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31,456 Article Results

Connected caregiving: investigating mothers in the era of digital access

10.11591/ijict.v14i1.pp347-354
Anissa Saidi , Wong Yee Von , Tirzah Zubeidah Zachariah@ Omar , Lim Seong Pek , Rita Wong Mee Mee , Khoo Kim Leng
Mothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion.
Volume: 14
Issue: 1
Page: 347-354
Publish at: 2025-04-01

Enhancing engineering education through virtual reality: a systematic study on immersive engineering education practices

10.11591/ijece.v15i2.pp1889-1899
Tarek Riaji , Sanae El Hassani , Fatima Ezzahrae M'hamdi Alaoui
This article explores the integration of virtual reality (VR) and associated technologies in engineering education, focusing on the pedagogical approaches adopted in this integration, which we refer to as immersive engineering education. This study considers the application possibilities and the transformative impact of VR on engineering education. The article addresses the critical collection and analysis of VR applications in engineering education. It covers main VR-related papers published from 2015 to February 2024 and indexed in Scopus, Web of Sciences, or both, and discussing design, development challenges, and collaborative tools. Empirical evidence showcases improved engagement, motivation, and learning outcomes. The findings offer modern insights for educators and researchers on leveraging VR for impactful learning experiences, while also noting the need for further research in this evolving field.
Volume: 15
Issue: 2
Page: 1889-1899
Publish at: 2025-04-01

An adaptive audio wave steganography using simulated annealing algorithm

10.11591/ijece.v15i2.pp2237-2253
Atef Ahmed Obeidat , Mohmmed Jazi Bawaneh , Sawsan Yousef Abu Shqair , Hamdi A. Al-Omari , Emad Fawzi Al-shalabi
The science of information security has increased in importance to encounter the espionage and information theft. This research proposes a new steganography framework that utilizes simulated annealing (SA) as an artificial intelligence algorithm to support the process of hiding a binary secret message file within an audio wave file. The best path for embedding the secret data inside the audio file is determined through SA that searches for the preferred path according to the content of the host audio file and secret message to be hidden. The least significant bit (LSB) technique was employed to hide message bytes, in which each audio-chosen byte will hold one bit from a secret message byte. The hiding process constructs the stego audio file and extraction key that will be required in an extraction process. The authorized user requires an extraction key and a decryption key to retrieve the hidden message. On the other hand, the attacker requires knowledge of the aforementioned keys and working algorithms that were employed in the hidden process. Robustness against data extraction, detection, imperceptibility (phonological hearing), security, peak signal to noise ratio (PSNR), mean square error (MSE) and capacity as security performance measures were used to evaluate the system. The maximum size of the data to be hidden may reach 12.5% of the data size of the host audio file, in which the average value of MSE and PSNR are (0.0041, 74.73), respectively.
Volume: 15
Issue: 2
Page: 2237-2253
Publish at: 2025-04-01

Vehicle side control of a wireless power transfer charger using optimized artificial neural network

10.11591/ijece.v15i2.pp1487-1498
Marouane El Ancary , Abdellah Lassioui , Hassan El Fadil , Anwar Hasni , Yassine El Asri , Zakariae El Idrissi
This paper investigates a new approach to control a wireless power transfer (WPT) charger for electric vehicles (EVs) employing an optimized artificial neural network (ANN). Enhancing the efficiency and robustness of such systems is crucial, and integrating artificial intelligence (AI)-based solutions has introduced innovative approaches in this field. The proposed method enables precise regulation of battery charging voltage even under challenging conditions, such as coil misalignment or shared grounding assemblies for multiple EVs. To assess the stability and robustness of the proposed controller, its performance was evaluated under scenarios of coil misalignment and shared grounding assemblies for EVs with varying battery voltages. The controller effectively eliminated overshoot and significantly reduced residual output voltage ripple by 4.33% compared to a conventional proportional-integral (PI) controller, demonstrating the superior performance and reliability of the ANN-based control approach.
Volume: 15
Issue: 2
Page: 1487-1498
Publish at: 2025-04-01

