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31,456 Article Results

Deep learning utilization in Sundanese script recognition for cultural preservation

10.11591/ijeecs.v36.i3.pp1759-1768
Rosalina Rosalina , Nunik Afriliana , Wiranto Herry Utomo , Genta Sahuri
This study addresses the challenge of preserving the Sundanese script, a traditional writing system of the Sundanese community in Indonesia, which is at risk of being forgotten due to technological advancements. To tackle this problem, we propose a deep learning approach using the YOLOv8 model for the automatic recognition of Sundanese characters. Our methodology includes creating a comprehensive dataset, applying augmentation techniques, and annotating the characters. The trained model achieved a precision of 95% after 150 epochs, demonstrating its effectiveness in recognizing Sundanese characters. While some variability in accuracy was observed for certain characters and real-time applications, the results indicate the feasibility and promise of using deep learning for Sundanese script recognition. This research highlights the potential of technological solutions to digitize and preserve the Sundanese script, ensuring its continued legacy and accessibility for future generations. Thus, we contribute to cultural preservation by providing a method to safeguard the Sundanese script against obsolescence.
Volume: 36
Issue: 3
Page: 1759-1768
Publish at: 2024-12-01

Survey of IoT and AI applications: future challenges and opportunities in agriculture

10.11591/ijeecs.v36.i3.pp1655-1663
Kamal Elhattab , Said Elatar
The internet of things (IoT) connects physical objects through sensors, software, and communication technologies, enabling efficient data collection and sharing. This interconnection promotes automation, real-time monitoring, and improved decision-making across various sectors. In agriculture, the integration of IoT with artificial intelligence (AI) is revolutionizing resource management by providing farmers with real-time information on crop health, climate conditions, and soil quality. This paper explores how IoT and AI are transforming traditional agricultural practices to enhance both efficiency and sustainability. Through an in-depth analysis of existing literature and practical applications in the sector, this study identifies significant advancements in crop management, reduction of losses, and resource optimization. Additionally, it highlights persistent challenges such as data security and interoperability. The aim is to address these challenges and propose innovative solutions to optimize agricultural processes. The results indicate that while IoT and AI offer substantial benefits, further advancements and solutions are needed to fully leverage these technologies for sustainable agricultural development.
Volume: 36
Issue: 3
Page: 1655-1663
Publish at: 2024-12-01

Automated blood cancer detection models based on EfficientNet-B3 architecture and transfer learning

10.11591/ijeecs.v36.i3.pp1731-1738
Nawaf Alshdaifat , Hamza Abu Owida , Zaid Mustafa , Ahmad Aburomman , Suhaila Abuowaida , Abdullah Ibrahim , Wafa Alsharafat
In blood smear images, there are difficulties in diagnosing blood cancer diseases like leukemia and lymphoma because of their various forms that appear in the human body. In this paper, a method for automatic detection of blood cancer is suggested that uses the EfficientNet-B3 architecture along with transfer learning techniques to improve accuracy and efficiency. We first fine-tuned the EfficientNet-B3 model, which was pre-trained on a large dataset consisting of annotated blood smear images, to capture pertinent features linked with blood malignant cells. To expedite the training process and adapt the model to our task, we use transfer learning. The proposed approach’s results from our experiments show that it outperforms traditional deep learning models and state-of-the-art methods in blood cancer detection. Additionally, with high precision and recall rates, this model also detects different types of blood cancers with robustness in its performance since its accuracy is over 99%. This means that when used together with the EfficientNet-B3 architecture, transfer learning can help the developed methods generalize among different types of blood cancers and conditions.
Volume: 36
Issue: 3
Page: 1731-1738
Publish at: 2024-12-01

Enhancing energy efficiency with poncebloc and hemp wool: a Moroccan case study

10.11591/ijaas.v13.i4.pp974-984
Yasser Boukioud , Kaoutar Senhaji Rhazi , Youssef Mejdoub
In recent years, the building sector's energy consumption in Morocco has been steadily increasing, highlighting the urgent need for sustainable building practices. Optimizing building parameters and the development of new insulation materials have become critical areas of research due to the significant energy losses associated with traditional insulation materials. Insulation materials can be a simple and cost-effective way to improve building energy efficiency and reduce environmental impact. Unfortunately, not all of these materials exhibit favorable thermal characteristics such as conductivity or heat capacity. Some involve complex production processes, while others are prohibitively expensive or toxic. At this juncture, the strategy of integrating ecological materials with insulation materials to enhance thermal performance holds considerable promise. This paper investigates the impact of the integration of poncebloc and hemp wool in inside temperature and annual consumption of a building construction numerically under a transient simulation program (TRNSYS). The results show that by using these two materials along with double-glazed windows, energy savings ranging from 50-55% can be achieved.
Volume: 13
Issue: 4
Page: 974-984
Publish at: 2024-12-01

