GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer

This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vegetables, cheese and fish.This part also serves as a literature review for Part II.Part II: GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer. This part introduces to an end-to-end deep neural network architecture that can predict the degree of acceptability by the consumer for a guava based on sensory evaluation

Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment

In the last decade, integrated logistics has become an important challenge in the development of wireless communication, identification and sensing technology, due to the growing complexity of logistics processes and the increasing demand for adapting systems to new requirements. The advancement of wireless technology provides a wide range of options for the maritime container terminals. Electronic devices employed in container terminals reduce the manual effort, facilitating timely information flow and enhancing control and quality of service and decision made. In this paper, we examine the technology that can be used to support integration in harbor's logistics. In the literature, most systems have been developed to address specific needs of particular harbors, but a systematic study is missing. The purpose is to provide an overview to the reader about which technology of integrated logistics can be implemented and what remains to be addressed in the future

A Genetic Programming Approach for Computer Vision: Classifying High-level Image Features from Convolutional Layers with an Evolutionary Algorithm

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceComputer Vision is a sub-field of Artificial Intelligence that provides a visual perception component to computers, mimicking human intelligence. One of its tasks is image classification and Convolutional Neural Networks (CNNs) have been the most implemented algorithm in the last few years, with few changes made to the fully-connected layer of those neural networks. Nonetheless, recent research has been showing their accuracy could be improved in certain cases by implementing other algorithms for the classification of high-level image features from convolutional layers. Thus, the main research question for this document is: To what extent does the substitution of the fully-connected layer in Convolutional Neural Networks for an evolutionary algorithm affect the performance of those CNN models? The proposed two-step approach in this study does the classification of high-level image features with a state-of-the-art GP-based algorithm for multiclass classification called M4GP. This is conducted using secondary data with different characteristics, to better benchmark the implementation and to carefully investigate different outcomes. Results indicate the new learning approach yielded similar performance in the dataset with a low number of output classes. However, none of the M4GP models was able to surpass the results of the fully-connected layers in terms of test accuracy. Even so, this might be an interesting route if one has a powerful computer and needs a very light classifier in terms of model size. The results help to understand in which situation it might be beneficial to perform a similar experimental setup, either in the context of a work project or concerning a novel research topic

Space station data system analysis/architecture study. Task 2: Options development DR-5. Volume 1: Technology options

The second task in the Space Station Data System (SSDS) Analysis/Architecture Study is the development of an information base that will support the conduct of trade studies and provide sufficient data to make key design/programmatic decisions. This volume identifies the preferred options in the technology category and characterizes these options with respect to performance attributes, constraints, cost, and risk. The technology category includes advanced materials, processes, and techniques that can be used to enhance the implementation of SSDS design structures. The specific areas discussed are mass storage, including space and round on-line storage and off-line storage; man/machine interface; data processing hardware, including flight computers and advanced/fault tolerant computer architectures; and software, including data compression algorithms, on-board high level languages, and software tools. Also discussed are artificial intelligence applications and hard-wire communications

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous publicly available real-world and simulated benchmark datasets have emerged from different sources, but their organization and adoption as standards have been inconsistent. As such, selecting and curating specific benchmarks remains an unnecessary burden on machine learning practitioners and data scientists. The present study introduces an accessible, curated, and developing public benchmark resource to facilitate identification of the strengths and weaknesses of different machine learning methodologies. We compare meta-features among the current set of benchmark datasets in this resource to characterize the diversity of available data. Finally, we apply a number of established machine learning methods to the entire benchmark suite and analyze how datasets and algorithms cluster in terms of performance. This work is an important first step towards understanding the limitations of popular benchmarking suites and developing a resource that connects existing benchmarking standards to more diverse and efficient standards in the future.Comment: 14 pages, 5 figures, submitted for review to JML