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31,042 Article Results

Efficient design of approximate carry-based sum calculating full adders for error-tolerant applications

10.11591/ijict.v14i3.pp1189-1198
Badiganchela Shiva Kumar , Galiveeti Umamaheswara Reddy
Approximate computing is an innovative circuit design approach which can be applied in error-tolerant applications. This strategy introduces errors in computation to reduce an area and delay. The major power-consuming elements of full adder are XOR, AND, and OR operations. The sum computation in a conventional full adder is modified to produce an approximate sum which is calculated based on carry term. The major advantage of a proposed adder is the approximation error does not propagate to the next stages due to the error only in the sum term. The proposed adder was coded in verilog HDL and verified for different bit sizes. Results show that the proposed adder reduces hardware complexity with delay requirements.
Volume: 14
Issue: 3
Page: 1189-1198
Publish at: 2025-12-01

Optimization of principal component analysis and k-nearest neighbors in cultivation area classification red onion

10.12928/telkomnika.v23i6.27103
Arif; Politeknik Negeri Medan Ridho Lubis , Purwa; Politeknik Negeri Medan Hasan Putra , Fahdi; International Islamic University Malaysia Saidi Lubis
This research aims to increase the effectiveness in classifying shallot cultivation areas through the combined application of principal component analysis (PCA) and k-nearest neighbors (KNN) methods. Shallot is an important agricultural commodity, and identification of optimal areas for its cultivation is essential to support food self-sufficiency. Onion cultivation is generally done in the highlands. One of the areas with shallot cultivation in North Sumatra Province is Berastagi, Karo Regency. This research was conducted by determining the spatial extent of upland land. In the use of data there are 2 types of data that will be used: land suitability dataset and land condition dataset for each region. The PCA method is utilized to simplify the data structure by reducing the number of dimensions and removing insignificant attributes, while KNN was used to classify regions based on their suitability for shallot cultivation. This research produces a classification map that can be used to identify the most optimal areas for shallot cultivation. The test results with the regional spatial dataset using precision, recall and fi-score testing accuracy value 0.92%, and macro avg value 0.94%, weighted avg value 0.93%.
Volume: 23
Issue: 6
Page: 1579-1589
Publish at: 2025-12-01

Spth-FCM: decision support tool for speech therapist based on fuzzy cognitive mapping

10.11591/ijict.v14i3.pp845-859
Maziz Asma , Taouche Cherif
The development and integration of medical information systems into a unified information space is a significant focus in the field of information technologies. It is essential to develop decision support systems (DSS) to enhance the effectiveness of medical and diagnostic procedures. This article presents a novel decision support tool for speech therapists, which is based on fuzzy cognitive maps (FCM). The latter is a method of modeling complex systems using knowledge of human existence and experience. The proposed tool is composed of three phases. The first phase focuses on entering patient information into the graphical interface developed in JAVA based on the most precise observations. An FCM will be automatically constructed, describing the type of disorder and the patient’s case during the second phase. Finally, in the third phase, FCM-based scenarios were built during the execution of the inference process under FCM expert. The system is presented and demonstrated using a real cases study for eight weeks. The results show that the tool makes it possible to display, guide, assist, and confirm the medical decision of the speech therapist for an appropriate diagnosis and treatment.
Volume: 14
Issue: 3
Page: 845-859
Publish at: 2025-12-01

Escalating QoS by firefly optimization of CGSTEB routing protocol with subordinate energy alert gateways

10.12928/telkomnika.v23i6.27007
R.; Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Madonna Arieth , Ramya; Vellore Institute of Technology (VIT) Govindaraj , Subrata; Sri Venkateswara College of Engineering and Technology (A) Chowdhury , Thu; Hanoi University of Industry Thi Nguyen , Tran; Phenikaa University Duc-Tan
Wireless sensor networks (WSNs) comprise large numbers of sensor nodes that are highly constrained by limited battery power, making energy-efficient routing essential for sustaining network lifetime and service quality. Among existing solutions, the general self-organized tree-based energy balancing (GSTEB) pro tocol with clustering has been widely adopted for energy-aware communication. However, GSTEB and its clustered variant often suffer from energy imbalance, high packet loss, and reduced quality of service (QoS) due to excessive load on cluster heads (CHs). To address these challenges, this paper introduces an enhanced routing framework that integrates firefly optimization with clustered GSTEB(CGSTEB)andintroduces subordinate energy alert gateways (SEAGs). The firefly algorithm is applied to optimize CH selection through a fitness func tion that balances residual energy and node proximity, ensuring efficient cluster formation and adaptive load distribution. Meanwhile, SEAGs establish a two hop communication model between CHs and the base station (BS), reducing CH energy consumption and preventing premature node failures. Simulation exper iments conducted in NS2 demonstrate that the proposed firefly-CGSTEB with SEAG significantly improves QoS metrics, including network lifetime, energy utilization, throughput, and packet loss rate, compared with conventional CG STEB. These results confirm the effectiveness of combining metaheuristic opti mization with gateway-assisted routing for resilient and energy-efficient WSNs.
Volume: 23
Issue: 6
Page: 1718-1728
Publish at: 2025-12-01

