- Все статьи 611
Статьи журнала - International Journal of Information Engineering and Electronic Business
Все статьи: 611
Статья научная
The growing need for accessible websites cannot be overemphasized as it has posed a major challenge in the world of Information and Communication Technology (ICT). Most businesses have gone online in order to improve their market value; the banking sector is not an exception. In an attempt to satisfying customers, websites developers have violated most of the websites standards. The banking sector is one area that carries out most of its activities online. Therefore, it is important that their websites be accessible to all especially people with visually impaired disability and more so, regardless of the browsing technology being used. This study evaluates the accessibility and usability of Nigeria banking websites using some automated tools and manual inspection method. This is done in order to know the conformance of the banking websites with standard as specified by Web Accessibility Initiate (WAI). Results from the study indicate that some of the websites do not conform to the expected standard. Hence, there is need for substantial improvements on most bank websites in Nigeria
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Knowledge Template Based Multi-perspective Car Recognition Algorithm
Статья научная
In order to solve the problem due to the vehicle-oriented society such as traffic jam or traffic accident, intelligent transportation system(ITS) is raised and become scientist’s research focus, with the purpose of giving people better and safer driving condition and assistance. The core of intelligent transport system is the vehicle recognition and detection, and it’s the prerequisites for other related problems. Many existing vehicle recognition algorithms are aiming at one specific direction perspective, mostly front/back and side view. To make the algorithm more robust, our paper raised a vehicle recognition algorithm for oblique vehicles while also do research on front/back and side ones. The algorithm is designed based on the common knowledge of the car, such as shape, structure and so on. The experimental results of many car images show that our method has fine accuracy in car recognition.
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LDASpike for Recognizing Epileptic Spikes in EEG
Статья научная
Manual processing of recorded EEG data for characteristics like epileptic spikes is very time consuming since the recording of EEG for a longer duration producing enormous amount of data. Therefore, automated systems are required to speed up the processing. In the current work, a classification method has been proposed for detecting the epileptic spikes in the recorded EEG data by using Linear Discriminant Analysis (LDA) and has been named LDASpike. The prerecorded EEG data files were used as input to LDASpike and the output produced was the total number of spikes present in each EEG file. The proposed method results on an average sensitivity 100% and selectivity 95.38%, when the training and testing data were same. However, with four fold cross-validation applied in this work, the sensitivity and selectivity were achieved as 98.45% and 96.06%, respectively, on an average. Though a little time initially is spent to train the system but the result produced by the system is very promising and can be compared with the existing standard methods. This system can also works with the real time recording and processing for a clinical setup.
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Lessons Towards Developing An Integrated Tool-support for Small Team Meetings
Статья научная
Teams within organisations meet regularly to review their progress and engage in collaborative activities within a team setting. However, the uptake of tools to support their activities within team meetings is limited. Research efforts on understanding the reasons for low rates of tool adoption and learning lessons in developing tools that could be readily adopted by team members within team meetings are largely unexplored. This qualitative study focuses on learning lessons towards developing an integrated tool-support for small team meetings within organisations using focus groups. Discussions were based on a tool-kit framework generated by observing their team meetings in an earlier study. The discussions were recorded and the transcripts were analysed using grounded theory approach to generate stories on team processes and potential tools that could assist team members during each process. The lessons derived from the study were based on three aspects of tool-support namely the potential users of the proposed tool-kit, processes within the team meetings that would be influenced by the introduction of the tool-kit and the technological aspects of the tool-kit.
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Linear antenna array pattern synthesis using elephant swarm water search algorithm
Статья научная
Linear antenna array pattern synthesis using computational method is an important task for the electronics engineers and researchers. Suitable optimization techniques are required for solving this kind of problem. In this work, Elephant Swarm Water Search Algorithm (ESWSA) has been used for efficient and accurate designing of linear antenna arrays that generate desired far field radiation pattern by optimizing amplitude, phase and distance of the antenna elements. ESWSA is inspired by water resource search procedure of elephants during drought. Two different fitness functions for two different benchmark problem of linear antenna array have been tested for validation of the proposed methodology. During optimization, three types of synthesis have been used namely: amplitude only, phase only and position only control for all cases antenna array. The results show that ESWSA is very efficient process for achieving desired radiation pattern while amplitude only control performed better compare to the others two controlling process for all benchmark problems.
