129 research outputs found
L?on-Gontran Damas : Cent ans en noir et blanc
No full textCollection of studies around L?on-Gontran Damas, Guyanese French-speaking author. Pivotal exponent of the Negritude, he is a constituent author of a poetics really American. The volume is organized in four parts : "des traces, des trac?s", "po?mes, de la po?sie", "des mots, des signes", "de part et d\u27autre". The bibliography and the index complete this work.Recueil d\u27?tudes autour de L?on-Gontran Damas, auteur francophone Guyanais. Figure charni?re de la N?gritude il est un auteur constitutif d\u27une po?tique v?ritablement am?ricaine. Volume organis? en quatre parties: "des traces, des trac?s", "po?mes, de la po?sie", "des mots, des signes", "de part et d\u27autre". La bibliographie et l\u27index compl?tent cet ouvrage
Introduction (? L?on-Gontran Damas : Cent ans en noir et blanc)
No full textPresentation of "L?on-Gontran Damas : Cent ans en noir et blanc", First result of an exploration to illustrate the communicative dynamism of an author capable of answering multiple subjects concerning the contemporary man And his position in the world.Pr?sentation de "L?on-Gontran Damas : Cent ans en noir et blanc", premier r?sultat d\u27une exploration visant ? illustrer le dynamisme communicatif d\u27un auteur capable de susciter des r?onses ? des sujet multiples concernant l\u27homme contemporain et sa position dans le monde
PENGARUH BAURAN PROMOSI TERHADAP KEPUTUSAN PEMBELIAN PRODUK KOSMETIK EMINA (Survei pada Mahasiswi Manajemen Fakultas Ekonomi Universitas Negeri Jakarta)
No full textABSTRAK :
Karya Ilmiah ini bertujuan untuk mengetahui: 1.) Untuk mengetahui deskripsi Bauran Promosi dan Keputusan Pembelian, 2.) Mengetahui pengaruh Bauran Promosi terhadap Keputusan Pembelian produk Kosmetik Emina. Metode pengumpulan data yang digunakan yaitu dengan metode survei dengan instrument Kuesioner. Sampel yang diambil sebanyak 152 responden. Subjek dari penelitian ini yaitu Mahasiswi Manajemen Fakultas Ekonomi Universitas Negeri Jakarta Angkatan 2016, 2017, 2018 yang pernah menggunakan Produk Kosmetik Emina. Metode analisis yang digunakan metode analisis deskriptif dan analisis regresi linier sederhana. Pengolahan data diolah dengan menggunakan SPSS 25.
Berdasarkan hasil penelitian, dapat diketahui bahwa Bauran Promosi berpengaruh positif terhadap Keputusan Pembelian Produk kosmetik Emina. Hal tersebut ditunjukkan dengan nilai Koefisien Determinasi (R²) sebesar 0,324 atau 32,4%.
Kata kunci: Bauran Promosi, Keputusan Pembelian, Produk kosmetik Emina
ABSTRACT :
The purpose of this research was to determine: 1.) to find out the description of the Promotional Mix and Purchase Decisions, 2.) to know the relationship between Promotional Mix of Purchase Decision of Emina Cosmetics Products. The method used in this research is survey method with questionnaire instrument. The Sampling put 152 respondents. The subject of this research is Management student Faculty Economics State University of Jakarta who ever used Emina Cosmetics Products. The data analysis are used statistic descriptive analysis and simple linier regression analysis. The author used SPSS 25 to process the data resource.
The result of this research, Promotion Mix has a positive influence on Purchase Decision Emina Cosmetics Product. This indicated by the coefficient of determination value of 0,324 or 32,4%.
Keyword: Promotional Mix, Purchase Decision, Emina Cosmetic
Normalized Neural Networks for Breast Cancer Classification
No full textIn almost all parts of the world, breast cancer is one of the major causes of death among women. But at the same time, it is one of the most curable cancers if it is diagnosed at early stage. This paper tries to find a model that diagnose and classify breast cancer with high accuracy and help to both patients and doctors in the future. Here we develop a model using Normalized Multi Layer Perceptron Neural Network to classify breast cancer with high accuracy. The results achieved is very good (accuracy is 99.27%). It is very promising result compared to previous researches where Artificial Neural Networks were used. As benchmark test, Breast Cancer Wisconsin (Original) was used.</p
Automatic Detection of Alzheimer Disease Based on Histogram and Random Forest
No full textAlzheimer disease is one of the most prevalent dementia types affecting elder population. On-time detection of the Alzheimer disease (AD) is valuable for finding new approaches for the AD treatment. Our primary interest lies in obtaining a reliable, but simple and fast model for automatic AD detection. The approach we introduced in the present contribution to identify AD is based on the application of machine learning (ML) techniques. For the first step, we use histogram to transform brain images to feature vectors, containing the relevant "brain" features, which will later serve as the inputs in the classification step. Next, we use the ML algorithms in the classification task to identify AD. The model presented and elaborated in the present contribution demonstrated satisfactory performances. Experimental results suggested that the Random Forest classifier can discriminate the AD subjects from the control subjects. The presented modeling approach, consisting of the histogram as the feature extractor and Random Forest as the classifier, yielded to the sufficiently high overall accuracy rate of 85.77%.</p
Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier
No full textIn this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F- measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).</p
Ensemble SVM Method for Automatic Sleep Stage Classification
No full textSleep scoring is used as a diagnostic technique in the diagnosis and treatment of sleep disorders. Automated sleep scoring is crucial, since the large volume of data should be analyzed visually by the sleep specialists which is burdensome, time-consuming tedious, subjective, and error prone. Therefore, automated sleep stage classification is a crucial step in sleep research and sleep disorder diagnosis. In this paper, a robust system, consisting of three modules, is proposed for automated classification of sleep stages from the single-channel electroencephalogram (EEG). In the first module, signals taken from Pz-Oz electrode were denoised using multiscale principal component analysis. In the second module, the most informative features are extracted using discrete wavelet transform (DWT), and then, statistical values of DWT subbands are calculated. In the third module, extracted features were fed into an ensemble classifier, which can be called as rotational support vector machine (RotSVM). The proposed classifier combines advantages of the principal component analysis and SVM to improve classification performances of the traditional SVM. The sensitivity and accuracy values across all subjects were 84.46% and 91.1%, respectively, for the five-stage sleep classification with Cohens kappa coefficient of 0.88. Obtained classification performance results indicate that, it is possible to have an efficient sleep monitoring system with a single-channel EEG, and can be used effectively in medical and home-care applications
A System Identification Approach to Determining Listening Attention from EEG Signals
No full textWe still have very little knowledge about how ourbrains decouple different sound sources, which is known assolving the cocktail party problem. Several approaches; includingERP, time-frequency analysis and, more recently, regression andstimulus reconstruction approaches; have been suggested forsolving this problem. In this work, we study the problem ofcorrelating of EEG signals to different sets of sound sources withthe goal of identifying the single source to which the listener isattending. Here, we propose a method for finding the number ofparameters needed in a regression model to avoid overlearning,which is necessary for determining the attended sound sourcewith high confidence in order to solve the cocktail party problem
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