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30,376 Article Results

Toward nuanced sentiment analysis through multi-sense emoji embedding

10.11591/ijeecs.v40.i3.pp1598-1606
Junita Amalia , Agnes Veronika Sihombing , Hanna Dhea Christi Sihombing , Nadya Dioranta Tambunan
This research investigates the role of emojis in sentiment analysis using a more comprehensive multi-sense skip-gram approach. Emojis, which can convey facial expressions, body movements, and intonations often challenging to express in text, enhance digital communication by enriching the meaning of messages. Previous studies have shown that emojis improve sentiment analysis, yet most focused solely on their positive and negative connotations. This study broadens the scope by incorporating positive, negative, and neutral sentiment contexts. In the experiments, emojis were embedded in text and converted into vector representations for further analysis. The classification of sentiment texts was performed using a bidirectional long short-term memory (Bi-LSTM) method enhanced with an attention layer. The experiments resulted in accuracy of 0.83, recall of 0.83, precision of 0.82, and F1-score of 0.82. Statistical tests confirmed the significance of these findings, indicating that the approach improves the accuracy of sentiment analysis involving emojis. Overall, the study demonstrates that the integration of text and emojis leads to a more nuanced and precise understanding of sentiment in sentences, confirming the effectiveness of this method.
Volume: 40
Issue: 3
Page: 1598-1606
Publish at: 2025-12-01

Predicting staple crop yields under climate variability using multiple regression techniques

10.11591/ijeecs.v40.i3.pp1531-1538
Richard D. Hortizuela , Thelma D. Palaoag
Global food systems rely on staple crops—rice, wheat, maize, potato, soybean, and sugarcane, which are vital in Asia, where production is high. However, climate change threatens crop yields, potentially increasing hunger and malnutrition. Yield variability due to climate factors like rainfall and temperature underscores the need for accurate crop yield predictions. This paper analyzed the relationships between staple crop yields, climate variables, and pesticide usage. It aimed to develop a predictive model for crop yields in Asia using multiple regression techniques in Google Colab. The model was evaluated using a hybrid set of metrics like mean absolute error (MAE), root mean squared error (RMSE), and R² score. Findings revealed that reliable yield predictions are achievable despite weak linear relationships among variables. The extreme gradient boosting (XGBoost) achieves the highest R² score of 0.958367, which indicates superior predictive performance for staple crop yield forecasting due to its lower overall error rates and greater consistency in performance. This highlights the effectiveness of ensemble methods like XGBoost in capturing complex crop yield patterns. Despite newer machine learning (ML) techniques, these models remain recommended for similar tasks due to their robust performance.
Volume: 40
Issue: 3
Page: 1531-1538
Publish at: 2025-12-01

Innovative automation and optimization of solar-powered water purification using siemens programmable logic controller and human-machine interface

10.11591/ijeecs.v40.i3.pp1285-1297
Ahmed Bouraiou , Azzeddine Dekhane , Mohamed Benghanem , Chouaib Rahli
This study presents a novel approach to optimizing water purification systems at the Zaouiet Kounta solar power plant through the integration of advanced automation and supervision technologies. By utilizing a siemens programmable logic controller (PLC) and human-machine interface (HMI) programmed via the totally integrated automation (TIA) Portal software, the project aimed to significantly enhance the performance of water production and distribution systems. The objectives included improving operational efficiency, reducing manual intervention, and increasing system reliability and precision. The results presented herein show significant improvements in operational efficiency, system reliability, and automation in a challenging environmental context. This research provides a comprehensive case study that not only highlights the feasibility of using Siemens PLC and HMI systems in solar-powered water purification systems but also proposes scalable solutions for similar industrial applications.
Volume: 40
Issue: 3
Page: 1285-1297
Publish at: 2025-12-01

Parametric optimization of microchannel heat exchanger using socio-inspired algorithms

10.11591/ijai.v14.i6.pp5303-5310
Vikas Gulia , Aniket Nargundkar
Miniaturized products and systems have emerged as game-changing innovations with huge potential in the modern period with increasing emphasis on sustainable development and green energy. Automotive, astronomical, electronics, and medical research are just a few of the industries where micro electro mechanical systems (MEMS) have found use. In addition to that, microchannel heat exchangers (MCHX) have been created in response to the growing demand for effective cooling solutions for these small systems. Optimization of these MCHX is important for improving the overall system efficiency. In this work, two popular socio inspired evolutionary algorithms viz. teaching learning-based optimization (TLBO) and cohort intelligence (CI) are applied for optimizing three objectives such as power density, compactness factor, and heat transfer with pressure drop (HTPD) for air-water MCHX. The results obtained are significantly improved when compared with genetic algorithm (GA). Moreover, both the techniques are observed to be robust. This study investigates the use of socio-inspired artificial intelligence (AI) algorithms to support the design and optimization of heat exchangers, highlighting their potential to address complex engineering challenges more efficiently.
Volume: 14
Issue: 6
Page: 5303-5310
Publish at: 2025-12-01

