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

29,939 Article Results

Multi-microcontroller system for Mecanum robots with gripper-shooter mechanisms

10.11591/ijra.v14i2.pp181-190
Mohammed Mareai , Juhen Fashikha Wildan , Mochamad Ridho Afkarean
This study presents the design and implementation of a multi-microcontroller digital control system for a Mecanum-wheeled robot with gripper and shooter mechanisms, tailored for agricultural applications. The proposed system integrates an Espressif32 master controller with Arduino Nano slave microcontrollers, enabling precise control of robot movement and functional components. Wireless control is facilitated by a PlayStation 3 controller, while Mecanum wheels ensure omnidirectional mobility in dynamic environments. Experimental results indicate a 66.67% success rate in seedling planting and an 83.33% success rate in ball collection tasks. Despite its notable performance, enhancements in sensor feedback and automation are recommended to improve efficiency. This research underscores the potential of cost-effective, multi-microcontroller systems for advancing real-time control and task execution in agricultural robotics.
Volume: 14
Issue: 2
Page: 181-190
Publish at: 2025-06-01

Plagiarism detection using text-representing centroids techniques

10.11591/ijeecs.v38.i3.pp1722-1734
Sureeporn Nualnim , Maleerat Maliyaem , Herwig Unger
This study addresses the limitations of traditional plagiarism detection methods by introducing the text-representing centroid (TRC) technique. TRC is designed to improve the accuracy of detecting semantic similarities and sophisticated forms of plagiarism. It utilizes a co-occurrence graph to identify centroid terms that represent the core meaning of text documents, effectively capturing the contextual associations between terms. Extensive experiments were conducted on a dataset of academic papers to assess TRC’s performance against traditional techniques across various categories of plagiarism, including near-copy, modified-copy, and paraphrasing. The results demonstrate the effectiveness of the TRC technique, achieving an average precision of 0.96 and a recall of 0.71. This performance surpasses methods such as Jaccard and Cosine similarity in accurately detecting more, complex forms of plagiarism. These findings highlight TRC’s potential as a robust tool for both academic and industry applications, helping to ensure integrity in textual content through precise and comprehensive plagiarism detection.
Volume: 38
Issue: 3
Page: 1722-1734
Publish at: 2025-06-01

Deep-feed: An Internet of things-enabled smart feeding system for pets powered by deep learning

10.11591/ijra.v14i2.pp227-236
Jameer Kotwal , Amruta Surana , Pallavi Adke , Krunal Pawar , Asma Shaikh , Vajid Khan
Internet of things (IoT) encompasses a variety of connected devices and technologies designed to improve care, monitoring, and management of pets. IoT technology enables voice or app-driven control of these feeders, allowing pet guardians to remotely dispense food to their pets anytime. In this paper, a novel deep-feed network has been proposed that combines Image and sensor data classification. The inputs, such as camera (images) and sensor data, are sent to the preprocessing stages, where images are preprocessed using a Bilateral filter, and the data using preprocessing techniques such as tokenization, lemmatization, etc. The preprocessed images are sent to the neural network, like a convolutional neural network (CNN) for image classification and a bidirectional gated recurrent unit (BiGRU) to predict the dog's behavior. Next, these two networks are fused, and the fuzzy concept identifies whether the dogs are near the food or not in a cage. If the dog is near the food cage, the control unit will allocate the food and water through the water pump in the dog cage. Then the control unit gives the order to fill the food and water pumps and alerts the user to identify the food in a cage via the Blynk application. The accuracy of the suggested method can reach 99.95%, compared to 84.9%, 87.58%, and 93.91% for conventional models like the cat's monitoring and feeding systems via IoT (CMFSVI), petification, and global system for mobile communications/general packet radio service (GSM/GPRS). In comparison to the current approaches, the accuracy of the suggested methodology increased by 16.09%, 13.8%, and 3.75%, for existing models like CMFSVI, petification, and GSM/GPRS, respectively.
Volume: 14
Issue: 2
Page: 227-236
Publish at: 2025-06-01

Camera-based simultaneous localization and mapping: methods, camera types, and deep learning trends

