International Journal of Applied Methods in Electronics and Computers
type: channel
description: The International Journal of Applied Methods in Electronics and Computers (IJAMEC) is a peer-reviewed, open-access journal dedicated to publishing high-quality research in the fields of electronics and computer science. Established in 2013, the journal provides a platform for interdisciplinary studies and innovative applications addressing real-world challenges in these dynamic fields. The journal welcomes research papers in all significant areas of electronics and computers that have practical applications in scientific problem-solving. It allows authors to retain unrestricted copyright and publishing rights. Open Access under the CC-BY license. IJAMEC does not impose any fees for article submissions or processing. It is free of charge. ISSN: 3023-4409 DOI Prefix: 10.58190/ijamec Frequency: Quarterly Scholar Google Cite Analysis for IJAMEC
publisher: PB Academy
language: en
publication name: International Journal of Applied Methods in Electronics and Computers
issn: 3023-4409
items: Trends and Research Directions in Electricity Load Forecasting: A Bibliometric Analysis
items: A Hybrid Model Approach Based on Swin Transformer and EfficientNetV2 for Maize Variety Classification
items: Predicting School Dropout Among International Students in Türkiye Using Machine Learning: A Case Study of Yemeni Students
items: Room Occupancy Prediction from Temperature Data with Deep Convolutional Neural Networks
items: Energy Production Prediction in Hydroelectric Power Plants with Multi-Layer Perceptron Algorithm, Menzelet Dam Example
items: Performance Comparison of SVM Kernel Functions for Date Fruit Classification
items: Hybrid RNN-LSTM Architecture for Sentiment Analysis of Algerian Dialectal Social Media Content
items: Development of an Embedded System Based Prototype Automatic Coffee Machine and Detailed Analysis of Its Modules
items: Examining the Relationship of Breast Cancer Data With Survival Chance and Comparison of Algorithms on Breast Cancer Prediction
items: Drone Detection with Deep Learning and Image Processing: CNN- Based Feature Extraction and Machine Learning Classification
items: User-Centric Design for Automated Metering Power Management Systems: A Case Study in Nigeria
items: Bibliometric Analysis of Logistics and Artificial Intelligence Research Trends in the Last 10 Years
items: Gradient-Based MPC Controller for One DOF Copter Control
items: Diagnosis of temporomandibular joint disorder using one-dimensional convolutional neural networks: A comparative study
items: An Artificial Neural Network-Based Caller Authentication and Identification Algorithm in Cellular Communication Networks
items: Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe
items: Political sentiment analysis using natural language processing on social media
items: Determination of Thermal Behavior from Core Flux Density with Image Processing Technique for Distribution Transformers
items: YOLO-V4 Based Real-Time Face Mask Detection via Unmanned Aerial Vehicle
items: Enhancing Travel Experience: Predicting Flight Delays for Informed Journey Planning
items: Development of a bulk material volume estimation system using automatic moving rail LiDAR technology
items: Analysis of Lorawan Protocol and Attacks Against Lorawan-Based IoT Devices
items: Robust fuzzy-logic flight control for unmanned aerial vehicles (UAVs)
items: Boosting the classification success in imbalanced data of bee larva cells
items: Hybrid cyber security of unmanned aerial vehicles
items: Feedforward Neural Network-Based Indoor Air Quality Detection System
items: BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID
items: Detection of Face-Mask in Real Time: A Cascaded Bi-Level Feature Extraction Technique Approach
items: A Fuzzy Inference-Based TCSC Control Technique to Improve Dynamic Power System Responses
items: Use of Machine Learning Algorithms in Location Determination for Safe Construction

Trends and Research Directions in Electricity Load Forecasting: A Bibliometric Analysis
type: item
description: Electricity load forecasting plays a critical role in strategic decision-making processes within the energy sector. Accurate and reliable forecasts are essential for ensuring the efficient operation of energy companies, distribution networks, and energy markets. Due to the complexity and dynamic nature of traditional forecasting methods, electricity load forecasting has become an increasingly challenging topic. In recent years, alongside traditional methods, there has been a growing use of novel and advanced techniques such as Empirical Mode Decomposition and Support Vector Regression in this field. These techniques offer effective alternatives for addressing complex data structures and nonlinear relationships. However, a comprehensive bibliometric analysis of studies and the application of these techniques in literature is yet to be conducted. This study aims to identify trends and future research directions in electricity load forecasting. To this end, a bibliometric analysis was performed using the Visualization of Similarities Viewer software. The findings of this study will serve as a valuable resource for researchers and industry professionals in the field of electricity load forecasting by guiding future research and shedding light on upcoming studies. This research encompasses articles containing the keywords "electrical," "load," and "estimation" published in scientific journals between 2015 and 2024.
subject: Research Articles
  topic: section
subject: Bibliometric Analysis, Electricity Load Forecasting, Energy Sector, VOSviewer, Forecasting Methods
  topic: keywords
creator: Oya KILCI
creator: Abdulkadir OZTURK
creator: Muslume Beyza YILDIZ
creator: Sude Nur ATIK
creator: Elham Tahsin YASIN
creator: Zeki Berk TONGUÇ
creator: Murat Koklu
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 65
ending page: 74
doi: 10.58190/ijamec.2025.130

A Hybrid Model Approach Based on Swin Transformer and EfficientNetV2 for Maize Variety Classification
type: item
description: In this study, two different deep learning-based models were proposed for the classification of the maize varieties Chulpi Cancha, Indurata, and Rugosa. In the first stage, a single model was developed using the Swin Transformer architecture with an attention mechanism. This model was then integrated with EfficientNetV2 to create a hybrid structure. The developed models were tested on a dataset consisting of 1050 images with a fixed background and high resolution. The Swin Transformer model produced successful results with 99.37% accuracy, while the hybrid model achieved 100% test accuracy, accurately classifying all samples. The findings demonstrate that the Swin Transformer and EfficientNetV2-based hybrid architectures offer high discrimination power and generalization capacity in image-based classification of maize varieties. Future studies are recommended to conduct additional tests using images taken under different environmental conditions and larger datasets encompassing a wider range of varieties.
subject: Research Articles
  topic: section
subject: classification
  topic: keywords
subject: Hybrid Deep Learning Model
  topic: keywords
subject: Maize Classification
  topic: keywords
creator: Hüseyin Bulduk
creator: Kadir Sabancı
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 84
ending page: 92
doi: 10.58190/ijamec.2025.132

