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Article Citation - WoS: 21Citation - Scopus: 35Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets(Mdpi, 2022) Kadhim, Yezi Ali; Khan, Muhammad Umer; Mishra, AlokComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.Article Citation - WoS: 1Citation - Scopus: 1Machine Vs. Deep Learning Comparision for Developing an International Sign Language Translator(Taylor & Francis Ltd, 2022) Eryilmaz, Meltem; Balkaya, Ecem; Ucan, Eylul; Turan, Gizem; Oral, Seden GulayThis study aims to enable deaf and hard-of-hearing people to communicate with other individuals who know and do not know sign language. The mobile application was developed for video classification by using MediaPipe Library in the study. While doing this, considering the problems that deaf and hearing loss individuals face in Turkey and abroad modelling and training stages were carried out with the English language option. With the real-time translation feature added to the study individuals were provided with instant communication. In this way, communication problems experienced by hearing-impaired individuals will be greatly reduced. Machine learning and Deep learning concepts were investigated in the study. Model creation and training stages were carried out using VGG16, OpenCV, Pandas, Keras, and Os libraries. Due to the low success rate in the model created using VGG16, the MediaPipe library was used in the formation and training stages of the model. The reason for this is that, thanks to the solutions available in the MediaPipe library, it can normalise the coordinates in 3D by marking the regions to be detected in the human body. Being able to extract the coordinates independently of the background and body type in the videos in the dataset increases the success rate of the model in the formation and training stages. As a result of an experiment, the accuracy rate of the deep learning model is 85% and the application can be easily integrated with different languages. It is concluded that deep learning model is more accure than machine learning one and the communication problem faced by hearing-impaired individuals in many countries can be reduced easily.Article Citation - WoS: 29Citation - Scopus: 43Text Classification Using Improved Bidirectional Transformer(Wiley, 2022) Tezgider, Murat; Yıldız, Beytullah; Yildiz, Beytullah; Aydin, Galip; Yıldız, BeytullahText data have an important place in our daily life. A huge amount of text data is generated everyday. As a result, automation becomes necessary to handle these large text data. Recently, we are witnessing important developments with the adaptation of new approaches in text processing. Attention mechanisms and transformers are emerging as methods with significant potential for text processing. In this study, we introduced a bidirectional transformer (BiTransformer) constructed using two transformer encoder blocks that utilize bidirectional position encoding to take into account the forward and backward position information of text data. We also created models to evaluate the contribution of attention mechanisms to the classification process. Four models, including long short term memory, attention, transformer, and BiTransformer, were used to conduct experiments on a large Turkish text dataset consisting of 30 categories. The effect of using pretrained embedding on models was also investigated. Experimental results show that the classification models using transformer and attention give promising results compared with classical deep learning models. We observed that the BiTransformer we proposed showed superior performance in text classification.Article Citation - WoS: 14Citation - Scopus: 26Deep Learning-Based Vehicle Classification for Low Quality Images(Mdpi, 2022) Tas, Sumeyra; Sari, Ozgen; Dalveren, Yaser; Pazar, Senol; Kara, Ali; Derawi, MohammadThis study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 x 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. Results indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.Review Citation - WoS: 7Citation - Scopus: 9A Survey of Covid-19 Diagnosis Using Routine Blood Tests With the Aid of Artificial Intelligence Techniques(Mdpi, 2023) Habashi, Soheila Abbasi; Koyuncu, Murat; Alizadehsani, RoohallahSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.Article Citation - WoS: 5Citation - Scopus: 8A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification(Mdpi, 2024) Kadhim, Yezi Ali; Guzel, Mehmet Serdar; Mishra, AlokMedicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.Article Citation - WoS: 103Citation - Scopus: 160Cassava Disease Recognition From Low-Quality Images Using Enhanced Data Augmentation Model and Deep Learning(Wiley, 2021) Abayomi-Alli, Olusola Oluwakemi; Damasevicius, Robertas; Misra, Sanjay; Maskeliunas, RytisImprovement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real-world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low-quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour-based features, which are less sensitive to the deficiencies of low-quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down-sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower-quality testing images when compared with the baseline network. The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.Article Citation - WoS: 3Citation - Scopus: 5Convolutional Neural Network-Based Vehicle Classification in Low-Quality Imaging Conditions for Internet of Things Devices(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Maiga,B.; Dalveren,Y.; Kara,A.; Derawi,M.Vehicle classification has an important role in the efficient implementation of Internet of Things (IoT)-based intelligent transportation system (ITS) applications. Nowadays, because of their higher performance, convolutional neural networks (CNNs) are mostly used for vehicle classification. However, the computational complexity of CNNs and high-resolution data provided by high-quality monitoring cameras can pose significant challenges due to limited IoT device resources. In order to address this issue, this study aims to propose a simple CNN-based model for vehicle classification in low-quality images collected by a standard security camera positioned far from a traffic scene under low lighting and different weather conditions. For this purpose, firstly, a new dataset that contains 4800 low-quality vehicle images with 100 × 100 pixels and a 96 dpi resolution was created. Then, the proposed model and several well-known CNN-based models were tested on the created dataset. The results demonstrate that the proposed model achieved 95.8% accuracy, outperforming Inception v3, Inception-ResNet v2, Xception, and VGG19. While DenseNet121 and ResNet50 achieved better accuracy, their complexity in terms of higher trainable parameters, layers, and training times might be a significant concern in practice. In this context, the results suggest that the proposed model could be a feasible option for IoT devices used in ITS applications due to its simple architecture. © 2023 by the authors.Conference Object Citation - Scopus: 1Toxicity Detection Using State of the Art Natural Language Methodologies(Ieee, 2023) Keskin, Enes Faruk; Acikgoz, Erkut; Dogan, GulustanIn this paper, the studies carried out to detect objectionable expressions in any text will be explained. Experiments were performed with Sentence transformers, supervised machine learning algorithms, and Bert transformer architecture trained in English, and the results were observed. To prepare the dataset used in the experiments, the natural language processing and machine learning methodologies of the toxic and non-toxic contents in the labeled text data obtained from the Kaggle platform are explained, and then the methods and performances of the models trained using this dataset are summarized in this paper.Article Citation - WoS: 6Deep Learning-Based Defect Prediction for Mobile Applications(Mdpi, 2022) Jorayeva, Manzura; Akbulut, Akhan; Catal, Cagatay; Mishra, AlokSmartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.
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