Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

25,002 Article Results

Improved convolutional neural network-based bearing fault diagnosis using multi-phase motor current signals

10.11591/ijece.v15i2.pp1656-1669
Hai Dang Huu , Ngoc-My Bui , Van-Phuc Hoang , Thang Bui Quy , Yen Hoang Thi
Diagnosing bearing faults of the induction motor is crucial for the maintenance of rotating electrical machines. Numerous methods have been developed and published for monitoring and classifying these faults using sensor data such as vibration, audio, and current signals. Ideally, the current phases are balanced; however, faults disrupt this symmetry, causing each phase to reveal unique diagnostic details. Consequently, studies that rely on a single phase of the current signal may not capture all fault-related characteristics. Research on motor bearing fault diagnosis using two current phases typically extracts features from each phase separately, applying machine learning to classify the faults. Currently, no approach has been proposed to extract features from both phases simultaneously. Furthermore, the proposed solutions have only been published with noise-free data. To address these challenges, this paper introduces an enhanced solution that improves the accuracy of motor bearing fault classification based on an improved convolutional neural network that processes current signals from two phases simultaneously. Experimental results demonstrate that the proposed method significantly outperforms traditional approaches, particularly in scenarios where the sample signals are noise-adding signals. Fault classification accuracy of the proposed improved convolutional neural network (MI-CNN) about 95.12% with noise-adding signals at the signal-to- noise ratio of 20 dB.
Volume: 15
Issue: 2
Page: 1656-1669
Publish at: 2025-04-01

Enhancing accuracy in greenhouse microclimate forecasting through a hybrid long short-term memory light gradient boosting machine ensemble approach

10.11591/ijece.v15i2.pp2392-2403
Mokeddem Kamal Abdelmadjid , Seddiki Noureddine , Bourouis Amina , Benahmed Khelifa
Greenhouse cultivation is one of the main methods for improving agricultural yield and quality. With the world needing more and more production, improving greenhouses using innovative technology becomes a must. These high-tech, aka, smart greenhouses depend much on the accuracy and availability of sensor data to perform at their best. In challenging situations such as sensor malfunctions or data gaps, utilizing historical data to predict microclimate parameters within the greenhouse is essential for maintaining optimal growing conditions and effective sustainable resource management control. In this work, and by employing a synthesis technique across various time series models, we forecast internal temperature and humidity, the two main parameters for a greenhouse, by incorporating diverse characteristics as input into a customized forecasting model. The selected architecture integrates deep learning and nonlinear learning models, specifically long short-term memory (LSTM) and light gradient boosting machine (LightGBM) as an ensemble approach, providing a comprehensive framework for time-series prediction, evaluated through mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) metrics. With a focus on improving accuracy in anticipating environmental changes, we have achieved high precision in predicting temperature (98.45%) and humidity (99.61%).
Volume: 15
Issue: 2
Page: 2392-2403
Publish at: 2025-04-01

Temperature response analysis between PD and PI controls applied to infant incubators

10.12928/telkomnika.v23i2.25879
Abd.; Poltekkes Kemenkes Surabaya Kholiq , Lamidi; Poltekkes Kemenkes Surabaya Lamidi , Farid; Poltekkes Kemenkes Surabaya Amrinsani , Anisia Yunita; Poltekkes Kemenkes Surabaya Maulani Argumery , Hafizh; Poltekkes Kemenkes Surabaya Aushaf Mahdy
Premature infants, born with low birth weight, require specialized care and isolation due to their vulnerability to infections in public settings. Baby incubators, classified as life support equipment, play a crucial role in safeguarding these infants by maintaining a consistent temperature and humidity similar to the mother’s womb. This study compares the temperature control systems in baby incubators, specifically proportional and derivative (PD) control versus proportional and integral (PI) control. LM35 and DS18B20 sensors were employed in the study. Results from PD control using the LM35 sensor show a rise time of 5 min and 40 sec, a settling time of 25 min, and an overshoot of 2.2 °C. The DS18B20 digital sensor, under PD control, achieves a rise time in 6 min and 30 sec, a settling time of 23 min, with an overshoot of 1.2 °C. For PI control with the LM35 sensor, there’s a 3 °C overshoot, a 5-minute rise time, and a 30-minute settling time. The DS18B20 sensor under PI control exhibits a 2.7 °C overshoot, a 5-minute rise time, and a 29-minute settling time. PD control demonstrates lower overshoot and faster response but longer rise times than PI control. Future research explores fuzzy control systems and proportional integral derivative (PID)-fuzzy hybrid control.
Volume: 23
Issue: 2
Page: 495-506
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

Android-based smart digital marketplace application on agricultural commodities using a new variant recommendation system