Predicting School Dropout Among International Students in Türkiye Using Machine Learning: A Case Study of Yemeni Students
type: item
description: International students often encounter a range of challenges that can put their academic progress at risk, sometimes leading to dropouts. This study focuses on identifying the key factors that influence such outcomes, using Yemeni students as a case group to explore broader patterns among international students. A structured survey was conducted with 583 Yemeni students. After removing incomplete responses, 545 valid cases remained. From these, 268 students who were still enrolled were excluded (because students who were still attending school had neither graduated nor dropped out and were outside our scope of study), leaving 277 complete records (128 graduates and 149 dropouts) for the modeling phase. A total of fifteen supervised machine learning algorithms were applied, with training and evaluation carried out using an 80/20 split. Model performance was assessed through common classification metrics such as accuracy, precision, recall, and F1-score. Several important predictors were identified, including academic performance (GPA), proficiency in Turkish, satisfaction with their academic department, financial stability (e.g., access to scholarships, family income), and levels of psychological stress. Among the tested models, XGBoost performed best, achieving 91% accuracy and an F1-score of 0.92 for the dropout class. To illustrate the practical implications of this research, a prototype web application was also developed. Overall, the study demonstrates that machine learning can be a valuable tool for anticipating dropout risks among international students and highlights the importance of early, targeted support in academic, linguistic, financial, and psychological domains.
subject: Research Articles
  topic: section
subject: School dropout
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Data analysis
  topic: keywords
subject: Prediction
  topic: keywords
subject: Classification
  topic: keywords
subject: International students
  topic: keywords
creator: Anas ALHARDI
creator: Selahattin ALAN
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 75
ending page: 83
doi: 10.58190/ijamec.2025.131

Room Occupancy Prediction from Temperature Data with Deep Convolutional Neural Networks
type: item
description: This study aims to estimate the number of people in a room using data from temperature sensors placed in a room. The study utilizes an open-source dataset comprising time-dependent temperature sensor data. The days when the number of people in the dataset was always zero were removed to avoid misleadingly increasing the success of the model, and the number of data points was reduced by averaging every 10 measurements. The temperature data were converted into RGB images of 28 x 28 pixels, and the measurements from each sensor were assigned to a different region in the image. A convolutional neural network model was trained by dividing these images into training, validation, and test sets. The model was able to predict the no-person and low-person classes with high accuracy. However, at higher headcounts, the model’s performance degraded. In particular, prediction errors increased in transition situations where the number of people changes rapidly. The accuracy of the model on the test dataset is obtained as 93.33%. The results show that temperature data can be effectively used to predict occupancy levels. This study lays a foundation for headcount prediction based on temperature data and offers significant potential in applications such as smart building systems.
subject: Research Articles
  topic: section
subject: Convolutional neural networks
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Occupancy Detection
  topic: keywords
creator: Özcan Çataltaş
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 37
ending page: 43
doi: 10.58190/ijamec.2025.121

Energy Production Prediction in Hydroelectric Power Plants with Multi-Layer Perceptron Algorithm, Menzelet Dam Example
type: item
description: Hydroelectric energy is connected to clean and extractable energy produced by electric generators that convert the movement of water falling into dams into energy. From the perspective of life cycle planning of energy production systems, estimating the energy to be produced from hydroelectric power plants is very important in terms of energy production efficiency management, but it is quite difficult to do. Because the flow of such energy production data depends on factors such as precipitation-flow, flow, temperature and evaporation. This causes energy changes and fluctuations in variables. In this paper, long-term energy production planning was made using Multi-Layer Perceptron (MLP) from Artificial Neural Network architectures for Menzelet Dam and HEPP located in Ceyhan Basin of Kahramanmaraş province. The activation functions used in this study are sigmoid and tanh and the models used for learning are quick propagation and conjugate gradient descent. In the study, the energy production data between (1999-2020) is used for the experiment. The training and test parts were run. The results of the prediction values were compared by looking at CCR and R2 values. According to the tests, the highest prediction value for energy is 0.9891.
subject: Research Articles
  topic: section
subject: Artificial neural networks
  topic: keywords
subject: Hydroelectric power plants
  topic: keywords
subject: Energy forecasting
  topic: keywords
subject: Multilayer perceptron
  topic: keywords
creator: Ezgi Öztürk İSPİR
creator: Hasan Erdinç KOÇER
creator: Şerife Yurdagül KUMCU
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 44
ending page: 49
doi: 10.58190/ijamec.2025.122

Performance Comparison of SVM Kernel Functions for Date Fruit Classification
type: item
description: In this study, the Support Vector Machines (SVM) algorithm was employed to classify different types of date fruits. The performances of various SVM kernel functions - namely Linear, Quadratic, Cubic, Medium Gaussian, and Coarse Gaussian- were compared during the classification process. The analyses were conducted using the Date Fruit Dataset, which was published on the Kaggle platform and comprises 34 numerical features. The Minimum Redundancy Maximum Relevance (MRMR) feature selection algorithm was utilized to identify the 13 most effective features for classification. Subsequently, classification was performed using both the complete feature set (34 features) and the selected subset (13 features). The findings revealed that the highest classification accuracy was achieved with the Linear kernel SVM model in both cases. When all features were used, the Linear SVM model reached an accuracy of 91.79%, whereas the accuracy increased to 92.07% when the 13 features selected by MRMR were employed. These results indicate that feature selection plays a significant role in improving classification performance.
subject: Research Articles
  topic: section
subject: Classification
  topic: keywords
subject: Date Fruit
  topic: keywords
subject: Support Vector Machines (SVM)
  topic: keywords
subject: Feature Selection
  topic: keywords
subject: Minimum Redundancy Maximum Relevance (MRMR)
  topic: keywords
creator: Hüseyin Bulduk
creator: Kadir Sabancı
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-09-30
publication date: 2025-09-30
starting page: 58
ending page: 64
doi: 10.58190/ijamec.2025.129

