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29,734 Article Results

Energy-efficient multilevel inverter for electric vehicles using wireless sensor network monitoring

10.11591/ijres.v15.i1.pp130-137
Nishalini Delcy , Francis Thomas Josh , Kannadhasan Suriyan
This research presents a unique energy-efficient routing strategy aimed at optimizing energy consumption and prolonging network longevity using an innovative clustering probability. Cluster-based routing algorithms facilitate versatile configurations and extend the network's lifetime until the last node ceases operation. This study introduces an energy-efficient hierarchical clustering algorithm for wireless sensor networks (WSNs), enhancing the low-energy adaptive clustering hierarchy (LEACH) algorithm. The objective of this algorithm is to reduce power consumption by the strategic selection of new cluster heads (CH) in each data transfer round and to prevent network conflicts. This objective is accomplished by employing an efficient function to identify the optimal CH nodes in each cycle, considering the current energy levels of the sensors. The suggested technique enhances the cluster formation process by utilizing the reduced distance to the base station. This study findings will enhance packet scheduling algorithms for data aggregation in WSNs to minimize the number of packets transmitted from sensors to CH. Simulation findings validate the system's efficacy in comparison to alternative compression techniques and non-compression scenarios utilized in LEACH and multi-hop LEACH.
Volume: 15
Issue: 1
Page: 130-137
Publish at: 2026-03-01

Inquisitive biometric feature analysis and implementation for recognition tasks using camouflaged segmentation with AI and IoT

10.11591/ijres.v15.i1.pp119-129
Mahesh Shankarrao Patil , Harsha J. Sarode , Abhijit Banubakode , Prakash Tukaram Patil , Nutan Patil , Vijayakumar Varadarajan , Deshinta Arrova Dewi
A vital role in reconfigurable and embedded systems which are deployed in smart environements and healthcare monitoring applications is played by human activity recognition (HAR). However, the potential leakage of sensitive user attributes raises serious privacy issues due to collection of data from the end devices and it needs to be transmitted to more powerful platforms for inference. Addressing this key challenge is principally crucial for resource-constrained embedded systems where efficiency of energy is a chief design requirement. The aim of this paper is present an energy-aware, privacy-preserving HAR framework appropriate for low-power embedded platforms. A machine learning–based camouflaged signal segmentation technique is proposed to transform the data collected from the sensor by eliminating sensitive information while preserving activity-relevant features. For characterization of trade off between the energy consumption and accuracy of recognition, parameters are extensively tuned by careful optimization in this proposed model. Experimental evaluations demonstrate that the method significantly reduces the inference of sensitive attributes such as gender, age, height, and weight, with minimal impact on HAR accuracy. Furthermore, the system supports configurable trade-offs between energy usage and classification performance, making it suitable for implementation on low-power embedded devices.
Volume: 15
Issue: 1
Page: 119-129
Publish at: 2026-03-01

FPGA implementation and bit error rate analysis of the forward error correction algorithms in voice signals

10.11591/ijres.v15.i1.pp86-96
Ramjan Khatik , Afzal Shaikh , Shraddha Sawant , Pritika Patil
The idea of codes (VITERBI) is broadly utilized as a part of the wireless communication system as a result of their less complex nature in the decoding of transmitted message. This paper attempts to develop a performance analysis of the decoder by methods for bit error rate (BER) examination. The Galois field based decoder calculation is only utilized as a part of the communication systems. The decoder calculation-based Viterbi based decoder is carried out using field programmable gate arrays (FPGA) and MATLAB. This paper looks at the execution examination of both the calculations. The reconfigurable processor called Microblaze on the Spartan 3E FPGA is utilized for this purpose. MATLAB based code is used to see the BER analysis after the FPGA implementation output.
Volume: 15
Issue: 1
Page: 86-96
Publish at: 2026-03-01

Multi-modal sensor integration in chicken-fish-vegetable greenhouse agriculture based on internet of things

