Solving classification tasks by a receptron based on nonlinear optical speckle fields

Among several approaches to tackle the problem of energy consumption in modern computing systems, two solutions are currently investigated: one consists of artificial neural networks (ANNs) based on photonic technologies, the other is a different paradigm compared to ANNs and it is based on random networks of nonlinear nanoscale junctions resulting from the assembling of nanoparticles or nanowires as substrates for neuromorphic computing. These networks show the presence of emergent complexity and collective phenomena in analogy with biological neural networks characterized by self-organization, redundancy, non-linearity. Starting from this background, we propose and formalize a generalization of the perceptron model to describe a classification device based on a network of interacting units where the input weights are nonlinearly dependent. We show that this model, called "receptron", provides substantial advantages compared to the perceptron as, for example, the solution of non-linearly separable Boolean functions with a single device. The receptron model is used as a starting point for the implementation of an all-optical device that exploits the non-linearity of optical speckle fields produced by a solid scatterer. By encoding these speckle fields we generated a large variety of target Boolean functions without the need for time-consuming machine learning algorithms. We demonstrate that by properly setting the model parameters, different classes of functions with different multiplicity can be solved efficiently. The optical implementation of the receptron scheme opens the way for the fabrication of a completely new class of optical devices for neuromorphic data processing based on a very simple hardware

Perceiving University Student's Opinions from Google App Reviews

Google app market captures the school of thought of users from every corner of the globe via ratings and text reviews, in a multilinguistic arena. The potential information from the reviews cannot be extracted manually, due to its exponential growth. So, Sentiment analysis, by machine learning and deep learning algorithms employing NLP, explicitly uncovers and interprets the emotions. This study performs the sentiment classification of the app reviews and identifies the university student's behavior towards the app market via exploratory analysis. We applied machine learning algorithms using the TP, TF, and TF IDF text representation scheme and evaluated its performance on Bagging, an ensemble learning method. We used word embedding, Glove, on the deep learning paradigms. Our model was trained on Google app reviews and tested on Student's App Reviews(SAR). The various combinations of these algorithms were compared amongst each other using F score and accuracy and inferences were highlighted graphically. SVM, amongst other classifiers, gave fruitful accuracy(93.41%), F score(89%) on bigram and TF IDF scheme. Bagging enhanced the performance of LR and NB with accuracy of 87.88% and 86.69% and F score of 86% and 78% respectively. Overall, LSTM on Glove embedding recorded the highest accuracy(95.2%) and F score(88%).Comment: Accepted in Concurrency and Computation Practice and Experienc

Review of machine learning and deep learning application in mine microseismic event classification

Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others. Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed. Findings. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment. They always need preprocessing in order to classify actual seismic events. Traditional detecting and classifying methods do not always yield precise results, which is especially disappointing when different events have a similar nature. The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one. This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events. Originality. Previously adopted methods are discussed in short, and a brief historical outline of Machine learning and deep learning development is presented. The recent advancement in discriminating MS events from other events is discussed in the context of these mechanisms, and finally conclusions and suggestions related to the relevant field are drawn. Practical implications. By means of machin learning and deep learning technology mine microseismic events can be identified accurately which allows to determine the source location so as to prevent rock burst.Мета. Аналіз й узагальнення технологій машинного і глибокого навчання в гірничодобувній промисловості для високоточної ідентифікації гірських мікросейсмічних (МС) подій на відміну від таких незначних подій як шум, вибух та інші. Методика. Описано традиційні методи класифікації МС подій і показана їх недостатня ефективність. Надана коротка історична довідка про розвиток методів машинного та глибокого навчання, розглянуті різні підходи до використання цих методів при класифікації МС подій. Результати. У статті наведено огляд новітніх способів застосування машинного та глибокого навчання для виявлення шахтних МС подій. Представлені сучасні досягнення в галузі ідентифікації МС подій серед подій іншого роду, зроблені остаточні висновки і пропозиції щодо розглянутих проблем. Відзначено, що відомі дані про МС події, пов’язані з процесом руйнування гірських порід, отримані за допомогою датчиків, не є достатньо точними у зв’язку з комплексним впливом гірського середовища і потребують попередньої обробки перш, ніж використовувати їх для класифікації реальних МС подій. Встановлено, що традиційні способи виявлення та класифікації МС подій не завжди дозволяють отримати точні результати, що особливо важливо, коли події однієї природи мають різні прояви. Розроблено класифікацію різних МС подій з більш високою точністю у порівнянні з існуючими методиками. Наукова новизна. Сформована концепція машинного і глибокого навчання в гірничодобувній промисловості, що дозволяє високоточно ідентифікувати гірські удари в шахтах на відміну від інших видів геодинамічних явищ. Практична значимість. Технології машинного і глибокого навчання дозволяють точно ідентифікувати шахтні МС події і визначити місце розташування їх джерела, що дозволяє запобігти гірського удару та підвищити безпеку ведення гірничих робіт.Цель. Анализ и обобщение технологий машинного и глубокого обучения в горнодобывающей промышленности для высокоточной идентификации горных микросейсмических (МС) событий в отличие от таких незначительных событий как шум, взрыв и другие. Методика. Описаны традиционные методы классификации МС событий и показана их недостаточная эффективность. Дана краткая историческая справка о развитии методов машинного и глубокого обучения, рассмотрены различные подходы к использованию этих методов при классификации МС событий. Результаты. В статье приведен обзор новейших способов применения машинного и глубокого обучения для обнаружения шахтных МС событий. Представлены современные достижения в области идентификации МС событий среди событий другого рода, сделаны окончательные выводы и предложения в отношении рассматриваемых проблем. Отмечено, что известные данные о МС событиях, связанных с процессом разрушения горных пород, полученные с помощью датчиков, не являются достаточно точными из-за комплексного влияния горной среды и нуждаются в предварительной обработке прежде, чем использовать их для классификации реальных МС событий. Установлено, что традиционные способы обнаружения и классификации МС событий не всегда позволяют получить точные результаты, что особенно важно, когда события одной природы имеют различные проявления. Разработана классификация различных МС событий с более высокой точностью по сравнению с существующими методиками. Научная новизна. Сформирована концепция машинного и глубокого обучения в горнодобывающей промышленности, позволяющая высокоточно идентифицировать горные удары в шахтах в отличии от других видов геодинамических явлений. Практическая значимость. Технологии машинного и глубокого обучения позволяют точно идентифицировать шахтные МС события и определить месторасположение их источника, что позволяет предотвратить горный удар и повысить безопасность ведения горных работ.This work has been performed under the acknowledged support of the research team of Mining System Engineering and National Natural Science Foundation of China, working in big data oriented safety hazard identification and accident evolution mechanism of metal underground mines, 52074022

Classificação de lesões da cavidade bucal baseada em aprendizagem profunda em ambiente remoto

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorTrabalho de Conclusão de Curso (Graduação)A displasia epitelial oral é um precursor relativamente comum do câncer de boca em que sua progressão para o câncer varia de 6% a 36%. Existem várias técnicas de imagem empregadas em seu diagnóstico. A análise histológica de imagens suportada pelos sistemas computacionais mostrou-se bastante eficaz no diagnóstico da doença. Neste trabalho, é apresentada uma abordagem para quantificar e classificar amostras de tecido bucal com uso de redes neurais convolucionais (AlexNet, Vgg16 e ResNet) e transferência de aprendizado. A transferência de aprendizado utilizada foi a baseada em rede, em que se emprega a reutilização de uma parte da rede pré-treinada no domínio da fonte, para aplicação no domínio destino. Devido ao tamanho do banco de imagens, aplicou-se uma etapa de aumento de dados para avaliar a acurácia das arquiteturas das redes. Definiu-se 30% das imagens, aleatoriamente selecionadas, para o grupo de teste e dentre 70% restantes, 90% ficariam para o grupo de treinamento e outros 10% para a validação. Ao final do treinamento obteve-se resultados relevantes, atingindo 96,56% de acurácia com a ResNet18 e 94,33% com a VGG16. Esse estudo ainda apresenta uma aplicação para dispositivos móveis para disponibilizar ao usuário um meio de classificar imagens histológicas. Essa aplicação é composta por um bot do Telegram e algoritmos em linguagem MatLab para as execução das CNNs. Levando em consideração o contexto pandêmico da COVID-19, esses resultados são considerados relevantes e essa abordagem pode ser útil como um protocolo que contribuirá na análise de diagnóstico de lesões da cavidade oral