Hybrid RNN-LSTM Architecture for Sentiment Analysis of Algerian Dialectal Social Media Content
type: item
description: Sentiment analysis in under-resourced dialects like Algerian Arabic (Darija) presents unique challenges due to code-switching, informal orthography, and cultural-linguistic nuances. This study addresses the binary sentiment classification task using a hybrid Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) architecture, designed to effectively capture sequential dependencies and long-term contextual information. The model is trained on DZSentiA, a curated dataset of annotated Algerian dialect social media posts, and achieves strong performance with an accuracy of 84.7%, an F1-score of 84.45%, a recall of 82,75%, and a precision of 84.7%. These results surpass several baseline methods, highlighting the potential of deep learning approaches in low-resource dialectal settings. This work contributes to dialect-specific Natural Language Processing (NLP) by demonstrating the feasibility and effectiveness of deep models in sentiment detection for Algerian Darija, and supports the broader goal of developing culturally aware tools for online discourse analysis.
subject: Research Articles
  topic: section
subject: Sentiment Analysis Applications
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: Djalila Boughareb
creator: Ammar Chahir Menasri
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-10-03
publication date: 2025-10-03
starting page: 50
ending page: 57
doi: 10.58190/ijamec.2025.128

Development of an Embedded System Based Prototype Automatic Coffee Machine and Detailed Analysis of Its Modules
type: item
description: Coffee is one of the most popular beverages in the world and has an important place in many cultures. With the developing technology, coffee preparation processes have also evolved and automatic coffee machines have entered our lives. These machines provide convenience and time savings to the user, while optimizing the ideal brewing conditions of the coffee and increasing the quality of its flavour. Modern automatic coffee machines have advanced systems that can precisely control the temperature of the water, the grinding degree of the coffee bean and the brewing time. Thus, coffee lovers can have a consistent and perfect coffee experience in every cup. This article comprehensively covers the design, construction and testing process of a prototype automatic coffee machine. First, the requirements and goals determined within the scope of the project were defined, then the components suitable for these requirements were selected. During the design phase, the focus was on basic functions such as adjusting the coffee strength (amount), grinding the coffee, heating the water, brewing time and frothing the milk. The electronic control unit was designed based on Arduino Mega and the various functions of the machine were performed automatically. The user interface is implemented with an application written in the C# programming language. This user interface has established communication with Arduino Mega and aims to enable the user to easily adjust brewing settings. Finally, the performance of the developed prototype has been evaluated with various tests, and detailed analyses have been made on the reliability and user-friendly design of the system. This study presents important findings regarding the design and development process of automatic coffee machines and aims to guide future studies.
subject: Research Articles
  topic: section
subject: coffee machine
  topic: keywords
subject: coffee grinding
  topic: keywords
subject: coffee brewing
  topic: keywords
subject: milk frothing
  topic: keywords
subject: embedded system
  topic: keywords
creator: Yavuz Selim Taşpınar
creator: Akbar Sheikh Akbari
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-06-30
publication date: 2025-06-30
starting page: 27
ending page: 36
doi: 10.58190/ijamec.2025.120

Examining the Relationship of Breast Cancer Data With Survival Chance and Comparison of Algorithms on Breast Cancer Prediction
type: item
description: This article compares the performance of machine learning algorithms on breast cancer data. The aim is to predict the survival status of breast cancer patients and contribute to the development of clinical decision support systems. Using a dataset obtained from the National Cancer Institute, XGBoost, Random Forest, Support Vector Machines (SVM), and Logistic Regression algorithms were compared. Data preprocessing steps were applied, correlation analysis was performed, and it was determined that the XGBoost algorithm showed the best performance with hyperparameter optimization. The metrics obtained after hyperparameter optimization of the XGBoost algorithm show an overall accuracy of 92%. Optimization has resulted in high performance for class 0 (precision 92%, recall 98%), but the recall for class 1 remains at 54%. The article discusses the effect of data imbalance on the results and offers suggestions for future studies.
subject: Research Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: • Breast Cancer
  topic: keywords
subject: • XGBoost
  topic: keywords
creator: Ali Murat Tiryaki
creator: Ahmet Can Mermer
creator: Bora Ugurlu
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 19
ending page: 26
doi: 10.58190/ijamec.2025.117

Drone Detection with Deep Learning and Image Processing: CNN- Based Feature Extraction and Machine Learning Classification
type: item
description: Drones are now widely used in the entertainment industry, cargo and transportation, security and logistics. However, drones can be confused with many objects and creatures that are similar to them or that they are in the same environment. Due to their widespread use and the fact that they have similar characteristics with other objects and living things, they pose some problems. These problems become problems that concern and sometimes disturb the society. For this reason, image processing methods have been used to classify drone and non-drone objects. Thanks to image processing, drones can be classified without any contact. Thanks to deep learning, data sets containing a large number of images can be classified quickly and accurately. In this study, it is aimed to classify drone and non-drone objects. In this study, a total of 1081 data sets consisting of 597 drone images, 484 non-drone objects and live images were used. 20% of the dataset was used for testing and 80% for training. Convolutional Neural Network (CNN) method was used to determine the features of these images. For each image, 4608 image features obtained from the CNN model were classified with Artificial Neural Network (ANN), K Nearest Neighbor (KNN) and Random Forest (RF) machine learning models. Precision, recall, F1 Score and accuracy metrics were used to evaluate the performance of the CNN model. Classification of the features obtained from the CNN model with ANN, KNN and RF models resulted in 89.9, 85 and 85 classification successes, respectively. The highest classification success was obtained from the ANN model in the classifications made with the features of the CNN model. With the results obtained, it is seen that the proposed classification and feature extraction models can be used to distinguish between drone and non-drone objects.
subject: Research Articles
  topic: section
subject: Drone
  topic: keywords
subject: Non-Drone
  topic: keywords
subject: Classification
  topic: keywords
subject: Cnn
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Begüm Özyer
creator: Ali YASAR
creator: Yavuz Selim TASPINAR
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 12
ending page: 18
doi: 10.58190/ijamec.2025.116

