Study on Image Classification with Convolution Neural Networks

The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems.

Deep learning and convolutional neural networks (CNNs) in particular are increasingly popular tools for segmentation and classification of medical images. CNNs were shown to be successful for segmentation of brain tumors into multiple regions or labels. However, in the environment which fosters data-sharing and collection of multi-institutional datasets, a question arises: does training with data from another institution with potentially different imaging equipment, contrast protocol, and patient population impact the segmentation performance of the CNN? Our study presents preliminary data towards answering this question. Specifically, we used MRI data of glioblastoma (GBM) patients for two institutions present in The Cancer Imaging Archive. We performed a process of training and testing CNN multiple times such that half of the time the CNN was tested on data from the same institution that was used for training and half of the time it was tested on another institution, keeping the training and testing set size constant. We observed a decrease in performance as measured by Dice coefficient when the CNN was trained with data from a different institution as compared to training with data from the same institution. The changes in performance for the entire tumor and for four different labels within the tumor were: 0.72 to 0.65 (p=0.06), 0.61 to 0.58 (p=0.49), 0.54 to 0.51 (p=0.82), 0.31 to 0.24 (p<0.03), and 0.43 to 0.31(p<0.003) respectively. In summary, we found that while data across institutions can be used for development of CNNs, this might be associated with a decrease in performance.

BackgroundClassifying and mapping vegetation are crucial tasks in environmental science and natural resource management. However, these tasks are difficult because conventional methods such as field surveys are highly labor-intensive. Identification of target objects from visual data using computer techniques is one of the most promising techniques to reduce the costs and labor for vegetation mapping. Although deep learning and convolutional neural networks (CNNs) have become a new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation is still difficult. In this study, we investigated the effectiveness of adopting the chopped picture method, a recently described protocol for CNNs, and evaluated the efficiency of CNN for plant community detection from Google Earth images.ResultsWe selected bamboo forests as the target and obtained Google Earth images from three regions in Japan. By applying CNN, the best trained model correctly detected over 90% of the targets. Our results showed that the identification accuracy of CNN is higher than that of conventional machine learning methods.ConclusionsOur results demonstrated that CNN and the chopped picture method are potentially powerful tools for high-accuracy automated detection and mapping of vegetation.

Chapter 2 - Convolution neural network and deep learning

Chapter 4 - Deep Learning in Texture Analysis and Its Application to Tissue Image Classification

The center of human activity and a key point of contact for social interaction between city people and the built environment is the street. With more street-level photos becoming available, urban landscape studies have more chances to examine and evaluate streetscapes closely and from various angles. This manuscript present the graph neural networks (GNN) optimized with gazelle optimization algorithm (GOA) for urban plants cape design based on large-scale street view (UPLS-GNN-GOA). Initially data is taken from ADE20K dataset. Afterward the data is fed to General Robust Subband Adaptive Filtering (GRSAF) based pre-processing process. The pre-processing output is given to Discrete Fractional Fourier Transforms (DFrFT) to extract the street greenery and openness of street canyons. After that, the extracted features are fed to Graph Neural Networks (GNN) to classify the public health, urban microclimate, human perception. The weight parameters of the GNN are optimized using Gazelle Optimization Algorithm (GOA).The UPLS-GNN-GOA method is implemented in Python, and its efficiency is determined using a series of performance evaluation metrics analysis, including accuracy, precision, recall, f1-score, and sensitivity. The proposed UPLS-GNN-GOA method shows the highest accuracy of 98%, precision of 98%, and F1-score of 97% while comparing other existing methods such as urban plantscape design based on convolutional neural network (UPLS-CNN), urban plantscape design based on deep convolutional neural network (UPLS-DCNN) and urban plantscape design based on deep learning and convolutional neural network (UPLS-DL-CNN) respectively.

Nowadays, people pay more attention in artificial intelligence(AI) research, and they try to make Al smarter. The machine learning became a popular subject, especially in object recognition area. Aiming at providing a faster and more accurate plant species recognition program, the author introduced the deep learning and convolution neural network(CNN), and decided to build a CNN project with pycharm, anaconda, kera to find the best way to improve recognition program accuracy and recognition speed. The author tried to change the learning epoch time and learning data set capacity to found the best solution. After tests were finished, the result of output plots analyze is that both adding learning epochs time and extend training image set are all helpful to improve recognition accuracy and speed. As for the effect of increase learning time, it is more obvious in improving accuracy while extend training set size, which is a better method to reduce recognition time. The end of the thesis contained the experiment result, the deficiency of this essay and the future prospect forecast of the machine learning applied in plant area.

