Int. J. Artif. Life Res.最新文献_第7页

Non Linear Dynamical Systems and Chaos Synchronization 非线性动力系统与混沌同步

Int. J. Artif. Life Res. Pub Date : 2010-04-01 DOI: 10.4018/jalr.2010040104

Ayub Khan, Prempal Singh

引用次数: 4

A New Approach to Pattern Recognition in Fractal Ferns 分形蕨类植物模式识别的新方法

Int. J. Artif. Life Res. Pub Date : 2010-04-01 DOI: 10.4018/jalr.2010040102

M. Rani, Saurabh Goel

引用次数: 12

An Optimal Balanced Partitioning of a Set of 1D Intervals 一组1D区间的最优平衡划分

Int. J. Artif. Life Res. Pub Date : 2010-04-01 DOI: 10.4018/jalr.2010040106

Chuan-Kai Yang

引用次数: 8

Propagation of Front Waves in Myelinated Nerve Fibres: New Electrical Transmission Lines Constituted of Linear and Nonlinear Portions 前波在有髓神经纤维中的传播:由线性和非线性部分组成的新型输电线路

Int. J. Artif. Life Res. Pub Date : 1900-01-01 DOI: 10.4018/jalr.2011010104

A. M. Nkomidio, P. Woafo

引用次数: 8

Existence of Positive Solutions for Generalized p-Laplacian BVPs 广义p-Laplacian bvp正解的存在性

Int. J. Artif. Life Res. Pub Date : 1900-01-01 DOI: 10.4018/jalr.2011010105

W. Lian, F. Wong, Jen-Chieh Lo, Cheh-Chih Yeh

引用次数: 8

Virtual Worlds and Social Media: Security and Privacy Concerns, Implications, and Practices 虚拟世界和社会媒体:安全和隐私问题，影响和实践

Int. J. Artif. Life Res. Pub Date : 1900-01-01 DOI: 10.4018/ijalr.2014010103

Greg Gogolin, E. Gogolin, Hwee-Joo Kam

引用次数: 8

Noise Power Spectrum for Firecrackers 鞭炮噪音功率谱

Int. J. Artif. Life Res. Pub Date : 1900-01-01 DOI: 10.4018/jalr.2011010107

K. B. Patange, A. Khan, S. Behere, Y. Shaikh

引用次数: 10

Knowledge in Memetic Algorithms for Stock Classification 基于模因算法的库存分类知识

Int. J. Artif. Life Res. Pub Date : 1900-01-01 DOI: 10.4018/ijalr.2014010102

Jie Du, R. Rada

引用次数: 5

T-Whale: Trust and Whale Optimization Model for Secure Routing in Mobile Ad-Hoc Network T-Whale:移动自组织网络中安全路由的信任与鲸鱼优化模型

Int. J. Artif. Life Res. Pub Date : 1900-01-01 DOI: 10.4018/IJALR.2018070104

Ram Mohan Chintalapalli, Venugopal Reddy Ananthula

{"title":"T-Whale: Trust and Whale Optimization Model for Secure Routing in Mobile Ad-Hoc Network","authors":"Ram Mohan Chintalapalli, Venugopal Reddy Ananthula","doi":"10.4018/IJALR.2018070104","DOIUrl":"https://doi.org/10.4018/IJALR.2018070104","url":null,"abstract":"One of the infrastructureless networks built by various independent mobile nodes is mobile ad hoc network (MANET), which is an emerging technology, requiring a secure routing protocol for data transmission. Accordingly, literature presents various secure routing protocols for MANETs by utilizing trust and data encryption. In this article, a whale optimization algorithm (WOA) is utilized for selecting the optimal secured routing path in the MANET. The WOA algorithm utilizes the trust factor and the distance between the nodes for computing the fitness for the routing path. Overall, the steps involved in the proposed routing algorithm are as follows: i) Measuring the trust and the distance-based metrics for every node; ii) Discovering k-disjoint path; and iii) Determining the optimal path based on the trust and the distance-based metrics. The performance of the trust-based WOA (T-Whale) is analyzed using the metrics, energy, throughput, and packet delivery rate. From the simulation results, it is evident that the T-Whale algorithm has the improved energy, throughput, and PDR values of 27.4520, 0.4, and 0.4, at the simulation time of 10 sec over the conventional trust random search algorithm when the node is under attack.","PeriodicalId":289669,"journal":{"name":"Int. J. Artif. Life Res.","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122053606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Forecasting Demand in Supply Chain Using Machine Learning Algorithms 利用机器学习算法预测供应链需求

Int. J. Artif. Life Res. Pub Date : 1900-01-01 DOI: 10.4018/IJALR.2015010104

N. Ampazis

{"title":"Forecasting Demand in Supply Chain Using Machine Learning Algorithms","authors":"N. Ampazis","doi":"10.4018/IJALR.2015010104","DOIUrl":"https://doi.org/10.4018/IJALR.2015010104","url":null,"abstract":"Managing inventory in a multi-level supply chain structure is a difficult task for big retail stores as it is particularly complex to predict demand for the majority of the items. This paper aims to highlight the potential of machine learning approaches as effective forecasting methods for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. For this purpose, we utilize Artificial Neural Networks ANNs trained with an effective second order algorithm, and Support Vector Machines SVMs for regression. We evaluated the effectiveness of the proposed approach using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during an especially critical for sales season, which is the Christmas holiday season. In our analysis we also integrated data from two other sources of information, namely an aggregator for movie reviews Rotten Tomatoes, and a movie oriented social network Flixster. Consequently, the approach presented in this paper combines the integration of data from various sources of information and the power of advanced machine learning algorithms for lowering the uncertainty barrier in forecasting supply chain demand.","PeriodicalId":289669,"journal":{"name":"Int. J. Artif. Life Res.","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126875837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7