User-Centric Design for Automated Metering Power Management Systems: A Case Study in Nigeria
type: item
description: Nigeria continues to face persistent power management challenges, with frequent outages affecting both urban and rural areas. Technical issues, such as open-circuit faults, contribute to significant losses in the low-voltage section of the power distribution system. Additionally, the lack of transparency in the current power management system fosters distrust between electricity consumers and providers. This study presents the Automated Metering Power Management System (AMPMS)—an integrated solution designed to improve energy monitoring, fault detection, and consumption planning. The system incorporates an automated digital meter, a Home Energy Planner (HEP), a web-based platform, and the "Meter Utility" mobile app to enhance energy management. The AMPMS utilizes a Hall effect current sensor, voltage sensor, Wi-Fi module, and Atmega328p microcontroller, enabling real-time fault detection, outage reporting, and load scheduling. Evaluation results demonstrate the system’s effectiveness in reducing power consumption. Households using the AMPMS recorded an energy consumption of 3.02 kWh, significantly lower than the 4.442 kWh observed with conventional prepaid meters—a 31.9% reduction. Furthermore, survey data revealed that 96.3% of respondents experienced delays in resolving open-circuit faults under the existing system, underscoring the need for an automated fault detection and reporting mechanism. By integrating these components, the proposed AMPMS improves power distribution accuracy, enhances consumer control over energy usage, and reduces downtime. The system is scalable for residential, small office, and potential industrial applications in developing nations. This research provides a practical and technologically driven approach to addressing Nigeria’s energy management challenges, promoting efficiency, transparency, and accountability in power distribution.
subject: Research Articles
  topic: section
subject: Automation
  topic: keywords
subject: Innovation
  topic: keywords
subject: Monitoring
  topic: keywords
subject: Power Management
  topic: keywords
creator: Orodje Edemirukaye Ukeh
creator: Apeh Simon T.
creator: Edoghogho Olaye
rights: Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2025-03-31
publication date: 2025-03-31
starting page: 1
ending page: 11
doi: 10.58190/ijamec.2025.115

Bibliometric Analysis of Logistics and Artificial Intelligence Research Trends in the Last 10 Years
type: item
description: In recent years, the integration of logistics and artificial intelligence has become increasingly important across various industries, fostering innovation and progress. This study seeks to uncover key contributors, prominent keywords, influential journals, and leading countries at the crossroads of logistics and AI to provide direction for future research. By analyzing 1118 articles from the past decade (2015–2024) using the Web of Science (WoS) database and VOSviewer software, several critical insights were derived. The analysis included co-occurrence of keywords, citation patterns (articles, sources, institutions, and countries), and co-authorship networks. Results from the keyword analysis reveal that “artificial intelligence” and “logistics” dominate, followed by terms such as “machine learning,” “deep learning,” “blockchain,” “optimization,” and “internet of things.” Citation analysis identified the study by Dwivedi et al. (2021) as the most cited work, with 1009 citations. Among journals, Engineering Applications of Artificial Intelligence stands out, featuring 58 papers and 894 citations. In co-authorship analysis, Angappa Gunasekaran emerges as the most impactful author with six publications and 330 citations. Institutionally, the Chinese Academy of Sciences leads with 342 citations, while China ranks first among countries with 3979 citations, followed by India and the United Kingdom. This bibliometric analysis highlights pivotal resources, influential studies, and leading contributors in the field of logistics and artificial intelligence, serving as a foundational guide and valuable reference for future researchers in this domain.
subject: Research Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Artificial Intelligence in Logistics
  topic: keywords
subject: Bibliometric Analysis
  topic: keywords
subject: Logistics
  topic: keywords
subject: Artificial Intelligence (AI), Artificial Intelligence in Logistics, Bibliometric Analysis, Logistics, Research Trends
  topic: keywords
creator: Selime Sinem BAHAR
creator: Muslume Beyza YILDIZ
creator: Serkan GERZ
creator: Elham Tahsin YASIN
creator: Ahmet GOKTAS
creator: Murat KOKLU
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-31
publication date: 2024-12-31
starting page: 119
ending page: 128
doi: 10.58190/ijamec.2024.112

Gradient-Based MPC Controller for One DOF Copter Control
type: item
description: This study tries to contrast the performance of Gradient Based Model Predictive Control (MPC) with the classical PID controllers in the control of a 1 DOF copter system. While PID controllers are easy to set up and be applied in a vast number of occasions, they may become insufficient when nonlinear systems or operating constraints are considered. To that end, results show that MPC, whose optimization formulation predicts the system dynamics, has better performance measures. Simulation results show that the gradient-based MPC performs well in dynamic and steady-state performance compared to PID controllers tuned with two different coefficients. In particular, the MPC is characterized by high accuracy and minimum long-term error, no overshoot and fast settling time. In noisy conditions, MPC is able to produce a stable control signal due to its predictive capability, whereas PID controllers are more sensitive to such conditions and produce a chattering signal. It is concluded that the Gradient Based MPC is a good choice for applications that require more precise control and robustness against noise, while PID controllers are preferable due to their simplicity and low computational cost.
subject: Research Articles
  topic: section
subject: Model Predictive Control (MPC)
  topic: keywords
subject: Gradient-Based Control
  topic: keywords
subject: PID Control
  topic: keywords
subject: One DOF Copter
  topic: keywords
subject: Noise Tolerance
  topic: keywords
creator: Okan Uyar
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-10
publication date: 2024-12-10
starting page: 97
ending page: 103
doi: 10.58190/ijamec.2024.110

Diagnosis of temporomandibular joint disorder using one-dimensional convolutional neural networks: A comparative study
type: item
description: Temporomandibular Joint (TMJ) is a joint located on both sides of the cranium that connects the mandible to the cranium. Temporomandibular Joint Disorder (TMD) is generally defined as pathological conditions resulting from abnormal movement of the TMJ. Symptoms of TMD usually occur in the form of pain in the mandible and the muscles that control mandibular movement. One of the clinical diagnostic methods is the auscultation of sounds coming from the joint during the opening and closing of the mandible. In this study, previously recorded TMJ sounds were analyzed for TMD diagnosis using one-dimensional (1-D) convolutional neural networks (CNN), a sub-branch of deep learning algorithms. The obtained results were compared with the results of previous outcomes of studies which were using deep learning algorithms such as two-dimensional CNN, which is generally used for image processing, and LSTM network, which is widely used for time series analysis. Comparison results indicate that 1-D CNNs are less effective than image-based CNN algorithms. Results show that 1-D CNN classification of Type-1 algorithm and Type 2 algorithm are 75% and 65% accuracy respectively. These figures are significantly lower than the 94% accuracy achieved by the 2-D image-based algorithm. When compared to LSTM networks, which have an accuracy of 70%, 1-D CNNs yield comparable results. For a more comprehensive analysis, precision, recall, specificity, and F1-Score metrics were evaluated, and the findings were interpreted accordingly.
subject: Research Articles
  topic: section
subject: Temporomandibular Joint Disorder
  topic: keywords
subject: Sound Classification
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: 1-D CNN
  topic: keywords
creator: Uğur TAŞKIRAN
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-10
publication date: 2024-12-10
starting page: 90
ending page: 96
doi: 10.58190/ijamec.2024.109

