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30,185 Article Results

Hydrothermal synthesis of ZnFe2O4@g-C3N4 for enhanced adsorption-photocatalytic degradation of ciprofloxacin

10.11591/ijaas.v15.i1.pp313-321
Medya Ayunda Fitri , Muchammad Tamyiz , Eko Prasetyo Kuncoro , Mamlu’atul Nihaya , Muhammad Abdul Basith Thom Thom , Cindy Dwi Cahyani , Bahauddin Alqostolani
The persistence of antibiotic contaminants such as ciprofloxacin (CIP) in aquatic environments poses significant environmental and health risks, necessitating the development of efficient removal strategies. In this work, a zinc ferrite-anchored two-dimensional carbon nitride nanocomposite (ZF@2DCN) was synthesized via a simple calcination and hydrothermal approach to achieve synergistic adsorption–photocatalytic degradation of CIP under visible light. Structural and optical characterizations confirmed the successful formation of a ZF–2DCN heterojunction with high crystallinity, strong interfacial interactions, and enhanced visible-light absorption. The incorporation of ZF reduced the bandgap of 2DCN from 2.8 to 2.6 eV, promoting improved charge separation. Adsorption studies revealed rapid equilibrium within 30 min and multilayer adsorption on heterogeneous active sites, with a maximum adsorption capacity of 11.7 mg g-1. Under visible-light irradiation, ZF@2DCN achieved up to 81% CIP degradation within 60 min, exhibiting an apparent reaction rate approximately 2.5 times higher than that of pristine 2DCN. The enhanced performance is attributed to the strong synergy between adsorption-driven pollutant enrichment and photocatalytic degradation. Overall, ZF@2DCN shows strong potential as an efficient material for antibiotic removal in wastewater treatment.
Volume: 15
Issue: 1
Page: 313-321
Publish at: 2026-03-01

Adaptive sentiment analysis for stock markets using deep learning

10.11591/ijaas.v15.i1.pp416-426
Talent Mawere , Selvaraj Rajalakshmi , Venu Madhav Kuthadi , Othlapile Dinekanyane
Stock markets are highly volatile, making price prediction very difficult. One of the factors influencing the volatility of financial markets is rapidly changing news sentiment. This study presents a novel adaptive deep learning (DL) framework for sentiment analysis with concept drift capabilities. The proposed model combines convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and attention mechanisms in its processing architecture. The model inputs preprocessed news headlines into both the CNN and BiLSTM-Attention networks to extract local features, model contextual dependencies, and prioritizes important sentiment cues in its prediction mechanism. We use FastText and Word2Vec for word embeddings, while incremental learning is used to manage concept drift. One key advantage of handling concept drift is that the model can continuously learn new patterns in data streams without needing to fully retrain the model. The model is validated on a curated dataset from various sources with superior performance across all metrics, like accuracy (0.9753) and an F1-score (0.98). It significantly outperforms benchmarks like distilled bidirectional encoder representations from transformers (DistilBERT), LSTM, and valence aware dictionary and sentiment reasoner (VADER). A run of ten iterations validated that the real-time pipeline did not exceed 200 ms in processing and classifying headlines. This signifies the practical viability of our model in fintech applications such as algorithmic trading and risk management.
Volume: 15
Issue: 1
Page: 416-426
Publish at: 2026-03-01

Extension of Hermite-Hadamard type inequalities to Katugampola fractional integrals

10.11591/ijaas.v15.i1.pp1-18
Dipak Kr Das , Shashi Kant Mishra , Pankaj Kumar , Abdelouahed Hamdi
In this study, we introduce several new Hermite-Hadamard type general integral inequalities for exponentially (s,m)-convex functions via Katugampola fractional integral. The Katugampola fractional integral is a broader form of the Riemann–Liouville and Hadamard fractional integrals. We utilized the power mean integral inequality, the H¨older inequality and a few additional generalizations to derive these inequalities. Numerous limiting results are derived from the main results presented in the remarks. Furthermore, we provide an example illustrating our theoretical findings, supported by a graphical representation.
Volume: 15
Issue: 1
Page: 1-18
Publish at: 2026-03-01

Adaptive sugarcane monitoring in Mojokerto using a hybrid powered IoT multi-sensor system and machine learning

