International Journal of Information Technology and Management (58 papers in press)
Investigating the Role of Information Technology in Developing the Learning Organization: Empirical Evidence from Egypt
by Hadia Hamdy, Karim Hodaib
Abstract: Developing a learning organization has been associated with organizational growth and innovation as it enables companies to maintain a memory for their created knowledge and experiences that can be shared among all organizational members. The most important tool identified to build such a memory is Information Technology knowledge management systems. However, despite their importance, their actual contribution to the development of the different dimensions of the learning organization has rarely been studied, especially in developing countries like Egypt.
This research investigates the effect of using IT knowledge management tools on the development of the learning organization using the dimensions of the learning organization questionnaire developed by Watkins and Marsick. A pre-post quasi experiment was conducted in an Egyptian organization to measure the dimensions of the learning organization before and after the application of a new IT system designed to enhance employees learning and knowledge sharing. Results indicate that the implementation of the new IT system significantly improved all the dimensions of organizational learning except for degree of teamwork that is essential for the process of knowledge integration. It concludes that IT systems facilitate the creation of the learning organization, and convey a positive message regarding organizational commitment to developing and empowering employees towards a shared organizational vision. However, they have to be implemented within an integrated strategy, structure, culture, and policy change management system to be able to achieve the desired outcome of transferring a rigid organization into a learning one.
Keywords: Learning organization; Organizational learning; Information Technology; Knowledge management systems; Egypt.
Enhancing Excel Business Tools with Additional Relational and Recursive Capabilities
by Pedro Ramos, Luís Botelho, Antonio Martins
Abstract: This paper presents a new plug in that enriches spreadsheet capabilities mainly in what concerns its potential regarding relational queries and recursive computational processes. Currently some apparently trivial and useful queries can only be handled with the support of programming skills. Spreadsheet users with low computer science skills should have a natural and easy way to handle those queries within the spreadsheet, without relying on external programming (e.g., VBA). The tool we have developed can be used with Prolog technology, and provides those features to the most used professional spreadsheet: Microsoft Excel. Throughout the paper we explore the plug-in features with several business examples.
Keywords: Dedutive Spreadsheet; Business Tools; Excel; Prolog; Recursive Processing; Relational Data.
Validation of Cognitive Inhibitors to Technology Adoption using Content Analysis
by Easwar Krishna Iyer, Sreehari Unnikrishnan, Paul Philip, Mallika Sundarrajan, Bharadhwaj Sivakumaran
Abstract: Inhibitors to technology play a crucial role in predicting eventual technology adoption. In the inhibitor space, studies have so far been limited to understanding affective inhibitors only. The central proposition of this paper is to identify two new cognitive inhibitor constructs. This study augments the existing body of knowledge in the area of technology acceptance behavior by positing Dissatisfiers and Risk as two cognitive inhibitor constructs to technology adoption. The methodology used for validating the proposed model is Content Analysis. The target technology chosen for validation is Internet of Things (IoT). The results obtained provide strong support for the proposed hypotheses thereby establishing Dissatisfiers and Risk as two new cognitive inhibitor constructs.
Keywords: Cognitive Inhibitors; Dissatisfiers; Risk; Content Analysis; Technology Adoption Model; Internet of Things.
Illusion of SNS Influence: Are Popular SNS Influential?
by DongBack Seo, Martin Bügel
Abstract: People influence each other in the environment of social networking sites (SNS). Some SNS have gained popularity. Practitioners and researchers assume that these popular SNS are influential. However, influence and popularity are two different concepts. This research aims to provide two clear concepts of influence and popularity as well as their relationship. Popularity is hypothesized that it is affected by elements in the surroundings (e.g. societal position of a SNS owner) of SNS based on a peripheral information processing mechanism, whereas influence is hypothesized that it is affected by core elements (e.g. quality of content on a SNS message) of SNS based on a central information processing mechanism. The results indicate that influence is solely affected by the central information processing mechanism, but popularity is affected by both central and peripheral information processing mechanisms. There is a relationship between influence and popularity but it is marginal. Therefore, it should not be assumed that popular SNS are influential and vice versa.
Keywords: Popularity; Influence; SNS; Micro-blog; Twitter; Central information processing; Peripheral information processing.
Information Systems Continuance:
The Role of Incentives and Goal Harmony
by Tove Boee, Boge Gulbrandsen, Tom Roar Eikebrokk
Abstract: This paper extends information systems continuance theory (ISCT) and integrates it with a managerial perspective. Our study integrates principalagent theory (PAT) to capture the managerial influence on users continued use of information technology. We argue that the two theories offer complementary explanations, ameliorate each others shortcomings, and enhance our ability to explain and predict IS continuance. As predicted by PAT, the study finds support for the effects of goal harmony and incentives on users continuance decisions. Additionally, goal harmony reduces the positive relationship between incentives and continuance. While the supported ISCT model shows an explained variance of 37%, the full model explains 51% of the variance of the dependent variable, indicating that the integrative model is more powerful in explaining user continuance than is ISCT in isolation. The study advances the theoretical understanding of continued use of IS from both a personal and a managerial perspective and offers proposals for organizational actions.
Keywords: Information Systems Continuance Theory; Agency Theory; Goal Harmony; Incentives.
A flexible and extensible project planning and management tool tailored for European projects
by Ruben Alonso, Daniele Bortoluzzi, Andrea Costa, Thomas Messervey, Massimiliano Raciti, Diego Reforgiato Recupero
Abstract: In this paper, we describe a software tool developed for the comprehensive management of any kind of project (research, industrial, etc.), from planning to execution and cost reporting within an organization (governmental, public, private). Our project management tool is particularly tailored for, although not limited to, European funded projects (such as FP7 and H2020). It is also capable of handling other common projects and it can easily be extended in cost reporting functionalities to cover any kind of project with other rules, funding mechanisms or other constraints and information required for specific purposes. At the current state, with the presented tool, it is possible to; 1) create new European, national level or company internal projects along with their information; 2) create and manage project activities, work packages and related tasks; 3) manage users and their seniority levels, (e.g., employees of the organizations vs. in-house consultants or owners to include contractual information); 4) assign users to specific work packages of existing projects where they are involved along with their yearly planned involvement in term of target working hours; 5) track worked hours during the projects execution; 6) create new funding types of projects (e.g., upcoming EU FP9) assigning new cost claiming schemes such as funding percentage per activity type, funding overhead etc.; 7) create reporting periods for each project and get costs automatically computed; 8) manage reimbursement associated to users and related projects for travels and purchases expenses. The tool is based on the open-source Frappe framework, has been further developed using web-based, open-source technologies, is available online, can be extended to cover other features and it exposes its data through the easy development of ad-hoc REST services that the tool supports.
Keywords: project planning and management tool; European projects; financial management; time tracking; reporting; reimbursement; calendar; time tracking feedback; full-stack web framework; FP7; H2020.
Impact of E-commerce on supply chain management
by Saleeshya P.G, Rahul R
Abstract: E-commerce is web-enabled technology that brought significant changes in the supply chain activities of anindustry. Supply chain management has become a major strategy in manufacturing and service industry. An attempt has made in this paper on identifying the significant factors that affect the e-commerce adoption in the supply chain of a company, identifying the effects of e-commerce on different aspects of supply chain. The study also identifies the major performance parameters of a supply chain and effect of e-commerce adoption on these supply chain variables. An attempt has been made to propose a conceptual model for the study based on the existing literature and field study; the model is validated by academic experts and also experts in industry. Correlation analysis is being carried out to find gap between the ideal and actual situation.rnrn
Keywords: supply chain; E-commerce; correlation; conceptual model.
