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30,411 Article Results

Investigating the performance of RNN model to forecast the electricity power consumption in Guangzhou China

10.11591/ijres.v14.i2.pp497-506
Han Mingying , Azman Ab Malik , Noormadinah Allias , Irni Hamiza binti Hamzah
The project initiatives to create a reliable prediction model for power loads in Guangzhou, China. The power industry is facing issues due to rapid market growth and the necessity for better grid management, prompting this response. In developing the models, conventional machine learning models have been used so far, but in this study, the performance of deep learning is investigated. Therefore, the recurrent neural network (RNN) was selected for the prediction of electricity consumption. Later, the performance of the model was compared with autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), and RNN. The experimental results show that the RNN outperforms ARIMA and LSTM, with an R² value of 0.92, an RMSE of 0.13107 and an MAE of 0.0176. The project improved power resource planning and management, selected an acceptable forecasting model RNN and contributed to forecasting technology developments. The study identified limits in historical data availability and quality, as well as external influences affecting the studies. RNN models can help optimize resource allocation and improve energy planning.
Volume: 14
Issue: 2
Page: 497-506
Publish at: 2025-07-01

Blockchain-based decentralized voting system with SHA-256 algorithm and facial recognition

10.11591/ijres.v14.i2.pp481-489
BJD Kalyani , Jaya Krishna Modadugu , Sarabu Neelima
Blockchain technology has completely changed the way data is stored and transactions are verified. It provides a dependable, transparent, and safe medium for communication and transaction validation. In order to solve the drawbacks of conventional electronic voting systems, the goal of this research project is to design a decentralized voting system based on blockchain technology. The suggested method offers an immutable and safe way to record and validate votes by utilizing the security and transparency capabilities of blockchain technology. The suggested approach provides an immutable and safe way to record and validate votes by utilizing the security and transparency capabilities of blockchain technology. This paper aims to provide a comprehensive process for digital identity authentication, create a voter interface that is compatible with Ethereum wallets, and apply smart contracts on the Ethereum network to speed up voter registration, ballot preparation, voting, and result tabulation. Additionally, this paper proposes to build up a multi-factor authentication system for election managers and validators to offer them safe and approved power over the voting process. By carefully examining the existing methods, this research highlights the flaws and weaknesses of traditional electronic voting systems and stresses the need for more trustworthy and secure voting technology. The proposed blockchain-based voting system offers an innovative solution to problems with voter fraud and election manipulation because of its irreversible blockchain record, which gives a high degree of transparency and integrity.
Volume: 14
Issue: 2
Page: 481-489
Publish at: 2025-07-01

Pipelined reconfigurable architecture for 5G software-defined radio systems

10.11591/ijres.v14.i2.pp320-327
Vijaya Bhaskar Chalampalem , Munaswamy Pidugu , Sanacarapu Nagaraju
The filters are used to allow a specific band of frequencies. In a wireless communication, the filter is used to select the frequency of operation with a narrow or broad band. As the generations increase the amount of data handled increases drastically. 5G data rate can be significantly deliver up to 20 Gigabits per second while 4G communication data rate is handled in the order of 100 Megabits per second. Now the challenge becomes processing data at such a speed with low power and low area specifications. The filters that can configure themselves as per the data received are reconfigurable filters so that the bandwidth is saved. Also, when the pipelining is introduced, the reconfigurable filter improves the performance of the design. This paper details about the pipelined reconfigurable finite impulse response (RFIR) filter with the simplest algorithm with auto updating capability. The design is modelled in Verilog hardware description language (HDL) language, synthesized for Cyclone III field-programmable gate array (FPGA). The results prove that the proposed filter increases only slightly with respect to delay and power dissipation with a trade off in area and maximum possible clock frequency.
Volume: 14
Issue: 2
Page: 320-327
Publish at: 2025-07-01

Enhancing scalability and efficiency in technological transaction utilizing dual-layer blockchain approach