Automatic kidney disease prediction using deep learning techniques

10.11591/ijeecs.v36.i3.pp1798-1806
Jency Rubia , Sherin Shibi , Babitha Lincy , Jenifer Pon Catherin , Vigneshwaran Vigneshwaran , Ezhil Nithila
The kidneys play an energetic role in eliminating excess products and fluids from the body, by a complex mechanism which is crucial for upholding a stable balance of body chemicals. Chronic kidney disease (CKD) is considered by an unhurried weakening in renal function that may eventually result in kidney injury or failure. The difficulty of diagnosing the illness rises as it worsens. However, using data from normal medical visits to evaluate the various phases of CKD could help with early detection and prompt care. Researchers suggest a classification strategy for CKD along with optimization strategies used in the learning process. The incorporation of artificial intelligence offers promise because it may often astonish with its skills and enable seemingly difficult undertakings. Modern machine learning techniques have been developed to detect renal illness in light of this. In the current study, a new deep learning model for CKD initial recognition and prediction is introduced. The main objective of the project is to build a strong deep neural network (DNN) and estimate its result outcomes in comparison to other leading-edge machine learning techniques. The outcomes demonstrate that the proposed strategy outperforms current approaches and has promise as a useful tool for CKD detection.
Volume: 36
Issue: 3
Page: 1798-1806
Publish at: 2024-12-01

Space vector pulse width modulation realization for three-phase voltage source inverter

10.11591/ijeecs.v36.i3.pp1976-1984
Ramasamy Palanisamy , Valarmathi Thangamani Santhakumari , Shanmugasundaram Venkatarajan , Selvaraj Hemalatha , Albert Alice Hepzibah , Ravindran Ramkumar , Vidyasagar Sugavanam
This paper presents the implementation of space vector pulse width modulation (SVPWM) for a three-phase voltage source inverter (VSI). SVPWM is a technique used to control the output voltage of VSIs with improved efficiency and precision. The abstract outlines the key steps involved in implementing SVPWM, including reference signal clarification, sector identification, determination of voltage vectors, and switching state calculation. This proposed system provides improved output voltage of the inverter, minimized voltage stress across the switches and reduced total harmonic distortion and electromagnetic interference. The proposed implementation aims to enhance the performance of three-phase VSIs in various applications, such as motor drives, renewable energy systems, and power converters. The simulation results of proposed system are verified using MATLAB Simulink.
Volume: 36
Issue: 3
Page: 1976-1984
Publish at: 2024-12-01

Self-regulation and resilience: the role on active procrastination of young adults

10.11591/ijphs.v13i4.23945
Ivy Josiah Vincent , Mei-Yui Law , Zariv Long-Kwan Chew , Chooi-Seong Lai
Our current dynamic digital era has generated active procrastination which is a popular trend of behaviour among young adults. Deciphering this reckless habit reveals a fascinating truth in which active procrastination thrives on strategic planning for task completion. This study examined i) self-regulation and resilience as the significant predictors of active procrastination among young adults, ii) the significant difference in active procrastination among university students and working young adults, iii) significant gender differences among university students, and iv) among working young adults. A total of 192 respondents completed the questionnaires, Self-Regulation Scale, Connor-Davidson Resilience Scale, and Active Procrastination Scale. Through quantile regression, results show that self-regulation is a significant predictor of active procrastination among young adults. Independent t-tests shows there is no significant differences in active procrastination among university students and working young adults. However, there are significant gender differences in active procrastination among university students, but not among working young adults. These findings indicate that self-regulation plays a significant role in cultivating active procrastinating among young adults. Furthermore, interesting findings are discovered for gender differences as males have relatively higher active procrastination scores. Hence, the findings emphasize the need for workshops to transform young adults to become active procrastinators through enhancing self-regulation.
Volume: 13
Issue: 4
Page: 1538-1547
Publish at: 2024-12-01

Entanglement classification – a comparative study of 𝑺𝑼(𝟐) and 𝑺𝑳(𝟐) developed operator model

10.11591/ijict.v13i3.pp556-562
Amirul Asyraf Zhahir , Siti Munirah Mohd , Mohd Ilias M. Shuhud , Bahari Idrus , Hishamuddin Zainuddin , Nurhidaya Mohamad Jan , Mohamed Ridza Wahiddin
Entanglement classification is a core aspect of quantum information theory. It ensures successful quantum information processing. This article presents a comparative study of entanglement classification using developed operator models for the special unitary group and special linear group. This study was built upon prior work in entanglement classification in a pure three-qubit quantum system environment, where the operator models for each mathematical group were independently developed. Through extensive analysis, both synthesized models are functionally effective and yield the desired results. However, the comparative analysis reveals that the operator model exhibits certain limitations, particularly in its early phase of development compared to. This study provides significant enlightenments into the practical abilities of the developed operator models in entanglement classification and underlines the theoretical distinction between and paving the path for future research in quantum information theory, specifically entanglement classification.
Volume: 13
Issue: 3
Page: 556-562
Publish at: 2024-12-01

