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31,456 Article Results

MPCNN: a novel approach for detecting human Monkeypox from skin lesion images leveraging deep neural network

10.11591/ijeecs.v36.i3.pp1573-1582
Sk. Shalauddin Kabir , Md. Apu Hosen , Shahadat Hoshen Moz , Syed Md. Galib
The global healthcare scenario encounters a substantial challenge caused by the widespread outbreak of Monkeypox affecting over 65 countries. Limited availability of polymerase chain reaction (PCR) tests and biochemical assays necessitates alternative strategies. This study explores the viability of computer-aided identification of Monkeypox through the analysis of skin lesion images, offering a potential solution, particularly in resource-constrained settings. Employing data augmentation techniques, we augment the dataset to enhance its robustness. Subsequently, we utilize various pre-trained deep learning models, including EfficientNetB3, VGG16, ResNet50, AlexNet, and EfficientNet for classification tasks related to Monkeypox and other diseases. The achieved accuracies for these models are 98.48%, 69.19%, 91.41%, 78.38%, and 94.44%, respectively. We introduce a novel modified convolutional neural network (CNN) architecture named MPCNN to further improve performance. Our proposed MPCNN model demonstrates exceptional accuracy, precisely identifying Monkeypox patients with a remarkable precision of 99.49%. This technological advancement in disease identification holds significant promise for enhancing healthcare strategies and response mechanisms in the context of global health concerns.
Volume: 36
Issue: 3
Page: 1573-1582
Publish at: 2024-12-01

Unraveling spinal cord injury caregiver burden in Indonesia

10.11591/ijphs.v13i4.24381
Vitriana Biben , Novitri Sumardi , Arya Wijaya , Septania Elfira Simatupang , Sitti Ayu Hemas Nurarifah
Diverse cultural, social, and economic backgrounds significantly influence the caregiver burden of spinal cord injury (SCI) patients, affecting their quality of life (QoL) and the overall care experience. This study was done to identify spinal cord caregivers' QoL in Indonesia through their burden and the variables that influence it. A cross-sectional study was conducted at Dr. Hasan Sadikin Hospital, Bandung, Indonesia, involved 137 primary SCI caregivers. The burden of these caregivers was evaluated using the Indonesian version of the Zarit Burden Interview (ZBI) and the World Health Organization Quality of Life-BREF questionnaires. The data was analyzed with multiple linear regression and spearman analysis using SPSS version 24. The majority of SCI caregivers have a mild to moderate burden (54.7%). The burden score was significantly correlated, especially with occupational status and the length of interaction each day (p<0.001). It also found a significant with strong negative correlation (referring to the Guilford criteria) between caregiver burden and QoL (r-value −0.750; p-value <0.001). Indonesian caregivers' cultural and economic backgrounds may alleviate their burden in caregiving, but attention to patient occupational status and care duration is crucial to prevent worsening their quality of life, particularly for SCI survivors they care for.
Volume: 13
Issue: 4
Page: 1634-1640
Publish at: 2024-12-01

Phishing detection using clustering and machine learning

10.11591/ijai.v13.i4.pp4526-4536
Luai Al-Shalabi , Yahia Hasan Jazyah
Phishing is a prevalent and evolving cyber threat that continues to exploit human vulnerability to deceive individuals and organizations into revealing sensitive information. Phishing attacks encompass a range of tactics, from deceptive emails and fraudulent websites to social engineering techniques. Traditional methods of detection, such as signature-based approaches and rule-based filtering, have proven to be limited in their effectiveness, as attackers frequently adapt and create new, previously unseen phishing campaigns. Consequently, there is a growing need for more sophisticated and adaptable detection methods. In recent years, machine learning (ML) and artificial intelligence (AI) have played a significant role in enhancing phishing detection. These technologies leverage large datasets to train models capable of recognizing subtle patterns and anomalies in both email content and website behavior. This research proposes a hybrid algorithm to detect phishing attacks based on clustering and classification machine learning methods (CMLM): deep learning (DL) and decision tree (DT). Simulation results show that the proposed technique achieves a high percentage of accuracy in detecting phishing. 
Volume: 13
Issue: 4
Page: 4526-4536
Publish at: 2024-12-01

Factors influencing the intention to use m-commerce in Malaysian: an extended IS success model

