Intelligent Methods In Engineering Sciences
Welcome to Intelligent Methods in Engineering Sciences (IMIENS) IMIENS is an international, interdisciplinary, peer-reviewed journal dedicated to advancing intelligent systems and applications across all fields of engineering. Our mission is to bridge the gap between theory and practice, fostering innovations in diverse areas such as nanotechnology, renewable energy, biomedical engineering, robotics, aerospace, industrial manufacturing, and more. As an Open Access Journal, IMIENS ensures global accessibility to all published articles without subscription fees. This commitment accelerates the dissemination of knowledge and enhances the visibility and impact of your research. ISSN: 2979-9236 DOI: 58190/imiens Explore our articles on Google Scholar. Join us in shaping the future of intelligent systems in engineering.
Digital Twin for Cryogenic Ejector Systems: Integrating Advanced Machine Learning and Dynamic Modeling
This paper presents an integrated Digital Twin (DT) framework for cryogenic ejector systems designed for Boil Off Gas (BOG) management in Liquefied Natural Gas (LNG) applications. Building on prior experimental and numerical studies, the proposed DT improves both predictive accuracy and dynamic adaptability by coupling Physics Informed Neural Networks (PINNs) with a transient dynamic model. The PINN integrates compressible flow conservation laws into its loss function, ensuring physical consistency during learning. A dataset of 1000 operating points was analyzed, revealing that the primary pressure (Pp) is the dominant factor influencing the entrainment ratio (ER). A baseline linear regression achieved an R2 equal to 0.791, while the PINN increased predictive accuracy to an R2 equal to 0.98. The dynamic model simulates the transient response of the ejector to sudden variations in BOG load, demonstrating the DT capability to anticipate system instability and enable real time control. Together, these components create a physically interpretable and computationally efficient digital framework capable of supporting the design, optimization, and operation of cryogenic ejectors. The results highlight the potential of the proposed DT to enhance energy efficiency, reliability, and safety in LNG processing systems through intelligent, physics based decision making.
Copyright (c) 2025 Intelligent Methods In Engineering Sciences https://creativecommons.org/licenses/by-sa/4.0
Convolutional Neural Network-Based Framework for the Detection of Tuberculosis
Tuberculosis (TB) remains a significant public health issue worldwide, especially in low- and middle-income countries, where access to accurate and rapid diagnostic tools is limited. Early diagnosis and treatment are essential to control spread and improve patient outcomes. Traditional TB diagnosis methods, such as sputum microscopy and culture, are time consuming and require specialized laboratory facilities. In this study, we explored the application of machine learning techniques in automating and enhancing TB detection, focusing on the analysis of chest radiograph images (X-ray). Hence, a Convolutional Neural Network-based framework is presented. The framework used advanced image preprocessing and augmentation techniques to enhance feature learning and mitigate data set imbalance to support early screening and clinical decision making. The system demonstrated high precision, correctly identifying 97% of normal chest x-rays and achieving a perfect 100% precision for TB cases, which means no false positives were recorded. In terms of recall, the model correctly detected all normal X-rays but misclassified 5% of TB cases as normal, resulting in a 95% recall for TB detection. The F1-score, which balances precision and recall, was 98% for both normal and TB cases, indicating strong classification performance. Additionally, the macro and weighted averages were both 98%, reflecting consistent and reliable model performance across different case distributions. The results indicate that the proposed CNN-based framework provides a robust, scalable, and cost-effective solution for automated TB detection, offering potential integration into computer-aided diagnostic systems.
Deep Learning Based Classification of Real and Synthetic Animal Images
This study aims to develop a convolutional neural network (CNN)-based classification framework that can distinguish between synthetic and real animal images generated by the Stable Diffusion Turbo model. Additionally, this study will evaluate the performance of different network architectures for this task. The study employed a balanced dataset of 31,995 images, including 16,000 real images and 15,995 synthetic images generated by Stable Diffusion Turbo. The dataset includes eight animal categories: dogs, cats, cows, rabbits, horses, sheep, chickens, and elephants. All images were resized to 224 by 224 pixels, and standard preprocessing techniques were applied. During the classification stage, five pretrained convolutional neural network architectures were retrained using transfer learning, including MobileNetV2, DenseNet121, DenseNet169, DenseNet201, and NASNetMobile. Model performance was evaluated using accuracy, precision, recall, the F1 score, the area under the curve of the receiver operating characteristic, confusion matrices, and training time. The experimental results demonstrate that MobileNetV2 and DenseNet201 achieved the highest classification performance, with respective accuracy rates of 99.58% and 99.56%, and perfect area under the curve values. All DenseNet variants exhibited complete sensitivity in detecting synthetic images, whereas NASNetMobile showed substantially lower performance compared to the other models. These results suggest that synthetic images produced by diffusion-based generative models can be reliably identified when appropriately designed CNN architectures and balanced datasets are used. This provides a significant methodological contribution to the discrimination of synthetic versus real images, the detection of fake content, and the verification of visual authenticity.
