International Journal of Applied Methods in Electronics and Computers
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
Remote Monitoring of Vital Signs for The Development of Decision Support Systems in Medical Health Services
Monitoring patients' vital signs such as pulse, body temperature, systolic-diastolic blood pressure, and SpO₂ plays a significant role in healthcare systems for post-operative patients, emergencies, infants, individuals with heart failure, pandemics, and similar situations. In this study, two different real-time remote monitoring and early warning systems (RTMEWS) are proposed to assist healthcare professionals and physicians in measuring and monitoring patients' vital signs. In the first proposed RTMEWS, SpO₂, pulse, and body temperature can be measured using a pulse oximeter developed with an ATMEGA328P microcontroller and MAX30100 sensor, and monitored via wireless transmission from the OLED GLCD screen on the prototype through an interface developed with the MIT2 application on a smartphone. In the second proposed system, when the measured pulse rate, body temperature, and systolic-diastolic blood pressure findings exceed the threshold range determined by the physician, a warning message can be sent to the mobile phone of the healthcare worker, doctor, or desired recipient via an MC35i terminal (GSM/GPRS modem) controlled by an AT command set connected to the computer where the data is stored. In conclusion, many factors influence the determination of normal and abnormal ranges of vital signs and intervention priorities. When unforeseen situations arise in clinical care processes, RTMEWS has been shown to assist professional interventions by generating timely alarms. The system provides a foundation for clinical decision support systems by offering real-time remote monitoring and autonomous threshold-based alerts.
Copyright (c) 2025 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
Determination of Sensitive Frequency Bands for Cytotoxicity Analysis Using Microwave Dielectric Measurements
This study presents a comprehensive frequency-resolution analysis framework to identify biologically significant frequency bands for cancer cell monitoring in the 10–20 GHz range. By integrating statistical significance, effect size, dielectric deviation magnitude, and temporal stability analysis, the spectral response of cell cultures exposed to cytotoxic agents was systematically evaluated. The results revealed a dual-optimization environment: the 13.9–14.2 GHz range showed a statistically significant global maximum for signal coherence, while higher frequencies demonstrated superior dielectric contrast due to γ-dispersion mechanisms. To resolve this balance, a composite sensitivity score and band-averaged analysis were used. The evaluation identified the 16–18 GHz band as the optimum working region, offering significantly enhanced physical contrast and temporal robustness while maintaining a composite score almost identical to the statistical peak (<0.7% difference). This study demonstrates that data-driven, multi-criteria frequency selection provides a robust foundation for sensor design and paves the way for the development of highly sensitive, frequency-selective microwave biosensors for toxicological screening.
Deep Learning-Based Skin Disease Detection: Comparative Performance Analysis of YOLOv8 and YOLOv11 Models
Recent advances in the field of deep learning have revealed significant potential for automating the detection of skin diseases by overcoming the limitations of traditional diagnostic methods in terms of speed and accessibility. This study presents a comparative analysis aimed at evaluating the performance of the state-of-the-art object detection models YOLOv8 and YOLOv11 in detecting ten different skin disease classes using a publicly available dataset. The dataset consists of a total of 2,486 images, divided into training, validation, and test subsets, and both models were trained under identical conditions for 100 epochs on a Tesla T4 GPU. To assess model effectiveness, performance metrics including precision, recall, F1-score, and mean Average Precision (mAP@0.5) were analyzed. The results indicate that, compared to YOLOv8, which achieved 84% precision and 91% recall, YOLOv11 attained 85% precision and 92% recall, demonstrating a limited improvement in both precision and recall metrics. Class-wise analyses revealed high detection performance for conditions such as Kurap (fungal infection), whereas relatively lower performance was observed for classes such as Bisul and Urticaria. Furthermore, YOLOv11 achieves this improvement while reducing the number of parameters by approximately 22%, thereby offering an advantage in terms of computational efficiency. These findings demonstrate that YOLOv11 provides a computationally efficient approach and indicate its potential use as a decision-support tool for dermatologists in resource-constrained environments.
