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29,922 Article Results

Optimizing social issue sentiment analysis with hybrid Chi-square and bayesian-optimized binary coordinate ascent

10.11591/ijeecs.v40.i2.pp772-779
Guilbert Nicanor Abiera Atillo , Ralph Alanunay Cardeno
Feature selection aims to reduce the dimensionality of the feature space and prevent overfitting. However, when striving to produce accurate models for sentiment classification, feature selection introduces several challenges, particularly concerning textual content. Consequently, many researchers are exploring hybrid feature selection methods to customize the selection process and develop more advanced automated techniques, recognizing that the performance of these methods depends on hyperparameters. Integrating Bayesian Optimization into binary coordinate ascent (BCA) enhances the search for optimal solutions and improves classification performance in sentiment analysis, explicitly focusing on classifying abortion sentiment using Naïve Bayes. The effectiveness of combining Chi2 feature selection with the hybridized BCA and Bayesian Optimization approach is tested across multiple n-gram configurations. Results demonstrate significant improvements in accuracy and recall compared to Chi2 and BCA hybrid methods. For instance, the Bayesian Optimization-enhanced approach achieved up to 93.80% accuracy (1-gram) and 100% recall (4-gram), outperforming the baseline method. The study highlights trade-offs between computational efficiency and performance, noting that while the Chi2 and BCA hybrid method has lower training time complexity, the Bayesian Optimization-enhanced method excels in accuracy and recall during testing. The findings suggest that integrating Bayesian Optimization into feature selection improves sentiment classification performance and recommend further exploration of this approach with other classification algorithms, especially for social issues like abortion sentiment analysis.
Volume: 40
Issue: 2
Page: 772-779
Publish at: 2025-11-01

Unveiling educational enrollment factors in Egypt via ensemble learning

10.11591/ijeecs.v40.i2.pp941-952
Fahad kamal Alsheref , Mostafa Sayed Mostafa El Misery , Mahmoud Mohamed Bahloul , Dalia A. Magdi , Ibrahim Eldesouky Fattoh
Education plays a vital role in the development of a nation and significantly influences the direction of societies. Understanding the various factors that impact educational enrollment is essential for policymakers and resource allocation strategies. This paper explores the factors impacting educational enrollment in Egypt using predictive modeling and machine learning techniques. The study evaluates six machine learning algorithms and ensemble learning approaches to predict enrollment rates, considering computational efficiency, robustness, and parameter sensitivity. By analyzing socio-economic and demographic indicators from Egyptian educational data, the research examines the interplay of these factors. Results highlight the effectiveness of these methods in elucidating enrollment patterns, with ensemble learning showing promising performance and significant improvements compared to traditional machine learning algorithms. This study offers insights into Egypt's educational landscape that could inform policy formulation and resource allocation strategies.
Volume: 40
Issue: 2
Page: 941-952
Publish at: 2025-11-01

Fuzzy multi-objective energy optimization of workflow scheduling

10.11591/ijeecs.v40.i2.pp871-882
Ayoub Chehlaf , Mohammed Gabli
Task scheduling is a key and challenging problem in cloud computing systems, requiring decisions regarding resource allocation to tasks to optimize a perfor mance criterion. This problem has required researchers and developers to over come significant challenges. Our goal in this study aims to minimize both the makespan and energy consumption in cloud computing systems by efficiently scheduling workflows. To achieve this, we first proposed a dynamic multi objective model, which wasthensimplified into a single-objective problem using dynamic weights. Then, we proposed a dynamic genetic algorithm (DGA) and a dynamic particle swarm optimization algorithm (DPSO) to address the prob lem. To deal with the situation where the makespan is uncertain and not exact, we present a fuzzy model, treating each value as a fuzzy number and we utilize both possibility and necessity metrics. The results are contrasted with the Het erogeneous earliest finish time (HEFT) algorithm and Considerably lowered the total energy consumption, especially for DGA.
Volume: 40
Issue: 2
Page: 871-882
Publish at: 2025-11-01