Classification of Obesity Levels Using Machine Learning Algorithms
Obesity has become a critical public health issue on a global scale due to the serious comorbidities and economic burden it brings. The aim of this study is to develop an effective machine learning model that can accurately determine obesity levels based on data including individuals' demographic characteristics and dietary habits, and to compare the performance of tree-based ensemble learning algorithms and Artificial Neural Network (ANN) approaches. In this context, classification was performed using Random Forest, XGBoost, CatBoost, and ANN (Artificial Neural Network) algorithms based on the open-source “Obesity Dataset” obtained from 1,610 participants and containing 14 different attributes. The models' performance was tested using a 5-fold cross-validation method and evaluated based on accuracy, f-score, precision, and recall using a confusion matrix. Experimental results show that tree-based ensemble models outperform the ANN approach in this dataset. The Random Forest algorithm was the most successful model with an accuracy rate of 94.34% and an F-score of 94.36, followed by XGBoost with an accuracy rate of 92.80%. In contrast, YSA remained at an accuracy rate of 82.98% and spent approximately 93 times more time in terms of training duration compared to Random Forest. When considering the obtained outputs, this study demonstrates that ensemble learning methods such as Random Forest are more efficient than ANN models in terms of both prediction accuracy and computational cost in the analysis of tabular health data, and that the developed model can be used as a reliable tool in clinical decision support systems.
CrackNet: Pavement Crack Detection and Classification Based on Deep Learning Models
The identification of pavement cracks is essential for reducing traffic accidents and minimizing road maintenance costs. Existing crack detection methods frequently encounter challenges related to inefficiencies and accuracy, resulting in billions of dollars spent globally on road repairs annually. This study introduces an enhanced deep learning-based network aims at improving the accuracy of pavement crack detection. The proposed CrackNet utilizes advanced geometric augmentation techniques to enhance the model performance in identifying cracks across a variety of road conditions. We introduced CrackNet, a custom-designed deep-learning classification framework for pavement images. CrackNet combines pre-trained backbone feature extractors (MobileNetV2, InceptionV4, SqueezeNet) with a lightweight classifier head and a comprehensive preprocessing + augmentation pipeline to improve generalizability and address class imbalance. We evaluated three CrackNet variants (Sqz-CrackNet, Mob-CrackNet, Incep-CrackNet), each distinguished by its backbone, and found Incep-CrackNet achieved the highest accuracy of 96.05%, surpassing the Mob-crackNet and Sqz-crackNet, which attained accuracies of 95.44% and 90.06%, respectively. These findings underscore the effectiveness of the proposed deep learning framework in accurately detecting pavement cracks, representing a significant advancement over traditional detection methods.
A Fixed-Grid Rdtm-Based Computational Strategy for Nonlinear Partial Differential Equations
In this study, a fixed-grid version of the Reduced Differential Transform Method (RDTM) is systematically implemented to obtain approximate solutions of linear and nonlinear partial differential equations. In this method, the solution range is divided into equal subregions and the fixed-grid algorithm is integrated into the RDTM framework. This approach provides an efficient and orderly computational process for solving complex partial differential equations. The effectiveness of the proposed method is demonstrated on the homogeneous Klein–Gordon equation (a representative hyperbolic equation) and the nonlinear Klein–Gordon equation, and the obtained approximate solutions are compared with known analytical solutions with high accuracy and consistency. Furthermore, the proposed fixed-grid RDTM (FGS-RDTM) framework offers potential integration with intelligent systems where accurate and efficient numerical solvers are required for modeling, control, and learning in dynamic environments. These results confirm the reliability and practical usefulness of the new method in addressing nonlinear partial differential equations in the context of intelligent computational systems.
Dual-Scale Transformer-Guided Attention Network for Efficient Multi-OAR Segmentation in Head and Neck Radiotherapy
Accurate segmentation of organ-at-risk (OARs) in head and neck CT images is crucial for radiotherapy planning, but it remains a challenging task due to anatomical complexity, low soft-tissue contrast, and the presence of small, variable structures. We propose DSTANet, a novel dual-scale transformer-guided attention network that integrates multi-resolution encoding, transformer-based global context fusion, and anatomically guided attention refinement to deliver precise multi-OAR segmentation. Unlike traditional CNN-based methods, DSTANet effectively models long-range spatial dependencies while preserving high-resolution boundary detail. On the HNSCC-3DCT-RT dataset, DSTANet achieved a mean Dice Score of 97.5% and a mean 95th percentile Hausdorff Distance (HD95) of 2.32 mm, while on the MICCAI 2015 benchmark dataset, it achieved 90.0% Dice, which surpasses several state-of-the-art approaches both in terms of overlap and geometric accuracy. These results, combined with a sub-20-second inference time, establish DSTANet as a robust and clinically viable solution for automated head and neck OAR segmentation.