Determinants of Success and Failure of Knowledge Transfer in Information Systems Offshoring: A Ranking-Type Delphi Study
by Artur Strasser, Susanne Strahringer, Markus Westner
Abstract: The transfer of knowledge from client to service provider poses major challenges in information systems (IS) offshoring projects. Knowledge transfer directly affects IS offshoring success. Therefore, associated challenges must be overcome. Our study examines the determinants of success and failure of knowledge transfer in IS offshoring projects based on a ranking-type Delphi study. We questioned 32 experts from Germany, each with more than ten years of experience in near- or offshore initiatives to seek a consensus among them. We identified 19 success and 20 failure determinants. These determinants are ranked in order of importance using best-worst scaling. Aspects of closer cooperation are critical for effective knowledge transfer. This includes regular collaboration, willingness to help and support, and mutual trust. In contrast, critical determinants of failure are concerned with fears and fluctuation of human resources. Hidden ambiguities or knowledge gaps, an unwillingness and disability to share knowledge, and high fluctuation of human resources negatively impact knowledge transfer.
Keywords: best-worst scaling; BWS; delphi; determinants of success; determinants of failure; information systems; IS; information systems offshoring; knowledge transfer; ranking-type delphi.
How to evaluate brand extension in the mobile Internet environment
by Wenlong Zhu, Shiye Wang
Abstract: How to successfully implement a brand extension has been a common topic among global enterprises. Mobile Internet (M-Internet), a new information and communication technology, creates suitable conditions for enterprise brand extension. However, most researches related to brand extension evaluation focus on traditional information technologies presently. Little research has addressed M-Internet. From the perspective of consumer, this study constructs brand extension evaluation model based on the Task-Technology Fit (TTF) and Aaker and Keller Model (A & K Model), and analyzes the influencing mechanism of brand extension evaluation by using the Structural Equation Modeling (SEM). The final results show that technical characteristics of M-Internet produce a positive effect on attitude of parent brand. Furthermore, attitude of parent brand influences the brand extension evaluation, brand trust and perceived fit positively. Lastly, brand extension evaluation is subject to positive impact of brand trust and perceived fit besides attitude of parent brand. The theoretical and practical implications of this study are discussed lastly.
Keywords: Mobile Internet; Brand extension evaluation; Structural Equation Modeling; Mediation effect.
QoE-based service differentiation: An analysis of the business implications for the mobile services market
by Luis Guillermo Martinez Ballesteros, Per Jonny Nesse, Jan Markendahl
Abstract: Mobile network operators (MNOs) face a future characterized with new challenges, such as growing data consumption, a slowdown in subscriber growth and reduced revenues due to the success of over-the-top providers. To remain competitive, MNOs must offer affordable services and provide innovative strategies to retain current customers. Quality of Experience (QoE) is a well-established methodology for measuring and understanding the overall level of customer satisfaction and has also been presented as a way to improve telecommunication services. Even though QoE can be used to solve problems, such as customer loyalty and optimization of network resources in mobile networks, there is still a lack of knowledge on how the MNOs can take advantage of QoE and its potential benefits. In this paper, we explored the implications of the incorporation of QoE feedback in mobile networks at the business level. The analysis, which is based on a combination of value network configuration and business model analysis of scenarios, shows that value-added offers of differentiated and personalized services can be seen as alternatives to generate new revenue streams in the mobile service market. An important finding from our study is that, due to the nature of the challenges facing the mobile services industry, a QoE analysis cannot be limited to only a technical discussion but needs to be combined with an informed analysis of the business implications of the proposed solution.
Keywords: Quality of Experience; Business Models; Mobile Networks; Service Differentiation.
Research on Evaluation of Network Payment Security
by Xiang Xie, Ying Cui
Abstract: The paper collects the data about the network payment fraud through the questionnaire and then analyzes the data. We propose a prediction model based on the hybrid support degree Apriori algorithm, and find out the factors that are closely related to the success of cheating. Then we find out the nodes of the decision and their importance tree use the ID3 algorithm to construct the decision tree, construct and verify the evaluation index system of the network payment from the user's point of view, considering the results of association rules and decision tree, combined with principal component analysis and text mining results, and then determine the weight using entropy method. So we can determine the probability of being deceived according to the situation of different people, not only remind people to be vigilant, but also provide reference for the community.
Keywords: Apriori algorithm; ID3 algorithm; Internet payment security; styling; Evaluation System; Principal component analysis; entropy method.
Scrutinizing medical practitioners twitter feeds analysis
by Arushi Jain, Vishal Bhatnagar, Nilanjan Dey, Amira Ashour, Fuqian Shi
Abstract: Mining of social media data has found widespread applications in recent times. Twitter feeds and facebook posts are being used to device product marketing strategies, sentiment analysis, financial predictions and forebode alarming situations. Twitter feeds analysis can be applied for analyzing the behavior and experiences of medical practitioners. Doctors informal conversations on Twitter can provide deep insights about their work experiences, their concerns about the profession, their feelings- pathos or excitement they feel and the affecting conditions. In the present work, twitter feed of Doctors along with the Twitter hashtags are used to collect data from tweets with hashtags such as #DoctorProblems. Afterward, data analysis was performed to determine the major problems faced by the members of the medical fraternity. These problems were categorized into five main categories. Further, based on these categories, the multi-label Na
Keywords: Big data; Hadoop distributed file system; MapReduce; Naïve Bayes multilabel classifier; Tweets; Evaluation based measure; Label based measure.
Big Data Block Impact within Big Data Environment
by Ron Ziv, Oded Koren, Nir Perel
Abstract: Handling data is becoming more and more complex. A higher velocity of data is created as more people have access to data generating devices such as computers, mobile phones, medical devices, home appliances, etc. Data files, such as user activity logs, system logs and so on, are stored in HDFS big data platform in various sizes, considering the business requirements, infrastructure parameters, administration decisions, etc. Dividing the data files (in various volumes), without taking into consideration the HDFS predefined block size may create performance issues which can affect the systems activity. This paper presents how HDFS block design affects the performance of Apache Hadoop
Keywords: architecture; HDFS; performance; big data.
Study on Optimization of Supply Chain Inventory Management Based on Particle Swarm Optimization
by Liangguang Mo
Abstract: Aiming at the problems of poor convergence, high cost and low efficiency of traditional supply chain inventory management model, an optimization method of supply chain inventory management based on particle swarm optimization (PSO) is proposed. Firstly, the whole process of particle swarm optimization (PSO) is described. Secondly, by introducing the inventory of different nodes in the supply chain, this paper studies the supply chain and designs the optimal inventory management model to meet the requirements of the supply chain model. Finally, the particle swarm optimization algorithm is used to design the optimal inventory management model and generate the optimal inventory. The experimental results show that the total inventory cost of the model is only 3.682 million yuan, which is much lower than other traditional models. The results show that the model can effectively reduce the cost of supply chain inventory management, has a high degree of convergence, and can reduce the work intensity of relevant personnel.
Keywords: particle swarm optimization; supply chain; inventory; management; model.
