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31,456 Article Results

Plant disease sensing using image processing (with CNN)

10.11591/ijict.v15i1.pp93-101
Haresh Rajkumar , Harry Jakin S. , Sudhakar Thirumalaivasal Devanathan , Booapthy Kannan
Plant disease is a significant challenge for agriculture, leading to reduced yield, economic loss, and environmental impact. Leveraging digital photos of plant leaves, convolutional neural networks (CNNs) have emerged as promising tools for disease detection. The methodology involves several steps, including image pre-processing, segmentation, feature extraction using CNNs. Crucially, a diverse dataset comprising images of both healthy and diseased leaves under varying conditions is necessary for training accurate models. Transfer learning, particularly with pre-trained models like ImageNet, can further enhance accuracy, allowing for better performance with fewer training samples. The proposed method demonstrates impressive results, achieving over 95% accuracy, outperforming existing state-of-the-art techniques. This system could serve as a valuable tool for farmers, facilitating timely disease identification and treatment, ultimately leading to increased agricultural yields, reduced financial losses, and the adoption of more sustainable farming practices. Additionally, beyond its practical applications, the proposed system holds promise for advancing sustainable agriculture by promoting environmentally friendly farming methods and contributing to the overall resilience and productivity of agricultural systems.
Volume: 15
Issue: 1
Page: 93-101
Publish at: 2026-03-01

Exploratory data analysis and forecasting of dengue outbreaks in Pangasinan using the ARIMA model

10.11591/ijict.v15i1.pp46-56
Patrick Mole , Thelma Palaoag
Dengue fever remains a critical public health concern in tropical countries like the Philippines, with Pangasinan frequently experiencing outbreaks due to favorable environmental conditions for mosquito breeding. Despite ongoing efforts to control the disease, the absence of a reliable forecasting tool limits the ability of health authorities to implement proactive measures. This study developed a forecasting model using the autoregressive integrated moving average (ARIMA) technique, following an initial exploratory data analysis (EDA) to identify trends and patterns in historical dengue case data from 2019 to 2024. The ARIMA model was trained and validated using historical data, capturing seasonal variations and projecting future dengue outbreaks. The evaluation metrics, including mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), indicated that the model achieved an accuracy of approximately 78.3%, suggesting reasonable predictive capability. Forecasts for the year 2025 indicate a potential rise in dengue cases, particularly during peak seasons, aligning with observed historical trends. These predictions offer valuable insights for local health authorities, enabling them to plan targeted interventions, allocate resources efficiently, and mitigate the impact of future outbreaks. The study demonstrates the practical application of time series analysis in public health forecasting and provides a proactive tool tailored for the needs of Pangasinan.
Volume: 15
Issue: 1
Page: 46-56
Publish at: 2026-03-01

Improving the energy efficiency of two-speed motors through the use of new pole-switched windings

10.11591/ijpeds.v17.i1.pp195-210
Zhanat Issabekov , Dauletbek Rismukhamedov , Khusniddin Shamsutdionov , Shakhobiddin Husanov , Sabit Rismukhamedov , Bibigul Issabekova , Assemgul Zhantlessova
This article addresses the design and manufacturing of two-speed asynchronous motors with pole-changing windings. The need for developing two-speed motors with a single pole-changing winding is justified from the standpoint of energy and resource efficiency, as well as improved starting performance of high-power electric drives. An analysis of existing pole-changing winding designs is presented, highlighting their practical limitations in industrial applications. A new pole-changing winding with a 4/2 pole ratio and 48 stator slots was developed using the discrete spatial functions method based on star–delta–double star configurations. The electromagnetic characteristics of the proposed winding were analyzed. Based on this design, a new 4A200L8/4U3 two-speed motor was manufactured and tested under production conditions at the energy motors plant. Experimental results show that at p1 = 4 pole pairs the motor delivers P2 = 20 kW with efficiency η = 87%, cos φ = 0.82, I1 = 43 A at slip s = 2.35%, while at p2 = 2 pole pairs it develops P2 = 36 kW with efficiency η = 91.5%, cos φ = 0.906, I1 = 66 A at slip s = 1.5%. The results confirm more efficient utilization of the active magnetic core at lower polarity and demonstrate the feasibility of implementing such motors for energy-saving applications in heavy-duty drives requiring two equivalent operating speeds.
Volume: 17
Issue: 1
Page: 195-210
Publish at: 2026-03-01