Modeling chemical kinetics of geopolymers using physics informed neural network

10.11591/ijict.v14i3.pp822-829
Blesso Abraham , Thirumalaivasal Devanathan Sudhakar
Using a physics informed neural network for the analysis of geopolymers as an alternate material for cement can be a viable approach, as neural networks are capable of modeling complex, nonlinear relationships in data, which can be beneficial for representing the dynamics of chemical properties. If you have a substantial amount of theoretical data, a neural network can learn patterns and relationships in the data, even when the underlying system dynamics are not well-defined or are difficult to model analytically. A welltrained neural network can generalize from the training data to make predictions for unseen scenarios, which can be useful for real-time analysis of the material.
Volume: 14
Issue: 3
Page: 822-829
Publish at: 2025-12-01

Enhanced speed regulation using separate P and I gain controllers in a fuzzy-PI framework

10.11591/ijpeds.v16.i4.pp2280-2295
Minh Duc Pham , Duong Nguyen Trong Qui , Truong Phuoc Hoa
This paper explores an enhanced method for regulating the speed of brushless DC (BLDC) motors using field-oriented control. Conventionally, a proportional-integral (PI) controller is employed to adjust output speed and current FOC method. While the PI controller is effective in many scenarios, it exhibits limitations including poor performance when the speed reference changes rapidly. To address these limitations, a fuzzy-PI control scheme is proposed in this study with the aim of improving the speed control performance of BLDC motors, especially under rapidly changing speed reference. The proposed two separate fuzzy logic controllers adaptively adjust the proportional and integral gains so that it combines the robustness of fuzzy logic with the steady-state error of PI control. Simulation and experimental results demonstrate that the fuzzy-PI control significantly outperforms the conventional PI controller in terms of BLDC stability, response time, and accuracy. The proposed approach ensures more reliable and efficient speed regulation for BLDC motors, making it a reliable solution for applications where speed reference fluctuate frequently.
Volume: 16
Issue: 4
Page: 2280-2295
Publish at: 2025-12-01

Soil moisture prototype soil moisture sensor YL-69 for Gaharu (Aquilaria malaccensis) tree planting media

10.11591/ijict.v14i3.pp1163-1171
Rikie Kartadie , Muhammad Agung Nugroho , Adiyuda Prayitna , Adi Kusjani , Ardeana Galih Mardika
Soil moisture, defined as the amount of water present in the spaces between soil particles, plays a critical role in plant growth. Excessive soil moisture can lead to issues such as root rot, deviating from the ideal conditions required for root absorption. To address this, we developed a prototype tool using the YL-69 soil moisture sensor to monitor and control the soil moisture levels in Agarwood/Gaharu tree planting media. The prototype was designed to activate a water pump when soil moisture exceeded 80%, ensuring optimal humidity for plant growth. Once the moisture level dropped below 80%, the pump was deactivated to prevent overwatering. The YL-69 sensor demonstrated an accuracy of 88.76% under controlled conditions. This study highlights the potential of using low-cost sensors for automated soil moisture management in small-scale Gaharu cultivation.
Volume: 14
Issue: 3
Page: 1163-1171
Publish at: 2025-12-01

Comparative analysis of various rotor types BLDC motor for residential elevator application

10.11591/ijpeds.v16.i4.pp2224-2233
Nor Aishah Md. Zuki , Raja Nor Firdaus Kashfi Raja Othman , Fairul Azhar Abdul Shukor , Kunihisa Tashiro
Brushless DC (BLDC) motors are widely used in applications where high efficiency is crucial. With advancements in permanent magnet technology, BLDC motors are increasingly suitable for high-torque applications such as residential elevators. Known for their high efficiency, low maintenance, and excellent controllability, BLDC motors are ideal candidates for this research. However, the challenge lies in identifying the most efficient rotor structure that can deliver the required torque for residential elevator applications while maintaining cost-effectiveness and compact design. This paper addresses this problem by simulating various rotor types of BLDC motors using the finite element method (FEM), Ansys Maxwell. four different rotor structures have been analyzed to evaluate their back electromotive force (EMF) and torque. The model generating the highest torque will be selected for manufacturing as a motor for residential elevators. Among the models studied, BLDC-ERA rotor structures produced the highest torque of 28 Nm, while BLDC-HR type generates the lowest torque. To ensure practicality and cost-effectiveness of installing elevators in double-story houses or smaller residences, the selected motor must be compact and affordable, enabling senior citizen to maintain their independence. This research not only aids other researchers in designing suitable motors for elevator applications but also contributes to societal well-being by promoting accessibility and independence for the elderly.
Volume: 16
Issue: 4
Page: 2224-2233
Publish at: 2025-12-01