10.11591/ijaas.v13.i4.pp957-969
Moussa Barry , Ahasanul Haque , Muhammad Tahir Jan
The progress of mobile technology has undergone substantial development in recent years, leading to the emergence of new and creative ideas. This paper investigates the factors influencing consumers' intentions to use mobile commerce in Malaysia. The DeLone and McLean updated information system success model served as the basis for this study's proposed model. A convenience sampling method was employed to collect 310 surveys from smartphone owners who conduct mobile commerce activities in Malaysia. A “two-stage structural equation modeling” (SEM) technique assessed the research model and the study's assumptions. The findings revealed that “information quality”, “service quality”, “system quality”, and “trust” significantly influence consumers' “intention to use mobile commerce in Malaysia”. The findings further reveal that “system quality” is the strongest factor influencing the “intention to use mobile commerce in Malaysia”. Therefore, the research outcomes will benefit academicians, researchers, policymakers, and practitioners in the mobile commerce industry in Malaysia. To the best of the authors’ knowledge, this is the first empirical study that expanded the “information system success model” by including “trust” in the context of Malaysian mobile commerce users’ “intentions”. However, further research is recommended to explore the factors influencing consumers' “intention to use mobile commerce in Malaysia”.
Volume: 13
Issue: 4
Page: 957-969
Publish at: 2024-12-01

Risk factors and trends analysis of unwanted pregnancy in Indonesia

10.11591/ijphs.v13i4.23702
Supriyadi supriyadi , Tris Eryando , Purnawan Junadi
Unwanted pregnancy has serious and adverse health, social, and economic impacts, as well as the risk of maternal and child mortality. In Indonesia, unwanted pregnancy is one of the most troubling public health problems and a major reproductive health problem. The sample consisted of 2002 (12,612), 2007 (14,471), 2012 (14,212), and 2017 (8,838), so the total sample was 50,233 couples of childbearing age by random sample method. This study was analyzed using logistic regression analysis. Unintended pregnancies were associated with maternal age, age at first marriage, number of children, maternal education, contraceptive use, contraceptive failure, residence, and the interval between pregnancies. Unwanted pregnancies at the mother's vulnerable age have increased over the past 10 years and decreased after entering the last 5 years, tend to be stable and constant at the age of first marriage. About one and a half women in Indonesia encounter unwanted pregnancies. Unwanted pregnancies fluctuate over 20 years. Women, especially the most vulnerable should be empowered to avoid unwanted pregnancies for their awareness and independence. Programs to expand access and quality of information and counseling centers, access and quality of women's health services, and specific reproductive health services as needed.
Volume: 13
Issue: 4
Page: 1864-1871
Publish at: 2024-12-01

Machine learning-driven design and performance analysis of microstrip antennas for sub-6 GHz/mm Wave 5G networks

10.11591/ijict.v13i3.pp462-469
Piske Laxmi Prasanna Kumar , Ramineni Padmasree , Korra Kiran , Banothu Sudheer
In the realm of modern communication systems, antennas are crucial components, with the microstrip patch antenna being particularly notable for its low profile and seamless integration. Despite its widespread use, designing this antenna involves complex simulations to optimize parameters, requiring significant expertise and consuming considerable time and energy. To streamline this process, machine learning (ML) algorithms are being utilized. This paper introduces an innovative approach that employs ML techniques to design a rectangular microstrip patch antenna operating within the sub-6 GHz frequency range (1-6 GHz) and the millimeter frequency range (28-40 GHz). The antenna design maintains consistent patch dimensions positioned strategically at the center, with a thorough examination of patch length and width to enhance performance. Datasets are meticulously prepared, covering output parameters such as beam area, directivity, gain, and radiation efficiency across the specified frequency ranges. By employing various ML algorithms, this study conducts a comprehensive analysis to identify the most effective algorithm for accurately predicting antenna characteristics. The K-nearest neighbor (KNN) algorithm achieved high accuracy across all parameters: gain at 94.23% under sub-6 GHz and 95.93% under millimeter frequency range, directivity at 99.02% and 98.59%, radiation efficiency at 93.94% and 94.28%, and beam area at 99.07% and 98.59% respectively. These results optimize microstrip antenna designs and enhance understanding of the relationship between design parameters and performance outcomes with ML.
Volume: 13
Issue: 3
Page: 462-469
Publish at: 2024-12-01

The production-inventory model with imperfect, rework, and scrap items under stochastic demand

10.11591/ijaas.v13.i4.pp896-906
Dana Marsetiya Utama , Artha Bripka Putri
Inventory is crucial in maintaining a smooth production process and meeting consumer demand for manufacturing companies. This research focuses on production problems involving defects, rework, and scrap items in stochastic demand. This research aims to develop a production-availability model by minimizing the expectation of total cost (ETC). The model includes four main decision variables, namely production quantity (Q), safety factor (k), production rate (P), and rework rate (P1). This research uses the Aquila optimizer algorithm to optimize the objective function. It compares with the heuristic procedure and Harris Hawk optimization algorithm. The results showed that the Aquila optimizer algorithm successfully optimized the production-availability problem. A comparison between algorithms indicates that the Aquila optimizer algorithm performs equivalently to the Harris Hawk optimization algorithm and outperforms the heuristic procedure. Sensitivity analysis shows that increasing demand uncertainty increases ETC and k. At the same time, it can decrease Q.
Volume: 13
Issue: 4
Page: 896-906
Publish at: 2024-12-01

