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networks</title> <meta name="description" content="Search results for: deep neural networks"> <meta name="keywords" content="deep neural networks"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="deep neural networks"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5280</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: deep neural networks</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5280</span> Forecasting the Temperature at a Weather Station Using Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Debneil%20Saha%20Roy">Debneil Saha Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast horizon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short%20term%20memory" title=" long short term memory"> long short term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/124787/forecasting-the-temperature-at-a-weather-station-using-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124787.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5279</span> Neural Style Transfer Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaik%20Jilani%20Basha">Shaik Jilani Basha</a>, <a href="https://publications.waset.org/abstracts/search?q=Inavolu%20Avinash"> Inavolu Avinash</a>, <a href="https://publications.waset.org/abstracts/search?q=Alla%20Venu%20Sai%20Reddy"> Alla Venu Sai Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=Bitragunta%20Taraka%20Ramu"> Bitragunta Taraka Ramu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We can use the neural style transfer technique to build a picture with the same "content" as the beginning image but the "style" of the picture we've chosen. Neural style transfer is a technique for merging the style of one image into another while retaining its original information. The only change is how the image is formatted to give it an additional artistic sense. The content image depicts the plan or drawing, as well as the colors of the drawing or paintings used to portray the style. It is a computer vision programme that learns and processes images through deep convolutional neural networks. To implement software, we used to train deep learning models with the train data, and whenever a user takes an image and a styled image, the output will be as the style gets transferred to the original image, and it will be shown as the output. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/167224/neural-style-transfer-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167224.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">95</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5278</span> Classification Based on Deep Neural Cellular Automata Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasser%20F.%20Hassan">Yasser F. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cellular%20automata" title="cellular automata">cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20cellular%20automata" title=" neural cellular automata"> neural cellular automata</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/104722/classification-based-on-deep-neural-cellular-automata-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104722.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5277</span> Exploring Deep Neural Network Compression: An Overview</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghorab%20Sara">Ghorab Sara</a>, <a href="https://publications.waset.org/abstracts/search?q=Meziani%20Lila"> Meziani Lila</a>, <a href="https://publications.waset.org/abstracts/search?q=Rubin%20Harvey%20Stuart"> Rubin Harvey Stuart</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20compression" title="model compression">model compression</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title=" deep neural network"> deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=pruning" title=" pruning"> pruning</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20distillation" title=" knowledge distillation"> knowledge distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=quantization" title=" quantization"> quantization</a>, <a href="https://publications.waset.org/abstracts/search?q=low-rank%20decomposition" title=" low-rank decomposition"> low-rank decomposition</a> </p> <a href="https://publications.waset.org/abstracts/185803/exploring-deep-neural-network-compression-an-overview" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185803.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">43</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5276</span> Positive Bias and Length Bias in Deep Neural Networks for Premises Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiaqi%20Huang">Jiaqi Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuheng%20Wang"> Yuheng Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20theorem%20proving" title="automated theorem proving">automated theorem proving</a>, <a href="https://publications.waset.org/abstracts/search?q=premises%20selection" title=" premises selection"> premises selection</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=interpreting%20deep%20learning" title=" interpreting deep learning"> interpreting deep learning</a> </p> <a href="https://publications.waset.org/abstracts/108017/positive-bias-and-length-bias-in-deep-neural-networks-for-premises-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">183</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5275</span> Robot Movement Using the Trust Region Policy Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Romisaa%20Ali">Romisaa Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title="deep neural networks">deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=proximal%20policy%20optimization" title=" proximal policy optimization"> proximal policy optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=state-of-the-art" title=" state-of-the-art"> state-of-the-art</a>, <a href="https://publications.waset.org/abstracts/search?q=trust%20region%20policy%20optimization" title=" trust region policy optimization"> trust region policy optimization</a> </p> <a href="https://publications.waset.org/abstracts/158075/robot-movement-using-the-trust-region-policy-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158075.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">169</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5274</span> Facial Emotion Recognition with Convolutional Neural Network Based Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Koray%20U.%20Erbas">Koray U. Erbas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20based%20FER" title=" deep learning based FER"> deep learning based FER</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20emotion%20recognition" title=" facial emotion recognition"> facial emotion recognition</a> </p> <a href="https://publications.waset.org/abstracts/128197/facial-emotion-recognition-with-convolutional-neural-network-based-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128197.