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30,938 Article Results

Tracking a person and determining the location by using convolutional neural network technology

10.11591/csit.v7i2.p203-213
Zinah Shiker Makki , Ahmet Zengin
Tracking individuals in real-world environments requires robust, non-intrusive methods that overcome the limitations of device-based systems. This study proposes a convolutional neural network (CNN)-driven person-tracking framework that identifies targeted individuals directly from camera feeds, eliminating the need for wearable or global positioning system (GPS) devices and addressing a major drawback of traditional tracking technologies. The system utilizes a TensorFlow-trained CNN model that can detect, recognize, and locate persons of interest in real-time, even under varying illumination conditions. Unlike conventional approaches, our method integrates facial feature extraction with encrypted identity management, enabling secure multi-person detection and rapid location reporting. Experimental results demonstrate a 92% accuracy in low-light settings and 100% accuracy under normal lighting, confirming the system’s effectiveness for security-oriented applications. The findings highlight the novelty of combining lightweight CNN architecture, real-time facial recognition, and hash-based identity protection within a unified tracking pipeline.
Volume: 7
Issue: 2
Page: 203-213
Publish at: 2026-07-01

Analysis of a compact wideband DGS-inspired octagonal patch antenna for sub-6 GHz 5G and IoT wireless systems

10.11591/ijres.v15.i2.pp479-489
Komalavalli Subramanian , Divya Subramani , Sornalatha Ravindran , Muthu Manickam Anbarasu , Mohan Chinnasamy , Anita Daniel
The proposed compact wideband antenna is developed to meet the increasing demand for efficient and miniaturized radiators in sub-6 GHz fifth generation (5G) and internet of things (IoT) wireless systems. The design features an octagonal radiating patch integrated with modified H-shaped slots to enhance the current path and impedance matching, while a graded defected ground structure (DGS) is introduced to improve bandwidth (BW) and suppress unwanted surface wave effects. Fabricated on an FR4 substrate and energised by a simple stripline feed, the antenna maintains a compact size of 18×15 mm² without compromising performance. It achieves a wide fractional BW of 42.81% spanning 3.1–5.8 GHz, with a resonance centered at 4.6 GHz and obtained reflection coefficient of −36 dB, indicating excellent impedance matching. Additionally, the suggested antenna provides the maximum gain of 3.14 dB and an overall radiation efficiency of 80.5%, demonstrating stable radiation characteristics while making it ideal for small, low-profile 5G and IoT communication devices.
Volume: 15
Issue: 2
Page: 479-489
Publish at: 2026-07-01

An IoT-enabled vision-aid for the blind integrating ultrasonic obstacle detection and GPS-based location tracking

10.11591/ijres.v15.i2.pp386-395
Varuna Kumara , Akshatha Naik , Ashwini Ashwini , Navilgone Krishna Vaishnavi , Ruchitha Kamath Subhashchandra , Trapthi Trapthi
Visual impairment significantly affects independent mobility and personal safety, creating a need for affordable and reliable assistive navigation technologies. This paper presents the design and implementation of a low-cost wearable Vision-Aid system to support visually impaired individuals during outdoor navigation. The primary objective of the study is to enhance obstacle awareness, location tracking, and emergency communication using accessible embedded technologies. The proposed system integrates ultrasonic sensors for real-time obstacle detection, an Arduino microcontroller for data processing, a global positioning system (GPS) module for location tracking, and a global system for mobile communication (GSM) module for emergency alert transmission. Audio feedback is provided through a voice module to guide the user safely. Experimental evaluations were conducted under various environmental conditions to assess obstacle detection accuracy, response time, and location reliability. The results demonstrate accurate obstacle detection, timely audio alerts, and reliable real-time location sharing with caregivers. The proposed system improves user confidence, mobility, and safety while maintaining low implementation cost. This work highlights the potential of embedded and internet of things (IoT)–based assistive devices to enhance autonomy for visually impaired individuals and provides a foundation for future integration of artificial intelligence (AI)-based object recognition.
Volume: 15
Issue: 2
Page: 386-395
Publish at: 2026-07-01

Implementation and design of GPS tracker monitoring system on car rental vehicles based on internet of things using Nodemcu ESP-32