10.11591/ijra.v14i2.pp162-172
Anak Agung Ngurah Bagus Dwimantara , Oskar Natan , Novelio Putra Indarto , Andi Dharmawan
The development of simultaneous localization and mapping (SLAM) technology is crucial for advancing autonomous systems in robotics and navigation. However, camera-based SLAM systems face significant challenges in accuracy, robustness, and computational efficiency, particularly under conditions of environmental variability, dynamic scenes, and hardware limitations. This paper provides a comprehensive review of camera-based SLAM methodologies, focusing on their different approaches for pose estimation, map reconstruction, and camera type. The application of deep learning also will be discussed on how it is expected to improve performance. The objective of this paper is to advance the understanding of camera-based SLAM systems and to provide a foundation for future innovations in robust, efficient, and adaptable SLAM solutions. Additionally, it offers pertinent references and insights for the design and implementation of next-generation SLAM systems across various applications.
Volume: 14
Issue: 2
Page: 162-172
Publish at: 2025-06-01

Experimental evaluation of bidirectional encoder representations from transformers models for de-identification of clinical document images

10.11591/ijra.v14i2.pp273-280
Ravichandra Sriram , Siva Sathya Sundaram , S. LourduMarie Sophie
Many health institutes maintain patients’ diagnosis and treatment reports as scanned images. For healthcare analytics and research, large volumes of digitally stored patient information have to be accessed, but the privacy requirements of protected health information (PHI) limit the research opportunities. Particularly in this artificial intelligence (AI) era, deep learning models require large datasets for training purposes, which hospitals cannot share unless the PHI fields are de-identified. Manual de-identification is beyond possible, with millions of patient records generated in hospitals every day. Hence, this work aims to automate the de-identification of clinical document images utilizing AI models, particularly pre-trained bidirectional encoder representations from transformers (BERT) models. For the purpose of experimentation, a synthetic dataset of 550 clinical document images was generated, encompassing data obtained from diverse patients across multiple hospitals. This work presents a two-stage transfer learning approach, initially employing Tesseract character recognition (OCR) to convert clinical document images into text. Subsequently, it extracts PHI fields from the text for de-identification. For the purpose of extraction, BERT models were utilized; in this work, we contrasted six pre-trained versions of such models to examine their effectiveness and achieve the F1 score of 92.45%, thus showing better potential for de-identifying PHI data in clinical documents.
Volume: 14
Issue: 2
Page: 273-280
Publish at: 2025-06-01

Understanding golfers’ acceptance behavior toward robotic golf caddies by merging the task technology fits theory and the perceived risk theory

10.11591/ijra.v14i2.pp173-180
Kyuhyeon Joo , Heather Markham Kim , Jinsoo Hwang
The current paper was designed to understand how to form the acceptance behavior of golfers toward robotic golf caddies, which conducted a hypothetico-deductive approach. The study focused on two questions: i) Can the TTF theory explain the acceptance behavior of golfers toward robotic golf caddies? ii) Do perceived risks negatively affect the acceptance behavior of golfers toward robotic golf caddies? Thus, the study postulated the impacts of task/technology characteristics and the five perceived risks (i.e. financial, time, privacy, performance, and psychological) on task technology fit, and the link between task technology fit and behavioral intentions. The data was collected from 387 golfers in South Korea, and the hypotheses tests were conducted by structural equation modeling. The results of the data analysis indicate that both task and technology characteristics increase task technology fit, and the four dimensions of perceived risks, which include time, privacy, performance, and psychology, have a negative influence on task technology fit. In addition, task technology fit also increases behavioral intentions. The study provides theoretical contributions by filling the acknowledged research gaps, and it also presents managerial implications in regard to commercializing robotic caddies in the golf industry.
Volume: 14
Issue: 2
Page: 173-180
Publish at: 2025-06-01