Cost-effective IoT-based automated vehicle headlight control system: design and implementation

10.11591/ijict.v14i1.pp325-333
Momotaz Begum , Nayeem Ullah , Mehedi Hasan Shuvo , Towhidul Islam , Thofazzol Hossen , Jia Uddin
The current world would be difficult without vehicles, which offer vital advantages for social connectivity, mobility, and technical advancement. Though motor vehicles provide benefits to passenger transportation, they also present certain challenges in their use. A major issue is nighttime traffic accidents caused by headlamps from automobiles traveling in reverse directions, that's why there is a high probability of accidents due to the glare on the driver's eyes. The phrase "Troxler effect" refers to an unexpected glare that a motorist recognizes. In this paper, we will provide an optimal solution to this challenge/Troxler effect. The primary objective of this paper is to design an internet of things (IoT)-based smart headlight control model. Our system introduced a cost-effective vehicle’s headlights controlled by light detection. According to this paper, a vehicle’s headlights are automatically rotated down when the sensor detects lights from the opposite direction of the vehicle headlights. We tried to reduce the road accident rate with our proposed system. This type of technology will prove useful in the motor vehicle sector and offer an innovative approach that ensures driver safety as well as increasing economic development.
Volume: 14
Issue: 1
Page: 325-333
Publish at: 2025-04-01

Classification of brain stroke based on susceptibility-weighted imaging using machine learning

10.11591/ijece.v15i2.pp1602-1611
Shaarmila Kandaya , Norhashimah Mohd Saad , Abdul Rahim Abdullah , Ezreen Farina Shair , Ahmad Sobri Muda , Muhammad Izzat Ahmad Sabri
Magnetic resonance imaging (MRI) is used to identify brain disorders, particularly strokes. Rapid treatment, often referred to as "time is brain," is emphasized in recent studies, stressing the significance of early intervention within six hours of stroke onset to save lives and enhance outcomes. The traditional manual diagnosis of brain strokes by neuroradiologists is both subjective and time-intensive. To tackle this challenge, this study introduces an automated method for classify brain stroke from MRI images based on pre- and post-stroke patients. The technique employs machine learning, with a focus on susceptibility weighted imaging (SWI) sequences, and involves four stages: preprocessing, segmentation, feature extraction, classification and performance evaluation. The paper proposes classification and performance evaluation to determine stroke region according to three types of categories, those are poor improvement, moderate improvement and good improvement stroke patients based on pre and post patients. Then, performance evaluation is verified using accuracy, sensitivity and specificity. Results indicate that the hybrid support vector machine and bagged tree (SVMBT) yields the best performance for stroke lesion classification, achieving the highest accuracy which is 99% and showing significant improvement for stroke patients. In conclusion, the proposed stroke classification technique demonstrates promising potential for brain stroke diagnosis, offering an efficient and automated tool to assist medical professionals in timely and accurate assessments.
Volume: 15
Issue: 2
Page: 1602-1611
Publish at: 2025-04-01

Collaborative singular value decomposition with user-item interaction expansion for first-time user and item recommendations

10.11591/ijict.v14i1.pp111-121
Manal Loukili , Fayçal Messaoudi
In today's digital landscape, recommendation systems are essential for delivering personalized content and improving user engagement across various platforms. However, a key challenge known as the cold-start problem—where limited user-item interaction data hampers the ability to generate accurate recommendations—remains a significant obstacle, particularly for new users and items. To address this issue, this paper introduces an enhanced methodology combining collaborative singular value decomposition (Co-SVD) with an innovative approach to reduce data sparsity. The objective of this research is to improve recommendation accuracy in sparse data environments by leveraging collaborative information in the user-item interaction matrix. Extensive experiments conducted on an e-commerce dataset validate the superiority of the proposed Enhanced Co-SVD model over traditional Co-SVD, content-based filtering, and random recommendation methods across multiple metrics. Our approach demonstrates particular strength in cold-start scenarios, providing precise recommendations with minimal user interaction data. These findings have important implications for e-marketing, personalized user experiences, and overall business success in online environments.
Volume: 14
Issue: 1
Page: 111-121
Publish at: 2025-04-01