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">274</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5273</span> Leveraging Deep Q Networks in Portfolio Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Liu">Peng Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title="deep reinforcement learning">deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20Q%20networks" title=" deep Q networks"> deep Q networks</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-period%20optimization" title=" multi-period optimization"> multi-period optimization</a> </p> <a href="https://publications.waset.org/abstracts/189031/leveraging-deep-q-networks-in-portfolio-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">32</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5272</span> A Case Study of Deep Learning for Disease Detection in Crops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Felipe%20A.%20Guth">Felipe A. Guth</a>, <a href="https://publications.waset.org/abstracts/search?q=Shane%20Ward"> Shane Ward</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20McDonnell"> Kevin McDonnell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title="convolutional neural networks">convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20detection" title=" disease detection"> disease detection</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20agriculture" title=" precision agriculture"> precision agriculture</a> </p> <a href="https://publications.waset.org/abstracts/95339/a-case-study-of-deep-learning-for-disease-detection-in-crops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95339.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">259</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5271</span> Nonparametric Sieve Estimation with Dependent Data: Application to Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chad%20Brown">Chad Brown</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sieve%20extremum%20estimates" title="sieve extremum estimates">sieve extremum estimates</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20estimation" title=" nonparametric estimation"> nonparametric estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=rectified%20linear%20unit" title=" rectified linear unit"> rectified linear unit</a>, <a href="https://publications.waset.org/abstracts/search?q=nonstationary%20processes" title=" nonstationary processes"> nonstationary processes</a> </p> <a href="https://publications.waset.org/abstracts/186727/nonparametric-sieve-estimation-with-dependent-data-application-to-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186727.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">41</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5270</span> Deep Learning Based, End-to-End Metaphor Detection in Greek with Recurrent and Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Perifanos">Konstantinos Perifanos</a>, <a href="https://publications.waset.org/abstracts/search?q=Eirini%20Florou"> Eirini Florou</a>, <a href="https://publications.waset.org/abstracts/search?q=Dionysis%20Goutsos"> Dionysis Goutsos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=metaphor%20detection" title="metaphor detection">metaphor detection</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=representation%20learning" title=" representation learning"> representation learning</a>, <a href="https://publications.waset.org/abstracts/search?q=embeddings" title=" embeddings"> embeddings</a> </p> <a href="https://publications.waset.org/abstracts/115854/deep-learning-based-end-to-end-metaphor-detection-in-greek-with-recurrent-and-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5269</span> Performance Evaluation of Distributed Deep Learning Frameworks in Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuen-Tai%20Wang">Shuen-Tai Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang-An%20Kuo"> Fang-An Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Chau-Yi%20Chou"> Chau-Yi Chou</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Bin%20Fang"> Yu-Bin Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a> </p> <a href="https://publications.waset.org/abstracts/110135/performance-evaluation-of-distributed-deep-learning-frameworks-in-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/110135.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">211</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5268</span> Speech Emotion Recognition: A DNN and LSTM Comparison in Single and Multiple Feature Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thiago%20Spilborghs%20Bueno%20Meyer">Thiago Spilborghs Bueno Meyer</a>, <a href="https://publications.waset.org/abstracts/search?q=Plinio%20Thomaz%20Aquino%20Junior"> Plinio Thomaz Aquino Junior</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=speech" title=" speech"> speech</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=human-robot%20interaction" title=" human-robot interaction"> human-robot interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/145908/speech-emotion-recognition-a-dnn-and-lstm-comparison-in-single-and-multiple-feature-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">170</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5267</span> Cells Detection and Recognition in Bone Marrow Examination with Deep Learning Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiyin%20He">Shiyin He</a>, <a href="https://publications.waset.org/abstracts/search?q=Zheng%20Huang"> Zheng Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cell%20detection" title="cell detection">cell detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cell%20recognition" title=" cell recognition"> cell recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Mask-RCNN" title=" Mask-RCNN"> Mask-RCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet" title=" ResNet"> ResNet</a> </p> <a href="https://publications.waset.org/abstracts/98649/cells-detection-and-recognition-in-bone-marrow-examination-with-deep-learning-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98649.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">190</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5266</span> Empirical Evaluation of Gradient-Based Training Algorithms for Ordinary Differential Equation Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martin%20K.