Review on class imbalance techniques to strengthen model prediction

10.11591/ijai.v14.i3.pp1727-1742
Hemalatha Putta , Geetha Mary Amalanathan
Data is a fundamental component in various fields, including science, business, health care, and technology. It is often processed, stored, and analyzed using computer systems and software applications. The importance of data lies in its ability to provide valuable insights, drive innovation, and improve decision-making processes. However, it’s essential to handle and manage data responsibly to address privacy and ethical considerations. Data mining (DM) involves discovering patterns, trends, correlations, or useful information from large datasets. Data dredging or DM and machine learning (ML) are closely related fields that both involve the analysis of data to discover patterns and make predictions. DM focuses on extracting knowledge from data; ML emphasizes the development of algorithms that can do analysis. The two fields are interconnected, and the techniques from one state of art integrated into the processes of the other. In ML the class imbalance problem occurs due to the class distribution in the training data is not equal. Imbalanced classification refers to a condition where a particular class (minority class) is under represented parallelled to another class (majority class) in a dataset. This paper furthermore emphasizes on the synthetic minority oversampling technique (SMOTE) variants employed by the researchers, and highlights the limitations the work.
Volume: 14
Issue: 3
Page: 1727-1742
Publish at: 2025-06-01

Comparative insights into nonlinear PID-based controller design approaches for industrial applications

10.11591/ijra.v14i2.pp191-203
Syed Najib Syed Salim , Mohd Fua’ad Rahmat , Lokman Abdullah , Shamsul Anuar Shamsudin , Khairun Najmi Kamaludin , Mazree Ibrahim
Proportional-integral-derivative (PID) controllers are established in manufacturing due to their simple design, robustness, and wide-ranging industrial applications. However, traditional PID controllers often struggle with the complexity and nonlinearity behaviors inherent in many control systems. As a result, ongoing and future research is focused on developing more stable PID controllers that function efficiently without heavily depending on exact mathematical models, by fine-tuning controller parameters. This study explores several PID-based controllers, including non-linear PID (N-PID), multi-rate non-linear PID (MN-PID), and self-regulating nonlinear PID (SN-PID), assessing and contrasting their performance. The efficacy and robustness of these control mechanisms are substantiated through comparative analyses with the sliding mode control technique, employing experimental data from a pneumatic actuator system to assess performance across varying load scenarios. SN-PID outperforms sliding mode controller (SMC) by 90.97% and PID by 89.90%, followed by MN-PID (85.58% over SMC, 83.86% over PID) and N-PID (78.08% over SMC, 75.49% over PID), while PID offers only 10.63% improvement over SMC. These findings provide valuable insights and recommendations for enhancing controller performance. These insights aim to guide control engineers in selecting the most appropriate N-PID design strategy for specific applications, ultimately improving system performance and operational efficiency in industrial environments.
Volume: 14
Issue: 2
Page: 191-203
Publish at: 2025-06-01

Development and implementation of a mobile robot for grouting floor tile joints

10.11591/ijra.v14i2.pp151-161
Anees Abu Sneineh , Wael A. Salah , Mohamed Elnaggar , Mai Abuhelwa
Many construction tasks need time and effort from people. Thus, modern technology is one of its purposes to aid task completion. These include grouting floor tile joints. It takes time and effort to complete this process. Traditional methods for grouting floor tile joints between tiles are inefficient and require the worker to stay on his knees for extended periods, which can cause health issues. Thus, mobile robots are needed to automate floor grouting. This study describes the design and development of a mobile robot model to grout floor tile joints uniformly and effectively. Compared to manual approaches, the proposed robot can clean tiles quickly and precisely. The robot fills based on user-defined workspace coordinates. Set the robot at the start location to begin grouting. The robot then follows the user-defined code and coordinates to fill the requirement. After grout filling, the robot returned to the starting position to clean. This model was evaluated and exhibited faster, more accurate grouting and a shorter injection process than manual approaches.
Volume: 14
Issue: 2
Page: 151-161
Publish at: 2025-06-01

Internet of Things-enabled smart robotic baggage monitoring and tracking system for enhanced traveler convenience and security

