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Household electric monitoring IoT system

10.11591/ijeecs.v40.i1.pp85-92
Joemar Corpuz , Kristine Joy S. Dela Cruz , Joan B. Palomar , Jackielyn Tamayo , Hohn Lois C. Bongao , Mark Joseph B. Enojas , Jane E. Morgado
In dense areas in the Philippines, there are recorded cases of power theft or known to be illegally tapping power lines from another household which results to complaints because of increased electricity bills. To address the power theft problems, this work uses internet of things system for household electric monitoring and control. A transmitter and receiver set up is designed to monitor the energy consumption at both ends. When there is discrepancy with the meter reading, an alert system sends notification that there is an illegal wiretapping. The load is monitored through electric meters and the powers measured are compared. These data are being sent wirelessly through a GSM module. The meter readings for both the transmitter and receiver can be viewed in a mobile phone through a web app developed. A minimum of 3W difference between the transmitter and the receiver will mean a discrepancy and notifies illegal wiretapping. Illegal connections are cutoff when an incident of tapping occurs. Based on the results of the test, the household electricity monitoring system through internet of things (IoT) is found to be 100% reliable in detecting and cutting off illegal connections. Additionally, the system is able to compute the monthly power consumption.
Volume: 40
Issue: 1
Page: 85-92
Publish at: 2025-10-01

DigiScope: IoT-enhanced deep learning for skin cancer prognosis

10.11591/ijeecs.v40.i1.pp202-215
Aymane Edder , Fatima-Ezzahraa Ben-Bouazza , Oumaima Manchadi , Idriss Tafala , Bassma Jioudi
In dermatology, early identification and intervention are crucial for optimizing patient outcomes in skin cancer care. Recent technological advances, particularly in the internet of things (IoT), have led to significant growth in telemedicine. This study introduces a cutting-edge system that proactively predicts the emergence of skin cancer by combining deep learning algorithms, IoT devices, and sophisticated medical imaging techniques. The experimental setup leverages a high-resolution mobile camera for dermoscopy, associated with a cloud-integrated machine learning framework. The proposed algorithm comprehensively examines lesion characteristics, Utilizing color, texture, and shape characteristics to evaluate the probability of malignancy. Subsequently, a cloud-hosted machine learning model analyzes and scrutinizes the collected data, yielding a thorough diagnostic evaluation. Initial results reveal that this system achieves an impressive predictive accuracy rate exceeding 97.6%, enabling swift and efficient skin cancer detection. These promising findings emphasize the potential for rapid, efficient, and proactive diagnosis, significantly improving patient prognosis and reinforcing the value of telemedicine in contemporary healthcare.
Volume: 40
Issue: 1
Page: 202-215
Publish at: 2025-10-01

Sentiment analysis of YouTube videos comments for children using machine learning and deep learning

10.11591/ijeecs.v40.i1.pp397-410
Amal Alrehaili , Abdullah Alsaeedi , Wael M.S. Yafooz
Nowadays, online connectivity is increasing with the rapid growth of the world wide web. Consequently, content shared across numerous platforms varies in appropriateness. it is necessary to ensure the suitability of the content since children are among the consumers of online content. A lot of children watch videos on YouTube these days, and such platforms can contain useful content. However, such videos can also have a negative impact on children. The suitability of these videos can be determined through sentiment analysis to refine the content for children on YouTube, by classifying the posted comments as either positive or negative. Therefore, this study utilizes natural language processing methods, machine learning classifiers (MLCs) and deep learning models (DLMs) to detect and classify negative user comments using the proposed dataset. Different MLCs such as random forest (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), decision tree (DT), K-nearest neighbour (KNN), AdaBoost, and support vector machine (SVM) have been used. Additionally, DLMs were also used such as artificial neural network (ANN), convolutional neural network (CNN) and long short-term memory (LSTM). Overall, the experimental results showed that the LR, RF, AdaBoost, ANN and LSTM classifiers outperformed all the other classifiers in terms of accuracy.
Volume: 40
Issue: 1
Page: 397-410
Publish at: 2025-10-01

