Bulletin of Electrical Engineering and Informatics, 2025
Blockchain technology has become a major focus in data security and reliability. A foundation for... more Blockchain technology has become a major focus in data security and reliability. A foundation for innovations such as non-fungible token (NFT), which opens up new opportunities in managing ownership of digital assets. We investigate NFTs in the form of voice, which is digital audio communication. During the COVID-19 pandemic, podcasts have been rampant, creating new business opportunities in digital media such as NFTs, which have explored and evolved in various markets; voice content has gained significant space in sales, promotion, and dissemination/innovation. This research presents a comprehensive analysis of NFTs from 2019 to 2022, focusing on the variable association consisting of the NFT category, the price of each of those NFT categories, NFT editions, and NFT marketplace. We used structural equation modeling (SEM) to clarify the relationship in partial least squares structural equation modeling (PLS-SEM). This study's findings suggest that music enthusiasts seek NFTs based on the NFT category. Therefore, it is crucial for NFT creators, who are musicians too, to exercise caution when choosing the NFT category that is most popular among music enthusiasts. We suggest that the musicians creating NFTs should consider establishing appealing NFT categories to attract music fans and other collectors.
Bulletin of Electrical Engineering and Informatics, 2025
Effective crop recommendation systems are crucial for modern agriculture, yet existing models oft... more Effective crop recommendation systems are crucial for modern agriculture, yet existing models often struggle to adapt to dynamic environmental conditions and incorporate expert knowledge. This paper proposed a novel model that fuses decision tree (DT) algorithms with ontologies, combining robust data analysis with semantic knowledge representation. DT provide transparent, adaptable decision rules that respond to changing environmental factors, while ontologies structure domain expertise to enable deeper reasoning and improve accuracy. This integrated approach achieved a remarkable 99.77% accuracy on an Indian crop recommendation dataset, significantly outperforming previous methods. By merging the strengths of DT and ontologies, this model offers a powerful, adaptable tool for informed decision-making, supporting farmers in today's complex agricultural landscape.
Bulletin of Electrical Engineering and Informatics, 2025
Image captioning has emerged as a vital research area in computer vision, aiming to enhance how h... more Image captioning has emerged as a vital research area in computer vision, aiming to enhance how humans interact with visual content. While progress has been made, challenges like improving caption diversity and accuracy remain. This study proposes transfer learning models and RNN algorithms trained on the microsoft common objects in context (MS COCO) dataset to improve image captioning quality. The models combine image and text features, utilizing ResNet50, VGG16, and InceptionV3 with LSTM, and BiLSTM. Performance is measured using metrics such as BLEU, ROUGE, and METEOR for greedy and beam search. The InceptionV3+BiLSTM model outperformed others, achieving a BLEU score of over 60%, a METEOR score of 28.6%, and a ROUGE score of 57.2%. This research contributes to building a simple yet effective image captioning model, providing accurate descriptions with human-like understanding. The error was analyzed to improve results while discussing ongoing research aimed at enhancing the diversity, fluency, and accuracy of generated captions, with significant implications for improving the accessibility and searchability of visual media and informing future research in this area.
Bulletin of Electrical Engineering and Informatics, 2025
Primary malignant brain tumors along with central nervous system cause a significant amount of de... more Primary malignant brain tumors along with central nervous system cause a significant amount of deaths every year, making brain cancer a major worldwide health problem. In South Asian countries, the number of patients suffering from brain cancer is steadily rising. Treatment effectiveness and improved patient outcomes depend on early detection. Using a dataset consisting of 6056 original raw MRI scans, this study evaluates how well convolutional neural networks (CNNs) diagnose brain cancer. We present ResNet50 and ResNet50V2 models assessed for their effectiveness in identifying brain cancers. Transfer learning and fine-tuning were employed to enhance model performance. The models demonstrated strong performance, with 87-99% accuracy rate. ResNet50V2 achieved the highest 99% accuracy. To detect tumor early, this work emphasizes how well the CNN-based machine learning methods help as timely intercession and patient care is necessary. Early prediction with 100% confidence and reliable precision is a critical issue in the modern world. Our goal is to use advanced algorithms to forecast images affected by cancer. Lastly, we will deploy an automated system that will enable us to confidently identify images affected by cancer. Our suggested methodology and its application could significantly impact the field of medical science by combining computer vision and health informatics.
