Program static analysis can be utilized to automatically investigate the execution procedures of programs. However, the proof of program completeness verification is less concerned by utilizing current static analysis tools, which only focus on the execution results verification. Therefore, we initially propose a novel completeness verification mechanism to provide a method to illustrating the targeted program is completed with required functions. We transfer the programming language into abstract symbols and establish the control flow graph of program, which can apply in all programming languages. Our proposed schemes significantly demonstrate completeness of program and more efficient than existing static analysis methods. From our experimental and evaluative results, proposed static analysis mechanism can effectively proof the completement of target program compared with existing methods and the computation cost is reasonable for the analysis period, which is decided by the size of programs.
Image classification is an essential method to dispose the practical issues including the medical image classification, detection of objectives and process downstream tasks. However, current researchers has utilized the neural network or machine learning model to classify the images based on the image characteristic, which is extremely relies the obtained image data and the training model is a black box. Inspired by the supervised learning algorithm, we proposed a novel self-supervised structure to classify the image data-set. The model structure is consisted of three primary operations including three layers of random transformation, a main neural network layer and prediction layer. In this paper, we specifically demonstrate each components and test our model on a written number data-set. From our extensive experimental results, our proposed mechanism can identify the correct labels in image data-set with acceptable accuracy and reasonable computation cost.
Several machine learning algorithms for image processing and computer vision applications have been proposed in the past decade. LBP, HAAR are some popular algorithms that are widely used in face recognition and produce excellent results. However, most of these algorithms are not suitable for real-time recognition in unconstrained environments. Recently, the most advanced deep learning technology has become the new favorite of traditional machine learning algorithms. In this paper, the obstacle recognition technology based on optimized deep learning algorithm, based on the traditional GS optimization algorithm, the gray wolf optimization algorithm is used to optimize the image features .Firstly, this paper creates an obstacle image database to provide deep learning data. Create cnn model in MATLAB to learn the features of pictures in the database and identify the categories of pictures.
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