10.11591/ijaas.v15.i1.pp384-395
Sekar Sari , Oktavia Citra Resmi Rachmawati , Tole Sutikno
This study develops a hybrid-powered IoT multi-sensor system integrated with machine learning for sugarcane monitoring in Mojokerto. Four sensors—soil moisture, pH, LM35 temperature, and LDR light—are connected to an Arduino UNO R4 WiFi microcontroller. A hybrid power supply (mains electricity and solar panels) and dual data storage (real-time transmission to Google Sheets and local SD backup) ensure resilience and reliability under field conditions. Sensor data are normalized and smoothed prior to analysis using K-Means clustering to map environmental states and a Random Forest classifier to predict crop health. Field validation demonstrates soil moisture as the most influential parameter, followed by temperature, pH, and light intensity. The Random Forest model achieved 93.01% accuracy, 93.88% precision, 99.02% recall, and a 96.38% F1-score on held-out data. By combining hybrid power, multi-sensor integration, dual storage, and machine learning, the system provides robust, data-informed monitoring that supports timely irrigation and management decisions in sugarcane cultivation.
Volume: 15
Issue: 1
Page: 384-395
Publish at: 2026-03-01

Artificial neural network-optimized bridgeless Landsman converter for enhanced power factor correction in electric vehicle applications

10.11591/ijape.v15.i1.pp238-247
Podila Purna Chandra Rao , Radhakrishnan Anandhakumar , T. Vijay Muni , L. Shanmukha Rao
Electric vehicles (EVs) are gaining popularity globally due to their energy-efficient battery storage systems, low carbon emissions, and eco-friendly operation. By transforming both the transportation and electrical sectors, EVs could create a synergistic relationship that reduces fossil fuel use and improves renewable energy integration. However, this convergence emphasizes the necessity for appropriate power factor correction (PFC) methods, especially in EV battery charging systems, to alleviate supply-end PQ concerns. Use of a bridgeless Landsman converter (BLC), noted for its efficiency and link voltage monitoring, is innovative in this research. A proportional-integral (PI) controller tuned by an artificial neural network (ANN) improves prediction and classification, especially response time. The ANN-based PI controller optimises system performance in real time using adaptive control. Using a hysteresis controller attached to a pulse width modulation (PWM) generator regulates the converter's steady-state switching frequency for accurate and consistent output. The proposed approach reduces harmonic distortions and improves operating efficiency. This comprehensive architecture improves power factor and addresses significant PQ concerns in EV charging infrastructure. Integrating improved control tactics and converter design shows that this approach may support electric car technology developments. MATLAB simulations show that power factor correction (PFC) charges EV batteries quickly and effectively. Findings suggest the technique could increase power quality, system efficiency, and EV uptake.
Volume: 15
Issue: 1
Page: 238-247
Publish at: 2026-03-01

Robust hall sensor signal conditioning for BLDC motor control using RC filters and optocoupler isolation

10.11591/ijape.v15.i1.pp373-382
Hasni Anwar , Intidam Abdessamad , El Fadil Hassan , Lassioui Abdellah , El Ancary Marouane , El Asri Yassine
Brushless DC (BLDC) motors require accurate rotor position feedback to guarantee reliable electronic commutation. However, hall-effect sensor signals are often degraded by high-frequency switching noise from the inverter, which can cause false commutations and control errors. Moreover, a direct connection to control hardware may introduce ground loops and jeopardize sensitive electronics. This study proposes a hardware-based hall signal conditioning method that integrates RC low-pass filters, designed with a 1.59 kHz cutoff frequency, to attenuate inverter-induced noise, and 4N35 optocouplers to provide galvanic isolation. Unlike existing approaches that rely primarily on algorithmic noise rejection or digital filtering, the proposed solution offers a compact, low-latency hardware implementation suitable for real-time embedded control. Experimental validation using a dSPACE DS1104 board shows a 14.7 dB improvement in signal-to-noise ratio (SNR) and a 36% reduction in timing jitter, ensuring clean and isolated hall signals for stable six-step commutation. These improvements directly translate into smoother torque production, enhanced speed stability, and increased protection of control electronics, making the method applicable to both research and industrial BLDC motor systems operating in noisy environments.
Volume: 15
Issue: 1
Page: 373-382
Publish at: 2026-03-01

