2016 International Conference on Information Technology (ICIT)最新文献_第2页

Centralized Solution to Securely Transfer Payment Information Electronically to Banks from Multiple Enterprise Resource Planning (ERP) Systems 集中解决方案安全地将支付信息从多个企业资源规划(ERP)系统电子化地转移到银行

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.062

Manu Kohli, Edgardo Suarez

{"title":"Centralized Solution to Securely Transfer Payment Information Electronically to Banks from Multiple Enterprise Resource Planning (ERP) Systems","authors":"Manu Kohli, Edgardo Suarez","doi":"10.1109/ICIT.2016.062","DOIUrl":"https://doi.org/10.1109/ICIT.2016.062","url":null,"abstract":"Financial transactions in an organization, for example payments to suppliers and employee salaries, are generated from Enterprise Resource Planning (ERP) applications and require secure transmission to bank. The payment models have been evolving over the years which confronted transformations from the usage of manual payment methods, cheque, cards, Electronic Fund Transfer (EFT) to Automatic Clearing House (ACH). Current models utilize Public key infrastructure methods for authorization procedures which require certification and verification of customer and payment information. The present case study proposes a consolidated model that has been developed and deployed using a centralized infrastructure enabling secured payment information exchange from various business units in an organization to the bank. The developed method achieves economies of scale and scope, provides set of standardized procedures that integrate multiple ERP applications deployed for various business units distributed over multiple geographies on a secured platform at a minimum reconstruction and waiting time. The model has proven effective leading to cost and schedule saving from 25 % to 75% and provides a plug and play platform to business units in an organization to exchange payment information securely with various banks.","PeriodicalId":220153,"journal":{"name":"2016 International Conference on Information Technology (ICIT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116631309","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: 1

Forward Load Aware Scheduling for Data-Intensive Workflow Applications in Cloud System 云系统中数据密集型工作流应用的正向负载感知调度

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.030

M. Kumar, Indrajeet Gupta, P. K. Jana

引用次数: 5

Novel System for Retrieval of Composite Trademarks Using Multi-feature Voting 基于多特征投票的组合商标检索系统

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.058

Akriti Nigam, R. Tripathi

引用次数: 0

Location of the Fault in TCSC-based Transmission Line Using SVR 基于tscc的输电线路故障SVR定位

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.061

P. Ray, D. Mishra, G. K. Budumuru

引用次数: 1

Simultaneous Feature Selection and Cluster Analysis Using Genetic Algorithm 基于遗传算法的特征选择与聚类分析

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.064

Sunanda Das, S. Chaudhuri, Sujata Ghatak, A. Das

{"title":"Simultaneous Feature Selection and Cluster Analysis Using Genetic Algorithm","authors":"Sunanda Das, S. Chaudhuri, Sujata Ghatak, A. Das","doi":"10.1109/ICIT.2016.064","DOIUrl":"https://doi.org/10.1109/ICIT.2016.064","url":null,"abstract":"Cluster analysis being one of the important techniques of data mining applied in several fields such as bioinformatics, social networks, computer vision, and so on. It is an unsupervised learning technique for exploring the structure of the data without class label. Many clustering algorithms have been proposed to analyze high volume of data, but very few of them evaluate the quality of the clusters due to irrelevant and inconsistent features present in the dataset. So, feature selection is an important pre-processing step in data analysis mainly for high dimensional dataset. In the paper, we select optimal subset of features and perform clusters analysis simultaneously using genetic algorithm. Basically, genetic algorithm is used to select the optimal subset of features which automatically finds optimal number of clusters sat the end of the process. Optimality of the clusters is measured by calculating various cluster validation indices. The overall performance of the method is investigated on popular UCI datasets and the experimental results are compared with Fuzzy C-Means algorithm to demonstrate effectiveness of the proposed method.","PeriodicalId":220153,"journal":{"name":"2016 International Conference on Information Technology (ICIT)","volume":"26 8-9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132363869","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: 2