10.11591/ijres.v15.i1.pp138-149
Muhammad Risal , Pujianti Wahyuningsih , Suwatri Jura , Irmawaty Iskandar , Abdul Jalil
Integrated chicken-fish-vegetable farming is a type of agriculture that combines the benefits of them within a single ecosystem. The objective of this study is to develop a control and monitoring system for integrated greenhouse-based chicken-fish-vegetable farming using the internet of things (IoT). The monitoring method employs the integration of multi-modal sensors in the greenhouse, consisting of a camera, water level, DHT11, pH, TDS, DS18B20, light dependent resistor (LDR), and infrared (IR) sensor. The camera functions as a visual monitoring tool for the farm, water level sensor detects hydroponic water levels, DHT11 measures air temperature and humidity, pH sensor measures water acidity, TDS sensor detects water nutrients, DS18B20 measures pond water temperature, LDR detects weather conditions, and IR sensor measures sunlight intensity. The processing units used to control the sensors and output devices are the ESP32 and Raspberry Pi. The system outputs include a relay for pump control, an LCD for text messages, and IoT information visualization using the Blynk platform. The results of this study demonstrate that the multi-modal sensor device can effectively monitor the conditions of integrated greenhouse-based chicken-fish-vegetable farming, achieving an accuracy of up to 96%, with an average data transmission time of 6 seconds through the Blynk IoT platform.
Volume: 15
Issue: 1
Page: 138-149
Publish at: 2026-03-01

Learning customer preference dynamics using rank-aware matrix factorization and enhanced collaborative filtering model

10.11591/ijres.v15.i1.pp159-169
Sathya Sundar , Eswara Thevar Ramaraj , Padmapriya Arumugam
Understanding how customer preferences evolve over time is a critical challenge for modern recommender systems operating in large-scale, implicit-feedback–driven e-commerce environments. The primary objective of this study is to develop a unified and interpretable framework that simultaneously models ranking-based preferences, collaborative similarity structures, and temporal behavioral evolution of customers. To achieve this, the study proposes a novel hybrid framework that integrates rank-aware matrix factorization (RA-MF), enhanced collaborative filtering (CF), K-means clustering, and temporal cluster migration matrices (TCMM) for learning customer preference dynamics. The ranking factorization model effectively captures implicit signals such as purchase frequency and recency decay, while CF provides complementary similarity-based insights. K-means segmentation reveals diverse customer personas, including high-value loyal buyers and exploratory shoppers, with significant differences in spending and purchasing behavior. Quantitative evaluations demonstrate strong performance improvements, with 11–18% gains in NDCG@10, 10–15% increases in Precision@10, and notable reductions in root mean square error (RMSE) and mean absolute error (MAE). The results highlight the framework’s ability to deliver both accurate recommendations and interpretable behavioral insights, offering valuable contributions to personalized marketing, customer retention, and data-driven e-commerce strategy.
Volume: 15
Issue: 1
Page: 159-169
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

Performance comparison of feature extraction methods for electroencephalogram-based recognition of Balinese script

10.11591/ijaas.v15.i1.pp55-64
I Made Agus Wirawan , Ida Bagus Nyoman Pascima , Gede Surya Mahendra , I Made Candiasa , I Nyoman Sukajaya
Recognizing Balinese script from electroencephalogram (EEG) signals remains a challenging problem due to low signal amplitude, non-stationary dynamics, and significant inter-subject variability. Despite previous attempts, no single feature extraction method has been universally effective in addressing these limitations. To fill this gap, this study systematically evaluates five feature extraction techniques—differential entropy (DE), power spectral density (PSD), discrete wavelet transforms (DWT), Hjorth parameters, and statistical features—on the Balinese imagined spelling using electroencephalography (BISE) dataset, which contains EEG recordings specifically designed for Balinese script recognition. For classification, both artificial neural networks (ANN) and support vector machines (SVM) are applied, and their performance is validated across multiple experimental settings. Results demonstrate that DE consistently provides more stable and discriminative features than the other methods, achieving the highest classification accuracy when combined with ANN. These findings highlight the potential of DE-based approaches to advance electroencephalogram driven Balinese script recognition, offering a culturally significant contribution to brain-computer interface (BCI) research and supporting future applications in inclusive artificial intelligence, digital heritage preservation, and assistive technologies.
Volume: 15
Issue: 1
Page: 55-64
Publish at: 2026-03-01