Forecasting industrial electricity demand using hybrid optimization methods

10.11591/ijeecs.v40.i3.pp1689-1697
Gawalee Phatai , Tidarat Luangrungruang
This study presents a hybrid machine learning framework for forecasting industrial electricity consumption by comparing backpropagation neural networks (BPNN) with models enhanced through metaheuristic optimization algorithms. Using 32 years of annual data from APEC economies, the research addresses rising electricity demand driven by economic and infrastructural development. A key limitation in traditional models— underfitting due to complex data patterns—is addressed via feature selection, which identifies the most relevant variables and reduces model complexity. Five metaheuristic algorithms—cuckoo search (CS), differential evolution (DE), harmony search (HS), particle swarm optimization (PSO), and teaching–learning-based optimization (TLBO)—are applied to optimize both feature selection and BPNN training. The proposed approach improves forecasting accuracy by handling noisy inputs and capturing the nonlinear relationships common in energy datasets. Among the tested methods, TLBO consistently delivers superior accuracy and robustness across most evaluated countries. The findings contribute an effective and adaptable forecasting model with significant implications for long-term energy planning and policy development.
Volume: 40
Issue: 3
Page: 1689-1697
Publish at: 2025-12-01

Cloud-based secure data storage in healthcare using elliptic curve cryptography

10.11591/ijaas.v14.i4.pp1281-1294
Gayathri Govindappa Nalina , Channakrishna Raju
The growth of cloud computing in the healthcare field has led to significant developments, but ensuring the confidentiality and protection of medical records such as electronic health records (EHRs) remains a major concern for healthcare service applications. In cloud computing, the basic authentication provided by most service providers is insufficient to ensure secure access to critical or sensitive resources. Moreover, most of the existing healthcare management systems are ineffective in handling a number of patient data, which leads to single points of failure. To address these issues, elliptic curve cryptography (ECC) with Curve25519 is utilized to enhance security in cloud storage, particularly within healthcare management systems. The ECC with Curve25519 is optimized for efficient and fast scalar multiplication, which reduces computational overhead and enhances performance. The curve parameters are selected to prevent vulnerabilities and ensure security against known attacks. Moreover, it is efficient in maintaining the integrity of patient records, which reduces storage and bandwidth requirements. The ECC with Curve25519 achieves lower Key-Gen, prove, verify, proving key size, and verification key size of 13.7 s, 48 s, 0.608 s, 13.27 Mb, and 123.70 Kb, respectively, in comparison with proxy re-encryption algorithm with zero-knowledge proof (ZKP).
Volume: 14
Issue: 4
Page: 1281-1294
Publish at: 2025-12-01

A hybrid DWT-DCT-SVD watermarking scheme using arnold transform

10.11591/ijeecs.v40.i3.pp1659-1668
Van-Thanh Huynh , Thai-Son Nguyen , Thanh-C Vo
In telemedicine, medical images and electronic patient records (EPRs) are frequently transmitted and stored, making them vulnerable to tampering and theft. To ensure data security and copyright protection, this paper proposes a hybrid watermarking scheme based on discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD). The method uses a two-level DWT to decompose the image, applies DCT to selected sub-bands, and embeds two watermarks. The first is a logo used for ownership verification, and the second is an EPR encrypted with the Arnold transform for privacy protection. SVD is then used to enhance robustness. Experimental results show that the proposed scheme achieves better image quality and stronger resistance to common attacks compared with existing watermarking methods.
Volume: 40
Issue: 3
Page: 1659-1668
Publish at: 2025-12-01

Adaptive fuzzy logic controller based BLDC motor to improve the dynamic performance for electric tractor application

10.11591/ijpeds.v16.i4.pp2186-2196
Ashwini Yenegur , Mungamuri Sasikala
Permanent magnet brushless DC (PMBLDC) motors are widely used in a variety of industrial applications due to their high-power density and ease of regulation. The three-phase power semiconductors bridge is the standard way for controlling these motors. In order to initiate the inverter bridge and switch on the power devices, rotor position sensors must be provided with the correct commutation sequence. The power devices commutate progressively 60 degrees, depending on the location of the rotor. The right speed controllers are necessary for the motor to run as efficiently as possible. PI controllers are commonly employed with permanent magnet motors to achieve speed control in simple manner. Nevertheless, these controllers provide challenges in managing control complexity, including nonlinearity, parametric fluctuations, and load disturbances. PI controllers need accurate linear mathematical models. To overcome this, in this paper adaptive fuzzy logic controller (FLC) for controlling the speed of a BLDC motor is presented. When the motor drive system uses the adaptive FLC technology for speed control, it exhibits better dynamic behavior and is more resistant to changes in parameters and load disturbances. The main objectives of this work are to analyze and appraise the functioning of an electric tractor driven by a PMBLDC motor drive using adaptive FLC. The PMBLDC motor drive controllers are simulated using MATLAB/Simulink software.
Volume: 16
Issue: 4
Page: 2186-2196
Publish at: 2025-12-01

