- All articles 732
-
International Journal of Information Engineering and Electronic Business @ijieeb
Journal articles - International Journal of Information Engineering and Electronic Business
All articles: 732
Multi quadrant operation of brushless direct current motor drive with pi and fuzzy logic controllers
Scientific article
This paper presents that the simulation of control of three phase Brushless Direct Current (BLDC) motor in all four quadrants with PI and Fuzzy Logic controllers (FLC). Traditionally the speed control of motors is carried out by conventional motors with using P, PI, PID and some other control techniques [5]. But it provides a chance to occurrence of nonlinearity & uncertainties that causes some internal and external parameter errors. The efficient speed control in four quadrant operation can be achieved by using a fuzzy logic controller. The improvisation of Brushless Direct current motor drive through fuzzy logic controller in all four quadrants is done using simulink/MATLAB [7].
Free
Multi-Attribute Decision Making Using Simple Additive Weighting and Weighted Product in Food Choice
Scientific article
This paper provide an overview of the analysis and implementation Multi-Attribute Decision Making (MADM) for food selection or food choice. Food choice aim to find the solution on lack of food. Food choice can be doing with diversification. Food diversification aims to find best choice of food alternatives. Food alternative is rice, corn, cassava, potato, sago, sorghum, wheat, and analog rice. The method used is Simple Additive Weighting (SAW) and Weighted Product (WP). The use of this method is expected to help and provide the best decision in food choice. Alternative on MADM as data training, alternative on SAW method and alternative on WP method as data testing. Experimental result on SAW method, best alternative (highest value) is wheat with value 0.8833. On WP method, best alternative (highest value) is wheat with value 0.1563. On SAW method and WP method, decision is the same with wheat as best alternative in MADM on food choice.
Free
Scientific article
Choosing the optimal software architecture for cloud-based systems is a critical and complex Multi-Criteria Decision Making (MCDM) problem, characterized by multiple, often conflicting, and interdependent criteria such as performance, cost, scalability, deployment speed, security, and maintainability. This research addresses this challenge by proposing and applying an integrated MCDM methodology that leverages Evidential Reasoning (ER) and Bayesian Inference (BI). The study's primary objective is to provide a robust and transparent framework for evaluating six common architecture styles: Monolithic, Microservices, Layered, Serverless, Event-Driven, and Service-Oriented Architecture (SOA). The methods employed involved a multi-stage process. First, criteria weights were derived using the Analytic Hierarchy Process (AHP) through expert pairwise comparisons. The techniques for handling uncertainty and dependencies were central. ER was utilized to aggregate subjective and objective assessments, representing them as belief distributions to explicitly account for imprecision and ignorance. Concurrently, BI was applied to model probabilistic interdependencies between criteria (Security influencing Performance, Performance influencing Scalability and Cost) within a Bayesian Network. The Intelligent Decision System (IDS) tool facilitated the operationalization of both ER aggregation and Bayesian inference. The results of the AHP weighting revealed the priorities: Performance (0.3930), Security (0.2355), Scalability (0.1420), Maintainability (0.1160), Deployment Speed (0.0568), and Cost (0.0568). The overall evaluation, integrating these weighted criteria with ER and BI, identified Monolithic architecture as the most suitable option, achieving a utility score of 0.81. This ranking was followed by Event-Driven (0.69), SOA (0.68), Serverless (0.68), Microservices (0.65), and Layered (0.47). A comprehensive sensitivity analysis was conducted to assess the robustness of this decision. Crucially, the analysis demonstrated that while the Monolithic architecture was initially optimal, significant shifts in criteria weights could alter the ranking. Specifically, when the weight of Security was substantially increased (to ~0.32) and Performance decreased (to ~0.25), the Serverless architecture emerged as the new top-ranked alternative (83% utility score), surpassing Monolithic (78%). This finding underscores the critical influence of strategic priorities on architecture selection. Future studies may also focus on developing data-driven, adaptive, and domain-specific decision frameworks to enhance the robustness, transparency, and real-world applicability of MCDM approaches for cloud-based software architecture selection.
Free
Scientific article
With the growing digitization of financial services, fraudulent activities in online transactions are becoming increasingly sophisticated and widespread. An automated and intelligent financial transaction fraud detection system is necessary to properly detect and categorize fraudulent incidents in order to address this challenge. The majority of previous research has been on using binary classification to identify fraudulent transactions. Previous efforts were often hindered by class imbalance, inadequate feature selection, suboptimal prediction accuracy, and insufficient hyperparameter tuning. This paper suggests a hybrid deep learning framework for multi-class financial fraud detection that combines Convolutional Neural Network (CNN) and Closed-form Continuous-time (CfC) models in order to handle these problems. By analyzing transactional and behavioral characteristics, the model is intended to categorize fraud situations into distinct groups, including Phishing, Account Takeover (ATO), Card Skimming, and No Fraud. The proposed CfC-CNN model was not only trained but also tested on a large transaction augmented dataset that was enhanced with contextual characteristics. This work uses the IBM financial fraud dataset and considers 5 million transactions. This work investigated four classes such as phishing, ATO, card skimming, and no fraud. Label encoding was used as a preprocessing method for the contextual and category information. The dataset was refined for best performance by feature selection using Chi-Square and the random search method for hyper parameter tweaking. The proposed CfC-CNN model achieved at least .03 percent accuracy gain and .49 percent F1 score gain over the compared previous works.