Hybrid deep learning approach for Indonesian hoax detection: a comparative evaluation with IndoBERT

10.11591/ijaas.v15.i1.pp322-332
Siti Mujilahwati , Moh. Rosidi Zamroni , Miftahus Sholihin
The spread of hoaxes in Indonesia has escalated significantly, with over 12,547 cases recorded between 2018 and 2023. Low public literacy and uncontrolled information flow contribute to the rapid dissemination of false content that fuels disinformation and social unrest. Previous studies have utilized artificial intelligence (AI) approaches such as Indonesia bidirectional encoder representations from Transformers (IndoBERT) and deep learning models like long short-term memory (LSTM), bidirectional LSTM (BiLSTM), convolutional neural network (CNN), and Transformer-based methods. However, most relied on a single modeling paradigm and did not address the trade-offs between classification performance and computational efficiency. This study proposes a hybrid architecture that integrates IndoBERT with bidirectional gated recurrent unit (BiGRU) and BiLSTM to enhance Indonesian hoax detection. Using 4,312 news articles and 10-fold cross-validation, we compare the performance of IndoBERT–BiGRU, IndoBERT–BiLSTM, and the proposed hybrid IndoBERT–BiGRU BiLSTM model. Evaluation metrics include accuracy, precision, recall, F1 score, and training time. The hybrid model achieved the best performance with 98.73% accuracy, 99.01% recall, 98.04% precision, and 98.98% F1 score, while also reducing training time compared to single models. These findings demonstrate that combining BiGRU and BiLSTM within the IndoBERT framework effectively balances performance and efficiency, making it a robust solution for Indonesian text classification.
Volume: 15
Issue: 1
Page: 322-332
Publish at: 2026-03-01

Design of a solar system with a PID controller based on the Tyrannosaurus optimization algorithm

10.11591/ijres.v15.i1.pp170-182
Kadhim Sabah Rahimah , Issa Ahmed Abed , Afrah Abood Abdul Kadhim
Although photovoltaic (PV) power generation systems are an efficient way to use solar energy, their conversion efficiency is very low. Keeping the DC output power from the panel consistent is the key challenge with solar PV systems. Radiation and temperature are two variables that can impact a panel's output power. This study proposes a unique hunting-based optimization technique called the Tyrannosaurus optimization algorithm (TROA). It is demonstrated that the TROA can be used to achieve maximum power point tracking (MPPT) for lithium-ion battery charging with solar panels. Tyrannosaurus Rex hunting techniques served as the model for this approach. MPPT is used to regulate the solar array's output in PV systems. A buck converter is used by the charge controller to convert DC to DC. To provide the most power, it is utilized to balance the impedance of batteries and solar panels. To maximize power transfer, the algorithm modifies the gating signal's duty cycle based on the voltage and current detected by the solar panel. Three well-known optimization methods are contrasted with TROA's performance: gorilla troops optimization (GTO) algorithm, perticle swarm optimization (PSO), and cultural algorithm (CA). In contrast to current approaches, the proposed approach has yielded superior results.
Volume: 15
Issue: 1
Page: 170-182
Publish at: 2026-03-01

Portable verification IP: a UVM-based approach for reusable verification environments in complex IP and SoC verification

