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
An Offline-First Mobile Reporting System for Digital One Health Surveillance in Resource-Constrained Settings
The increasing interdependence of human, animal, and environmental health highlights the need for integrated One Health surveillance systems capable of supporting timely and coordinated disease reporting. In many low- and middle-income settings, existing surveillance workflows remain fragmented, paper-based, and poorly suited for field conditions. This study employs the Design Science Research Methodology (DSRM) to design, develop, and evaluate a mobile One Health surveillance application that addresses these limitations. The proposed solution is a cross-platform, offline-capable mobile system supporting structured digital reporting, local data storage, and an AI-assisted conversational reporting interface aimed at reducing reporting burden. The artifact was demonstrated through simulated real-world usage scenarios and evaluated using functional testing and usability assessment. Results show a System Usability Scale (SUS) score of 78.4, indicating good overall usability, while the conversational interface reduced average reporting time despite a higher learning curve. The findings demonstrate the feasibility of offline-first mobile surveillance systems for improving One Health data capture in resource-constrained environments.
Copyright (c) 2026 International Journal of Applied Methods in Electronics and Computers https://creativecommons.org/licenses/by-sa/4.0
Comparative Evaluation of Machine Learning Algorithms for Raisin Variety Classification Based on Morphological Features
Machine learning algorithms enable the rapid, accurate, and automated classification of agricultural products. Compared to human visual assessment, these algorithms provide more consistent and highly accurate determination of product characteristics such as variety and quality. In this study, machine learning algorithms were employed to classify Keçimen and Besni raisin varieties cultivated in Turkey. The Raisin dataset obtained from the UCI Machine Learning Repository was used as the data source. For the classification task, k-Nearest Neighbors (k-NN), Multilayer Perceptron (MLP), and Support Vector Machines (SVM) algorithms were utilized. The classification process was carried out using the WEKA software. Initially, the dataset was divided into 80% training and 20% testing subsets, and classification was performed accordingly. Subsequently, the classification process was repeated using the 10-fold cross-validation method. The obtained performance results were evaluated comparatively among the applied algorithms. The MLP algorithm achieved the highest performance, with an accuracy of 92.78% and an AUC of 0.968 under the 80% training–20% testing scheme. Under 10-fold cross-validation, MLP also produced the highest accuracy and AUC values of 87.22% and 0.928, respectively. Although the k-NN and SVM algorithms also produced satisfactory results, they did not reach the performance level of MLP. The findings demonstrate that the evaluated machine learning algorithms can provide effective classification performance for the two raisin varieties under the experimental conditions considered in this study.
Deep Learning-Based Lung Cancer Classification and Grad-Cam++ With Lime-Supported Explainability Analysis
This study aims to classify lung cancer using deep learning-based methods and to interpret the obtained model results using explainable artificial intelligence (XAI) approaches. The dataset used in this study consisted of 1933 chest computed tomography images, which were classified as normal, benign, and malignant. In the classification process, EfficientNet-B4, MobileNetV3-Large, and ResNet50 deep learning architectures were trained using a transfer learning approach, and their performance was evaluated using a 10-fold cross-validation method. The performance of the models was analyzed using accuracy, precision, recall, F1-score, and ROC-AUC metrics. According to the results obtained in the study, the MobileNetV3-Large model showed the highest overall performance with 97.05% accuracy and 99.70% ROC-AUC value. The EfficientNet-B4 and ResNet50 models also provided high and balanced performance values, achieving effective results in lung cancer classification. Grad-CAM++ and LIME methods were used to make model decisions more clinically reliable and interpretable. Grad-CAM++ analyses reveal that the models primarily focus on anatomically significant regions within the lung parenchyma during classification. LIME analyses, on the other hand, have enabled the superpixel-level explanation of local regions that contribute most to classroom decisions. Explainability maps obtained for normal, benign, and malignant classes showed that each class exhibited distinct spatial attention and contribution patterns. In conclusion, this study demonstrates that deep learning-based lung cancer classification offers a more reliable and transparent framework for clinical decision support systems, not only when evaluated with high-performance metrics but also when considered in conjunction with Grad-CAM++ and LIME-supported explainability analyses. The findings demonstrate that explainable artificial intelligence approaches play a significant role in improving model reliability in medical image analysis.
Domain-Specific Fine-Tuning of LLMs for Elevator Emergency Response Systems
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their application in safety-critical domains such as elevator emergency response requires domain-specific knowledge and reliable performance, particularly when deployed as offline edge AI systems with constrained computational resources. This study presents fine-tuning of the Gemma-2-9B-Instruct model using Quantized Low-Rank Adaptation (QLoRA) for Turkish elevator emergency scenarios, targeting deployment on embedded touchscreen control panels inside elevator cabins. Unlike cloud-based systems leveraging internet connectivity for retrieval, our edge deployment operates entirely offline, making fine-tuning essential for encoding domain knowledge directly into model parameters. We developed a specialized Turkish dataset containing 1,155 question-answer pairs covering diverse emergency situations. Our evaluation demonstrates significant improvements: ROUGE-1 scores increased from 0.259 to 0.317 (22.49%), BLEU improved by 218.11%, and hallucination rates reduced from 54% to 22% while achieving 49% faster inference. The resulting 4.8GB quantized model runs entirely on embedded hardware without network dependencies, providing immediate, reliable emergency guidance. These results validate parameter-efficient fine-tuning for safety-critical edge AI applications.