Modelling and control of LCL voltage source converter-based hybrid high voltage alternating current/high voltage direct current system

10.11591/ijece.v15i2.pp1297-1321
Mahmood Saadeh , Mohammad Hamdan , Osama Saadeh
Voltage source converters (VSCs) have revolutionized high voltage direct current (HVDC) transmission, offering numerous advantages such as black start capability, absence of commutation failure, and efficient control of bidirectional power flow. This study introduces a comparative analysis of advanced VSC technologies, focusing on a novel series hybrid converter incorporating an inductor-capacitor-inductor (LCL) passive circuit. This configuration is explored for its potential to enhance both high voltage alternating current (HVAC) and high voltage direct current (HVDC) side fault suppression capabilities and improve DC output voltage quality, addressing critical drawbacks of traditional VSCs. Through comprehensive simulations in MATLAB/Simulink, this research evaluates and compares three different converter topologies: the three-level neutral point clamped converter, the hybrid converter with AC side cascaded H-bridge cells, and the LCL hybrid converter. The comparison is based on key performance metrics such as DC output voltage quality, fault suppression capabilities, and system efficiency during normal and fault conditions. The study finds that the LCL hybrid converter outperforms traditional converters by significantly improving DC output voltage quality and enhancing fault suppression capabilities in HVDC systems. It effectively reduces ripple and maintains stability during faults, making it a superior choice for future HVDC converter designs and applications, offering valuable insights for advancing HVDC technology.
Volume: 15
Issue: 2
Page: 1297-1321
Publish at: 2025-04-01

Tackling the anomaly detection challenge in large-scale wireless sensor networks

10.11591/ijece.v15i2.pp2479-2490
Tamara Zhukabayeva , Aigul Adamova , Lazzat Zholshiyeva , Yerik Mardenov , Nurdaulet Karabayev , Dilaram Baumuratova
One of the areas of ensuring the security of a wireless sensor network (WSN) is anomaly detection, which identifies deviations from normal behavior. In our paper, we investigate the optimal anomaly detection algorithms in a WSN. We highlight the problems in anomaly detection, and we also propose a new methodology using machine learning. The effectiveness of the k-nearest neighbor (kNN) and Z Score methods is evaluated on the data obtained from WSN devices in real time. According to the experimental study, the Z Score methodology showed a 98.9% level of accuracy, which was much superior to the kNN 43.7% method. In order to ensure accurate anomaly detection, it is crucial to have access to high-quality data when conducting a study. Our research enhances the field of WSN security by offering a novel approach for detecting anomalies. We compare the performance of two methods and provide evidence of the superior effectiveness of the Z Score method. Our future research will focus on exploring and comparing several approaches to identify the most effective anomaly detection method, with the ultimate goal of enhancing the security of WSN.
Volume: 15
Issue: 2
Page: 2479-2490
Publish at: 2025-04-01

The role of disruptive technologies in the metaverse worlds: state of the art survey