10.11591/ijece.v15i2.pp1968-1977
Subiyanto Subiyanto , Sucihatiningsih Dian Wisika Prajanti , Nur Azis Salim , Setya Budi Arif Prabowo , Deyndrawan Sutrisno , Andika Anantyo , Dewi Anggriani
In the marketing of agricultural products, addressing the challenges associated with extensive distribution chains is essential, as these directly affect sellers. Additionally, the vast array of available product options often overwhelms customers, complicating their efforts to identify and purchase items that align with their preferences. This work aims to develop a smart e-commerce application for agribusiness, specifically designed for agricultural products on the Android platform. The application integrates a recommendation system that utilizes geolocation-aware neural graph collaborative filtering (GA-NGCF), which facilitates product marketing for farmers and streamlines the product search and selection process for users based on personalized preferences. The development process encompassed various stages, from planning to rigorous testing. The application’s recommendation system, which implements GA-NGCF, operates based on three primary elements: the creation of a geolocation graph of user-item data, the integration of information between neighboring nodes, and the prediction of user preferences. The resulting smart agribusiness e-commerce application, enhanced by GA-NGCF, demonstrated marked improvements in recommendation accuracy and overall application performance during testing. Empirical results indicated substantial enhancements in recommendation metrics, with GA-NGCF achieving a recall of 0.34, a precision of 0.36, and normalized discounted cumulative gain of 0.37, thereby outperforming existing models.
Volume: 15
Issue: 2
Page: 1968-1977
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

Two-scale decomposition and deep learning fusion for visible and infrared images

10.11591/ijece.v15i2.pp1593-1601
Ruhan Bevi Azad , Hari Unnikrishnan , Lokesh Gopinath
The paper focuses on the fusion of visible and infrared images to generate composite images that preserve both the thermal radiation information from the infrared spectrum and the detailed texture from the visible spectrum. The proposed approach combines traditional methods, such as two-scale decomposition, with deep learning techniques, specifically employing an autoencoder architecture. The source images are subjected to two-scale decomposition, which extracts high-frequency detail and low-frequency base information. Additionally, an algorithmic unravelling technique establishes a logical connection between deep neural networks and traditional signal processing algorithms. The model consists of two encoders for decomposition and a decoder after the unravelling operation. During testing, a fusion layer merges the decomposed feature maps, and the decoder generates the fused image. Evaluation metrics including entropy, average gradient, spatial frequency and standard deviation are employed to subjectively assess fusion quality. The proposed approach demonstrates promise for effectively combining visible and infrared imagery for various applications.
Volume: 15
Issue: 2
Page: 1593-1601
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

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

Graphene-based THz antenna with a wide bandwidth for future 6G short-range communication

10.12928/telkomnika.v23i2.26562
Md. Kawsar; Daffodil International University Ahmed , Md. Sharif; Daffodil International University Ahammed , Md. Ashraful; Daffodil International University Haque , Narinderjit Singh; INTI International University Sawaran Singh , Jamal; Daffodil International University Hossain Nirob , Redwan; Daffodil International University A. Ananta , Kamal; Daffodil International University Hossain Nahin , Liton; Pabna University of Science and Technology Chandra Paul
In this study, we present the design and investigation of a terahertz (THz) frequency antenna optimized for the 2-10 THz range, featuring both single-element and multiple-input multiple-output (MIMO) configurations, with a focus on industrial and innovative applications to enhance future 6G communication systems. The antenna, constructed on a polyimide substrate with dimensions of 90×30 µm, achieves a bandwidth from 4.0328 to 10 THz. The MIMO configuration, which includes two ports, demonstrates excellent isolation with a value of -27 dB. The proposed antenna system achieves a gain of 12.38 dB and an efficiency of 89%, making it highly appropriate for THz communication applications. Furthermore, the envelope correlation coefficient (ECC) of 0.002 and diversity gain (DG) of 9.99 affirm the antenna’s effectiveness in MIMO systems. A resistance inductance capacitance (RLC) circuit model was employed to accurately represent the S11 curve, ensuring precise characterization of the antenna’s performance. These results underscore the probability of the proposed antenna for high-speed, short-range communication systems.
Volume: 23
Issue: 2
Page: 306-315
Publish at: 2025-04-01

Human–robot collaboration with mixed reality for interactive and safe workspaces

10.12928/telkomnika.v23i2.26275
Sanghun; Changwon National University Nam
Realizing seamless collaboration between humans and robots in shared workspaces requires advanced systems that can ensure safety and efficiency while considering the inherent unpredictability of human movement. This paper proposes a system that integrates mixed reality (MR) and robotics through a unified coordinate system to facilitate real-time interaction and collaboration. By leveraging a MR interface, human collaborators can visualize and interact with the projected paths of the robotic arms, thereby enhancing both spatial awareness and task coordination. The proposed system adapts the robot’s movement path dynamically using the Voronoi diagram algorithm to modify trajectories in response to the detection of a human hand within a predefined caution zone. This mechanism reduces the risk of collisions, which ensures safer collaborative environments. The proposed system’s ability to exchange motion information between the operator and the robot supports real-time adjustments and promotes an intuitive and efficient collaborative experience. Our findings suggest that integrating MR technology in human–robot collaboration systems can improve safety protocols and operational fluidity dramatically, thereby representing a significant step forward in the development of safe, efficient, and effective interactive robot systems.
Volume: 23
Issue: 2
Page: 526-532
Publish at: 2025-04-01