10.11591/csit.v7i2.p214-223
Indah Purnama Sari , Al-Khowarizmi Al-Khowarizmi , Asrar Aspia Manurung
Internet of things (IoT) based vehicle tracking system is an effective solution to overcome various problems in the vehicle rental industry, such as asset loss, route misuse, and late returns. This study aims to design and implement a real-time vehicle position monitoring system using the NodeMCU ESP-32 module integrated with the NEO-6M GPS module and Wi-Fi connectivity to send data to a cloud-based server. This system is designed to display the vehicle position directly through a web-based digital map interface, which can be accessed by vehicle owners anytime and anywhere. The methodology used includes hardware and software design, location accuracy testing, and data integration with a web-based visualization platform using a map API. The test results show that the system is capable of sending vehicle location data with a position accuracy level of up to ±5 meters and data updates every 10 seconds under stable network conditions. In addition, the system has good power efficiency, with an average current consumption of 80–100 mA when active. All data was successfully stored and visualized in real-time using the Google Maps API, and the system was able to operate stably for 24 hours of non-stop testing. Based on these results, the IoT-based GPS tracker system with NodeMCU ESP-32 can be effectively implemented on rental vehicles as a modern monitoring solution that is cost-effective, flexible, and easily accessible. This system provides added value in fleet monitoring and supports faster and data-based decision making.
Volume: 7
Issue: 2
Page: 214-223
Publish at: 2026-07-01

Performance evaluation of the deep learning system for weed recognization

10.11591/csit.v7i2.p167-178
Abd Abrahim Mosslah , Reyadh Hazim Mahdi , Hassan Kassim Albahadily
Numerous approaches based on machine learning have emerged in recent years to enhance crop protection efficiency. One example is the utilization of deep neural networks (DNNs) to differentiate between various weed types in actual events scenarios. Nevertheless, these methods often need substantial input from experts who work iteratively to design the robust deep learning system. To simplify such process and conserve resources, researchers have explored a fresh method known as automated deep learning our technology’s recognization of weeds through the use of machine learning was evaluated using plant seedlings and weed collections from plants dataset to address a issue of weed recognization. The study compared various configurations, including plant segmentation, using a collection of classifiers in place of Softmax, and training with datasets that contain noise. The findings indicated ensuring performance, with F1-scores of 93.1% and 90.2% based on the dataset utilised. These results align together with automated machine learning (AutoML-linked) studies, while fall short of manually fine-tuned deep-learning-based systems created through human specialists. To conclude, exploring the potential of combining manual expert work and automated deep learning could be a promising direction for enhancing efficiency in plant defence.
Volume: 7
Issue: 2
Page: 167-178
Publish at: 2026-07-01

Fuzzy logic–based consensus protocol for educational blockchain networks

10.11591/csit.v7i2.p131-140
Igor Ivanov , Svetlana Zhdanova
This paper addresses the growing challenge of ensuring trust, authenticity, and transparency in the management and verification of educational credentials within modern, digitally oriented learning ecosystems. Rapid expansion of e-learning, lifelong learning, and global mobility has intensified document fraud, revealing the limitations of traditional verification mechanisms. To respond to these systemic risks, the study proposes a socially oriented block-validation protocol integrated into a distributed blockchain environment designed specifically for educational data security. The protocol forms the core of the EduBLOCK system, developed by the authors, and introduces an innovative consensus mechanism that incorporates human-centered reputation assessments rather than computational or financial power. The approach employs fuzzy-set theory to evaluate user activity, institutional credibility, and delegate reputation, enabling a more nuanced and context-sensitive model of trust. Delegates responsible for validating blocks are selected through a dynamic, reputation-driven procedure that excludes financial contributions and subjective parameter tuning. The proposed algorithm combines cryptographic guarantees, peer-to-peer (P2P) communication, and soft-computing methods to ensure fairness, prevent manipulation, and maintain stable system functioning. Block validity is determined through open voting, requiring approval by more than two-thirds of elected delegates.
Volume: 7
Issue: 2
Page: 131-140
Publish at: 2026-07-01

Adaptive fractional-order PID-controlled DVR optimized by zebra algorithm for harmonic suppression

