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International Journal of Information Engineering and Electronic Business @ijieeb
Статьи журнала - International Journal of Information Engineering and Electronic Business
Все статьи: 626
OpenMP Dual Population Genetic Algorithm for Solving Constrained Optimization Problems
Статья научная
Dual Population Genetic Algorithm is an effective optimization algorithm that provides additional diversity to the main population. It deals with the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. But dual population introduces additional search space that increases time required to find an optimal solution. This large scale search space problem can be easily solved using all available cores of current age multi-core processors. Experiments are conducted on the problem set of CEC 2006 constrained optimization problems. Results of Sequential DPGA and OpenMP DPGA are compared on the basis of accuracy and run time. OpenMP DPGA gives speed up in execution.
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Operator Design Methodology and Application in H.264 Entropy Coding
Статья научная
Currently ASIC applications, such as multimedia processing, require shorter time-to-market and lower cost of Non Recurring Engineering (NRE). Also, with the IC manufacturing technology developing continually, from transistor level to logic gate level, the size of design cells in digital circuits is increasing correspondingly. New design methodology is in urgent need to meet the requirement for the developing processing technology and shorter time-to-market in IC industry. This paper proposed the concepts and principles of operator design methodology, then focused on the entropy coding application based on the operators and finally presented the implementation results. The results show that with the proposed methodology, a comparable hardware performance can be obtained against the traditional standard cell based design flow. Furthermore, the design speed can be improved efficiently.
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Opinion based on Polarity and Clustering for Product Feature Extraction
Статья научная
In recent time, with the rapid development of web 2.0 the number of online user-generated review of product is increases very rapidly. It is very difficult for user to read all reviews and handle all websites to make a valuable decision at feature level. The feature level opinion mining has become very infeasible when people write same feature with contrary words or phrases. To produce a relevant feature based summary of domain synonyms words and phrase, need to be group into same feature group. In this work, we focus on feature based opinion mining and proposed a dynamic system for generate feature based summary of specific feature with specific polarity of opinion according to customer demand on periodic base and changed the summary after a span of period according to customer demand. First a method for feature (frequent and infrequent) extraction using the probabilistic approach at word-level. Second identify the corresponding opinion word and make feature-opinion pair. Third we designed an algorithm for final polarity detection of opinion. Finally, assigning the each feature-opinion pair into the respective feature based cluster (positive, negative or neutral) to generate the summary of specific feature with specific opinion on periodic base which are helpful for user. The experiment results show that our approach can achieves 96%accuracy in feature extraction and 92% accuracy in final polarity detection of feature-opinion pair in feature based summary generation task.
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Optimal Clustering Algorithms for Data Mining
Статья научная
Data mining is the process used to analyze a large quantity of heterogeneous data to extract useful information. Meanwhile, many data mining techniques are used; clustering classified to be an important technique, used to divide data into several groups called, clusters. Those clusters contain, objects that are homogeneous in one cluster, and different from other clusters. As a reason of the dependence of many applications on clustering techniques, while there is no combined method for clustering; this study compares k-mean, Fuzzy c-mean, self-organizing map (SOM), and support vector clustering (SVC); to show how those algorithms solve clustering problems, and then; compares the new methods of clustering (SVC) with the traditional clustering methods (K-mean, fuzzy c-mean and SOM). The main findings show that SVC is better than the k-mean, fuzzy c-mean and SOM, because; it doesn’t depend on either number or shape of clusters, and it dealing with outlier and overlapping. Finally; this paper show that; the enhancement using the gradient decent, and the proximity graph, improves the support vector clustering time by decreasing its computational complexity to O(nlogn) instead of O(n2d), where; the practical total time for improvement support vector clustering (iSVC) labeling method is better than the other methods that improve SVC.
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Optimal Playing Position Prediction in Football Matches: A Machine Learning Approach
Статья научная
Deciding optimal playing position can sometimes a challenging task for anyone working in sport management industry, particularly football. This study will present a solution by implementing Machine Learning approach to find and help football managers determine and predict where to place individual existing football players/potential players into different positions such as Attacking Midfielder (AM), Defending Midfielder (DMC), All-Around Midfielder (M), Defender (D), Forward Winger (FW), and Goalkeeper (GK) in a specific team formation based on their attributes. To aid in this identification process, it may be beneficial to understand how a player’s playstyle can affect where a player will be positioned in a team formation. The attributes used in facilitating the identification of the player position will be based on Passing Capabilities (AveragePasses), Offensive Capabilities (Possession, etc), Defensive Capabilities (Blocks, Through Balls, Tackles, etc), and Summary (Playtime, Goals, Assists, Passing Percentage, etc). The data that will be analysed upon will be scrapped manually from a popular football site that present football players statistics in a structured and ordered manner using a scrapping tool called Octoparse 8.0. Afterwards, the data that has been processed will be used to create a machine learning predictor modelled using various classification algorithms, which are KNN, Naive Bayes, Support Vector Machine, Decision Tree, and Random Forest ,coded using the Python programming language with the help of various machine learning and data science libraries, further enriched with copious graphs and charts which provides insight regarding the task at hand. The result of this study outputted in the form of the model predictor’s evaluation metric proves the Decision Tree algorithm have both the highest accuracy and f1-score of 76% and 75% respectively, while Naïve Bayes sits the lowest at both 69% accuracy and f1-score. The evaluation has prioritized validating and filtering algorithms that have overfitting in copious amounts which are evident in both the KNN and Support Vector Machine algorithms. As a result, the model formed in this study can be used as a tool for prediction in facilitating and aiding football managers, team coaches, and individual football players in recognizing the performance of a player relative to their position, which in turn would help teams in acquiring a specific type of player to fill a systematic frailty in their existing team roster.