Optimized ultra-low power and reduced delay GNR Ternary SRAM using a 7-transistor architecture

10.11591/ijict.v14i3.pp1044-1055
Ravikishore Gaddikoppula , Nandhitha Nathakattuvalasu Muthu
Greater need and evolution in electronics require a memory device that can go with a decreased power delay, SRAM plays an important role as a storage element in digital circuit design. Power and delay are vital problems faced by today’s RAM technology. It is necessary to lessen the power and increase the speed. There is a need to reduce power utilization and time delay. The proposed method is seen in the Electronics technical tool H-Spice technology. The technique proposed on DRG 7T- transistors SRAM consumes less power and delay. After the analysis and enhancement of the circuit, this approach gives the power delay product of the graphene nanoribbon (GNR) 7T SRAM as 80% at 0.7 V, 59% at 0.8 V, 34 % at 0.9 V, which is much less when compared to conventional SRAM power delay product.
Volume: 14
Issue: 3
Page: 1044-1055
Publish at: 2025-12-01

The bootstrap procedure for selecting the number of principal components in PCA

10.11591/ijict.v14i3.pp1136-1145
Borislava Toleva
The initial step in determining the number of principal components for both classification and regression involves evaluating how much each component contributes to the total variance in the data. Based on this analysis, a subset of components that explains the highest percentage of variance is typically selected. However, multiple valid combinations may exist, and the final choice is often made manually by the researcher. This study introduces a novel yet straightforward algorithm for the automatic selection of the number of principal components. By integrating ANOVA and bootstrapping with principal component analysis (PCA), the proposed method enables automatic component selection in classification tasks. The algorithm is evaluated using three publicly available datasets and applied with both decision tree and support vector machine (SVM) classifiers. Results indicate that this automated procedure not only eliminates researcher bias in selecting components but also improves classification accuracy. Unlike traditional methods, it selects a single optimal combination of principal components without manual intervention, offering a new and efficient approach to PCAbased model development.
Volume: 14
Issue: 3
Page: 1136-1145
Publish at: 2025-12-01

ANN based speed control of switched reluctance motor using MATLAB-interfaced DSP controller

10.11591/ijpeds.v16.i4.pp2243-2256
Veena Wilson , Latha Padinjaredath Govindan
The switched reluctance motor (SRM) is gaining significance as a competitive motor in industries due to its prominent features such as absence of rare-earth elements, strong fault tolerance, and competitive efficiency. This paper presents a comprehensive framework to a novel and simplified hardware implementation of SRM drive, accompanied by a stepwise procedure to develop the control process that includes system modelling with simulation analysis and experimental validation, useful for the novice researchers. A precise hardware control environment is introduced, by integrating MATLAB/Simulink platform with digital signal processor (DSP) microcontroller - TMS320F280049C, which minimizes the complexities of traditional controller coding. The paper provides an in-depth explanation of deployment of artificial neural network (ANN) speed control block, offering valuable insights into the practical aspects of ANN-based control in MATLAB. The paper also compares closed-loop speed control using proportional-integral (PI) and ANN control in SRM, and the results demonstrate accurate and adaptive performance of ANN control for variable speed- load conditions.
Volume: 16
Issue: 4
Page: 2243-2256
Publish at: 2025-12-01

An artificial intelligent system for cotton leaf disease detection

10.11591/ijict.v14i3.pp950-959
Priyanka Nilesh Jadhav , Pragati Prashant Patil , Nitesh Sureja , Nandini Chaudhari , Heli Sureja
This study aims to develop a deep learning-based system for the detection and classification of diseases in cotton leaves, with the goal of aiding in early diagnosis and disease management, thereby enhancing agricultural productivity in India. The study utilizes a dataset of cotton leaf images, classified into four categories: Fusarium wilt, Curl virus, Bacterial blight, and Healthy leaves. The dataset is used to train and evaluate various CNN models such as basic CNN, VGG19, Xception, InceptionV3, and ResNet50. These models were evaluated on their accuracy in identifying the presence of diseases and classifying cotton leaf images into the respective categories. The models were trained using standard deep learning frameworks and optimized for high performance. The results indicated that ResNet50 achieved the highest accuracy of 100%, followed by InceptionV3 with 98.75%, and VGG19 and Xception both with 97.50%. The basic CNN model showed an accuracy of 96.25%. These models demonstrated strong potential for accurate multi-class classification of cotton leaf diseases. This study emphasizes the potential of deep learning in agricultural diagnostics. Future research can focus on improving model robustness, incorporating larger datasets, and deploying the system for real-time field use to assist farmers in disease management and improving cotton production.
Volume: 14
Issue: 3
Page: 950-959
Publish at: 2025-12-01