Enhanced incremental conductance MPPT method for maximizing photovoltaic power generation

10.11591/ijpeds.v16.i4.pp2757-2767
Asnil Asnil , Refdinal Nazir , Krismadinata Krismadinata , Muhammad Nasir
This research proposes an enhanced maximum power point tracking (MPPT) algorithm that integrates the variable step size (VSS) method to significantly improve power extraction from photovoltaic (PV) systems. The primary objective is to optimize performance under dynamic environmental conditions. Through comprehensive experimental studies, the proposed algorithm’s performance was evaluated and directly compared against conventional incremental conductance (INC) and perturb and observe (P&O) algorithms. The results demonstrate a substantial increase in power generation, with the proposed algorithm delivering 18.79% more power compared to INC and 39.67% more power than P&O. These findings underscore the efficacy of the developed algorithm at improving the efficiency and robustness of PV power generation, particularly in variable operating environments.
Volume: 16
Issue: 4
Page: 2757-2767
Publish at: 2025-12-01

Improvement of DSIM control using fuzzy third-order sliding mode approach optimized by MOA

10.11591/ijpeds.v16.i4.pp2321-2331
Rahma Belkaid , Lamia Youb , Farid Naceri , Ghoulem Allah Boukhalfa
This study focuses on the contribution of a new hybrid controller based on the sliding mode technique associated with fuzzy logic and optimized by an innovative approach called the mayfly optimization algorithm (MOA) to improve the drive of the dual star induction motor (DSIM). The performance and robustness of this system are analyzed under different operating conditions with three proposed strategies and compared with each other under the MATLAB/Simulink environment. Through the simulation results obtained, we realize that the method that integrates the MOA with a hybrid controller associating the third order sliding mode with fuzzy logic (MOA-FTOSMC) makes a significant contribution to research work in this field and offers the best dynamic performance and adequately manages the uncertainty and variation of the system parameters under different operating regimes.
Volume: 16
Issue: 4
Page: 2321-2331
Publish at: 2025-12-01

Performance placement of BESS in the Sulawesi-Southern interconnected power system

10.11591/ijpeds.v16.i4.pp2819-2830
Zaenab Muslimin , Indar Chaerah Gunadin , Fitriyanti Mayasari , Muhira Dzar Faraby , Asma Amaliah , Isminarti Isminarti
Frequency regulation and active power loss management are crucial aspects of power system operations. Battery energy storage systems (BESS) have emerged as an innovative solution to enhance grid performance, especially in addressing frequency fluctuations and reducing power losses. This study explores the role of BESS in optimizing frequency regulation and managing active power losses in the power system through several BESS integration scenarios. In this study, a BESS with a capacity of 8.437 MW was used and analyzed using symmetric steady-state simulations in DigSILENT PowerFactory software. The simulations aim to test the effectiveness of BESS in frequency regulation and minimizing active power losses in the Sulbagsel system. The analysis results show that implementing BESS can respond effectively to both over-frequency and under-frequency conditions in the Sulbagsel system. In the discharge scenario, BESS can reduce the system's average frequency by 0.02 Hz and decrease active power losses by up to 1.09 MW. Conversely, in the charge scenario, active power losses increase by 1.22 MW when the BESS is installed on Bus Tonasa. This study provides valuable insights for developing BESS-based frequency regulation strategies that contribute to the stability and efficiency of the power system.
Volume: 16
Issue: 4
Page: 2819-2830
Publish at: 2025-12-01

Effect of gas flow rate on ionizing power characteristics of penning type ion source

10.11591/ijpeds.v16.i4.pp2562-2569
Silakhuddin Silakhuddin , Bambang Murdaka Eka Jati , Dwi Satya Palupi , Taufik Taufik , Idrus Abdul Kudus , Fajar Sidik Permana , Suharni Suharni
An experimental observation on the effect of hydrogen gas flow rate value on ionization power characteristics of penning type ion source has been conducted. The experiments were conducted in the range of gas flow rate values between 3 and 8 sccm, which is a range of discharge that is generally used in cyclotron operations. The characteristic of ionization power is the change in power which is determined from the cathode voltage and cathode current that occurs when the gas flow rate is varied. The fixed operating parameter is the magnetic field at a value of 1.29 T. The characteristic data is presented in graphs and analyzed theoretically. The experiment was conducted at the DECY-13 cyclotron. The results of the analysis show that the effect of increasing the gas flow rate does not significantly affect the characteristics of ionization power. However, further analysis shows that the increase in gas flow rate will have a significant effect on the increase in ion formation rate in the ionization chamber due to a significant increase in the increase in gas pressure in the chamber. The benefit of the results of this study is as an initial capital to increase ion productivity from ion sources.
Volume: 16
Issue: 4
Page: 2562-2569
Publish at: 2025-12-01

