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29,602 Article Results

Semantic segmentation for data validation in unmanned robotic vehicles

10.11591/ijra.v15i1.pp71-79
Ivan Sunit Rout , P Pal Pandian , Anil Raj , Anil Melwyn Rego , Sajna Parimita Panigrahi
Semantic segmentation is a vital aspect of computer vision, widely used in fields such as autonomous driving, medical imaging, and industrial automation. Maintaining high-quality datasets is crucial for enhancing model accuracy and minimizing real-world errors. This paper focuses on developing a comprehensive data validation pipeline for semantic segmentation using OpenCV. The proposed framework integrates automated integrity checks, preprocessing techniques, and consistency verification to manage large-scale datasets effectively. Key validation processes include image quality assessment (detection of blurriness and noise), verification of annotation accuracy, class distribution analysis, and identification of anomalies. Additionally, OpenCV-powered preprocessing steps, such as image resizing, normalization, contrast optimization, and data augmentation, are applied to refine dataset quality for segmentation models. This paper also addresses scalability concerns associated with processing extensive datasets, introducing optimized batch handling and parallel validation techniques. By implementing a structured validation workflow, this research enhances the reliability, robustness, and overall effectiveness of semantic segmentation models, ensuring high-quality training data for deep learning applications.
Volume: 15
Issue: 1
Page: 71-79
Publish at: 2026-03-01

Design and drag force analysis of an autonomous underwater remotely operated vehicles for coral reef health assessment

10.11591/ijra.v15i1.pp181-189
Pandiyarajan Rajendran , Srinivasan Alavandar
This research presents the conception and building of an inexpensive remotely operated vehicle (ROV) system to ease the tasks of underwater inspection and environmental monitoring in areas where the global positioning system (GPS) signal is not available. A Raspberry Pi-based control unit, an inertial measurement unit (IMU), and depth sensors are merged in the system so that simple data acquisition and remote operation can be carried out. ROV hydrodynamic drag and stability for a state of ideal balance and maneuverability were assessed through tests based on preliminary simulations in Fusion 360 and empirical calculations. The ROV is confirmed to be behaving as expected in terms of stability, imaging capabilities, and responsiveness to operator control in the testing that was done in controlled water environments. This paper, the work, and the testing, in fact, present the initial design, but it is a significant step towards the consideration of the possible further embedding of autonomous features “simultaneous localization and mapping (SLAM)-based navigation, doppler velocity log (DVL), light detection and ranging (LiDAR) systems” for completely autonomous underwater guided missions.
Volume: 15
Issue: 1
Page: 181-189
Publish at: 2026-03-01

Modeling and control of a 3D under-actuated bipedal robot using partial feedback linearization

10.11591/ijra.v15i1.pp122-135
Ali Guessam , Foudil Abdessemed , Abdelmadjid Chehhat
This article presents a dynamic modeling and control framework for a 3D underactuated five-link bipedal robot with 14 degrees of freedom (DoF) and eight actuators. The robot exhibits highly nonlinear, strongly coupled, and hybrid dynamics, posing challenges for conventional control approaches. To address these issues and introduce our research contribution, a partial feedback linearization (PFL)-based tracking framework is proposed, which analytically decouples the system into actuated and unactuated subsystems, enabling efficient real-time control. Unlike hybrid zero dynamics (HZD) methods that enforce virtual constraints online and require offline gait optimization, or model predictive control (MPC) schemes that are online optimization based dependent and computationally demanding, the proposed PFL approach achieves computational simplicity and fast implementation through closed-form control laws. In contrast to zero-moment point (ZMP)-based controllers, PFL enables dynamic underactuated walking with PD feedback for accurate trajectory tracking and disturbance attenuation, though robustness to large uncertainties and disturbances may require additional mechanisms, such as adaptive control, sliding-mode, or fuzzy logic. Simulation results of the applied control method demonstrate the periodic nature and stability of generated walking gaits, which proves the effectiveness and reliability of the proposed control approach.
Volume: 15
Issue: 1
Page: 122-135
Publish at: 2026-03-01

EdgeRetina: Hybrid multimedia architecture for diabetic retinopathy screening on low-cost mobiles

10.11591/ijra.v15i1.pp234-246
Guidoum Amina , Achour Soltana , Maamar Bougherara , Amara Rafik , Mhamed Tayeb
Diabetic retinopathy (DR) is a major cause of preventable blindness, particularly in areas with limited medical resources where access to ophthalmologists is critical. Existing automated solutions struggle to balance clinical performance, cost-effectiveness, and robustness in the face of fundus image variability—including lighting differences, artifacts, and uneven capture quality. To address this challenge, we propose EdgeRetina, an integrated solution for diabetic retinopathy screening on low-cost mobiles. Our approach combines lightweight preprocessing (128×128 resizing, intensity normalization, and targeted augmentations simulating real-world conditions) with a hybrid SqueezeNet-MobileViT architecture (1.4 million parameters), optimized by dynamic threshold calibration (median: 0.3), maximizing clinical utility. Clinically calibrated INT8 quantization reduces the model to 8.27 MB (-92%) without altering diagnostic performance (sensitivity of 90.7% for referable diabetic retinopathies), while preserving compatibility with floating point 32 (FP32)-based gradient-weighted class activation mapping (Grad-CAM) visualizations. Evaluated on the APTOS 2019 dataset, this solution achieves an AUC of 0.96 with a latency (inference time) of 15.43 ms, reducing CPU consumption by 43% compared to FP32. The dynamic threshold/INT8 coupling decreases false positives by 71.4%. This pipeline thus enables accurate, accessible, and early screening of diabetic retinopathy on low-cost mobile devices, combining operational efficiency and diagnostic reliability in constrained environments, which is crucial to prevent avoidable blindness.
Volume: 15
Issue: 1
Page: 234-246
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