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Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review
Статья научная
Cloud computing is an emerging technology which provides unlimited access of versatile resources to users. The multifaceted and dynamic aspects of cloud computing require efficient and optimized techniques for resource provisioning and load balancing. Cloud monitoring is required identifying overutilized and underutilized of physical machines which hosting Virtual Machines (VMs). Load balancing is necessary for efficient and effective utilization of resources. Most of the authors have taken the objective to reduce the makespan for executing requests on multiple VMs. In this paper, a thorough review on scheduling and load balancing techniques has been done and different techniques have been analyzed on the basis of SLA Violations, CPU utilization, energy consumption and cost parameters.
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Optimized Solution of Two Bar Truss Design Using Intuitionistic Fuzzy Optimization Technique
Статья научная
The main goal of the structural optimization is to minimize the weight of structure or the vertical deflection of loaded joint while satisfying all design requirements imposed by design codes. In general fuzzy sets are used to analyze the fuzzy structural optimization. In this paper, a planer truss structural model in intuitionistic fuzzy environment has been developed. This paper proposes an intuitionistic fuzzy optimization approach to solve a non-linear programming problem in the context of a structural application. This approximation approach is used to solve structural optimization model with weight as objective function. This intuitionistic fuzzy optimization (IFO) approach is illustrated on two-bar truss structural design problem. The result of the intuitionistic fuzzy optimization obtained is compared with the other results of optimization algorithms from the literary sources. It is shown that the proposed intuitionistic fuzzy optimization approach is more efficient than the analogous fuzzy technique for structural design.
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Статья научная
Recent advancements in pest classification using deep learning models have shown promising results in various agricultural contexts. The VGG16 model, known for its robust performance in image classification, has been applied to the task of classifying pests in oil palm plants. This study aims to evaluate the effectiveness of the VGG16 model in identifying pests on oil palm, comparing the performance of default settings with models fine-tuned using grid search and random search techniques. We employed a quantitative approach, training the VGG16 model with three different configurations: default, fine-tuned with grid search, and fine-tuned with random search. Evaluation metrics including precision, recall, F1-Score, and overall accuracy were used to assess model performance across different pest categories: Metisa plana, Setora nitens, and Setothosea asigna. The default VGG16 model achieved precision, recall, and F1-Score values around 90% for Metisa plana, Setora nitens, and Setothosea asigna, with an overall accuracy of 91.00%. Fine-tuning with grid search improved these metrics, with precision, recall, and F1-Score reaching approximately 93.88%, 92%, and 92.93% respectively, and an overall accuracy of 93%. The random search fine-tuning resulted in even higher performance, with precision of about 95.92%, recall of 94%, and F1-Score of 94.95% for Metisa plana, and overall accuracy of 94.67%. The VGG16 model demonstrated strong performance in pest classification on oil palm, with significant improvements achieved through fine-tuning techniques. The study confirms that grid search and random search fine-tuning can substantially enhance model accuracy and efficacy. Future research should focus on expanding the dataset to include more diverse pest species, incorporating attention mechanisms, and leveraging automated control technologies like drones and the Internet of Things (IoT) to further improve pest management practices.
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Статья научная
This study aims to develop a web-based parking lot management system using multi-paradigm programming languages. This application is designed to help parking lot owners in monitoring the ins and outs of the parking spaces including the income they generated from it. The researchers used multi-paradigm programming languages where more than one programming paradigm was employed. This allows them to use the most suitable programming style and associated language constructs to build the system. Specifically, the researchers made use of the following languages in creating the system: HTML5, CSS3, JavaScript, PHP, MySQL, and Flutter. The study utilized developmental research methods in which the product-development process is analyzed and described, and the final product is evaluated. As a result, the creation of the system has been successful.