Enhanced voltage stability in power distribution networks through optimal reconfiguration using hybrid metaheuristic algorithms

10.11591/ijpeds.v16.i4.pp2582-2591
Mohammed Zuhair Azeez , Abbas Swayeh Atiyah , Yaqdhan Mahmood Hussein , Hatem Oday Hanoosh
An optimal network reconfiguration (ONR) is used in distribution power systems to improve voltage decreases within the permitted period and minimize real power losses. Consequently, attaining optimal reconfiguration in distribution systems is regarded as the primary objective of numerous researchers. Conventional heuristic techniques such as genetic algorithms (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) can reduce active power losses and enhance network stability. These algorithms indicate a greater number of difficulties, including inadequate convergence characteristics, a reduction in power loss, and an increase in bus voltage. This research proposes effective optimization strategies utilizing the salp swarm algorithm (SSA) and whale optimization algorithm (WOA) to augment bus voltage, reduce distribution losses, and improve network dependability. The proposed algorithms are executed and evaluated on the IEEE 33-bus and 69-bus networks to determine the ideal network architecture. The efficacy of the examined methodologies is illustrated through MATLAB under steady-state conditions, showcasing benefits in the reduction of active power loss relative to current algorithms. The comparison indicates that the SSA algorithm exhibits superior performance in terms of power losses and bus voltage enhancement relative to the WOA method. due to its enhanced exploration and exploitation capabilities, which help avoid local optima and ensure a more effective search for optimal solutions. SSA's adaptive mechanism and cooperative behavior improve convergence speed and solution accuracy, making it more efficient for optimization in network reconfiguration.
Volume: 16
Issue: 4
Page: 2582-2591
Publish at: 2025-12-01

Small signal modeling of restructured boost converter in continuous conduction mode

10.11591/ijpeds.v16.i4.pp2500-2508
Anwar Muqorobin , Sulistyo Wijanarko , Muhammad Kasim , Pudji Irasari , Ketut Wirtayasa , Puji Widiyanto
This paper introduces small signal modeling of the restructured boost converter (RBC) in continuous conduction mode (CCM) by using the circuit averaging technique. The averaging technique produces linear transfer functions of the converter. The transfer functions relating the duty cycle to output voltage, duty cycle to inductor current, input voltage to output voltage, and input voltage to inductor current are obtained. To validate the converter model, power simulation (PSIM) simulations are developed, and experiments are conducted. The function of RBC is similar to a conventional boost converter, i.e., to level up the input voltage. A comparative analysis between the RBC and conventional boost converter is performed. The results highlight the advantages of RBC over a conventional boost converter.
Volume: 16
Issue: 4
Page: 2500-2508
Publish at: 2025-12-01

SCADE: a deep learning ensemble for semantic flow analysis in smart contract vulnerability detection

10.11591/ijeecs.v40.i3.pp1417-1429
Muralidhara Srirama , Usha Banavikal Ajay
A vulnerability in smart contracts refers to weaknesses in the code that can be exploited by attackers, leading to security breaches and unintended behavior. With the growing use of smart contracts in decentralized blockchain systems, particularly in internet of things (IoT) environments, ensuring their security has become increasingly critical. Traditional vulnerability detection techniques, such as formal verification and symbolic execution, face significant limitations, including high rates of false positives and negatives, scalability issues, and difficulty in detecting complex vulnerabilities. To address these challenges, this paper proposes semantic contract flow analysis and deep learning ensemble (SCADE) for smart contract vulnerability detection. SCADE leverages semantic flow analysis combined with an ensemble of deep learning models, including convolutional neural networks (CNN), bidirectional sequence encoder (BSE), layered probabilistic neural network (LPNN), and adaptive context learning network (ACLN), to detect vulnerabilities effectively. The methodology breaks down the smart contract code into structured components through a contract structure mapper, followed by extracting semantic paths and converting them into sequential vector representations. These representations are then processed through a deep learning ensemble to identify potential vulnerabilities such as reentrancy, timestamp dependency, code injection, and hardcoded gas amounts.
Volume: 40
Issue: 3
Page: 1417-1429
Publish at: 2025-12-01
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