Bulletin of Electrical Engineering and Informatics, 2025
This paper presents a thorough examination of two prominent speech-to-text translation (STT) mode... more This paper presents a thorough examination of two prominent speech-to-text translation (STT) models: the end-to-end (E2E) model and the cascade model. STT is a critical technology in today's multilingual society, facilitating communication across language barriers. The study focuses on comparing these models using a multicriteria approach to evaluate their effectiveness in translating speech to text. The E2E model represents a unified architecture that directly translates speech into text, while the cascade model involves separate modules for speech recognition and machine translation (MT). Both models have distinct advantages and challenges, which are explored in detail. Through a multicriteria comparison, this research assesses various performance metrics and criteria to determine the strengths and weaknesses of each model. The weighted sum method is employed to assign weights to evaluation criteria, providing a systematic evaluation framework. The findings have implications for researchers and developers in STT. By understanding the comparative performance of E2E and cascade models, researchers can make informed decisions regarding model selection based on criteria such as accuracy, speed, robustness, and resource requirements. This research advances the understanding of speech translation technologies and provides a foundation for future studies to refine evaluation methodologies, explore hybrid models, and enhance translation quality.
Bulletin of Electrical Engineering and Informatics, 2025
Organizations worldwide commonly utilize cloud infrastructure to manage large volumes of data, ma... more Organizations worldwide commonly utilize cloud infrastructure to manage large volumes of data, making the optimization of storage crucial for enhancing cloud performance. One effective optimization technique is data deduplication, which identifies duplicate objects and ensures that only one copy of unique data is stored in the cloud. While several deduplication schemes currently exist, there is a pressing need to improve efficiency in cloud storage through innovative approaches. In this paper, we propose a new system model designed to facilitate an efficient deduplication process. Our algorithm, called deduplication in cloud infrastructure (DCI), offers a systematic and effective method for handling deduplication challenges related to redundant data storage. DCI focuses on hash generation, metadata comparison, and pointer-based deduplication, providing a comprehensive strategy for optimizing cloud storage resources and minimizing duplication. This ultimately enhances both the efficiency and cost-effectiveness of cloudbased data management. A simulation study using CloudSim and the Hadoop distributed file system (HDFS) simulator demonstrates that the proposed deduplication method is effective. Experimental results show that our algorithm outperforms many existing solutions, achieving the highest deduplication ratio of 6.7 and saving 85.09% of storage space due to its efficient deduplication approach. The proposed system can be used in cloud infrastructures for efficiency.
Bulletin of Electrical Engineering and Informatics, 2025
Current source inverter (CSI) transforms DC current into a predetermined AC current. In practice,... more Current source inverter (CSI) transforms DC current into a predetermined AC current. In practice, the DC current are acquired by connecting inductors with the DC power source. Common-emitter current source inverter (CE-CSI) is an inverter where the emitter terminals of the insulated gate bipolar transistors (IGBTs) or metal oxide semiconductor field effect transistors (MOSFETs) switches are connected at a common voltage. This inverter requires two nonisolated DC current sources as input power. The two level CE-CSI is the simplest circuit of the CE-CSIs. The circuit was able in simplifying inverter circuits compared to the three-level CE-CSI in case of device number, i.e., diodes, IGBTs/MOSFETs, and gate drive circuits. This paper studied the basic characteristics of the two-level CE-CSI when two reactors with a single magnetic core were used. The inverter circuit was examined and evaluated through computer tests, and experimentally. The two-level CE-CSI was able to generate a low distortion of sinusoidal AC load current with total harmonic distortion (THD) value 1.92%. Test data showed that the magnitudes of low order harmonics were less than 0.3% of the fundamental frequency. Moreover, the inverter efficiency can be increased due to reduction of the power losses caused by power switching devices.
Bulletin of Electrical Engineering and Informatics, 2025
The rapid growth of internet accessibility requires strong data security measures, mainly for saf... more The rapid growth of internet accessibility requires strong data security measures, mainly for safeguarding sensitive information. Since many threats and attacks steal our private data. Data encryption standard (DES) is one of the cryptographic methods that uses a symmetric key encryption method to resist various types of cryptographic attacks. This work proposes an improved key scheduling algorithm (KSA) to enhance DES security. The modified KSA is evaluated using criteria such as frequency test, hamming weight, and bit difference to measure round key randomness and resilience. Moreover, the avalanche effect is evaluated to assess the diffusion and confusion character of the generated ciphertext. The final result indicates that the enhanced KSA attains better frequency distribution (0.89-1.0), increased hamming weight consistency (97.13%), and high bit transition rates compared to the original DES KSA. These enhancements demonstrate increased randomness and complexity, making the algorithm more resistant to brute-force and other cryptographic attacks. Our proposed work shows enhanced security capabilities, albeit with increased computational requirements, and establishes a foundation for future improvement in symmetric key cryptography.