Outage analysis of a single-threshold hard-switching hybrid FSO/RF system for reliable pico-macrocell backhauling

10.11591/ijeecs.v36.i3.pp1543-1554
Abduljalal Yusha’u Kassim , Vitalice Kalecha Oduol , Aliyu Danjuma Usman
In the quest for high-speed, reliable and cost-effective backhaul solutions for modern cellular networks, the hybrid free space optical (FSO) and radio frequency (RF) communication system is envisaged to be a promising technology. The hybrid system merges the benefits of both RF and FSO subsystems, delivering high data rates and reliability. The integration of both technologies improves the communication system's performance by addressing the inherent limitations of each. This study proposes a single-threshold hard-switching hybrid FSO/RF system for reliable pico-macrocell backhauling applications. We formulated closed-form expressions for the cumulative density functions (CDFs), probability density functions (PDFs), and outage probability (OP) for RF-only, FSO-only and hybrid FSO/RF links. The rician fading and gamma-gamma (G-G) channel distributions were utilized, respectively. The average received signal-to-noise ratio (SNR) determines the switching mechanism based on the defined threshold and atmospheric condition. Simulation results and analysis demonstrated that, at any average SNR above the defined threshold, the hybrid system’s OP outperforms that of the RF-only and FSO-only links under most conditions. The analysis illustrates that employing the hybrid FSO/RF system enhances reliability and boosts overall system performance in pico-macrocell backhauling scenarios, surpassing the performance of standalone FSO-only or RF-only links.
Volume: 36
Issue: 3
Page: 1543-1554
Publish at: 2024-12-01

Application of K-means clustering and B-value algorithms for analysis of earthquake-dangerous zones in Java Island

10.11591/ijaas.v13.i4.pp911-919
Bentang Anggarajati , Y Yatini , Wiji Raharjo
Java Island is an island with a high earthquake vulnerability. Therefore, earthquake mitigation measures are needed to reduce the impact of earthquakes. Earthquake mitigation is done by knowing the zones with a high risk of earthquakes and high levels of rock stress. The methods used to map earthquake-prone zones are K-means clustering and B-value. The K-means clustering method can provide earthquake clusters based on their characteristics and the B-value can produce rock stress conditions in the area. The results of this study are that the K-means clustering method produces 7 earthquake clusters with 5 classifications of very low, low, medium, high, and very high. In contrast, the B-value process has a high B-value with a value of 1.2-1.5 in West Java and a low B-value with a value of 0.9-1.2 in the central to the eastern part of Java.
Volume: 13
Issue: 4
Page: 911-919
Publish at: 2024-12-01

An efficient convolutional neural network for adversarial training against adversarial attack

10.11591/ijeecs.v36.i3.pp1769-1777
Srinivas A. Vaddadi , Sanjaikanth E. Vadakkethil Somanathan Pillai , Santosh Reddy Addula , Rohith Vallabhaneni , Bhuvanesh Ananthan
Convolutional neural networks (CNN) are widely used by researchers due to their extensive advantages over various applications. However, images are highly susceptible to malicious attacks using perturbations that are unrecognized even under human intervention. This causes significant security perils and challenges to CNN-related applications. In this article, an efficient adversarial training model against malevolent attacks is demonstrated. This model is highly robust to black-box malicious examples, it is processed with different malicious samples. Initially, malicious training models like fast gradient descent (FGS), recursive-FGSM (I-FGS), Deep-Fool, and Carlini and Wagner (CW) techniques are utilized that generate adversarial input by means of the CNN acknowledged to the attacker. In the experimentation process, the MNIST dataset comprising 60K and 10K training and testing grey-scale images are utilized. In the experimental section, the adversarial training model reduces the attack accuracy rate (ASR) by an average of 29.2% for different malicious inputs, when preserving the accuracy of 98.9% concerning actual images in the MNIST database. The simulation outcomes show the preeminence of the model against adversarial attacks.
Volume: 36
Issue: 3
Page: 1769-1777
Publish at: 2024-12-01

Quantum computing impact of cybersecurity identity verification measures on WhatsApp resilient infrastructure

