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Smartphone-based fingerprint authentication using siamese neural networks with ridge flow attention mechanism

10.11591/ijeecs.v39.i3.pp1622-1632
Benchergui Malika Imane , Ghazli Abdelkader , Senouci M. Benaoumeur
Authenticating finger photo images captured using a smartphone camera provides a good alternative solution in place of the traditional method-based sensors. This paper introduces a novel approach to enhancing fingerprint authentication by leveraging images captured via a mobile camera. The method employs a siamese neural network (SNN) combined with a ridge flow attention mechanism and convolutional neural networks (CNN). Our approach begins with collecting a dataset consisting of finger images from two individuals then we apply multiple preprocessing techniques to extract fingerprint images, followed by generating augmented data to improve model robustness, scaling, and normalizing them to form images suitable for model training. Next, we generate positive and negative pairs for training a SNN. We used the SNN with CNN for feature extraction, combined with an attention mechanism that focuses on the ridge flow pattern of fingerprints to improve feature relevance which significantly contributed to the performance enhancement. As for the testing performance, our model has an accuracy of 90%, precision of 89%, recall of 83%, F1 score of 86%, area under the curve (AUC) 95 %, and 13% of equal error rate (EER) when using smartphone-captured images for fingerprint recognition.
Volume: 39
Issue: 3
Page: 1622-1632
Publish at: 2025-09-01

Performance evaluation of multicarrier quadrature phase shift keying-based system under noisy channel conditions

10.11591/ijaas.v14.i3.pp693-701
Deepa Narayana Reddy , Aishwarya Nagaraju , Deepti Hosakere Prabhakara , Deekshitha Beeraganahalli Srinivas , Gandlaparthi Navyatha
A comprehensive analysis of quadrature phase shift keying (QPSK) modulation in both single input single output (SISO) and multiple input multiple output (MIMO) systems is conducted using MATLAB. The investigation focuses on evaluating QPSK performance with metrics such as signal-to-noise ratio (SNR) and bit error rate (BER) across diverse channel conditions. Furthermore, the study extends to encompass the integration of QPSK with orthogonal frequency division multiplexing (OFDM), with a particular emphasis on assessing spectral efficiency and error rate implications. To validate the accuracy of the simulations, QPSK and QPSK-OFDM configurations are implemented on the WiComm-T hardware platform, enabling a direct comparison of real-world performance metrics against simulation results. By offering practical insights and recommendations for the deployment of robust communication systems, this research underscores the inherent advantages of integrating OFDM with QPSK across both SISO and MIMO configurations.
Volume: 14
Issue: 3
Page: 693-701
Publish at: 2025-09-01

Solar cell-based garden light automation for environmentally friendly technology learning

10.11591/ijpeds.v16.i3.pp1457-1471
Afrizal Mayub , Fahmizal Fahmizal , Lazfihma Lazfihma
This research aims to: 1) Produce a prototype design for a solar cell-based automatic garden lighting system; 2) Determine the relationship between current, power, and voltage and light intensity; 3) Describe the feasibility of an environmentally friendly technology practicum guidebook; and 4) Describe teacher and student responses to the environmentally friendly technology practice guidebook. This research is R&D type Analysis, Design, Development, Implementation and Evaluation (ADDIE) Analysis, design, development, implementation and evaluation. The research sample used 44 class IX students at MTS Rahmatullah. According to students, aspects of teaching materials, aspects of content, and difficulty of teaching materials at school are inadequate at 84.25%, 80% and 82.5%. Student interest in environmentally friendly technology practicum guidebooks was 84.25%. The higher the light intensity, the higher the current, power, and voltage. Expert validation shows; the prototype of an automatic garden lighting system based on solar cells and a practical guidebook on environmentally friendly technology are very suitable for use (89.14% and 90.75%). The use of environmentally friendly technology practicum guidebooks increased students' critical thinking skills in the high category (N-Gain = 0.7937) and received responses from teachers and students in the "almost all" category (91.50% and 89.9%).
Volume: 16
Issue: 3
Page: 1457-1471
Publish at: 2025-09-01

Self-development moderates the impact of digital literacy and talent on human error

10.11591/ijaas.v14.i3.pp682-692
Achmad Mirza , Isnurhadi Isnurhadi , Muhammad Ichsan Hadjri
Effective public services are important for increasing community satisfaction and organizational credibility. This study aims to explore the influence of digital literacy, underutilized talent, and human error on the effectiveness of public services, with self-development as a moderating variable. This study was conducted with employees of the Trade Office of South Sumatra Province. The research method used was quantitative data analysis, which was performed using partial least squares structural equation modeling (PLS-SEM). The results of this study show that digital literacy and self-development play an important role in reducing human error and increasing the effectiveness of public services. These findings have practical implications for human resource management in the public sector, focusing on improving digital literacy and employee self-development. 
Volume: 14
Issue: 3
Page: 682-692
Publish at: 2025-09-01

