Wed, 05 Dec 2018 22:36:00 GMT forecasting methods and applications 3rd pdf PDF | On Jan 1, 1984, S ~G Makridakis and others published Forecasting: Methods and Applications Tue, 04 Dec 2018 23:06:00 GMT (PDF) Forecasting: Methods and Applications ResearchGate Forecasting: methods and applications. This book was published in 1998, and for nearly 20 years I maintained an associated website at this address. Fri, 30 Nov 2018 14:35:00 GMT Forecasting: methods and applications | Rob J Hyndman Prod 2100-2110 Forecasting Methods 2 1. Framework of planning decisions Let us first remember where the inventory control decisions may take place. Fri, 07 Dec 2018 14:13:00 GMT Forecasting Methods UCLouvain 2002 Forecasting: Methods and Applications Makridakis, ... this 3rd edition very wisely includes some more advanced forecasting methods such as dynamic regression, ... Sat, 01 Dec 2018 22:41:00 GMT 2002 Forecasting: Methods and Applications HEPHAESTUS Methods and Applications Third Edition Spyros Makridakis European Institute of Business ... major forecasting methods 516 The use of different forecasting Tue, 04 Dec 2018 22:37:00 GMT Methods and Applications Max Planck Society MATH6011: Forecasting â€œAll models are wrong, ... S.C. and Hyndman, R.J. 1998, Forecasting: Methods and Applications 3rd Ed., New York: Wiley as text book. Wed, 21 Nov 2018 17:31:00 GMT MATH6011: Forecasting University of Southampton Save As PDF Ebook forecasting methods and applications ... FOUR LAMAS OF DOLPO AUTOBIOGRAPHIES OF FOUR TIBETAN LAMAS INTRODUCTION AND TRANSLATIONS VOL I 3RD [PDF] Tue, 04 Dec 2018 19:10:00 GMT forecasting methods and applications makridakis pdf ... forecasting methods and applications 3rd ed Download forecasting methods and applications 3rd ed or read online books in PDF, EPUB, Tuebl, and Mobi Format. Thu, 06 Dec 2018 07:26:00 GMT forecasting methods and applications 3rd ed | Download ... INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 : ... Some applications of forecasting ... Qualitative techniques in forecasting Time series methods Mon, 19 Nov 2018 11:49:00 GMT INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT 6 ... 3 Hierarchical forecasting 9 3 Advanced methods 9. Forecasting: principles and practice 7 Assumptions â€¢ This is not an introduction to R. I assume you are broadly ... Thu, 06 Dec 2018 22:49:00 GMT Forecasting: Principles & Practice, Rob J Hyndman, 2014 forecasting methods and applications 3rd ed Download forecasting methods and applications 3rd ed or read online here in PDF or EPUB. Please click button to get ... Mon, 03 Dec 2018 08:27:00 GMT Forecasting Methods And Applications 3rd Ed | Download ... Forecasting methods can be classified as qualitative or quantitative. ... practical applications. 15-4 Chapter 15 Time Series Analysis and Forecasting Fri, 07 Dec 2018 12:33:00 GMT PDF Time Series Analysis and Forecasting Cengage FORECASTING METHODS AND APPLICATIONS 3RD EDITION PDF READ Forecasting Methods And Applications 3rd Edition pdf. Download Forecasting Methods And Applications 3rd ... Sun, 11 Nov 2018 17:14:00 GMT Free Forecasting Methods And Applications 3rd Edition PDF Forecasting Methods and Applications. 3rd ed. New York: John Wiley & Sons, 1998. Sat, 08 Dec 2018 09:40:00 GMT Forecasting Methods and Applications Book Harvard ... Preface In preparing the manuscript for the third edition of Forecasting: methods and applications, one of our primary goals has been to make the book as complete and ... Wed, 05 Dec 2018

Forecasting methods and applications.

Systematic evaluation of convolution neural network advances on the Imagenet

This text is a survey on cross-validation We define all classical cross-validation procedures, and we study their properties for two different goals: estimating the risk of a given estimator, and selecting the best estimator among a given family For the risk estimation problem, we compute the bias (which can also be corrected) and the variance of cross-validation methods For estimator selection, we first provide a first-order analysis (based on expectations) Then, we explain how to take into account second-order terms (from variance computations, and by taking into account the usefulness of overpenalization) This allows, in the end, to provide some guidelines for choosing the best cross-validation method for a given learning problem

Cross-validation

Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a new liver and brain tumor classification method is proposed by using the power of convolutional neural network (CNN) in feature extraction, the power of discrete wavelet transform (DWT) in signal processing, and the power of long short-term memory (LSTM) in signal classification. A CNN–DWT–LSTM method is proposed to classify the computed tomography (CT) images of livers with tumors and to classify the magnetic resonance (MR) images of brains with tumors. The proposed method classifies liver tumors images as benign or malignant and then classifies brain tumor images as meningioma, glioma, and pituitary. In the hybrid CNN–DWT–LSTM method, the feature vector of the images is obtained from pre-trained AlexNet CNN architecture. The feature vector is reduced but strengthened by applying the single-level one-dimensional discrete wavelet transform (1-D DWT), and it is classified by training with an LSTM network. Under the scope of the study, images of 56 benign and 56 malignant liver tumors that were obtained from Firat University Research Hospital were used and a publicly available brain tumor dataset were used. The experimental results show that the proposed method had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM). By using the CNN–DWT–LSTM hybrid method, an accuracy rate of 99.1% was achieved in the liver tumor classification and accuracy rate of 98.6% was achieved in the brain tumor classification. We used two different datasets to demonstrate the performance of the proposed method. Performance measurements show that the proposed method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying.

https://www.mdpi.com/1424-8220/19/9/1992/pdf

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks.

