High-Performance Deep learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems

Nowadays, technology and computer science are rapidly developing many tools and algorithms, especially in the field of artificial intelligence. Machine learning is involved in the development of new methodologies and models that have become a novel machine learning area of applications for artificial intelligence. In addition to the architectures of conventional neural network methodologies, deep learning refers to the use of artificial neural network architectures which include multiple processing layers./nIn this paper, models of the Convolutional neural network were designed to detect (diagnose) plant disorders by applying samples of healthy and unhealthy plant images analyzed by means of methods of deep learning. The models were trained using an open data set containing (18,000) images of ten different plants, including healthy plants. Several model architectures have been trained to achieve the best performance of (97 percent) when the respectively [plant, disease] paired are detected. This is a very useful information or early warning technique and a method that can be further improved with the substantially high-performance rate to support an automated plant disease detection system to work in actual farm conditions

Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images

Artificial Intelligence & Machine Learning in Computer Vision Applications

Deep learning and machine learning innovations are at the core of the ongoing revolution in Artificial Intelligence for the interpretation and analysis of multimedia data. The convergence of large-scale datasets and more affordable Graphics Processing Unit (GPU) hardware has enabled the development of neural networks for data analysis problems that were previously handled by traditional handcrafted features. Several deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM)/Gated Recurrent Unit (GRU), Deep Believe Networks (DBN), and Deep Stacking Networks (DSNs) have been used with new open source software and libraries options to shape an entirely new scenario in computer vision processing

ARTIFICIAL INTELLIGENCE IN BLOCKCHAIN-PROVIDE DIGITAL TECHNOLOGY

Artificial intelligence technologies, today, are rapidly developing and are an important branch of Computer Science. Artificial intelligence is at the heart of research and development of theory, methods, technologies, and applications for modeling and expanding human intelligence. Artificial intelligence technology has three key aspects, namely data, algorithm, and computing power, in the sense that training an algorithm to produce a classification model requires significant data, and the learning process requires improved computing capabilities. In the age of big data, information can come from a variety of sources (such as sensor systems, Internet of Things (IoT) devices and systems, as well as social media platforms) and/or belong to different stakeholders. This mostly leads to a number of problems. One of the key problems is isolated data Islands, where data from a single source/stakeholder is not available to other parties or training an artificial intelligence model, or it is financially difficult or impractical to collect a large amount of distributed data for Centralized Processing and training. There is also a risk of becoming a single point of failure in centralized architectures, which can lead to data intrusion. In addition, data from different sources may be unstructured and differ in quality, and it may also be difficult to determine the source and validity of the data. There is also a risk of invalid or malicious data. All these restrictions may affect the accuracy of the forecast. In practice, artificial intelligence models are created, trained, and used by various subjects. The learning process is not transparent to users, and users may not fully trust the model they are using. In addition, as artificial intelligence algorithms become more complex, it is difficult for people to understand how the result of training is obtained. So, recently there has been a tendency to move away from centralized approaches to artificial intelligence to decentralized ones

Challenging the Computational Metaphor: Implications for How We Think

This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think

Assessing hyper parameter optimization and speedup for convolutional neural networks

The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures