Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Deep Learning Techniques for Depression Assessment
2018
Depression is a typical mood disorder, which affects a significant number of individuals worldwide at an increasing rate. Objective measures for early detection of signs related to depression could be beneficial for clinicians with regards to a decision support system. In this paper, assessment of depression is done by applying three deep learning techniques of Convolutional Neural Network (CNN). These techniques are transfer learning using AlexNet, fine-tuning using AlexNet and building an end to end CNN. The inputs of the CNNs are a combination of Motion History Image, Landmark Motion History Image and Gabor Motion History Image, and have been generated on a depression dataset. Accuracy o…
Prediction of arrival times and human resources allocation for container terminal
2011
Increasing competition in the container shipping sector has meant that terminals are having to equip themselves with increasingly accurate analytical and governance tools. A transhipment terminal is an extremely complex system in terms of both organisation and management. Added to the uncertainty surrounding ships’ arrival time in port and the costs resulting from over-underestimation of resources is the large number of constraints and variables involved in port activities. Predicting ships delays in advance means that the relative demand for each shift can be determined with greater accuracy, and the basic resources then allocated to satisfy that demand. To this end, in this article we pro…
A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition
2022
Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and background variations. Therefore, developing and exploring an expert system for automatic fruits’ recognition is getting more and more important after many successful approaches; however, this technology is still far from being mature. The deep learning-based models have emerged as state-of-the-art techniques for image segmentation and classification and have a lot of promise in challenging domains such as agriculture, where they can …
A sentence based system for measuring syntax complexity using a recurrent deep neural network
2018
In this paper we present a deep neural network model capable of inducing the rules that identify the syntax complexity of an Italian sentence. Our system, beyond the ability of choosing if a sentence needs of simplification, gives a score that represent the confidence of the model during the process of decision making which could be representative of the sentence complexity. Experiments have been carried out on one public corpus created specifically for the problem of text-simplification.
A recurrent deep neural network model to measure sentence complexity for the Italian Language
2019
Text simplification (TS) is a natural language processing task devoted to the modification of a text in such a way that the grammar and structure of the phrases is greatly simplified, preserving the underlying meaning and information contents. In this paper we give a contribution to the TS field presenting a deep neural network model able to detect the complexity of italian sentences. In particular, the system gives a score to an input text that identifies the confidence level during the decision making process and that could be interpreted as a measure of the sentence complexity. Experiments have been carried out on one public corpus of Italian texts created specifically for the task of TS…
Mapping and holistic design of natural hydraulic lime mortars
2020
Supplementary data to this article can be found online at https://doi.org/10.1016/j.cemconres.2020.106167.
Generative Adversarial Networks in Cardiology
2021
A B S T R A C T Generative Adversarial Networks (GANs) are state-of-the-art neural network models used to synthesize images and other data. GANs brought a considerable improvement to the quality of synthetic data, quickly becoming the standard for data generation tasks. In this work, we summarize the applications of GANs in the field of cardiology, including generation of realistic cardiac images, electrocardiography signals, and synthetic electronic health records. The utility of GAN-generated data is discussed with respect to research, clinical care, and academia. Moreover, we present illustrative examples of our GAN-generated cardiac magnetic resonance and echocardiography images, showin…
Hybrid (diffractive-refractive) optical processor for space-variant color pattern recognition
2002
Space-variant optical processing constitutes an interesting approach in information processing techniques when the location of the reference object is of as much importance as its identification. Applications range from machine vision, optical logic, or neural network systems, to cryptography. First results of positional sensitivity were obtained in the past few years by Fresnel transform correlators with coherent light [1,2]. On the other hand, optical Fresnel cor-relators working under broadband point-source illumination allow us to exploit color information of input scenes and present a discrimination ability higher than its monochromatic counterparts. However, the use of the wavelength …
32×32 winner-take-all matrix with single winner selection
2010
A 32 × 32 winner-take-all (WTA) matrix with single winner selection is introduced. A high-resolution gain-boosted regulated-cascode WTA circuit is used in a first competition stage. Because of the large number of competing cells the possibility of a multiple winners situation arises. A single winner is obtained by means of a digital inhibitory circuit following each WTA analogue amplifier. Simulations show that this mixed analogue-digital circuit achieves its objective with a current resolution of approximately 10 nA (0.8% of the maximum input current in the simulated case). A time response of ?s can be achieved.
MLP Neural Network Implementation on a SIMD Architecture
2002
An Automatic Road Sign Recognition System {A(RS)2} is aimed at detection and recognition of one or more road signs from realworld color images. The authors have proposed an A(RS)2 able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sign detection and classification rates, the entire process requires a large computational time, so real-time applications are not allowed. In this paper we present the implementation of the neural layer on the Georgia Institute of Technology …