Towards efficient fog computing in smart cities: balancing energy consumption and delay

10.11591/ijict.v15i1.pp332-342
Ida Syafiza Md Isa , Nur latif Azyze Mohd Shaari Azyze , Haslinah Mohd Nasir , Vigneswara Rao Gannapathy , Ashwini Jayadevan Naidu
In this work, we propose fog-based energy-delay optimization (F-EDO) approach and benchmark its performance against the cloud-based energydelay optimization (C-EDO) method, focusing on energy consumption and delay. Unlike previous studies that optimize energy or delay separately, FEDO minimizes both metrics simultaneously, achieving up to 52.2% energy savings with near-zero delay. Additionally, increasing the number of users also leads to energy savings. This is due to the optimized placement of fog servers at the access layer which reduces network energy consumption compared to C-EDO. F-EDO also significantly reduces delay, with negligible delay compared to C-EDO due to fog servers are placed closer to the users which minimized the transmission distances. Besides, the results also show that the energy saving in F-EDO compared to the C-EDO increased as the processing capacity of the processing server increased while maintaining its minimal delay. Overall, F-EDO proves to be a more energyefficient and lower-delay solution for IoT networks, offering a better alternative to cloud-based offloading.
Volume: 15
Issue: 1
Page: 332-342
Publish at: 2026-03-01

A high linearity low noise amplifier with modified differential inductor for bluetooth profiles

10.11591/ijict.v15i1.pp323-331
Ghattamaneni Usharani , Sourirajan Varadarajan
In today’s rapidly evolving communication landscape, electronic devices rely heavily on high-performance components to ensure seamless connectivity. A low-noise amplifier (LNA) is a critical front-end element in any receiver chain, where its performance significantly influences the overall system efficiency. As integrated circuits continue to shrink with advancements in technology, challenges such as linearity degradation have become increasingly prominent. This work presents a modified derivative (MD) narrowband common source low-noise amplifier (CSLNA) designed using 0.13 µm CMOS technology, offering improved linearity and frequency characteristics. The proposed design adopts a hybrid architecture, combining a folded cascode gain stage with a common-gate configuration. An optimized modified differential inductor is employed at the input for effective impedance matching and reduced noise figure (NF). The implemented LNA achieves a gain of 25.81 dB, an input return loss of –24.86 dB, and maintains a low NF of 0.3 dB at an operating frequency of 2.4 GHz. Furthermore, the linearity metrics-third-order input intercept point (IIP3) and 1 dB compression pointare significantly improved to –16.70 dBm and –21.89 dBm, respectively. These results highlight the LNA's suitability for Bluetooth and other shortrange wireless communication applications.
Volume: 15
Issue: 1
Page: 323-331
Publish at: 2026-03-01

Analytical formulation of relationship between ionization current and extracted ion beam current in a Penning ion source

10.11591/ijpeds.v17.i1.pp629-639
Silakhuddin Silakhuddin , Idrus Abdul Kudus , Bambang Murdaka Eka Jati , Dwi Satya Palupi , Taufik Taufik , Emy Mulyani , Heranudin Heranudin
A study on the performance of the Penning-type internal ion source of the DECY-13 cyclotron has been conducted to evaluate the relationship between cathode current and extracted ion beam current, as well as the stability of the extracted beam. The DECY-13 cyclotron, developed at the Research Center of Accelerator Technology, BRIN, is designed to produce 13 MeV protons for radioisotope production. In the experiment, the cathode current was varied between 200-400 mA, while the magnetic field and extraction voltage at 1.25 T and 3 kV, respectively. The results indicate a clear power-law dependence between cathode current (Ic) and extracted beam current (Iext), expressed as Iext=343.8 Ic^1.42 . This relationship suggests that ionization efficiency increases sharply with cathode current. Stability tests at 400 mA cathode current showed that the extracted beam current remained stable at ~70 μA over 45 minutes, with only minor fluctuations. These findings demonstrate that cathode current is an effective parameter for controlling extracted beam current. The results contribute to a better understanding of ion source behavior in cyclotron systems and provide a foundation for further optimization of Penning ion sources for radioisotope production.
Volume: 17
Issue: 1
Page: 629-639
Publish at: 2026-03-01

