Intelligence Topi Pakistan

ISBN: (纸本)9798350349719

Generative AI and text-based image synthesis are being pursued with more interest than ever and adopt a growing audience. This work offers a new way of producing images within a broad range of quality levels, deducing the extra value of the increase resolution. The system applies sophisticated technologies such as diffusion and upscaling models that are guided by the pre-Training of the Contrastive Language-Image PreTraining (CLIP). However, the model shines primarily in its ability to generate visually cohesive and artistic images which are a good illustration of each specified textual prompt. Upsampling technique resolved the major translation problems by generating more high resolution and detailed resultant images of base images. Beyond that, the system also thoroughly illustrated its diversity by accurately producing a comprehensive collection of images based on the text input. This work would largely be a demonstration of the suitability of it for different fields like digital art, advertising, and content creation. Moreover, it delimits the chances of producing innovative and unique content. Future development of many other applications of the system will include the improvement of the system handling more complex textual descriptions, the addition of user-interactive generation features, the optimization of system performance and expanding to multimodal content generation. Our results here only represent a small but important step in this exciting direction. By means of this survey we enrich further knowledges on the field and its farther issues. © 2024 IEEE.

关键词： Signal sampling

来源：评论

学校读者我要写书评

暂无评论

Critical Evaluation of the Role of Cloud Systems and Networking in the Security and Growth of the Business Market 2

Critical Evaluation of the Role of Cloud Systems and Network...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Fakhouri, Hussam N. Alhadidi, Basim Alsharaiah, Mohammad A. Al Naddaf, Heba Alomoush data, Ashraf Saleh University Of Petra Faculty Of Information Technology Data Science And Artificial Intelligence Department Amman Jordan Al-Balqa Applied University Department Of Computer Information Systems Salt19117 Jordan Al-Ahliyya Amman University Data Science And Artificial Intelligence Department Amman Jordan Damascus University Faculty Of Media Deprtment Of Electronic Media Damascus Syria University Of Petra Faculty Of Information Technology Science And Artificial Intelligence Department Amman Jordan

ISBN: (纸本)9798350394962

This paper aims to assesses the multifaceted role of cloud systems in stimulating and protecting business market growth, with the goal of exploring the transformative benefits of cloud computing, such as lower costs, increased flexibility, improved data security, efficient collaboration, and greater integration, while also highlighting the challenges and risks associated with it. These include data protection, legal concerns, and compliance, which are especially critical given the growing incidence of cybersecurity risks. Furthermore, the paper dives into the many types of cloud services, such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS), outlining their distinct benefits, potential downsides, and applicability in various business situations. It also looks at new trends and solutions, particularly the rise of hybrid cloud systems. © 2024 IEEE.

关键词： Infrastructure as a service (IaaS)

来源：评论

学校读者我要写书评

暂无评论

Optimisation and Performance Computation of a Phase Frequency Detector Module for IoT Devices

Annals of Emerging Technologies in Computing

引用

Annals of Emerging Technologies in Computing 2024年第1期8卷 13-21页

作者： Hemel, Md. Shahriar Khan Reaz, Mamun Bin Ibne Ali, Sawal Hamid Bin Md Bhuiyan, Mohammad Arif Sobhan Miraz, Mahdi H. Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia Malaysia Department of Electrical and Electronic Engineering Independent University Bangladesh School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Malaysia School of Computing and Data Science Xiamen University Malaysia Malaysia Faculty of Arts Science and Technology Wrexham University United Kingdom Faculty of Computing Engineering and Science University of South Wales United Kingdom

The Internet of Things (IoT) is pivotal in transforming the way we live and interact with our surroundings. To cope with the advancement in technologies, it is vital to acquire accuracy with the speed. A phase frequency detector (PFD) is a critical device to regulate and provide accurate frequency in IoT devices. Designing a PFD poses challenges in achieving precise phase detection, minimising dead zones, optimising power consumption, and ensuring robust performance across various operational frequencies, necessitating complex engineering and innovative solutions. This study delves into optimising a PFD circuit, designed using 90 nm standard CMOS technology, aiming to achieve superior operational frequencies. An efficient and high-frequency PFD design is crafted and analysed using cadence virtuoso. The study focused on investigating the impact of optimising PFD design. With the optimised PFD, an operational frequency of 5 GHz has been achieved, along with a power consumption of only 29 µW. The dead zone of the PFD was only 25 ps. © 2024 by the author(s).

关键词： Phase locked loops

来源：评论

学校读者我要写书评

暂无评论

Proactive Alzheimer's Disease Identification Through EEG-Based Biomarkers 1

Proactive Alzheimer's Disease Identification Through EEG-Bas...

引用

1st International Conference on artificial intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Bhendare, Gaurav Dewangan, Leelkanth Datta Meghe Institute of Higher Education & Research Artificial Intelligence and Machine Learning Faculty of Engineering &technology Maharashtra Wardha India Datta Meghe Institute of Higher Education & Research Artificial Intelligence and Data Science Faculty of Engineering &technology Maharashtra Wardha India

ISBN: (纸本)9798350330915

To detection of Alzheimer's disease (AD) therapy is crucial for patients. Because it enables patients to take prevention actions before brain damage takes place. Although inherited effects limit most computer detection methods, computers have been employed in numerous research to identify AD The 'Electroencephalogram (EEG)' signal is used in this study to implement a deep learning detecting of Alzheimer's disease (AD). Initially, conventional internet databases are used to get the necessary EEG signal and there after used the The signal is divided into many current Neural Networks using the 'Lifting Wavelet Transform (LWT)' decomposition level. Extracts the temporal properties from the signal that has been divided up. © 2023 IEEE.