Bulletin of Electrical Engineering and Informatics, 2025
Since electric vehicles (EVs) emit less carbon dioxide, their number is rapidly increasing. As th... more Since electric vehicles (EVs) emit less carbon dioxide, their number is rapidly increasing. As the number of EVs grow, these added loads strain the distribution grid, introducing new challenges. Key concerns for network operators include voltage fluctuations and increased power losses. Properly deploying throughout the grid, photovoltaic (PV) systems and electric vehicle charging stations (EVCS) can assist in lowering power losses and improving the bus voltage profile. A MATLAB implementation of the metaheuristic algorithm called Harris Hawk optimization (HHO) algorithm is developed to select the best locations for integrating EVCSs and PVs, with the goals of enhancing the voltage profile and reducing power losses across buses. IEEE 12-bus and 14-bus systems and real-time distribution grid data were used to test the method. For the 26-bus real-time system, the results demonstrated a notable 24% decrease in overall power loss as compared to the base case and improved voltage regulation, as indicated by a lower average voltage deviation index (AVDI) value of 0.0929. A comparative analysis was performed between optimized and random placements of EVCSs and PVs, as well as against the grey wolf optimization (GWO) algorithm. The results provide a framework for implementing solar-powered EV charging infrastructure. This can reduce costs, enhance energy reliability, and contribute to a cleaner environment.
Bulletin of Electrical Engineering and Informatics, 2025
Texture analysis is a fundamental approach in image processing for identifying specific patterns ... more Texture analysis is a fundamental approach in image processing for identifying specific patterns or structures. One widely used method is the grey-level co-occurrence matrix (GLCM), which computes the frequency of pixel value pairs at certain distances and angles. This study adapts the GLCM method for 1D electroencephalogram (EEG) signal analysis, focusing on extracting features such as contrast, energy, homogeneity, correlation, and entropy. EEG signals are normalized to the range 0-255, and the extracted features are classified using a support vector machine (SVM). Experimental results show that combining features across multiple distances (d=1 to 20) achieves classification accuracy of 78.8% for five classes (Z/O/N/F/S), 94.0% for three classes (O/F/S), and 94.3% for another three-class group (Z/N/F). The method shows strong performance for short to medium distances and fewer class combinations. However, performance declines when dealing with more complex class sets and longer distances, where texture features become less effective. The drop in accuracy for Z/O/N/F/S beyond d=5 underscores the challenges of maintaining feature robustness at extended distances. Despite this, GLCM remains a promising approach for EEG signal classification. Future work should focus on optimizing distance parameters and feature combinations to further enhance classification performance.
Bulletin of Electrical Engineering and Informatics, 2025
This paper presents a product brand recognition method based on the YOLOv8 algorithm. The perform... more This paper presents a product brand recognition method based on the YOLOv8 algorithm. The performance evaluation of the proposed method is conducted on two datasets consisting of GroZi-120 and GroZi-3.2K. The results show that the proposed method can achieve high accuracy. The precision and F1-score on the GroZi-120 and GroZi-3.2K datasets reach of {74.77%, 80%} and {99.86%, 100%}, respectively. The comparison with previous studies shows that the precision and F1-score obtained by the YOLOv8 method outperform some previous studies. Additionally, the effectiveness of the proposed method is also evaluated on a dataset of 6,170 images for twelve real products collected from supermarkets for use in order payment. The results show that the proposed method can be applied in singleorder payment as well as multiple simultaneous orders with high accuracy in product recognition ranging from 94% to 98%. Therefore, the proposed method can be applied in order quick payment at supermarkets.
Bulletin of Electrical Engineering and Informatics, 2025
Recently, simultaneous wireless information and power transfer (SWIPT) emerged as the best soluti... more Recently, simultaneous wireless information and power transfer (SWIPT) emerged as the best solution for resource-constrained internet of things (IoT) networks. SWIPT ensures the provision of parallel information and power transfer in the network. Under the SWIPT model, many researchers use two well-established protocols: time switching (TS) and power splitting (PS). TS is better than PS when the signal is weak but inserts an extra delay because energy harvesting (EH) and information decoding (ID) happened two different times. However, PS protocol performs poorly in hard situations with low signal strength even if it conducts EH and ID simultaneously. Hence, this paper proposed a new model called mixed-SWIPT (MSWIPT) which combines TS and PS protocols in an intuitive manner. Further, this work proposes a multi-source EH mechanism in which the receiving node harvests energy from multiple sources which is different from single source, i.e., desired node's radio frequency (RF) signal. The multiple sources include non-participated Neighbor Node's RF signal, sink node and cochannel interference and noise. Under the routing, the node selection is formulated as maximum link capacity problems and solved through several constraints. Extensive simulations on proposed model prove the superiority in terms of EH and energy efficiency from the state-of-the-art methods.
