- All articles 719
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International Journal of Information Engineering and Electronic Business @ijieeb
Journal articles - International Journal of Information Engineering and Electronic Business
All articles: 719
Mitigating Coordination Costs in Global Software Development Using Scrum
Scientific article
Global Software Development (GSD) is the most recent and major trend in software engineering domain. It provides many benefits but also faces various challenges in control, communication and coordination due to socio-cultural, geographical and temporal distance. Scrum is increasingly being applied in GSD as it supports teamwork between developers and customers. Scrum method offers a distinctive feature to mitigate the effects of socio-cultural and geographical but not temporal distance on coordination in GSD projects. This paper explains how Scrum helps to mitigate the effects of temporal distance which includes increased coordination costs in GSD projects. A web application called (Distributed Scrum Web Application) provides various advantages for Scrum teams. The main advantage of this application is to facilitate communication among distributed team members.
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Mobile Augmented Integrated Framework for Citizen Centric E-Governance Services-MAIFCCES
Scientific article
The increasing adoption of improving mobile technologies has opened up a window for research and innovative services development opportunities in diversified dimensions. Government system anticipates delivery of citizen centric services through hand held devices in an integrated manner. This paper has made an attempt to develop user friendly citizen centric interface to avail government services in faster and cost-effective way through mobile device. The proposed MAIFCCES framework provides an interface for citizen to apply for government services and monitor it's status through mobile device. Also suggest a mechanism to process and monitor the applications of citizen centrally in government data centre.
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Mobile phone ranking by analytical hierarchical process: a case study
Scientific article
Mobile phones are one of the highly used gadgets now a day. These handheld devices serve multiple purposes through different available functionalities. Demand of services and functionalities vary with time and person concern. Before purchasing a new mobile phone, one has to judge specifications like functionalities, hardware capabilities and efficiencies available with the particular model of the device. We often find it difficult to identify or decide the best model among the available multiple alternatives by heuristics quick analysis of the specifications and prices. This paper proposes a method for ranking mobile phone models based on Analytical Hierarchical Process (AHP), one of the typically used mathematical models for Multi Criteria Decision Making (MCDM) problems. The effectiveness of the proposed method is analyzed through a case study consisting of various sophisticated approaches based on AHP. A novice mobile phone buyer will be benefitted by the use of the proposed method incorporated through e-commerce sites.
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Mobile-Based Fuzzy Expert System for Diagnosing Malaria (MFES)
Scientific article
Malaria is a deadly disease that claims yearly lives of millions in Africa, and other endemic continents. The prevalence of malaria in these endemic regions is majorly attached to the lack of competent medical experts who are capable of providing medical care for the affected victims. This study considers developing a mobile based fuzzy expert system that could assist in diagnosing malaria. The fuzzification of crisp inputs by the system was carried out using an inter-valued and triangular membership functions while the deffuzification of the inference engine outputs was performed by weighted average method. Root sum square method of drawing inferences has been employed while the whole development has been achieved with the help of Java 2 Micro Edition of Java. This expert system executes on the readily available mobile devices of the patients. This fuzzy system was finally evaluated and confirmed effective in providing a human-expert like diagnosis.