Gender identification from tribal speech using several learning techniques

10.11591/ijeecs.v40.i1.pp316-326
Subrat Kumar Nayak , Kumar Surjeet Chaudhury , Nirmal Keshari Swain , Yugandhar Manchala , Ajit Kumar Nayak , Smitaprava Mishra , Nrusingha Tripathy
Language processing and linguistics researchers are interested in gender identification through audio, as human voices have many distinctive features. Although several gender identification algorithms have been developed, the accuracy and efficiency of the system can still be improved. Despite extensive studies on the topic in various languages, there aren’t many studies on gender identification in the KUI language. Using a variety of machine learning (ML) and deep learning (DL) classifiers, including decision tree (DT), multilayer perceptron (MLP), gradient boosting (GB), linear discriminant analysis (LDA), recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), and transformer, the goal of this study is to assess the accuracy of gender identification among diverse KUI language speakers. To verify the effectiveness of the suggested model, several prediction evaluation metrics were calculated, such as the area under the receiver operating characteristic curve (AUC), F1-score, precision, accuracy, and recall. While the findings are compared to other learning models, the gradient-boosting strategy yielded better results with an accuracy rate of 97.0%.
Volume: 40
Issue: 1
Page: 316-326
Publish at: 2025-10-01

Determining social assistance recipients using fuzzy-TOPSIS method in Sumur Bandung district Indonesia

10.11591/ijeecs.v40.i1.pp366-378
Rangga Sanjaya , Irmeila Cahaya Fatihah , Titik Khawa Abdul Rahman
This study aims to improve the selection process for social assistance recipients in the Sumur Bandung District, Indonesia, using the fuzzyTOPSIS method. The research establishes eligibility criteria and evaluates alternatives based on data from April 2024. By combining multi-criteria decision-making with fuzzy logic, the fuzzy-TOPSIS approach enhances the accuracy and fairness of recipient selection. The methodology involves determining criteria weights, fuzzification, and ranking alternatives against ideal solutions. The results demonstrate that fuzzy-TOPSIS significantly improves decision-making, leading to more objective and reliable outcomes than traditional methods. These findings underscore the potential of fuzzyTOPSIS in optimizing social assistance distribution, ensuring that assistance reaches the most deserving recipients efficiently.
Volume: 40
Issue: 1
Page: 366-378
Publish at: 2025-10-01

Design of high-efficiency microinverter for a photovoltaic system with low harmonic distortion

10.11591/ijeecs.v40.i1.pp67-77
Walter Naranjo Lourido , Jhon Manuel Sanchez Fierro , Diana Paola Monroy Cadena , Javier Eduardo Martínez Baquero
This article presents the design of a modular pure sine wave microinverter with a high-efficiency maximum power point tracking (MPPT) regulator for photovoltaic (PV) systems. The design starts with a DC/DC buck-boost chopper regulator, simulated using the perturb and observe (P&O) algorithm. Next, a high-frequency DC/AC conversion stage is implemented using a toroidal transformer to achieve various voltage levels and isolated power sources. Finally, a 27-level multilevel inverter is designed to produce a pure sine wave with minimal total harmonic distortion (THD). Simulation results indicate that the microinverter achieves a total efficiency of 90% and produces a pure wave output with 3% harmonic distortion. Compared to commercial solutions, the proposed design enhances efficiency while integrating key components. Additionally, the system maintains a cost-effectiveness and directly proportional to its energy efficiency, making it a viable and cost-effective solution for PV energy conversion.
Volume: 40
Issue: 1
Page: 67-77
Publish at: 2025-10-01

War strategy assisted Bi-LSTM for sentiment analysis of customer review

10.11591/ijeecs.v40.i1.pp480-489
Anilsagar T. , Syed Abdul Syed S.
Sentiment analysis (SA) stands as a valuable tool for categorizing reviews to discern positive or negative sentiments. Satisfaction of customers holds a pivotal role in the realm of customer service. Presently, customer expression entails a significant volume of reviews on online platforms. For extracting useful information from massive reviews, the categorization of reviews into positive or negative SA is essential. For enhancing the efficiency of customer review detection, this work presents a war strategy algorithm (WSA)-bidirectional long short-term memory (Bi-LSTM) for customer review classification using the TripAdvisor dataset. Initially, the preprocessing stage is carried out, and the skip-gram-based word embedding is performed. For categorizing the extracted features, the deep learning model Bi-LSTM-WSA is presented. Accuracy and precision values achieved are 97.1% and 97.5% respectively.
Volume: 40
Issue: 1
Page: 480-489
Publish at: 2025-10-01

A comprehensive analysis of smartphones and tablets in graphic design and digital art

10.11591/ijeecs.v40.i1.pp146-155
Jirawat Sookkaew , Nakharet Chaikaew , Nakarin Chaikaew
This paper discusses how smartphones and tablets have changed creativity and graphic design. These portable tools and easy apps have transformed the creative process, allowing artists, designers, and students to create high-quality work anywhere. Mobile design apps promote creativity, accessibility, and skill development across broad user groups, according to the study. Unlike desktop tools, it addresses key constraints. Mobile apps sometimes struggle with smaller screens, restricted processing power, and reduced capabilities for complicated tasks like multi-layer editing and advanced graphics. These restrictions may inhibit expert designers working on complex, precise designs. Even Nevertheless, mobile technology like larger screens, stylus support, and cloud-based solutions are making mobile devices more feasible for creative work. The findings emphasise the relevance of integrating mobile technology into education and professional workflows and its complementarity to desktop solutions for resource-intensive jobs. In the developing digital landscape, our dual-platform approach maximizes creativity and flexibility.
Volume: 40
Issue: 1
Page: 146-155
Publish at: 2025-10-01

