0000000000943036

AUTHOR

Michel Chammas

0000-0003-3261-9417

showing 2 related works from this author

An efficient data model for energy prediction using wireless sensors

2019

International audience; Energy prediction is in high importance for smart homes and smart cities, since it helps reduce power consumption and provides better energy and cost savings. Many algorithms have been used for predicting energy consumption using data collected from Internet of Things (IoT) devices and wireless sensors. In this paper, we propose a system based on Multilayer Perceptron (MLP) to predict energy consumption of a building using collected information (e.g., light energy, day of the week, humidity, temperature, etc.) from a Wireless Sensor Network (WSN). We compare our system against four other classification algorithms, namely: Linear Regression (LR), Support Vector Machin…

General Computer ScienceMean squared errorComputer scienceReal-time computing02 engineering and technology[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]7. Clean energy[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR]0202 electrical engineering electronic engineering information engineeringElectrical and Electronic Engineering020206 networking & telecommunicationsEnergy consumption[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationRandom forestSupport vector machineMean absolute percentage error13. Climate actionControl and Systems Engineering[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Multilayer perceptron020201 artificial intelligence & image processing[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET]Gradient boosting[INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Wireless sensor network
researchProduct

Writer identification for historical handwritten documents using a single feature extraction method

2020

International audience; With the growth of artificial intelligence techniques the problem of writer identification from historical documents has gained increased interest. It consists on knowing the identity of writers of these documents. This paper introduces our baseline system for writer identification, tested on a large dataset of latin historical manuscripts used in the ICDAR 2019 competition. The proposed system yielded the best results using Scale Invariant Feature Transform (SIFT) as a single feature extraction method, without any preprocessing stage. The system was compared against four teams who participated in the competition with different feature extraction methods: SRS-LBP, SI…

Writer identificationComputer sciencebusiness.industryFeature extractionhistorical documentsScale-invariant feature transform020207 software engineeringPattern recognition02 engineering and technologyartificial intelligenceConvolutional neural networkSupport vector machineIdentification (information)sift descriptors0202 electrical engineering electronic engineering information engineeringIdentity (object-oriented programming)Unsupervised learning020201 artificial intelligence & image processing[INFO]Computer Science [cs]Artificial intelligencebusiness
researchProduct