关键词： AD (Alzheimer's disease) LWT (Lifting Wavelet Transform) EEG (Electroencephalogram)

来源：评论

学校读者我要写书评

暂无评论

An Empirical Study on: Time Series Forecasting of Amazon Stock Prices using Neural Networks LSTM and GAN models 5

An Empirical Study on: Time Series Forecasting of Amazon Sto...

引用

5th International Conference on Information Management and Machine intelligence, ICIMMI 2023

作者： Bhardwaj, Anjul Singh, Uday Pratap Dept. Artificial Intelligence & Data Science Poornima Institute of Engineering & Technology Rajasthan Jaipur India

ISBN: (纸本)9798400709418

The OHLCV (Open, High, Low, Close, Volume) data used in this study is used to forecast time series and anticipate stock price movement. We investigate a wide variety of models, including traditional statistical approaches and cutting-edge deep learning strategies combined with sentiment analysis, feature extraction, and hyperparameter tweaking. Instead of focusing on absolute stock prices, our main goal is to predict swings in stock prices, as this has been shown to produce more accurate outcomes. We start our research by obtaining historical Amazon stock data via the Yahoo API, and then we go on a thorough analytical journey. We generate features on the OHLCV data first, and then we design and test Fourier and Autoregressive Integrated Moving Average (ARIMA) models. We then switch to more sophisticated deep learning methods, using the pre-processed data to apply Long Short-Term Memory (LSTM) models. Interestingly, we add sentiment analysis to the LSTM study, which expands its scope and lets us consider market sentiment as a possible influencing factor. To guarantee the stability of our models, we use a careful train-test split technique and organize the data in a time series manner. The field of financial forecasting and trading methods will ultimately benefit from the insightful information our study's findings provide on the efficacy of different modeling techniques and their capacity to forecast stock price movements. © 2023 ACM.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Improving Cybersecurity Measures Through the Revelation of Malignant URLs using Machine Learning Techniques 5

Improving Cybersecurity Measures Through the Revelation of M...

引用

5th International Conference on data intelligence and Cognitive Informatics, ICDICI 2024

作者： Saravanan, T.M. Muthusamy, A. Thamil Selvi, C.P. Senthil Kumar, M. Maheshwari, D. Kongu Engineering College Department of Computer Technology Perundurai Erode India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thudupathi Erode India Kpr College of Arts Science and Research Department of Computer Technology Coimbatore India

ISBN: (纸本)9798350389609

In today's world the internet has ingrained itself deeply into our lives, and web searching has become essential for people of all ages, locations, and occupations. However, due to the rise in internet usage, there has been a rise in spoofing attacks through malicious websites. Online purchases, reservations, recharges, and various other transactions are now commonly conducted online. Internet users are increasing;it has become crucial to develop automatic URL detection systems to protect users. This proposed research aims to compare different categorization models like Decision Tree, Random Forest and Logistic Regression to determine which one achieves finest accuracy in distinguishing among genuine websites and False websites. Objective is to identify attacking sites effectively and determine the artificial intelligence algorithm that provides the best accuracy for this use. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

来源：评论

学校读者我要写书评

暂无评论

Android Malware Detection

Android Malware Detection

引用

2023 IEEE World Conference on Communication and Computing, WCONF 2023

作者： Gagan, N. Sai Kumar, S. Keerthana, M. Vaidya, Samarth S Rastogi, Vanshika Dept. Artificial Intelligence and Data Science East Point College of Engineering and Technology Bangalore India

ISBN: (纸本)9798350311204

Since android smartphones are so popular, malware writers have found them to be lucrative targets, which poses a serious risk to user security and privacy. To address this problem, several malware detection strategies have been put out recently. In this review paper, we examine an efficient method for detecting malware on Android that makes use of machine learning techniques. Our method divides Android applications into benign and harmful categories using a combination of feature extraction methods and machine learning algorithms. Overall, our suggested method offers a dependable and efficient alternative for detecting Android malware, giving consumers a more dependable and safe environment for mobile computing. © 2023 IEEE.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

引用

Neural Computing and Applications 2024年第27期36卷 17199-17219页

作者： Hosney, Reham Talaat, Fatma M. El-Gendy, Eman M. Saafan, Mahmoud M. Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Nile Higher Institute for Engineering and Technology Mansoura Egypt

Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication and social interaction difficulties. Conventional methods for diagnosing ASD may not be effective in the early stages of the disorder. Hence, early diagnosis is crucial to improving the patient's overall health and well-being. One alternative and effective method for early autism diagnosis is facial expression recognition since autistic children typically exhibit distinct facial expressions that can aid in distinguishing them from other children. This paper provides a deep convolutional neural network (DCNN)-based real-time emotion recognition system for autistic kids. The proposed system is designed to identify six facial emotions, including surprise, delight, sadness, fear, joy, and natural, and to assist medical professionals and families in recognizing facial expressions in autistic children for early diagnosis and intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm for facial expression recognition is proposed, which enhances the YOLOv8 model's performance by integrating an attention mechanism. The proposed method (AutYOLO-ATT) outperforms all other classifiers in all metrics, achieving a precision of 93.97%, recall of 97.5%, F1-score of 92.99%, and accuracy of 97.2%. These results highlight the potential of the proposed method for real-world applications, particularly in fields where high accuracy is essential. © The Author(s) 2024.

关键词： Diagnosis

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：