A reliability and security enhanced framework for cloud-based storage systems
by Peng Xiao
Abstract: In cloud computing environments, reliable and secure data storage service plays a more and more important role in many data-intensive applications. However, existing storage systems either fail to provide them or provide them in a cost-ineffective manner. To provide better storage service in nowadays cloud environments, we propose a novel framework called Reliability and Security enhanced Cloud Storage (RSCS), which consists of several well-designed components to improve low-level data reliability as well as guarantee up-level data accessing security. In the RSCS framework, a simple yet effective file system scheme is proposed, which can mirror striped data among different nodes to improve the reliability while maintaining a high aggregated throughput. A centralized lease-based mechanism is designed to allow simultaneous accesses to different portions of a single file while maintaining the multiple-reader-single-writer semantics to each requested data portion. Finally, we provide a secure data accessing tunnel technology which allows the RSCS to establish secure communication channels between users and storage nodes without introducing too many extra costs. Extensive experiments are conducted in a real-world cloud storage platform, and the results indicate that the performance of the proposed RSCS framework can easily meet the requirements for most of current cloud systems.
Keywords: cloud storage; data security; replication service; file system.
Special Issue on: Information Technology/Information Systems Applications in Enterprise Systems
Investigation and Analysis on Crowdsourcing for Improving Enterprise QoS
by Remya S, Sasikala R
Abstract: Crowd sourcing is treated as an open contest for a crowd of people known as workers. All workers can contribute their suggestions and solutions to the platform. Hence crowd sourcing can connect a large number of people and they can share their knowledge. The amount of unstructured data is increasing now. This is where crowd sourcing can help big data by breaking down data into mini chunks and have the power of crowd to do the organizing task. This helps big analytics companies focus on the core aspect of infrastructure and security. It also makes sense of the data and not invests resources in organizing data and this distributed environment can be solved intelligently. Here various crowd sourcing techniques in different aspects related to data pre-processing, performance approaches, security issues and applications are analysed. Out of these approaches the most efficient one in each are characterized. This survey helps to analyse the various issues in crowd sourcing and proposed some solutions for improving the quality and security of workers in crowd sourcing based on the literature survey
Keywords: Enterprise; Crowd sourcing; Bigdata; K-means; QoS.
Assessing the Impact of Information Technology on Human Resource Practices: Evidence from Organisations in Ghana.
by Mayqueen Attatsitsey, Noble Osei-Bonsu
Abstract: Information Technology (IT) is universally regarded as an essential tool in enhancing the competitiveness of the economy of a country. There is consensus that IT has significant effects on the productivity of firms. This study focused on how modern Information Technology impacts on effectiveness and efficiency of HR practices. Human Resource Management practitioners generally use IT in the form of Human Resource Information System (HRIS) for the purposes of decision making in the field of HR. The main objective of this study was to examine the impact of IT on HR practices in organisations in Ghana. One hundred organizations were purposively sampled for the study. The descriptive survey methodology was used. Data was collected using a self-designed questionnaire made up of closed and open-ended questions and statements. Results revealed organizations awareness and use of the various HR-related software, and despite the fact that these software are costly, they, at the same time, generates terrific benefits.
Keywords: Information Technology; Human Resource; Human Resource Information Systems; Ghana.
Congestion Management with Improved Real Power Transfer Using TCSC in Thirty Bus System
by Mohana Sundaram Kuppusamy, Kalaimani P
Abstract: Secure operation and reliable utilization of transmission lines is a challenging issue in deregulated power system. The scheduled power transactions are difficult due to the overloading of transmission lines in restructured power system as the electricity market has become more competitive. Due to congestion of transmission lines, the transfer of real power and the power system voltage profile are greatly affected in the power system. The aim of this research work is to increase the real power and the reactive power flowing in the lines of multibus system using thyristor controlled series compensator (TCSC). Real power transfer with reduced losses and improved voltage stability is an important factor in the present global scenario. This paper deals with the improvement of power flow in power transmission lines by series compensation device in thirty bus system with reduced congestion. The thirty bus system without and with thyristor controlled series compensation device (TCSC) is modeled and simulated and the results are presented. The simulation studies indicate a significant improvement in the real and the reactive power flow with the introduction of TCSC .The advantages of the proposed system include the smooth variations of the real and the reactive powers.
Keywords: TCSC; Congestion Management; Real power flow; Voltage Stability; Available Transfer Capability.
CLASSIFICATION OF CRICKET VIDEOS USING FINITE STATE MACHINES
by VIJAYAN ELLAPPAN
Abstract: The problem of classifying scenes from cricket video is addressed and a robust framework for this problem is proposed. It is proposed that the finite state machines (FSM) are suitable for detecting and classifying scenes and their usage is demonstrated for three types of events: wicket, six, four. This framework utilises the structural information of the scenes together with the low-level and mid-level features. Low level features of the video including motion and audio energy and a mid-level feature, body, are used in this approach. The transitions of the FSMs are determined by the features from each shot in the scene. The FSMs have been experimented on over 80 clips and convincing results have been achieved.
Keywords: Fine State Machine.
Automatic Brain Tumor Detection using Image Processing and Data Mining Techniques
by Geetha Ramani R, Febronica Faustina, Shalika Siddique, Sivaselvi Krishnamoorthy
Abstract: In recent days, analysis on Magnetic Resonance Imaging (MRI) has extensively performed to understand the complex information in the human brain. Mostly, the pathological regions in the brain are detected using various MRI techniques. Depending upon the MRI technique specific regions may be exhibited better than other regions. These images are computationally analysed to identify the abnormal regions. In this work, glioma images are involved to detect the tumor regions in the brain using image processing and data mining techniques. Broadly, the pixels are grouped into tumor and tumor pixels using unsupervised as well as supervised data mining methods. Further, the tumor pixels are classified into four classes namely, edema, necrosis, enhancing tumor and non-enhancing tumor using supervised classification methods. K-means clustering could detect the tumor pixels with the accuracy of 94.64% whereas Random Forest classifier with 99.5% could identify the pixel classes correctly.
Keywords: Image Processing; Data Mining; Clustering; Classification; Random Forest; Brain Tumor Detection;.
An Improved Mean Curvature Based Bending Model for Cloth Simulation
by Xiaohui Tan
Abstract: In cloth animation, the bending behavior of cloth is important for cloth simulation effects. The presentation of cloth bending properties plays a key role in cloth animation research because cloth is characterized by strong resistance to stretch while weak resistance to bending. This paper proposed an improved approximate nonlinear bending model based on local geometric information. In the dynamic simulation, cloth was divided into several regions according to mean curvature of surface. The bending force was updated according to the changes of the mean curvature in each region. The calculation of bending force was simple and accurate with the proposed model. Experimental results show that wrinkles and folds generated in a natural way with the improved model and the efficiency of simulation is improved compared with the original algorithm.
Keywords: bending model; cloth simulation; mean curvature; surface segmentation.
Frequency Variations Management in Deregulated Environment using Intelligent Controller
by Chockalingam Aravind Vaithilingam, Yi Heng Ser, Ramani Kannan, Charles R Sarimuthu
Abstract: Information of the frequency variations is critical to restore the dynamics of power system network. In this paper a modified load frequency control method in the deregulated power system to restore back the frequency is proposed and analysed. The research is done using industry standard modelling tool and the frequency variations are investigated through the construction of the power system network. The distribution company participation matrix is used in the deregulated environment with number of Generation Company (GENCO) and Distribution Company (DISCO). Three type of controllers are applied to the proposed two-area system through load frequency control including the conventional and intelligent controllers. The deviation of the output frequency in each area and tie line exchange are studied. It is concluded that the modified load frequency control method using intelligent controller shows 10% improvement on the settling time and about 20% improvement on undershooting.