Blockchain-based handle-research data sharing: a blockchain-based handle system to enhance the privacy and security of research data sharing

10.11591/ijeecs.v40.i2.pp1065-1086
Mahamat Ali Hisseine , Deji Chen , Yang Xiao
The increasing demand for secure, persistent and interoperable research data (RD) sharing makes traditional systems vulnerable. All research objects should be findable, accessible, interoperable and reusable (FAIR) for machines and people. This paper proposes a novel framework called blockchain-based handle- RD sharing (BHRDS), which integrates the handle system for persistent identifiers (PIDs) with a smart contract for access control and mirror-specific encryption, BLAKE2-based hashing for identity binding and irregularity detection. The system utilizes swarm, a decentralized storage layer, for off-chain data storage while storing only credential metadata and access conditions on-chain. The framework enables secure identity data management, and verifiable credential distribution across multiple mirror sites. We conducted experiments under growing user numbers (10 to 10,000), different encryption key strengths (AES 128, 192, and 256 bits), and blockchain load conditions. Results show that BHRDS achieves high irregularity detection rates (above 97%) and maintains low response times even at scale. In all the test instances, the system performed accurately, demonstrating that BHRDS offers a decentralized data access model that is scalable and aligned with the FAIR principle, making it suitable for next-generation scientific and institutional data sharing. 
Volume: 40
Issue: 2
Page: 1065-1086
Publish at: 2025-11-01

Optimizing clustering efficiency with weighted k-means: a machine learning-driven approach for enhanced accuracy and scalability

10.11591/ijeecs.v40.i2.pp1121-1128
Vishal Kaushik , Abdul Aleem
Data analysis unlocks the hidden, latent patterns and structures within datasets. Clustering algorithms, the cornerstone of any data analysis, are usually challenged by high-dimensionality, complexity, or large-scale data. This research proposes a hybrid model that merges neural networks and clustering techniques to handle these problems. Neural networks are used for feature extraction and dimensionality reduction; raw data will be transformed into a robust, low-dimensional representation. With these refined features, the performance of clustering algorithms improves in terms of scalability, efficiency, and accuracy. The proposed model is tested on diversified datasets such as the wisconsin breast cancer dataset (WBCD), GEO Dataset, and image and text data benchmarks for which substantial improvements in clustering metrics such as silhouette score, purity, and computational efficiency are reported. The results demonstrate the efficacy of the hybrid approach in optimizing clustering applications across domains, such as bioinformatics, health care, and image analysis.
Volume: 40
Issue: 2
Page: 1121-1128
Publish at: 2025-11-01

Multi-visual modality for collaborative filtering-based personalized POI recommendations

10.11591/ijeecs.v40.i2.pp978-987
Sudarat Arthan , Kreangsak Tamee
Point-of-interest (POI) recommendation systems help users discover locations that match their interests. However, these systems often suffer from data sparsity due to limited user check-in history. To address this challenge, this study proposed a novel user profiling framework that incorporates multiple visual modalities derived from user-generated photos. Three types of visual-based user profiles were constructed: image label-based, image feature-based, and a fused profile, combining both modalities through score-level fusion. We conducted extensive experiments on two real-world datasets. The results demonstrate that visual-based profiles, particularly the image feature-based profile, consistently improve recommendation performance under sparse data conditions. Although the fused profile offered stable results, it did not consistently outperform the single modality. Furthermore, performance was sensitive to the number of nearest neighbors and the amount of training data. These findings highlight the importance of modality selection and fusion strategy in visual-based POI recommendation systems.
Volume: 40
Issue: 2
Page: 978-987
Publish at: 2025-11-01

Automated defect detection in submersible pump impellers using image classification