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Mobile-based Attendance Monitoring System Using Face Tagging Technology
Scientific article
Manual checking of attendance may lead to inconsistency of data inputs and may generate unreliable attendance result. Hence, Radio Frequency Identification (RFID) system has been developed to solve this problem, but it allows only checking student’s attendance as they enter and exit the school premise only. In consequence, teachers in every subject still need to check and monitor students’ attendance manually. Nevertheless, due to a usual large number of students entering and existing the school premise as they are tapping their RFID card, there is always a possibility of proxy attendance. Thus, Mobile-Based Attendance Monitoring System Using Face Tagging Technology (MBAMSUFTT) was developed to provide an attendance monitoring system through biometric authentication such as face recognition. The system serves as a tool for teachers to check and monitor student’s attendance in most reliable and accurate way using their smart phones. The MBAMSUFTT generates attendance report intended for close monitoring and printing of student’s attendance result. But the reliability of the attendance result (output) of the system depends on the quality of picture (input) sent by the user. Camera specification, ambiance lighting condition, and proper position of students while taking photo is exclusively required. The server and the mobile part can only run together if Wireless Fidelity is on, otherwise, monitoring will not be executed. As a developmental research, this study used the Agile Model based on System Development Life Cycle (SDLC) intended for building a project that can adapt to change requests quickly. The MBAMSUFTT was evaluated based on the ISO/IEC 25010; MBAMSUFTT’s software quality characteristics by the IT experts, and its functionality, performance efficiency, and usability by the teachers. The analysis of the data revealed that the MBAMSUFTT serves its intended purpose in checking and monitoring students’ attendance per subject area with more accurate and reliable attendance results and has also met the ISO software quality standards.
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Model Real-Time Viewer Monitoring Based on SDLC for Student Learning Outcomes
Scientific article
In modern educational environments, particularly within computer laboratory settings in higher education institutions, the lack of effective real-time supervision and streamlined assessment processes presents a persistent challenge. Most current systems still rely on manual monitoring and evaluation, which are not only inefficient and time-intensive but also vulnerable to academic dishonesty, such as copy-paste behaviour during lab work. This study identifies and addresses this critical gap by proposing the development and implementation of an integrated real-time monitoring and assessment system tailored for use in academic computer labs. The proposed solution is a desktop-based application that incorporates four key features: Real-Time Viewer (RTV) for live monitoring of student activities, Block Inappropriate Websites (BIW) to restrict access to non-educational or harmful content, Manage Computer Time (MCT) to regulate system usage duration, and Form Learning Assessment (FLA) for digitalized and efficient performance evaluation. The development process followed the System Development Life Cycle (SDLC) framework, ensuring a structured approach across analysis, design, implementation, testing, and maintenance stages. Empirical testing involved a series of functional test cases simulating real-use conditions. All seven critical scenarios—such as input validation, session management, access control, and data deletion—were executed and passed successfully, indicating the system’s robustness and usability. In a pilot study conducted at Pekanbaru College of Technology, the application was tested among 30 students across multiple laboratory sessions. The results demonstrated a notable improvement in student engagement and learning performance. Quantitatively, students achieved learning assessment scores ranging from 84 to 96, with a calculated mean of 89.6 and a standard deviation of 4.1. These outcomes suggest that the introduction of automated, real-time monitoring significantly enhances not only instructional supervision but also the accuracy and fairness of learning assessments. This research contributes to the field by bridging the gap between digital classroom management and performance assessment in a higher education context. It introduces an innovative and practical approach for educators to maintain instructional quality while managing multiple learners in digital settings. Moreover, the findings provide empirical evidence supporting the integration of real-time supervision tools into educational systems to foster accountability, deter academic misconduct, and support data-driven instructional improvements.
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Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator
Scientific article
The main purpose of this paper is to design a suitable control scheme that confronts the uncertainties in a robot. Sliding mode controller (SMC) is one of the most important and powerful nonlinear robust controllers which has been applied to many non-linear systems. However, this controller has some intrinsic drawbacks, namely, the chattering phenomenon, equivalent dynamic formulation, and sensitivity to the noise. This paper focuses on applying artificial intelligence integrated with the sliding mode control theory. Proposed adaptive fuzzy sliding mode controller optimized by Particle swarm algorithm (AFSMC-PSO) is a Mamdani’s error based fuzzy logic controller (FLS) with 7 rules integrated with sliding mode framework to provide the adaptation in order to eliminate the high frequency oscillation (chattering) and adjust the linear sliding surface slope in presence of many different disturbances and the best coefficients for the sliding surface were found by offline tuning Particle Swarm Optimization (PSO). Utilizing another fuzzy logic controller as an impressive manner to replace it with the equivalent dynamic part is the main goal to make the model free controller which compensate the unknown system dynamics parameters and obtain the desired control performance without exact information about the mathematical formulation of model.