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M-Commerce in Bangladesh –Status, Potential and Constraints
Статья научная
Mobile Commerce often referred to as "M-Commerce" or "mCommerce" is a new dimension or extension of e-Commerce that is performed by mobile devices and Personal Digital Assistants (PDA) using mobile phone networks. As the number of mobile users is increasing dramatically the prospect of m-commerce is also increasing day by day in developing countries like Bangladesh. Though there are a lot of researchers has written about the prospect and adoption of M-commerce but in my research, I tried to find out the statistical analysis of mobile users, mobile internet users; M-commerce current status in Bangladesh. Research also has been done for a number of visitors of stakeholder's site, using the ranking tools, uses of mobile apps by customers and limitation of mobile commerce adoption in Bangladesh those were not discussed earlier. Here, I collected data through web and phone call from various stakeholders, in the top line m-commerce business, studied and identified the problem, shown the current status and major barriers of M-commerce and suggested a methodological framework.
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Machine Learning and Software Quality Prediction: As an Expert System
Статья научная
To improve the software quality the number of errors from the software must be removed. The research paper presents a study towards machine learning and software quality prediction as an expert system. The purpose of this paper is to apply the machine learning approaches, such as case-based reasoning, to predict software quality. The main objective of this research is to minimize software costs. Predict the error in software module correctly and use the results in future estimation. The novel idea behind this system is that Knowledge base (KBS) building is an important task in CBR and the knowledge base can be built based on world new problems along with world new solutions. Second, reducing the maintenance cost by removing the duplicate record set from the KBS. Third, error prediction with the help of similarity functions. In this research four similarity functions have been used and these are Euclidean, Manhattan, Canberra, and Exponential. We feel that case-based models are particularly useful when it is difficult to define actual rules about a problem domain. For this purpose we have developed a case-based reasoning model and have validated it upon student data. It was observed that, Euclidean and Exponential both are good for error calculation in comparison to Manhattan and Canberra after performing five experiments. In order to obtain a result we have used indigenous tool. For finding the mean and standard deviation, SPSS version 16 and for generating graphs MATLAB 7.10.0 version have been used as an analyzing tool.
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Machine learning based business forecasting
Статья научная
The business sectors directly contribute to the growth of any nation. Moreover, the business is an activity of producing, buying, and selling the goods and services to generate the money. The business directly involves in the gross domestic product (GDP). The business forecasting is the activity of predicting or estimating the feature position of the sales, expenditures, and profits of any business. However, the business forecasting helps to the business sectors for planning, decision making, resource utilization, business success, etc. Therefore, business forecasting is a pressing need for the growth of any business. In recent past, many researches attempt to carry out the business forecasting using different tools. However, this paper presents the business forecasting for sales data using machine learning technique and the obtained results are presented and discussed..
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Статья научная
Malware classification has already been a prominent concern for decades, and malware attacks have proliferated at an astounding rate, constituting a significant threat to cyberspace. Deep learning (DL) and malware image approaches are becoming more prevalent in the field of malware analysis, with spectacular results. This work focuses on the challenge of classifying malware variants that are represented as images. This study employs visualization and proposes a convolutional neural network (CNN) based DL model to effectively and accurately classify malware. The proposed model is trained and tested on a very challenging and heterogeneous dataset, and it achieves accuracy of 98.179%, precision of 97.39%, a F1-score of 97.70%, and a fast classification speed (3 seconds needed to test 934 unseen malware). This demonstrates the proposed model's incredibly quick, effective and accurate performance. The proposed model outperformed existing traditional DL models in terms of various performance measures and demonstrated its usefulness in classifying malware families through visualization. This study and experimental results reveal that small-scale malware images and a simple CNN architecture alone are capable of accurately classifying malware families with high classification accuracy.
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Статья научная
The study observes the metadata identification and analysis for Document, Audio, Image, and Videos. The process uses MapReduce and Match Aggregate Pipeline to identify, classify, and categories for identification purposes. The inputs are FITS array results and processed in form of XML. The works consist of the extraction process, identification and analysis, classification, and metadata information. The objective is establishing the file information based on volume, variety, veracity, and velocity criteria as part of task identification component in Self-Assignment Data Management. Testing is done for all file types with the number of files and the size of the file according to the grouping. The results show that there is a pattern where the match-aggregate-pipeline has a longer processing time than MapReduce on a small block size, shown in a block size of 64 Mb, 128 Mb, and 256 Mb. But once the block size is magnified the match-aggregate-pipeline has faster processing time at 1024 Mb and 2048 Mb. The results have a contribution in the metadata processing for large files can be done by arranging the block sizes in Match Aggregate Pipeline.