Performance evaluation of distribution network with change of load by connecting wind DG

10.11591/ijeecs.v39.i3.pp1459-1466
Swathi Sankepally , Sravana Kumar Bali
The aim of this research is to determine the optimal location and size of a minimum number of distributed generators (DGs) needed to maintain the stable operation of an IEEE 85-bus distributed network. The main objective is to ensure the stability of the distribution network by optimizing the placement and capacity of DGs. This is accomplished through the utilization of particle swarm optimization (PSO). The stability of the distribution network is checked by evaluating the voltages and power losses using load flow. The stability of the distribution network is assessed using boundary criteria that are not altered by more than 5% of the nominal voltage value. The distribution network voltage stability is assessed using various case studies, one of that involves a change in load driven by connecting WDG and the other by a change in power supply from wind DGs due to varying wind speed. The PSO is implemented in IEEE-85 bus distribution network using MATLAB software.
Volume: 39
Issue: 3
Page: 1459-1466
Publish at: 2025-09-01

Modern research of using alternative energy resources in Azerbaijan

10.11591/ijaas.v14.i3.pp907-915
Ramil Sadigov Ali , Mushkunaz Nazarova Kichmirza , Garayeva Irada Eyvaz , Gunay Mammadova Israphil , Turkan Hasanova Allahverdi , Muhammad Madnee
The article provides a comprehensive analysis of modern trends and prospects for the use of solar batteries in various sectors of the economy and the agricultural sector. The purpose of this article is to analyze the possibility of energy saving for a private residential building in Gobustan using solar energy storage in a greenhouse extension and a heat pump to transfer heat to the heating system. The calculation showed that in the coldest month, December, the potential of solar thermal energy is 15-38% of the required heat demand, depending on the material used in the extension design. In March and April, excess heat is generated, which can be used for hot water supply needs. Thus, for an individual residential building, the use of solar heat accumulated in a greenhouse extension is relevant as an additional source of heat for the heating system. Surface density of solar radiation flux, W/m2: surface density of direct solar radiation flux: 1,680 (November), 1,530 (December), 1,870 (January), 2,730 (February), 3,270 (March), 3,180 (April); Surface density of diffuse solar radiation flux: 650 (November), 450 (December), 480 (January), 680 (February), 1180 (March), 1,830 (April).
Volume: 14
Issue: 3
Page: 907-915
Publish at: 2025-09-01

Comprehensive secure code review analysis of web application security vulnerabilities

10.11591/ijeecs.v39.i3.pp1807-1814
Azlinda Abdul Aziz , Nur Razia Mohd Suradi , Rahayu Handan , Mohd Noor Rizal Arbain
A secure code review is a process of software development involves systematic examination of application code. However, web applications evolving of cyber threats makes it challenging to conduct adequate security. Therefore, this paper conducts a comprehensive secure code review analysis to protect any crucial aspect of web security from potential threats and vulnerabilities. The application code is scanned for security issues during the real review and the results are classified according to the areas of vulnerability. As a result, the application code risk level and list of risk categories were defined. This result assists in prioritizing issues for resolution, beginning with the most critical problems to lower risk levels. Next, list of risk categories that give the most significant security vulnerabilities affect to application codes are defined. SQL injection, weak password handling, insecure direct object reference, information exposure, improper session management, missing input validation, deprecated functions, and lack of comments are defined as a risk category. Moreover, the result of application code weakness in the security of the application code is determined based on the level of risk and categories. Thus, analysis result offers the developers a clear perspective on protects the web applications from threats and vulnerabilities.
Volume: 39
Issue: 3
Page: 1807-1814
Publish at: 2025-09-01

Comprehensive multiclass debris detection for solar panel maintenance using ANN models

10.11591/ijeecs.v39.i3.pp1489-1498
Renuka Devi S. M. , Vaishnavi J. , Gayatri A. , Ragini K. , Ramesh Reddy K. , Koti Reddy B.
Solar photovoltaic (PV) technology has emerged as a leading renewable energy solution globally. However, maintaining optimal performance remains a challenge due to the accumulation of debris, including dust, bird droppings, and other contaminants on the panels. These deposits significantly reduce the efficiency of solar panels, necessitating regular monitoring and cleaning. Automated inspection systems provide a cost-effective alternative to traditional methods by minimizing labor-intensive efforts. This study proposes a machine learning-based framework for detecting and classifying several types of debris on solar panels. The methodology utilizes gray-level co-occurrence matrix (GLCM) texture features and key statistical features extracted from RGB, HSV, and LAB color spaces. A dataset comprising 19 distinct classes, such as “Without Dust,” “Bird Droppings,” “Black Soil,” and “Sand,” was employed to train and evaluate the models. Among the tested classification techniques, artificial neural networks (ANN) achieved a notable accuracy of 93.94%, demonstrating their effectiveness in identifying and categorizing debris. This work underscores the potential of machine learning-based feature extraction and classification techniques to automate solar panel inspection and facilitate targeted cleaning interventions, thereby enhancing overall system efficiency.
Volume: 39
Issue: 3
Page: 1489-1498
Publish at: 2025-09-01

