Journal of Information Technology Research

The paper presents an approach to generate and optimize test sequences from the input UML activity diagram. For this, an algorithm is proposed called Unified Modelling Language for Test Sequence Generation (UMLTSG) that uses a search-based algorithm, named Test Sequence Prioritization using Ant Colony Optimization (TSP ACO) to generate and optimize test sequences. The algorithms overcome the existing limitations of handling complex decision-making activity such as conditional activity, fork activity, and join the activity. The optimization process helps to reduce the number of processing nodes that leads to minimizing the time and cost. The proposed approach experiments on a well-known application Railway Ticket Reservation System (RTRS). APFD metric measures the effectiveness of our approach and found that the prioritized order of test sequences achieved 20% higher APFD score. Apart from this, the authors have also experimented on six real life case studies and obtained an average of 52.16% reduction in redundant test paths.

Gayatri Nayak

Mitrabinda Ray

Model-Based Test Sequence Generation and Prioritization Using Ant Colony Optimization

基于蚁群优化的模型驱动测试序列生成与优先级排序

The field of information retrieval (IR) is an important area in computer science, this domain helps us to find information that we are interested in from an important volume of information. A search engine is the best example of the application of information retrieval to get the most relevant results. In this paper, we propose a new recommendation approach for recommending relevant documents to a search engine’s users. In this work, we proposed a new approach for calculating the similarity between a user query and a list of documents in a search engine. The proposed method uses a new reinforcement learning algorithm based on n-grams model (i.e., a sub-sequence of n constructed elements from a given sequence) and a similarity measure. Results show that our method outperforms some methods from the literature with a high value of accuracy.

Amine El Hadi

Youness Madani

Rachid El Ayachi

Mohamed Erritali

Finding Relevant Documents in a Search Engine Using N-Grams Model and Reinforcement Learning

使用N-Gram模型和强化学习在搜索引擎中查找相关文档

Most of the traditional face modeling methods adopt the parameter-based method to construct, but the face model constructed by this method is too smooth and ignores the detailed features of the face. To solve this problem, a virtual face modeling method based on 3D motion capture data is proposed in this paper. In order to improve the deformation method for the realistic modeling purpose, this paper divides the modeling process of personalized face into two parts: overall modification and local deformation. The overall deformation modifies the facial shape and the position of the facial features of Maya universal model. Based on the principle of radial basis function interpolation algorithm, a smooth interpolation function is constructed, and the new position coordinates of non-characteristic points are obtained by solving the linear equations, so that they are more in line with the physiological characteristics of human faces. The end result is a more realistic virtual face that you model.

Xiangzhen He

Shengyin Zhu

Yihao Zhang

Yerong Hu

Dengyun Zhu

Xiaoyue Liu

Fucheng Wan

Research on Photorealistic Virtual Face Modeling

真实感虚拟人脸建模研究

Prior studies have found that mobile cloud computing could bring substantial cost savings to firms, ultimately resulting in reduced transaction cost to customers. Despite this, financial firms in Fiji are slow adopters of mobile cloud computing. The study identifies the challenges faced by financial firms in the adoption of mobile cloud computing to advance the literature on innovation adoption with evidence from a unique context – a Pacific island country. The context is important as the issues are likely to be similar in other developing and remote island countries but the extant research is largely confined to developed countries. Our findings suggest that the lack of mobile cloud computing policy, infrastructure constraints, and security constraints, among others are the main barriers to the adoption thereof. The study contributes by presenting a revised model based on factors that emerged from the study.

Milind Sathye

Sam Goundar

Akashdeep Bhardwaj

Determinants of Mobile Cloud Computing Adoption by Financial Services Firms

金融服务企业移动云计算采纳的决定因素

A number of numerical practices exist that actuaries use to predict annual medical claims expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This paper presents the development of Artificial Neural Network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models were finished, the focus was to decrease the Mean Absolute Percentage Error by adjusting the parameters such as epoch, learning rate and neuron in different layers. Both Feed Forward and Recurrent Neural Networks were implemented to forecast the yearly claims amount. In conclusion, the Artificial Neural Network Model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims. Recurrent neural network outperformed Feed Forward neural network in terms of accuracy and computation power required to carry out the forecasting.

Suneet Sonal Prakash

Pranil Sadal

Use of Artificial Neural Network for Forecasting Health Insurance Entitlements

人工神经网络在预测医疗保险权益中的应用

Investigated in this study is the influence of information communication technology (ICT) on the exchange rate in Zambia from 2010 to 2021. The research methods employed involve using descriptive, exploratory, and experimental designs. The results are reported focusing on ICT acquisition, usage, and production in relation to the performance of the exchange rate: imports of ICTs from the Zambia Revenue Authority (ZRA) represented the uptake while the performance of the exchange rate from the Bank of Zambia represented actual volatilities. It was further discovered that the ICTs influence movements in financial transactions by increasing the quantum of transactions. The impact on the movements of currencies advertently affects the exchange rate causing volatility. The role of ICTs in the amalgamation of markets, trade linkages, and openness, is key. Conclusively, ICTs influence the exchange rate: inherent in productive means and permeate consumptive aspects of ICTs and financial transactions. The main limitation arose from data collection on ICT software and services due to lack of a consolidated data capturing information system.

Oliver Bwalya

Chitra Dhawale

Information Communication Technology's Influence on Exchange Rate

信息通信技术对汇率的影响

This research was conducted to perform an in-depth analysis of the coupling metrics of 10 Open Source Software (OSS) projects obtained from the Comets dataset. More precisely, we analyze the dataset of object-oriented OSS projects (having 17 code related metrics such as coupling, complexity, and size metrics) to (1) examine the relationships among the coupling and other metrics (size, complexity), (2) analyze the pattern in the growth of software metrics, and (3) propose a model for prediction of coupling. To generalize the model of coupling prediction, we have applied different machine learning algorithms and validated their performance on similar datasets. The results indicated that the Random forests algorithm outperforms all other models. The relation analysis specifies the existence of strong positive relationships between the coupling, size, and complexity metrics while the pattern analysis pinpointed the increasing growth trend for coupling. The obtained outcomes will help the developers, project managers, and stakeholders in better understating the state of software health

Munish Saini

Raghuvar Arora

Sulaimon Oyeniyi Adebayo

In-Depth Analysis and Prediction of Coupling Metrics of Open Source Software Projects

开源软件项目耦合度量的深入分析与预测

Abbreviation	J. Inf. Technol. Res.
Journal Impact	0.46
Quartiles(Global)	COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS(Q4)

Publication Information	Publisher: IGI Global，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2010，Proportion of original research papers: 97.78%，Self Citation Rate:0.00%， Gold OA Rate: 59.35%

Journal of Information Technology Research

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Model-Based Test Sequence Generation and Prioritization Using Ant Colony Optimization

Finding Relevant Documents in a Search Engine Using N-Grams Model and Reinforcement Learning

Research on Photorealistic Virtual Face Modeling

Determinants of Mobile Cloud Computing Adoption by Financial Services Firms

Use of Artificial Neural Network for Forecasting Health Insurance Entitlements

Information Communication Technology's Influence on Exchange Rate

In-Depth Analysis and Prediction of Coupling Metrics of Open Source Software Projects

Model-Based Test Sequence Generation and Prioritization Using Ant Colony Optimization

Finding Relevant Documents in a Search Engine Using N-Grams Model and Reinforcement Learning

Research on Photorealistic Virtual Face Modeling