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Статья научная
Construction of Eukaryotic recombinant expression Vector pCI-ORF5-ORF6/ pCI-ORF5-ORF6B-Hsp70 Containing Hsp70 and PRRSV GP5/M (encoded by ORF5 and ORF6 genes), and to study its immune effect. After being identified by enzyme analysis and nucleotide sequencing test, the repression vector plasmid was transfected into COS-7 cells. The transient expression protein was detected by Western-blotting. The immunogenicities of this DNA vaccine constructs were firstly investigated in a mouse moder. IFN-γ, IL-4 of cytokine, and the spleen T-lymphocyte subgroup quantity (CD4+/CD8+) were detected, DNA vaccine distribution in mice by PCR .The result shows that the recombinant plasmid pCI-ORF5-ORF6-Hsp70 could induce higher response of cellular immune responses and specific immune responses in mouse, the DNA Vaccines in mice Vaccinated via as heart and liver ,lung and kidney, muscle and brain each time step after immunity. providing the clinical basic data and theoretical basis for success of the DNA vaccine development.
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Parallel Algorithms for Freezing Problems during Cryosurgery
Статья научная
Treatment planning based on numerical simula-tion before cryosurgery is an indispensable way to achieve exactly killing of tumors. Furthermore, intraoperative pre-diction based on monitoring results can lead to more accu-rate ablation. However, conventional serial program is diffi-cult to meet the challenge of real-time assistance with com-plex treatment plans. In this study, two parallel numerical algorithms, i.e. parallel explicit scheme and Alternating Direction Implicit (ADI) scheme using the block pipelined method for parallelization, based on an effective heat capac-ity method are established to solve three-dimensional phase change problems in biological tissues subjected to multiple cryoprobes. The validation, speedups as well as efficiencies of parallelized computations of the both schemes were com-pared. It was shown that the parallel algorithms developed here can perform rapid prediction of temperature distribu-tion for cryosurgery, and that parallel computing is hopeful to assist cryosurgeons with prospective parallel treatment planning in the near future.
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Статья научная
The estimation of aeroengine component deviation parameters (CDP) is an important portion of aeronautical propulsion system performance-seeking control (PSC), which employs linear Kalman filter based on piecewise state variable model (SVM) traditionally. But it’s not easy to get SVM, and the process of linearizing the nonlinear model to get the SVM will introduce errors. So parameters nonlinear estimation was introduced based on the nonlinear aeroengine model directly. The nonlinear estimation model is established according to aeroengine operation balance and the measured and calculated values matching of measurable parameters. The nonlinear estimation was changed to a problem of solving complex nonlinear equations, which is equal to an optimization problem. Time-varying inertia weight particle swarm optimization (PSO) with constriction factor was employed to solve the problem in order to satisfy the requirement of precision and calculation speed. The simulation results of a given turbofan engine show that utilizing the improved PSO algorithm can estimate the CPD precisely with satisfied converging speed.
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Performance Analysis of MAC Layer Protocols in Wireless Sensor Network
Статья научная
Media Access Control (MAC) layer protocols have a critical role in making a typical Wireless Sensor Network (WSN) more reliable and efficient. Choice of MAC layer protocol and other factors including number of nodes, mobility, traffic rate and playground size dictates the performance of a particular WSN. In this paper, the performance of an experimental WSN is evaluated using different MAC layer protocols. In this experiment, a WSN is created using OMNeT++ MiXiM network simulator and its performance in terms of packet delivery ratio and mean latency is evaluated. The simulation results show that IEEE 802.11 MAC layer protocol performs better than CSMA, B-MAC and IEEE 802.15.4 MAC layer protocols. In the considered scenario, IEEE 802.15.4 is ranked second in performance, followed by CSMA and B-MAC.
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Performance Comparison of Kalman Filter and Mean Shift Algorithm for Object Tracking
Статья научная
Object tracking is one of the important tasks in the field of computer vision. Some of the areas which need Visual object tracking are surveillance, automated video analysis, etc. Mean shift algorithm is one of the popular techniques for this task and is advantageous when compared to some of the other tracking methods. But this method would not be appropriate in the case of large target appearance changes and occlusion. In addition, this method fails when the object is under the action of non-linear forces like that of the gravity e.g. a ball falling under the action of gravity. Another popular method used for tracking is the one that uses Kalman filter, with measurements (often noisy) of position of object to be tracked as input to it. This paper is based on a simulative comparison of both of these algorithms which will give a proper outline of which method will be more appropriate for object tracking, given the nature of motion of object and type of surroundings. Observations based on these methods are present in the literature but there is no evidence based on implementation of these algorithms that shows a quantitative comparison of the said algorithms.
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Performance Evaluation of Bagged RBF Classifier for Data Mining Applications
Статья научная
Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The feasibility and the benefits of the proposed approaches are demonstrated by the means of data mining applications like intrusion detection, direct marketing, and signature verification. A variety of techniques have been employed for analysis ranging from traditional statistical methods to data mining approaches. Bagging and boosting are two relatively new but popular methods for producing ensembles. In this work, bagging is evaluated on real and benchmark data sets of intrusion detection, direct marketing, and signature verification in conjunction with radial basis function classifier as the base learner. The proposed bagged radial basis function is superior to individual approach for data mining applications in terms of classification accuracy.
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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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