检索结果-内蒙古大学图书馆

A Modified jellyfish Search algorithm for Task Scheduling in Fog-Cloud Systems

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第9-11期37卷

作者： Jangu, Nupur Raza, Zahid Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi India

Integration of fog and cloud has become increasingly important in the age of IoT, where everything is connected to the Internet. The cloud-only models face many challenges when serving the requests from IoT devices due to several factors such as latency, network congestion, data privacy, and security. Despite the popularity and numerous advantages of hybrid models, task scheduling is still an unsolvable multiobjective optimization problem. This research uses an improved bioinspired jellyfish search algorithm to solve the nonlinear np-hard task scheduling optimization problem. The work proposes a multiobjective improved jellyfish search (MOIJS) framework using a multiobjective adaptation function to minimize the make-span, cost, and power consumption that benefit customers and providers by considering the expenses associated with execution and power consumption. The performance of MOIJS is evaluated by comparing it with the discrete nondominated sorting genetic algorithm II using a MATLAB simulator. The experimental outcomes demonstrate the proposed work's efficacy in reducing the make-span, cost, and energy in cloud-fog environments in different batches of tasks.

关键词： cloud computing fog computing genetic algorithm jellyfish algorithm task scheduling

来源：评论

学校读者我要写书评

暂无评论

Seismic refraction data inversion via jellyfish search algorithm for bedrock characterization in dam sites

引用

SN APPLIED SCIENCES 2022年第10期4卷 1-11页

作者： Poormirzaee, Rashed Urmia Univ Technol Dept Min Engn POB 17165-57166 Orumiyeh Iran

Seismic refraction method is an efficient tool for the investigation of dam construction sites. Velocity inversion has an essential role in an accurate seismic refraction data interpretation. This study aims to develop a new inversion algorithm to estimate P-wave velocity (Vp) structure from seismic refraction travel times. The introduced inversion algorithm is based on a recently developed nature-inspired algorithm, i.e., jellyfish search (JS) optimizer. First, the JS-based inversion algorithm was tested by several synthetic models in the presence of noise and without noise. Then, the performance of the applied inversion algorithm was evaluated by the seismic refraction travel times at a realistic dam construction site. The main objective of the actual data set analysis is the determination of Vp structure to find overburden thickness. The JS-based inversion algorithm in both synthetic models and actual data set shows acceptable performance. Results show three distinct seismic layers at the dam site. The velocities of the first, second and third layers, respectively, were estimated 400 m/s, 600 m/s and 1400 m/s. Also, the overburden thickness was estimated about 23 m, which was consistent with borehole data. The performance of the applied algorithm in the analyzing of actual data set was compared with the tomography interpretation method that the results revealed the efficiency of the JS-based inversion method.

关键词： Seismic refraction data P-wave velocity Overburden thickness jellyfish algorithm Inversion

来源：评论

学校读者我要写书评

暂无评论

Fog-based architecture and efficient task offloading methodology in IoT-based applications for smart irrigation system

引用

COMPUTING 2025年第3期107卷 1-33页

作者： Sohrabi, Sakine Sakhaei-nia, Mehdi Nassiri, Mohamad Mohammadi, Reza Bu Ali Sina Univ Fac Engn Dept Comp Engn Hamadan Iran

Fog computing enhances the Quality of Service for Internet of Things applications, addressing issues arising from the significant data transfer volume of Internet of Things devices that leads to increased latency and bandwidth demands in cloud computing. The growth of the Internet of Things generates vast data for processing, straining the fog-cloud network. High data transmission rates can disrupt system operations, necessitating effective resource management. Prioritized tasks are executed through scheduling algorithms, with high-priority tasks executed at nearby fog nodes and complex tasks sent to cloud data centers. While FC reduces response time, it raises energy consumption for users, and vice versa for cloud computing. Addressing these challenges is crucial for optimal task allocation in fog-cloud computing systems. Therefore, this study proposes a hybrid multi-objective task scheduling algorithm using the jellyfish algorithm and simulated annealing algorithm to reduce energy consumption and execution time while respecting task priorities, which play a fundamental role in task distribution. The simulation results confirm the efficiency of the IJFSA algorithm when compared with advanced algorithms such as PWAO, EETSPSO, and IJFA in reducing execution time and energy consumption.

