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29,167 Article Results

6G internet of things networks for remote location surgery also a review on resource optimization strategies, challenges, and future directions

10.11591/ijece.v15i6.pp5968-5977
Md Asif , Tan Kaun Tak , Pravin R. Kshirsagar
Remote location surgery presents stringent requirements for wireless communication, particularly in terms of reliability, speed, and low latency. The emergence of sixth-generation (6G) wireless networks is expected to address these challenges effectively. With the rapid expansion of internet of things (IoT) applications in healthcare, maintaining real-time connectivity has become essential. Ensuring such performance in 6G-enabled IoT networks relies heavily on the implementation of advanced resource optimization techniques. Recent studies have focused on improving key performance metrics, including latency, reliability, energy efficiency, spectral efficiency, data rate, and bandwidth usage. Comprehensive reviews of these techniques reveal a growing emphasis on multi-objective optimization strategies to balance conflicting requirements. Research has also highlighted limitations in existing approaches, suggesting the need for further innovation, particularly for mission-critical applications like remote surgery. Within this context, 6G IoT systems have demonstrated the potential to maintain high data rates and stable throughput, both of which are essential for safe and responsive surgical operations conducted over long distances. These findings underscore the importance of continued development in resource management to fully enable remote healthcare delivery through advanced wireless technologies.
Volume: 15
Issue: 6
Page: 5968-5977
Publish at: 2025-12-01

Image-based assessment of cattle manure-induced soil erosion in grazing systems

10.11591/ijece.v15i6.pp5360-5370
Cristian Gómez-Guzmán , Yeison Alberto Garcés-Gómez
Extensive livestock farming significantly impacts soil erosion, necessitating accurate monitoring and assessment to mitigate environmental damage and enhance sustainable pasture management. This study employs unsupervised classification of high-resolution drone imagery to detect and quantify soil erosion associated with cattle manure in pastures, focusing on evaluating classification algorithms, identifying relevant spectral and textural features, and quantifying the extent and severity of erosion. The results demonstrate the effectiveness of unsupervised classification in identifying erosion zones and their impact on soil health and water quality. Field validation confirms the accuracy of the analysis, emphasizing the need for sustainable management practices such as controlled manure redistribution and soil conservation to mitigate erosion and protect natural resources. This approach offers practical tools for mitigating the environmental impacts of semi-extensive livestock farming and promoting more sustainable management. The findings provide practical recommendations for sustainable pasture management, contributing to environmental conservation and the long-term health of live-stock systems.
Volume: 15
Issue: 6
Page: 5360-5370
Publish at: 2025-12-01

Integrity verification of medical images in internet of medical things for smart cities using data hiding scheme

10.11591/ijece.v15i6.pp5770-5781
Kilari Jyothsna Devi , Ravuri Daniel , Bode Prasad , Mohamad Khairi Ishak , Dorababu Sudarsa , Pasam Prudhvi Kiran
As technology has advanced, the internet of medical things (IoMT) has become incredibly useful. It is used to transmit a wide variety of medical images. Sensitive patient data may be altered during transmission or subject to illegal access. To overcome all of these challenges and preserve the integrity of medical images while transmission over IoMT, a blind region-based data concealing approach called medical image watermarking (MIW) is suggested. The region of interest (ROI) and region of non-interest (RONI) are the two sections that make up the medical image. The aim of the suggested MIW technique is to prevent transmission-related manipulation of medical image ROI. To provide high imperceptibility and resilience, confined integrity verification and recovery bits (CIVRB) bits are embedded in the RONI using hybrid integer wavelet transform–singular value decomposition (IWT-SVD). According to the experimental results, the suggested system is highly imperceptible (average peak signal-to-noise ratio (PSNR)=56dB), robust (average NC=0.99), and exhibits integrity verification accuracy of over 98% against a variety of image processing attacks. In terms of several watermarking properties, the proposed technique performs over state-of-the-art schemes. This method offers a dependable framework for protecting medical images in real-time IoMT applications and is suitable for smart healthcare environments.
Volume: 15
Issue: 6
Page: 5770-5781
Publish at: 2025-12-01

Combination of rough set and cosine similarity approaches in student graduation prediction

10.11591/ijece.v15i6.pp6001-6011
Ratna Yulika Go , Tinuk Andriyanti Asianto , Dewi Setiowati , Ranny Meilisa , Christine Cecylia Munthe , R. Hendra Kusumawardhana
Higher education institutions must deliver high-quality education that produces graduates who are knowledgeable, skilled, creative, and competitive. In this system, students are a vital asset, and their timely graduation rate is an important factor to consider. In the department of computer science, a challenge arises in distinguishing between students who graduate on time and those who do not. With a low on-time graduation rate of just 1.90% out of 158 graduates, this issue could negatively affect the institution's accreditation evaluation. This research employs the Case-Based Reasoning method, enhanced with an indexing process using rough sets and a prediction process utilizing cosine similarity. The testing, conducted using k-fold validation with 60%, 70%, and 80% of the data, produced average accuracy rates of 64.2%, 66.3%, and 65.6%, respectively. The test results indicate that the highest average accuracy of 66.3% was achieved with 70% of the cases.
Volume: 15
Issue: 6
Page: 6001-6011
Publish at: 2025-12-01