Development of machine learning techniques for automatic modulation classification and performance analysis under AWGN and fading channels

10.11591/ijict.v15i1.pp287-301
P. G. Varna Kumar Reddy , M. Meena
Automatic modulation classification (AMC) is essential in modern wireless communication for optimizing spectrum usage and adaptive signal processing. This study explores the use of various machine learning (ML) methods for AMC, focusing on their performance in additive white Gaussian noise (AWGN) and fading channels. This study evaluates of ML classifiers such as support vector machines (SVM), K-nearest neighbors (KNN), decision trees (DT), and ensemble methods with a dataset spanning signalto-noise ratios (SNRs) from -30 dB to +30 dB. Higher-order statistical features including moments and cumulants are used to train the classifiers for AMC. Performance is measured in terms of classification accuracy and computational efficiency across different SNR levels. The findings show that linear SVM, fine KNN, and fine trees consistently achieved high classification accuracy, even at low SNRs. From the analysis, it is observed that linear SVM and fine KNN achieve over 96% accuracy at 0 dB SNR. These classifiers demonstrate significant robustness, maintaining performance in challenging noise conditions. The research highlights the promise of ML techniques in improving AMC, providing a detailed comparison of classifiers and their strengths.
Volume: 15
Issue: 1
Page: 287-301
Publish at: 2026-03-01

GSM based load monitoring system with ADL classification and smart meter design

10.11591/ijict.v15i1.pp74-83
Debani Prasad Mishra , Rudranarayan Senapati , Rohit Kumar Swain , Subhankar Dash , Raj Alpha Swain , Surender Reddy Salkuti
This paper introduces a method for the classification of activities of daily living (ADL) by utilizing smart meter and smart switch data in a synergistic approach. Through the integration of these internet of things (IoT) devices, the paper aims to enhance the application of ADL classification. Guided by recent advancements in load monitoring and energy management systems, the methodology incorporates machine learning techniques to analyze data streams from both the smart meter and smart switch. Drawing inspiration from prepaid smart meter monitoring systems, IoT-based smart energy meters for optimizing energy usage, and energy metering chips with adaptable computing engines, our design incorporates diverse perspectives. Additionally, we consider the utilization of mobile communication for prepaid meters, remote detection of malfunctioning smart meters, and an empirical investigation into the acceptance of IoT-based smart meters. We substantiate our proposed approach through experimental results, showcasing its effectiveness in accurately classifying diverse ADL scenarios. This research contributes to the field of smart home technology by offering an advanced method for ADL classification. The integration of smart meter and smart switch data provides a comprehensive understanding of energy consumption patterns, opening avenues for improved energy management and informed decision-making within smart homes.
Volume: 15
Issue: 1
Page: 74-83
Publish at: 2026-03-01

Design of the wireless EV charger to meet the performance requirement of SAE J2954 standard