10.11591/ijres.v14.i2.pp452-462
T. Kanimozhi , M. Inbavalli
The leather industry encounters significant challenges in integrating blockchain technology and smart contracts into its complex supply networks. Despite technological advancements, existing supply chain management systems suffer from inefficiencies, opacity, and vulnerabilities to fraud. Blockchain offers promising solutions such as immutable ledgers, decentralized governance, and smart contract automation. However, scalability limitations hinder the efficient handling of high transaction volumes, impacting procurement, production, inventory management, and distribution processes, leading to delays and increased costs. This research aims to address these challenges by exploring innovative approaches, including dual-layer blockchain architectures incorporating sharding and state channels, tailored to the unique needs of the leather industry. By overcoming scalability barriers, the research seeks to unlock the transformative potential of blockchain technology and smart contracts, enhancing transparency, traceability, and efficiency in leather supply chains while ensuring global interoperability and regulatory compliance. Through empirical validation and comparative analysis, this study provides understandings into the practical implementation of blockchain solutions within the leather industry, offering strategic guidance for sustainable supply chain management practices.
Volume: 14
Issue: 2
Page: 452-462
Publish at: 2025-07-01

Different methods of antenna reconfiguration by switches: a review

10.11591/ijres.v14.i2.pp301-310
Reji Valsalam , Perumal Ramani , Pandian Sharmila
The rapid advancement of wireless communication technology has focused researcher's attention on reconfigurable antennas with multiple input and output (MIMO) and cognitive radio operation in high-data-rate modern wireless applications. Reconfigurable antennas perform various functions in terms of operating frequency, radiation pattern, and polarization. Electronic, mechanical, physical, and optical switches are used in reconfigurable antennas as control elements to adjust the switching mechanism and accomplish dynamic tuning. Electronic switches are the most widely used component in reconfigurable antennas because of their effectiveness, dependability, and simplicity in integrating with microwave circuitry. In this paper, a review of various kinds of efficient implementation methods for electrically controlled frequency reconfigurable antennas are proposed. More electrical switches are being used for reconfiguration such as micro electromechanical systems (MEMS), P-type, intrinsic, N-type (PIN), and varactor diodes. Even though PIN diodes are more frequently employed for reconfiguration due to their stability and constant variation in internal inductor and capacitor values. This study provides a deep analysis of the PIN diode usage in reconfigurable antennas and how to reduce the diodes in different microstrip reconfigurable antenna structures.
Volume: 14
Issue: 2
Page: 301-310
Publish at: 2025-07-01

Optimizing social media analytics with the data quality enhancement and analytics framework for superior data quality

10.11591/ijres.v14.i2.pp472-480
B. Karthick , T. Meyyappan
his paper introduces the data quality enhancement and analytics (DQEA) framework to enhance data quality in social media analytics through machine learning (ML) algorithms. The efficacy of the framework is validated through features tested against human coders on Amazon Mechanical Turk, achieving an inter-coder reliability score of 0.85, indicating high agreement. Furthermore, two case studies with a large social media dataset from Tumblr were conducted to demonstrate the effectiveness of the proposed content features. In the first case study, the DQEA framework reduced data noise by 30% and bias by 25%, while increasing completeness by 20%. In the second case study, the framework improved data consistency by 35% and overall data quality score by 28%. Comparative analysis with state-of-the-art models, including random forest and support vector machines (SVM), showed significant improvements in data reliability and decision-making accuracy. Specifically, the DQEA framework outperformed the random forest model by 15% in accuracy and 20% in true positive rate, and the SVM model by 10% in error rate reduction and 18% in reliability. The results underscore the potential of advanced data analytics tools in transforming social media data into a valuable asset for organizations, highlighting the practical implications and future research directions in this domain.
Volume: 14
Issue: 2
Page: 472-480
Publish at: 2025-07-01

Enhanced fault detection in photovoltaic systems using an ensemble machine learning approach