10.11591/ijece.v15i2.pp2211-2223
Jamal N. Al-Karaki , Amjad Gawanmeh , Ahmed Awad , Natnael Zerai Teklesenbet
The metaverse has emerged as an immersive and interactive virtual world that has the potential to revolutionize various industries. The use of disruptive technologies, such as blockchain, artificial intelligence (AI), digital twin, internet of things (IoT), cloud, big data, and cybersecurity, has and will play a significant role in enhancing the capabilities of the metaverse. This paper provides a state-of-the-art survey on the role of disruptive technologies in the metaverse. The paper presents a taxonomy of the use of disruptive technologies in the metaverse and a comprehensive literature review on the application areas of the metaverse in education, healthcare, tourism, gaming, and smart cities. The paper compares the adoption of technologies in the metaverse and identifies current and future research directions. The paper contributes to understanding disruptive technologies’ potential in the metaverse. It provides insights for researchers, practitioners, and policymakers to explore the opportunities and challenges of the metaverse.
Volume: 15
Issue: 2
Page: 2211-2223
Publish at: 2025-04-01

Enhancing data security using a multi-layer encryption system

10.11591/ijece.v15i2.pp1961-1967
Osama Abd Qasim , Sajjad Golshannavaz
This study highlights the interesting potential of a new multilayer cryptography scheme for reliable data protection in the field of cybersecurity. To do so, an intensive examination of a multi-layer encryption mechanism is proposed to reinforce the defenses in opposition to online threats to touchy data. The strategy is multilevel, with a superior digital dictionary serving as the foundation for the primary layer. The laborious procedures that went into making this dictionary, including rotation differences, ASCII conversion, and chaotic matrix era, upload to its encoding trouble. A modified model of the advanced encryption standard (AES) algorithm with a brand-new key generation technique, which is based on the chaos idea is furnished through layer 2. A parameter is encrypted using the Rivest-Shamir-Adleman (RSA) method, and further precautions are taken to assure the security of the encryption key. When it comes to encryption time, the first layer significantly outperforms the AES method. In addition to exhibiting instantaneous efficiency in data protection, the first layer outperformed the AES algorithm in terms of encryption time which took more than 3 seconds, and the first layer took less than 0.01 seconds, while both approaches functioned identically in terms of information decryption. In-depth talks are given to customize the suggested method's performance. The results demonstrate the effectiveness of the suggested multi-stage encryption and decryption system and demonstrate its efficacy in protecting text documents.
Volume: 15
Issue: 2
Page: 1961-1967
Publish at: 2025-04-01

Enhancing spatiotemporal weather forecasting accuracy with 3D convolutional kernel through the sequence to sequence model

10.11591/ijece.v15i2.pp2022-2030
Renaldy Fredyan , Karli Eka Setiawan , Kelvin Asclepius Minor
Accurate weather forecasting is important when dealing with various sectors, such as retail, agriculture, and aviation, especially during extreme weather events like heat waves, droughts, and storms to prevent disaster impact. Traditional methods rely on complex, physics-based models to predict the Earth's stochastic systems. However, some technological advancements and the availability of extensive satellite data from beyond Earth have enhanced meteorological predictions and sent them to Earth's antennae. Deep learning models using this historical data show promise in improving forecast accuracy to enhance how models learn the data pattern. This study introduces a novel architecture, convolutional sequence to sequence (ConvSeq2Seq) network, which employs 3D convolutional neural networks (CNN) to address the challenges of spatiotemporal forecasting. Unlike recurrent neural network (RNN)--based models, which are time-consuming due to sequential processing, 3D CNNs capture spatial context more efficiently. ConvSeq2Seq overcomes the limitations of traditional CNN models by ensuring causal constraints and generating flexible length output sequences. Our experimental results demonstrate that ConvSeq2Seq outperforms traditional and modern RNN-based architectures in both prediction accuracy and time efficiency, leveraging historical meteorological data to provide a robust solution for weather forecasting applications. The proposed architecture outperforms the previous method, giving new insight when dealing with spatiotemporal with high density.
Volume: 15
Issue: 2
Page: 2022-2030
Publish at: 2025-04-01

Maximum expansion with contiguity constraints scheduling algorithm: enhancing uplink transmission in long-term evolution vehicular environments