An Artificial Neural Network-Based Caller Authentication and Identification Algorithm in Cellular Communication Networks
type: item
description: Cellular communication companies have created tens of thousands of jobs within the industry. However, while business opportunities have expanded for legitimate enterprises, criminal gangs have also exploited the new business environment to further their illegal activities. Weaknesses in the authentication algorithm allow criminals to commit fraud while remaining anonymous, thereby facilitating wireless crime. This paper presents the development of an artificial neural network-based caller authentication and identification algorithm for cellular communication networks that addresses these weaknesses. Voice data were collected from volunteer participants and the Mozilla Common Voice (MCV) database. Voice feature vectors were extracted from the voice data using the Mel Frequency Cepstral Coefficient (MFCC) technique. The extracted voice feature vectors were used to train a Multilayer Perceptron (MLP) neural network for voiceprint generation. The MLP neural network architecture was optimized through Neural Architecture Search (NAS) using the Neural Network Intelligence (NNI) toolkit. The optimized MLP neural network architecture was trained with the extracted voice feature vectors to generate a voiceprint generation model. The developed voiceprint model was then deployed to create an artificial neural network-based caller authentication and identification algorithm in cellular communication networks. The developed algorithm was evaluated in the cellular communication emulation setup using accuracy, false acceptance rate (FAR), and false rejection rate (FRR) as metrics. The results of the performance evaluation of the developed algorithm for authenticating first-time users of a new SIM and identifying third-party SIM users both showed 100% accuracy, 0% FAR, and 0% FRR. These results indicate that the developed algorithm has strong potential to prevent the use of black market SIMs and deter perpetrators from using third-party SIMs to access network services while remainin…
subject: Research Articles
  topic: section
subject: Artificial Neural Networks
  topic: keywords
subject: Caller Authentication
  topic: keywords
subject: Cellular Communication
  topic: keywords
subject: Prevention of Black Market SIMs
  topic: keywords
subject: SIM User Identification
  topic: keywords
creator: Bem Sombo
creator: Simon Tooswem Apeh
creator: Isi Arthur Edeoghon
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-18
publication date: 2024-12-18
starting page: 104
ending page: 118
doi: 10.58190/ijamec.2024.111

Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe
type: item
description: The analysis of sports movements is of great importance for optimizing sports performance, minimizing injury risks, and ensuring that athletes work with correct techniques. Powerlifting is a power sport consisting of fundamental movements such as bench press, deadlift, and squat. These movements are inherently complex and challenging to execute. Therefore, it is of great importance to perform these movements with the correct technique and safely. The aim of this study was to classify these movements using deep learning methods to ensure that the basic movements in powerlifting sports (bench press, deadlift, and squat) are applied with correct techniques and to minimize the risk of injury. In this study, feature extraction was performed on powerlifting movements using the deep learning-based SqueezeNet model, followed by classification using machine learning methods. The dataset was compiled from 876 images of bench press, deadlift, and squat movements sourced from various online platforms. Additionally, the dataset was expanded through data augmentation techniques, and key points of posture estimation were added to the images using the Mediapipe library. The obtained datasets were classified using Neural Network, Logistic Regression, Support Vector Machine and Random Forest algorithms and model performances were evaluated using various metrics. The findings revealed that the Neural Network model demonstrated superior performance, achieving the highest accuracy (0.989). Additionally, the integration of pose estimation and data augmentation techniques significantly enhanced classification accuracy and overall model performance. The findings of this study show that deep learning methods are powerful tools in sports movement analysis and can make significant contributions to the evaluation of athletes' performance.
subject: Research Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Mediapipe
  topic: keywords
subject: Movement Analysis
  topic: keywords
subject: Powerlifting
  topic: keywords
subject: Sports Movements
  topic: keywords
creator: Ahmet ÇELİKEL
creator: Ilker Ali OZKAN
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-09-30
publication date: 2024-09-30
starting page: 72
ending page: 80
doi: 10.58190/ijamec.2024.107

Political sentiment analysis using natural language processing on social media
type: item
description: In this contemporary era , social media has become an essential component of daily life as a result of the extensive use of the internet. This paper explores sentiment analysis of political topics through social media comments. We collected a large dataset of over 14,000 political comments and applied advanced machine learning models such as logistic regression , linear support vector classification , random forest, decision tree classification , and naive bayes to evaluate expressed sentiments. Performance metrics , including accuracy , precision , recall , and F1 scores , were utilized to assess these models , with Linear SVC achieving the highest accuracy at 91.18% , closely followed by Logistic Regression at 90%. This research not only evaluates model performance on political sentiment data but also addresses data imbalance, presenting actionable insights into each algorithm’s suitability. Our study introduces a refined approach to political sentiment analysis by optimizing model selection for high accuracy and robustness, thus setting a foundation for effective political sentiment understanding on social media platforms.
subject: Research Articles
  topic: section
subject: Sentiment Classification
  topic: keywords
subject: Sentiment Analysis Applications
  topic: keywords
subject: Sentiment analysis
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: Md Shakil Hossain
creator: MD Rashidul Islam
creator: Dr. Basheer Riskhan Riskhan
creator: MD MEHEDI HASAN HASAN
creator: RABIUL ISLAM ISLAM
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-12-10
publication date: 2024-12-10
starting page: 81
ending page: 89
doi: 10.58190/ijamec.2024.108

Determination of Thermal Behavior from Core Flux Density with Image Processing Technique for Distribution Transformers
type: item
description: Considering the design and operating conditions of transformers, electromagnetic and mechanical stresses cause aging and negatively affect their operating performance. Advanced fault diagnosis methods have been developed based on information system-based remote online monitoring or electrical data obtained from sensors or sample windings added to the transformer core. The electromagnetic field distribution in the core structure of the transformer can respond quickly and effectively to fault situations. Therefore, changes in flux density within the core can be analyzed using image processing and/or data analysis methods. In this study, electromagnetic modeling of a distribution transformer with nominal values of 34.5/0.4 kV and 2000 kVA was conducted using Finite Element Analysis (FEA) software. Image processing techniques were applied to observe the behavior of the flux distributions on the core when the transformer was under nominal sinusoidal voltage. Then, considering the effect of the flux distribution in the core on the thermal state of the transformer, the thermal behavior of the core was derived with mathematical equations and shown on the transformer. Thus, the flux distribution in the core of a distribution transformer operating under nominal power conditions was examined and, a novel approach based on simulation studies was proposed to determine the flux distributions and thermal behavior of the transformer under fault conditions.
subject: Research Articles
  topic: section
subject: Transformer
  topic: keywords
subject: flux distribution
  topic: keywords
subject: image processing
  topic: keywords
subject: thermal behavior
  topic: keywords
subject: fault diagnosis
  topic: keywords
creator: Busra Aslan
creator: Selami Balci
creator: Ahmet Kayabasi
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-09-30
publication date: 2024-09-30
starting page: 54
ending page: 64
doi: 10.58190/ijamec.2024.105