Anxiety and self-belongingness of inclusive learners: the stance, facet overcoming

10.11591/ijere.v14i2.29993
Panneerselvam G. , Bella Wiselet S.
It was inevitable that young learners at this time would have to pay special attention to both prosaic life and their schoolwork. It is also acknowledged that the majority of learners live in a multi varied situations. This study focuses on and analyses academic anxiety and self-belongingness in learners in an inclusive setup. Academic anxiety is related to distinct characteristic of learner, including gender, venue, instructional media, and family structure. The study focused on these four personal characteristics, and an experimental design technique was used to perform the analysis. This was accomplished using a convenience sampling procedure, which yielded 284 samples. The descriptive analysis is assisted to examine the collected data. However, there is a significant variance regarding their locality of residency. As a result, there is an essential geographical divide between them. The study outcomes also revealed a correlation between learners’ academic concerns and sense of belonging. Significant differences in personnel parameters, such as the residential location of learners, were observed.
Volume: 14
Issue: 2
Page: 1286-1294
Publish at: 2025-04-01

Influence of social networks on the mental health of university students in Huancayo, Peru

10.11591/ijere.v14i2.31094
Nilton David Vilchez Galarza , Luis Angel Huaynate Espejo , Carmen Rocío Ricra Echevarría
Since the appearance of signed social networks (SSNs), their use has increased steadily among young people, not only in terms of the number of users but also in terms of the time they devote to managing the platforms, a situation that may be influencing their behavior. This study aimed to analyze the influence of the use of social networks (SNs) on the mental health of young university students. For this purpose, a quantitative, basic, and correlational study was carried out. We worked with a sample of 361 undergraduate students in health careers at a university in Huancayo. The PERMA-Profiler scale for mental health and the brief social network addiction questionnaire were used as data collection instruments to evaluate the use of SNs. The results indicate that there is a statistically significant influence of the use of SNs on the mental health of students, which explains a variability of 53.5% to 79.9%, according to the values of the Nagelkerke Pseudo X2 calculation for SNs. This suggests that the use of SNs hurts students’ mental health.
Volume: 14
Issue: 2
Page: 1134-1140
Publish at: 2025-04-01

Improving water quality parameter prediction with multi-level linear regression model and hybrid feature selection

10.11591/ijece.v15i2.pp2381-2391
Aleefia Khurshid , Samruddhi Korke , Yudhir Kothari , Shruti Alone , Khushali Bais
Predicting and modeling the quality of water is essential to guarantee that the water is safe to drink. The chlorine content in water needs to be monitored in real-time to provide a consistent supply of drinkable water. Additionally, potassium and chlorine have a major impact on how appealing the water is, as they are important components that influence taste and odor. Therefore, to evaluate the levels of chlorine and potassium, this work presents a multivariable linear regression approach backed by a hybrid feature extraction method. To bridge the gap between the filter and wrapper approaches, a hybrid approach is used to remove unnecessary information and reduce processing time and complexity. Here the quantitative parameters, in conjunction with categorical parameters, are instrumental in enabling accurate prediction of two water quality parameters. The two developed multi-level regression (MLR) models for the prediction of potassium and chloride are useful when factors affecting water parameters fluctuate at the site level as well as over larger spatial or temporal scales giving consumers a visual representation of how each parameter influences prediction. The converged model outperforms in comparison with other machine learning algorithms with an MAE of 7.42e-15 for potassium and 3.72e-14 for chloride.
Volume: 15
Issue: 2
Page: 2381-2391
Publish at: 2025-04-01

Buffers balancing of buffer-aided relays in 5G non-orthogonal multiple access transmission internet of things networks

10.11591/ijece.v15i2.pp1774-1782
Mohammad Alkhwatrah , Nidal Qasem
Buffer-aided cooperative non-orthogonal multiple access (NOMA) enhances the efficiency of utilizing the spectral by allowing more users to share the same re- sources to establish massive connectivity. This is remarkably attractive in the fifth generation (5G) and beyond systems, where a massive number of links is essential like in the internet of things (IoT). However, the capability of buffer co-operation in reducing the outage is limited due to empty and full buffers, where empty buffers can not transmit and full buffers can not receive data packets. Therefore, in this paper, we propose balancing the buffer content of the inter-connected relays, so the buffers that are more full send packets to the emptier buffers, hence all buffers are more balanced and farther from being empty or full. The simulations show that the proposed balancing technique has improved the network outage probability. The results show that the impact of the balancing is more effective as the number of relays in the network is increased. Further- more, utilizing the balancing with a lower number of relays may lead to better performance than that of more relays without balancing. In addition, giving the balancing different levels of priorities gives different levels of enhancement.
Volume: 15
Issue: 2
Page: 1774-1782
Publish at: 2025-04-01
1 2 3
...
1666 1667
Show 26 of 1667

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration