- Все статьи 584
Статьи журнала - International Journal of Information Engineering and Electronic Business
Все статьи: 584
Статья научная
This paper analyzes the performance of hybrid gigabit passive optical network (GPON) and wireless communication (FSO) system using EDFA and EYDWA as pre-amplification configuration to compensate the losses due to the fiber cable and the free space channel for 2.5 Gb/s of data. The performance for both amplifiers has been compared on the basis of distance, FSO range and number of users, however the results of simulation show enhancement offered by EYDWA as compared as EDFA amplifier. This amplifier was able to reach transmission distance over 245 Km optical fiber and 1Km range FSO with best BER value around 10-9 and good eye diagram. Whereas, the fiber distance has been limited to 64 Km by using EDFA amplifier.
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Phylogenetic Method for High-Throughput Ortholog Detection
Статья научная
Accurate detection of orthologous proteins is a key aspect of comparative genomics. Orthologs in different species can be used to predict the function of uncontrived genes from model organisms as they retain the same biological function through the path of evolution. Orthologs can be inferred using phylogenetic, pair-wise similarity or synteny based methods. The study here describes a computational method for detecting orthologs of a protein. A phylogenetic tree based approach is used for identification of orthologous proteins. A Combination of species overlap algorithm and patristic distances is used for detecting orthologs of a protein from a set of FASTA sequences. Patristic distances have been used to drill the orthology predictions of any protein down to its closest orthologs. The approach gives a considerably good accuracy and has high specificity and precision. The use of Distance threshold allows controlling the stringency level of predictions so that the closeness and proximity between the protein of interest and its orthologs can be adjusted.
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Platform Screen Doors Enhanced Bus Rapid Transit Intelligent Performance
Статья научная
It is the last straw to clutch at to solve the urban traffic issue, to develop high-capacity rapid transit and promote transit priority. Characterized by low cost, short construction cycle and flexible development, Bus Rapid Transit (BRT) has been favored by more and more cities in the world. The Platform Screen Doors (PSDs) system is an important component at BRT stations, and it is original motive to be designed to satisfy the growing demand from BRT application to provide increased safety and comfort in the first. With the continual increased demand for BRT intelligent performance, the PSDs system is applied to get Bus Location Information with accurate position of arrival and departure at stop, to provide Real-Time Information (RTI) for BRT passengers using PSDs/GPS compound location technology, to put into practice Bus Fleet Management (BFM). Considering the capability of accurate location, it can be applied to actualize Bus Sign Priority in the future. The authors are luck to take in part the practice of BRT system, especially in the BRT intelligent systems, the papers will introduce upwards application and conceive in detail.
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Power Aware Reliable Virtual Machine Coordinator Election Algorithm in Service Oriented Systems
Статья научная
Service oriented systems such as cloud computing are emerging widely even in people’s daily life due to its magnificent advantages for enterprise and clients. However these computing paradigms are challenged in many aspects such as power usage, availability, reliability and especially security. Hence a central controller existence is crucial in order to coordinate Virtual Machines (VM) placed on physical resources. In this paper an algorithm is proposed to elect this controller among various VM which is able to tolerate multiple numbers of faults in the system and reduce power usage as well. Moreover the algorithm exchanges dramatically fewer messages than other relevant proposed algorithms.
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Статья научная
A novel lumped-parameter model is proposed to help with establish practice criteria of decompressive craniectomy and explain post-craniectomy intracranial dynamics. Besides traditional four major parts of arterial and venous blood, cerebrospinal fluid and brain tissue, another compartment produced by secondary intracranial hypertension is included here. The elliptical deflection solution under uniformly distributed pressure is introduced to compute the craniectomy compartment volume and incorporate it into existing differential equations. Under particular pathology in this paper our model predicts the waveform of post-craniectomy intracranial pressure, which measures the clinical effectiveness of such an operation. Then a statistical model—Gaussian fitting model is used to fit our simulation data. This quantitative model provides a possible way to designate the operation criteria such as the size of decompressive craniectomy. Finally we propose the optimal interval of craniectomy size as from 100 to 300 square centimeters and that larger than 400 square centimeters would not obviously reinforce pressure reduction anymore.
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Predicting Shelf Life of Burfi through Soft Computing
Статья научная
Soft computing cascade multilayer models were developed for predicting the shelf life of burfi stored at 30oC. The experimental data of the product relating to moisture, titratable acidity, free fatty acids, tyrosine, and peroxide value were input variables, and the overall acceptability score was the output variable. The modelling results showed excellent agreement between the experimental data and predicted values, with a high determination coefficient (R2 = 0.993499439) and low RMSE (0.006500561), indicating that the developed model was able to analyze nonlinear multivariate data with very good performance, and can be used for predicting the shelf life of burfi.