10.11591/ijeecs.v40.i2.pp1158-1166
Deepa Somasundaram , V. Pramila , G. Ezhilarasi , D. Lakshmi , P. Kavitha , R. Kalaivani
Casting is a crucial manufacturing process used to produce complex metal parts, but it is often plagued by defects such as cracks, porosity, shrinkage, and cold shuts, which can compromise quality and lead to financial losses. Traditional visual inspection methods for detecting these defects are slow and prone to human error, making them inefficient for large-scale production. This project proposes automating the defect detection process using advanced AI-powered non-destructive testing (NDT) techniques. Specifically, convolutional neural networks (CNNs), a deep learning model, are employed for real-time visual inspection of castings. CNNs, trained on high-resolution images, can accurately identify surface defects such as cracks, scratches, and dimensional irregularities, significantly improving inspection speed and accuracy. The performance metrics of the system include defect detection accuracy, false positive and false negative rates, processing time, and scalability for high-volume production environments. By minimizing human intervention, this automated system reduces error rates, enhances product quality, and lowers production costs. Furthermore, the real-time capabilities of CNNs allow for rapid feedback, preventing defective parts from advancing through the production line. Overall, the integration of AI-based vision systems boosts efficiency, sustainability, and profitability in manufacturing, ensuring castings meet customer specifications with minimal errors.
Volume: 40
Issue: 2
Page: 1158-1166
Publish at: 2025-11-01

Maximizing QoS in railway radio networks: leaky cable and ray-tracing for optimal BER on bridges

10.11591/ijeecs.v40.i2.pp678-686
Maksim Sidorovich , Ponomarchuk Yulia
The future railway mobile communication system (FRMCS) standard is crucial for advancing railway communication and implementing intelligent train control systems. This research focuses on development of an efficient modeling method to evaluate and optimize FRMCS performance on railway bridges, particularly under high-density modulation and radio noise interference. The key aspect of this study involves computer modeling of the deployment of a leaky coaxial cable (LCX) and comparison of its performance to traditional methods of radio coverage modeling. Using the single-slot radiation pattern, we evaluate the quality of radio communication by comparison of the bit error rate (BER) metrics for the Ray Tracing propagation model with and without the use of LCX. The results show that the use of LCX significantly reduces BER values, providing a much clearer and more reliable signal. This improvement is crucial for the safety and reliability of railway operations, ensuring effective communication for train control and reducing the risk of accidents in complex and high-demanding transport networks. This research contributes to the optimization of railway information infrastructure, with the aim of ensuring safe, reliable, and efficient operations.
Volume: 40
Issue: 2
Page: 678-686
Publish at: 2025-11-01

Enhancing the ternary neural networks with adaptive threshold quantization

10.11591/ijeecs.v40.i2.pp700-706
Son Ngoc Truong
Ternary neural networks (TNNs) with weights constrained to –1, 0, and +1 offer an efficient deep learning solution for low-cost computing platforms such as embedded systems and edge computing devices. These weights are typically obtained by quantizing the real weight during the training process. In this work, we propose an adaptive threshold quantization method that dynamically adjusts the threshold based on the mean of weight distribution. Unlike fixed-threshold approaches, our method recalculates the quantization threshold at each training epoch according to the distribution of real valued synaptic weights. This adaptation significantly enhances both training speed and model accuracy. Experimental results on the MNIST dataset demonstrates a 2.5× reduction in training time compared to conventional methods, with a 2% improvement in recognition accuracy. On Google Speech Command dataset, the proposed method achieves an 8% improvement in recognition accuracy and a 50% reduction in training time, compared to fixed-threshold quantization. These results highlight the effectiveness of adaptive quantization in improving the efficiency of TNNs, making them well-suited for deployment on resource constrained edge devices.
Volume: 40
Issue: 2
Page: 700-706
Publish at: 2025-11-01