VIDEO FOREGROUND LOCALIZATION FROM TRADITIONAL METHODS TO DEEP LEARNING

These days, detection of Visual Attention Regions (VAR), such as moving objects has become an integral part of many Computer Vision applications, viz. pattern recognition, object detection and classification, video surveillance, autonomous driving, human-machine interaction (HMI), and so forth. The moving object identification using bounding boxes has matured to the level of localizing the objects along their rigid borders and the process is called foreground localization (FGL). Over the decades, many image segmentation methodologies have been well studied, devised, and extended to suit the video FGL. Despite that, still, the problem of video foreground (FG) segmentation remains an intriguing task yet appealing due to its ill-posed nature and myriad of applications. Maintaining spatial and temporal coherence, particularly at object boundaries, persists challenging, and computationally burdensome. It even gets harder when the background possesses dynamic nature, like swaying tree branches or shimmering water body, and illumination variations, shadows cast by the moving objects, or when the video sequences have jittery frames caused by vibrating or unstable camera mounts on a surveillance post or moving robot. At the same time, in the analysis of traffic flow or human activity, the performance of an intelligent system substantially depends on its robustness of localizing the VAR, i.e., the FG. To this end, the natural question arises as what is the best way to deal with these challenges? Thus, the goal of this thesis is to investigate plausible real-time performant implementations from traditional approaches to modern-day deep learning (DL) models for FGL that can be applicable to many video content-aware applications (VCAA). It focuses mainly on improving existing methodologies through harnessing multimodal spatial and temporal cues for a delineated FGL. The first part of the dissertation is dedicated for enhancing conventional sample-based and Gaussian mixture model (GMM)-based video FGL using probability mass function (PMF), temporal median filtering, and fusing CIEDE2000 color similarity, color distortion, and illumination measures, and picking an appropriate adaptive threshold to extract the FG pixels. The subjective and objective evaluations are done to show the improvements over a number of similar conventional methods. The second part of the thesis focuses on exploiting and improving deep convolutional neural networks (DCNN) for the problem as mentioned earlier. Consequently, three models akin to encoder-decoder (EnDec) network are implemented with various innovative strategies to improve the quality of the FG segmentation. The strategies are not limited to double encoding - slow decoding feature learning, multi-view receptive field feature fusion, and incorporating spatiotemporal cues through long-shortterm memory (LSTM) units both in the subsampling and upsampling subnetworks. Experimental studies are carried out thoroughly on all conditions from baselines to challenging video sequences to prove the effectiveness of the proposed DCNNs. The analysis demonstrates that the architectural efficiency over other methods while quantitative and qualitative experiments show the competitive performance of the proposed models compared to the state-of-the-art

Метод сегментации изображения рака прямой кишки на основе сети U- Net

В статье используется сеть U-Net для интеллектуальной сегментации изображений КТ рака прямой кишки, применяются такие методы, как улучшение изображения и пакетная нормализация для облегчения явления переподгонки, определяется оптимальная начальная скорость обучения и количество сверточных ядер путем нескольких экспериментов, достигается идеальная сегментация опухолей рака прямой кишки с помощью сети U-Net: 85.76%. Эксперименты показывают, что U-Net хорошо работает для сегментации медицинских изображений на небольших наборах данных, и сходство сегментации может быть точно измерено с помощью коэффициентов Dice для наборов данных с чрезвычайно перекошенными положительными и отрицательными образцами.The paper uses U-Net network for intelligent segmentation of rectal cancer CT images, incorporates techniques such as image enhancement and batch normalization to alleviate the overfitting phenomenon, and determines the optimal initial learning rate and the number of convolutional kernels through several experiments, and achieves the ideal segmentation of rectal cancer tumors using U-Net network: 85.76%. The experiments show that U-Net works well for medical image segmentation on small data sets, and the similarity of segmentation can be accurately measured using Dice coefficients for data sets with extremely skewed positive and negative samples