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Статья научная
A modern technology used for extracting knowledge from a huge amount of data using different models and tasks such as prediction and description is called data mining. The data mining approach has a great contribution on solving a different problem for data miners. This paper focuses on the application of data mining in health centers using different models. The model development process helps to identify or predict the behavior of blood donors whether they are eligible or ineligible to donate blood by their right status way and protects any blood bank health center from the collection of unsafe blood. Classification techniques are used for the analysis of Blood bank datasets in this study. For continuous blood donors, it will help to enable to donate voluntary individuals and organizations systematically. J48 decision tree, neural network as well as naïve Bays algorithms have been implemented in Weka to analyze the dataset of blood donors. The study is used to classify the blood donor's eligibility or ineligibility status based on their genders, deferral time, weight, age, body priced, tattoos, HIV AIDS, blood pressure, donation frequency, hepatitis, illegal drug use attributes. From the 11 attributes, gender does not affect the result. We have used 1502 datasets for the train set and 100 datasets for testing the model using cross-fold validation. Cross-fold data, partition was used in this study. The efficiency and effectiveness of the algorisms are measured automatically by the system. The obtained result showed that the J48 classifier outperforms the best result as well as both neural network and navies, Bayes, in terms of matrix evolution, with its 97.5% overall model accuracy has offered interesting rules.
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Preparing Mammograms for Classification Task: Processing and Analysis of Mammograms
Статья научная
Breast cancer is the most common cancer found in women in the world. Mammography has become indispensable for early detection of breast cancer. Radiologists interpret patients' mammograms by looking for some significant visual features for decision making. These features could have different interpretations based on expert's opinion and experience. Therefore, to solve the problem of different interpretations among experts, the use of computer in facilitating the processing and analysis of mammograms has become necessary. This study enhanced and segmented suspicious areas on mammograms obtained from Radiology Department, Obafemi Awolowo University Teaching Hospital, Ile-Ife, Nigeria. Also, Features were extracted from the segmented region of interests in order to prepare them for classification task. The result of implementation of enhancement algorithm used on mammograms shows all the subtle and obscure regions thereby making suspicious regions well visible which in turn helps in isolating the regions for extraction of textural features from them. Also, the result of the feature extraction shows pattern that will enable a classifier to classify these mammograms to one of normal, benign and malignant classes.
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Статья научная
In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the “discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers’ monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day; thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year’s worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.
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Статья научная
The present system performs analysis of snapshots of cursive and non-cursive font character text images and yields customizable text files using optical character recognition technology. In the previous versions the authors have discussed the user training mechanism that introduces new non-cursive font styles and writing formats into the system and incorporates optimization, noise reduction and background detection modules. This system specifically focuses on enhancing the process of character recognition by introducing a mechanism for handling simple cursive fonts.
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Problems of Regulation and Prospective Development of E-commerce Systems in the Post-coronavirus Era
Статья научная
The article examines the application of e-commerce systems and technologies that have a positive impact on the development of the economy of the post-coronavirus period and the formation of appropriate technical and technological infrastructure for it, as well as promising features and directions of e-commerce. The physical and virtual opportunities created by e-commerce technologies for buyers and sellers are explained. The advantages of e-commerce in the international economic space have been identified. The functions of e-business models in accordance with the commercial stages of enterprises are explained. It was noted that the development of ICT has accelerated the process of transition from traditional commerce to e-commerce, led to the emergence of new global trends in e-commerce. These innovations have raised the issue of the application of modern ICT in the development of e-commerce on the platform of the 4.0 Industrial Revolution. Taking into account these factors, the presented article discusses the application of modern technologies in e-commerce systems, such as 3D modeling, the Internet of Things, artificial intelligence, big data. Features of application and regulation mechanisms of E-commerce systems in real economic sectors, which have a direct stimulating effect on economic growth in Azerbaijan, have been studied. Recommendations were given for the modernization and use of e-commerce systems with the application of the latest ICT technologies.
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Proposal of Enhanced Extreme Programming Model
Статья научная
Extreme programming is one of the commonly used agile methodologies in software development. It is very responsive to changing requirements even in the late phases of the project. However, quality activities in extreme programming phases are implemented sequentially along with the activities that work on the functional requirements. This reduces the agility to deliver increments continuously and makes an inverse relationship between quality and agility. Due to this relationship, extreme programming does not consume enough time on making extensive documentation and robust design. To overcome these issues, an enhanced extreme programming model is proposed. Enhanced extreme programming introduces parallelism in the activities' execution through putting quality activities into a separate execution line. In this way, the focus on delivering increments quickly is achieved without affecting the quality of the final output. In enhanced extreme programming, the quality concept is extended to include refinement of all phases of classical extreme programming and creating architectural design based on the refined design documents.
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Proposal to Decrease Code Defects to Improve Software Quality
Статья научная
Software quality is an important topic of software development and it is always challenging to deliver high-quality software. The major challenges, to complete the software, are time and cost without losing the software quality. Software quality has a significant impact on software performance. The acceptability, success, and failure of software are depending on its level of quality and number of defects. Software defects are one of the fundamental factors that can determine the time of software delivery. In addition, defects or errors need to be eliminated before software delivery. Software companies spend a lot to reduce code defects. The aim is to detect defects early with cheaper methods. This paper proposes a code quality scanner to decrease the code defects. The proposed solution is a combination of code scanner and code review. Moreover, the paper presents results using quantitative analysis to show the effectiveness of the proposed solution. The results are found encouraging.