Modeling And Detection of Cracks in Earthenware Water Jugs Using Artificial Neural Networks and Image Processing Techniques
Quality control in traditional pottery production is a labor-intensive process that relies heavily on manual visual inspection to detect structural defects. To address this, the present study proposes an automated framework combining image processing techniques with Artificial Neural Networks (ANN) to detect and classify cracks in handmade earthenware jugs. Images acquired via smartphone were preprocessed using Otsu’s thresholding and median filtering to effectively isolate defect regions and suppress noise. A dataset of 189 samples was utilized to train and test the ANN model. The proposed model achieved a classification accuracy of 92.98%. Confusion matrix analysis confirmed robust performance, demonstrating high capability in distinguishing between intact and defective samples with no significant bias toward either class. This approach offers potential for modernizing pottery production, enhancing efficiency, and ensuring quality.
Trends and Research Directions in Electricity Load Forecasting: A Bibliometric Analysis
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.
Bibliometric Analysis, Electricity Load Forecasting, Energy Sector, VOSviewer, Forecasting Methods
Bidirectional Puc Converter Based Battery Storage System for Active and Reactive Power Control
Battery storage systems (BSS) play a crucial role in enhancing power quality, reliability, and renewable energy integration. However, multilevel converters used in BSS often face challenges of high component count and capacitor voltage balancing. This study addresses these issues by proposing a single-phase battery storage system based on a seven-level packed U-cell (PUC) converter with two DC-links. Each DC-link is regulated by a bidirectional DC-DC converter, ensuring stable capacitor voltage control and enabling seamless operation in both grid-tied and stand-alone modes under a unified control scheme. Unlike conventional approaches that redesign controllers for different modes, the proposed system only modifies the reference voltage generation for pulse-width modulation. Simulation results confirm effective capacitor voltage balancing, smooth bidirectional active power transfer, and reliable reactive power exchange, allowing the BSS to function as a single-phase STATCOM. Harmonic analysis demonstrates compliance with IEEE 519–2014 standards, ensuring high power quality in grid-tied and stand-alone operation.
A Hybrid Model Approach Based on Swin Transformer and EfficientNetV2 for Maize Variety Classification
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.
Predicting School Dropout Among International Students in Türkiye Using Machine Learning: A Case Study of Yemeni Students
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.
Room Occupancy Prediction from Temperature Data with Deep Convolutional Neural Networks
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.
Energy Production Prediction in Hydroelectric Power Plants with Multi-Layer Perceptron Algorithm, Menzelet Dam Example
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.
Performance Comparison of SVM Kernel Functions for Date Fruit Classification
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.
Minimum Redundancy Maximum Relevance (MRMR)
Hybrid RNN-LSTM Architecture for Sentiment Analysis of Algerian Dialectal Social Media Content
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.
Development of an Embedded System Based Prototype Automatic Coffee Machine and Detailed Analysis of Its Modules
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.
Examining the Relationship of Breast Cancer Data With Survival Chance and Comparison of Algorithms on Breast Cancer Prediction
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.
Drone Detection with Deep Learning and Image Processing: CNN- Based Feature Extraction and Machine Learning Classification
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.
User-Centric Design for Automated Metering Power Management Systems: A Case Study in Nigeria
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.
Bibliometric Analysis of Logistics and Artificial Intelligence Research Trends in the Last 10 Years
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.
Artificial Intelligence (AI), Artificial Intelligence in Logistics, Bibliometric Analysis, Logistics, Research Trends
Copyright (c) 2024 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
Gradient-Based MPC Controller for One DOF Copter Control
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.
Diagnosis of temporomandibular joint disorder using one-dimensional convolutional neural networks: A comparative study
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.
An Artificial Neural Network-Based Caller Authentication and Identification Algorithm in Cellular Communication Networks
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 remaining anonymous.
Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe
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.
Political sentiment analysis using natural language processing on social media
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.
Determination of Thermal Behavior from Core Flux Density with Image Processing Technique for Distribution Transformers
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.
YOLO-V4 Based Real-Time Face Mask Detection via Unmanned Aerial Vehicle
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.
Enhancing Travel Experience: Predicting Flight Delays for Informed Journey Planning
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.
Development of a bulk material volume estimation system using automatic moving rail LiDAR technology
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%.
Analysis of Lorawan Protocol and Attacks Against Lorawan-Based IoT Devices
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.
IoT, IoTSecurity, LoRaWAN, Risk Analysis, LPWAN
Robust fuzzy-logic flight control for unmanned aerial vehicles (UAVs)
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.
Boosting the classification success in imbalanced data of bee larva cells
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.
Hybrid cyber security of unmanned aerial vehicles
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.
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