10.11591/ijeecs.v42.i3.pp786-797
Milind Paraye , Rajendra G. Sutar
Dynamic voltage restorers (DVRs) are widely employed to mitigate power quality disturbances in modern power grids. Existing DVR control strategies frequently struggle to adequately suppress harmonic distortions and voltage sags due to nonlinear grid behaviour, rapidly varying disturbances, and limited tuning flexibility. We suggest a grid-connected DVR with an adaptive fractional order proportional integral derivative (FOPID) controller whose parameters are improved using an improved zebra algorithm (IZA) in order to close this gap. The IZA algorithm is used to improve the FOPID controller parameters, ensuring rapid convergence and superior accuracy. The effectiveness of the proposed system is assessed under two different operating conditions. In case 1, the harmonic compensation is analyzed, in which the DVR reduces systemic harmonic disturbances. The results reveal that the proposed controller reduces the total harmonic distortion (THD) from 1.36% to 0.01% while maintaining a constant voltage amplitude of around 0.9986 V, demonstrating strong harmonic suppression capability. Voltage sag mitigation is assessed in Case 2. The load voltage is effectively restored from 0.722 V to 0.9986 V by the DVR, which also reduces THD from 32.97% to 1.6% by injecting the required compensatory current. Overall, the results confirm that the adaptive FOPID–IZA controlled DVR significantly improves power quality and voltage stability in grid-connected systems by effectively mitigating both harmonic distortion and voltage sags.
Volume: 42
Issue: 3
Page: 786-797
Publish at: 2026-06-10

Development of an IoT-based waste monitoring and notification system for smart environmental management

10.11591/ijeecs.v42.i3.pp729-741
Enggar Utari , Ika Rifqiawati , Wahyuni Martiningsih , Izzal Ihasani , Aditya Rahman , Bagus Dwicahyono
Rapid urban population growth has intensified solid waste generation, while many existing waste management systems still rely on manual inspection and single-parameter monitoring, resulting in delayed responses and inefficient handling. Previous studies have primarily focused on isolated sensing or offline monitoring, highlighting the need for integrated, real-time, and user-oriented waste monitoring solutions. This study used RnD method, proposes a smart garbage level and information hub (SIGALIH), an IoT based waste monitoring and notification system designed to address these limitations. SIGALIH combines multi-parameter sensing, including waste level, temperature–humidity, gas concentration, and ambient light, with an ESP32 microcontroller, a cloud-based data platform, and a real-time notification service using a messaging bot. System evaluation involved sensor accuracy testing, communication latency analysis, and functional verification. Experimental results indicate an average sensor accuracy of 96.8%, with an average data transmission latency of 1.84 seconds and a notification delay of 2.14 seconds, indicating reliable real-time performance under varying network conditions. Functional testing confirmed stable operation of all system modules. The system was also integrated into an Environmental Education learning module to support environmental literacy and awareness through contextual learning on sustainable waste management. SIGALIH is designed for small- to medium-scale urban and community-based applications. However, performance depends on wireless network availability, which may reduce reliability in low-connectivity areas. Overall, SIGALIH provides a low-cost, scalable, integrated solution supporting smart environmental management and sustainable urban waste initiatives.
Volume: 42
Issue: 3
Page: 729-741
Publish at: 2026-06-10

Enhanced detection of chronic obstructive pulmonary disease via exhaled breath analysis: internet of things and electronic nose system

10.11591/ijeecs.v42.i3.pp875-883
Nur Hidayah Naimah Harahap , Budi Yanti , Muhammad Ilham , Muhammad Suhaili , Dzakiroh Mufidah Hasibuan , Farah Narizki
Chronic obstructive pulmonary disease (COPD) remains a major global health burden, highlighting the need for accessible, non-invasive screening tools. This study aims to develop a portable, real-time internet of things (IoT)-integrated electronic nose (e-nose) system for COPD detection using exhaled volatile organic compounds (VOCs). Breath samples from 44 participants (healthy, smokers, and COPD) were analyzed using a MOS based e-nose, and four machine-learning classifiers were evaluated. Data were processed through cloud-based pipelines enabling real-time acquisition and automated analysis. The random forest (RF) model achieved the highest performance (accuracy 86%) in distinguishing COPD-related VOC patterns. This approach overcomes limitations of earlier offline Tedlar-bag methods by enabling direct, real-time breath analysis. The prototype dashboard provides immediate visualization for potential remote monitoring. Key limitations include the small sample size and non-standardized breath sampling, which may affect VOC variability. Overall, this work contributes a cost-effective, portable, IoT-enabled framework demonstrating the feasibility of real-time VOC analysis for early COPD screening and future integration into telehealth and community-based diagnostics.
Volume: 42
Issue: 3
Page: 875-883
Publish at: 2026-06-10

