210 research outputs found
Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository
Machine learning qualifies computers to assimilate with data, without being
solely programmed [1, 2]. Machine learning can be classified as supervised and
unsupervised learning. In supervised learning, computers learn an objective
that portrays an input to an output hinged on training input-output pairs [3].
Most efficient and widely used supervised learning algorithms are K-Nearest
Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor
(LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this
paper is to implement these elegant learning algorithms on eleven different
datasets from the UCI machine learning repository to observe the variation of
accuracies for each of the algorithms on all datasets. Analyzing the accuracy
of the algorithms will give us a brief idea about the relationship of the
machine learning algorithms and the data dimensionality. All the algorithms are
developed in Matlab. Upon such accuracy observation, the comparison can be
built among KNN, SVM, LMNN, and ENN regarding their performances on each
dataset.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities
Density-based spatial clustering of applications with noise (DBSCAN) is a
data clustering algorithm which has the high-performance rate for dataset where
clusters have the constant density of data points. One of the significant
attributes of this algorithm is noise cancellation. However, DBSCAN
demonstrates reduced performances for clusters with different densities.
Therefore, in this paper, an adaptive DBSCAN is proposed which can work
significantly well for identifying clusters with varying densities.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm
In recent days, Artificial Neural Network (ANN) can be applied to a vast
majority of fields including business, medicine, engineering, etc. The most
popular areas where ANN is employed nowadays are pattern and sequence
recognition, novelty detection, character recognition, regression analysis,
speech recognition, image compression, stock market prediction, Electronic
nose, security, loan applications, data processing, robotics, and control. The
benefits associated with its broad applications leads to increasing popularity
of ANN in the era of 21st Century. ANN confers many benefits such as organic
learning, nonlinear data processing, fault tolerance, and self-repairing
compared to other conventional approaches. The primary objective of this paper
is to analyze the influence of the hidden layers of a neural network over the
overall performance of the network. To demonstrate this influence, we applied
neural network with different layers on the MNIST dataset. Also, another goal
is to observe the variations of accuracies of ANN for different numbers of
hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on
Electrical Engineering and Information & Communication Technology (iCEEiCT
2018
INVESTIGATION OF THE BULK, SURFACE AND TRANSFER PROPERTIES OF CHLORINE BLEACHED DENIM APPAREL AT DIFFERENT CONDITION
Oxidizing bleaching agent (calcium hypochlorite or bleaching powder) is widely used in the apparel washing plant as a color removing and cost effective finishing chemical. As the faded denim or old look denim is preferred by the today’s youth it has become a crucial issue for the technologists to modify denim apparel to fulfill the demand of existing trend. Calcium hypochlorite (Ca(OCl)Cl) fades the denim effectively but a significant changes are happened in the properties of the denim apparel. The main objective of this paper was to investigate the changes of bulk, surface and transfer properties of denim apparel after the chlorine bleach action at varying length of washing time (10,15 and 30 min) with fixed concentration and temperature (50°C). These properties are related to the performance of the end product. 100% cotton indigo dyed 2/1 twill denim apparel was treated with 5gm/l (Ca(OCl)Cl). To determine the end use performance of the modified denim the changes of tensile strength, stiffness, dimensional stability (bulk properties), hand roughness, rubbing fastness (surface properties), air permeability, water repellency (transfer properties) of the modified denim apparel were evaluated. It has been monitored from the experimental data that the bulk properties play down but the surface properties have a noticeable improvement after the chlorine bleach action. It is also noticed that washing time has a significant effect on air permeability of the treated denim apparel
INVESTIGATION OF THE BULK, SURFACE AND TRANSFER PROPERTIES OF CHLORINE BLEACHED DENIM APPAREL AT DIFFERENT CONDITION
Oxidizing bleaching agent (calcium hypochlorite or bleaching powder) is widely used in the apparel washing plant as a color removing and cost effective finishing chemical. As the faded denim or old look denim is preferred by the today’s youth it has become a crucial issue for the technologists to modify denim apparel to fulfill the demand of existing trend. Calcium hypochlorite (Ca(OCl)Cl) fades the denim effectively but a significant changes are happened in the properties of the denim apparel. The main objective of this paper was to investigate the changes of bulk, surface and transfer properties of denim apparel after the chlorine bleach action at varying length of washing time (10,15 and 30 min) with fixed concentration and temperature (50°C). These properties are related to the performance of the end product. 100% cotton indigo dyed 2/1 twill denim apparel was treated with 5gm/l (Ca(OCl)Cl). To determine the end use performance of the modified denim the changes of tensile strength, stiffness, dimensional stability (bulk properties), hand roughness, rubbing fastness (surface properties), air permeability, water repellency (transfer properties) of the modified denim apparel were evaluated. It has been monitored from the experimental data that the bulk properties play down but the surface properties have a noticeable improvement after the chlorine bleach action. It is also noticed that washing time has a significant effect on air permeability of the treated denim apparel
PALMAR: Towards Adaptive Multi-inhabitant Activity Recognition in Point-Cloud Technology
With the advancement of deep neural networks and computer vision-based Human
Activity Recognition, employment of Point-Cloud Data technologies (LiDAR,
mmWave) has seen a lot interests due to its privacy preserving nature. Given
the high promise of accurate PCD technologies, we develop, PALMAR, a
multiple-inhabitant activity recognition system by employing efficient signal
processing and novel machine learning techniques to track individual person
towards developing an adaptive multi-inhabitant tracking and HAR system. More
specifically, we propose (i) a voxelized feature representation-based real-time
PCD fine-tuning method, (ii) efficient clustering (DBSCAN and BIRCH), Adaptive
Order Hidden Markov Model based multi-person tracking and crossover ambiguity
reduction techniques and (iii) novel adaptive deep learning-based domain
adaptation technique to improve the accuracy of HAR in presence of data
scarcity and diversity (device, location and population diversity). We
experimentally evaluate our framework and systems using (i) a real-time PCD
collected by three devices (3D LiDAR and 79 GHz mmWave) from 6 participants,
(ii) one publicly available 3D LiDAR activity data (28 participants) and (iii)
an embedded hardware prototype system which provided promising HAR performances
in multi-inhabitants (96%) scenario with a 63% improvement of multi-person
tracking than state-of-art framework without losing significant system
performances in the edge computing device.Comment: Accepted in IEEE International Conference on Computer Communications
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