10.11591/ijres.v15.i1.pp78-85
Harinagarjun Chippagi , Vangala Sumalatha
Reusable and portable verification techniques are becoming more and more necessary due to the growing complexity of system-on-chip (SoC) designs and the need for quick time-to-market. In order to facilitate cross-project reusability, automation, and scalability in SoC verification, this paper introduces a portable verification IP (PVIP) framework based on the universal verification methodology (UVM). The suggested framework improves coverage efficiency and verification portability across heterogeneous platforms by integrating UVM with the portable stimulus standard (PSS). In comparison to traditional UVM-based methods, experimental evaluation shows that the PVIP framework achieves 92% functional coverage, enhances reusability by 87%, and shortens verification cycle time by 27%. These findings demonstrate how PVIP can greatly speed up verification closure, minimize engineering effort, and assist in the development of the next generation of intelligent, scalable, and industry-ready SoC verification environments.
Volume: 15
Issue: 1
Page: 78-85
Publish at: 2026-03-01

Energy-efficient reconfigurable architectures for Edge AI in healthcare IoT: trends, challenges, and future directions

10.11591/ijres.v15.i1.pp1-20
Tole Sutikno , Aiman Zakwan Jidin , Lina Handayani
The integration of Edge artificial intelligence (AI) with internet of things (IoT) technologies is transforming healthcare applications, including wearable monitoring, telemedicine, and implantable medical devices, by enabling low-latency and intelligent data processing close to patients. However, stringent requirements on energy efficiency, reliability, real-time responsiveness, and data privacy continue to hinder scalable and long-term deployment in resource-constrained healthcare environments. Energy-efficient reconfigurable architectures—such as field-programmable gate arrays (FPGAs), coarse-grained reconfigurable arrays (CGRAs), and emerging memory-centric and heterogeneous platforms—have emerged as promising solutions to address these challenges by balancing flexibility, adaptability, and power efficiency. This review systematically examines recent advances in reconfigurable Edge AI architectures for healthcare IoT, highlighting key trends in hardware–software co-design, AI-assisted design automation, memory-centric optimization, and domain-specific overlays. It further identifies critical challenges, including energy–performance trade-offs, runtime reconfiguration overheads, security and privacy vulnerabilities, limited standardization, and reliability concerns in dynamic clinical settings. Finally, future research directions are outlined, emphasizing self-optimizing and context-aware architectures, secure and trustworthy reconfiguration mechanisms, unified frameworks for heterogeneous healthcare workloads, and sustainable, carbon-aware edge computing. Collectively, this review positions energy-efficient reconfigurable architectures as a foundational enabler for next-generation Edge AI in IoT-enabled healthcare systems.
Volume: 15
Issue: 1
Page: 1-20
Publish at: 2026-03-01

Heart disease prediction using hybrid deep learning and medical imaging with wavelet-based feature extraction

10.11591/ijres.v15.i1.pp183-193
Chairmadurai Palanisamy , Kavitha Pachamuthu , Arun Kumar Ramamoorthy
The process of heart disease prediction is based on patient medical information, which can be addressed in terms of medical image as well as the results of an electrocardiogram (ECG) conducted to determine the risk of developing heart disease. The hybrid deep learning (DL) algorithms are developed using past data that can identify trends related to cardiovascular disease (CVDs). In the current paper, it is possible to offer a new method of heart disease prediction that would combine high-quality image processing and hybrid DL to enhance the effectiveness of predictions and avoid the shortcomings of the modern approaches. First, medical images like ECG images are pre-processed with butterworth adaptive 2D wavelet filter, which ensures maximal noise reduction, followed by maintenance of spatial and frequency information. The Gabor Wavelet-based feature extraction technique is applied to extract meaningful patterns, including both spatial and frequency domain information, which is essential for detecting heart-related anomalies. The resultant features are then categorized, along with both convolutional neural networks (CNN) and long short-term memory (LSTM), to make reliable and precise predictions of heart disease. The performance indicators, including accuracy (92.4%), precision (91.2%), recall (93.5%), and F1-score (91.0%), are utilized. Applying the model yields significant levels of reliability and generalization compared to traditional applications.
Volume: 15
Issue: 1
Page: 183-193
Publish at: 2026-03-01

Home grocery listing hardware system and mobile application with speech recognition feature