Bulletin of Electrical Engineering and Informatics, 2025
This paper presents a novel hybrid fuzzy logic approach for the classification of thyroid disease... more This paper presents a novel hybrid fuzzy logic approach for the classification of thyroid disease. Hybrid fuzzy logic approaches have brought many benefits to the medical data classification problems such as reasoning on uncertain or incomplete data. The machine learning algorithms had been used with the fuzzy expert systems to define the fuzzy rule base. The optimization techniques had been used in the fuzzy expert systems for optimizing the fuzzy membership functions and fuzzy rules. Enhancing the machine learning algorithms and optimization techniques that are integrated with the fuzzy logic method can improve the overall performance of the fuzzy expert system. To deal with the curse of dimensionality problem and to enhance the integration of machine learning algorithm and fuzzy logic method, this paper presents an incremental learning based parallel fuzzy reasoning system (IL-PFRS) for medical diagnosis. In this research work, the decision tree classifier is used to extract the features from dataset. IL-PFRS is applied to classify the thyroid disease which is serious disease that needs attention and earlier detection. The thyroid disease dataset obtained from the UCI machine learning repository is used in this research work where the IL-PFRS showed the classification accuracy of 99% when testing using this dataset.
Bulletin of Electrical Engineering and Informatics, 2025
Due to the widespread availability of the internet all across the world, people prefer shopping o... more Due to the widespread availability of the internet all across the world, people prefer shopping online rather than going to a shop. There are various online marketplaces available in Bangladesh, like Daraz, Pickaboo, Rokomari, Othoba, Bikroy, Food Panda, and Robi Shop. With the increasing quantity of customers on online shopping platforms, the number of product reviews also increases with it. Data is classified utilizing machine learning (ML), deep learning (DL), transfer learning, and other data mining algorithms to facilitate the customer's comprehension of the primary subject of the review before making a purchase. Natural language processing techniques are employed to categorize data in any given language for such issues. There are no Bengali shopping review datasets available on online sites. So, we manually collected a dataset of 2,600 reviews. In this paper, reviews are classified into 5 categories (satisfied, very satisfied, not satisfied, fairly satisfied, and satisfied but delivery problem). DL (long short-term memory (LSTM) and convolutional neural network (CNN)) and ML (support vector machine (SVM), random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGBoost)) model have been applied. Among the DL models, CNN has the best accuracy (91.27%), and the RF classifier provides the highest accuracy (84.39%) out of all the ML models.
Bulletin of Electrical Engineering and Informatics, 2025
Sentiment analysis is an area of computational linguistics that studies natural language processi... more Sentiment analysis is an area of computational linguistics that studies natural language processing. The most significant subtasks are gathering people's thoughts and organizing them into groups to determine how they feel. The primary purpose of sentiment analysis is to determine whether the individual who created a piece of material has a positive or negative opinion about a subject. It has been claimed that sentiment analysis and social media mining have contributed to the recent success of both private sector and the government. Emotional analysis has applications in practically every aspect of modern life, from individuals to corporations, telecommunications to medical, and economics to politics. This article describes an improved sentiment analysis model based on gray level co-occurrence matrix (GLCM) texture feature extraction and a convolutional neural network (CNN). This model was created using tweets. First, texture characteristics are extracted from the input data set using the GLCM technique. This feature extraction improves categorization accuracy. CNNs are used to classify objects. It outperforms both the support vector machine and the AdaBoost algorithms in terms of accuracy. CNN has achieved an accuracy of 98.5% for sentiment analysis task.
Bulletin of Electrical Engineering and Informatics, 2025
Scheduling refers to the process of allocating cloud resources to several users according to a sc... more Scheduling refers to the process of allocating cloud resources to several users according to a schedule that has been established in advance. It is not possible to get acceptable performance in settings that are distributed without proper planning for simultaneous processes. When developing productive schedules in the cloud, it is necessary for work scheduling to take a variety of constraints and goals into consideration.When dealing with activities that have performance optimization limits, resource allocation is a very important aspect to consider. When it comes to cloud computing, the only way to achieve great performance, high profits, high scalability, efficient provisioning, and cost savings is with an exceptional task scheduling system. This article presents a grey wolf optimization (GWO) based framework for efficient task scheduling in cloud computing environment. The proposed algorithm is compared with particle swarm optimization (PSO) and flower pollination algorithm (FPA) and GWO is performing task scheduling in less execution time and cost in comparison with PSO and FPA techniques. Execution time taken by GWO to finish 200 task in 120.2 ms. It is less than the time taken by PSO and FPA algorithm to finish same number of tasks.
