Neural networks are a set of algorithms whose operation is inspired by biological neurons, these networks have been developed to solve problems: control, recognition of shapes or words, decision, and memorization. In this work, we tried to make an implementation that combines the advantages of the compact point cloud representation but uses the traditional 2D ConvNet to learn the prior knowledge about the shapes. And by combining the 3 modules together, the convolution structure generator 2D and the merge and pseudo-rendering modules, we have obtained an end-to-end model that learns to generate a compact point cloud representation from a single 2D image, using only a convolution structure generator 2D. And at the end we got as final result: from a single RBG image → 3D point cloud

This paper aims to test the most common imputation methods' effectiveness and choose the most appropriate methods for our data model. In the experimental study, we applied imputation to missing data using the imputation methods: fFill, bFfil, Drop, and Support Vector Regression (SVR). An easy and practical means of comparison is used to evaluate the effectiveness of imputation methods. Therefore, the classification quality criterion is used, and column reference graphs are used because they have a statistically significant relationship. The SVR imputation method was very reliable, and it helped us make a reasonable classification.

Many cloud providers offer very high precision services to exploit optical character recognition (OCR). However, there is no provider that offers Tifinagh optical character recognition (OCR) as web services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a web service. In this paper, the authors present a new architecture of Tifinagh handwriting recognition as a web service based on a deep learning model via Google Colab. For the implementation of the proposal, they used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The resultsshow that the method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machines.

Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS). However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM) to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.

Remaining Useful Life (RUL) prediction for aircraft engines based on the available run-to-failure measurements 5 of similar systems becomes more prevalent in Prognostic Health Management (PHM) thanks to the new advanced 6 methods of estimation. However, feature extraction and RUL prediction are challenging tasks, especially for data-driven 7 prognostics. The key issue is how to design a suitable feature extractor that is able to give a raw of time-varying sensors 8 measurements more meaningful representation to enhance prediction accuracy with low computational costs. In this 9 paper, a new Denoising Online Sequential Extreme Learning Machine (DOS-ELM) with double dynamic forgetting factors 10 (DDFF) and Updated Selection Strategy (USS) is proposed. First, depending on the characteristics of the training data that 11 comes from aircraft sensors, robust feature extraction using a modified Denoising Autoencoder (DAE) is introduced to 12 learn important patterns from data. Then, USS is integrated to ensure that only the useful data sequences pass through 13 the training process. Finally, OS-ELM is used to fit the non-accumulative linear degradation function of the engine and to 14 address dynamic programming by trucking the new coming data and forgetting gradually the old ones based on the 15 proposed DDFF. The proposed DOS-ELM is tested on the public dataset of commercial modular aeropropulsion system 16 simulation (C-MAPSS) of a turbofan engine and compared with OS-ELM trained with ordinary Autoencoder (AE), basic 17 OS-ELM and pervious works from the literature. Comparison results prove the effectiveness of the new integrated robust 18 feature extraction scheme by showing more stability of the network responses even under random solutions.

The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

This book is copyright material and must not be copied, reproduced, transferred, distributed, leased, licensed or publicly performed or used in any way except as specifically permitted in writing by the publishers, as allowed under the terms and conditions under which it was purchased or as strictly permitted by applicable copyright law. Any unauthorized distribution or use of this text may be a direct infringement of the author s and publisher s rights and those responsible may be liable in law accordingly.

This paper addresses the problem of fault diagnosis from observed data containing missing values amongst the inputs. In order to provide good classification accuracy for the decision function, a novel approach based on support vector machine and extreme learning machine is developed. SVM mixture model is used to model the data distribution, which is adapted to handle missing values, while extreme learning machine enables to devise a multiple imputation strategy for final estimation. In order to prove the efficiency of our proposed method, we have developed the classifier using the condition monitoring observations from milk pasteurisation data. The experiments show that the proposed algorithm gives improved results compared to recent methods, essentially if the number of missing data is significant. The results show that our approach can perfectly detect dysfunction, identify the fault, and is strong in …