Exploring stock price portfolio clusters in foreign exchange markets

10.11591/ijeecs.v40.i2.pp735-744
Challa Madhavi Latha , S. Bhuvaneswari , K. L. S. Soujanya , A. Poongodai
This study explores a novel portfolio management approach dividing the currency pairs into clusters of periodic returns. The primary purpose is to improve diversification and risk-return ratios with currencies. This research studied USD, Euro, and Chinese Yuan to collect historical data from April 2012 to March 2022. The present study makes use of K-means clustering to find clusters of assets with similar return patterns, which constitute diversified portfolios. Optimized portfolio vs. benchmark portfolio performance was also evaluated based on critical performance measures like cumulative return, Sharpe ratio, and volatility. The clustering approach was also tested through sensitivity analysis to check how market-specific it is. The results suggest that more clustered portfolios outperform traditional benchmarks and provide a better risk-adjusted return. The conclusion drawn here from the findings is that portfolio segmentation is a superior approach because of risk management in ever-changing volatile markets and identifying situations that link currency pairs. This is beneficial for those investors and portfolio managers looking to maximize their foreign exchange (FOREX) investments by allowing greater visibility into how the market is functioning, which can, in turn, improve decision-making processes. According to the study, portfolio clustering substantially enhances a portfolio's return for the foreign exchange market.
Volume: 40
Issue: 2
Page: 735-744
Publish at: 2025-11-01

MQTT live performance on the INA-CBT communication system: a measurement-based evaluation

10.11591/ijeecs.v40.i2.pp687-699
A. A. N. Ananda Kusuma , Tahar Agastani , Rifqi F. Giyana , Sakinah P. Anggraeni , Arfan R. Hartawan , Toto B. Palokoto , Widrianto S. Pinastiko
Cable-based tsunameters have been deployed in Indonesia under the name of the INA-CBT project. Currently, the system operated at the Labuan Bajo landing station works well and sends aggregated data from the seafloor sensors to a central or read down station in Jakarta for further processing. The current scheme makes use of a publish and subscribe indirect communication among the landing station (LS) as the publisher and various clients as subscribers for the sensor data. Message queue telemetry transport (MQTT) was selected as the application-layer protocol for implementing this communication scheme. This paper presents a measurement-based evaluation of the MQTT live performance by observing the MQTT messages’ latencies received at the subscriber of the INA-CBT’s MQTT broker. The results give insight on the general achievable performance of the INA-CBT communication system in providing reliable data for the tsunami detection system. Furthermore, the results obtained can be used as communication parameters for making a more realistic virtual testbed for designing a more appropriate and scalable CBT system.
Volume: 40
Issue: 2
Page: 687-699
Publish at: 2025-11-01

Deep-learning-based hand gestures recognition applications for game controls

10.11591/ijeecs.v40.i2.pp883-897
Huu-Huy Ngo , Hung Linh Le , Man Ba Tuyen , Vu Dinh Dung , Tran Xuan Thanh
Hand gesture recognition is among the emerging technologies of human computer interaction, and an intuitive and natural interface is more preferable for such applications than a total solution. It is also widely used in multimedia applications. In this paper, a deep learning-based hand gesture recognition sys tem for controlling games is presented, showcasing its significant contributions toward advancing the frontier of natural and intuitive human-computer interac tion. It utilizes MediaPipe to get real-time skeletal information of hand land marks and translates the gestures of the user into smooth control signals through an optimized artificial neural network (ANN) that is tailored for reduced com putational expenses and quicker inference. The proposed model, which was trained on a carefully selected dataset of four gesture classes under different lighting and viewing conditions, shows very good generalization performance and robustness. It gives a recognition rate of 99.92% with much fewer param eters than deeper models such as ResNet50 and VGG16. By achieving high accuracy, computational speed, and low latency, this work addresses some of the most important challenges in gesture recognition and opens the way for new applications in gaming, virtual reality, and other interactive fields.
Volume: 40
Issue: 2
Page: 883-897
Publish at: 2025-11-01

Development and integration of a privacy computing gateway for enhanced interoperability