Performance Analysis of Machine Learning Algorithms on Wisconsin Diagnostic Breast Cancer Data Set Enriched with Data Augmentation Technique
Breast cancer is a life-threatening cancer worldwide and is commonly seen in women. Early and accurate diagnosis is a key to successful treatment and better survival rates. In this study, using 569 samples from the Wisconsin Diagnostic Breast Cancer dataset, the classification performances of Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), Random Forest (RF), Naïve Bayes (NB) and k-Nearest Neighbor (kNN) algorithms are compared. To improve accuracy, the train dataset was expanded from 381 to 1524 samples using a three-stage method involving random scaling, Gaussian noise, and mathematical transformations. Test dataset remained unchanged. Statistical analyses confirmed that the augmented data presented sufficient variability to enhance generalization while preserving the original distribution. Model performance analysis was conducted using accuracy, precision, sensitivity, F1 score, FDR, MCC and AUC values. Compared to the original dataset, the accuracy of the SVM algorithm improved from 98.94% to 99.47% with the data augmentation techniques. This result shows that data augmentation techniques are effective in improving the classification performance of machine learning models for breast cancer diagnosis.
Classification of Orthopedic Diseases Using Biomechanical Properties with Machine Learning Methods
Orthopedic diseases significantly affect the musculoskeletal system, reducing patients’ functional capacity and quality of life. Accurate and early classification of such conditions is therefore critical for effective clinical decision-making. This study proposes a machine learning-based framework for orthopedic disease classification using biomechanical features, with a particular emphasis on handling class imbalance. A set of classification algorithms, including Random Forest, Support Vector Machine, Naive Bayes, Logistic Regression, XGBoost, LightGBM, and a Soft Voting Ensemble (XGBoost + LightGBM), were evaluated on a dataset of 310 patients using 10-fold cross-validation. The impact of the Synthetic Minority Over-sampling Technique (SMOTE) was systematically analyzed by comparing model performance with and without its application. Evaluation metrics included Accuracy, Precision, Recall, F1-score, and macro-average ROC-AUC. Results indicate that addressing class imbalance significantly improves model performance, particularly in terms of ROC-AUC. Among the tested methods, Logistic Regression demonstrated the most stable and competitive results. The best performance was achieved by Logistic Regression with SMOTE, yielding 87% accuracy and a macro-average ROC-AUC of 0.96. These findings highlight the importance of imbalance-aware modeling strategies in orthopedic disease classification.
Simulation-Based Evaluation of LiDAR–Photogrammetry Fusion via NeRF Reconstruction and ICP Registration in Urban Scenes
Reliable integration of LiDAR and photogrammetric point clouds is essential for high-precision 3D mapping, yet systematic evaluations of fusion accuracy under controlled conditions remain limited. This study presents a simulation-based assessment framework for LiDAR–photogrammetry fusion using a neural radiance field (NeRF) representation and iterative closest point (ICP) registration. Experiments were conducted in the CARLA simulator (Town 10), where a drone-mounted multi-sensor platform (RGB, LiDAR, GPS, IMU) generated spatially aligned datasets. Photogrammetric reconstructions were produced using the Nerfstudio–Nerfacto pipeline with varied architectural and sampling configurations to analyze their impact on cross-modal registration. Quantitative evaluation employed Chamfer and cloud-to-cloud (C2C) distances to assess NeRF reconstruction fidelity, and ICP root-mean-square error (RMSE), inlier fitness, and runtime to evaluate registration performance. Results show that tuning NeRF’s hidden dimensions and sampling levels yields up to 25% lower ICP RMSE and faster convergence across object categories. The proposed framework enables reproducible benchmarking of LiDAR–photogrammetry fusion and provides a foundation for extending NeRF-based methods to real-world urban mapping scenarios.
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.
Early Diagnosis of Tomato Diseases Using Machine Learning to Reduce Pesticide Application
Pesticides, which are widely used today to increase yield and prevent diseases in agricultural products, have negative effects on human health and the environment. Due to these effects, efforts to restrict pesticide use gained momentum with environmental protection movements starting in the 1970s. Strict controls and licensing processes have been introduced for pesticide production to prevent their unconscious use. The aim of this study is to classify diseases observed in tomato plants, thereby preparing a scientific groundwork for reducing pesticide use. For this purpose, a dataset comprising a total of 13,883 records of tomato plants belonging to six different classes was utilized. For the classification of the data, the following Machine Learning methods were employed: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) algorithms. Model performance was evaluated using metrics such as accuracy, precision, recall, and F1 Score, along with the confusion matrix and ROC analysis. The classification success rates obtained from model training and testing were determined as: ANN 100.0%, KNN 99.8%, SVM 99.9%, and LR 99.9%. The results indicate that all models achieved high and very similar success rates. In conclusion, it is expected that all classification models used can be successfully utilized for the reliable diagnosis of tomato plant diseases, and this capability is projected to make significant contributions to organic farming applications.
Analysis of Potato Diseases Using Image Processing Methods for Detection
Potatoes are one of the basic food sources consumed by living organisms. Potatoes are naturally vulnerable to diseases. Potatoes are a food that can spoil over time, and their nutritional value gradually decreases during this process. Therefore, the quality of potatoes needs to be continuously monitored during the production phase. For this reason, image processing tools are now being used to detect potato diseases. Based on this motivation, this study uses data from 451 different potato images. This dataset contains 7 disease classes: Black Scurf, Blackleg, Common Scab, Dry Rot, Healthy Potatoes, Miscellaneous, and Pink Rot. Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR) methods were used for the classification of this data. Confusion matrix and accuracy, precision, recall, and F1 Score metrics were used to analyze the classification success of the models. As a result of training and testing the models, classification success rates of 68.5% were achieved with the ANN model, 54.8% with the KNN model, 65.6% with the SVM model, and 67.0% with the LR model. The highest classification success was obtained from the ANN model. In conclusion, it can be said that all classification models can be used to detect potato diseases.
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.