10.11591/ijres.v15.i1.pp109-118
Mohamad Faris Eizlan Suhaimi , Aiman Zakwan Jidin , Haslinah Mohd Nasir , Mohd Haidar Md Hamzah , Mohd Syafiq Mispan
A home grocery list is a crucial aspect of household management that ensures sufficient kitchen supplies. The classic pen-and-paper grocery list is ineffective since it is time-consuming and prone to human error. Therefore, in this study, we proposed a microcontroller-based home grocery listing system using a barcode scanner and speech recognition. The proposed system consists of hardware and a mobile application. The main hardware components are the ESP32-S3 microcontroller, MH-ET barcode scanner v3.0, 20×4 LCD, and 2.4 GHz wireless keyboard. The mobile application is developed using the MIT App Inventor. Through the hardware, the system receives user input from barcode scanning or manual data entry using the keyboard. The data captured using a barcode scanner or keyboard is stored in the memory. Subsequently, the data is transmitted to the mobile application of the home grocery listing system via WiFi. Moreover, the mobile application is also equipped with user input via speech recognition and manual data entry using the keyboard. Hence, users have the flexibility to input the grocery list using four methods within the system. The developed home grocery listing system gives a new, satisfying experience to the users and a convenient way for them to make a home grocery list.
Volume: 15
Issue: 1
Page: 109-118
Publish at: 2026-03-01

Advanced MRI-based deep learning for brain tumors: a five-year review of oncology–radiology–AI synergy

10.11591/ijres.v15.i1.pp214-223
Shrisha Maddur Ramesh , Chitrapadi Gururaj
Rapid advancements in computer vision and machine learning have significantly revolutionized medical imaging one such application is brain tumor detection and classification. Deep learning has emerged as a powerful tool, which offers exceptional capabilities in handling complex medical datasets. However, the current systems still face challenges in achieving optimal accuracy, robustness and clinical interpretability. This study presents a comprehensive survey of brain tumor segmentation, classification and detection techniques using deep learning, metaheuristic and hybrid approaches. The detailed quantitative evaluations of conventional and emerging methods are conducted by examining key performance metrics, dataset characteristics, strengths, and limitations. This review highlights recent breakthroughs by analyzing state-of-the-art techniques from the past five years, research gaps and potential directions for future advancements. These findings provide insights into novel architectures, optimization strategies and clinical applications which ultimately guide researchers towards more robust, interpretable and clinically impactful artificial intelligence (AI)-driven solutions for brain tumor analysis.
Volume: 15
Issue: 1
Page: 214-223
Publish at: 2026-03-01

IoT cloud integration with EfficientNet-B7 for real-time pest monitoring and leaf-based classification

10.11591/ijres.v15.i1.pp150-158
Sabapathi Shanmugam , Vijayalakshmi Natarajan
The increasing prevalence of pest infestations poses a significant threat to global agricultural productivity, often resulting in substantial yield losses and economic damage. To address this challenge, this paper proposes an intelligent, cloud-enabled pest detection and classification framework leveraging state-of-the-art deep learning techniques. The proposed system integrates YOLOv8 for rapid and accurate pest detection with EfficientNet-B7 for fine-grained species-level classification. The framework is trained and evaluated using the Pestopia dataset, which contains annotated images representing diverse pest species. To enhance data diversity, robustness, and model generalization, data augmentation techniques such as center cropping and horizontal flipping are applied during preprocessing. YOLOv8 is employed to detect and localize pest instances within images, while EfficientNet-B7 extracts high-level discriminative features from detected regions to enable precise species identification. Furthermore, the system incorporates cloud-based real-time monitoring through Adafruit IO, enabling scalable, remote access to pest information for timely decision-making. The performance of the proposed framework is evaluated using standard metrics, including accuracy, precision, recall, and F1-score, achieving values of 97.8%, 98.9%, 98.4%, and 98.9%, respectively. The experimental results demonstrate the effectiveness and reliability of the proposed approach for real-time pest management. The cloud-integrated architecture facilitates proactive pest control strategies, supporting smarter, data-driven agricultural practices, and improved crop protection.
Volume: 15
Issue: 1
Page: 150-158
Publish at: 2026-03-01
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