10.11591/ijaas.v13.i4.pp844-853
JosephNg Poh Soon , Nair Preeta , Kumar Praveen , Kok Peng Yew , Phan Koo Yuen
This study investigates the impact and implications of implementing cybersecurity identity verification on the popular instant messaging platform, WhatsApp. Specifically, it focuses on the effects of such a measure on resilient infrastructure, user perceptions of the platform's branding and trustworthiness, and the cost-effectiveness of this approach in preventing scams, phishing, fraud, and cybercrime. Empirical data collected from a diverse group of WhatsApp users highlight that the introduction of identity verification could potentially enhance transaction efficiency, foster trust, and boost user satisfaction, particularly regarding the platform's security. However, the cost and complexity of implementation pose significant challenges. Despite these hurdles, most users perceive the potential benefits to outweigh the associated costs, pointing to a broad acceptance of such security measures. The study provides practical and theoretical contributions, offering valuable insights for managers and decision-makers at WhatsApp, as well as contributing to academic discussions on digital platform management and security. The research also underscores the societal implications of such a measure, suggesting an increase in trust in digital communication platforms and supporting safer financial transactions on social media. The integration of identity verification on WhatsApp being a responsive institution emerges as a valuable but complex endeavor, demanding careful planning to ensure maximum value creation for all stakeholders.
Volume: 13
Issue: 4
Page: 844-853
Publish at: 2024-12-01

A set of embedding rules in IWT for watermark embedding in image watermarking

10.11591/ijeecs.v36.i3.pp1512-1520
Muhammad Afnan Hafidz , Ferda Ernawan , Suraya Abu Bakar , Mohammad Fakhreldin
The development of new technologies has made image watermarking crucial in the digital era to preserve and protect illegal distribution of images against unauthorized users. This paper presents a robust image watermarking technique that employs a set of embedding rules in the three-level of integer wavelet transform (IWT). The proposed method aims to achieve high robustness of image watermarking while maintaining the imperceptibility. The proposed scheme divides the red and green layers into non-overlapping 16×16 blocks. Three levels of IWT are applied to obtain 2×2 LL sub-band, four coefficients of IWT are then modified based on the proposed set of rules for embedding watermark. The experimental results demonstrate a comparison of the proposed embedding and the existing methods. The proposed scheme produced an average NC value of 0.965 against the median filter. The results also showed the imperceptibility of the the image with a PSNR of 45.1760 db and SSIM of 0.9995.
Volume: 36
Issue: 3
Page: 1512-1520
Publish at: 2024-12-01

Artificial intelligence-based weather prediction framework using neural networks

10.11591/ijeecs.v36.i3.pp1836-1848
Keshav Kaushik , Gunjan Chhabra , Salil Bharany , Ateeq Ur Rehman , Habib Hamam
For humans, weather prediction is vital in making rational everyday choices and avoiding risk. Accurate weather forecasting is regarded as one of the world’s most difficult issues. New weather forecasting, unlike conventional techniques, relies on a mixture of computer simulations, observation (via balloons and satellites), and information of patterns and trends (via local weather analysts and weather stations). Predictions are rendered with fair precision using such techniques. Prediction algorithms based on complicated formulas run the majority of computational models used for prediction. This paper highlights the prediction of weather with the artificial neural networks (ANN) using the latest available smart computing devices. To assess the effectiveness of the model, comparison research is conducted with the other existing models in the same area. The result demonstrates that our approach is better in comparison to other similar research and products. The comparative analysis has been undergone which confirms the superiority of our proposed techniques with an accuracy of 90.4%.
Volume: 36
Issue: 3
Page: 1836-1848
Publish at: 2024-12-01

Impact of high-k insulators on electrical properties of junctionless double gate strained transistor

10.11591/ijeecs.v36.i3.pp1437-1447
Khairil Ezwan Kaharudin , Fauziyah Salehuddin , Anis Suhaila Mohd Zain , Nabilah Ahmad Jalaludin , Faiz Arith , Siti Aisah Mat Junos , Ibrahim Ahmad
High-k dielectric insulators are required to reduce leakage and increase transistor performance. They are able to impact the mobility of carriers in transistors positively, leading to better device performance in advanced transistor architecture. Nevertheless, an in-depth analysis of how high-k dielectric insulators influence transistor characteristics must be carried out to determine their suitability. The objective of this study is to investigate the impact of high-k insulators towards electrical properties of junctionless double gate strained transistor. The simulation works is done using process/device simulator Silvaco Athena/Atlas. Based on the retrieved results, the magnitude of ION, on-off ratio, gm, and Cint for TiO2-based device are approximately 63%, 99%, 62%, and 89% respectively higher than the lowest permittivity material-based device. The TiO2-based device also exhibits the lowest magnitude in IOFF and SS compared to others. However, a significant degradation in fT magnitude have been observed for TiO2-based device significantly due to its large capacitances
Volume: 36
Issue: 3
Page: 1437-1447
Publish at: 2024-12-01
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