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Map-Reduce based Multiple Sub-Graph Enumeration Using Dominating-Set Graph Partition
Статья научная
The purpose of this paper is to find all the instances of a given set of pattern graphs (sub-graphs) in a large data graph using a single round of Map-Reduce. For the simplest pattern graphs like a triangle and rectangle we propose the solution. This paper enumerates complex pattern graphs using the enumeration of simple pattern graphs. We proposed Dominating set based graph partition, it generates non-overlapped sub-graphs. Each sub-graph is processed by one machine in the cluster. We analyze both the communication cost and the total computational cost. Communication cost is reduced by using Map-Reduce based dominating set graph partition. At the same time Multiple pattern (sub-graphs) graphs can be enumerated with the same communication cost. The proposed method is not always superior to the conventional sub-graph enumeration, but in some cases involving large-scale data where this method wins, including (1) Adjacency list representation of the graph is the input (2) Number of partitions are decided based on the graph size. We experimentally show that our approach decreases significantly the computation cost, communication cost and scales the enumeration process with a large graph database.
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Measuring the Effect of CMMI Quality Standard on Agile Scrum Model
Статья научная
Agile development gets more appreciation from the market due to its flexible nature and high productivity. Scrum provides better management of the processes as compared to other agile modes. Scrum model has several features and strengths but still it lacks in engineering practices and quality. This research deals with the improvement of Scrum processes for better management and quality of software using the infusion of different practices from internationally renowned capability maturity model integration (CMMI) quality standard. Survey is used as a research design to validate the proposed framework. Statistical analysis shows that there is a profound effect on Scrum model due to the proposed research developing high quality software.
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Measuring the Performance of IT Management in Financial Enterprise by Using COBIT
Статья научная
In financial enterprise, electronic banking is an entirely financial enterprise integrated IT which is used for financial data transaction, human resources, and other important financial data management. It should be managed very well otherwise problem in data processing will bring harm to the enterprise. Data loss, transaction failure, and many others problems will bring a long term negative impact towards the enterprise. The presence of local state regulation issued by Bank Indonesia which requires financial institutions to audit electronic banking externally and internally is one of the reasons why this study is conducted. The methodology that used to measure the performance of IT management in a financial enterprise (e.g. Bank X) is the ones based on Framework COBIT 4.1. A mapping is done to make financial enterprise goals in line with COBIT purposes so the relevant domain will be gained to be able to do further assessment. From the questionnaire and interview done in Bank X, it was found that the maturity level average was 3-defined – IT management performance has developed until a phase where standard procedure and documentation process has taken place because of formal training for the users. But, the training was not fixed yet and as a result many shortages could not be detected maximally by the management although there was a policy created previously. The policy was not able to reach best practice level (i.e. level 5-optimized).
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Memetic Programming Approach for Floorplanning Applications
Статья научная
Floorplanning is a very crucial step in modern VLSI design. It dominates the top level spatial structure of a chip and initially optimizes the interconnections. Thus a good floorplan solution among circuit modules definitely has a positive impact on the placement, Routing and even manufacturing. In this paper the classical floorplanning that usually handles only block packing to minimize silicon rate, so modern floorplanning could be formulated as a fixed outline floorplanning. It uses some algorithms such as B-TREE representation, simulated annealing and adaptive fast simulated annealing, comparing above three algorithms the better efficient solution came from adaptive fast simulated annealing, it's leads to faster and more stable convergence to the desired floorplan solutions, but the results are not an optimal solution, to get an optimal solution it's necessary to choose effective algorithm. Combining global and local search is a strategy used by many optimization approaches. Memetic algorithm is an evolutionary algorithm that includes one or more local search phases within its evolutionary cycle. The algorithm combines a hierarchical design technique, genetic algorithms, constructive techniques and advanced local search to solve VLSI floorplanning problem.
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Metadata Management System for E-learning Objects using Cloud
Статья научная
Existing e-learning systems suffer seriously from the problems of scalability, maintainability, manageability, which can be overcome by merging e-learning with the cloud environment. Cloud technologies can satisfy the need of efficient e-learning system, resource management, user management and security for maintaining the secret information. Efficient delivery of the learning content can be done using Learning Content Management System. It is an environment where multiple users can create, accumulate, reuse, administer and deliver learning content from a central object repository. Learning Content Management System contains only the content description. So metadata for that content created which acts as an index for the learning content and holds details regarding author, location, format etc., Metadata is data about the data which is used for efficient searching.