Social and epistemological bases of technology transfer: The case of artificial intelligence

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis addresses a problem in the literature on technology transfer of understanding the local appropriation of knowledge. Based on interpretive and analytic traditions developed in Science and Technology Studies (STS) and ethnomethodology, I conceptualise technology transfer as involving communication between discursive communities. I develop the idea of 'performance of community' to argue that explanations of research and technology, and readings of those explanations, are sites for the elaboration of the identity of a discursive community. I explore this approach through a case study in the field of artificial intelligence (AI). I focus on what I call 'explanatory practices', that is practices of describing, identifying and explaining Al, and trace the differences in these practices, according to location, context and audience. The novelty of my thesis is to show the pervasiveness of performance of community within these explanatory practices, through showing the differences in the claimed identity and significance of Al, associated with different locations, contexts and audiences. I draw out some of the implications of my approach by counterposing it to a theory of technology transfer as the passing of neutral units of information, which I argue is implicit in a complaint made by Al vendors that the Al marketplace had been damaged by overselling or hype. In particular, I show that disclaimers of hype (more than the perpetration of it) had always been associated with the marketing of Al. More generally, my claim is that it is politically important to understand that neutral information is not available even as an ultimate standard, and that the local appropriation of knowledge is not an aberration to be controlled, but a component of both successful and unsuccessful communication between discursive communities

Aide à la décision pour la détection et l’analyse des défauts de surface dans les structures immergées

This study concerns the damages detection and diagnosis for immersed structure. The structures are metallic plates. The proposed method focuses on the analysis of ultrasonic acoustic measurements obtained by submarine echography. It combines signal processing tools and Gaussian neural networks for classification purpose. Methods with and without reference models are proposed. The usual detection technics with contact are not applicable for the considered systems like stream turbines. This research consists to use a single and a single transducer under different incidence angles opposed to others technics using numerous sensors and their accurate location. The present research use Lamb wave according to their sensibility to the structural damages. The different stages are the following : - 1. Experimental setup for Lamb wave generation and acquisition. - 2. Study of the Lamb wave processing on immersed structures, in particular in metallic plate immersed in water. - 3 .Signal characterization for different types of damages. - 4. Estimation of the angle and lift-off distance.Cette thèse concerne le développement de méthodes de détection et de diagnostic des défauts de surface dans les structures immergées. Les structures étudiées sont formées de plaques métalliques. Les méthodes proposées sont basées sur une analyse de mesures acoustiques ultrasonores issues d’échographie sous-marine. Cette analyse combine des outils usuels du traitement du signal et des méthodes de classification à base de réseaux de neurones gaussiens. Des variantes avec et sans modèle de référence sont proposées. Les techniques usuelles d’évaluation par contact montrent leurs limites pour le diagnostic des structures telles que les hydroliennes. Le présent travail de recherche consiste à utiliser un seul et unique transducteur sans contact sous différents angles contrairement à d’autres techniques qui nécessitent un grand nombre de capteurs et une connaissance précise de leur positionnement. Notre étude utilise les ondes de Lamb car elles sont très sensibles aux anomalies structurelles. Les principales étapes et outils utilisés sont les suivants : - 1. Utilisation d’un dispositif de génération et d’acquisition d’ondes de Lamb. - 2. Étude de la propagation d’ondes de Lamb dans les structures en immersion, en particulier dans les plaques métalliques immergées dans l’eau. - 3. Caractérisation des signaux pour différents types de défauts. - 4. Estimation de l’angle d’acquisition et de la distance du transducteur par rapport au centre de la plaque