Cryptographically secure digital certificates on a distributed ledger

10.11591/ijeecs.v40.i1.pp236-262
Umna Iftikhar , Hafiz Muhammad Attaullah , Inam Ullah Khan , Muhammad Mansoor Alam , Mazliham Mohd Su’ud , Ahthasham Sajid
Verification of a qualification, achievement, quality, or aspect of a person’s background is one of the biggest problems nowadays as we have seen many platforms where students can get fake credentials. Every organization must select professional and academically qualified employees to give quality service. As a result, corporations rely on academic certifications to confirm and measure their prospective employees’ academic qualifications. On the other hand, these employers lack a standardized process for confirming the legitimacy of academic certificates or degrees. Because the present procedures for verifying educational certifications are time-consuming, exhausting, and costly, just a few employers verify certificates for prospective employees. This research examines the issues that are related to the smart verification of someone’s credentials. To make the process of verifying digital credentials quicker, simpler, and more cost-effective, we suggest decentralized architecture. We present the prototype, design, and implementation of the proposed framework.
Volume: 40
Issue: 1
Page: 236-262
Publish at: 2025-10-01

Forecasting of nuclear energy trends in Romania using XGBoost

10.11591/ijeecs.v40.i1.pp78-84
Suman Chowdhury , Dilip Kumar Das
The energy demand continues to rise due to the exponential growth of the world's population. In today's world, every aspect of life, including industry, education, household, transport, and healthcare, relies on energy. Generating power in an environmentally friendly manner is a major concern. Predicting nuclear energy production depends on various factors. Researchers used the extreme gradient boost (XGBoost) machine learning algorithm for prediction. The study revealed that the RMSE validation value is 25.10810, while the training value is 15.01759 after 2000 iterations. According to the study, Romania has the potential to produce 1,300 MW of electricity in a single day through nuclear energy. Nuclear energy production can be a viable solution for decarburization and meeting energy needs. The prompt of nuclear energy in the present world is harnessing to the utmost level so that energy crisis can be mitigated for a long run. This paper tries to show the potentiality of nuclear energy in Romania predicting the future trends with the help of time series analysis.
Volume: 40
Issue: 1
Page: 78-84
Publish at: 2025-10-01

Optimal placement of wind turbine to minimize voltage variance in distributed grid considering harmonic distortion

10.11591/ijeecs.v40.i1.pp57-66
Dinh Chung Phan , Dinh Truc Ha
This paper suggested an algorithm to choose the optimal location of wind turbines (WT) in a distribution grid. The optimal position is calculated so that the maximal voltage variance in the distribution grid is minimized. This paper considers the harmonic current emitted by WT and the limitation of total harmonic distortion of voltage waves at nodes in the distribution grid. This proposed approach is written in MATLAB software and validated through a sample distribution grid, IEEE 33-bus. The verifying results demonstrated that by applying the suggested algorithm, the maximal voltage variance due to the variation of the power output of WT is minimized, the total harmonic distortion value at all buses remains within the operating range, and the electrical loss in the grid is reduced. Moreover, by considering the limitation of total harmonic distortion, the number of WT allowed to be installed in the grid is able to limited.
Volume: 40
Issue: 1
Page: 57-66
Publish at: 2025-10-01

The road conditions detection using the convolutional neural network

10.11591/ijeecs.v40.i1.pp327-345
Sujittra Sa-ngiem , Kwankamon Dittakan , Saroch Boonsiripant
Poor road conditions present considerable obstacles for individuals, resulting in asset loss, bodily harm, and time inefficiency. Approximately 1.35 million fatalities are attributable to road traffic incidents. The Department of Public Works and Town & Country Planning conducted road surveys to assess and strategize maintenance efforts. The manual car survey requires additional time and an excessive budget. The automated system of artificial intelligence (AI) is widely recognized. This paper presents a model to detect road surface conditions utilizing video data. Four versions of convolutional neural networks (CNN) were utilized for this work. The model evaluation employed the mean average precision (mAP) measure. The video data was acquired via a smartphone mounted in a vehicle, comprising 10,984 photos for training and 2,198 images for testing. We trained and evaluated four versions of CNN architectures named YOLO, utilizing our data and GPU, with a specific emphasis on identifying cracks, potholes, and the condition of manhole covers. Of the architectures evaluated, YOLO V6 attained the greatest mAP score in comparison YOLO V5 to YOLO V8. The testing results with batch sizes of 4, 8, 16, and 32 are effective. The batch size of 32 yields the highest performance, achieving 87.38% mAP. Conduct the dropout normalization using rates of 0.25, 0.50, 0.75, and 1. The maximum mAP is observed with a dropout rate of 0.25, yielding a mAP of 85.40%. The model indicates that the government conducted road surface inspections with enhanced efficiency, enabling the planning of road repairs for public utility issues, which can lower transportation costs. Additionally, the model can be utilized to identify hazardous road conditions and minimize vehicular accident rates.
Volume: 40
Issue: 1
Page: 327-345
Publish at: 2025-10-01