YOLO-V4 Based Real-Time Face Mask Detection via Unmanned Aerial Vehicle
type: item
description: COVID-19, which started in Wuhan city of China's Hubei province and then affected the whole world, continues to spread despite the measures taken. One of the most important of these measures is to use a mask. In some countries, while wearing a mask is mandatory in crowded environments, it is just as difficult to control it. Failure to detect individuals violating the mask causes the virus to spread, resulting in an increase in the number of cases and an increase in the number of deaths. Therefore, detecting the mask and taking action against it is an extremely important issue. In this study, in addition to making mask detection easily and quickly, mask detection is made by using unmanned aerial vehicles (UAVs) using images taken from different angles and different heights. For mask detection from UAV images, training and validation processes were applied using the YOLO-v4 algorithm on a public dataset containing 1510 masked and unmasked human face images. As a result of the training on this dataset, mean-average precision (mAP) was achieved with a success rate of 92.06%. Then, real-time mask detection was performed on the images taken with the DJI Ryze Tello quadcopter using the trained network. The results showed that the UAV is applicable in crowded environments required for autonomous mask detection and gives successful results.
subject: Research Articles
  topic: section
subject: YOLO-v4
  topic: keywords
subject: object detection
  topic: keywords
subject: mask detection
  topic: keywords
subject: unmanned aerial vehicle
  topic: keywords
subject: COVID-19
  topic: keywords
creator: Mucahit Akar
creator: Muhammet Fatih Aslan
creator: Kadir Sabanci
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-09-30
publication date: 2024-09-30
starting page: 65
ending page: 71
doi: 10.58190/ijamec.2024.106

Enhancing Travel Experience: Predicting Flight Delays for Informed Journey Planning
type: item
description: Flight delays pose significant inconveniences for travelers, potentially causing missed connections, schedule adjustments, and time wastage. This study presents a machine-learning driven approach to mitigate these challenges by developing an application that predicts flight delays, empowering passengers with insights to minimize travel disruptions. Leveraging diverse machine learning algorithms and datasets from the United States Department of Transportation and the National Oceanic and Atmospheric Administration Service, our model aids travelers in making informed decisions by suggesting optimal flight times and carriers based on historical flight data and weather conditions. Addressing the issue of imbalanced data, we explore techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and under-sampling. Our comparative analysis highlights the superior performance of Light Gradient Boosting Machine (L-GBM) in predicting flight delays. With an F1-score of and an AUC value of , our study offers a promising solution to enhance passenger experiences through improved flight recommendations.
subject: Research Articles
  topic: section
subject: Classification
  topic: keywords
subject: Flight Delay Prediction
  topic: keywords
subject: Flight Recommendation
  topic: keywords
subject: L-GBM
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Esma Ergün
creator: Süha Tuna
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 40
ending page: 47
doi: 10.58190/ijamec.2024.96

Development of a bulk material volume estimation system using automatic moving rail LiDAR technology
type: item
description: This paper focuses on developing a bulk material volume estimation system employing automatic moving rail optical distance measuring technology. The research objective is to devise a system capable of estimating warehouse bulk material volumes utilizing point cloud data. Additionally, the research proposes guidelines for enhancing efficiency in bulk material volume estimation processes. A prototype system was developed and tested using dry rice husk with a humidity level of 15% as the sample material. The testing environment comprised a laboratory with dimensions of 36 square meters and a height of 3 meters, wherein the sample material was arranged in a cone shape with a volume of 1 cubic meter. The system was designed to test with movement speed range from 2 to 30 centimeters per second, and the scanning angles of 0, 45, and 90 degrees. Statistical principles were applied to analyze the collected data, determining averages, and comparing results with actual data to assess accuracy and precision in volumetric measurements. Furthermore, the research evaluated the advantages and disadvantages of alternative tools for bulk material volume estimation in comparison to the developed system, considering factors such as data collection duration, operational costs, and safety measures. Experimental results revealed that at a scanning angle of 90 degrees and a moving speed of 20 centimeters per second achieve a volumetric data accuracy of 97%.
subject: Research Articles
  topic: section
subject: Bulk material
  topic: keywords
subject: Volume estimation
  topic: keywords
subject: LiDAR technology
  topic: keywords
subject: Point cloud data
  topic: keywords
creator: Chirananchai Sritap
creator: Paphakorn Pitayachaval
creator: Suradet Tantrairatn
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 48
ending page: 53
doi: 10.58190/ijamec.2024.97

Analysis of Lorawan Protocol and Attacks Against Lorawan-Based IoT Devices
type: item
description: In this study, the LoRaWAN protocol, which is a promising candidate for Low-Power Wide Area Network (LPWAN) in terms of network capacity, network security, battery life, low cost and data transmission capabilities, has been selected to examine and analyze its security risks. The Internet of Things (IoT) concept has been explored, and various network types and architectures have been investigated and compared with the LoRaWAN protocol. Subsequently, attacks on IoT networks, the countermeasures taken by manufacturers, and relevant academic studies on this topic have been examined. The main subject of the study, LoRaWAN, has been explained in terms of its versions, architecture, components, classes, and network layers. Vulnerabilities, security risks, and types of attacks have been categorized and thoroughly explained and a risk assessment has been conducted to evaluate the impact of these attack types. Based on the findings, solutions to prevent or mitigate attacks for four specific types of attacks against the LoRaWAN protocol have been proposed and presented to end users.
subject: Research Articles
  topic: section
subject: IoT, IoTSecurity, LoRaWAN, Risk Analysis, LPWAN
  topic: keywords
subject: Iot
  topic: subjects
subject: Computer Engineering
  topic: disciplines
creator: Kadir Tepecik
creator: Ahmet Furkan Ağrak
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-06-30
publication date: 2024-06-30
starting page: 33
ending page: 39
doi: 10.58190/ijamec.2024.95