%20Steiger">Martin K. Steiger</a>, <a href="https://publications.waset.org/abstracts/search?q=Lukas%20Heisler"> Lukas Heisler</a>, <a href="https://publications.waset.org/abstracts/search?q=Hans-Georg%20Brachtendorf"> Hans-Georg Brachtendorf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep neural networks and their variants form the backbone of many AI applications. Based on the so-called residual networks, a continuous formulation of such models as ordinary differential equations (ODEs) has proven advantageous since different techniques may be applied that significantly increase the learning speed and enable controlled trade-offs with the resulting error at the same time. For the evaluation of such models, high-performance numerical differential equation solvers are used, which also provide the gradients required for training. However, whether classical gradient-based methods are even applicable or which one yields the best results has not been discussed yet. This paper aims to redeem this situation by providing empirical results for different applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title="deep neural networks">deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient-based%20learning" title=" gradient-based learning"> gradient-based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=ordinary%20differential%20equation%20networks" title=" ordinary differential equation networks"> ordinary differential equation networks</a> </p> <a href="https://publications.waset.org/abstracts/145435/empirical-evaluation-of-gradient-based-training-algorithms-for-ordinary-differential-equation-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145435.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5265</span> Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rosdyana%20Mangir%20Irawan%20Kusuma">Rosdyana Mangir Irawan Kusuma</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Chun%20Kao"> Wei-Chun Kao</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-Thi%20Trang"> Ho-Thi Trang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Yen%20Ou"> Yu-Yen Ou</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai-Lung%20Hua"> Kai-Lung Hua</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stock market prediction is still a challenging problem because there are many factors that affect the stock market price such as company news and performance, industry performance, investor sentiment, social media sentiment, and economic factors. This work explores the predictability in the stock market using deep convolutional network and candlestick charts. The outcome is utilized to design a decision support framework that can be used by traders to provide suggested indications of future stock price direction. We perform this work using various types of neural networks like convolutional neural network, residual network and visual geometry group network. From stock market historical data, we converted it to candlestick charts. Finally, these candlestick charts will be feed as input for training a convolutional neural network model. This convolutional neural network model will help us to analyze the patterns inside the candlestick chart and predict the future movements of the stock market. The effectiveness of our method is evaluated in stock market prediction with promising results; 92.2% and 92.1 % accuracy for Taiwan and Indonesian stock market dataset respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=candlestick%20chart" title="candlestick chart">candlestick chart</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20prediction" title=" stock market prediction"> stock market prediction</a> </p> <a href="https://publications.waset.org/abstracts/98615/using-deep-learning-neural-networks-and-candlestick-chart-representation-to-predict-stock-market" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98615.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5264</span> Delay-Dependent Passivity Analysis for Neural Networks with Time-Varying Delays</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Y.%20Jung">H. Y. Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing%20Wang"> Jing Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20H.%20Park"> J. H. Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Shen"> Hao Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This brief addresses the passivity problem for neural networks with time-varying delays. The aim is focus on establishing the passivity condition of the considered neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=passivity%20analysis" title=" passivity analysis"> passivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=time-varying%20delays" title=" time-varying delays"> time-varying delays</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20matrix%20inequality" title=" linear matrix inequality"> linear matrix inequality</a> </p> <a href="https://publications.waset.org/abstracts/3026/delay-dependent-passivity-analysis-for-neural-networks-with-time-varying-delays" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3026.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">570</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5263</span> A Survey of Field Programmable Gate Array-Based Convolutional Neural Network Accelerators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Zhang">Wei Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20programmable%20gate%20array" title=" field programmable gate array"> field programmable gate array</a>, <a href="https://publications.waset.org/abstracts/search?q=FPGA" title=" FPGA"> FPGA</a>, <a href="https://publications.waset.org/abstracts/search?q=hardware%20accelerator" title=" hardware accelerator"> hardware accelerator</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a> </p> <a href="https://publications.waset.org/abstracts/128017/a-survey-of-field-programmable-gate-array-based-convolutional-neural-network-accelerators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128017.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">128</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5262</span> Artificial Neural Networks in Environmental Psychology: Application in Architectural Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diego%20De%20Almeida%20Pereira">Diego De Almeida Pereira</a>, <a href="https://publications.waset.org/abstracts/search?q=Diana%20Borchenko"> Diana Borchenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks are used for many applications as they are able to learn complex nonlinear relationships between input and output data. As the number of neurons and layers in a neural network increases, it is possible to represent more complex behaviors. The present study proposes that artificial neural networks are a valuable tool for architecture and engineering professionals concerned with understanding how buildings influence human and social well-being based on theories of environmental psychology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=environmental%20psychology" title="environmental psychology">environmental psychology</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20and%20social%20well-being" title=" human and social well-being"> human and social well-being</a> </p> <a href="https://publications.waset.org/abstracts/147521/artificial-neural-networks-in-environmental-psychology-application-in-architectural-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147521.