10.11591/ijece.v15i2.pp1709-1719
Shafinaz Ismail , Shajahan Maidin , Darmawaty Mohd Ali , Mohd Kamarulnizam Abdul Rahim
Uplink scheduling has become increasingly important due to increased activities like uploading videos, photos, and file sharing. Many users share or stream live videos and engage with social networks, significantly increasing uplink data traffic volume. Single carrier frequency division multiple access (SC-FDMA) is favored for its power efficiency and high data rates, benefiting user equipment (UE) battery life. However, maintaining the contiguity of resource blocks (RBs) poses challenges in uplink scheduling. The maximum expansion with contiguity constraints (MECC) algorithm has been introduced to address this challenge. MECC prioritizes contiguity, fairness, and throughput for users at the cell edge. The algorithm operates in two phases: initially allocating RBs proportionally and assigning RBs with the highest metrics while ensuring contiguity. Performance evaluation of MECC, conducted under conditions simulating vehicular movement at 30 km/h, demonstrates its superiority over other algorithms. MECC provides high fairness and throughput for both real-time (RT) and non-real-time (NRT) traffic, making it the preferred scheduler for ensuring quality of service (QoS) for both traffic types. Its focus on contiguity, fairness, throughput, and spectral efficiency establishes MECC as a valuable tool for optimizing uplink transmission in mobile networks, addressing the evolving needs of users in today's digital landscape.
Volume: 15
Issue: 2
Page: 1709-1719
Publish at: 2025-04-01

A unique YOLO-based gated attention deep convolution network-Lichtenberg optimization algorithm model for a precise breast cancer segmentation and classification

10.11591/ijece.v15i2.pp1670-1685
Vinoth Rathinam , Sasireka Rajendran , Valarmathi Krishnasamy
A novel you only look once (YOLO)-based gated attention deep convolution network (GADCN) classification algorithm is developed and utilized in this present study for the detection of breast cancer. In this framework, contrast enhancement-based histogram equalization is applied initially to produce the normalized breast image with reduced noise artifacts. Then, the breast region is accurately segmented from the preprocessed images with low complexity and segmentation error using the YOLO-based attention network model. To diagnose breast cancer with better accuracy, the GADCN model is used to predict the exact class of image (i.e., benign or malignant). During classification, the activation function is optimally computed with the use of the Lichtenberg optimization algorithm (LOA). It aids in achieving improved classification performance with little complexity in training and assessment. The significance of the present study includes the use of a unique, YOLO-based GADCN-LOA model that helps in the prediction of breast cancer with higher accuracy. It was observed that the model exhibited 99% accuracy for the datasets utilized. In addition, the selected model outperforms well with sensitivity, specificity, precision, and F1-score. Hence the proposed model could be exploited for the diagnosis of breast cancer at an early stage to enable preventive care.
Volume: 15
Issue: 2
Page: 1670-1685
Publish at: 2025-04-01

A convex hull based geofencing system to eradicate COVID

10.11591/ijece.v15i2.pp1817-1825
Parul Arora , Suman Deswal
The World Health Organization (WHO) has identified coronavirus disease (COVID-19), as a global pandemic due to its quick global spread to more than 183 countries. Many countries have used movement control orders (MCO) and high alert levels to halt the spread. The primary goal of this research is to provide a geofencing architecture that is specially tailored to the MCO's standard requirement for monitoring an individual's whereabouts during a lockdown. Whenever an individual tests Corona positive, Geofencing uses technology to notify an anticipated network of people who may be affected and to enable traceability for potential patients. Computational techniques such as Delaunay triangulation (inpolygon) and triangle weight characterization (inside polygon) are applied to analyze the geographical boundary in which the patient is isolated. Convex hull, on the other hand, is a better technique than computational algorithms. It is considered the best mathematical technique because it takes the least amount of time (0.014985 sec) to detect the patient within the geofence layer and has the lowest standard deviation when compared with the other computational techniques.
Volume: 15
Issue: 2
Page: 1817-1825
Publish at: 2025-04-01
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