Robust fuzzy-logic flight control for unmanned aerial vehicles (UAVs)
type: item
description: Researches on Unmanned Aerial Vehicles (UAVs) have been recently attracting considerable interest in the field of control theory applications. They are used in a wide range of areas thanks to having the potential of high manoeuvrability, hovering and flying, taking off and landing capabilities. However, to maintain robust control action towards changing conditions of the system is not an easy matter since quadrotor UAVs are highly unstable systems with high precision. Therefore, the main purpose of this study is to control a quadrotor UAV by using a proposed multi-input single-output (MISO) fuzzy-logic controller that ensures robustness if model parameters and trajectory change. For that reason, a 2-dimensional 3 degree-of-freedom quadrotor was used in this study to better evaluate the performance of proposed controller on UAVs. Afterwards, numerical analysis was performed and the findings were analysed. Consequently, the single most striking observation to emerge from the study is that the satisfactory results have been obtained demonstrating that the proposed fuzzy logic controller has remarkable advantage on the robustness of quadrotor UAVs.
subject: Research Articles
  topic: section
subject: Fuzzy Control
  topic: keywords
subject: Quadrotors
  topic: keywords
subject: UAVs
  topic: keywords
subject: Robustness
  topic: keywords
creator: Cengiz ÖZBEK
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-03-27
publication date: 2024-03-27
starting page: 16
ending page: 21
doi: 10.58190/ijamec.2024.79

Boosting the classification success in imbalanced data of bee larva cells
type: item
description: Selecting the appropriate honey harvesting method is crucial for sustainable beekeeping and optimal honey production. The use of primitive harvesting methods can lead to the death of bees and a decrease in honey yield. This study aims to address the issue of detecting and classifying young larvae on honeycombs. However, the area where young larvae are found is limited compared to other areas. In this study, the dataset obtained from honeycombs was imbalanced, which has used the Synthetic Minority Oversampling TEchnique (SMOTE) algorithm to balance it. The SMOTE algorithm is a synthetic data generation method. The balanced dataset was then used for classification processes with k-Nearest Neighbors algorithm (k-NN), Decision Trees, and Support Vector Machines. The evaluation of the classification results included the F1-Score, G-Mean, and AUC metrics. The results showed that the classification of the dataset balanced with synthetic data was more successful.
subject: Research Articles
  topic: section
subject: Imbalanced dataset
  topic: keywords
subject: Oversampling method
  topic: keywords
subject: SMOTE
  topic: keywords
subject: Beekeeping
  topic: keywords
subject: Detection of larval cells
  topic: keywords
subject: Computer Engineering
  topic: disciplines
creator: Serkan Özgün
creator: Mehmet Akif Şahman
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-03-27
publication date: 2024-03-27
starting page: 10
ending page: 15
doi: 10.58190/ijamec.2024.78

Hybrid cyber security of unmanned aerial vehicles
type: item
description: Unmanned aerial vehicle technological improvement has had a direct influence on today's warfare. As today's battles take place in cities, the distinction between civilians and soldiers has grown increasingly hazy. There is a wealth of literature on autonomy and ethical concerns concerning autonomous weapon systems. Unmanned aerial vehicles must contend with a slew of security risks, ranging from electronic jammers to missile strikes. The societal acceptance of these techniques, ethical value judgments, and long-term consequences are all important topics to discuss. Unmanned aerial vehicles have a wide range of applications in the military, from gathering intelligence to destroying targets, and in the civilian arena, from agriculture to photography. Furthermore, unmanned aerial vehicles have the potential to be used not just for military or commercial goals, but also for international crimes. Nowadays unmanned aerial vehicles are used in electronic warfare to monitor enemy positions, prevent their communications by jamming systems, instantly monitor the country's border security, render the electronic systems of the determined targets inoperable, and listen for information via communication systems. Under the conditions specified above, the study seeks to provide a hybrid approach to unmanned aerial vehicles. The cybersecurity of unmanned aerial vehicles, software security, physical security, and social and cultural security will all be handled from a single point of view.
subject: Research Articles
  topic: section
subject: Cyber security
  topic: keywords
subject: Unmanned aerial vehicle
  topic: keywords
subject: Semi autonomous system
  topic: keywords
subject: Military weapon systems
  topic: keywords
subject: Hybrid System
  topic: keywords
subject: Hybrid security
  topic: keywords
creator: Orhun DÖŞ
creator: Yunus Emre KARAKOCA
creator: Ebutalha CAMADAN
creator: Fikret BAYKALI
rights: Copyright (c) 2023 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-27
publication date: 2023-12-27
starting page: 179
ending page: 185
doi: 10.58190/ijamec.2023.65

Feedforward Neural Network-Based Indoor Air Quality Detection System
type: item
description: Indoor air quality is crucial for the sustainability of human life quality. Therefore, improving indoor air quality is critical for enhancing life quality. In this study, an artificial intelligence-based indoor air quality monitoring system is designed. The system consists of two main parts, hardware and software. The hardware part includes a control card and various sensors. The software part includes a C-based IDE software and a feedforward network, a deep learning algorithm, for establishing the connection between the control card and the sensors. The temperature, humidity, and gas concentration values obtained from the sensors at certain intervals were fed to the feedforward network's input layer through the control card. The feedforward network consists of the input layer, hidden layer, and output layer, and the decision on whether the air quality is normal or not was made at the output. The system described in this study is intended to provide a real-time monitoring solution for indoor air quality. By using a feedforward neural network, the system is able to learn patterns in the sensor data and detect changes in air quality that may indicate a problem. The system can be customized to suit the needs of different environments and can be used in a variety of settings, including homes, offices, and public spaces. Ultimately, the goal of the system is to improve human health and well-being by ensuring that indoor air quality is at a safe and healthy level.
subject: Research Articles
  topic: section
subject: Indoor air quality
  topic: keywords
subject: Internet of things
  topic: keywords
subject: Smart home systems
  topic: keywords
subject: IoT-based air quality
  topic: keywords
subject: Air pollution monitoring
  topic: keywords
creator: Cihat Ediz AKBABA
creator: Gökay Dişken
rights: Copyright (c) 2023 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-25
publication date: 2023-12-25
starting page: 174
ending page: 178
doi: 10.58190/ijamec.2023.64