关键词： Task scheduling Fog computing Metaheuristics Priority Energy consumption jellyfish algorithm

来源：评论

学校读者我要写书评

暂无评论

Deep neural network for emojis-based sentiment classification

引用

International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Balaji, Prasanalakshmi Alsid, Linda Elzubir Gasm Sagar, Kalpna Prakasam, Shanmugapriya Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science and Business Systems Rajalakshmi Engineering College Thandalam India

Sentiment classification using emojis on social media has become increasingly crucial in recent years. Social media commonly uses emojis to convey feelings, emotions, and moods. Hence, in this article, a jellyfish algorithm based on a Deep Convolution Neural Network (JA-DCNN) is developed for the sentiment classification of the emojis. Initially, the emoji images are collected from the open-source system. The proposed sentiment classification process uses a jellyfish algorithm (JA) optimized Deep Convolution Neural Network (DCNN). In DCNN, the hyperparameters are selected with the help of JA to improve accuracy and efficiency. The proposed method aims to improve the training features, maintain the resolution, reduce memory consumption, and improve the system’s accuracy. The proposed method decreases the computation cost and memory consumption, which empowers the system’s performance in emojis-based sentiment classification. The proposed method is implemented in MATLAB, and performance metrics of similarity and quality measurements evaluate performances. To validate the performance of the proposed methodology, it is compared with conventional methods such as MobilenetV2, ResNet18, and ResNet50, which have been proven to obtain better performance metrics. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Classification Deep convolution neural network Emojis jellyfish algorithm Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

CALCULATING TWO-STRAND jellyfish RELATIONS

引用

PACIFIC JOURNAL OF MATHEMATICS 2015年第2期277卷 463-510页

作者： Penneys, David Peters, Emily Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA Loyola Univ Dept Math & Stat Chicago IL 60660 USA

We construct a 3(Z/4) subfactor using an algorithm which, given generators in a spoke graph planar algebra, computes two-strand jellyfish relations. This subfactor was known to Izumi, but has not previously appeared in the literature. We systematically analyze the space of second annular consequences, adapting Jones' treatment of the space of first annular consequences in his quadratic tangles article. This article is the natural followup to two recent articles on spoke subfactor planar algebras and the jellyfish algorithm. Work of Bigelow and Penneys explains the connection between spoke subfactor planar algebras and the jellyfish algorithm, and work of Morrison and Penneys automates the construction of subfactors where both principal graphs are spoke graphs using one-strand jellyfish. This is the published version of arXiv:1308.5197.

关键词： subfactors planar algebras jellyfish algorithm principal graphs

来源：评论

学校读者我要写书评

暂无评论

An improved deep belief neural network based civil unrest event forecasting in twitter

引用

APPLIED INTELLIGENCE 2023年第5期53卷 5714-5731页

作者： Iyda, J. Joslin Geetha, P. Rajalakshmi Engn Coll Dept Informat Technol Chennai 602105 Tamil Nadu India Anna Univ Dept Informat Sci & Technol CEG Campus Chennai 600025 Tamil Nadu India

Nowadays, event forecasting in Twitter can be considered an essential, significant and difficult issue. Maximum conventional methods are focusing on temporal events like sports or elections. These methods do not calculate the spatial features too their correlation analysis. Hence, this paper proposes an Improved Deep Belief Neural Network (iDBNN) for civil unrest event forecasting in twitter data. This proposed method is utilized to forecast the future event with the consideration of the tweets. The proposed method is designed with three phases named as pre-processing phase, feature extraction phase, and civil unrest event forecasting. Initially, the proposed method is used to train the Hong Kong Protest event 2019 tweet data for forecasting events. In the pre-processing phase, removal of special symbol, removal of URL, username removal, tokenization and stop word removal are done. After that, the essential features such as domain weight, event weight, textual similarity, spatial similarity, temporal similarity, and Relative Document-Term Frequency Difference (RDTFD) are extracted and then applied for training the proposed model. To empower the training phase of proposed iDBNN method, the jellyfish algorithm is utilized to select optimal weight parameter coefficients of DBNN for training the model parameters. The projected technique is authenticated by statistical capacities and compared with the conventional methods such as Hidden Markov Model (HMM) and Random Forest (RF) respectively. Comparing with other traditional methods, the proposed model shows better performance in terms of prediction and processing time. The iDBNN model shows 91% prediction accuracy that is much higher than the traditional DBNN.