Sensor-based prediction of ALS progression: exploring PHI and feature engineering

10.11591/ijeecs.v42.i3.pp835-845
Chibuzor Chukwuemeka Okere , Edwin Thuma , Gontlafetse Mosweunyane
Amyotrophic lateral sclerosis (ALS) is a serious disease that affects nerve and muscle function, with no known cure. Early and accurate monitoring is essen tial to help physicians provide better care. Although machine learning has been applied to predict the progression of ALS, many models struggle with issues such as poor data quality and missing information, which affect accuracy. In this paper, our aim is to improve existing models by introducing better features to enhance prediction performance. A key contribution is the development of a new feature called the physical health index (PHI), which combines four im portant patient attributes: body mass index (BMI), weight, forced vital capacity (FVC), and basal calories. This feature provides a clearer view of the physical health of the patient, enabling the model to learn more effectively. We used the IDPP CLEF 2024 BTO dataset and performed three experiments: using 50 raw features, 29 engineered features, and 25 further engineered features including PHI. The results showed that the R-squared of the XGBoost model improved from 0.9573 to 0.9663 and finally 0.9828, while RMSE decreased from 0.2317 to 0.1801 and then 0.1182 with PHI. This study highlights how targeted feature engineering can improve the prediction of ALS using machine learning.
Volume: 42
Issue: 3
Page: 835-845
Publish at: 2026-06-10

On exploring text mining approaches to sentiment analysis based on the combination of word-based and ontology-based approaches

10.11591/ijeecs.v42.i3.pp827-834
Suthira Plansangket , Supaporn Kansomkeat , Supasit Kajkamhaeng
Currently, sentiment analysis plays an important role in business. Entrepreneurs try to understand customer needs for products and services. If they know about the needs, they can create the marketing plans or strategy plans in their business that help improve products and services. Therefore, this study explores two novel approaches to improve the classification accuracy of sentiment analysis data using a combination of a word-based approach (TF-IDF or CSDF) and an ontology-based approach (ontoSen) to provide two new methods, called ontoTF IDF and ontoCSDF. The experimental results show that CSDF method had the best classification accuracy among all the methods in this study: ontoCSDF did not improve further the classification accuracy of sentiment analysis data. Furthermore, ontoTFIDF method improved the classification by IBk algorithm significantly (p
Volume: 42
Issue: 3
Page: 827-834
Publish at: 2026-06-10

Adaptive fractional-order PID-controlled DVR optimized by zebra algorithm for harmonic suppression

10.11591/ijeecs.v42.i3.pp786-796
Milind Paraye , Rajendra G. Sutar
Dynamic voltage restorers (DVRs) are widely employed to mitigate power quality disturbances in modern power grids. Existing DVR control strategies frequently struggle to adequately suppress harmonic distortions and voltage sags due to nonlinear grid behaviour, rapidly varying disturbances, and limited tuning flexibility. We suggest a grid-connected DVR with an adaptive fractional order proportional integral derivative (FOPID) controller whose parameters are improved using an improved zebra algorithm (IZA) in order to close this gap. The IZA algorithm is used to improve the FOPID controller parameters, ensuring rapid convergence and superior accuracy. The effectiveness of the proposed system is assessed under two different operating conditions. In case 1, the harmonic compensation is analyzed, in which the DVR reduces systemic harmonic disturbances. The results reveal that the proposed controller reduces the total harmonic distortion (THD) from 1.36% to 0.01% while maintaining a constant voltage amplitude of around 0.9986 V, demonstrating strong harmonic suppression capability. Voltage sag mitigation is assessed in Case 2. The load voltage is effectively restored from 0.722 V to 0.9986 V by the DVR, which also reduces THD from 32.97% to 1.6% by injecting the required compensatory current. Overall, the results confirm that the adaptive FOPID–IZA controlled DVR significantly improves power quality and voltage stability in grid-connected systems by effectively mitigating both harmonic distortion and voltage sags.
Volume: 42
Issue: 3
Page: 786-796
Publish at: 2026-06-10

Velocity hemodynamic patterns in aortic valve stenosis: a study of inlet velocity during systole phase

