Current Issue : April - June Volume : 2019 Issue Number : 2 Articles : 5 Articles
In this research article, we study the problem of employing a neural machine translation model to translate Arabic dialects to\nModern Standard Arabic. The proposed solution of the neural machine translation model is prompted by the recurrent neural\nnetwork-based encoder-decoder neural machine translation model that has been proposed recently, which generalizes machine\ntranslation as sequence learning problems. We propose the development of a multitask learning (MTL) model which shares one\ndecoder among language pairs, and every source language has a separate encoder.The proposed model can be applied to limited\nvolumes of data aswell as extensive amounts of data. Experiments carried out have shown that the proposed MTL model can ensure\na higher quality of translation when compared to the individually learned model....
One of the most important ecosystems in the Amazon rainforest is the Mauritia flexuosa\nswamp or â??aguajalâ?. However, deforestation of its dominant species, the Mauritia flexuosa palm,\nalso known as â??aguajeâ?, is a common issue, and conservation is poorly monitored because of the\ndifficult access to these swamps. The contribution of this paper is twofold: the presentation of a\ndataset called MauFlex, and the proposal of a segmentation and measurement method for areas\ncovered in Mauritia flexuosa palms using high-resolution aerial images acquired by UAVs. The method\nperforms a semantic segmentation of Mauritia flexuosa using an end-to-end trainable Convolutional\nNeural Network (CNN) based on the Deeplab v3+ architecture. Images were acquired under different\nenvironment and light conditions using three different RGB cameras. The MauFlex dataset was\ncreated from these images and it consists of 25,248 image patches of 512 Ã? 512 pixels and their\nrespective ground truth masks. The results over the test set achieved an accuracy of 98.143%,\nspecificity of 96.599%, and sensitivity of 95.556%. It is shown that our method is able not only to\ndetect full-grown isolated Mauritia flexuosa palms, but also young palms or palms partially covered\nby other types of vegetation...
False data injection attacks (FIDAs) against state estimation in power system\nare a problem that could not be effectively solved by traditional methods. In\nthis paper, we use four outlier detection methods, namely one-Class SVM,\nRobust covariance, Isolation forest and Local outlier factor method from\nmachine learning area in IEEE14 simulation platform for test and compare\ntheir performance. The accuracy and precision were estimated through simulation\nto observe the classification effect....
Trying to provide a medical data visualization analysis tool, the machine\nlearning methods are introduced to classify the malignant neoplasm of lung\nwithin the medical database MIMIC-III (Medical Information Mart for Intensive\nCare III, USA). The K-Nearest Neighbor (KNN), Support Vector\nMachine (SVM) and Random Forest (RF) are selected as the predictive tool.\nBased on the experimental result, the machine learning predictive tools are\nintegrated into the medical data visualization analysis platform. The platform\nsoftware can provide a flexible medical data visualization analysis tool for the\ndoctors. The related practice indicates that visualization analysis result can be\ngenerated based on simple steps for the doctors to do some research work on\nthe data accumulated in hospital, even they have not taken special data analysis\ntraining....
Traffic light detection and recognition (TLR) research has grown every year. In addition,\nMachine Learning (ML) has been largely used not only in traffic light research but in every field\nwhere it is useful and possible to generalize data and automatize human behavior. ML algorithms\nrequire a large amount of data to work properly and, thus, a lot of computational power is required to\nanalyze the data. We argue that expert knowledge should be used to decrease the burden of collecting\na huge amount of data for ML tasks. In this paper, we show how such kind of knowledge was used\nto reduce the amount of data and improve the accuracy rate for traffic light detection and recognition.\nResults show an improvement in the accuracy rate around 15%. The paper also proposes a TLR\ndevice prototype using both camera and processing unit of a smartphone which can be used as a\ndriver assistance. To validate such layout prototype, a dataset was built and used to test an ML model\nbased on adaptive background suppression filter (AdaBSF) and Support Vector Machines (SVMs).\nResults show 100% precision rate and recall of 65%....
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