Optimizing distance vector-hop localization in wireless sensor networks using the grasshopper optimization algorithm

10.11591/ijeecs.v40.i1.pp461-479
Janani Selvaraj , Hymlin Rose Sasijohn Gloryrajabai , Sivarathinabala Mariappan , Backia Abinaya Antony Samy , Sudhakar Kalairishi
In scenarios involving mobile sensors within distributed sensor systems, such as those often encountered in wireless sensor networks (WSNs) or the internet of things (IoT), the ability to ascertain the origin of sensor data holds significant importance. Range-free Monte Carlo Localization methods offer an energy-efficient solution that eliminates the need for extra hardware, as they solely rely on the radio hardware already present on sensor nodes. But there are certain disadvantages when implemented, as it occupies more amount of power and some inaccuracies might happen in accessing the data from the sensor node. In this paper, we suggest the grasshopper optimization algorithm (GOA) strategy, which incorporates the distance-vector hop (DVHop) and three-anchor methods. It displays its usefulness in terms of both overall localization accuracy and resistance to hostile attacks or malfunctioning nodes. Nonetheless, the incorporation of dead reckoning based on motion sensor data significantly enhances the precision of location estimates and bolsters the network's robustness against both faulty components and malicious agents.
Volume: 40
Issue: 1
Page: 461-479
Publish at: 2025-10-01

Alzheimer’s disease stage prediction using a novel transfer learning-Alzheimer’s network architecture

10.11591/ijeecs.v40.i1.pp518-529
Pothala Ramya , Chappa Ramesh , Odugu Srinivasa Rao
The root cause of Alzheimer’s disease (AD) is unknown except for a very tiny number of family instances caused by a genetic mutation. A thorough examination of particular brain disorders’ tissues is necessary to correctly identify the circumstances using scans of magnetic resonance imaging (MRI), and specific non-brain tissues, like the neck, skin, muscle, and fat, make further investigation challenging and can be seen in MRI scans. This work aims to use the FSL-BET skull stripping tool to remove non-brain tissues and extract the significant region of the brain- deep learning (DL) techniques rather than machine learning (ML) models helpful in classification and predictions. The most frequent issue with DL models is which needs a lot of training data, causes to problems with class imbalance. To avoid imbalance issues, we used data augmentation to ensure that the samples were distributed equally among the classes. A novel transfer learning Alzheimer’s disease network (TL-AzNet) based visual geometry group-19 (VGG19) technique was developed in this study. Conducted a comparison study using the base and suggested models, comparing over data with oversampling versus non-oversampling. The novel model predicted AD with a 95% accuracy rate.
Volume: 40
Issue: 1
Page: 518-529
Publish at: 2025-10-01

Integrating swarm intelligence with deep learning for enhanced social media sentiment analysis

10.11591/ijeecs.v40.i1.pp280-287
Parminder Singh , Saurabh Dhyani
Understanding user views on social media in the advent of internet content demands sentiment analysis. This study introduces a novel approach called particle swarm-accelerated model (PSAM), that integrates deep learning with long short-term memory (LSTM) with two hyper-parameters and swarm intelligence through particle swarm optimization (PSO). In the sentiment classification of YouTube movie reviews for “Pushpa 2,” the recommended approach classifies opinions as “positive,” “negative,” or “neutral,” with an accuracy score of 95.3%. The process involved utilizing YouTube API to collect user-genearted comments, followed by advanced preprocessing steps such as punctuation removal, stopword filtering, slang normalization, and emoji handling. PSO performs feature selection to boost the efficiency of classification systems. The PSAM model reaches superior outcome results compared to support vector machines (SVM), Naive Bayes, CNN, and random forest classifiers when evaluated based on F1-score and accuracy metrics. The proposed hybrid model demonstrates its ability to boost sentiment analysis in different social media platforms according to research findings.
Volume: 40
Issue: 1
Page: 280-287
Publish at: 2025-10-01
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