Redesign the layout of the raw material warehouse from randomized storage to class-based storage

10.11591/ijaas.v14.i3.pp773-783
Nur Iftitah , Qurtubi Qurtubi , Danang Setiawan , Vembri Noor Helia
The company has a problem of ineffectiveness in the layout of the raw material warehouse due to the use of storage methods that ignore factors such as the type, dimensions, and condition of the goods. This reduces the optimal function of the warehouse and increases the time to retrieve goods. This research aims to redesign the suitable and practical layout of the raw material warehouse by considering its form and function, as well as filling methodological gaps from previous research. The method used is class-based storage. Based on ABC analysis, the category with the highest value is class C goods, with 73 units. Meanwhile, from the fast, slow, non-moving (FSN) analysis, class F (fast-moving) goods have the highest frequency of movement, with a movement percentage of 63% for 10 units of goods. The warehouse slotting analysis shows an increase in the number of shelves from nine to 15 shelves with five different shelf models and layout changes in raw material warehouses 1 and 2. The class-based storage method results in a more organized layout, efficient movement of goods, and faster picking time to optimize warehouse functions.
Volume: 14
Issue: 3
Page: 773-783
Publish at: 2025-09-01

Comprehensive structured analysis of machine learning in safety models

10.11591/ijaas.v14.i3.pp627-638
Mohd Shukri Abdul Wahab , Syed Tarmizi Syed Shazali , Noor Hisyam Noor Mohamed , Abdul Rani Achmed Abdullah
Machine learning (ML) integration into various industries has revolutionized operations recently, enhancing efficiency and predictive capabilities. However, the rapid adoption of ML models also presents significant safety concerns that are highly demanded. To achieve this, scholarly articles from reputable databases such as Scopus and Web of Science (WoS) focus on studies published between 2022 and 2024, which were extensively searched. The study's flow is based on the PRISMA framework. The database found (n=40) that the final primary data was analyzed. The findings were divided into three themes: i) safety and risk management, ii) ML and artificial intelligence (AI) applications in safety, and iii) smart technology for safety. The conclusion highlights the need for continuous monitoring and updating of the safety protocols to keep in step with the growing ML landscape. This review contributes to the understanding of ML safety. It offers global lessons that can guide future research and policy-making efforts to ensure ML technologies' safe and ethical use.
Volume: 14
Issue: 3
Page: 627-638
Publish at: 2025-09-01

SDN multi-access edge computing for mobility management

10.11591/ijeecs.v39.i3.pp1846-1854
Sri Ramachandra Lakkaiah , Hareesh Kumbhinarasaiah
In recent trends, multi-access edge computing (MEC) is becoming a realistic framework for extensive social networking. The rapid proliferation of internet of things (IoT) devices has led to an unprecedented increase in data generation, placing significant strain on conventional cloud computing infrastructure. MEC also supports ultra-reliable and low latency communications (URLLC) by delivering information and computational resources more quickly to mobile users. As a result, the need for low-latency and reliable communication has become paramount. This paper proposes an MEC architecture that integrates software defined networking (SDN) and virtualization techniques, where MEC enables the orchestration and organization of mobile edge hosts (MEH). Furthermore, the proposed MEC-SDN design minimizes latency while ensuring consistent ultra-low latency communications. The result analysis clearly demonstrates that the proposed MEC-SDN model achieves latency of 6-14 ms, bandwidth of 5.2 Mbits/sec, and SDN-BWMS of 5.4 Mbits/sec, outperforming the existing SDN-Mobile Core Network model. Mobile edge systems are enabled in this research to provide mobility support for users.
Volume: 39
Issue: 3
Page: 1846-1854
Publish at: 2025-09-01

An improved hybrid AC to DC converter suitable for electric vehicles applications

