A Hybrid Neural Network Architecture for Texture Analysis in Digital Image Processing Applications

A new hybrid neural network model capable of texture analysis in a digital image processing environment is presented in this thesis. This model is constructed from two different types of neural network, self-organisation and back-propagation. Along with a brief resume of digital image processing concepts, an introduction to neural networks is provided. This contains appropriate documentation of the neural networks and test evidence is also presented to highlight the relative strengths and weaknesses of both neural networks. The hybrid neural network is proposed from this evidence along with methods of training and operation. This is supported by practical examples of the system's operation with digital images. Through this process two modes of operation are explored, classification and segmentation of texture content within images. Some common methods of texture analysis are also documented, with spatial grey level dependence matrices being chosen to act as a feature generator for classification by a back-propagation neural network, this provides a benchmark to assess the performance of the hybrid neural network. This takes the form of descriptive comparison, pictorial results, and mathematical analysis when using aerial survey images. Other novel approaches using the hybrid neural network are presented with concluding comments outlining the findings presented within this thesis

Quantum machine learning for image classification

Image recognition and classification are fundamental tasks with diverse practical applications across various industries, making them critical in the modern world. Recently, machine learning models, particularly neural networks, have emerged as powerful tools for solving these problems. However, the utilization of quantum effects through hybrid quantum-classical approaches can further enhance the capabilities of traditional classical models. Here, we propose two hybrid quantum-classical models: a neural network with parallel quantum layers and a neural network with a quanvolutional layer, which address image classification problems. One of our hybrid quantum approaches demonstrates remarkable accuracy of more than 99% on the MNIST dataset. Notably, in the proposed quantum circuits all variational parameters are trainable, and we divide the quantum part into multiple parallel variational quantum circuits for efficient neural network learning. In summary, our study contributes to the ongoing research on improving image recognition and classification using quantum machine learning techniques. Our results provide promising evidence for the potential of hybrid quantum-classical models to further advance these tasks in various fields, including healthcare, security, and marketing.Comment: 9 pages, 8 figure

MEAT QUALITY PREDICTION USING MACHINE LEARNING

Meat quality is an essential aspect of the food industry. However, traditional methods of meat quality prediction have limitations in terms of accuracy, cost, and time efficiency. This project focused on utilizing advanced Deep learning and Machine learning algorithms to develop- machine learning models that could predict the freshness (or spoilage) of meat with a 100% accuracy, based on image data. In addition to accuracy, this study emphasizes the significance of speed and time in selecting the optimal machine learning model. The research questions are: Q1. What hybrid neural networks should be used to predict freshness? Q2. How do hybrid neural networks determine the freshness of the meat based on the image? Q3. How can accuracy and performance speed be improved? A dataset from the Kaggle repository was used to explore various machine learning algorithms such as Support Vector Machines, Decision Trees, and Random Forests with a combination of Convolutional Neural Network, a deep learning network. The findings are: Q1. A combination of Support Vector Machines-Convolutional Neural Network, Decision Trees-Convolutional Neural Network, and Random Forests-Convolutional Neural Network were used to predict freshness. 2) The hybrid neural networks were trained using the tensorflow.keras.models, a high-level neural networks API of the TensorFlow library, which allowed the creation and training of complex machine learning models in a simple and straightforward manner. 3) The accuracy and performance speed of the model can be improved by utilizing a distributed computing environment for training, which involves the collaboration of multiple machines to carry out computations. The conclusion from our project is that Utilizing the hybrid neural networks developed, it is possible to classify meat products as either fresh or spoiled using image analysis. This approach not only reduces the reliance on human input for meat classification but also decreases the time taken to complete the classification process. Furthermore, emerging areas for future research that emerged from this study is to develop machine learning models that can integrate and fuse multi-modal data such as genetics, feeding and processing techniques to make more accurate predictions of meat quality

Improving Trust in Deep Neural Networks with Nearest Neighbors

Deep neural networks are used increasingly for perception and decision-making in UAVs. For example, they can be used to recognize objects from images and decide what actions the vehicle should take. While deep neural networks can perform very well at complex tasks, their decisions may be unintuitive to a human operator. When a human disagrees with a neural network prediction, due to the black box nature of deep neural networks, it can be unclear whether the system knows something the human does not or whether the system is malfunctioning. This uncertainty is problematic when it comes to ensuring safety. As a result, it is important to develop technologies for explaining neural network decisions for trust and safety. This paper explores a modification to the deep neural network classification layer to produce both a predicted label and an explanation to support its prediction. Specifically, at test time, we replace the final output layer of the neural network classifier by a k-nearest neighbor classifier. The nearest neighbor classifier produces 1) a predicted label through voting and 2) the nearest neighbors involved in the prediction, which represent the most similar examples from the training dataset. Because prediction and explanation are derived from the same underlying process, this approach guarantees that the explanations are always relevant to the predictions. We demonstrate the approach on a convolutional neural network for a UAV image classification task. We perform experiments using a forest trail image dataset and show empirically that the hybrid classifier can produce intuitive explanations without loss of predictive performance compared to the original neural network. We also show how the approach can be used to help identify potential issues in the network and training process