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Статья научная
Server-side scripts like Hyper-Text Preprocessor, Active Server Pages, and their interaction with databases, has been one of the most popularly used packages for Large Database Intensive Enterprise Software today. In this paper, an approach based on a detailed and efficient method for compiling Web-Applications into executable formats to increase ease of software distribution, where limited internet access exists, is proposed. By this approach, first, one of the server-side scripts and a very popular web-application language in the world, Hyper-Text Preprocessor, is employed as a case study. Second, the methodology of using C++ for writing server installation scripts and creating Graphics User Interface Applications with Qt is shown, with tested applications. Third, Inno Setup Compiler scripts are written and used for compiling into installation and uninstallation setup files. Finally, the relevance of offline Web-Applications for solving scientific problems, the enhancement of C++ codes powered by Graphics User Interface for scientific computation, through inter-channels communication using Qt, and the steps required to easily conquer the challenges faced during the installation of Web-Applications' Servers and Databases like MySQL, are discussed. This approach is efficiently manifested by indicating and confirming this computational potential in the installation and usage of offline web-applications.
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Статья научная
Microarray Data, often characterised by high-dimensions and small samples, is used for cancer classification problems that classify the given (tissue) samples as deceased or healthy on the basis of analysis of gene expression profile. The goal of feature selection is to search the most relevant features from thousands of related features of a particular problem domain. The focus of this study is a method that relaxes the maximum accuracy criterion for feature selection and selects the combination of feature selection method and classifier that using small subset of features obtains accuracy not statistically indicatively different than the maximum accuracy. By selecting the classifier employing small number of features along with a good accuracy, the risk of over fitting (bias) is reduced. This has been corroborated empirically using some common attribute selection methods (ReliefF, SVM-RFE, FCBF, and Gain Ratio) and classifiers (3 Nearest Neighbour, Naive Bayes and SVM) applied to 6 different microarray cancer data sets. We use hypothesis testing to compare several configurations and select particular configurations that perform well with small genes on these data sets.
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Microcontroller-Based Automobile Tracking System with Audio Surveillance using GPS and GSM Module
Статья научная
This paper presents automobile tracking system with audio surveillance using GPS and GSM Module to provide an alternative solution to security challenges experienced by car owner, and to develop a system that can track the location of vehicles. We make use of Microcontroller Unit, GPS, and GSM unit. The design is an embedded application, which will continuously monitor a moving Vehicle and report the status of the Vehicle on demand. The PIC18F452 microcontroller is interfaced serially to a GSM Modem and GPS Receiver. A GSM modem is used to send the position (Latitude and Longitude) of the vehicle from a remote place. The GPS modem will continuously give the information indicating the position of the vehicle. The GPS modem gives many parameters as the output, but only the data coming out is read and sent to the user's phone number. We use RS-232 protocol for serial communication between the modems and the microcontroller. A serial driver IC is used for converting TTL voltage levels to RS-232 voltage levels. When the request by user is sent to the number in the modem, the system automatically sends a return reply to the mobile phone indicating the position of the vehicle in terms of latitude and longitude from this information we can track our vehicles.
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Mining Educational Data to Reduce Dropout Rates of Engineering Students
Статья научная
In the last two decades, number of Engineering Institutes and Universities grows rapidly in India. This causes a tight competition among these institutions and Universities while attracting the student to get admission in these Institutions/Universities. Most of the institutions and courses opened in Universities are in self finance mode, so all time they focused to fill all the seats of the courses not on the quality of students. Therefore a large number of students drop the course after first year. This paper presents a data mining application to generate predictive models for student's dropout management of Engineering. Given new records of incoming students, the predictive model can produce accurate prediction list identifying students who tend to need the support from the student dropout program most. The results show that the machine learning algorithm is able to establish effective predictive model from the existing student dropout data.
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Mitigating Coordination Costs in Global Software Development Using Scrum
Статья научная
Global Software Development (GSD) is the most recent and major trend in software engineering domain. It provides many benefits but also faces various challenges in control, communication and coordination due to socio-cultural, geographical and temporal distance. Scrum is increasingly being applied in GSD as it supports teamwork between developers and customers. Scrum method offers a distinctive feature to mitigate the effects of socio-cultural and geographical but not temporal distance on coordination in GSD projects. This paper explains how Scrum helps to mitigate the effects of temporal distance which includes increased coordination costs in GSD projects. A web application called (Distributed Scrum Web Application) provides various advantages for Scrum teams. The main advantage of this application is to facilitate communication among distributed team members.
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