Free
Scientific article
Internet acts as the best medium for proliferation and diffusion of fake news. Information quality on the internet is a very important issue, but web-scale data hinders the expert’s ability to correct much of the inaccurate content or fake content present over these platforms. Thus, a new system of safeguard is needed. Traditional Fake news detection systems are based on content-based features (i.e. analyzing the content of the news) of the news whereas most recent models focus on the social features of news (i.e. how the news is diffused in the network). This paper aims to build a novel machine learning model based on Natural Language Processing (NLP) techniques for the detection of ‘fake news’ by using both content-based features and social features of news. The proposed model has shown remarkable results and has achieved an average accuracy of 90.62% with F1 Score of 90.33% on a standard dataset.
Free
NeuroFortis: Blockchain-Powered Federated Learning for ADHD Diagnosis via IoMT Data
Scientific article
Attention-Deficit Hyperactivity Disorder (ADHD) represents a challenging neurodevelopmental disorder that consistently displays three major symptoms involving inattention and hyperactivity alongside impulsivity. Traditional approaches for diagnosis use behavioral evaluations that create both wrong conclusions and delayed help timing. This research develops a complete diagnostic solution involving deep learning federated learning and blockchain security to analyze actigraphy signals originating from IoMT devices. This method first uses UMAP as well as PCA and t-SNE to reduce data dimensions before implementing a hybrid CNN-Transformer neural network to achieve improved classification results. A distributed learning method helps medical institutions run model training autonomously while satisfying privacy rules and addressing data centralization challenges. Model updates on blockchain systems gain protection through smart contracts and cryptographic hashing to stop adversarial attacks and sustain data authenticity. Laboratory tests reveal that this approach reaches 99.2% classification precision without significant performance impact, establishing its effectiveness. This presented study provides on-the-next level ADHD diagnosis features with the help of an AIbased system that ensures privacy and guarantees tampering and scalable operations. Such results allow advancing accurate medical works by real-time monitoring of ADHD and offer safe application of medical Artificial Intelligence to distributed healthcare processes. This will provide objective and credible evaluations that will exist on a global scale.
Free
Neutrosophic Crisp Open Set and Neutrosophic Crisp Continuity via Neutrosophic Crisp Ideals
Scientific article
The focus of this paper is to propose a new notion of neutrosophic crisp sets via neutrosophic crisp ideals and to study some basic operations and results in neutrosophic crisp topological spaces. Also, neutrosophic crisp L-openness and neutrosophic crisp L- continuity are considered as a generalizations for a crisp and fuzzy concepts. Relationships between the above new neutrosophic crisp notions and the other relevant classes are investigated. Finally, we define and study two different types of neutrosophic crisp functions.
Free
New Contribution on Compression Color Images: Analysis and Synthesis for Telemedicine Applications
Scientific article
The wavelets are a recent tool for signal processing analysis, for multiple time scale. It gives rise to many applications in various fields such as geophysics, astrophysics, telecommunications, imaging, and video coding. They are the basis of new analytical techniques and signal synthesis and some nice applications for general problems such as compression. This paper introduces an application for color medical image compression based on the wavelet transform coupled with SP?HT coding algorithm. In order to enhance the compression by this algorithm, we have compared the results obtained with wavelet transform application in natural, medical and satellite color image field. For this reason, we evaluated two parameters known for their calculation speed. The first parameter is the PSNR; the second is MSSIM (structural similarity).
Free
New Fusion Algorithm Provides an Alternative Approach to Robotic Path Planning
Scientific article
For rapid growth in technology and automat- ion, human tasks are being taken over by robots as robots have proven to be better with both speed and precision. One of the major and widespread usage of these robots is in the industrial businesses, where they are employed to carry massive loads in and around work areas. As these working environments might not be completely localized and could be dynamically changing, new approaches must be evaluated to guarantee a crash-free way of performing duties.This paper presents a new and efficient fusion algorithm for solving path planning problem in a custom 2D environment. This fusion algorithm integrates an improved and optimized version of both, A* algorithm and the Artificial potential field method. Firstly, an initial or preliminary path is planned in the environmental model by adopting A* algorithm. The heuristic function of this A* algorithm is optimized and improved according to the environmental model. This is followed by selecting and saving the key nodes in the initial path Lastly, on the basis of these saved key nodes, path smoothing is done by artificial potential field method. Our simulation results carried out using Python viz. libraries indicate that the new fusion algorithm is feasible and superior in smoothness performance and can satisfy as a time-efficient and cheaper alternative to conventional A* strategies of path planning.
Free
New Operations on Intuitionistic Fuzzy Soft Sets based on Second Zadeh's logical Operators
Scientific article
In this paper, three new operations have been introduced on intuitionistic fuzzy soft sets. They are based on Second Zadeh's implication, conjunction and disjunction operations on intuitionistic fuzzy sets. Some examples of these operations were given and a few important properties were also studied.
Free
New Results of Intuitionistic Fuzzy Soft Set
Scientific article
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.
Free
Scientific article
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.
Free
New metrics for effective detection of shilling attacks in recommender systems
Scientific article
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.
Free
Scientific article
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.
Free
Scientific article
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.
Free
No-Code and Low-Code Approach: AI Data-Driven Python Modules through Jakarta Faces Web App
Scientific article
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.
Free
Nonlinear Analysis of Human Gait Signals
Scientific article
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.
Free
Novel design of 32-bit asynchronous (RISC) microprocessor & its implementation on FPGA
Scientific article
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.
Free
Novel framework to improve communication and coordination among distributed agile teams
Scientific article
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.
Free
Numerical Analysis of Dynamic Mechanical Properties for Rock Sample under Strong Impact Loading
Scientific article
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.
Free