Improved convergence speed using hybrid AI for TD EM modeling in power electronics

10.11591/ijape.v13.i4.pp973-981
Bessem Zitouna , Mohamed Tlig , Sassia Hedia , Jaleleddine Ben Hadj Slama
This paper presents a time-domain (TD) approach based on hybrid artificial intelligence (AI) to speed up convergence of radiating sources characterization in power electronics. To obtain a representative equivalent model of device under test, a dedicated optimization framework has been developed in TD using a particle swarm optimization (PSO) toolbox. In addition, for elementary feature extraction, a pseudo-Zernike moment invariant (PZMI) descriptor has been defined. Finally, with the aim of identifying remaining dipole parameters and classification problems, artificial neural networks (ANN) have been implemented. A coupling of TD electromagnetic (EM) inverse method based on a PSO algorithm along with PZMI and ANN application has been investigated and applied to a real test case. Experimental measurements have been conducted using the near-field scanning technique above an alternating current (AC)/direct current (DC) converter. Obtained results are discussed based on a comparison between measured and estimated EM field distributions using both the hybrid AI method and a conventional TD inverse method based on genetic algorithms (GA) only. This study confirms that, compared with those given by non-hybrid method, the proposed algorithm further improves the convergence speed while maintaining high accuracy. Hence, the present work offers an impressive perspective for radiated emissions characterization using hybrid AI algorithms.
Volume: 13
Issue: 4
Page: 973-981
Publish at: 2024-12-01

Performance analysis of CKF algorithm for battery SoC estimation with its aging effect

10.11591/ijape.v13.i4.pp886-899
G. Geetha Ravali , K. Narasimha Raju
The penetration of electric vehicle (EV) in automobile market is very much dependent on the battery technology. Its size, weight, and cost are issues of concern. To effectively utilize the battery expertise, precise estimate of state of charge (SoC) is vital which greatly depends on the battery model. Current models lack consideration for variations in battery capacity over their lifespan. This paper develops a battery model which depicts the depletion of battery capacity with its life. Subsequently, this model has been utilized for estimation using advanced Kalman filtering (KF) algorithms. For the developed model, the design and effectiveness of the cubature Kalman filter (CKF) is applied as a proposed robust state-estimator for this problem. Moreover, a comparative analysis was undertaken with existing non-linear KFs based on performance metrics. The optimal choice of estimator is identified, through the results obtained from the Octave/MATLAB simulation. The outcomes show CKF algorithm based SoC estimator is superior to others in ensuring high accuracy, strong robustness even under changes in initial conditions (i.e., initial SoC, process and sensor noise levels), system's ability to converge quickly while ensuring that the maximum error in state of charge (SoC) estimation remains within 1% after convergence.
Volume: 13
Issue: 4
Page: 886-899
Publish at: 2024-12-01

Enhanced scene text recognition using deep learning based hybrid attention recognition network

10.11591/ijai.v13.i4.pp4927-4938
Ratnamala S Patil , Geeta Hanji , Rakesh Huded
The technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology presented in this research, is intended to greatly improve efficiency and accuracy of text recognition in complicated scene situations. HARN makes use of cutting-edge elements including alignment-free sequence-to-sequence (AFS) module, creative attention mechanisms, and hybrid architecture that blends attention models with convolutional neural networks (CNNs). Thanks to its novel attention processes, HARN is capable of comprehending wide range of scene text components by capturing both local and global context information. Through faster network convergence, shorter training times, and better utilization of computing resources, the suggested technique raises bar for state-of-the-art. HARN’s versatility makes it a good choice for range of scene text recognition applications, including multilingual text analysis and data extraction. Extensive tests are conducted to assess the effectiveness of HARN approach and demonstrate it is ability to greatly influence real-world applications where accurate and efficient text recognition is essential.
Volume: 13
Issue: 4
Page: 4927-4938
Publish at: 2024-12-01

User perceptions of artificial intelligence powered phishing attacks on Facebook's resilient infrastructure

10.11591/ijaas.v13.i4.pp878-886
JosephNg Poh Soon , Rou Qian Chan , Qian Hui Lee , Dick En Loke , Stevenson Ling Heng Chun , Phan Koo Yuen
This study focuses on examining the user perceptions of a cybersecurity certificate transparency (CT) monitoring tool in the context of artificial intelligence (AI) powered phishing attacks on the Facebook platform. Implementing CT monitoring tools is one strategy for preventing these attacks. It reveals a significant level of concern among respondents regarding the potential risks associated with phishing attacks, indicating a growing awareness of the severity of such threats for future resilient infrastructure development. Users' knowledge and understanding of AI-driven phishing threats were found to vary, emphasizing the need for awareness campaigns towards sustainable development education. The study also highlights varying levels of confidence among users in effectively identifying and thwarting phishing efforts, suggesting the importance of user empowerment through improved training, tools, and technologies as responsive institutions. These findings underscore the significance of addressing user concerns, enhancing security awareness, and providing users with the necessary resources to protect themselves against sophisticated phishing attacks. The research contributes to the understanding of user perceptions and lays the groundwork for further improvements in security measures and user education in the fight against phishing threats on Facebook's inclusive growth.
Volume: 13
Issue: 4
Page: 878-886
Publish at: 2024-12-01