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">496</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5261</span> Tumor Detection Using Convolutional Neural Networks (CNN) Based Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinai%20K.%20Singh">Vinai K. Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lobular%20carcinoma" title="lobular carcinoma">lobular carcinoma</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks%20%28CNN%29" title=" convolutional neural networks (CNN)"> convolutional neural networks (CNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=histopathological%20imagery%20scans" title=" histopathological imagery scans"> histopathological imagery scans</a> </p> <a href="https://publications.waset.org/abstracts/146403/tumor-detection-using-convolutional-neural-networks-cnn-based-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146403.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5260</span> Taxonomic Classification for Living Organisms Using Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saed%20Khawaldeh">Saed Khawaldeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Elsharnouby"> Mohamed Elsharnouby</a>, <a href="https://publications.waset.org/abstracts/search?q=Alaa%20%20Eddin%20Alchalabi"> Alaa Eddin Alchalabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Usama%20Pervaiz"> Usama Pervaiz</a>, <a href="https://publications.waset.org/abstracts/search?q=Tajwar%20Aleef"> Tajwar Aleef</a>, <a href="https://publications.waset.org/abstracts/search?q=Vu%20Hoang%20Minh"> Vu Hoang Minh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20networks" title="deep networks">deep networks</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=taxonomic%20classification" title=" taxonomic classification"> taxonomic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20sequences%20classification" title=" DNA sequences classification "> DNA sequences classification </a> </p> <a href="https://publications.waset.org/abstracts/65170/taxonomic-classification-for-living-organisms-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65170.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">442</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5259</span> Comparison of Classical Computer Vision vs. Convolutional Neural Networks Approaches for Weed Mapping in Aerial Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Cesar%20Pereira%20Junior">Paulo Cesar Pereira Junior</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandre%20Monteiro"> Alexandre Monteiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafael%20da%20Luz%20Ribeiro"> Rafael da Luz Ribeiro</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Carlos%20Sobieranski"> Antonio Carlos Sobieranski</a>, <a href="https://publications.waset.org/abstracts/search?q=Aldo%20von%20Wangenheim"> Aldo von Wangenheim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title="convolutional neural networks">convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20image%20processing" title=" digital image processing"> digital image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20agriculture" title=" precision agriculture"> precision agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20segmentation" title=" semantic segmentation"> semantic segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=unmanned%20aerial%20vehicles" title=" unmanned aerial vehicles"> unmanned aerial vehicles</a> </p> <a href="https://publications.waset.org/abstracts/112982/comparison-of-classical-computer-vision-vs-convolutional-neural-networks-approaches-for-weed-mapping-in-aerial-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112982.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5258</span> An Empirical Study on Switching Activation Functions in Shallow and Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Apoorva%20Vinod">Apoorva Vinod</a>, <a href="https://publications.waset.org/abstracts/search?q=Archana%20Mathur"> Archana Mathur</a>, <a href="https://publications.waset.org/abstracts/search?q=Snehanshu%20Saha"> Snehanshu Saha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Though there exists a plethora of Activation Functions (AFs) used in single and multiple hidden layer Neural Networks (NN), their behavior always raised curiosity, whether used in combination or singly. The popular AFs –Sigmoid, ReLU, and Tanh–have performed prominently well for shallow and deep architectures. Most of the time, AFs are used singly in multi-layered NN, and, to the best of our knowledge, their performance is never studied and analyzed deeply when used in combination. In this manuscript, we experiment with multi-layered NN architecture (both on shallow and deep architectures; Convolutional NN and VGG16) and investigate how well the network responds to using two different AFs (Sigmoid-Tanh, Tanh-ReLU, ReLU-Sigmoid) used alternately against a traditional, single (Sigmoid-Sigmoid, Tanh-Tanh, ReLUReLU) combination. Our results show that using two different AFs, the network achieves better accuracy, substantially lower loss, and faster convergence on 4 computer vision (CV) and 15 Non-CV (NCV) datasets. When using different AFs, not only was the accuracy greater by 6-7%, but we also accomplished convergence twice as fast. We present a case study to investigate the probability of networks suffering vanishing and exploding gradients when using two different AFs. Additionally, we theoretically showed that a composition of two or more AFs satisfies Universal Approximation Theorem (UAT). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activation%20function" title="activation function">activation function</a>, <a href="https://publications.waset.org/abstracts/search?q=universal%20approximation%20function" title=" universal approximation function"> universal approximation function</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=convergence" title=" convergence"> convergence</a> </p> <a href="https://publications.waset.org/abstracts/160024/an-empirical-study-on-switching-activation-functions-in-shallow-and-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160024.