10.11591/ijpeds.v17.i1.pp11-24
Patcharapon Kaewnoen , Supapong Nutwong , Nattapong Hatchavanich , Ekkachai Mujjalinvimut
To address the need for a reproducible design process for an efficient wireless electric vehicle (EV) charging system that guarantees compliance with the SAE J2954 standard, this paper proposes a systematic, flowchart-based optimization technique. Unlike methods that focus solely on coil performance, the proposed approach integrates standard-specific constraints, such as inductance and geometric limits, from the outset to ensure the final design meets stringent performance benchmarks for efficiency and misalignment tolerance. A circular flat spiral coil structure has been adopted for both the transmitter and receiver coils to enhance manufacturability and achieve uniform magnetic field distribution. A flowchart-based design technique has been developed to optimize key coil parameters, including the number of turns and coil diameters, subject to constraints of 200 µH inductance and a maximum outer diameter of 700 mm. Finite element analysis (FEA) simulations verify that the proposed design approach achieves maximum magnetic coupling under various air gap distances and misalignment conditions. An experimental validation of a 2-kW prototype demonstrates close agreement with simulations, achieving coil-to-coil efficiencies between 92.61% and 96.67%, and overall system efficiency exceeds 80% under all tested conditions. These results confirm that the proposed design method effectively meets performance requirements set by the SAE J2954 standard.
Volume: 17
Issue: 1
Page: 11-24
Publish at: 2026-03-01

Fetal electrocardiogram extraction and signal quality assessment using statistical method

10.11591/ijict.v15i1.pp217-227
Li Mun Ng , Nur Anida Jumadi , Farah Najidah Noorizan
Abdominal electrocardiogram (aECG) can be used to monitor fetal heart rate (fHR), providing critical insights into fetal health during pregnancy. However, separating the mixed signals of fetal ECG (fECG) and maternal ECG (mECG) within the aECG remains a critical challenge. This paper investigates the integration of statistical metrics, including signal-to-noise ratio (SNR), skewness, kurtosis, standard deviation, and variance to assess fECG signal quality during extraction using three adaptive filtering metods ((Least mean square (LMS), normalized LMS (NLMS), and recursive least square (RLS)) and independent component analysis (ICA). The findings reveal that RLS achieves the best performance among the three AF methods, with the highest SNR of 5.6 dB at the step size, µ of 0.9. For ICA with a bandpass Chebyshev filter (low-cut frequency = 1 Hz, high-cut frequency = 50 Hz) produces an SNR of 0.86 dB. Additionally, both RLS and ICA yield similar fHR values of 133 bpm with a PE measurement of 0.9%. In conclusion, integrating statistical metrics with ICA and RLS effectively extracts fECG with good signal quality. Future research could explore other ECG datasets and incorporate machine learning to further improve fECG extraction and signal quality assessment.
Volume: 15
Issue: 1
Page: 217-227
Publish at: 2026-03-01

DFIG integration with ReLIFT converter for grid-connected systems: ANFIS MPPT control

10.11591/ijict.v15i1.pp21-29
Aravindhan Karunanithy , Chidambararaj Natarajan , Sanjay Selvan Arul Manimaran Malathy , Siva Malavan Elantherayan Sharmila
Although dispersed generation and non-linear loads provide difficulties for contemporary power systems that depend on power electronics, renewable energy sources (RES) are essential for meeting the world’s energy demands. This paper provides a unique method for maximum power point tracking (MPPT) in doubly fed induction generators (DFIG) system using an Adaptive network based fuzzy inference system (ANFIS) inference system. The suggested ANFIS MPPT controller adaptively modifies discontinuous control gain to reduce chattering phenomena in the excitation system while preserving the resilience of the closed-loop system. Prior to using a DQ control theory controller for rotor magnitude adjustment to accomplish vector control of active and reactive power, the turbine and DFIG must be modeled. The converter maximizes output current while striving for unity power factor and allowable harmonic content.
Volume: 15
Issue: 1
Page: 21-29
Publish at: 2026-03-01

State of charge prediction for new and second-life lithium-ion batteries based on the random forest machine learning technique