10.11591/ijra.v14i2.pp290-300
Anita Gehlot , Rajesh Singh , Rahul Mahala , Vivek Kumar Singh , Mahim Raj Gupta
Baggage travel is a significant issue, causing inconveniences and financial losses for travellers. The rise in efficient international and domestic travel has led to the need for live baggage tracking systems. Traditional methods, such as manual tracking and locks, are inefficient and counterproductive due to power limitations. IoT has revolutionized baggage management by providing real-time tracking feedback and enhancing security. IoT-enhanced smart luggage systems use biometric locks, GPS tracking, and smart locking mechanisms to prevent theft and unauthorized usage. Geofencing allows users to draw boundaries for luggage, and smart luggage systems can adapt to airport security requirements. Some smart suitcases also have self-following features, allowing travellers to have better control over their bags. IoT-enabled baggage solutions also improve airport and travel centre efficiency. RFID and barcode identification devices enable airline employees to quickly recognize, monitor, and manage luggage, reducing waiting times and loss risks. Cloud-based systems allow users to remember their luggage and receive travel suggestions based on predicted frequency of use. IoT-enabled baggage management systems have the potential to transform airport ecosystems into smarter ones through automated tracking with minimal human involvement and errors. AI and machine learning can also proactively address concerns and improve the overall customer journey.
Volume: 14
Issue: 2
Page: 290-300
Publish at: 2025-06-01

Design of H-/H∞ based fault detection filter for linear uncertain systems using linear matrix inequalities

10.11591/ijra.v14i2.pp214-226
Masood Ahmad , Rosmiwati Mohd-Mokhtar
One of the significant challenges in model-based fault detection is achieving robustness against disturbances and model uncertainties while ensuring sensitivity to faults. This study proposes an optimized approach for designing fault detection filters for discrete-time linear systems with norm-bounded model uncertainties. The design leverages the H-/H∞ optimization framework and is expressed through linear matrix inequality constraints. The filter is designed to produce a residual signal that balances two opposing objectives: minimizing the impact of disturbances and model uncertainties while maximizing fault sensitivity. The effectiveness of the proposed method is demonstrated through simulations involving sensor and actuator fault detection in the well-known three-tank system. Simulation results illustrate the method's ability to maintain robustness against disturbances and uncertainties while effectively detecting faults in the system.
Volume: 14
Issue: 2
Page: 214-226
Publish at: 2025-06-01

A novel approach to enhance rice foliar disease detection: custom data generators, advanced augmentation, hybrid fine-tuning, and regularization techniques with DenseNet121

10.11591/ijra.v14i2.pp237-247
Govindarajan Subburaman , Mary Vennila Selvadurai
Rice leaf diseases impact crop yield, leading to food shortages and economic losses. Early, automated detection is essential but often hindered by accuracy challenges. This study contributes to improving model robustness against diverse and adversarial inputs by proposing a custom data generator that applies Albumentation-based advanced augmentations, such as Gaussian blur, noise addition, brightness/contrast adjustments, and coarse dropout, to enhance model generalization. Five deep learning architectures—simple convolutional neural network (CNN), ResNet50, EfficientNetB0, Inception v3, and DenseNet121—were evaluated for classifying six categories: bacterial blight, brown spot, leaf blast, leaf scald, narrow brown spot, and healthy leaf. A hybrid model approach is proposed, fine-tuning the DenseNet121 model by unfreezing its last 20 layers, which balances transfer learning benefits with domain-specific feature extraction. Regularization techniques, including L2 regularization and a reduced dropout rate, are incorporated to control overfitting. Additionally, a custom learning rate scheduler is proposed to promote stable training. DenseNet121 achieved the highest performance, with an accuracy of 98.41%, demonstrating the effectiveness of these advanced augmentation and tuning strategies in rice leaf disease classification.
Volume: 14
Issue: 2
Page: 237-247
Publish at: 2025-06-01