dual-scale transformer-guided attention network
Binary Aquila Optimizer with Taper-Shaped Transfer Function: An Application for Merkle-Hellman Knapsack Cryptosystems
Metaheuristic algorithms are powerful methods used to solve large and complex optimization problems. Thanks to their flexibility, they provide effective results in various fields and also have an important place in security applications such as the Merkle-Hellman Knapsack Crypto System. Aquila Optimizer is an optimization algorithm inspired by the hunting behavior of aquilas. It provides fast and effective solutions to complex problems. In this study, Aquila Optimizer is discretized using taper-shaped transfer functions. Taper-shaped transfer functions help the algorithm obtain more precise and effective results by increasing its performance. Four BinAO versions obtained in this way were tested on the Merkle-Hellman Knapsack Cryptosystem. In the tests conducted for the cryptanalysis of "CAT" and "MACRO" messages, the version achieved more successful results. Additionally, tests conducted with the algorithms in the literature clearly showed that the proposed algorithm is successful and effective.
Cybersecurity in FinTech: A Machine Learning-Based Framework for Threat Detection in Mobile Payments
The rapid evolution of Financial Technology (FinTech) has revolutionized mobile payment systems, offering seamless, efficient, and real-time financial services. However, this digital transformation has simultaneously introduced complex cybersecurity challenges, particularly as cybercriminals increasingly exploit mobile platforms. This study proposes a novel machine learning-based framework for proactive threat detection in mobile payment environments, integrating behavioral analytics, device fingerprinting, and network anomaly detection. The framework leverages supervised and unsupervised learning models—such as Random Forest, Isolation Forest, and Autoencoders—to identify both known and zero-day threats with high precision. A hybrid feature engineering pipeline is also introduced, combining static application metadata with dynamic transaction behavior to enhance detection accuracy. Experimental results on real-world mobile payment datasets demonstrate that the proposed framework achieves superior performance in terms of precision, recall, and F1-score compared to traditional signature-based and rule-based detection systems. This research contributes to the advancement of secure FinTech ecosystems by offering a scalable and adaptive solution for real-time cyber threat mitigation in mobile payments.
Object Detection and Visual Intelligence in Retail Environments: A Deep Learning Approach for Inventory and Behavior Analytics
The integration of visual intelligence technologies in retail environments has revolutionized inventory tracking and customer behavior analysis. This study proposes a comprehensive deep learning-based framework that leverages advanced object detection models to enhance retail operations through real-time visual insights. Our method integrates state-of-the-art architectures such as YOLOv8 and Mask R-CNN to accurately identify, track, and classify products on shelves while simultaneously analyzing shopper interactions and movement patterns. By utilizing annotated datasets collected from real-world retail scenarios, the system demonstrates high accuracy in both inventory status recognition and behavioral inference, outperforming traditional sensor-based methods. Furthermore, we introduce a hybrid loss function and a scene-aware postprocessing module that improves detection in occluded or dynamic environments. The experimental results show that our approach enables automated planogram compliance checks, customer heatmap generation, and actionable analytics, thus supporting intelligent decision-making for retailers. This research contributes a scalable and real-time visual system that bridges the gap between deep learning and practical retail intelligence.
Behavior Analytics, Deep Learning, Inventory Management, Object Detection, Visual Intelligence
Fusion-Aware Lightweight CNN with Channel-Wise Attention for Rose Leaf Disease Classification in Multispectral Color Domains
The rose plant holds significant economic and cultural value, not only as an ornamental species but also due to its widespread use across various industrial domains such as cosmetics, medicine, and perfumery. In this context, the early and accurate detection of leaf diseases is crucial for ensuring the healthy cultivation of rose plants. This study presents a novel approach that integrates image processing and deep learning techniques for the detection of common leaf diseases in rose plants. The proposed method utilizes the RoseNet dataset, which consists of three classes: Black Spot, Downy Mildew, and Fresh Leaf. Input images were converted into RGB, HSV, and YCrCb color spaces and then fused at the channel level to enhance spectral diversity and improve the model's learning capacity. The developed convolutional neural network (CNN) architecture was enriched with channel attention mechanisms, namely Squeeze-and-Excitation (SE) and Efficient Channel Attention (ECA) blocks. Class imbalance issues were addressed through class weighting and label smoothing strategies. The model's performance was evaluated using multiple metrics such as accuracy, precision, recall, and F1-score. Achieving an accuracy of 95.65%, the proposed model outperformed widely used CNN architectures in the literature. Furthermore, with a low parameter count (1.03M) and a fast test time (376 ms), the model is well-suited for deployment on embedded systems. The findings demonstrate that attention mechanisms are effective in enhancing class discrimination, particularly in low-sample-size datasets. Thus, the proposed model offers a reliable, cost-effective, and AI-based solution for the diagnosis of plant leaf diseases.