Enhancing semantic segmentation with a boundary-sensitive loss function: a novel approach

10.11591/ijece.v15i6.pp5327-5335
Ganesh R. Padalkar , Madhuri B. Khambete
Semantic segmentation is crucial step in autonomous driving, medical imaging, and scene understanding. Traditional approaches leveraging manually extracted pixel properties and probabilistic models, have achieved reasonable performance but suffer from limited generalization and the need for expert-driven feature selection. The rise of deep learning architectures has significantly improved segmentation accuracy by enabling automatic feature extraction and capturing intricate object details. However, these methods still face challenges, including the need for large datasets, extensive hyperparameter tuning, and careful loss function selection. This paper proposes a novel boundary-sensitive loss function, which combines region loss and boundary loss, to enhance both region consistency and edge delineation in segmentation tasks. Implemented within a modified SegNet framework, the approach proposed in the paper is evaluated with the semantic boundary dataset (SBD) dataset using standard segmentation metrics. Experimental results indicate improved segmentation accuracy, substantiating to proposed method.
Volume: 15
Issue: 6
Page: 5327-5335
Publish at: 2025-12-01

Intuitive effectiveness degree of research methodologies for spectrum sensing in cognitive radio network

10.11591/ijece.v15i6.pp5699-5707
Pushpa Yellappa , Dr.Keshavamurthy Keshavamurthy
The phenomenon of spectrum sensing plays an essential role in cognitive radio network (CRN) that is performed in real-time for better adaptability to dynamic usage of spectrum. However, efficient decision-making is often noted to be affected by dynamic environmental condition, interference, and noise leading to declination in performance. In recent times, there are proposals for various methodologies addressing such issues targeting towards improving spectrum sensing along with machine learning and energy detection approach, which is gaining its pace for technical research implementation. Irrespective of this advancement, ambiguity shrouds regarding the contrast effectiveness associated with these methods and their appropriateness in different situation. Hence, this manuscript presents a comprehensive and yet crisp review work to offer concise assessment of latest methodologies towards spectrum sensing used in CRN ecosystem. The paper has an inclusion of existing techniques, presents their potentials and shortcomings, exhibited evolving trends of research, extracts key gaps and challenges. The prime intention of this review work is towards guiding the future researchers and scholars by facilitating deeper insight towards the recent state of technologies in spectrum sensing.
Volume: 15
Issue: 6
Page: 5699-5707
Publish at: 2025-12-01

Prospective classroom teachers’ views on instructional technologies and web-based digital educational tools

10.11591/ijere.v14i6.34918
Görkem Avcı , Elvan Subaşıoğlu
This study examined prospective classroom teachers’ perceptions of instructional technologies and the web-based digital tools they actively use. Using a case study design with semi-structured interviews, data were collected from 15 prospective teachers who had completed an instructional technology course. The findings show that participants strongly emphasized the necessity of technology integration in education. The most commonly used tools included assessment, visual–infographic design, coding, drawing–shaping, augmented and virtual reality, animation, interactive presentations, and artificial intelligence. These tools were found to significantly support effective and efficient learning, enhance motivation, and promote sustainable learning. Accordingly, the study recommends the systematic use of web-based digital tools to support digital transformation in education.
Volume: 14
Issue: 6
Page: 5219-5228
Publish at: 2025-12-01

Design and implementation of solar-grid based charging station for electric vehicle with fault detection method using R-Pi and IoT processor

10.11591/ijape.v14.i4.pp794-802
M. Vaigundamoorthi , S. Karthick , V. S. Chandrika , D. Chithra , K. V. Balaramakrishna , K. Lakshmi Khandan , Lakshmana Phaneendra Maguluri , S. Chandrasekar , M. Janarthanan
In this research describes the electrical vehicle (EV) charging station using PV panel with fault detection methods. The PV modules will failure for some time, because of some external factors and internal factors. In direct fault condition the monitor and analyze the external factors such as the life span, high intensity and breakage of the PV panels using Raspberry Pi (R-Pi) processor with internet of things (IoT) system. In power demand/day on the PV panel will be evaluated and analyzed through R-Pi processor and IoT. The efficiency and the range values of the PV panels will be monitored and analyzed through IoT. Proposed work explains, how the fault detection techniques have been improved and adopted in using R-Pi processor through IoT platform. The proposed dataset pre-processing system is incorporated with IoT module. The grid fault clearing time will be compared with the actual values through R-Pi processor. The PV panel faults are detected using thermal image processing, that image parameter values analysis through IoT based internal monitoring system.
Volume: 14
Issue: 4
Page: 794-802
Publish at: 2025-12-01