Keywords: Load Frequency Control; Multi-Area Power System; Reliable Grid; Energy storage systems.
Automation of Smart Monitoring for Person Localization & Alerting Network
by Deepika Kripanithi
Abstract: my Person Localization and Alerting Network (myPLAN) is an application for cautioning connections in deadlock situations. myPLAN is a mobile Application (App) developed for Android enabled smartphone. The App invokes Global Positioning System (GPS) Application Programming Interface (API) from the smartphone. Android Operating System (OS) enables the GPS API to retrieve the geographical location data. The whereabouts of an individual are incorporated using GPS module and Wi-Fi networks that are embedded in the device. GPS and Wi-Fi sensors utilize triangulation techniques to pinpoint the exact location on the global map. The application involves in linking the entity with the blood relations. myPLAN App opens by passing an unique code in the phone. The entity can send help request to family associations and emergency vehicle equipments through the App. An individual can inform the associated people about the whereabouts in case of trouble. The person can alert the connections about the location and time via Short Message Service (SMS) and Electronic Mailing System (E-MAIL). Emergency dispatchers can be chosen based on the necessity. The GPS API in the mobile phone locates the relevant assistance in the specified radius from the place where the individual positioned. The information is sent to the emergency carriers through SMS with the entity details along with the location co-ordinates.
Keywords: Application; Emergency; Global Positioning Systems; Location;rnLocalization; Positioning; Security; Sensors.
A Review on Feature Selection Methods for Improving the Performance of Classification in Educational Data Mining
by Maryam Zaffar, Manzoor Hashmani, Sameer Khan
Abstract: Educational Data Mining (EDM) evaluates and predicts students performance that assists to discover important factors affecting students academic performance and also guides educational managers to make appropriate decisions accordingly. The most common technique for discovering meaningful information from the educational database is classification. The accuracy of classification algorithms on educational data can be increased by applying Feature Selection Algorithms. Feature Selection Algorithms help in selecting robots and meaningful features for predicting students performance with high accuracy. This paper presents different EDM approaches for forecasting students performance using different data mining techniques. In addition, this paper also presents an evaluation of recent classification algorithms and feature selection algorithms used in Educational Data Mining. Furthermore, the paper will guide the researchers on new and possible dimensions in building a prediction model in EDM.
Keywords: Educational Data Mining (EDM); Feature Selection in Educational Data Mining; Filter Feature Selection; Wrapper Feature Selection.
PERFORMANCE ANALYSIS OF IRIS BIOMETRIC SYSTEM USING GKPCA AND SVM
by Suganthy M, Manjula S
Abstract: Among all biometric technologies, iris recognition is most accurate and high confidence authentication system. Due to the limitations in PCA based system, modified Principal Component Analysis (PCA) based feature extraction is proposed in iris recognition system. In the proposed system, features are extracted using Gaussian Kernal PCA (GKPCA) and classified using Support Vector Machine (SVM). GKPCA and SVM algorithms are evaluated using CASIA V3 Iris database. The performances are compared with the existing PCA based system. The proposed system achieves 96.67% of accuracy for 256 features using GKPCA linear SVM. False Acceptance Rate (FAR) and False Rejection Rate (FRR) are 0 and 3 respectively, for linear SVM. The results show that the proposed system performs accurate localization of patterns even in non-ideal conditions.
Keywords: Gaussian Kernal Principal Component Analysis; Support Vector Machine; Iris recognition; False Acceptance Rate.
Mobile Application for Children to Learn Hadith: Hidup Cara Rasullullah
by Aliza Sarlan
Abstract: Informal learning for many people starts at home from the moment they were born until they die. Starting from as simple as learning Arabic alphabet, informal learning of Islamic context for children is a vital part in any Muslim child development. However, the platform for children to obtain informal learning within Islamic context is limited. The rapid development of emerging technologies for mobile devices has increase the possibility to exploit them for creation of Islamic contexts apps necessary for children informal learning phase. As such, this project aims at developing a mobile application for android that enables children and parents to learn Islamic Hadiths in an interactive and engaging manner called Hidup Cara Rasullullah. User acceptance and usability testing results demonstrate an acceptable level of user acceptance and usability level. The mobile apps able to assist parents and teachers in promoting hadith learning among children in an informal learning environment.
Keywords: Mobile learning; children education; informal learning;rnIslamic education; hadith;.
Promoting Business -IT Alignment through Agent Metaphor Based Software Technology
by Venkatesan Devanathan
Abstract: Business-IT Alignment (BITA) not only enables enterprises to synchronise their effort among stakeholders but also facilitates efficient achievement of organizational goals. A strategy to facilitate BITA in enterprises is to adopt suitable Enterprise Architecture (EA). Popular software modeling approaches like unified modeling language (UML), Business Process Modeling Language (BPML) are used in EA to depict its IT models. So there is a direct interdependency or connection among BITA, EA, and software technology. Aligning the modeling terminologies of BITA, EA and software technology, facilitate CASE automation. It also improves traceability among different kinds of enterprise models, and facilitates change propagation. By this enterprises can avoid unnecessary effort to synchronise different models and terminologies using error prone and hard to comprehend model connectors. This result in enhanced stakeholder performance by reducing their effort needed to comprehend and perform BITA, EA and software modeling tasks. The present study evaluates the suitability of the object-oriented and agent-oriented modelling for the development of business model aligned software. EA alignment capabilities of these technologies are compared using Zachman Framework (ZF). The comparison is carried out by evaluating the property of the models of these technologies in filling up the grid cell units of ZF, in a manner preserving the syntactic and sematic relationship between grid cell units. This study presents the fact that agent models provide better business process software model alignment between gird cells of ZF due to the syntactic continuity of model abstractions in grid cell diagrams. It improves stakeholder communication, reduces the possibility of misunderstanding of business flow and augments savings due to fewer errors. This study also demonstrates that models created using agent abstraction can satisfy the information requirements as needed in ISO 19440/19439 and other enterprise modelling standards.
Keywords: Keywords: Enterprise Architecture - Enterprise Architecture Framework- Business Model Alignment- BITA- Model Driven Development – Zach man Framework- Enterprise Integration – Information System Design.
Special Issue on: LISS 2017 Emerging Trends, Issues and Challenges in Big Data and Its Implementation
Evolution of intellectual structure of data mining research based on keywords
by Yue Huang
Abstract: Data mining has made rapid progress in the past decade and detecting intellectual structure of data mining research is of great help to researchers. We retrieved 5380 papers, published in 11 leading journals of data mining defined by Google Scholar, from SCIE under Web of Science and Scopus databases between 2007 and 2016 to carry out bibliometric analysis. As indicated by the analysis on the evolution of keyword frequency, the research focus of data mining has shifted from such topics as association rule mining to large-scale complex networks. Matrices of high-frequency keywords were also built for different time periods, namely 2007 to 2016 for the whole picture during these years, 2007 to 2011 and 2012 to 2016 for two periods. Clustering results show there are four main data mining topics and the attention has been paid more to graph data mining and complex network analysis in the past 5 years.
Keywords: data mining; intellectual structure; co-word analysis; clustering; evolution analysis.