关键词： Event forecasting DBNN jellyfish algorithm Document term frequency difference Spatial features Temporal events

来源：评论

学校读者我要写书评

暂无评论

Vs Profiling by the Inversion of Rayleigh Wave Ellipticity Curve Using a Hybrid Artificial Intelligence Method

引用

PURE AND APPLIED GEOPHYSICS 2024年第6期181卷 1831-1844页

作者： Angardi, Shahram Poursorkhabi, Ramin Vafaei Shirvanehdeh, Ahmad Zarean Dabiri, Rouzbeh Islamic Azad Univ Dept Civil Engn Tabriz Branch Tabriz Iran Islamic Azad Univ Robot & Soft Technol Res Ctr Tabriz Branch Tabriz Iran Islamic Azad Univ Dept Civil Engn Shabestar Branch Shabestar Iran

Adequate estimation of S-wave velocity (Vs) structure is a significant parameter in the seismic micro zonation studies. To this purpose, different techniques, such as down-hole measurements and inversion of surface wave's dispersion curves are proposed for modeling VS profile. In the last decade, modeling Vs profile from the Rayleigh wave's ellipticity curve (H/V) is more applicable owing to its rapid and simple data gathering procedure. However, regarding the ambiguities in the inversion of H/V curves, obtaining the reliable results priori information, such as down-hole measurement, to constrain the final Vs model is vital. This study addressed this challenge, and based on a hybrid artificial intelligence method introduced a new technique to invert the Rayleigh wave ellipticity curve with acceptable performance. To do that, first model parameters (i.e. number of layers and corresponding thicknesses and shear wave velocities) by the ensemble of neural networks (ENN) were predicted, and then further inversion by jellyfish searching (JS) algorithm (named ENN-JS inversion method) was carried out to obtain a more reasonable Vs model. To build the ensemble system, ten base networks were arranged. To train the neural networks, synthetic Rayleigh wave ellipticity data by forward modeling approach were generated. The combination of the outputs of based networks was performed using the averaging method. Then, JS inversion algorithm was applied to estimate the final adequate Vs model. ENNs provide essential information to the JS searching algorithm on the number of layers and proper search spaces for model parameters. Synthetic and actual datasets tested the ENN-JS inversion technique. Findings show the proposed method provides a robust method for the inversion of Rayleigh wave ellipticity data.

关键词： Ensemble of neural networks jellyfish algorithm Rayleigh wave ellipticity curve

来源：评论

学校读者我要写书评

暂无评论

Real-Time Economic Dispatch Considering Adverse Weather Conditions in Renewable Generation 34

Real-Time Economic Dispatch Considering Adverse Weather Cond...

引用

34th Australasian Universities Power Engineering Conference

作者： Islam, Md Mainul Nagrial, Malmiood Kizk, Tamal Hellany, Ali Western Sydney Univ Sch Engn Design & Built Environm Kingswood Australia

ISBN: (纸本)9798350377958;9798350377941

Economic dispatch (ED) of the microgrid (MG) is challenging for real-time power generation and storage because of the unpredictability of renewable energy sources. The MG ED with the battery storage system is therefore essential to diminish the uncertainty. This study suggests a novel binary jellyfish search algorithm to solve the MG ED problem with adverse weather conditions. The results show that incorporating the battery storage unit can appreciably reduce the real-time operating costs by more than 5%. Furthermore, the simulation results also show the effectiveness of the suggested MG ED approach.

关键词： battery storage economic dispatch jellyfish algorithm microgrid renewable energy

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：