Implementation of a Heuristic Algorithm for the Solution of Discrete Time Cost Trade-off Problem
Time cost trade-off problem aims to minimize the total project cost by crashing the critical activities. This problem is solved by mathematical programming and meta-heuristic algorithms. However, construction sector has minimum priority on the theoretical knowledge to implement robust optimization algorithms. For this reason aforementioned optimization algorithms can be hardly implemented for the private construction companies. The nature of the time cost trade-off algorithm is not challenging and can be solved heuristically. In this study, a spreadsheet application is developed by utilizing in-app excel functions to identify the critical activities of the project and the paths of the project. The construction schedule is entered to the spreadsheet application as acticity on arrow diagram and the logical relationships between the activities are defined. Forward and backward pass computations are given as formulations which includes the actual activity durations. The developed application calculates the crashing costs of the critical activities and highlights the activities with the cheapest crashing costs. Prepaperd spreadsheet application implements a heuristic solution algorithm which is based on minimum cost slope of the construction activities. The user can easily execute the proposed or user selected crashing alternative and the schedule is updated according to the selection. The application is tested on 6 activity project and the optimum solution is obtained by crashing the activities sequentially. The proposed technique can be utilized to reduce the total project cost of the construction sector.
Internet Traffic Classification through Supervised Learning: Exploring Machine Learning Techniques
The increasing complexity and volume of internet traffic have led researchers to explore machine learning as an effective approach for traffic classification. By integrating intelligence into network processes, machine learning enhances network management and optimization. This study investigates four supervised learning techniques—Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT)—to forecast network traffic categorization. Through a comparative analysis, we evaluate the performance of these algorithms in terms of accuracy, precision, recall, and computational efficiency using a standardized dataset. The results demonstrate that while each algorithm has its strengths and weaknesses, our findings indicate that Random Forest outperforms the other algorithms in most metrics, providing valuable insights for future applications in network management. This study provides valuable insights into the applicability of these algorithms for real-time internet traffic management.
Realising Quality Control of Metal Products Used in Industry Through Deep Learning
This study was carried out with the aim of detecting the defects on the surfaces of metallic products, which are frequently encountered in our daily lives and widely used in industry, with deep learning. Many metal products in the industry undergo different processes during the production phase. As a result of these processes, the detection of defects such as breakage, cracking, etc. on the surfaces of metal products is carried out by quality control personnel or production personnel. However, this error detection made by manpower both slows down production and causes overlooked errors. In order to easily detect these defects, in our study, we used a dataset consisting of 285 images in total with images of two different surface defects and images containing flawless metal parts and a CNN architecture, ResNet50 architecture, to defects. There are three classes in total in the dataset. Two of these classes consist of images of different types of defects on the surface of the metal piston part used in air conditioners, and one of them consists of images of perfect metal piston parts. Convolutional Neural Network (CNN) method was used to determine the features of the images. Precision, recall, F1-Score and accuracy metrics were used to measure the performance of the model. With the ResNet50 architecture, defects on the surfaces of metal piston parts are detected quickly and with high accuracy. As a result of the study, it was suggested that the proposed model can detect surface defects that occur in the usage areas of metal products in various sectors more quickly and accurately using deep learning. This shows that the quality control problems experienced in the industry can be reduced by using deep learning, saving time and manpower.
Copyright (c) 2024 Intelligent Methods In Engineering Sciences https://creativecommons.org/licenses/by-sa/4.0
Comparison of Data Reduction Algorithms for Real-Time Data Processing in Embedded Systems
In embedded systems, large datasets are difficult to process in real-time due to limited processing power, memory capacity, and energy resources. In order to solve these difficulties, the use of algorithms that reduce data size and complexity has become a critical requirement. This study examines the techniques of five algorithms used for data reduction in embedded systems. The techniques of dimensionality reduction, numerosity reduction, data compression, data cube aggregation, and discretization algorithms are applied to a dataset. The dataset consists of load and angle data recorded every five seconds for three months. The selected data reduction techniques are evaluated to reduce data processing load, optimize storage requirements, and reduce energy consumption. The results show that each algorithm offers advantages according to different application requirements. The findings obtained in this study provide a guiding framework for the optimization of data processing processes in embedded systems. The results provide important information that can help system designers select algorithms suitable for application requirements. In the future, combining these algorithms with hybrid approaches can further increase the data processing capacity and efficiency of embedded systems.