Smart wearable glove for enhanced human-robot interaction using multi-sensor fusion and machine learning

10.11591/ijece.v15i6.pp5162-5172
Nourdine Herbaz , Hassan El Idrissi , Hamza Sabir , Abdelmajid Badri
Hand gesture recognition (HGR) using flexible sensors (flex-sensor) and the MPU6050 sensor has proved to be a key area of research in human-machine interaction, with major applications in biasing, rehabilitation, and assisted robotics. This paper proposes a wearable intelligent glove designed to operate a robotics arm in real time, relying on multi-sensor fusion and machine learning methods to enhance the system's responsiveness and precision. The proposed system enables the intuitive reproduction of hand movements and precise control of the robotic arm. In the context of Industry 4.0 and internet of things (IoT), the classification of gestures is necessary for maintaining operational efficiency. To guarantee gesture recognition, data signals from the smart glove are collected and trained by a recurrent neural network (RNN), which achieves 98.67% accuracy for real-time classification of seven gestures. Beyond industrial applications, the wearable smart glove can be exploited in a recognized circuit of all systems, including rehabilitation exercises that involve recording the progression of muscular activity for the assessment of motor functions and serve as a tool for patient recovery.
Volume: 15
Issue: 6
Page: 5162-5172
Publish at: 2025-12-01

Design and analysis of a new scheme of the FOSTA for DFIG based wind turbine

10.12928/telkomnika.v23i6.27222
Kheira; Tahar Moulay University of Saida Belgacem , Houaria; Tahar Moulay University of Saida Abdelli , Mebarka; Tahar Moulay University of Saida Atig , Abdelkader; Tahar Moulay University of Saida Mezouar
An super-twisting algorithm (STA)-based controller was designed and implemented in this study to achieve precise control over the stator active and reactive power of a doubly fed induction generator (DFIG)-equipped wind turbine device. The fractional calculus theory (FCT) allowed the STA to maximize its effectiveness and performance. A distinct form is sent to the FCT-based STA controller. The stator flux orientation technique uses control that is independent of stator active and reactive powers. In order to achieve a quick system with sufficient precision and a robust control strategy, the hybrid method control is based on the fractional-order super twisting algorithm (FOSTA) and FCT. To demonstrate the performance, efficacy, and resilience of the stated nonlinear approach, a number of simulations are provided.
Volume: 23
Issue: 6
Page: 1696-1705
Publish at: 2025-12-01

The effectiveness of bentonite in reducing soil resistance in acidic water swampland

10.12928/telkomnika.v23i6.27094
Dian; Universitas Sriwijaya Eka Putra , Muhammad; Sriwijaya University Irfan Jambak , Zainuddin; Sriwijaya University Nawawi
This study aims to evaluate the effectiveness of bentonite mixtures in reducing grounding resistance in acidic swampy areas. The method used is an experiment comparing resistance before and after the addition of bentonite in various compositions (25%, 50%, 75%, and 100%), supplemented with linear regression analysis. The results showed that bentonite significantly reduced soil resistance in three types of electrodes: iron rebar, copper-coated iron, and galvanised iron. The highest reduction in resistance was achieved in iron rebar electrodes, from 35.93 Ω to 22.46 Ω (a 37% reduction) with the addition of 25% bentonite. Linear regression analysis showed a consistent negative relationship between the percentage of bentonite and grounding resistance, with a coefficient of determination (R²) varying between 26.40% and 73.39%. These findings indicate that bentonite is effective as a natural grounding material in acidic swampy areas. This research makes an important contribution to the development of more efficient and safer electrical systems in swampy areas and challenging environments, while also supporting the use of natural materials to reduce dependence on synthetic chemicals.
Volume: 23
Issue: 6
Page: 1657-1665
Publish at: 2025-12-01

Performance enhancement of PV generator using a sensor based dual axis solar tracking system in Algeria