10.11591/ijeecs.v42.i3.pp884-891
Nur’Afifah Yousri , Nabilah Ibrahim , Ishkrizat Taib
This work highlighted a close-up version of the aortic valve that provide detail parameter and clearer graphics compared to the 3D version. Therefore, four simplified models are designed and simulated by using computational fluid dynamics (CFD) which are one healthy valve model (100%) and three stenotic models with varying valve opening (70%, 50%, and 30%). The model dimensions and setup parameters are determined by comparing the healthy aortic valve with the previous data. The analysis focused on two different views, which are the view on a targeting line velocity along the x-axis, and the view at the y-axis around the aortic valve. Results on the evaluation graph at the x-axis and y-axis show significant differences in flow patterns between healthy and aortic valve stenosis. The healthy model of 100% valve opening depicted a lower velocity (m/s) at 1.5m/s compared to the stenotic model of 70%, 50%, and 30% valve opening that showed higher velocities of 3.24 m/s, 6.09 m/s, and 14.57 m/s, respectively, due to the narrowing of the valve opening. Thus, the smallest orifice of the valve produced a higher velocity. This finding highlights the importance of hemodynamic assessment in aortic valve stenosis by providing valuable insight for clinicians in pre-surgical evaluation.
Volume: 42
Issue: 3
Page: 884-891
Publish at: 2026-06-10

Improved interactivity and automated response for visual question answering

10.11591/ijeecs.v42.i3.pp742-752
Nguyen Ha Manh Khang , Nguyen Tuan Anh , Nguyen Minh Hoang , Bui Thanh Hung
Visual question answering (VQA) systems have made substantial progress, yet they still face limitations in handling complex or ambiguous queries and supporting real-time interaction due to reliance on large, computationally expensive models that increase latency and restrict practical deployment, particularly in educational contexts. This study aims to develop an efficient and interactive VQA system that enhances answer accuracy while enabling natural two-way communication with users. To achieve this goal, we propose a lightweight multimodal framework based on pre-trained vision language models such as BLIP and fine-tuning T5, combined with prompt engineering to improve question understanding and answer generation. The system further incorporates conversational context memory and a feedback mechanism that generates clarification questions when user inputs are ambiguous, thereby strengthening interaction capabilities. Experiments are conducted on public benchmark dataset Flickr8k, using single-GPU computational settings to evaluate accuracy, response latency, and interaction effectiveness. The experimental results demonstrate that the proposed approach achieves competitive or superior accuracy compared to heavier baseline models, while significantly reducing inference time and enabling real-time interaction. The main contributions of this work include a lightweight, prompt-driven VQA architecture, an interactive strategy for resolving ambiguous queries, and empirical evidence that efficient models can support accurate and conversational VQA for education and other real world applications.
Volume: 42
Issue: 3
Page: 742-752
Publish at: 2026-06-10

Optimization of glioma segmentation using 3D U-Net++ in MRI surgical planning and patient safety outcomes

10.11591/ijeecs.v42.i3.pp798-808
Ahmed Bounegta , Mustapha Khelifi , Mohammed Beladgham
The main goal of this study is to develop and evaluate a novel 3D U-Net++ convolutional neural network for accurate segmentation of glioma sub regions in MRI scans, aiming to enhance surgical planning, targeted radiotherapy, and patient safety. Precise segmentation of glioma sub-regions is a persistent challenge in neuro-oncology due to substantial morphological variability across patients. To address this, we introduce an automatic segmentation model based on a 3D U-Net++ architecture with dense skip connections, which improves spatial feature extraction and the delineation of tumor boundaries. Utilizing volumetric data from the BraTS 2020 benchmark, the model automatically segments three clinically relevant substructures: tumor core, the enhancing tumor, and whole tumor. The integration of dense connections with 3D convolutional layers facilitates the detection of subtle tissue variations, including necrosis and edema. Quantitative evaluation demonstrates that the proposed 3D U-Net++ surpasses conventional architectures such as standard U-Net and DeepMedic in Dice coefficient, sensitivity, and specificity, yielding more homogeneous and continuous segmentations while reducing manual and semi-automatic annotation efforts. This approach supports advanced clinical decision making and workflow automation, and offers potential for application to other tumor types or integration into real-time clinical practice.
Volume: 42
Issue: 3
Page: 798-808
Publish at: 2026-06-10
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