Grid Based Data Gathering in Multi-channel Wireless Sensor Network 基于网格的多通道无线传感器网络数据采集

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.034

H. Sarma

引用次数: 4

Text Categorization via Attribute Distance Weighted k-Nearest Neighbor Classification 基于属性距离加权k近邻分类的文本分类

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.053

H. Wandabwa, Defu Zhang, Korir Sammy

{"title":"Text Categorization via Attribute Distance Weighted k-Nearest Neighbor Classification","authors":"H. Wandabwa, Defu Zhang, Korir Sammy","doi":"10.1109/ICIT.2016.053","DOIUrl":"https://doi.org/10.1109/ICIT.2016.053","url":null,"abstract":"Text categorization entails making a decision on whether a document belongs to a set of pre-specified classes of other documents. This can be in a supervised way in classification tasks or unsupervised reminiscent of clustering related tasks. Categorization can be a challenging task especially when the discriminating words are large. K-Nearest Neighbor is an instance based learning algorithm that has proven to be effective in such classification tasks including documents. The key element of this algorithm lies in the similarity measurement principle that is capable of identifying neighbors of a particular document to high accuracies. The only drawback of this approach is in the weighting of all features to determine the distance among the documents in question. This is not only time consuming but also overuses computer resources without adding anything substantial to the overall results. In our approach (Attribute Distance Weighted - KNN), we do not make use of all features in the corpus but first extract the most relevant ones by weighting them in relation to the corpus. We then calculated the distance between the highly ranked features in the corpus alone as a representative of the entire document set. So far no known literature has inclined towards this approach thus our comparison will be in relation to the classical KNN measure. Our approach showed marginal performance in distance measure compared to classical KNN.","PeriodicalId":220153,"journal":{"name":"2016 International Conference on Information Technology (ICIT)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124159393","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

Genetic Algorithm Based Parallel Matrix Factorization for Recommender Systems 基于遗传算法的并行矩阵分解推荐系统

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.051

B. V. Srivastava, Shashikant Sharma, Deepanwita Datta, G. Sriram, S. Jambhulkar, Shashank Naik, Ganapa Jaswanth Reddy

{"title":"Genetic Algorithm Based Parallel Matrix Factorization for Recommender Systems","authors":"B. V. Srivastava, Shashikant Sharma, Deepanwita Datta, G. Sriram, S. Jambhulkar, Shashank Naik, Ganapa Jaswanth Reddy","doi":"10.1109/ICIT.2016.051","DOIUrl":"https://doi.org/10.1109/ICIT.2016.051","url":null,"abstract":"Matrix Factorization is one of the popular approaches for learning the latent characteristics from the sparse utility matrix of recommendation systems. In recent times, Coordinate Descent based matrix factorization approach (CCD) have outperformed the other existing approaches such as Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD). While ALS is not scalable due to its cubic time complexity, SGD suffers from slow convergence. An improved version of CCD, CCD++ was recently proposed to overcome the shortcomings of CCD. The difference in these two approaches lies in their update rules and the update sequences. CCD++ was shown to converge faster than CCD. In this paper, we hypothesize that use of Genetic Algorithm (GA) for initializing matrices may significantly speed up the convergence of CCD++. Also, parallelism could be exploited more efficiently at update stage. We update the rating matrix at regular intervals with GA, so that the convergence of CCD++ is relatively fast. Our experimental results show that optimum update of matrices enhances the convergence of CCD++ appreciably.","PeriodicalId":220153,"journal":{"name":"2016 International Conference on Information Technology (ICIT)","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122759411","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: 2

Extraction of Network Information - Quality and Quantity - from Nodes of Neuronal Network 从神经网络节点中提取网络信息的质量和数量

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.035

Sayan Biswas

引用次数: 5

Synaptic Coupling Strength and Seizures 突触耦合强度与癫痫发作

2016 International Conference on Information Technology (ICIT) Pub Date : 2016-12-01 DOI: 10.1109/ICIT.2016.038

Harshvardhan, D. K. Lobiyal

引用次数: 0