An uneven cluster-based routing protocol for WSNs using a hybrid MCDM and max-min ant colony optimization

10.11591/csit.v7i1.p74-82
Man Gun Ri , Pyong Gwang Kim , JinSim Kim
In energy-constrained wireless sensor networks (WSNs) composed of sensor nodes (SNs) characterized by multi-criteria contradictory with each other, it is still one of the challenges to be solved to figure out how to combine multi-criteria with each other and how to use an intelligent optimization (IO) algorithm for developing an optimal cluster-based routing protocol. In this article, we overture a new routing protocol based on uneven cluster using the hybrid FCNP-VWA-TOPSIS (FVT) and an improved max-min ant colony optimization (ACO). This scheme uses the hybrid FVT to perform the clustering, and uses an improved max-min ACO to configure a routing tree for the relay transmission of sensed data. The extensive simulation experiments have been carried out to show that the proposed scheme greatly prolongs the network lifetime (NL) by achieving an energy consumption balance superior to the previous schemes.
Volume: 7
Issue: 1
Page: 74-82
Publish at: 2026-03-01

Optimizing interconnection call routing: a machine learning approach for cost and quality efficiency

10.11591/csit.v7i1.p56-65
Ivy Anesu Mudari , Mainford Mutandavari , Kenneth Chiworera
This study presents the design and development of an automated least cost routing (LCR) model for telecommunications interconnection calls using machine learning. Leveraging a random forest regressor, the model predicts the most cost-effective call routing path based on pricing and network latency. Trained on real-world call detail records (CDRs) from TelOne Zimbabwe, the model achieved a high R² score of 0.851, with a mean absolute error (MAE) of $0.0482 per minute. Evaluation results demonstrate an average cost reduction of 46.75% compared to traditional routing methods, with prediction times under 0.1 seconds and latency remaining within acceptable thresholds. This work provides a practical, scalable, and efficient solution for telecom. operators seeking to reduce interconnection costs and maintain service quality through intelligent routing automation. The model architecture and performance to make it viable for integration into real-time telecom infrastructure.
Volume: 7
Issue: 1
Page: 56-65
Publish at: 2026-03-01

Raindrop and bit drop effects on millimeter wave network performance: a critical review

10.11591/csit.v7i1.p83-92
Victor Dela Gordon , Amevi Acakpovi , George Kwamena Aggrey , Michael Gameli Dziwornu
This PRISMA guided review examines how rain precipitation degrades 5G millimeter wave (mmWave) network performance, with emphasis on rain induced bit drop and its impact on end-to-end quality of service (QoS). From an initial corpus of 13,317 publications screened across IEEE Xplore, ACM Digital Library, ScienceDirect, Google Scholar, and ELICIT, 18 peer reviewed studies published between 2018 and 2024 met the inclusion criteria. Findings show that rainfall significantly weakens mmWave signals, with specific attenuation ranging from approximately 4 to 45 dB/km at 100 mm/h, particularly in tropical regions. When QoS outcomes are reported, these losses manifest as increased bit error rates, rain driven bit drop along the link, higher packet loss and delay, and reduced throughput. Key deficiencies identified include limited empirical validation of attenuation models against packet level QoS, lack of standardized propagation datasets for short range links, and weak treatment of bit level impairments within QoS analysis. To address these gaps, the review recommends enhancing ITU R P.530 and Mie scattering models with region specific measurements, implementing rain aware adaptive protocols, and adopting standardized benchmarking frameworks that link rain attenuation, bit drop, and QoS. This synthesis offers guidance for building climate aware mmWave systems and positions bit drop as a practical metric for precipitation resilience assessment.
Volume: 7
Issue: 1
Page: 83-92
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

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

Car selection in games using multi-objective optimization by ratio analysis based on player achievement

10.11591/csit.v7i1.p30-45
Caesar Nafiansyah Putra , Fresy Nugroho , Mochamad Imamudin , Dwi Pebrianti , Jehad Abdelhamid Hammad , Tri Mukti Lestari , Dian Maharani , Alfina Nurrahman
The selection menu in some racing games usually uses a random system for vehicle selection. However, this random feature generally randomizes the selection of the index without considering factors that support the player's abilities. Therefore, this study aims to develop a racing game that can suggest vehicles that have been adjusted to the player's performance. Vehicle recommendations are made using the multi-objective optimization on the basis of ratio analysis (MOORA) method as its method. The MOORA calculation ranks vehicles based on criteria such as mileage, fuel efficiency, speed, agility, and others collected in previous games. The results of this study show the effectiveness of using the MOORA method in recommending vehicles that match the player's skills, thereby improving the overall player experience. In addition, the usability test produced a system usability scale (SUS) score of 82.4, so it is included in the very good category.
Volume: 7
Issue: 1
Page: 30-45
Publish at: 2026-03-01