Predicting Student Dropout Using Machine Learning Algorithms
This article comprehensively examines the use of machine learning algorithms to predict and reduce student dropout rates. These methods, developed to monitor and support student achievement in education, also aimed to enhance success rates in education and ensure more effective student engagement in the learning process. Big data analysis and machine learning models provide important contributions to the development of strategic solutions to the problem of school dropout by predicting student movements and trends. This study uses a dataset consisting of 4424 student data and has 37 features. The dataset is divided into three classes: "Dropout", "Enrolled" and "Graduate" according to the students' school dropout status. Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) competitions, which are frequently used in such training studies in the literature, are aimed at this dataset. According to the obtained operations, DT showed moderate performance with an accuracy rate of 70.1%. The RF algorithm showed higher success with an accuracy rate of 75.5%. The highest success was achieved by the ANN algorithm with an accuracy rate of 77.3%. ANN's flexible structure has produced superior results compared to other algorithms for this dataset, its ability provide successful classification in complex datasets. The article ultimately demonstrates how machine learning-based prediction models can have a significant impact on student achievement and offer a powerful tool for reducing school dropouts.
Classification of Raisin Grains Using Different Artificial Neural Network Methods
In addition to its nutritional properties, raisins are also a beneficial food in terms of health due to its vitamins, minerals, antioxidants and phenolic compounds. Turkey ranks first in global raisin production with a production capacity of 24%. Many problems are encountered in the classification of raisins according to their type and quality by traditional methods. In order to overcome these problems, artificial intelligence systems, whose usage area is increasing day by day, are utilized. In this study, raisin grains were classified using 3 different Artificial Neural Network (ANN) methods using the ‘Raisin’ dataset from the UCI Machine Learning Repository. Performance measurements of Competitive Layer Neural Network (CLNN), Pattern Recognition Artificial Neural Network (PRNN) and Self-Organizing Map (SOM) methods used in classification were performed. In the obtained performance measurements, PRNN has the highest success, while SOM is weaker compared to the other two methods. CLNN, on the other hand, remains at similar levels to PRNN and offers a good alternative to PRNN.
pattern recognition artificial neural network
Computer Science – Focus on machine learning, artificial intelligence, and algorithm development.
Performance Evaluation of Machine Learning Algorithms in Estimating Taxi Times at Istanbul Airport
This article evaluates the performance of regression algorithms used to estimate taxi-out times at Istanbul Airport. Artificial neural networks, random forest, gradient boosting, and decision trees algorithms were studied to determine the algorithms with the highest accuracy. Principal Component Analysis (PCA) was used to reduce the data's dimensionality and improve model performance. The findings of the study provide valuable insights for more effective management of airport operations and reduction of flight delays. PCA-applied Artificial Neural Networks (ANN) emerged as the most successful algorithm, demonstrating the highest accuracy (R²: 95.89%) and lowest error margins (MAE: 0.016, MSE: 0.001) in predicting taxi-out times. This superior performance indicates that ANN can effectively capture the complex relationships and variability inherent in airport operational data. Following ANN, the PCA-applied Random Forest algorithm also showed commendable accuracy (R²: 94.89%), providing robust predictions with slightly higher error margins (MAE: 0.157, MSE: 0.044) compared to ANN. These results underline the potential of using advanced machine learning techniques to enhance the efficiency of airport operations, thereby minimizing delays and optimizing resource allocation. Overall, the application of these machine learning models, particularly ANN and Random Forest, offers a significant improvement over traditional methods. The study's outcomes suggest that incorporating these advanced algorithms can lead to more accurate predictions of taxi-out times, supporting better decision-making processes and operational strategies at airports.
Deep Learning-Based Classification of Skin Lesion Dermoscopic Images for Melanoma Diagnosis
This study examined the effectiveness of artificial intelligence and machine learning methods in accurately categorizing skin cancers. We used the ISIC 2019 Skin Lesion dataset, which consists of images divided into eight categories. To prepare the data, we applied preprocessing techniques and extracted features using the SqueezeNet deep learning model. The dataset was divided into training and test sets using cross validation. Four well-known machine learning algorithms, namely Artificial Neural Network (ANN), k-nearest neighbors (kNN), Random Forest (RF), and Logistic Regression (LR), were employed to perform classification tasks. Each algorithm was specifically designed to suit its processing methodology. The algorithms are assessed based on several important measures. The results indicate that the Artificial Neural Network (ANN) achieved the highest accuracy rate of 71.80%, whereas the k-nearest neighbors (kNN), Random Forest (RF), and Logistic Regression (LR) achieved accuracy rates of 69.70%, 67.00%, and 67.20%, respectively. The results emphasize the capacity of machine learning algorithms to augment clinical decision-making in the field of dermatology, with the goal of enhancing early detection and treatment effectiveness for individuals with skin cancer.