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">158</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5257</span> Analysis of Multilayer Neural Network Modeling and Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danilo%20L%C3%B3pez">Danilo López</a>, <a href="https://publications.waset.org/abstracts/search?q=Nelson%20Vera"> Nelson Vera</a>, <a href="https://publications.waset.org/abstracts/search?q=Luis%20Pedraza"> Luis Pedraza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title=" long short-term memory"> long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neuronal%20network" title=" recurrent neuronal network"> recurrent neuronal network</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20analysis" title=" mathematical analysis"> mathematical analysis</a> </p> <a href="https://publications.waset.org/abstracts/63507/analysis-of-multilayer-neural-network-modeling-and-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63507.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">420</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5256</span> Unsupervised Images Generation Based on Sloan Digital Sky Survey with Deep Convolutional Generative Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guanghua%20Zhang">Guanghua Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Fubao%20Wang"> Fubao Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Weijun%20Duan"> Weijun Duan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Convolution neural network (CNN) has attracted more and more attention on recent years. Especially in the field of computer vision and image classification. However, unsupervised learning with CNN has received less attention than supervised learning. In this work, we use a new powerful tool which is deep convolutional generative adversarial networks (DCGANs) to generate images from Sloan Digital Sky Survey. Training by various star and galaxy images, it shows that both the generator and the discriminator are good for unsupervised learning. In this paper, we also took several experiments to choose the best value for hyper-parameters and which could help to stabilize the training process and promise a good quality of the output. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolution%20neural%20network" title="convolution neural network">convolution neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminator" title=" discriminator"> discriminator</a>, <a href="https://publications.waset.org/abstracts/search?q=generator" title=" generator"> generator</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20learning" title=" unsupervised learning"> unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/89010/unsupervised-images-generation-based-on-sloan-digital-sky-survey-with-deep-convolutional-generative-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89010.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">268</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5255</span> Using Gene Expression Programming in Learning Process of Rough Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sanaa%20Rashed%20Abdallah">Sanaa Rashed Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Yasser%20F.%20Hassan"> Yasser F. Hassan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20sets" title="rough sets">rough sets</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20programming" title=" gene expression programming"> gene expression programming</a>, <a href="https://publications.waset.org/abstracts/search?q=rough%20neural%20networks" title=" rough neural networks"> rough neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/41805/using-gene-expression-programming-in-learning-process-of-rough-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41805.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5254</span> Image Classification with Localization Using Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhuyain%20Mobarok%20Hossain">Bhuyain Mobarok Hossain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title="image classification">image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</a> </p> <a href="https://publications.waset.org/abstracts/139288/image-classification-with-localization-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139288.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5253</span> Prediction of Remaining Life of Industrial Cutting Tools with Deep Learning-Assisted Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gizem%20Eser%20Erdek">Gizem Eser Erdek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=remaining%20life%20of%20industrial%20cutting%20tools" title=" remaining life of industrial cutting tools"> remaining life of industrial cutting tools</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet" title=" ResNet"> ResNet</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=VggNet" title=" VggNet"> VggNet</a> </p> <a href="https://publications.waset.org/abstracts/166428/prediction-of-remaining-life-of-industrial-cutting-tools-with-deep-learning-assisted-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166428.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">77</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5252</span> Study of the Use of Artificial Neural Networks in Islamic Finance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kaoutar%20Abbahaddou">Kaoutar Abbahaddou</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Salah%20Chiadmi"> Mohammed Salah Chiadmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Islamic%20finance" title="Islamic finance">Islamic finance</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20price%20prediction" title=" stock price prediction"> stock price prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/142047/study-of-the-use-of-artificial-neural-networks-in-islamic-finance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">237</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5251</span> Amplifying Sine Unit-Convolutional Neural Network: An Efficient Deep Architecture for Image Classification and Feature Visualizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamshaid%20Ul%20Rahman">Jamshaid Ul Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Faiza%20Makhdoom"> Faiza Makhdoom</a>, <a href="https://publications.waset.org/abstracts/search?q=Dianchen%20Lu"> Dianchen Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amplifying%20sine%20unit" title="amplifying sine unit">amplifying sine unit</a>, <a href="https://publications.waset.org/abstracts/search?q=activation%20function" title=" activation function"> activation function</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=oscillatory%20activation" title=" oscillatory activation"> oscillatory activation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=CIFAR-10" title=" CIFAR-10"> CIFAR-10</a> </p> <a href="https://publications.waset.org/abstracts/169054/amplifying-sine-unit-convolutional-neural-network-an-efficient-deep-architecture-for-image-classification-and-feature-visualizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169054.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks&page=2">2</a></li> <li class="page-item"><a class="page-link" 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