BLDC Motor speed control with dynamic adjustment of PID coefficients: Comparison of fuzzy and classic PID
type: item
description: Brushless DC (BLDC) motors, which have small volumes, are widely used in many areas from the aviation industry to industrial applications due to their high efficiency and torque. In parallel with the development of technology, the field of use continues to expand with the development of BLDC engine (BLDCM) control strategies and the decrease in control costs. In this thesis study, it is aimed to minimize the observed changes in rotor speed compared to the reference speed. To achieve this, PID parameters were tried to be changed simultaneously with fuzzy control techniques, taking the error value as a reference. The control system of the BLDC engine was designed in the MATLAB/Simulink environment. In the simulation, the operating stability of classical PID and PID with updated fuzzy-based parameters on two engines with the same features was compared at different speeds. As a result of the research, it was concluded that the correction of the speed observed in the rotor of the PID-controlled motor, whose fuzzy logic-based coefficients were updated, based on the reference speed was more stable and the percentage of exceedance for the reference value was lower, compared to the classical PID controlled motor.
subject: Research Articles
  topic: section
subject: BLDC motor
  topic: keywords
subject: PID controller
  topic: keywords
subject: fuzzy PID
  topic: keywords
creator: Selahattin GUNTAY
creator: Ismail SARITAS
rights: Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2024-03-27
publication date: 2024-03-27
starting page: 22
ending page: 32
doi: 10.58190/ijamec.2023.80

Detection of Face-Mask in Real Time: A Cascaded Bi-Level Feature Extraction Technique Approach
type: item
description: Due to COVID-19's rapid spread, millions of people around the world have been affected and there has been extensive destruction. One of the most effective ways of preventing its spread and transmission during the pandemic period wearing of a mask and was required then in most public areas. As a result, this necessitate the use of programmed real-time mask detection devices in place of manual reminders. Face mask detection requires real-time processing of a huge amount of data with constrained processing resources, hence local descriptors that are quick to calculate, quick to match, and cheap to store are highly sought after. To achieve improved matching and reduction in memory use and computational complexity, the study offers a combination of Features from Accelerated Segment Test (FAST) corner detector with Histogram of Oriented Gradient (HOG) feature descriptor to allow faster matching and minimize memory usage and computation cost. The features obtained were then classified into face mask present and face mask absent using SVM, NB and CNN. Results obtained gives an accuracy of 99.41% which was higher than that previous results of 99.27% and 95% accuracy. Furthermore, it took the system only 48secs to extract the features obtained from face for training and testing. This outcome confirmed the suitability of the suggested method for real-time face mask detection.
subject: Research Articles
  topic: section
creator: Solomon Adepoju
creator: Thoma Eneobong Adahada
creator: Opeyemi Aderiike Abisoye
creator: Abdumalik Danlami Mohammed
rights: Copyright (c) 2023 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-27
publication date: 2023-12-27
starting page: 186
ending page: 196
doi: 10.58190/ijamec.2023.66

A Fuzzy Inference-Based TCSC Control Technique to Improve Dynamic Power System Responses
type: item
description: With the expansion of transmission systems, the devices that contribute to the overall power system performance must be adequate to the increasing modeling complexity and requirements. In this sense, the Flexible AC transmission system (FACTS) devices are often employed to improve stability and power quality, while the Thyristor-Controlled Series Compensator (TCSC) is a common example, able to change the equivalent transmission line impedance, improving power flow. This work discusses two established techniques for the control of TCSC devices based on the lead-lag model, whose parameters are defined through metaheuristic techniques, such as Genetic Algorithm and Particle Swarm Optimization. The results found in these publications are implemented in the Matlab/Simulink environment and confronted with the proposed Fuzzy logic application, written with linguistic inference, simplified rules, and simple membership functions. The two published models and the proposed Fuzzy logic performed satisfactorily with very similar results in all scenarios simulated considering a Single Machine Infinite Bus (SMIB) equivalent system model. The big advantage of the use of Fuzzy logic is its modeling simplicity, unlike the heuristic techniques that require much more modeling time and, sometimes, a large number of iterations to achieve acceptable parameters.
subject: Research Articles
  topic: section
subject: fuzzy control
  topic: keywords
subject: Genetic Algorithm
  topic: keywords
subject: particle swarm optimization
  topic: keywords
creator: Giuseppe Silva
creator: Gabriel A. F. Souza
creator: Benedito I. Fuly
creator: Pedro N. Vasconcelos
rights: Copyright (c) 2023 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-27
publication date: 2023-12-27
starting page: 203
ending page: 212
doi: 10.58190/ijamec.2023.68

Use of Machine Learning Algorithms in Location Determination for Safe Construction
type: item
description: Disasters are events that affect life activities that cause physical, economic and social losses. These events cause loss of life and property, as well as damage to structures such as schools and hospitals that will affect the continuation of education and health services. There are two types of disasters. The first is man-made disasters and the second is natural disasters. Natural disasters occur as a result of natural events. Earthquake is a natural disaster. Disaster management is a process that covers pre-disaster, disaster and post-disaster. This study focuses on pre-earthquake disaster management. Safe construction is necessary to reduce the effects of earthquakes. Soil class is very important in a safe construction. Soil classification was made according to TBDY-2018 by using machine learning techniques for a safe construction in the Mediterranean region. 12 different machine learning algorithms were used for Classification and the results were analyzed. As a result of the analysis, the accuracy values of the algorithms are respectively: Naive Bayes 87%, LDA 88%, KNN 84%, Adaboost 96%, Logit boost 95%, Ultraboost 92%, BF Tree 98%, Extra Tree 84%, Random Forest 93%, Random Tree%. 95, Rep Tree 96%, SimpleCart 98%. The most successful algorithms in classification are Simle Cart and BT tree. The least successful algorithm is the Extra Tree algorithm.
subject: Research Articles
  topic: section
subject: Earthquake
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Soil characterization
  topic: keywords
subject: Safe construction
  topic: keywords
subject: TBDY-2018
  topic: keywords
creator: Ebru Efeoğlu
rights: Copyright (c) 2023 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
date: 2023-12-27
publication date: 2023-12-27
starting page: 197
ending page: 202
doi: 10.58190/ijamec.2023.67