Detecting Industrial Potato Chips Defects with Machine Learning Methods Using Deep Features
Detection of production defects in industrial foods is vital to protect consumer health. Early detection of these errors can minimize the economic losses of manufacturers by reducing the costs of recalls and production stoppages. Additionally, continuous monitoring and improvement of product quality can increase brand reliability and customer satisfaction. Image processing can detect product defects, minimize human error and increase efficiency by performing uninterrupted inspection on the production line. Based on these reasons, this study aimed to detect potato chip errors with image processing. PepsiCo Lab Potato Chips Quality Control image dataset was used in the study. There are two classes in the dataset: defective and not defective. There are 967 images in total. SqueezeNet Convolutional Neural Network (CNN) architecture was used to extract the features of the images. With this architecture, 1000 features obtained for each image were classified with Artificial Neural Network (ANN), K Nearest Neighbor (KNN), Random Forest (RF) machine learning methods. As a result of the classifications, 0.986 classification accuracy was obtained from the ANN model, 0.927 from the KNN model, and 0.962 from the RF model. F1 Score, precision, recall and specificity metrics were used to compare the models in detail. According to the data obtained from the experimental results, it is predicted that the proposed feature extraction and classification models can detect industrial production errors occurring in potato chips.
Classification of Environmental Attitudes with Artificial Intelligence Algorithms
The study aims to examine people's attitudes towards the environment. Environmental education provides the necessary awareness to effectively address environmental issues. It is stated that attitudes towards the environment are very important and negative attitudes can worsen environmental problems. For this purpose, a dataset was obtained by using a scale consisting of 37 variables to a participant group consisting of 384 people. With this dataset, attitudes towards the environment have been analyzed using various classification algorithms. Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT) models were used in the research. The LR, SVM, and DT models achieved 94.53%, 92.96%, and 82.55% classification success, respectively It is seen that the classification achievements of the models are at an acceptable level compared to the literature. As a result, the research sheds light on people's attitudes towards the environment through classification processes. Despite the acceptable classification achievements, alternative artificial Intelligence approaches can also be used to improve performance.
Classification of Industrial and Commercial Facilities Using Machine Learning Techniques
The performance of machine learning algorithms for the automatic classification of industrial and commercial facilities were examined within the scope of this project. A dataset containing a total of 47,683 data points, including 27,691 warehouses, 6,441 retail stores, and 13,551 factories, was used in this study. To classify these facilities as "factory," "warehouse," or "retail," ANN, RF, and kNN machine learning models were applied and compared. The ANN achieved the highest classification accuracy with 76.9%. This was followed by the RF algorithm with 73.9% and the kNN algorithm with 63.9%. The high performance demonstrated by the ANN indicates that it could be a powerful tool for automatic facility classification in industrial and commercial sectors. This classification can provide significant contributions such as increasing operational efficiency of businesses, more effectively guiding marketing strategies, and better management of resources. Future studies can expand research in this field by further increasing model accuracy and testing in various application scenarios.
Classification of Diseases in Tomato Leaves Using Deep Learning Methods
The automatic detection of diseases in tomato leaves significantly contributes to tomato production and enables farmers to manage these issues more effectively. Tomatoes are a crucial commercial crop for local markets and exports, representing a significant agricultural sector in our country. Diseases affecting tomato leaves directly influence tomato yield and quality, making early detection and intervention paramount. Our study aims to address tomato losses due to leaf diseases using computer technology. Recently, Convolutional Neural Networks (CNN) have been employed in various fields including agriculture, military, robotics, and medicine for classification, object detection, and segmentation tasks. The integration of computer vision and image processing with deep learning architectures has led to notable advancements in these areas, offering solutions with higher accuracy and reducing human error. In our research, a dataset was created using images of tomato leaf diseases selected from Kaggle. Algorithms such as k-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Neural Networks (NN) were applied using Orange, a data visualization and analysis software. Moreover, a custom algorithm developed in Python demonstrated the highest accuracy. while the highest classification accuracy of classification made with machine learning algorithms was 95.6%, the classification accuracy was achieved about 96% with the developed deep learning model. This system was integrated into an Amazon Web Services (AWS) Lambda function, subsequently utilized in a mobile application developed using Flutter for the UI and Dart for backend, ensuring connectivity with the Lambda function.