10.11591/ijpeds.v17.i1.pp487-501
Masoud A. Sahhouk , Mohd Junaidi Abdul Aziz , Mohd Ibthisham Ardani , Nik Rumzi Nik Idris , Tole Sutikno , Bashar Mohammad Othman
Accurate state of charge (SOC) estimation is a critical requirement for the safe and efficient operation of lithium-ion batteries (LIBs), particularly in second-life battery (SLB) applications where battery ageing, nonlinear degradation, and measurement noise introduce uncertainty. Although numerous SOC estimation techniques have been proposed, reliable prediction for new and second-life batteries under varied operating conditions remains challenging. In this study, a comparative investigation of the conventional coulomb counting (CC) method and a data-driven random forest (RF) model is conducted for SOC prediction in new and second-life LIBs. Experimental data are obtained from Murata US18650VTC5D cells under pulse discharge tests (PDT), constant discharge tests (CDT), and dynamic stress tests (DST) across a wide range of C-rates. PDT is conducted at 0.24 C, CDT at 0.2 C, 0.5 C, 1 C, and 2 C, while DST is performed at C-rates ranging from 0.5 C to 4 C at a controlled ambient temperature of 25 °C. The RF model is trained using voltage, current, and time features and evaluated against CC using MAE, MSE, RMSE, and R² metrics. Results show that RF consistently outperforms CC under all conditions, particularly for SLBs, achieving significantly lower errors and R² values approaching 0.998. These findings confirm the effectiveness of RF-based SOC estimation for intelligent battery management systems (BMS).
Volume: 17
Issue: 1
Page: 487-501
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

Design and implementation of a buck converter-based PV emulator using dynamic evolution control

10.11591/ijpeds.v17.i1.pp809-822
Ahmad Saudi Samosir , Dikpride Despa , Herri Gusmedi , Sony Ferbangkara
This paper presents the design, simulation, and experimental implementation of a photovoltaic (PV) emulator based on a buck converter controlled using the dynamic evolution control (DEC) technique. The proposed system accurately reproduces the nonlinear current-voltage (I-V) and power-voltage (P-V) characteristics of a commercial GREEN CELL SM100-18P (100 Wp) PV module under standard test conditions (1000 W/m2, 25 °C). The electrical characteristics of the reference module are embedded in the controller through a lookup table (LUT), which is integrated with the DEC algorithm to enable adaptive real-time regulation of output voltage and current. System modeling and validation are first conducted in MATLAB/Simulink to analyze steady-state and transient performance. A hardware prototype based on an XL4016 buck converter and Arduino Nano microcontroller is then implemented, with real-time monitoring provided via an ILI9341 TFT display. Experimental results show that the emulator achieves a maximum power deviation of 0.8%, a normalized root mean square error (RMSE) of 0.015, a settling time of approximately 12 ms, overshoot below 1.5%, voltage ripple under 2%, and peak conversion efficiency of 94% near the MPP region. These results confirm that the proposed PV emulator provides accurate static and dynamic reproduction of PV characteristics, offering a low-cost, stable, and repeatable platform for laboratory-scale evaluation of PV-related power electronic converters.
Volume: 17
Issue: 1
Page: 809-822
Publish at: 2026-03-01

Development of a mathematical model for electric drive dynamics in belt conveyors: A Simulink-based analysis of transient behavior

10.11591/ijpeds.v17.i1.pp69-81
Khalaf Y. Alzyoud , Jawdat S. Alkasassbeh , Ayman Y. Al-Rawashdeh , Vlademer Е. Pavlov
This paper presents a detailed study of developing a mathematical model and experimental analysis of electric drive processes in belt conveyors. The proposed model simplifies the complex real mechanical system by substituting distributed parameters, such as the transported load's mass and the traction element's elasticity, with concentrated equivalents. A comprehensive investigation of key transient processes including stator currents speed, torque and resistance forces was performed using MATLAB's Simulink environment. The findings reveal significant differences in performance between the initial startup phase and operation under loaded conditions. To validate the model's accuracy, the authors employed numerical analyses utilizing regression metrics such as root mean square error (RMSE) and correlation coefficients. The results show that the proposed model significantly outperforms similar models in the literature with a notable RMSE of 12.5 A for stator current, reflecting an 18% improvement and 8.7 Nm for torque prediction, indicating a 15% enhancement. Furthermore, the model achieved a correlation coefficient of 0.98, confirming its high accuracy in experimental data fitting. By effectively capturing oscillatory phenomena during both unloaded and loaded startup conditions, this work establishes the model as a reliable representation of belt conveyor dynamics, setting a new benchmark in the field.
Volume: 17
Issue: 1
Page: 69-81
Publish at: 2026-03-01
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