Author: Muhammad Mokhlesur Rahman
Abstract: A glioma is a sort of tumor that begins in the glial cells of the brains or the spine. Gliomas contain around 30 percent of all brain tumors and focal sensory system tumors and 80 percent of all dangerous brain tumors. Brain tumors occur when there is a defect in the DNA of normal brain cells. To understand the genetic basis of the tumor, we need to identify the candidate genes. Genes are segments of DNA that contain the code for a specific protein that functions in one or more types of cells in the body. Genes are high dimensional and vary in size, depending on the sizes of the proteins for which they code. Due to the complexity and high dimensionality of genes, the classification of tumor samples remains a challenge. In this work, we have focused on a comparative study of different feature selection methods and proposed a new methodological approach to identify patterns of gene expression effectively that is useful to classify unknown samples. If the tumor types are classified correctly and predict survival time, the plan of treatment can be improved. We propose a novel pipeline framework for glioma analysis that uses several feature selection algorithms followed by effective classifier selection to predict the tumor type based on genes expressions data and find the top n number of genes as features for tumor type and also find the possible survival time in days. We applied twelve well-known machine learning algorithms such as Decision Trees (DT), Random Forrest (RF), Bagging (BAG), Gradient Boosting (GB), Gaussian Na¨ıve Bayes (NB), Multi-Layer Perception (MLP), Support Vector Machines (SVM), Logistic Regression (LR), K-nearest Neighbors (KNN), AdaBoost (AB), Linear Discriminant Analysis (LDA), and Extra Trees Classifier (ET), with five different feature selection methods, including Univariate Feature Selection, Principal component analysis (PCA), Kernel Principal component analysis (KPCA), Independent Component Analysis (ICA) and Factor Analysis(FA) on two datasets. Datasets are collected from the National Center for Biotechnology Information to analyze the accuracy of tumor type known as Grade classification and Survival time in days. The best performance was achieved by using Univariate Feature Selection for both datasets comparing with other feature selection methods. It is observed that using the Freije dataset, the best classification accuracy achieved by the AdaBoost classifier (98.75%) for grade classification, whereas using the Phillips dataset, Extra tree classifier has the best accuracy(95.67%). For survival classification, the best accuracy achieved by SVM for both the Freije dataset(94.5%) and the Phillips dataset(85.75%). The Median Survival time for the Freije dataset is 1098 days, and for the Phillips dataset, Median Survival time is 2275 days.

Author: Muhammad Mokhlesur Rahman, Shalima Binta Manir
Abstract: Long Term Evolution (LTE) is consents pliable spectrum distribution which renders enriched wireless data services to users at lower latency and multi-megabit throughput. LTE uses Orthogonal Frequency Division Multiple Access (OFDMA) and Single Carrier Frequency Division Multiple Access (Sc-FDMA) for downlink and Uplink transmission where OFDMA has been acquired in LTE for downlink transmission which diminishes the terminal cost and power consumption and Sc-FDMA has been allocates multiple users to a shared communication resources. Frequency Division Duplex (FDD) and Time Division Duplex (TDD) are the prevailing duplexing scheme in LTE that provides deployable tractability according to spectrum assignation. In this paper, we analyze the performance of SC-FDMA and OFDMA in LTE Frame Structure based on Peak to Average Power Ratio (PAPR) analysis. ITU Pedestrian A channel and ITU Vehicular A channel and also Additive White Gaussian Noise (AWGN) channel are used for analyzing the error performance between SC-FDMA and OFDMA

Author: Shalima Binta Manir; Muhammad Mokhlesur Rahman; Tanvir Ahmed
Abstract: Long Term Evolution (LTE) is the largest wireless communication technology which renders substantially increased data rates to gain higher efficiency in multimedia system. LTE provides flexibility of using existing and new frequency band as well as reduced cost per bit, increased services provisioning, lower response time and wider spectrum. To conciliate all types of spectrum resources Frequency Division Duplex (FDD) and Time Division Duplex (TDD) duplexing schemes are used in LTE to share the same underlying framework. This paper is an overview of comparison between FDD and TDD based on Peak-to-average power ratio (PAPR) analysis in Single-carrier frequency-division multiple access (SC-FDMA). The error performance is simulated based on Signal-to-Noise ratio (SNR) Vs. Bit Error Rate (BER) for two types of multipath channel i.e. ITU Pedestrian A channel and ITU Vehicular A channel and also Additive White Gaussian Noise (AWGN) channel is used.