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 202