Optimal battery selection for electric vehicles: a comparative ranking approach

10.11591/ijape.v15.i1.pp319-327
Ramana Pilla , Rebba Sasidhar , Malleti Sreedhar , Tentu Papi Naidu , Shaik Rafi Kiran , Vasupalli Manoj , Kalyana Kiran Kumar
Electric vehicles (EVs) have emerged as an eco-friendly alternative to traditional internal combustion engines, with battery technology playing a pivotal role in their success. Key factors like energy density, power output, charging speed, durability, cost, safety, and environmental impact hinge on the choice of battery. Various technologies in lithium-ion batteries are assessed for their suitability in EVs. The right battery is essential for optimal performance, extended range, and sustainability. This paper offers an in-depth look at battery selection in EVs, examining different types in lithium-ion and their pros and cons. Additionally, it explores into three prominent decision-making methods: fuzzy analytic hierarchy process (FAHP), evaluation based on distance from average solution (EDAS), and preference ranking organization method for enrichment evaluation-II (PROMETHEE II). FAHP ranks batteries based on their relevance to specific EV requirements, while EDAS and PROMETHEE II provide a multi-criteria framework. These methods offer valuable insights into choosing the most suitable lithium-ion battery for EVs. The study underscores the importance of meticulous battery selection and highlights the efficacy of decision-making approaches like FAHP, EDAS, and PROMETHEE II. As battery tech advances, future research on alternatives like solid-state and sodium-ion batteries could revolutionize the EV industry.
Volume: 15
Issue: 1
Page: 319-327
Publish at: 2026-03-01

Experimental validation of a trajectory tracking controller for a two-wheeled mobile robot

10.11591/ijra.v15i1.pp33-42
Boualem Kazed , Abderrezak Guessoum
One of the most important and challenging problems of any kind of autonomous mobile robot is the ability to accurately control its onboard actuators, enabling it to fulfill a specified task. In the case of a two-wheeled mobile robot, this can only be achieved through a pair of adequate steering control signals. The main goal of this paper is to design a nonlinear multivariable controller allowing a self-made mobile robot prototype to track a prescribed trajectory. The basic principle of this control approach uses the Lyapunov theory as a primary tool to derive two steering control laws, making a three-state error vector converge to zero. Tuning the proposed controller parameters is carried out using an equivalent dynamic simulated model. This controller is then applied to generate the resulting command signals to the actual robot. This is achieved through a real-time high-speed serial communication between a stationary personal computer (PC), on which a MATLAB/Simulink version of this controller is performing, and an onboard Microchip 16 bits dsPIC33FJ64MC802 microcontroller running a firmware that takes care of all the data exchange with the connected PC and a set of two proportional integral derivative (PID) controllers ensuring that the rotational speeds of the robot wheels are kept very close to those required by the main controller, running on this PC. The performance of the proposed controller is evaluated using two different shaped trajectories. These tests show that the robot is able to gradually follow the required path with minimal lateral error. The robustness of this controller is demonstrated through its capability to reject external disturbances triggered during these experimental tests.
Volume: 15
Issue: 1
Page: 33-42
Publish at: 2026-03-01

Autonomous reconstruction of strip-shredded documents via self-supervised deep learning and global optimization

10.11591/ijra.v15i1.pp107-121
Yi-Chang Wu , Pei-Shan Chiang , Yao-Cheng Liu
Autonomous reconstruction of mechanically shredded documents is a labor-intensive challenge in forensic and archival workflows, particularly for scripts with complex structures such as Simplified Chinese. While traditional manual reassembly is tedious, existing digital tools typically rely on extensive human intervention. This paper presents an automated reassembly framework that integrates a lightweight convolutional feature extractor with global combinatorial optimization. By adapting the established SqueezeNet v1.1 backbone, we employ a task-specific self-supervised learning strategy trained on synthetically shredded samples, enabling the adapted model to capture local stroke continuity and edge-geometry cues without manual annotation. The framework infers pairwise relationships from calibrated edge-region inputs, organizing compatibility scores into an asymmetric traveling salesman problem (ATSP) formulation. The optimal fragment sequence is solved deterministically using the Concorde TSP solver, yielding a globally consistent reconstruction. Experimental results on physically shredded documents demonstrate reconstruction accuracies of 86.5% for Simplified Chinese and 94.8% for Western scripts. These results indicate that the proposed pipeline effectively generalizes from synthetic training data to real-world scenarios, providing a practical, high-throughput foundation for automated document recovery under computational constraints typical of robotic or embedded systems.
Volume: 15
Issue: 1
Page: 107-121
Publish at: 2026-03-01