Hyperspectral imaging technology combined with deep learning for hybrid okra seed identification

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Convolutional Neural Network in Deep learning is a type of deep neural networks, generally put in an application to analyze visual images. The project name entitled “Devnagari Lipi Recognition using Deep Learning Techniques” is a machine learning-based project in which we are recognizing Hindi characters by gesticulating a Hindi “Akshar” or alphabet in front of our webcam the machine will recognize which letter is being completed. For the project, we will train our machine with all the Devanagari alphabets and after the training, our machine will get expertise in recognizing Hindi alphabets in no time. Not only the machine will recognize the character but it will tell the user how to pronounce it in English by writing its English pronunciation on the screen.

Understanding the underlying processes of aesthetic perception is one of the ultimate goals in empirical aesthetics. While deep learning and convolutional neural networks (CNN) already arrived in the area of aesthetic rating of art and photographs, only little attempts have been made to apply CNNs as the underlying model for aesthetic perception. The information processing architecture of CNNs shows a strong match with the visual processing pipeline in the human visual system. Thus, it seems reasonable to exploit such models to gain better insight into the universal processes that drives aesthetic perception. This work shows first results supporting this claim by analyzing already known common statistical properties of visual art, like sparsity and self-similarity, with the help of CNNs. We report about observed differences in the responses of individual layers between art and non-art images, both in forward and backward (simulation) processing, that might open new directions of research in empirical aesthetics.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting reproductive-aged women, characterized by hormonal imbalance and ovarian cysts. Timely and accurate diagnosis of PCOS is crucial for effective management and prevention of associated complications. In this paper, we propose an automated diagnostic system for PCOS using deep learning and convolutional neural networks (CNNs). The dataset comprises ultrasound images of ovaries categorized into infected (PCOS) and not infected (non-PCOS) classes. Two CNN architectures, a deep CNN model and a CNN model with a last layer replaced by a support vector machine (SVM), are implemented and evaluated. Evaluation metrics include accuracy, precision, recall, and F1-score, demonstrating promising performance in accurately distinguishing between PCOS and non-PCOS cases. This automated diagnostic system offers a reliable and efficient approach for PCOS diagnosis, potentially reducing dependence on manual interpretation of ultrasound scans and improving healthcare outcomes for affected individuals. Key Words: Polycystic ovary syndrome (PCOS) , Deep learning, Convolutional neural networks (CNNs), Data augmentation, Ultrasound Scans

Artificial Intelligence (AI) and its tools are being widely used worldwide. In the area of agriculture, AI is being widely studied and expanding. The use of AI in agriculture is being widely studied and expanding from pre-harvest to post-harvest. The increase in world population has triggered the need to increase food production. This need has triggered a search for solutions that promote increased food production and quality. One way to increase food production and quality is pest control. AI and its tools have proven to be a growing and rising solution in controlling and combating pests. This research focuses on reviewing and demonstrating the advances in combating and controlling pests using AI tools and images. It stands out: the classification of pests; insect identification; use and capture of Unmanned aerial vehicle (UAV) footage; using Deep Learning (DL) and Convolutional Neural Network (CNN). A search engine was applied to 5 databases. Cutting criteria were applied in 3 stages, and there were 71 papers at the end. The 71 went through 3 quality assessment questions, leaving 47 works for final analysis. This study demonstrated that the DL and the CNN tool using real images have the potential for insect control and combat solutions. Another tool in recent studies associated with CNN is the attention mechanism, improving pest identification results. Identification of insects through leaf images using CNN requires.

In this paper we propose a website aesthetic evaluation method. For achieving better performance, we have applied convolutional neural networks, which are one of the methods of deep learning research area. Using deep learning and convolutional neural networks for feature representation is one of the main tips that makes difference between our work and previous ones. Our system takes a screenshot of the website as input, and finally reports it is a good or bad website based on users' country or not. For evaluation process, we represent the website screenshot using MemNet convolutional neural network. Then we decrease the extracted features dimension using principal component analysis algorithm. Finally, we classify them using a SVM classifier, which trained, based on users' ratings. Furthermore, aesthetics evaluation in this research is language independent. It means the website's language is not important and our method works for all languages.

At present, deep learning and convolutional neural networks are currently two of the fastest rising trends as the tool to perform a multitude of tasks such as image classification and computer vision. However, vulnerabilities in such networks can be exploited through input modification, leading to negative consequences to its users. This research aims to demonstrate an adversarial attack method that can hide its attack from human intuition in the form of a QR code, an entity that is most likely to conceal the attack from human acknowledgment due to its widespread use at the current time. A methodology was developed to demonstrate the QR-embedded adversarial patch creation process and attack existing CNN image classification models. Experiments were also performed to investigate trade-offs in different patch shapes and find the patch’s optimal color adjustment to improve scannability while retaining acceptable adversarial efficacy.KeywordsDeep learningAdversarial attackConvolutional neural networks