10.11591/ijeecs.v39.i3.pp1499-1513
Khaled A. Mahafzah , Mohamad A. Obeidat , Hesham Alsalem , Ayman Mansour , Eleonora Riva Sanseverino
This paper introduces a novel hybrid AC-DC converter designed for various applications like DC micro-grids, Electric Vehicle setups, and the integration of renewable energy resources into electric grids. The suggested hybrid converter involves a diode bridge rectifier, two interconnected single ended primary inductor converter (SEPIC) and Flyback converters, and two additional auxiliary controlled switches. These extra switches facilitate switching between SEPIC, Flyback, or a combination of both. The paper ex-tensively discusses the operational modes using mathematical equations, deriving specific duty cycles for each switch based on the circuit parameters. This hybrid converter aims to decrease total harmonic distortion (THD) in the line current. The findings exhibit a THD of approximately 14.51%, showcasing a 3% reduction compared to prior hybrid converters, thereby enhancing the power factor of the line current. Furthermore, at rated load conditions, the proposed converter achieves 90% efficiency. To validate the proposed hybrid converter’s functionality, a 4.5 kW converter is simulated and performed using MATLAB/Simulink after configuring the appropriate passive parameters.
Volume: 39
Issue: 3
Page: 1499-1513
Publish at: 2025-09-01

Optimizing retail systems: using big data and power business intelligence for performance insights

10.11591/ijaas.v14.i3.pp945-954
Huu Dang Quoc , Ha Le Viet
In the rapid development of information technology, using enterprise data to support timely management decisions is crucial in helping businesses operate effectively and improve competitiveness. This study uses Microsoft power business intelligence (MPBI) to analyze data in retail systems, allowing managers to grasp the business situation in real time, track advanced sales, optimize inventory control, and analyze customer behavior and supply chain visibility. From the data generated by the business, the study uses the streaming extract transform load (ETL) model to support real-time data aggregation, then converts to the MPBI data visualization system to convert data into visual charts, helping businesses easily monitor, track, analyze, and make decisions to promote business activities. The study proposes a data structure to organize retail information storage. It proposes a system of calculation formulas and data synthesis, making integrate and convert tabular data into visual charts. Through analysis of real data from the LH83 retail system, the study shows the feasibility of implementing a data visualization system and the difficulties encountered when businesses want to deploy this model.
Volume: 14
Issue: 3
Page: 945-954
Publish at: 2025-09-01

A solar PV-fed MF-DVR for compensation of grid-islanding issues and power-quality issues in grid-connected distribution system

10.11591/ijeecs.v39.i3.pp1480-1488
Tharinaematam Bhavani , Durgam Rajababu , Md Mujahid Irfan
Difficulties with the quality of power come up as an effect of the inte-conneted renewable energy through grid called as distribution generation (DG) scheme. The voltage harmonics and swell-sag are happened in the utility grid as a result of power quality issues, affecting end-level consumers. Moreover, grid islanding issues is considered the most affected problem in distribution system for affecting the uninterrupted energy-flow to respective load demand. The main aim of this paper provides affective designing of the suitable cost-effective multi-functional dynamic voltage restorer (MF-DVR) has been proposed for resolving the problems. The major objective is mitigation of voltage-interruptions during grid-islanding, voltage-sag, voltage-swell and voltage-harmonics, any voltage quality in the utility grid, by utilizing the solar photovoltaic (PV) integrated MF-DVR as DG scheme through synchronous reference frame (SRF) control theory. Also, it can regulate the voltage and phase of the distribution system during sudden voltage interruptions occurred in grid-islanding. The performance of the proposed SRF controlled MFDVR for power-quality (PQ) improvement and DG integration during grid-islanding has been validated via Matlab/Simulink computing tool; the simulation findings are shown with an appealing comparison analysis.
Volume: 39
Issue: 3
Page: 1480-1488
Publish at: 2025-09-01

Enhancing Qur'anic recitation through machine learning: a predictive approach to Tajweed optimization

10.11591/ijeecs.v39.i3.pp1562-1570
Mohamed Amine Daoud , Nayla Fatima Hadjar Kherfan , Abdelkader Bouguessa , Sid Ahmed Mokhtar Mostefaoui
The human voice is a powerful medium for conveying emotion, identity, and intellect. Arabic, as the language of the Qur'an, holds deep spiritual and linguistic importance. Reciting the Qur'an correctly involves following Tajweed rules, which ensure phonetic precision and aesthetic quality. However, mastering these rules is challenging due to complex pronunciation and articulation variations, often requiring expert guidance. Traditional learning methods lack personalized feedback, making it difficult for learners to identify and correct errors. With the rise of machine learning, new opportunities have emerged to support Qur’anic recitation through intelligent analysis of Tajweed patterns and error prediction. This study presents a predictive model that identifies Qur’an reciters using ensemble learning techniques. By incorporating deep learning models like gated recurrent units (GRUs), long short-term memory (LSTM), and recurrent neural network (RNN), the system effectively captures the vocal features unique to each reciter. The model achieves an accuracy rate of 88.57%, demonstrating its potential to support Qur’anic learning and preservation. Nonetheless, its performance may be affected by audio quality and limited training data diversity. To improve adaptability and robustness, future work will focus on enriching the dataset and optimizing the model to generalize better across a broader range of reciters.
Volume: 39
Issue: 3
Page: 1562-1570
Publish at: 2025-09-01
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