10.11591/ijres.v14.i2.pp507-517
Mohammed Salah Ibrahim , Hussein k. Almulla , Anas D. Sallibi , Ahmed Adil Nafea , Aythem Khairi Kareem , Khattab M. Ali Alheeti
Malfunctioning of photovoltaic (PV) systems is a main issue affecting solar panels and other related components. Detecting such issues early leads to efficient energy production with low maintenance costs and high system performance consistency. This paper proposed an ensemble model (EM) for fault detection (FD) in PV systems. The proposed model utilized advanced machine learning algorithms containing random forest (RF), k-nearest neighbors (KNN), and gradient boosting (GB). Traditional approaches often do not handle the several situations that PV systems can have. Our EM leveraged the power of GB’s algorithm in handling complex data patterns through iterative boosting, KNN’s capability in capturing local data structures, and RF’s strength in handling overfitting and noise through its tree structure randomness. Combining these models enhanced fault detection capabilities, providing excellent accuracy compared to individual models. To evaluate the performance of our EM, different experiments were conducted. The results demonstrated substantial improvements in detection fault, achieving an accuracy rate of 95%. This accuracy rate considered high underscores the model’s capability to handle fault detection of PV systems, posing a consistent solution for instant fault detection and maintenance scheduling.
Volume: 14
Issue: 2
Page: 507-517
Publish at: 2025-07-01

Machine learning methods for energy sector in internet of things

10.11591/ijres.v14.i2.pp538-545
Reyhane Hafezifard , Soodeh Hosseini
This research paper focuses on exploring machine learning studies and conducting a comparative analysis of their advantages, disadvantages, implementation environments, and algorithms. A key aspect of the study involves evaluating the energy efficiency using machine learning algorithms to predict energy consumption. Additionally, a feature selection algorithm is employed to rank the features, with the highest-ranking feature identified as one of the most significant. The experimentation is conducted using the Weka tool, incorporating several machine learning algorithms such as linear regression, k-nearest neighbors, decision stump, radial basis function (RBF) network, and isotonic regression. The RBF algorithm, which relies on RBF, shares similarities with neural network algorithms. Results indicate a minimum error value of 1.546 for cooling load and 1.364 for heating load. The random forest algorithm emerges as the most suitable choice within the context of this study.
Volume: 14
Issue: 2
Page: 538-545
Publish at: 2025-07-01

IoT-based smart agriculture system using fuzzy logic: case study in Vietnam

10.11591/ijres.v14.i2.pp440-451
Le Phuong Truong , Le Nam Thoi
This paper presents an internet of things (IoT)-based smart agriculture system using fuzzy logic. This system automatically supervise and regulate pivotal parameters like temperature, humidity, pH, nutrients (NPK), and electrical conductivity (Ec) for vegetables. Data from the cultivation environment is gathered by sensors system and processed by fuzzy logic algorithms to make appropriate control decisions, ensuring optimal crop growth conditions. Additionally, a web application was developed to monitor temperature, humidity, Ec, pH, and NPK content. Moreover, when any of the NPK, Ec, pH, temperature or humidity indices fall outside allowed ranges, the system send warning notifications through the web application. Furthermore, an IP camera was installed to take images of the garden and send them to users via this web app. Experimental results demonstrate the system's reliability with a pH root mean square error (RMSE) of 0.22 and temperature RMSE of 0.93, corresponding to low errors of 0.034% and 0.056% respectively. Concurrently, this system optimizes resource utilization including water and electricity to assist in reducing production costs.
Volume: 14
Issue: 2
Page: 440-451
Publish at: 2025-07-01

Design and structural modelling of patient-specific 3D-printed knee femur and tibia implants

