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