The Effects of Relationship Quality and Knowledge Sharing on Service Innovation Performance: Organizational learning as a mediator
by Zhaoquan Jian, MOHAMED A.L.I. OSMAN, L.E.I. LI
Abstract: Drawing on RBV and service dominant logic and using data collected from 243 companies, this paper aims at examining the interplays between relationship quality, knowledge sharing, organizational learning, and service innovation performance. This empirical research found that knowledge sharing and relationship quality were significantly related to organizational learning, that in turn significantly affected service innovation performance. Moreover, better relationship quality would yield improved knowledge sharing. Furthermore, we propose that organizational learning is a significant mediator through which knowledge sharing influences firm performance, and that relationship quality is also a critical factor that facilitates service innovation.
Keywords: Relationship Quality; Knowledge Sharing; Organizational Learning; Service Innovation Performance.
Special Issue on: ICSS 2018 Advances of Service Science in Information Technology
A Construction and Self-Learning Method for Intelligent Domain Sentiment Lexicon
by Shaochun Wu, Qifeng Xiao, Ming Gao, Guobing Zou
Abstract: A new method of building intelligent sentiment lexicon based on LDA and word clustering is put forward in this paper. In order to make seed words more representative and universal, this method uses LDA topic model to build the term vectors and select seed words. The improved SO-PMI algorithm has been used to calculate the emotional tendency of each sentiment word. In addition, the domain sentiment lexicons automatic extension and update method is designed to deal with dynamic corpus data. Experiments show that the proposed method can build the sentiment lexicon with higher accuracy, and can reflect the change of words emotional tendency in real time. It is proved in this paper that this method is more suitable for processing a large number of dynamic Chinese texts.
Keywords: Sentiment Lexicon;SO-PMI algorithm;seed words;LDA Topic Model;word clustering; incremental text processing.
Comprehensive Evaluation of Cloud Services based on Fuzzy Grey Method
by Wenjuan Li, Jian Cao, Shiyou Qian
Abstract: Cloud-based applications have become more and more popular. However, it remains a big challenge to comprehensive evaluate the reliability and performance of cloud providers and services due to the reason that Cloud is extremely dynamic and uncertain, resources distributed, virtualized, and freedom of entry and exit. The grey theory is adapted to handle the problems of blurring and uncertainty. Therefore, based on the grey relational analysis and grey comprehensive evaluation method, this paper proposes a novel trust comprehensive evaluation method for cloud services, also a comprehensive user satisfaction evaluation method for the better selection of suitable providers. In addition, it discuss in detail the construction and calculation of the evaluation model by case study.
Keywords: Cloud computing; Grey comprehensive evaluation; trust; user satisfaction.
Predicting Service Collaboration for Users based on Data Variation Patterns
by Jiaqiu Wang, Zhongjie Wang
Abstract: Service collaboration allows the realization of more complicated business logic by using existing services. Nowadays, users use lots of services in their daily work. For example, developers use a large number of services (e.g., Stack Overflow, Github, Blogger, etc.) to develop programs. Services are used continuously. Since most of the users\' data is distributed in these different service providers, these data are separated from each other although they are correlated. If we coordinate different services based on these correlation data, we can provide users with seamless and effective support. This is very significant because it greatly increases users\' productivity. However, due to the segregation of data, it is difficult to coordinate different services based on data correlation. To deal with this challenge, we propose a novel deep recurrent neural network (runs in a centralized service) to predict future services collaboration and their generated data. The network captures the correlation between different data and discovers patterns of data variation by using multiple hidden layers, which are beneficial to services collaboration prediction. Extensive experiments are conducted on the real world data set. Experimental results show that our model significantly outperforms a few competitive baseline methods.
Keywords: Service Collaboration Prediction; Correlation Data; Data Variation; Deep Recurrent Neural Network.
A Caching Strategy Based on Dynamic popularity for Named Data Networking
by Meiju Yu, Ru Li
Abstract: Named Data Networking (NDN) is a prominent architecture for the future Internet. In NDN, routers have the capacity of in-network cache, which can completely improve network performance. However, the cache capacity in routers is limited and how to utilize the cache resources effectively is still a great challenge. To solve the problem, this study presents a dynamic popularity caching strategy based on additive increase multiplicative decrease for NDN (DPCA). DPCA takes content popularity and caching capacity into account and it utilizes AIMD algorithm to adjust the popularity threshold dynamically. At the same time, it also proposes a evict algorithm which takes the historical information of content popularity, the trend of content request and the interval from the last request time into account. The simulation results show that the DPCA strategy can effectively improve cache hit ratio, decrease network throughput and reduce the average hit distance compared with other schemes.
Keywords: named data networking; caching replacement policy; dynamic content popularity; additive increase multiplicative decrease; evict algorithm.
Log Automaton under Conditions of Infrequent Behavior Mining
by Xianwen FANG, Juan LI, Lili WANG, Huan FANG
Abstract: In the existing process mining methods, infrequent behaviors are often considered as noise is ignored, but some infrequent behaviors have an important role in business process management. Firstly, the knowledge of log automaton is applied to the low-frequency log to delete infrequent behavior in the logs; secondly, the processed logs are added into attributes. Then, the condition-dependent value of the communication characteristics of different module networks is compared with the threshold, and the effective infrequent log is retained to optimize the model. Finally, a practical case is applied, which indicates the effectiveness and validation of the proposed method.
Keywords: process mining; Log automaton; infrequent behavior; Conditional dependency measure.
A Dynamic Programming-based Approach for Cloud Instance Type Selection and Optimization
by Pengwei Wang, Wanjun Zhou, Caihui Zhao, Yinghui Lei, Zhaohui Zhang
Abstract: With the advantages of cloud computing gradually highlighted, users increasingly want to deploy their applications and services on the cloud to reduce costs and obtain high computing capacity. Nowadays, cloud providers
(e.g. Amazon, Microsoft) at home and abroad provide a large amount of cloud instance types optimized to fit different use cases, such as compute optimized and memory optimized. Due to the potentially large quantity of cloud instance types in the public cloud market, it is often a challenge for users to select an optimal set of cloud instance types subject to limited resource capacity. In this paper, a dynamic programming-based approach is proposed for cloud instance type selection, which can provide optimal combination of cloud instance types
to users. Experiments are performed based on real-world cloud information to evaluate the proposed method.
Keywords: cloud computing; cloud instance type; dynamic programming; selection and optimization.
Learning context-dependent word embedding based on dependency parsing
by Ke Yan, Jie Chen, Wenhao Zhu, Xin Jin, Guannan Hu
Abstract: Word embeddings constitute the basic method of text representation. Whether they are the input to a machine learning algorithm or the features used in a natural language processing application, such embeddings have proven helpful in solving various text processing tasks. In natural language texts, contextual information exerts a crucial influence on the semantics of word representations. In current research, most training models are based on shallow textual information and do not fully exploit deep relationships in sentences. To overcome this problem, this paper proposes the dependency-based continuous bag-of-words (DCBOW) model. This model integrates the dependency relationships between words and sentences into the context in the form of weights, thereby increasing the influence of specific contextual information on the prediction of target words. This method increases the abundancy of word context information and enhances the semantics of word embeddings. The experimental results show that relative to syntactic similarity, the proposed method highlights semantic relations and improves the performance of word representations.
Keywords: word embedding; context-dependent; dependency.
A Recommendation Algorithm for Point of Interest Using Time-based Collaborative Filtering
by Jun Zeng, Yingbo Wu
Abstract: Location-based social networks (LBSNs) make it possible for people to share their visited places by uploading the check-in information. To improve the efficiency of recommendation algorithm, researchers introduce check-in data into point of interest (POI) recommendation to help users find new and interesting place. However, some researches ignore the signification of time factor for POI recommendation in LBSNs. In this paper, we propose a time-based collaborative filtering algorithm according to the similarity between users which combines the global similarity during a long period and local similarity within a short time interval. The experimental results show that the method we proposed can get more accurate recommendation.
