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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: 642
New Results of Intuitionistic Fuzzy Soft Set
Статья научная
In this paper, three new operations are introduced on intuitionistic fuzzy soft sets .They are based on concentration, dilatation and normalization of intuitionistic fuzzy sets. Some examples of these operations were given and a few important properties were also studied.
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Статья научная
In this article some modern techniques have been used to diagnose the oral and dental diseases. The symptoms and causes of such disease has been studied that may cases many other serious diseases .Many cases have been reviewed through patients' records, and investigation on such causes of oral and dental disease have been carried out to help design a system that helps diagnose oral and classify them, and that system was made according to the decision tree, (Id3 and J48) and artificial neural network techniques. Sample of oral and dental diseases were collected with their symptoms to become a data base so as to help construct a diagnostic system. The graphical interface were formed in C# to facilitate the use's diagnosis process where the patient chooses the symptoms through the interface which he suffered from ,and they are analyzed using the classification techniques and then re diagnosed the disease for the user.
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New metrics for effective detection of shilling attacks in recommender systems
Статья научная
Collaborative filtering techniques are successfully employed in recommender systems to assist users counter the information overload by making accurate personalized recommendations. However, such systems are shown to be at risk of attacks. Malicious users can deliberately insert biased profiles in favor/disfavor of chosen item(s). The presence of the biased profiles can violate the underlying principle of the recommender algorithm and affect the recommendations. This paper proposes two metrics namely, Rating Deviation from Mean Bias (RDMB) and Compromised Item Deviation Analysis (CIDA) for identification of malicious profiles and compromised items, respectively. A framework is developed for investigating the effectiveness of the proposed metrics. Extensive evaluation on benchmark datasets has shown that the metrics due to their high Information Gain lead to more accurate detection of shilling profiles compared to the other state of the art metrics.
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Статья научная
The sudden surge of digitalization escalates the challenges faced by traditional tax systems to detect and combat tax evasion, and it is a pivotal concern for the smooth functioning and sustainable development of any nation. The paradigm shift offered by the emergence of new-age technologies presents unprecedented opportunities to tap their potential for administering effective tax systems. In our paper, we provide a systematic scientometric analysis of existing literature to analyze four focal new-age technologies in combating tax evasion. We also propose a novel holistic framework to understand the intricacies of this multifaceted landscape of tax evasion from technological, ethical, legal, social, and economic (TELSE) perspectives. The research methodology gives a quantitative scientific mapping to analyze research publications from Web of Science and Scopus databases using Biblioshiny. A total of 117 documents were examined, spanning over the last decade (2014-2024). The research findings highlight considerable traction regarding the number of publications from the two most populated countries in the world. The analysis of the most frequent keywords yields an increasing trend towards the adoption of other new-age technologies as well and depicts different factors that affect tax evasion, which is in line with varied laws and regulations across countries. The interdisciplinary research efforts need to be aligned to tap the full potential of these technologies and to develop effective intelligent taxation systems that are fair, accountable, and explainable.
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Статья научная
Electronic passports (e-passports) are to prevent the illegal entry of traveller into a specific country and limit the use of counterfeit documents by more accurate identification of an individual. The e-passport, as it is sometimes called, represents a bold initiative in the deployment of two new technologies: cryptography security and biometrics (face, fingerprints, palm prints and iris). A passport contains the important personal information of holder such as photo, name, date of birth and place, nationality, date of issue, date of expiry, authority and so on. The goal of the adoption of the electronic passport is not only to expedite processing at border crossings, but also to increase security. The paper explores the privacy and security implications of this impending worldwide experiment in biometrics authentication technology.
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No-Code and Low-Code Approach: AI Data-Driven Python Modules through Jakarta Faces Web App
Статья научная
This research presents a framework that integrates no-code and low-code approaches with AI-driven Python modules for data analysis and visualization, embedded within Jakarta Faces web applications through TCP socket communication. The framework addresses the challenge of enabling non-technical users to perform complex data analysis tasks without requiring extensive programming knowledge. By leveraging Python’s powerful data libraries, the system automates code generation based on user input, offering a seamless environment for data-driven decision-making. The proposed framework demonstrates significant benefits in democratizing access to AI tools, improving development efficiency, and fostering a user-friendly interface for real-time data analysis and visualization. Rigorous testing of the prototype indicates enhanced usability, scalability for moderate-sized datasets, and practical applications across multiple industries, including healthcare and education. This research contributes to the growing body of work on no-code and low-code platforms by offering a novel integration of Python-based data analysis into Java-based web environments, laying the groundwork for more accessible and scalable AI-driven solutions in web development.