CP_SDUNet: road extraction using SDUNet and centerline preserving dice loss

10.11591/ijra.v14i2.pp260-272
Bayu Satria Persada , Muhammad Rifqi Priyo Susanto , Laksmita Rahadianti , Aniati Murni Arymurthy
Existing automatic road map extraction approaches on remote sensing images often fail because they cannot understand the spatial context of an image. Mainly because they could not learn the spatial context of the image and only knew the structure or texture of the image. These approaches only focus on regional accuracy instead of connectivity. Therefore, most approaches produce discontinuous outputs caused by buildings, shadows, and similarity to rivers. This study addresses the challenge of automatic road extraction, focusing on enhancing road connectivity and segmentation accuracy by proposing a network-based road extraction that uses a spatial intensifier module (DULR) and densely connected U-Net architecture (SDUNet) with a connectivity-preserving loss function (CP_clDice) called CP_SDUNet. This study analyzes the CP_clDice loss function for the road extraction task compared to the BCE Loss function to train the SDUNet model. The result shows that CP_SDUNet, performs best using an image size of 128×128 pixels and trained with the whole dataset with a combination of 20% clDice and 80% dice loss. The proposed method obtains a clDice score of 0.85 and an Interest over Union (IoU) score of 0.65 for the testing data. These findings demonstrate the potential of CP_SDUNet for reliable road extraction.
Volume: 14
Issue: 2
Page: 260-272
Publish at: 2025-06-01

Susceptibility of Aedes aegypti to malathion and permethrin insecticides in Enrekang Regency: an experimental study

10.11591/ijaas.v14.i2.pp291-299
Sulasmi Sulasmi , Hamsir Ahmad , Juherah Juherah , Iwan Suryadi , Siti Rachmawati
Insecticide resistance in Aedes mosquitoes can undermine arbovirus control efforts. Malathion and permethrin insecticides belong to the group of insecticides used for control and if used continuously will cause immunity of target mosquitoes. This study aims to assess the level of susceptibility of Aedes aegypti to insecticides commonly used in public health in the Enrekang Regency. The type of research used was experimental research. Female Aedes aegypti were collected from rearing results with a total sample size of 240 mosquitoes which were divided into 120 mosquitoes each in 4 treatments and 2 controls on malathion 0.8% and permethrin 0.25% insecticides. The results obtained from the research on insecticide susceptibility test results using malathion 0.8% in 60 minutes of exposure averaged 55% dead and exposure for 24 hours averaged 90% mosquito death, while permethrin 0.25% insecticide in 60 minutes of exposure averaged 90% dead mosquitoes and 24 hours exposure averaged 100% mosquito death, while for the control all live. The conclusion of the study was the susceptibility test of Aedes aegypti mosquitoes to malathion 0.8% insecticide in the category of moderate resistance while permethrin 0.25% insecticide in the category of susceptible.
Volume: 14
Issue: 2
Page: 291-299
Publish at: 2025-06-01

Averaged bars for cryptocurrency price forecasting across different horizons

10.11591/ijai.v14.i3.pp1910-1918
Ahmed El Youssefi , Abdelaaziz Hessane , Imad Zeroual , Yousef Farhaoui
Technical analysis uses past price movements and patterns to predict future trends and help traders make informed decisions about their cryptocurrency portfolios. This study investigates the effectiveness of different forecasting algorithms and features in predicting the future log return of cryptocurrency close price across various horizons. Specifically, we compare the performance of AdaBoost, light gradient boosting machine (LightGBM), random forest (RF), and k-nearest neighbor (KNN) regressors using Kline open, high, low, close (OHLC) prices data and averaged bars (Heikin-Ashi) features. Our analysis covers ten of the most capitalized cryptocurrencies: Cardano, Avalanche, Binance Coin, Bitcoin, Dogecoin, Polkadot, Ethereum, Solana, Tron, and Ripple. We have observed nuanced patterns in predictive performance across different cryptocurrencies, forecasting horizons and features. Then we have found that AdaBoost and RF models consistently exhibit a competitive performance, with LightGBM showing promising results for specific cryptocurrencies. The impact of forecast horizons on forecasting performance underscores the need for tailored forecasting models. In summary, the use of Kline OHLC data as features outperforms averaged bars in forecasting the first and second horizons, while averaged bars outperform Kline OHLC data for mid- to relatively long-term horizons (starting from the third horizon). Our findings suggest that averaged bars merit more attention from researchers instead of relying solely on Kline OHLC data.
Volume: 14
Issue: 3
Page: 1910-1918
Publish at: 2025-06-01
1 2 3
...
1995 1996
Show 207 of 1996

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