Keywords: location-based social networks; recommendation system; point of interest recommendation; time-based collaborative filtering;.
Special Issue on: Control and Management of Logistic Systems Based on Information Technologies
Big Data Prediction Method of Traffic Logistics Demands Based on Regional Differences
by Rongting Sun, Yiqun Guo
Abstract: Aiming at the optimization effect of traditional methods on logistics transportation, and the inaccurate prediction of logistics demand, this paper proposes a big data forecasting method for traffic logistics demand based on regional differences.Based on the regional differences, a linear statistical programming model for the prior data of traffic logistics demand data is established. On this basis, the association rule feature decomposition and average mutual information analysis are carried out for the traffic logistics demand big data.The BP fuzzy decision classification model is adopted for feature information clustering and information fusion processing of traffic logistics demand big data to optimize the big data prediction model. The simulation results show that the model has higher accuracy and better global convergence in the big data forecast of traffic logistics demand, which improves the overall forecasting ability, and the forecasting time is reduced by 12.8% compared with the traditional method.
Keywords: Regional difference; traffic logistics; big data prediction; fuzzy decision; linear planning model.
Research on Regional Spatial Logistics Information Integration Method Based on Big Data
by Xiangdong Chen, Gregory Kalra
Abstract: Aiming at the shortcomings of current regional spatial logistics, such as low efficiency of logistics resource utilization, high cost of logistics transportation and slow speed of goods transportation, a regional spatial logistics information integration method based on large data is proposed. Firstly, the integration principle and process of regional spatial logistics information are described. Then, the logistics transportation route optimization model is assumed. Finally, the integration of regional spatial logistics information is realized by using the logistics transportation route optimization model with time windows. The experimental results show that the proposed regional spatial logistics information integration method can improve the utilization efficiency of logistics resources, and the data consistency can reach 96.9%. When the number of goods is 10,000, the transportation cost of the proposed method is the lowest of 12,300 yuan, so the transportation time of the method is the shortest.
Keywords: Regional Spatial Logistics; Information Integration; Path Optimization.
Intelligent Classification of Logistics Multi-Distribution Resources Based on Information Fusion
by Xinxian Qiu
Abstract: Aiming at the problems of low recall rate and low precision of intelligent classification of logistics multi-distribution resources, an intelligent classification method of logistics multi-distribution resources based on information fusion is proposed. The discrete transformation of measurement equation and state equation is used to describe the state parameters of logistics distribution vehicles and roads. The state estimation component is used to estimate the state of logistics multi-distribution vehicles, and the state equation of logistics multi-distribution vehicles is obtained. The state error of multi-distribution vehicles is used to realize the intelligent classification of logistics multi-distribution resources. The experimental results show that this method has high recall rate and accuracy in intelligent classification of logistics multi-distribution resources, and can get more accurate classification results, at the same time, it takes less time to classify resources, which is conducive to promoting the development of logistics distribution technology.
Keywords: Information Fusion; Logistics Multi-Distribution; Resources; Intelligent Classification.
Research on Routing Optimization of Logistics Distribution Vehicle Based on Cloud Model
by Zhongmin Liu
Abstract: The path optimization problem is very important in logistics distribution. Under the current situation that urban traffic road congestion is serious and user service demand is gradually improved, logistics distribution path optimization is not the simple combination optimization problem. To this end, combined with the deep belief network and the cloud model, the cloud-based logistics distribution vehicle routing optimization algorithm is proposed. After pre-processing of the collected urban traffic data for repair, denoising, etc., the data is trained and learned using the Deep Belief Network Model (DBN). According to the manually set tag data and model training results, the road conditions of the logistics distribution route are predicted; then, the time-sharing weighted traffic network is established. Combined with genetic algorithm, the cloud model theory is introduced and to realize real-time solution and update of time-sharing weights of time-division weighted network paths. The global optimal solution is acquired and the optimal solution for the logistics distribution vehicle path is obtained. The experimental results show that the performance of the logistics model based on cloud model is better than the current algorithm.\r\n\r\n
Keywords: cloud model; logistics and distribution; path optimization;.
Design of Logistics Transportation Monitoring System Based on GPS/DR Combined Positioning Technology
by Wenlian Deng, Aida Maki
Abstract: Through the logistics transportation monitoring system, logistics companies and customers can grasp the status of cargo transportation in real time. The current research on logistics transportation monitoring has problems such as poor positioning accuracy, low actual load rate and high cost. Therefore, the logistics transportation monitoring system based on GPS/DR combined positioning technology is proposed. According to the actual needs of logistics and transportation monitoring, the overall framework of the system is designed. The designed system hardware has functional modules such as tracking, query, scheduling, monitoring center, and alarm of the transportation tool. According to the overall structure and hardware design of the system, the main process of the system operation is as follows: After receiving the GPS/DR combined positioning data, the coordinates of the current location of the vehicle are automatically calculated by the system. Every once in a while, the communication network will restore the received coordinate information and other related data. It is then registered with the electronic map of the system\'s underlying data module. At the same time, combined with the database of other functional modules, the transportation status is presented on the electronic map interface to realize the positioning monitoring of logistics transportation. The experimental results show that compared with the current research results, the proposed system has high positioning accuracy and real load rate, low transportation cost and high reliability.
Keywords: GPS; DR; location technology; logistics and transportation; monitoring system.
Design of Logistics Operation Management Algorithm based on Information Technology on Internet
by Lili Shao, Fuxian Huang, Yuzhen Yang
Abstract: Aiming at the problems of slow convergence speed, high cost and low search efficiency when ant colony algorithm is used to solve the distribution routing optimization problem in logistics operation management, a logistics operation management algorithm based on hybrid intelligent optimization algorithm is proposed and designed. Based on the establishment of a time-limited one-way distribution routing optimization model for logistics enterprise product and its constraints, artificial fish swarm is adopted. In the early stage, the artificial fish swarm algorithm is used to obtain the initial solution of the model, select the update strategy of ant pheromone concentration and improve the transition probability of ant state. In the later stage, the basic ant colony algorithm is improved by introducing the concept of crowding degree to improve the ability of ant optimization. The parameters affecting the performance of the algorithm are analyzed and set up, and the optimal solution of the model is obtained. The results show that the design algorithm is superior to the basic ant colony algorithm in terms of optimization efficiency, convergence speed and minimum logistics distribution cost.\r\n\r\n
Keywords: Internet; Information technology; Logistics operation management; Basic ant colony algorithm; Artificial fish swarm algorithm.
Integrated Equilibrium Planning for Emergency Logistics Warehouse Allocation based on Internet Plus Mode
by Chaosheng Han, Kim R. Thorup
Abstract: The development mode of integration of emergency logistics warehouse distribution affects the security and timeliness of emergency supplies supply. When using the current method to plan the integration of emergency logistics warehouse allocation, demand points have low satisfaction with the services provided by emergency logistics centers, high cost of transportation of emergency materials, long time of transportation of emergency materials, and problems of low service level, high transportation cost and low transportation efficiency. In the Internet plus mode, we propose an integrated planning method for emergency logistics warehouse allocation. Under the premise of service capacity constraint, operation cost constraint, land resource constraint and timeliness constraint, the costs and maximum service level as the objective function of the integrated planning for emergency logistics warehouse allocation are minimized, and a multi-objective integration equilibrium programming model for emergency logistics warehouse in Internet plus mode is built. The multi-objective equilibrium planning model for enterprises emergency logistics warehouse allocation is solved by genetic algorithm, and the optimal planning strategy is obtained to realize the integration of enterprises emergency logistics warehouse allocation. The experimental results show that the proposed method has high service level, low transportation cost and high transportation efficiency.\r\n\r\n
Keywords: Internet plus mode; emergency logistics; balanced planning.