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Nonlinear Analysis of Human Gait Signals
Статья научная
Nonlinear dynamics has been introduced to the analysis of biological data and increasingly recognized to be functionally relevant. The aim of this study is to evaluate nonlinear and chaotic dynamics of gait signals. For this purpose, we analyzed gait data in ten healthy subjects who walked for an hour at their usual, slow and fast paces. Poincare plots, Hurst Exponents and the Lyapunov Exponents of gait signals were calculated. The results show that the Hurst Exponents are significantly increased during slow and fast paces. For all subjects, the Lyapunov Exponents are increased during normal gait, which indicates that signals are more chaotic. This can be due to decreased nonlinear interaction of variables in slow and fast paces. The finite values of Hurst Exponents and positive values of Lyapunov Exponents suggest that all of gait signals have low dimensional chaos. In addition, the complexity of signals is decreased during slow and fast gait. Results are useful for the early diagnosis of common gait pathologies.
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Novel design of 32-bit asynchronous (RISC) microprocessor & its implementation on FPGA
Статья научная
As the efficiency and power consumption plays an important role in electronic system design, an asynchronous design is used to reduce such challenges faced in synchronous architectures. The asynchronous processors have a number of advantages, especially in SoC (System on chip) including reduced crosstalk between analog and digital circuits, ease of integrating multi-rate circuits, ease of component reuse and less power consumption as well. This paper deals with the novel design and implementation of such type of asynchronous microprocessor by using VHDL on Xilinx ISE tool wherein it has the capability of handling even I-Type, R-Type and Jump instructions with multiplier instruction packet. Moreover, it uses separate memory for instructions and data read-write that can be changed at any time.
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Novel framework to improve communication and coordination among distributed agile teams
Статья научная
This paper discusses the roles of communication and coordination (C&C) in the agile teams. C&C are important activities that a project manager has to deal with tactically during the development of software projects to avoid the consequences. Their importance further increases especially in case of distributed software development (DSD). C&C are considered as project drivers to accomplish a project successfully within budget and schedule. There are several issues associated to poor C&C those can lead to fail software projects such as budget deficit, delay in delivery, conflicts among team members, unclear goals of project and architectural, technical and integration dependencies. C&C issues are critical and vital for collocated teams but their presences in distributed teams are disastrous. Scrum is one of the most widely practiced agile models and it is gaining further popularity in the agile community. Therefore, a novel framework is proposed to address the issues that are associated to C&C using Scrum methodology. The proposed framework is validated through a questionnaire. The results are found supportive to reflect that it will help to resolve the C&C issues effectively and efficiently.
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Numerical Analysis of Dynamic Mechanical Properties for Rock Sample under Strong Impact Loading
Статья научная
Stress wave propagation effect and failure characteristic of limestone were studied by one-stage light-gas gun induced-plate impact experiment technology. The experiment results indicate that dispersion effect and attenuation characteristic exist in impacting rock. The failure of rock sample has division characteristics, which are head failure zone, middle tension-compression failure zone and tail fracture failure zone. On this basis, the dynamic mechanical response of rock target under impact loading was analyzed by LS-DYNA finite element method. Stress-time curves in different impact velocities were obtained by sensors buried in rock target. The comparative analysis of experiment and simulation show that the main reason of rock failure is the joint action of longitudinal compression wave and transverse sparse wave, and the conclusions have some significance on guiding farther dynamic mechanical experiment of rock.
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Object Oriented Software Usability Estimate with Adaptive Neuro Fuzzy, Fuzzy Svm
Статья научная
In this paper, we present many intelligent models to estimate the usability of object oriented software. In our proposed system, fuzzy svm has less errors and system worked more accurate and appropriative than prior methods.
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On Fuzzy Soft Matrix Based on Reference Function
Статья научная
In this paper we study fuzzy soft matrix based on reference function.Firstly, we define some new operations such as fuzzy soft complement matrix and trace of fuzzy soft matrix based on reference function.Then, we introduced some related properties, and some examples are given. Lastly, we define a new fuzzy soft matrix decision method based on reference function.
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On a Class of Dual Risk Model with Dependence based on the FGM Copula
Статья научная
In this paper, we consider an extension to a dual model under a barrier strategy, in which the innovation sizes depend on the innovation time via the FGM copula. We first derive a renewal equation for the expected total discounted dividends until ruin. Some differential equations and closed-form expressions are given for exponential innovation sizes. Then the optimal dividend barrier and the Laplace transform of the time to ruin are considered. Finally, a numerical example is given.
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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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