Real-time low-drift global optimization for dynamic scene LiDAR SLAM localization

10.11591/ijra.v15i1.pp1-20
Peiyan Yang , Jiuyang Yu , Pan Liu , Wenfeng Xia , Yaonan Dai
To address challenges like global drift, unstable matching, and high computational cost in light detection and ranging simultaneous localization and mapping (LiDAR SLAM) under complex conditions, this paper proposes an improved algorithm based on the LeGO-LOAM framework. A Newton-optimized normal distributions transform (NDT) is integrated to improve point cloud registration by constructing a negative log-likelihood objective and optimizing pose estimation. Using initial pose information from LeGO-LOAM accelerates convergence and enhances system robustness. This work addresses the problem of insufficient adaptability of existing algorithms in real scenarios. By deploying an independently designed four-wheel omnidirectional mobile robot platform, a hybrid LiDAR SLAM framework is used for precise positioning and map construction in complex campus environments, successfully reducing the positioning error to the centimeter level. Experiments on the KITTI dataset show a 43.51% reduction in maximum localization error and a 30.83% decrease in average error. Field tests in real-world campus environments with pedestrians, bicycles, and vehicles demonstrate strong reliability, adaptability, and resistance to interference. Horizontal error was reduced by about 58.26%, lowering the average error from 4.60 m to 1.92 m. Although computational load increases, it is offset by using high-performance LiDAR and processors. The enhanced accuracy and drift reduction significantly outperform traditional methods. At critical time points such as 50 seconds and 100 seconds, the system achieved high-precision pose estimation and accurate environmental reconstruction.
Volume: 15
Issue: 1
Page: 1-20
Publish at: 2026-03-01

Vibration control of semi-active suspension system using super-twisting sliding mode controller

10.11591/ijra.v15i1.pp171-180
Liuding Sun , Siti Azfanizam Ahmad , Jun Kit Ong , Suhadiyana Hanapi , Azizan As'arry
The development of suspension systems arises from the impact of vehicle vibrations caused by road irregularities on passengers. Among various suspension systems, semi-active suspension (SAS) is favored for its cost-effectiveness and power efficiency. Magnetorheological (MR) dampers are commonly used in SAS to enhance vibration control by adjusting the magnetic field. However, the traditional sliding mode control (SMC) method often causes chattering, which affects performance. This study proposes the application of a super-twisting sliding mode controller (STSMC) to improve vibration control in SAS and overcome the chattering problem. Simulations and experimental evaluations were conducted on a quarter-car test bench with different road excitations. The results show that the STSMC-based system outperforms the traditional controller in vibration suppression. Specifically, the suppression effect on the root mean square value of body acceleration on a sinusoidal road surface can reach up to 38.2%. Therefore, the STSMC controller demonstrates superior vibration control in SAS systems equipped with MR dampers, providing a valuable reference for future research on SAS vibration control.
Volume: 15
Issue: 1
Page: 171-180
Publish at: 2026-03-01

Multi-modal transformer and convolutional attention architectures for melanoma detection in dermoscopic images

10.11591/ijra.v15i1.pp136-148
Guidoum Amina , Maamar Bougherara , Amara Rafik
The deadliest type of skin cancer, melanoma, requires early and accurate detection for a successful course of treatment. Traditional diagnostic techniques, which rely on visual inspection and dermoscopy, are frequently arbitrary and prone to human error. Automated melanoma detection exemplifies the integration of multimedia, a truly interdisciplinary field that melds visual data processing, human-computer interaction, and digital technologies. This study presents a multi-modal architecture: a multi-modal transformer network (MMTN) and a convolutional attention mechanism multi-modal (CAMM) that combines clinical data and dermoscopy images to enhance melanoma detection. The models achieve higher performance compared to other approaches by utilizing the strengths of architecture based on transformers, an encoder for image processing, dense layers for clinical data also Spatial Attention for the second architecture proposed. We evaluate the models on the entire set of ISIC 2019 data, showing significant improvements in accuracy and AUC. The models achieve high accuracy and AUC using CPU in both architectures. Our findings highlight the potential of a multi-modal learning architecture to enhance clinical decision-making and diagnostic accuracy in dermatology. To our knowledge, this is the first implementation combining MobileNet, transformer encoder attention, and clinical data fusion for the ISIC 2019 dataset, providing a significant advancement in the automated categorization of skin malignancies.
Volume: 15
Issue: 1
Page: 136-148
Publish at: 2026-03-01