Evaluation of Machine Learning and Deep Learning Approaches for Automatic Detection of Eye Diseases
There are many ocular diseases present in the world. These diseases may arise from factors such as genetic predisposition, environmental influences, and aging. In recent years, advancements in technology have facilitated the detection of ocular pathologies through machine learning techniques. Machine learning models can serve as decision support mechanisms in diagnostic scenarios. In this study, the aim is to detect ocular diseases using machine learning and deep learning techniques. To enhance the results obtained from classification with 4,217 images in the study, 705 images were added to the glaucoma class and 370 images were added to the Diabetic Retinopathy class. The supplemented dataset with additional images comprises a total of four classes. One class represents the control group and is labeled as "normal." The remaining three classes represent disease categories: Diabetic Retinopathy, Cataract, and Glaucoma. To extract deep features from the images, a pre-trained InceptionV3 model was utilized, resulting in 2048 features extracted. These extracted features were then classified using Neural Network (NN), Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Random Forest (RF) machine learning models. Before the dataset supplemented with additional images, the classification accuracies of the machine learning models were as NN 89.2%, LR 87.3%, k-NN 81.2%, and Random Forest 76.9%. Upon examining the classification accuracies after dataset supplemented with additional images, the following improvements were observed: NN 90.9% with a 1.7% increase, LR 90.2% with a 2.9% increase, k-NN 84.6% with a 3.4% increase, and Random Forest 82% with a 5.1% increase. Performance evaluation was conducted using recall, precision, and F-1 score metrics. Additionally, the learning performance of the machine learning models was assessed through Receiver Operating Characteristic (ROC) curves and Area Under Curve (AUC) values.
Automatic Classification and Detection of Faulty Packaging using Deep Learning Algorithms: A Study for Industrial Applications
In order to market the product to the consumer with the correct methods and to increase the reliability and sustainability of the brand in many stages from the production stage to the launch of the product in the national and international environment, to prevent faulty problems that may be encountered, the project will classify the packages with computer vision within the framework of deep learning algorithms and detect faulty packages. Studies have been carried out in this direction with the aim of saving labor and time, reducing the margin of error and increasing efficiency. In the study, a total of 3000 images, 1000 from each class, were used in three classes of fruit juice boxes called "Flawless", "Pressed" and "Stained" to ensure the image distribution ratio according to classes. In the study, training and testing of the model was carried out using the YoloV8 object detection algorithm. In addition, in order to make comparisons, SqueezeNet and IncepptionV3 classification models were trained and tested using images. Values of 99.5% for mAP50 and 97.9% for mAP50-95 were obtained from the YoloV8 model. 100% classification success was achieved from the SqueezeNet model and 99.9% classification success was achieved from the InceptionV3 model. The performance results obtained from the tests of the models were analyzed and evaluated, and then real-time testing was carried out. The accuracy of the study was evaluated by taking real-time images of the juice boxes moving on the conveyor with a camera. It is thought that the system created as a result of the study can be used in the industrial field.
Emotion Detection with Pre-Trained Language Models BERT and ELECTRA Analysis of Turkish Data
Developments in artificial intelligence have led to positive developments in many fields. Sentiment analysis, one of these areas, has become more applicable with the models and architectures developed. In this study, emotion detection and emotion analysis were performed on the transcribed data of Turkish voice recordings. In the emotion detection phase, after the emotional states (positive, negative, neutral) of the data were detected with BERT and ELECTRA models, which are transformer-based structures, machine learning algorithms were used in the accuracy analysis of these emotional states and the Google Colaboratory platform was used in the application phase. Naive Bayes, Random Forest, Support Vector Machine and Logistic Regression algorithms were used in the accuracy analysis. As a result of the study, both Naive Bayes and Logistic Regression algorithms achieved the best accuracy rate in emotion detection with the BERT model with a rate of 70%. In emotion detection with the ELECTRA model, both Random Forest and Logistic Regression algorithms achieved the best accuracy rate of 72%. BERT and ELECTRA methods are used to provide a better understanding of understanding and classification of emotional content in Turkish texts and contribute to the development of sentiment analysis-based applications. In addition, two Turkish emotion data sets were obtained, and by using more than one method in emotion analysis, our study has been a unique study in the field, allowing the analysis of the study to be done more effectively.