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Scientific article
Three phase induction motor are very used widely, especially in industries because they bring advantages, namely simple and sturdy motor construction, relatively cheap prices, easier maintenance compared to other types of motors. At CFPP Tanjung Enim 3x10 MW, for example, a three phase induction motor used to support operational activities of the generator is a coal feeder with a motor power of 7.5 kW and a volt of 380 V. Damage to the induction motor at CFPP Tanjung Enim 3x10 MW has an impact on the a decrease in the performance of the Power Plant and can cause a loss of electricity production. Induction motor damage simulation is carried out by providing temporary disturbances, namely inter-phase disturbances and phase-to-ground faults which assume a sudden disturbance when the motor is operating normally. The parameters observed in this simulation are voltage (V), current (A) and motor rotation (Rpm). simulation results that have been carried out, it is found that disturbances in a three phase induction motor greatly affect the performance of the motor, such as when getting a phase to phase and phase to ground short circuits, the voltage will drop, the current will rise rapidly and rotation the motor will stop. By using Matlab software the author hopes to provide the right modeling to analyze the damage to the induction motor.
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Scientific article
In Algeria, as in many other countries, the Emergency Department (ED) of the hospital, is the main entrance to the hospital, which provides Healthcare to patients threatened with death, and which faces several issues, emphasized by resource limitation. Our work presents a description of patient flow inside the ‘ED’ of Chalabi Abdelkader Hospital, Mascara, Algeria. This study aims to prevent the care complication scheme by adopting a workflow approach in order to design the patient flow in the chosen ‘ED’. The objective is to enhance patients’ flows, to improve the quality of the patient supervision, by targeting the minimization of the total and waiting times. A simulation model of the study system will be built based on the acquired data, and it will be validated by domain experts for a maximal rapprochement to the reality. Then, many simulations instances will be realized using Rockwell ARENA simulator to evaluate the impact of the proposed solutions. As a result of this study, we provided to ‘ED’ supervisors many improvement solutions and recommendations to the issues identified in the modeling phase.
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Monitoring of Wide Area Power System Network with Phasor Data Concentrator (PDC)
Scientific article
This paper presents the fault detection technique in wide area power system network using Phasor Data Concentrator (PDC). Single line to ground (SLG) fault data of transmission line is processed using the WAMS technology. Phasor Data Concentrator (PDC) is primarily data collecting centres located at the control centre processing unit and is responsible for collecting all the PMUs data that is transmitted over the communication link, which will detect fault in transmission line. This will help the operators in control centre to monitor the health of wide area power system network, and can initiate corrective actions when time is the critical issue. In this paper, the voltage and current phasor information, and other transmission line parameters from 14 IEEE bus system is processed and detects the fault in transmission lines. The proposed fault detection technique in transmission line of wide area power system network is very useful for improving power system reliability and power quality.
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Multi Genre Music Classification and Conversion System
Scientific article
Artificial Intelligence (AI) has a huge scope in automating, stream- lining, and increasing productivity of Music Industry. Here, we look upon AI based techniques for classifying a piece of music into multiple genres and then later converting it into another user-specified genre. Plenty of work has been done in classification, but using traditional machine learning models which are limited in term of accuracy and rely heavily on features to train the model. The novelty of this work lies in its attempt to covert genre of music from one type to another. This paper focuses on classification achieved by using a model trained via Convolutional Neural Networks. Conversion of music genre, a relatively less worked upon field has been discussed in this paper along with details of implementation. For Conversion, we initially convert the input file to spectrogram. A database of all genre is maintained at all times and a random file from user selected genre is also converted to spectrogram. Later, these spectrograms are processed and converted back to signals. Finally the user can listen to the converted audio file. Validation of the conversion was performed via a survey with the help of end users. Thus, a novel idea of doing Music Genre Conversion was put forth and was validated with positive outcomes.
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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].
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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.
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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.
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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.
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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.
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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.
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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).
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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.
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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.
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