An improved black-winged kite algorithm optimized back-propagation neural network for biceps curl classification

10.11591/ijra.v15i1.pp247-256
Chunqing Liu , Kim Geok Soh , Hazizi Abu Saad , Haohao Ma
Accurately identifying and classifying biceps curl types is of vital importance for sports training and upper limb joint rehabilitation training. It can improve the effect and reduce the risk of injury caused by incorrect training. In this study, a dataset of biceps curl training was obtained by measuring wearable sensors. After data preprocessing, 340 samples of 35-dimensional feature data were obtained. The classification labels of the dataset were marked as 1-5 according to the five types of biceps curl. This study proposed a black-winged kite algorithm (IBKA) that uses the good point set (GPS) method and the adaptive spiral search rule, a multi-strategy. IBKA optimized the initial weights, biases, and hidden layer numbers and provided them to the back-propagation neural network (BPNN) to establish the IBKA-BPNN model. The constructed IBKA-BPNN model improved the classification accuracy of the training set from 79.83% to 94.54%, and the accuracy of the test set from 69.61% to 88.33%. The IBKA-BPNN model proposed in this study provides a reliable decision-making basis for real-time coaching, athlete performance analysis, and upper limb rehabilitation. Future work will expand the dataset, integrate more bio signals, and explore lightweight deployment on wearable hardware.
Volume: 15
Issue: 1
Page: 247-256
Publish at: 2026-03-01

A review of human swarm interaction

10.11591/ijra.v15i1.pp80-88
Jan Carlo Barca
A review of recent activities in Human Swarm Interaction (HSI) research is presented in this paper. The paper begins with providing a short description of swarming. It then discusses HSI and explains why it is beneficial to enable human operators to supervise swarms of robots. Then, a wide range of papers, which present novel methods for interacting with swarms of robots, are reviewed. Four control methods that can be used to transmit an operator’s intent to a swarm are also discussed. Levels of autonomy and flexible autonomy in HSI are furthermore described. At the end of the paper, a discussion of the gaps in knowledge that still must be filled to enable swarms of robots to operate in the real world is presented. It is suggested that more research into techniques for remote interaction with robotic swarms be conducted. This includes methods that enable remote interaction with swarms of swarms. More work on HSI in degraded communications environments is also required. Additional research into swarm autonomy is furthermore needed to facilitate efficient supervisory control. Lastly, there is room for more work on trust in HSI, as robotic swarms can only be used by humans if they can be trusted.
Volume: 15
Issue: 1
Page: 80-88
Publish at: 2026-03-01

Development of autonomous quadcopter unmanned aerial vehicle using APM 2.8 flight controller

10.11591/ijra.v15i1.pp63-70
Mohd Yusuf Amran , Mohd Ariffanan Mohd Basri , Aminurrashid Noordin
This paper presents the development of a quadcopter unmanned aerial vehicle (UAV) using the APM 2.8 flight controller as the core of its navigation and control system. The project aims to design, assemble, and evaluate a stable and cost-effective quadcopter platform suitable for basic autonomous flight tasks such as waypoint navigation and altitude hold. The system incorporates essential components, including brushless DC motors, ESCs, a GPS module, a telemetry radio, and a power distribution system, integrated with the APM 2.8 running on the ArduPilot firmware. Waypoints are planned via Mission Planner software, with a flight control system embedded in the firmware. Real-world flight tests were conducted to evaluate the UAV’s performance in executing autonomously predefined survey grid and zigzag waypoints trajectories over open terrain. The root mean square error (RMSE) was calculated to assess the performance of waypoint tracking accuracy. The results show that the quadcopter UAV achieved an RMSE of 1.78 meters during zigzag waypoint tracking and 1.56 meters during survey grid, demonstrating reliable flight control performance offered by the APM 2.8 for basic autonomous mission tasks. This work highlights the feasibility of using APM 2.8 for cost-effective UAV development in research, education, and prototyping purposes.
Volume: 15
Issue: 1
Page: 63-70
Publish at: 2026-03-01
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