10.12928/telkomnika.v23i6.26872
Sakina; Udes/Centre De Développement des Énergies Renouvelables (CDER) Atoui , Harb; University of Algiers 1 Benyoucef Benkhedda Hadjer , Belaïd; University of Algiers 1 Benyoucef Benkhedda Abdelghani
This article presents the implementation of a two-axis solar tracking system and its impacts to increase the performance of the photovoltaic system in northern Algeria. The system enhances the efficiency of solar systems by optimizing their exposure to sunlight making the sunbeam perpendicular to solar panel. The main objective of the study is to develop a technically proficient and economically viable solution to increase solar energy production. The design relies on integrating light sensors and motors controlled by an Arduino board, enabling automatic adjustment of solar panel positions. This approach offers dynamic and precise orientation, based on light dependent resistor (LDR) sensor design and threshold value, resulting in a significant increase in energy output. The results show that the dual-axis solar tracking system can capture 60.64% more solar energy, taking into account the power consumption of the two electric actuators. The findings of this study will positively influence the promotion of clean and sustainable energy sources while providing a practical solution for more efficient utilization of solar energy in Algeria.
Volume: 23
Issue: 6
Page: 1706-1717
Publish at: 2025-12-01

Object detection and tracking with decoupled DeepSORT based on αβ filter

10.12928/telkomnika.v23i6.27500
Lakhdar; University of Sciences and Technology of Oran (USTO-MB) Djelloul Mazouz , Abdessamad; University of Sciences and Technology of Oran (USTO-MB) Kaddour Trea , Tarek; University of Sciences and Technology of Oran (USTO-MB) Amiour , Abdelaziz; University of Sciences and Technology of Oran (USTO-MB) Ouamri
With the rapid growth of the population, the demand for autonomous video surveillance systems has substantially increased. Recently, artificial intelligence has played a key role in the development of these systems. In this paper, we present an enhanced autonomous system for object detection and tracking in video streams, tailored for transportation and video surveillance applications. The system comprises two main stages: detection stage; this stage employs you only look once (YOLO)v8m, trained on the KITTI dataset, and is configured to detect only pedestrians and cars. The model achieves an average precision of 97.3% and 87.1% for cars and pedestrians classes respectively, resulting a final mean average precision (mAP) of 92.2%. Tracking stage; the tracking component utilizes the DeepSORT algorithm, which originally incorporates a Kalman filter for motion prediction and performs data association using cosine and Mahalanobis distances to maintain consistent object identifiers across frames. To improve tracking performance, we introduce two key modifications to the original DeepSORT: architecture modification and Kalman filter replacement. The tracking tests are carried out on KITTI and MOTChallenge Benchmarks. The final order tracking accuracy (HOTA) scores achieve 77.645 and 54.019 for Cars and Pedestrians classes respectively in the KITTI-Benchmark and 45.436 for the Pedestrians class in the MOTChallenge-Benchmark.
Volume: 23
Issue: 6
Page: 1729-1742
Publish at: 2025-12-01

Novel fractional order sinusoidal oscillators using operational trans resistance amplifier

10.12928/telkomnika.v23i6.27250
Battula; University College of Engineering Kakinada Tirumala Krishna , Vanitha; GITAM University Kakollu , Manchala; Jawaharlal Nehru Technological University Kakinada Madhusudhan Prasad
The design of fractional order circuits in very large-scale integration (VLSI) domain is gaining the interest of many researchers. At the same time design of fractional circuits using the current mode devices is attracting the research community. In this paper, several possible fractional order sinusoidal oscillators using operational trans resistance amplifier (OTRA) as a basic building block is presented. The necessary condition for the frequency of oscillation and condi tion for oscillations is derived. Fractional order operator sα is the most crucial one to be approximated. In this paper, the fractional order element is approxi mated by the continued fraction expansion (CFE). The approximation is carried out up to fifth order. The circuits are tested with the simulation software named LTspice. The results agree with the theoretical one. The proposed circuits of fers a frequency of 15 MHz, 20 MHz, and 25 MHz which is higher in value as compared to the existing circuits. The proposed circuits finds applications in bio medical, communication circuits.
Volume: 23
Issue: 6
Page: 1635-1645
Publish at: 2025-12-01

Exploring cookies vulnerabilities: awareness, privacy risks and exploitation

10.11591/ijece.v15i6.pp5792-5803
Nor Anisah Amir Hamzah , Anis Safiyyah Adnan , Norsaremah Salleh
This study investigates cookie vulnerabilities, focusing on awareness, privacy risks, and exploitation techniques. We used a mixed-method approach that combines insights from a survey study and a systematic mapping study of 27 papers from online databases to comprehensively address the research topic. The results show a moderate level of user awareness about cookie-related privacy risks, with significant concerns over user tracking and profiling, identified in 88% of the reviewed studies. Key risks include sensitive data exposure, privacy and consent issues, targeted advertising, ineffective mitigation measures, and cyberattacks. Tracking via cookies, and especially third-party cookies were found to pose the greatest risk to end-users. Their widespread use for cross-site tracking and extensive fingerprinting often occurred without users’ awareness or explicit consent. These insights suggest the need for stricter privacy laws, better practices on cookies, and improved user awareness to mitigate concerning risks.
Volume: 15
Issue: 6
Page: 5792-5803
Publish at: 2025-12-01
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