Empowering low-resource languages: a machine learning approach to Tamil sentiment classification

10.11591/ijict.v14i3.pp941-949
Saleem Raja Abdul Samad , Pradeepa Ganesan , Justin Rajasekaran , Madhubala Radhakrishnan , Peerbasha Shebbeer Basha , Varalakshmi Kuppusamy
Sentiment analysis is essential for deciphering public opinion, guiding decisions, and refining marketing strategies. It plays a crucial role in monitoring public sentiment, fostering customer engagement, and enhancing relationships with businesses' target audiences by analyzing emotional tones and attitudes in vast textual data. Sentiment analysis is extremely limited, particularly for languages like Tamil, due to limited application in diverse linguistic contexts with fewer resources. Given its global impact and linguistic diversity, addressing this gap is crucial for a more nuanced understanding of sentiments in India. In the context of Tamil, the need for sentiment analysis models is particularly crucial due to its status as one of the classical languages spoken by millions. The cultural, social, and historical nuances embedded in Tamil language usage require tailored sentiment analysis approaches that can capture the subtleties of sentiment expression. This paper introduces a novel method that assesses the performance of various text embedding methods in conjunction with a range of machine learning (ML) algorithms to enhance sentiment classification for Tamil text, with a specific focus on lyrics. Experiments notably emphasize FastText word embedding as the most effective method, showcasing superior results with a remarkable 78% accuracy when coupled with the support vector classification (SVC) model.
Volume: 14
Issue: 3
Page: 941-949
Publish at: 2025-12-01

Fuzzy logic-based adaptive PLL switching strategy for voltage control in DVR assisted grid tied PV systems

10.11591/ijpeds.v16.i4.pp2353-2368
R. Srilakshmi , V. Chayapathy
This study aims to enhance power quality in grid-connected photovoltaic (PV) systems by introducing an intelligent fuzzy logic-based adaptive control strategy for dynamic PLL switching in a DVR-supported configuration. A 100-kW grid-tied PV system is modeled with a digital phase-locked loop (DPLL), a conventional synchronous reference frame PLL (CTPLL), and a dynamic voltage restorer (DVR). A Mamdani-type fuzzy inference system (FIS) performs real-time PLL selection based on phase-wise real-time fault monitoring. The system was tested under symmetrical and asymmetrical 20% sag and swell conditions, evaluating voltage stability at both PCC and load, total harmonic distortion (THD), recovery time, and synchronization accuracy. Results show that the proposed method reduces unnecessary DVR voltage injection from ~50 V to ~5-6 V under healthy conditions, maintains a near-unity power factor (< 0.95), and achieves up to 15% THD reduction in inverter current and PCC currents compared to DPLL-only operation. Recovery times improved by up to 25%, with stable synchronization maintained in all fault cases. The integration of adaptive PLL switching and targeted DVR activation offers a novel, hardware-efficient approach to harmonic suppression, voltage stabilization, and fault resilience in medium-scale PV systems.
Volume: 16
Issue: 4
Page: 2353-2368
Publish at: 2025-12-01

A comprehensive review of efficient wireless power transfer for electric vehicle charging: advancements, challenges, and future directions

10.11591/ijpeds.v16.i4.pp2156-2169
Md. Ashraf Ali Khan , Kuber Kuber , Yusra Wahab , M. Saad Arif , Shahrin Md. Ayob , Norjulia Mohamad Nordin
Electric vehicles (EVs) have transformed the transportation sector, offering a sustainable alternative to fossil-fuel-powered vehicles. However, their widespread adoption faces challenges such as inadequate charging infrastructure, range anxiety, and concerns about user convenience. Wireless power transfer (WPT) technology provides an efficient, reliable, and user-friendly charging solution that eliminates physical connections, enabling both static and dynamic charging applications. This review explores key components of WPT systems, including wireless charging schemes, compensation circuits, coupling pad structures, and misalignment tolerance, emphasizing their impact on system efficiency and reliability. Findings highlight that WPT can enhance charging convenience, reduce dependence on large battery capacities, and support seamless EV integration into daily life. Additionally, WPT systems improve safety, lower maintenance needs, and create opportunities for autonomous charging. Key advancements in compensation topologies, coupling pad geometries, and misalignment-tolerant capabilities are discussed alongside their role in enhancing power transfer efficiency. By offering insights into the current state-of-the-art and future directions, this paper aims to support the development and deployment of WPT systems, contributing to the global transition toward sustainable transportation.
Volume: 16
Issue: 4
Page: 2156-2169
Publish at: 2025-12-01
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