Analysis of congestion management using generation rescheduling with augmented Mountain Gazelle optimizer

10.11591/ijict.v15i1.pp57-65
Chidambararaj Natarajan , Aravindhan Karunanithy , S. Jothika , R. P. Linda Joice
This study presents an original blockage of the executive’s approach utilizing age rescheduling with the augmented mountain gazelle optimizer (AMGO). Enlivened by the versatility of mountain gazelles, AMGO is applied to enhance age plans for a reasonable power framework situation. The strategy successfully mitigates clogs, taking into account functional imperatives, market elements, and vulnerabilities. Recreation results show AMGO’s heartiness, seriousness, and proficiency in contrast with existing strategies. Notwithstanding its heartiness in blockage the board, the AMGO presents a state-of-the-art versatile element, enlivened by the spryness of mountain gazelles, empowering constant changes in accordance with developing power framework conditions and contrasted and genetic algorithms and PSO. The review adds to propelling streamlining methods for clogging the executives, offering a promising device for improving power framework, unwavering quality and productivity.
Volume: 15
Issue: 1
Page: 57-65
Publish at: 2026-03-01

Enhancing intellectual property rights management through blockchain integration

10.11591/ijict.v15i1.pp111-119
Raghavan Sheeja , Sherwin Richard R. , Shreenidhi Kovai Sivabalan , Srinivas Madhavan
The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.
Volume: 15
Issue: 1
Page: 111-119
Publish at: 2026-03-01

Classification and regression tree model for diabetes prediction

10.11591/ijict.v15i1.pp207-216
Farah Najidah Noorizan , Nur Anida Jumadi , Li Mun Ng
Diabetes mellitus is characterized by excessive blood glucose that occurs when the pancreas malfunctions while producing insulin. High blood glucose levels can cause chronic damage to organs, particularly the eyes and kidneys. Diabetes prediction models traditionally use a variety of machine learning (ML) algorithms by combining data from the glucose levels, patient health parameters, and other biomarkers. Prior research on diabetes prediction using various algorithms, such as support vector machine (SVM) and decision tree (DT) models, demonstrates an accuracy rate of approximately 70%, which is relatively modest. Therefore, in this study, a classification and regression tree (CART) multiclassifier model has been proposed to improve the accuracy of diabetes prediction, which is based on three classes: non-diabetic, pre-diabetic, and diabetic. The study involved data preprocessing steps, hyperparameter tuning, and evaluation of performance metrics. The model achieved 97% accuracy while utilizing the value of 5 for the number of leaves per node, the value of 10 for the maximum number of splits, and deviance as the split criterion, which also resulted in a precision of 98%, recall of 97%, and F1-score of 98%, showing that the proposed multiclassifier model can accurately predict diabetes. In conclusion, the proposed CART model with the best hyperparameter setting can enable the highest accuracy in predicting diabetes classes.
Volume: 15
Issue: 1
Page: 207-216
Publish at: 2026-03-01

Reputation-enhanced two-way hybrid algorithm for detecting attacks in WSN

10.11591/ijict.v15i1.pp428-437
Divya Bharathi Selvaraj , Veni Sundaram
Wireless sensor networks (WSNs) are susceptible to a variety of attacks, such as data tampering attacks, blackhole attacks, and grayhole attacks, that can affect the reliability of communication. We proposed a reputationenhanced two-way hybrid algorithm (RCHA) that uses cryptographic hash functions and reputation-based trust management to detect and de-escalate attacks accurately. The RCHA algorithm implements two hash functions RACE integrity primitives’ evaluation message digest (RIPEMD) and secure hash algorithm (SHA-3), to initiate the integrity check for the entire packet sent across the network. Every node in the WSN tracks a reputation score for each neighbor the node is connected to, and this score is dynamically updated based on the behavior of each neighbor. If a neighboring node’s reputation drops below a threshold, the node is sent a maliciousness designation. At that time, the node will broadcast an alert message to its neighboring nodes and begin to reroute its data through one of its trusted neighbors to ensure the reliability of the communication. The simulation results reported that the RCHA algorithm improved the accuracy of the attack detection rate and the number of packets delivered compared to traditional attack detection methods. The RCHA algorithm was able to maintain low computational and energy overhead for the WSN, making it an attractive option for a resource-constrained application in a WSN. Given the trends towards more collaborative networks, the reputation mechanism in the RCHA algorithm improves the overall reliability and capabilities of the WSN, regardless of adversaries.
Volume: 15
Issue: 1
Page: 428-437
Publish at: 2026-03-01
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