10.11591/ijres.v14.i2.pp575-586
Bolugoddu Sandeep , Saravanan Dhanushkodi , Sudhakar Kumarasamy
Arthritis is a degenerative joint condition that progressively damages the knee, leading to pain, stiffness, and limited mobility. To alleviate these symptoms and restore joint functionality, total knee arthroplasty (TKA) is performed. This procedure becomes necessary due to either sudden trauma to the knee or gradual wear and tear of the meniscus and cartilage. TKA involves meticulous planning, precise bone cutting, and the placement of prosthetic components made from high-density polyethylene and metal alloys. However, traditional methods creating customized knee implants are expensive and time-intensive. This study explores the challenges in manufacturing personalized knee implants for TKA and evaluates the potential of three-dimensional (3D) printing technology in this process. Variations in knee joint anatomy across populations complicate surgery, as optimal outcomes rely on precise alignment and implant dimensions. A preoperative computed tomography (CT) scan identifies the region of interest (ROI), such as the knee joint. The scan data is then processed using computer-aided design (CAD) software to generate a printable file. The patient’s CT scan data is converted into a standard triangulation language (STL) file and CAD models of the knee joint. Errors such as overlapping triangles or open loops in the STL file are corrected, and unwanted geometries near the ROI are removed. Resection techniques are applied to create CAD models tailored to the patient’s bone morphology. Fused deposition modeling (FDM) is then used to produce prototypes of the knee joint and implants. Despite visible layer lines in the printed prototypes, challenges encountered during the process were effectively resolved.
Volume: 14
Issue: 2
Page: 575-586
Publish at: 2025-07-01

A custom reduced instruction set computer-V based architecture for real-time electrocardiogram feature extraction

10.11591/ijres.v14.i2.pp412-427
Vinayak Vikram Shinde , Sheetal Umesh Bhandari , Deepti Snehal Khurge , Satyashil Dasharath Nagarale , Ujwal Ramesh Shirode
The growing demand for energy-efficient and real-time biomedical signal processing in wearable devices has necessitated the development of application-specific and reconfigurable embedded hardware architectures. This paper presents the register transfer level (RTL) design and simulation of a custom reduced instruction set computer-V (RISC-V) based hardware architecture tailored for real-time electrocardiogram (ECG) feature extraction, focusing on R-peak detection and heart rate (HR) calculation. The proposed system combines ECG-specific functional blocks including a specialized ECG arithmetic logic unit and a finite state machine-based ECG control unit with a compact 16-bit RISC-V control core. Hardware optimized algorithms are used to carry out pre-processing activities such high-pass and low-pass filtering as well as feature extraction processes including moving average filtering, derivative calculation, and threshold based peak identification. Designed to reduce memory footprint and control complexity, a custom instruction set architecture supports modular reconfigurability. Functional validation is carried out by Xilinx Vivado simulating RTL components described in very high speed integrated circuit (VHSIC) hardware description language (VHDL). The present work shows successful simulation of important architectural components, complete system-level integration and custom ECG data validation. This work provides the basis for an application-specific, reconfigurable, power efficient hardware solution for embedded health-monitoring devices.
Volume: 14
Issue: 2
Page: 412-427
Publish at: 2025-07-01

Exploring the landscape of approximate subtraction methods in ASIC platform

10.11591/ijres.v14.i2.pp388-397
M. Priyadharshni , Rajermani Thinakaran , Grande Naga Jyothi , Vijayakumar Varadarajan , C. Srinivasa Murthy
Approximate computing has emerged as a crucial technique in modern computing, offering significant benefits for error-resilient applications. Error resilient applications include signal, image, audio processing, and multimedia. These applications will accept the errored results with some degree of tolerance. This approach allows these applications to process and embrace data that may deviate slightly from perfect accuracy. The utility of approximate computing extends to both hardware and software domains. In hardware, arithmetic units are particularly important, among that approximate subtractors have gained attention for their role in these units. A comparative study was conducted on various approximate subtractors from existing literature, considering structural analysis in all scenarios. These approximate subtractors are coded in Verilog hardware description language (HDL) and synthesized in Synopsys electronic design automation (EDA) Tool using Taiwan Semiconductor Manufacturing Company (TSMC) 65 nm technology. Out of the available choices, approximate subtractor 3 is particularly well-suited for processing higher bit data due to its reduced hardware complexity and minimal error. Notably, it outperforms exact subtractors by achieving a notable reduction of 20% in the area delay product (ADP) and 15% in the power delay product (PDP) as process innovation. These improvements highlight the efficiency and effectiveness of approximate subtractor 3, making it a compelling option for various computing applications which accept the inaccurate results.
Volume: 14
Issue: 2
Page: 388-397
Publish at: 2025-07-01

