Source code classification (SCC) is a task to assign codes into different categories according to a criterion such as according to their functionalities, programming languages or vulnerabilities. Many source code archives are organized according to the programming languages, and thereby, the desired code fragments can be easily accessed by searching within the archive. However, manually organizing source code archives by field experts is labor intensive and impractical because of the fastgrowing available source codes. Therefore, this study proposes new convolutional neural network (CNN) architectures to build source code classifiers that automatically identify programming languages from source codes. This is the first study in which the performances of deep learning algorithms on programming language identification are compared on both image and text files. In this study, the experiments are performed on three source code datasets to identify eight programming languages, including C, C++, C# , Go, Python, Ruby, Rust, and Java. The comparative results indicate that although textbased SCC and image-based SCC approaches achieve very high ( > 93.5% ) and similar accuracies, text-based classification has significantly better performance in terms of execution time.
Traditional indoor human activity recognition (HAR) has been defined as a time-series data classification problem and requires feature extraction. The current indoor HAR systems still lack transparent, interpretable, and explainable approaches that can generate human-understandable information. This paper proposes a new approach, called Human Activity Recognition on Signal Images (HARSI), which defines the HAR problem as an image classification problem to improve both explainability and recognition accuracy. The proposed HARSI method collects sensor data from the Internet of Things (IoT) environment and transforms the raw signal data into some visual understandable images to take advantage of the strengths of convolutional neural networks (CNNs) in handling image data. This study focuses on the recognition of symmetric human activities, including walking, jogging, moving downstairs, moving upstairs, standing, and sitting. The experimental results carried out on a real-world dataset showed that a significant improvement (13.72%) was achieved by the proposed HARSI model compared to the traditional machine learning models. The results also showed that our method (98%) outperformed the state-of-the-art methods (90.94%) in terms of classification accuracy.
The high resolution of the image is very important for applications. Publicly available satellite images generally have low resolutions. Since low resolution causes loss of information, the desired performance cannot be achieved depending on the type of problem studied in the field of remote sensing. In such a case, super resolution algorithms are used to render low resolution images high resolution. Super resolution algorithms are used to obtain high resolution images from low resolution images. In studies with satellite images, the use of images enhanced with super resolution is important. Since the resolution of satellite images is low, the success rate in the classification process is low. In this study, super resolution method is proposed to increase the classification performance of satellite images. The attributes of satellite images were extracted using AlexNet, ResNet50, Vgg19 from deep learning architecture. Then the extracted features were then classified into 6 classes by giving input to AlexNet-Softmax, ResNet50-Softmax, Vgg19-Softmax, Support Vector Machine, K-Nearest Neighbor, decision trees and Naive Bayes classification algorithms. Without super resolution and with super resolution feature extraction and classification processes were performed separately. Classification results without super resolution and with super resolution were compared. Improvement in classification performance was observed using super resolution.
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