Special Issue on: Intelligent Service Computing in Advanced Technology Management
Fine-grained sentiment classification based on semantic extension of target word
by Xindong You, Pengfei Guan, Xueqiang Lv, Baoan Lv
Abstract: Current existing fine-grained sentiment analysis method usually extract the context of the sentence while ignoring the semantic representation of the target words. We extent the target words in comments as the additional input parameters to the deep learning model in this paper. And the influence of the number of extended words on the models performance is also discussed thoroughly during the experimenting process. Main procedures of our proposed fine-grained sentiment classification method can be described as: (1) Firstly, target words are expanded by using the semantic distance of the word embedding, which used as the key information. (2) Bidirectional LSTM neural network is used to extract the semantic information afterwards. (3) Additionally, the attention mechanism is employed to learn the sentiment weight distribution of the target words among the text automatically. Experiments conducted on the SemEval 2014 Task 4 corpus showed that the proposed method outperforms the other LSTM model.
Keywords: Fine-grained sentiment analysis; Target word extension; Bi-directional LSTM; Attention mechanism.
Research on Evaluation of Minimum Backbone Grid of Transmission Network Based on Differentiation Planning
by Xinyang Deng, Tao Wang, Yihe Wang, Na Zhang, Kai Liu, Fangyuan Yang
Abstract: The essence of the differential planning of transmission network is to evaluate the minimum backbone networks of transmission grids, and the evaluation of the backbone network of transmitting minimum network includes the evaluation of important nodes and the evaluation of important lines. In this paper, the important node evaluation of the transmission network takes into account the structural fragile nodes and the important load nodes, and the important line evaluation is based on the line number, which is considered synthetically from the point of view of the structural fragile line and the important load line. The minimum backbone network of transmission network is constructed by the structure backbone network and the important load backbone networks.
Keywords: Electrical network architecture;The power system;The grid.
Research on Control Strategy of Grid-Connected Inverter Based on Three-loop Structure
by Yan Geng, Jianwei Ji, Bo Hu
Abstract: In this paper, a grid-connected inverter system is deeply researched based on the two-stage single-phase PV grid-connected inverter. Full bridge structure is adopted in the backend inverter circuit. In order to realize the balanced flow of power, to improve the system's response speed and to eliminate the influence of the resonance peak of LCL filter, the three loop control strategy is adopted in the inverter system. At the same time, the reason of generation of the dead-time and its influence on the output of the grid-connected inverter are analyzed in detail. And an improved dead-time compensation method is proposed for zero-current clamping phenomenon. The output voltage error of inverter is extended from two to seven in this method, which effectively solves the problems, such as zero-crossing distortion of the grid-connected current, more odd harmonic content, higher distortion rate and lower amplitude of fundamental wave.
Keywords: Grid-Connected inverter; Dead-time Compensation; PV.
An Intelligent Image Denoising Method Using Weighted Multi-scale CB Morphological Filter Algorithm
by Yongjie Tan, Jie Qin
Abstract: In order to improve the accuracy of paper disease recognition in paper making process, a paper image denoising method based on multi-scale contour bougie (CB) element morphological filter is proposed. The small-scale structural elements in CB morphological filtering algorithm have better detail protection ability, and the large-scale structural elements have stronger noise suppression ability. By selecting several structural elements to filter the image, and then fusing the filtered images at different scales, the final denoising image can be obtained. The simulation results on the holes paper disease image with Gauss noise and salt and pepper noise show that the PSNR reaches more than 43dB and 38dB respectively, which proves that this method can suppress the noise in the image and keep the image details well.
Keywords: image denoising; CB morphological filter; multi-scale structural elements; weighted fusion.
Network intrusion detection method based on improved ant colony algorithm combined with cluster analysis in cloud computing environment
by Xifeng Wang, Xiaoluan Zhang
Abstract: Aiming at the low detection accuracy of traditional clustering algorithm in intrusion detection under cloud computing platform, a network intrusion detection method based on improved ant colony algorithm combined with clustering analysis is proposed. In this paper, ant colony optimization clustering algorithm is proposed to improve the current mainstream clustering algorithm. This step mainly designs an ant colony clustering processing module suitable for intrusion detection, aiming at the improvement of the function parameters, objective function and calculation method of clustering center of ant colony algorithm. The purpose of the ant colony clustering module is to distinguish most of the clusters belonging to the same type again by clustering algorithm. Each feature vector is used as the clustering center to process and analyze them, so as to realize the separation of legal and illegal acts of network data as far as possible. Finally, the KDDcup99 data set which is recognized by the current research on intrusion detection algorithm is used to carry out experiments. In the experiment, cluster identification technology is used to identify a small number of anomalous intrusion data. Experiments show that the accuracy of the algorithm can achieve at 94.3% for DoS intrusion types and 94.1% for U2R intrusion types, which is higher than that of the contrast methods. It further proves that the proposed improved algorithm has a good clustering effect.
Keywords: Network intrusion detection; Ant colony algorithm; Cluster analysis; Genetic algorithm; Abnormal data.
An Intelligent Image Detection Method Using Improved Canny Edge Detection Operator
by Qian Wang, Wenxia Chen, Haiyun Peng
Abstract: In order to meet the requirement of edge detection of paper disease image in papermaking process, a paper disease image detection method based on improved Canny operator is proposed. Firstly, according to the principle of Gauss filtering and the method of feature statistical analysis, the filtering function and window are selected adaptively. Then, in the gradient solution, the traditional 2?2 neighborhood is replaced by the 3?2 or 2?3 neighborhood which enhances the weight of the intermediate pixel, and the accuracy of edge detection is improved by enhancing the influence of the intermediate pixel. Finally, the iterative averaging method is used to determine the optimal threshold and reduce the error rate of image edge segmentation. The experimental results show that this method can effectively detect the edges of paper disease area and has good edge continuity.
Keywords: Intelligent image detection; edge detection; improved Canny operator; Gauss filtering; feature statistical analysis; Adaptive.
Special Issue on: Information Management in the Information Age
Optimization Decision Model of Enterprise Financial Risk Management Combining Stochastic Demand
by Dandan Zhao, Lin Li
Abstract: The Corporate financial management has certain risks. The influencing factors include the laws of economy, politics, and the market itself. Constructing a suitable enterprise financial risk management model based on the market mechanism can effectively reduce the enterprises financial risks and increase the investment return rate. The random demand is a kind of artificial intelligence optimization decision model with better optimization ability, but sometimes will fall into a local optimal solution. Combining random demand with optimization decision and adding optimization decision random number can make the algorithm jump out of the local optimal solution and get the global optimum so as to improve the accuracy of the algorithm. Then the random demand is used to optimize the enterprise financial risk management model. Experiments show that the random demand, which has better convergence accuracy and convergence speed, can achieve a better optimization effect on the enterprise financial risk management model, reduce the risks, and increase the income.
Keywords: Optimization Decision Model; Enterprise Financial Risk Management; Optimum; Accuracy of the algorithm.
