Showing papers in "Procedia Computer Science in 2020"

TL;DR: Artificial Neural Network and Random Forest techniques have been utilized for predicting the next day closing price for five companies belonging to different sectors of operation and show that the models are efficient in predicting stock closing price.

TL;DR: This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values.

TL;DR: A Convolution Neural Network based approach is applied for the disease detection and classification of tomato and the experimental results shows the efficacy of the proposed model over pre-trained model i.e. VGG16, InceptionV3 and MobileNet.

TL;DR: This study aims to assess the risk of diabetes among individuals based on their lifestyle and family background using different machine learning algorithms as these algorithms are highly accurate which is very much required in the health profession.

TL;DR: After applying the different methods, it was found that classes were imbalanced in the confusion matrix and the f1 score measure was added, which helped identify the best accuracy model among the five applied algorithms as the Random Forest classifier.

TL;DR: A dataset was created from the Indian stock market and an LSTM model was developed, optimized by comparing stateless and stateful models and by tuning for the number of hidden layers.

TL;DR: There is an attempt to explore the possibility to use Naive Bayes, Support Vector Machine, Logistic Regression, KNN, Neural Network and Convolutional Neural Network for predicting and analysis of ASD problems in a child, adolescents, and adults.

TL;DR: The recent advancement in the IDS datasets that can be used by various research communities as the manifesto for using the new IDS dataset for developing efficient and effective ML and DM based IDS.

TL;DR: The comparative study of various ML algorithms used in IDS for several applications such as fog computing, Internet of Things (IoT), big data, smart city, and 5G network is explored and their efficiency was measured and also compared along with the latest researches.

TL;DR: The interest of this paper is to serve and guide researchers by review recent works on automatic facial emotion recognition FER via deep learning and providing insights to make improvements to this field.

TL;DR: A sustainable approach to irrigation is provided in this paper by establishing a distributed wireless sensor network (WSN), wherein each region of the farm would be covered by various sensor modules which will be transmitting data on a common server.

TL;DR: A hybrid model of a powerful Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) for recognition of handwritten digit from MNIST dataset is developed, which achieves recognition accuracy of 99.28% over MNIST handwritten digits dataset.

TL;DR: In this article, machine learning predictive models for forecasting particulate matter concentration in atmospheric air are investigated on Taiwan Air Quality Monitoring data sets, which were obtained from 2012 to 2017, and the performance of these models was evaluated with statistical measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), mean square error (MSE), and Coefficient of Determination (R2).

TL;DR: The proposed concept for the learning system in metaverse is explained and the significance for current research collaboration is pointed out.

TL;DR: An automated way of resume Classification and Matching could really ease the tedious process of fair screening and shortlisting, it would certainly expedite the candidate selection and decisionmaking process.

TL;DR: The presented corn plant disease recognition model is capable of running on standalone smart devices like raspberry-pi or smart-phone and drones, and achieves an accuracy of 88.46% demonstrating the feasibility of this method.

TL;DR: Authors survey on various segmentation and feature extraction methods in medicinal images used for preprocessing in order to find the inner or outer construction of mortal body.

TL;DR: The paper addresses the influential academic achievements and innovations in the field of AI; their impact on the entrepreneurial activities and thus on the global market; and investigates the overall impact of AI - from research and innovation to deployment.

TL;DR: The collection and decomposition of waste in the smart way so that benefit from the waste is maximized and the actual waste is minimized efficiently is discussed.

TL;DR: This is the first survey to review recent advances in UWB-based methods that enable ad-hoc and dynamic deployments; collaborative localization techniques; and cooperative sensing and cooperative maneuvers such as UAV docking on mobile platforms.

TL;DR: A blockchain-based architecture for the IoT applications is presented, which brings distributed data management to support transactions services within a multi-party apparel business supply chain network.

TL;DR: The performance evaluation of Docker containers and virtual machines using standard benchmark tools such as Sysbench, Phoronix, and Apache benchmark, which include CPU performance, Memory throughput, Storage read/write performance, load test, and operation speed measurement are provided.

TL;DR: This paper amalgamates the potentials of blockchain technology as a promising security measure, highlights potential challenges in the healthcare domain, and provides an analysis of different blockchain-based security solutions.

TL;DR: The results show that GLCM in combination with PCA for feature reduction gives high classification accuracy when classifying images using Artificial Neural Network (ANN).

TL;DR: Experimental results showed the accuracy rate of the proposed method using DNN for identifying the attacks in IoT showed that accuracy rate is above 90% with each dataset.

TL;DR: The study has found that the machine learning strategies in computer vision are supervised, un-supervised, and semi- supervised; the commonly used algorithms are neural networks, k-means clustering, and support vector machine.

TL;DR: This work compares K-Means and Gaussian Mixture Model to evaluate cluster representativeness of the two methods for heterogeneity in resource usage of Cloud workloads and finds that GaussianMixture Model provides better clustering with distinct usage boundaries.

TL;DR: This paper is discussing how manufacturing could develop a digital transformation strategy that including a different aspect of the strategy tailored to the nature of the manufacturing sector.

TL;DR: A methodology for the recognition of hand gestures, which is the prime component in sign language vocabulary, based on an efficient deep convolutional neural network (CNN) architecture is proposed.

TL;DR: This paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models and comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.