10.11591/ijeecs.v40.i2.pp1011-1022
Akhila Reddy Yadulla , Vinay Kumar Kasula , Bhargavi Konda , Mounica Yenugula , Supraja Ayyamgari
A new design of privacy computing gateway stands as the solution to secure efficient interoperability between heterogeneous platforms. The growing importance of data privacy, along with rising collaborative data analysis operations, creates an immediate need for standardized privacy-preserving frameworks that are adaptable to diverse situations. A three-layered architecture consisting of application protocol and communication layers receives support from an Adaptation mechanism designed for compatibility between separate privacy computing systems. Testing of the framework uses standard machine learning methods together with horizontal and vertical federated learning using diverse data quantities and feature distribution patterns. The gateway achieves satisfactory model performance and protects data privacy integrity in combination with platform interoperability. area under the curve (AUC) along with F1 score metrics, proves that the proposed system reaches performance equivalence with centralized models when operating within privacy-limited environments. The research introduces an effective solution for securing cross-platform data sharing that will enable secure inter-sector collaboration in finance, healthcare, and government applications.
Volume: 40
Issue: 2
Page: 1011-1022
Publish at: 2025-11-01

Application of Naïve Bayes Algorithm in Expert System for Diagnosing Chilli Plant Diseases Based on Growth Phase on Peatland

10.11591/ijeecs.v40.i2.pp829-839
fatayat fatayat fatayat , Wahyu Lestari Wahyu Lestari Wahyu Lestari , Alfirman Alfirman Alfirman
Agricultural development on peatlands has its own challenges, especially in the cultivation of chili plants that are susceptible to various diseases. Therefore, an expert system is needed that can help farmers diagnose chili plant diseases quickly and accurately based on the plant growth phase. This research aims to apply the Naïve Bayes algorithm to the expert system for diagnosing Capsicum annum L (Chilli) plant diseases. The results of the expert system research offer an innovative and adaptive solution for the management of plant diseases in peatlands, with great potential to increase agricultural productivity and plant resistance to disease. The expert system is able to diagnose several types of diseases on chili plants in peatlands, such as anthracnose, fusarium wilt, and leaf curl disease. Each diagnosis is based on symptoms observed in each phase of plant growth, from the vegetative phase to the generative phase. Expert system testing results. This system is expected to increase the productivity and quality of chili crops on peatlands, as well as reduce losses due to disease attacks. In addition, this research also shows that the Naive Bayes algorithm has great potential to be applied in expert systems in other agricultural fields.
Volume: 40
Issue: 2
Page: 829-839
Publish at: 2025-11-01

Hash-based message authentication code with secure hash algorithm-256 for efficient data sharing in blockchain

10.11591/ijeecs.v40.i2.pp780-788
Naveenkumar Lingaraju , Manjula Haladappa Sunkadakatte
Recently, cloud servers have increasingly been utilized for storing a large amount of data, which is stored in the form of ciphertext. In a decentralised system, the communication overhead on the network is recognized as the main problem due to the numerous transaction data recorded across the data Sharding and nodes with authorized users. Hash-Based Message Authentication Code with Secure Hash Algorithm-256-bit (HMAC-SHA-256 bit) is proposed for secure and effective data sharing in blockchain to overcome this issue. The secure algorithm HMAC serves for authenticating both the data origin and integrity. That uses a cryptographic hash procedure in combination with a confidential key to validate both the verification and tamper-proof content of a message. HMAC consists of a particular content and an authentication key with a hashing code value. In the Blockchain framework, the HMAC algorithm is utilized with the SHA-256bits to generate and validate the signatures of many transactions. SHA-256 is a hash algorithm that creates a 256-bit cryptographic checksum. The blockchain uses HMAC along with SHA-256bits, which is a safe and clearly expressed algorithm to allocate or convey the data securely. The Authentication of HMAC-SHA-256bits achieves the optimal retrieval times of 0.4s, 1.0s, 1.5s, 1.9s, 2.2s, and 2.8s for file sizes of 50KB, 100KB, 150KB, 200KB, 250KB, and 300KB, correspondingly, when compared to interplanetary file system (IPFS).
Volume: 40
Issue: 2
Page: 780-788
Publish at: 2025-11-01
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