181 research outputs found
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 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
A Logic Simplification Approach for Very Large Scale Crosstalk Circuit Designs
Crosstalk computing, involving engineered interference between nanoscale
metal lines, offers a fresh perspective to scaling through co-existence with
CMOS. Through capacitive manipulations and innovative circuit style, not only
primitive gates can be implemented, but custom logic cells such as an Adder,
Subtractor can be implemented with huge gains. Our simulations show over 5x
density and 2x power benefits over CMOS custom designs at 16nm [1]. This paper
introduces the Crosstalk circuit style and a key method for large-scale circuit
synthesis utilizing existing EDA tool flow. We propose to manipulate the CMOS
synthesis flow by adding two extra steps: conversion of the gate-level netlist
to Crosstalk implementation friendly netlist through logic simplification and
Crosstalk gate mapping, and the inclusion of custom cell libraries for
automated placement and layout. Our logic simplification approach first
converts Cadence generated structured netlist to Boolean expressions and then
uses the majority synthesis tool to obtain majority functions, which is further
used to simplify functions for Crosstalk friendly implementations. We compare
our approach of logic simplification to that of CMOS and majority logic-based
approaches. Crosstalk circuits share some similarities to majority synthesis
that are typically applied to Quantum Cellular Automata technology. However,
our investigation shows that by closely following Crosstalk's core circuit
styles, most benefits can be achieved. In the best case, our approach shows 36%
density improvements over majority synthesis for MCNC benchmark
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
Superiority of Islamic Banking in Comparison with Conventional Banking in Bangladesh - A Comparative Study
This paper investigates the financial performance of interest- based conventional commercial banks and interestfree Islamic banks in Bangladesh using descriptive statistics ttest and test of hypotheses Data has been processed through Statistical Package for Social Science SPSS software The data consist of accounting figures of 4 interests based conventional commercial banks and 4 interest free Islamic banks from 2009 to 2013 The study revealed mixed results The study found that conventional commercial banks are superior in terms of performance regarding in commitment to economy and community development productivity and efficiency where performance of Islamic banks in terms of business development profitability liquidity and solvency is superior to that of conventional bank
Capacitance-Voltage Characteristics of Nanowire Trigate MOSFET Considering Wave Function Penetration
Short channel effects on the gate capacitance of nanowire trigate MOS field-effect transistors are studied considering wave function penetration. Capacitance-Voltage (C‑V) measurements are commonly used in studying gate-oxide quality in detail. C‑V test results offer a wealth of device and process information, including bulk and interface charges. Capacitance indicates switching speed of the MOSFET. It is our goal to minimize capacitance as possible as we can in MOSFET. Due to our necessary to compact the Integrated Circuit as possible as we can for getting small electronics devices. Capacitance determines the speed of the IC. Every engineer in this section should know capacitance of his implementing device MOSFET to get exact result from this device. Whenever we deal with 10X10 nm scale or less device of MOSFET. We must concern the effect of wave function penetration into device in this stage classical mechanics fails to describe exact result of the system because electron can move in only one direction (x), in 3 Dimension, it cannot move in other two direction (y, z). i.e. confined in two direction which is not predictable by classical mechanics here quantum mechanics (QM) gives better solution of this problem. Therefore we consider QM in our study. Here we presented how wave function play vital role considering small area of trigate MOSFET. It is the analytical approach of QM and highly recommendation to use QM rather than CM to get accuracy. This result will be helpful for determining capacitance of trigate MOSFET.DOI:http://dx.doi.org/10.11591/ijece.v2i6.178
Prediction of addiction to drugs and alcohol using machine learning: A case study on Bangladeshi population
Nowadays addiction to drugs and alcohol has become a significant threat to the youth of the society as Bangladesh’s population. So, being a conscientious member of society, we must go ahead to prevent these young minds from life-threatening addiction. In this paper, we approach a machinelearning-based way to forecast the risk of becoming addicted to drugs using machine-learning algorithms. First, we find some significant factors for addiction by talking to doctors, drug-addicted people, and read relevant articles and write-ups. Then we collect data from both addicted and nonaddicted people. After preprocessing the data set, we apply nine conspicuous machine learning algorithms, namely k-nearest neighbors, logistic regression, SVM, naïve bayes, classification, and regression trees, random forest, multilayer perception, adaptive boosting, and gradient boosting machine on our processed data set and measure the performances of each of these classifiers in terms of some prominent performance metrics. Logistic regression is found outperforming all other classifiers in terms of all metrics used by attaining an accuracy approaching 97.91%. On the contrary, CART shows poor results of an accuracy approaching 59.37% after applying principal component analysis
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