Enhanced U-Net models with encoder and augmentation for phytoplankton segmentation

10.11591/ijaas.v13.i4.pp1009-1018
Ovide Decroly Wisnu Ardhi , Tri Retnaningsih Soeprobowati , Kusworo Adi , Esa Prakasa , Arief Rachman
This study comprehensively analyzes U-Net models for semantic segmentation in phytoplankton image recognition, leveraging encoders such as EfficientNet-B5, MobileNetV2, ResNet50, and ResNeXt50 and employing the Adam optimizer. The research highlights the U-Net MobileNetV2 model with optical distortion, which achieves notable test scores with 93.69% Dice, 88.14% intersection over union (IoU), 99.89% Precision, and 100% Recall, underscoring the efficacy of the applied augmentation strategies, including geometric and distortion transforms, and color and blur techniques. The U-Net ResNet50 model with mix transform consistently demonstrates high accuracy in critical metrics, outperforming others, while EfficientNet-B5 with blur suggests increased model sensitivity with improved recall. These results underscore the crucial role of encoder-augmentation synergy in model performance. Training and testing times across models have remained under 250 seconds, reflecting methodological efficiency. Overall, these results demonstrate the model's excellent performance for the semantic segmentation task.
Volume: 13
Issue: 4
Page: 1009-1018
Publish at: 2024-12-01

A multi-criteria trust-enhanced collaborative filtering algorithm for personalized tourism recommendations

10.11591/ijeecs.v36.i3.pp1919-1928
Qusai Y. Shambour , Mahran M. Al-Zyoud , Adeeb M. Alsaaidah , Mosleh M. Abualhaj , Ahmad A. Abu-Shareha
The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry.
Volume: 36
Issue: 3
Page: 1919-1928
Publish at: 2024-12-01

Minimization of the circuit components with modified cascaded multilevel inverter topology

10.11591/ijape.v13.i4.pp798-807
Raghvendra Pratap Singh , Prateek Nigam , Kishor Thakre
This article examines a modified multilevel inverter circuit that uses basic units connected in cascade. The suggested circuit can be used with an inverter that is symmetrical or asymmetric. The magnitude of the DC voltage source is determined using a variety of methods in order to generate a large number of voltage levels. For both symmetrical and asymmetrical configurations, the magnitude of two DC sources in basic units can be used. The DC voltage source's magnitude is the same for each unit in the symmetrical configuration. However, in an asymmetrical configuration, the value of the DC source for the fundamental units is inequitable, and their magnitudes are obtained using various techniques. Comparison study demonstrates that the suggested circuit requires minimum components, reduces power loss, and boosts inverter efficiency. Additionally, in comparison to modern topologies, the standing voltage across the switches is acceptable. To verify the effectiveness of the investigated topology, simulation results for 15, 17, 23, and 31-level inverters are analysed.
Volume: 13
Issue: 4
Page: 798-807
Publish at: 2024-12-01

Evaluation of the time-of-use tariff responsiveness for plug-in electric vehicle home charging in Malaysia

10.11591/ijaas.v13.i4.pp769-776
Nurliyana Baharin , Mohamad Fani Sulaima , Nofri Yenita Dahlan , Hazlie Mokhlis
Plug-in electric vehicles (PEVS) have become increasingly popular as a viable transportation option as owners can charge them at home. This will add much energy to the house if the users charge their PEVs at home. The PEV charging load will lead to extra energy demand on the distribution network, and the users will need to pay more for electricity if they use the current domestic tariff in Malaysia. This research aims to analyze the PEV charging costs using time-of-use (ToU) tariffs with different time segmentations and price elasticity. The effect of four residential load profile patterns has also been investigated in Malaysia as a case study. Four PEV charging scenarios were created, and the charging times were set according to Malaysian driving styles, with charging times starting at 6 PM, 10 PM, and 9 AM. The PEV and electric vehicle supply equipment (EVSE) are set to be homogeneous, and the EV was assumed to have a minimum state-of-charge of 20%. The main contribution of this paper is the selection of the ToU tariff segmentation, where the structure of the smallest time segmentation gave the lowest electricity bill per month compared to the Tenaga Malaysia Berhad (TNB) domestic tariff.
Volume: 13
Issue: 4
Page: 769-776
Publish at: 2024-12-01
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