In recent years, autonomous driving technologies have attracted broad and enormous interests from both academia and industry and are under rapid development. High-Definition (HD) Maps are widely used as an indispensable component of an autonomous vehicle system by researchers and practitioners. HD Maps are digital maps that contain highly precise, fresh and comprehensive geometric information as well as semantics of the road network and surrounding environment. They provide critical inputs to almost all other components of autonomous vehicle systems, including localization, perception, prediction, motion planning, vehicle control etc. Traditionally, it is very laborious and costly to build HD Maps, requiring a significant amount of manual annotation work. In this paper, we first introduce the characteristics and layers of HD Maps; then we provide a formal summary of the workflow of HD Map creation; and most importantly, we present the machine learning techniques being used by the industry to minimize the amount of manual work in the process of HD Map creation.

Machine Learning Assisted High-Definition Map Creation

Vehicle color information is one of the important elements in ITS (Intelligent Traffic System). In this paper, we present a vehicle color recognition method using convolutional neural network (CNN). Naturally, CNN is designed to learn classification method based on shape information, but we proved that CNN can also learn classification based on color distribution. In our method, we convert the input image to two different color spaces, HSV and CIE Lab, and run it to some CNN architecture. The training process follow procedure introduce by Krizhevsky, that learning rate is decreasing by factor of 10 after some iterations. To test our method, we use publicly vehicle color recognition dataset provided by Chen. The results, our model outperform the original system provide by Chen with 2% higher overall accuracy.

Vehicle Color Recognition using Convolutional Neural Network

This study discusses the appropriate method to be applied in a presence system using faces by comparing two deep learning architectural models, they are FaceNet and Openface. FaceNet is a model developed by Google researchers that has the highest accuracy in face recognition. While Openface is a development from FaceNet that is trained with smaller datasets but has an accuracy that is almost equal to FaceNet. This will start by taking the employee's face into an image dataset. From the dataset, the face preprocessing will be performed by detecting, cropping, and resizing the face. Then extracting facial features into 128 dimensions using the FaceNet and Openface. With the Support Vector Machine (SVM), the classification of facial features will be carried out to obtain accuracy. To validate the model, 5 fold cross-validations are used. FaceNet accuracy results that obtained are higher with perfect accuracy that is 100%, while Openface only 93.33% accuracy. The implementation using the model with the highest accuracy (FaceNet) has the same results as the model testing that is 100% using the introduction threshold probability of 0.25.

Face Recognition System using Facenet Algorithm for Employee Presence

Single image vehicle classification using pseudo long short-term memory classifier

Article history: Received: 20 November, 2020 Accepted: 28 December, 2020 Online: 22 January, 2021 This research designed an image encryption system that focused on securing teledermatology data in the form of skin disease images. The encryption and decryption process of this system is done on the client side using chaos-based encryption with confusion and diffusion techniques. Arnold’s cat map is the chaotic map model used for confusion, while the Henon map is used for diffusion. The initial values of both chaotic maps are obtained from a 30-digit secret key that is generated using Diffie–Hellman key exchange. During Arnold’s cat map generation, different p and q values are used for every iteration. On the other side, the precision of the Henon map’s x and y values is 10–14. From the tests that have been done, histograms of the encrypted images are relatively flat and distributed through all the gray values. Moreover, the encrypted images have average correlation coefficients of 0.003877 (horizontal), -0.00026 (vertical) and -0.00049 (diagonal) and an average entropy of 7.950304. According to the key sensitivity test, a difference of just one number in the secret key causes big differences, as both results have a similarity index of 0.005337 (0.5%). Meanwhile, in the decryption process, that small key difference cannot be used to restore the encrypted image to its original form and generate another chaotic image with average entropies of 7.964909333 (secret key difference) and 7.994861667 (private key difference).

/pdf/chaos-based-image-encryption-using-arnold-s-cat-map-2y29q3ii1x.pdf

Chaos-based image encryption using Arnold's cat map confusion and Henon map diffusion

Image-based facial expression recognition is an important problem especially for analyzing the human emotion or feeling under a specific condition, such as while watching a movie scene or playing a computer game. Furthermore, the convolutional neural network (CNN) is one of the underlying technology proven to be applicable to image-based facial expression recognition problem. Unfortunately, the available CNN architecture that applied for image-based facial expression recognition problem only focuses on the accuracy instead of other factors, such as the number of parameters and the execution time. In this paper, we investigated whether transfer learning from a medium-size and large-size dataset is feasible to improve the performance of lightweight CNN architecture on image-based facial expression recognition problem. We use lightweight residual-based CNN architecture originally used for CIFAR dataset to analyze the effect of the transfer learning from five different datasets, including CIFAR10, CIFAR100, ImageNet32, CINC-10, and CASIA-WebFace. The FER+ (Facial Expression Recognition Plus) dataset is used to evaluate the lightweight CNN architecture performance. Experiments show that our lightweight CNN classifier can also be improved even when the transfer learning performing from middle-size dataset comparing when training the classifier from scratch.

Reza Fuad Rachmadi

Papers

Vehicle Color Recognition using Convolutional Neural Network

Face Recognition System using Facenet Algorithm for Employee Presence

Single image vehicle classification using pseudo long short-term memory classifier

Chaos-based image encryption using Arnold's cat map confusion and Henon map diffusion

Improving Lightweight Convolutional Neural Network for Facial Expression Recognition via Transfer Learning