Detection of Emergency Words with Automatic Image Based Lip Reading Method
Lip reading automation can play a crucial role in ensuring or enhancing security at noisy and large-scale events such as concerts, rallies, public meetings, and more by detecting emergency keywords. In this study, the aim is to automatically detect emergency words from the lip movements of a person using images extracted from silent video frames. To achieve this goal, an original dataset consisting of silent video images in which 8 emergency words were spoken by different 14 speakers was used. The lip regions of the images obtained from the videos in the dataset were labeled through relevant region detection. Labeled data were then evaluated using the SSD (Single Shot MultiBox Detector) deep learning method. Subsequently, subsets of labeled data with 8, 6, and 4 classes were created. The SSD algorithm was evaluated separately for each of these subsets. During the training of the SSD algorithm, weight initialization methods such as 'he,' 'glorot,' and 'narrow-normal' were used, and their performances were compared. Additionally, the SSD algorithm was trained with two different values of the maxepochs parameter, which were 20 and 30, respectively. According to the results, the lowest accuracy value was found for the 8-class subset, with an accuracy of 42% obtained using 20 epochs of training and the 'narrow-normal' weight initialization method. The highest accuracy value was achieved for the 4-class subset, with an accuracy of 76% obtained using the 30 epochs of training and the 'glorot' weight initialization method.
A Detailed Analysis of Detecting Heart Diseases Using Artificial Intelligence Methods
Hearts are crucial for maintaining a healthy lifestyle and are ranked high among the organs that need special care. Globally, heart disease is one of the leading causes of death, posing a considerable public health challenge in low-income countries in particular. Early diagnosis and the identification of risk factors are critical when dealing with these diseases, as early symptoms are often not evident. Heart disease can be caused by several factors, including smoking, poor diet, stress, a lack of physical activity, and excessive alcohol consumption. During the diagnosis process, doctors may encounter various challenges, including vague symptoms, misleading test results, and other medical complications. It is currently possible to diagnose heart disease more accurately and effectively using machine learning algorithms. The present study examines seven different machine learning algorithms on a dataset consisting of 4,238 records and 16 different patient characteristics. Among the classification models, Naive Bayes, Decision Trees, Random Forests, Support Vector Machines (SVM), Artificial Neural Networks (ANNs), K Nearest Neighbors, and Logistic Regressions yielded 78.9%, 79.9%, 83.9%, 70.9%, 83.7%, 83.4%, and 85.5%, respectively.
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Digitization and Archiving of Company Invoices using Deep Learning and Text Recognition-Processing Techniques
Nowadays, it is crucial to transfer official documents such as invoices, dispatch notes, and receipts into digital environments and establish correct semantic relationships. However, understanding and processing these documents is a difficult process that requires significant time and effort. In recent years, the use of deep learning, image preprocessing, text detection, and optical character recognition (OCR) technologies have made this process easier. However, for text recognition and processing techniques to produce accurate results, documents must be clean and readable. Additionally, difficulties arising from time-consuming, tiring, error-prone, and cost-incurring human-powered digitalization processes must be reduced. The aim of this study is to digitize and archive scanned invoices and similar official documents using current artificial intelligence technologies, thereby enabling the most effective use of components such as time, cost, and human resources. The dataset used in the study includes 10,000 ".jpg" image files and 10,000 ".xml" data files. The model trained with the ResNet-50 architecture can detect text with accuracy rates of up to 97% on randomly selected images from the dataset. In an environment where a person can process an average of 2,112 documents per month, it is predicted that the trained artificial intelligence model can process 108,000 documents per month. With this developed method, businesses can quickly digitize and archive official documents such as invoices, dispatch notes, and receipts. Future studies propose the development of new methods that can produce better results using larger and more diverse datasets.
Predicting Future Demand Analysis in the Logistics Sector Using Machine Learning Methods
In this study, the potential of machine learning methods for analyzing future needs in the logistics sector was investigated. The research is conducted using the MATLAB platform. Numeric pallet demand data obtained from a logistics company are employed to train MLP, LSTM, and CNN models. Data security and confidentiality take priority during the data collection process. This dataset, comprising a total of 3,062 daily records, serves as the primary data source for the study. In the data preprocessing phase, missing or erroneous data is rectified, and outliers are detected and corrected. The models are tested to predict pallet quantities over periods of 25 days and 4 weeks. The results are evaluated by comparing the model predictions with actual data. Model performances are assessed using metrics such as MSE, RMSE, NRMSE, MAE, ESD, and RC. The outcomes of the last 25 days demonstrate that the LSTM model exhibits the lowest MSE (6,410.5571) and RMSE (80.0660) values. For the MLP model, the MSE value is calculated as 20,536.5564, and the RMSE value is 143.3058. Performance evaluations for the CNN model yield an MSE of 8,492.4297 and an RMSE of 92.1544. Furthermore, it is observed that the MLP model provides the best results for the 4-week forecasts. The results of this study indicate the success of the models used for predicting pallet transportation quantities in the logistics sector. In addition to this study, a contribution is made toward enabling logistics companies to make more informed and strategic decisions.