Bulletin of Electrical Engineering and Informatics, 2025
Diabetic retinopathy (DR) is a disease that affects the blood vessels that are located in the ret... more Diabetic retinopathy (DR) is a disease that affects the blood vessels that are located in the retina. Loss of vision due to diabetes is a common consequence of the illness and a key factor in the progression of vision loss and blindness. Both ophthalmology and diabetes research have become more dependent on computer vision and image processing techniques in recent years. Fundus photography, also known as a fundus image, is a method that may be used to capture an image of the back of a person's eye. This article presents optimized deep learning model for diagnostic marking in retinal fundus images towards accurate detection of retinopathy. For experimental work, 500 images were selected from available open source Kaggle data set. 400 images were used to train deep learning model and remaining 100 images were used to validate the model. Images were enhanced using the contrast limited adaptive histogram equalization (CLAHE) algorithm. Pre trained convolutional neural network (CNN) models-AlexNet, VGG16, GoogleNet, and ResNet are used for classification and prediction of images. Accuracy, specificity, precision and F1-score of AlexNet is better than VGG16, ResNet-50, and GoogleNet. Sensitivity of ResNet-50 is higher than other pre trained CNN models.
Bulletin of Electrical Engineering and Informatics, 2025
The advent of fifth-generation (5G) technology and progressing further to sixgeneration (6G) tech... more The advent of fifth-generation (5G) technology and progressing further to sixgeneration (6G) technology has created a new era of high-speed wireless communication, demanding antennas with enhanced capabilities to fulfill the dynamic demands of various applications. This paper presents novel approaches to designing antennas in the GHz frequency range for 5G networks by incorporating re-configurability features. Adaptive antennas provide the flexibility to alter their radiation configurations, frequencies, or polarization states, allowing them to optimize performance under different operating conditions. The theoretical foundations are explored, and reconfigurable antennas are simulated using HFSS, focusing on frequency and pattern variation at GHz frequencies using different types of switches such as pin diodes and rods. Through simulations, the antenna's S parameters are evaluated, demonstrating its capacity to meet the rigorous specifications of 5G applications. Its adaptive nature enhances connectivity and overall network performance, supporting the successful deployment and advancement of 5G technology in diverse real-world applications.
Bulletin of Electrical Engineering and Informatics, 2025
Accurate classification of fresh fruit types is essential in the agricultural sector for ensuring... more Accurate classification of fresh fruit types is essential in the agricultural sector for ensuring quality control, minimizing waste, and enhancing food safety across the supply chain. This study evaluates the performance of four machine learning algorithms-artificial neural network (ANN), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF)-in classifying fruit freshness based on data obtained from electronic noses equipped with MQ array sensors. Experiments were conducted using a comprehensive dataset comprising various fruit combinations, and model performance was assessed using accuracy, precision, recall, and F1 score metrics. Results indicate that the RF algorithm achieved the highest accuracy (100%) and precision (1.00), demonstrating superior performance in both classification accuracy and computational efficiency. ANN and KNN also performed well, with accuracies of 96.80% and 97.10%, respectively, while LR yielded a lower but still effective accuracy of 91.16%. Statistical analysis confirms that RF's superior performance is statistically significant when compared to the other algorithms. These findings suggest that RF is the most effective algorithm for fruit freshness classification using electronic nose data, offering fast and reliable results that are well-suited for integration into real-time monitoring systems in agricultural and food retail applications.
Bulletin of Electrical Engineering and Informatics, 2025
The grading of arecanuts before their sale is significant for enhancing profitability. The assess... more The grading of arecanuts before their sale is significant for enhancing profitability. The assessment of areca nut quality widely utilizes and respects both producer-level and wholesale dealer-level grading methods. This study proposes an advanced grading framework for white Chali-type arecanuts by developing a standardized image database and utilizing deep learning-based feature extraction. This research presents a novel approach by combining a representational deep neural network (ResNet) for automatic feature extraction with various spectral analysis methods, such as the Fourier transform and wavelet transform, to capture frequency-domain features. The support vector machine (SVM) model classifies these extracted features. The proposed system achieves an accuracy of 97.8%, which is significantly better than existing methods SVM with 72.5%, convolutional neural network (CNN) with 92.9%, AlexNet with 90.6%, and VGG19 with 90.2%. The results show that the proposed hybrid ResNet-SVM method improves accuracy, precision, recall, and F1-score, making it a more reliable and automated way to grade areca nuts. This method thus enhances efficiency, reduces manual effort, and ensures consistent quality assessment.
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