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Quality Test Template toward Multi-user Access Control of Internet-Based System
Статья научная
Aiming at three kinds of Internet-based system quality problems, which is performance, liability and security, the paper proposes a kind of test template during multi-user login and resource access control, which includes test requirement, login script, role-resource correlating and mutation test technique. Some Internet-based systems are tested and diagnosed by automation test technique of test template. At last, system quality can be verified and improved through the realization mechanism of test template.
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Quality of Experience Assessment of Banking Service
Статья научная
In this paper, Quality of Experience (QoE) is used to assess and improve Bank’s customer satisfaction and provide quality of service (QoS) according to their demands. QoE based web platform was developed for the assessment of customer satisfaction. The Eclipse Neon Enterprise Edition was used for the design and development of platform and MySQL database was used for backend database storage. The front interface of the platform provided user facility to enter their complaints and information, which will store in the database. The stored data will be used for the analysis of a particular employee’s evaluations of his performance and behavior with customers. Management can observe the performance of the bank’s employees and can overcome their flaws by providing the required training. If one employee is lacking communication skills and is unable to convey his message to the customer of the bank, then the management can arrange training for improving his/her communication skills.
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Quantum Particle Swarm Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem
Статья научная
This paper presents a quantum behaved particle swarm algorithm for solving the multi-objective reactive power dispatch problem .Particle swarm optimization (PSO) is a population-based swarm intellect algorithm that share various similarities with evolutionary computation methods. Yet, PSO is determined by the imitation of a societal psychosomatic metaphor aggravated by cooperative behaviours of bird and other societal organisms instead of, the endurance of the fittest individual. Stimulated by the traditional PSO method and quantum procedure theories, this work presents a new Quantum behaved PSO (QPSO). The simulation results reveal high-quality performance of the QPSO in solving an optimal reactive power dispatch problem. In order to appraise the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.
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Queuing Effect on Multipath Routing in Mobile Ad Hoc Networks
Статья научная
In Mobile Ad Hoc Network, data delivery is very challenging through single path due to dynamic changes in the network topology. To cope up this issue multipath data delivery is very useful. Recently, many works have been carried out in this domain but few are addressing the queueing effect on multipath scenarios. In this paper, we have designed a network model that based on the existence of multipath between source and destination node and every node behave as M/M/1 queue. In order to do this we generate K (K=1, 2, 3…i) paths are available between each source toward the destination node. The traffic arrivals in each node follow poisson process with arrival rate λ packets/sec. The simulation work of this multipath scenario based on varying mean inter-arrival time. The effect of arrival rate on the performance of multipath network model is analysed and compared. To better understand the effect of arrival rate in application and network layer various QoS metrics are computed. Significant performance of individual node is noticed in the obtained results with various arrival rates.
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Rank University Websites Using Fuzzy AHP and Fuzzy TOPSIS Approach on Usability
Статья научная
With the advent of dynamic website usually all business processes of a business organization are linked with the website of the organization. This is resulted in designing of a complex and gigantic website which may result in slow download and unfriendly navigation. Satisfying the end user need is one of the key principles of designing an effective website. As there are different users for given website, hence there are different criteria on which user wants to get satisfied, hence evaluating a website is a multi-criteria decision making problem. In order to incorporate uncertainties and vagueness in decision making Fuzzy Analytic Hierarchy (FAHP) approach is extended with Fuzzy TOPSIS approach, where different decision makers (DM's) opinion was considered for ranking the website.
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Real Time Speaker Recognition System for Hindi Words
Статья научная
Real time speaker recognition is needed for various voice controlled applications. Background noise influences the overall efficiency of speaker recognition system and is still considered a challenge in Speaker Recognition System (SRS). In this paper MFCC feature is used along with VQLBG algorithm for designing SRS. A new approach for designing a Voice Activity Detector (VAD) has been proposed which can discriminate between silence and voice activity and this can significantly improve the performance of SRS under noisy conditions. MFCC feature is extracted from the input speech and then vector quantization of the extracted MFCC features is done using VQLBG algorithm. Speaker identification is done by comparing the features of a newly recorded voice with the database under a specific threshold using Euclidean distance approach. The entire processing is done using MATLAB tool.The experimental result shows that the proposed method gives good results.
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Real estate recommendation using historical data and surrounding environments
Статья научная
Recommending appropriate things to the user by analyzing available data is becoming popular day by day. There are no sufficient researches on Real-estate recommendation with historical data and surrounding environments. We have collected real-estate, historical and point of interest (POI) data from the various sources. In this research, a hybrid filtering technique is used for recommending real-estate consisting of collaborative and content-based filtering. Generally, in every website user ratings are collected for the recommendation. But we have considered historical data and surrounding environments of a real-estate location for recommendation by which it will be easy for a user to decide that which place would be better for him/her. If any user request for any specific location then the system will find the POI data using google map API. Then the system will consider historical data of that area, got from the trusted sources. So considering the minimum price and optimal facilities, our system will recommend top-k real-estate. After extensive experiments on real and synthetic data, we have proved the efficiency of our proposed recommender system.
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