FPGA-based implementation of a substitution box cryptographic co-processor for high-performance applications

10.11591/ijres.v14.i2.pp587-596
Moulai Khatir Ahmed Nassim , Ziani Zakarya
The increasing demand for reliable cryptographic operations for securing current systems has given birth to well-advanced and developed hardware solutions, in this paper we consider issues within the traditional symmetric advanced encryption standard (AES) cryptographic system as major challenges. Additionally, problems such as throughput limitations, reliability, and unified key management are also discussed and tackled through appropriate hierarchical transformation techniques. To overcome these challenges, this paper presents the design and field programmable gate array (FPGA)-based implementation of a cryptographic coprocessor optimized for substitution box (S-Box) operation which is considered as a key component in many cryptographic algorithms such as AES. The architecture of the co-processor proposed in this article is based on the advanced characteristics of FPGAs to accelerate the S-Box transformation, improve throughput and reduce latency compared to software implementations. We discussed carefully the design considerations along with resource utilization, speed optimization, and energy efficiency. The obtained experimental results present significant performance improvements, the FPGA-based implementation ensured higher throughput and lower execution time compared to traditional central processing unit (CPU)-based methods. We presented in this work the effectiveness of using FPGAs for the acceleration of cryptographic operations in secure applications which will therefore be a robust solution for the next generation of secure systems.
Volume: 14
Issue: 2
Page: 587-596
Publish at: 2025-07-01

A novel approach to transparent and accurate fuel dispensing for consumer protection

10.11591/ijres.v14.i2.pp353-364
Gayatri Phade , Sharada Narsingrao Ohatkar , Murugan Pushpavalli , Vidya Chitre , Vijaya Pawar , Omkar Suresh Vaidya , Harikrishnan Ramachandran
Consumer rights are exploited around the world and it is necessary for to protect consumer rights by means of safeguarding consumers from various unfair trade practices. Those most vulnerable to such exploitation must be shielded, and this is achieved through consumer protection measures. One such example of unethical behavior is fuel stealing at fuel stations. To overcome this critical issue, a low-cost fuel quantity sensing and monitoring system is proposed in this paper. A fuel detection system will ensure the exact quantity of fuel filled in fuel tank and will detect fuel theft, if any, at fuel pumps. An embedded system is developed for this purpose, consisting of sensors, display devices, communication devices and microcontroller. The quantity of fuel filled in the tank is transmitted to mobile phone of the consumer to avoid fuel theft. Performance of the system is validated by comparing the displayed amount of fuel dispensed and actual filled in the tank and achieve 99.95% accuracy. With this consumer right to get the value for amount paid for the petrol will be protected. This novel feature can be added in the fuel tank of the smart vehicle development and design as a future scope.
Volume: 14
Issue: 2
Page: 353-364
Publish at: 2025-07-01

Building a photonic neural network based on multi-operand multimode interference ring resonators

10.11591/ijres.v14.i2.pp311-319
Thanh Tien Do , Hai Yen Pham , Trung Thanh
Photonic neural networks (PNNs) offer significant potential for enhancing deep learning networks, providing high-speed processing and low energy consumption. In this paper, we present a novel PNN architecture that employs nonlinear optical neurons using multi-operand 4×4 multimode interference (MMI) multi-operand ring resonators (MORRs) to efficiently perform vector dot-product calculations. This design is integrated into a photonic convolutional neural network (PCNN) with two convolutional layers and one fully connected layer. Simulation experiments, conducted using Lumerical and Ansys tools, demonstrated that the model achieved a high test accuracy of 98.26% on the MNIST dataset, with test losses stabilizing at approximately 0.04%. The proposed model was evaluated, demonstrating high computation speed, improved accuracy, low signal loss, and scalability. These findings highlight the model’s potential for advancing deep learning applications with more efficient hardware implementations.
Volume: 14
Issue: 2
Page: 311-319
Publish at: 2025-07-01
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