Special Issue on: Internet of Things-Related Open Approaches
Evaluation of Educational Hospitals Portal as a Tool for Patient Access to Information
by Fatemeh Rangraz Jeddi, Hamidreza Gilasi, Sahar Khademi, Sara Chopannejad, Razieh Farrahi
Abstract: The purpose of this study was to evaluate the portal quality of educational hospitals affiliated to the universities of Medical Sciences in Iran. A descriptive cross-sectional study was conducted in 2015. Two hundred and eleven portals were evaluated. Data were collected using a checklist containing 33 questions related to six qualitative criteria which included usability, ease of use and user-friendly, validity, efficiency, services, and interaction. Data gathering was performed through observation. Data were analyzed by descriptive statistics. The obtained score for usability criteria was 3.9 (good), for ease of use and user-friendliness was 3.6 (good), for validity was 4.5 (very good), for efficiency was 4.1 (very good), for service was 2.9 (average), and for interaction was 1.5 (poor). Increasing interactions and providing services through the hospital's website is essential. Therefore, it is recommended to increase, appointing patients for services such as visit, para-clinical services through the provided website.
Keywords: Access to Information; Computer System; Evaluation; Hospital; Information Services; Internet; Patient Portals; Portals' Quality; Quality; University.
Entrepreneurial orientation vignettes into open innovation of the internet of things: Advancing into the age of service dominant reasoning
by Suresh Sood
Abstract: Open Innovation Internet of Things (OI-IoT) is in a nascent stage of development. IoT vignettes enable an understanding of the entrepreneurial orientation and open innovation in nurturing IoT and achieving enormous future growth and pervasiveness in everyday life. The interplay of Open innovation in IoT (OI-IoT) suggests a two-step long run arrival of the IoT revolution during 2012- 2030 with OI-IoT becoming a fabric of successful IoT implementations emerging in 2020 and beyond. Several propositions provide guidance for entrepreneurs developing IoT products as a service with new revenue streams and business models and builds on the early stage of IoT and entrepreneurial moves away from a closed technology assemblage towards embracing service dominant reasoning with an OI-IoT.
Keywords: entrepreneur; IoT; innovation; internet; open; service: things; vignettes.
Internet of Things: The Acceptance and Its Impact on Well-Beings among Millennial
by Zam Zuriyati Mohamad, Siti Ummaizah Meor Musa, Rizalniyani Abdul Razak, Thavamalar Ganapathy, Nur Aliah Mansor
Abstract: This study explores millennials intention to use smart home appliances and devices, and the impact on their perceived quality of life. It focuses on perceived usefulness, perceived ease of use, attitude and behavioural intention. Considering the intention-behavioural gap, this study extends the Technology Acceptance Model by including expected quality of life as an effect of behavioural intention. A total of 206 respondents completed the questionnaire. The findings reveal that perceived usefulness, perceived ease of use and attitude have a positive significant influence on millennials intention to use smart home devices and appliances. Further, the findings demonstrate that attitude partially mediates the relationship between perceived usefulness and behavioural intention. The outcome shows that intention to use smart technology will influence ones expectation of a better quality of life. This study contributes to the development of the smart home device and appliance industry, particularly in developing new technology.
Keywords: Internet of Things; Technology Acceptance Model; well-being; smart devices and appliances; millennials; quality of life; perceived usefulness; perceived ease of use; attitude; behavioural intention.
Technical capabilities are not enough: Deploying Internet of Things in the metals and mining industry
by Shan Gao, Esko Hakanen
Abstract: Technologies enabling the Internet of Things (IoT) have emerged in business operations across industries. Our research investigates the related changes in the metals and mining industry. Based on 53 qualitative interviews among experts and managers within this industry, we identified considerable misalignment between the user expectations and the supplied technologies. Hence, we suggest a more collaborative approach across the industry participants. Openness helps in acquiring the broad set of capabilities (analytic capability, IoT competency, business development, and substantive expertise) that are needed for the implementation of the IoT technologies in the industry-specific context.
Keywords: Internet of Things; implementation; application; metals; mining; capabilities; case study; business models; intelligent products; product intelligence; open innovation; open approach.
The Creation and Capture of Value through Open Platform: The Business Model Utilizing Two-Sided Markets by Managing Standardisation
by Haruo Awano, Masaharu Tsujimoto
Abstract: The emergence of the Internet of Things (IoT) requires substantial increases in the storage needed to store IoT data. This storage technology and products are important to realize the IoT. LTO format is a key to storage of data transmitted by Internet for IoT system. This paper examines a business model involving the LTO (Linear Tape Open) and DLT (Digital Linear Tape) storage formats to clarify how value is created and captured by such open platforms as these storage formats. Recent studies have clarified that successful firms must openly disclose the external interfaces necessary to create complements while still protecting their competitive advantage through proprietary architecture. Few studies have examined the difference between open and proprietary architectures in the case of two-sided markets from a standpoint of obtaining profits. This research has found that the proprietary architecture to close the core technology need not necessarily be built for two-sided market.
Keywords: value creation; value capture; platform; business model; two-sided market; Linear Tape Open; Digital Linear Tape; proprietary architecture; open platform; standardisation; royalty; Internet of Things; IBM; HP; patent.
Smart Parking: An investigation of Users Satisfaction in Kingdom of Bahrain
by Reem AL-Kaabi, Hayat Ali, Shoaib Ahmed, Kainat Ahmed
Abstract: With progressions in Technology, city foundations are becoming robust day by day, progressively engaged towards arrangement of fundamental human needs. However, parking stays one of the under looked issue, although it has a great significance with the growing population and emerging Smart City Concepts. Smart Parking has turned into an imperative thought for improvement of Smart urban communities, which would tackle issues with shortage of vehicle leaving enhancements and traffic supervision. The purpose of this research is to investigate the factors that overall improve the users satisfaction of Smart Parking in Kingdom of Bahrain. Towards this aim, a quantitative methodology followed in which a questionnaire utilized as an information gathering tool and 385 responses gathered from smart parking users in Kingdom of Bahrain. The outcome uncovers that all factors including timeliness, accuracy, information quality, Usability, appearance, system quality and service quality have a critical positive effect on users satisfaction. The contribution of this research resides in proposing a model of satisfaction for smart parking that can be embraced in different nations. In addition, the proposals recommended by the authors will give insights for the legislature and concerned gatherings with smart parking in Kingdom of Bahrain toward better users satisfaction.
Keywords: Internet of Things (IoT); Smart parking; Smart city.
Two dimensions of the evolution process by R&D subsidiaries in MNCs: Comparative analysis of Coca-Cola and 3M in Japan
by Kazumi Tada, Masahiro Ida
Abstract: This study discusses the evolution process of R&D overseas subsidiaries that develop new global products within their own multinational companies. Based on a survey of prior studies, this study analysed the process of the result in product development activities (PDA) by overseas subsidiaries, focusing on MNC and local environmental factors. In addition, we focused on two dimensions of PDA: originality and geographical scope. We conducted a comparative analysis of the Coca-Cola Company\'s Japanese subsidiary (CCJC) and 3Ms Japanese subsidiary (3MJ), both of which are one of the largest R&D subsidiaries in their groups. Consequently, we noted that MNC factor is likely to promote the expansion of geographical scope of PDA results, while the local environmental factor is likely to promote originality. Furthermore, we noted that the influence of the parent company might suppress originality in PDA results. We believe that these findings will contribute to future research.
Keywords: overseas subsidiary; multinational company; product development; R&D; evolution process; Coca-Cola; 3M; comparative analysis; local environment.