scholarly journals Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

2021 ◽  
Vol 15 (4) ◽  
pp. 1-18
Author(s):  
Gururaj T. ◽  
Siddesh G. M.
Keyword(s):  
Processing Time ◽  
Gene Expression Analysis ◽  
Gene Sequence ◽  
Hybrid Approach ◽  
Basic Operation ◽  
Sequence Matching ◽  
Hadoop Mapreduce ◽  
Proposed Model ◽  
Performance Issues ◽  
Memory Utilization

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.

scholarly journals Hybrid Approach for Enhancing Performance of Genomic Data for Stream Matching

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Processing Time ◽  
Gene Expression Analysis ◽  
Gene Sequence ◽  
Hybrid Approach ◽  
Basic Operation ◽  
Sequence Matching ◽  
Hadoop Mapreduce ◽  
Proposed Model ◽  
Performance Issues ◽  
Memory Utilization

In gene expression analysis, the expression levels of thousands of genes are analyzed, such as separate stages of treatments or diseases. Identifying particular gene sequence pattern is a challenging task with respect to performance issues. The proposed solution addresses the performance issues in genomic stream matching by involving assembly and sequencing. Counting the k-mer based on k-input value and while performing DNA sequencing tasks, the researches need to concentrate on sequence matching. The proposed solution addresses performance issue metrics such as processing time for k-mer counting, number of operations for matching similarity, memory utilization while performing similarity search, and processing time for stream matching. By suggesting an improved algorithm, Revised Rabin Karp(RRK) for basic operation and also to achieve more efficiency, the proposed solution suggests a novel framework based on Hadoop MapReduce blended with Pig & Apache Tez. The measure of memory utilization and processing time proposed model proves its efficiency when compared to existing approaches.

scholarly journals EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

2018 ◽  
Vol 7 (2) ◽  
pp. 7 ◽  
Author(s):  
S Subasree ◽  
N P Gopalan ◽  
N K Sakthivel
Keyword(s):  
Classification Accuracy ◽  
Processing Time ◽  
Research Work ◽  
Time Processing ◽  
Multi Objective ◽  
Time Sensitivity ◽  
Proposed Model ◽  
Sensitivity Specificity ◽  
Cancer Pattern ◽  
Memory Utilization

Microarray based Cancer Pattern Classification is one of the popular techniques in Bioinformatics Research. This Research Work is noticed that for studying the expression levels through the Gene Expression profiling experiments, thousands of Genes have to be simultaneously studied to understand the patterns of the Gene Expression or Cancer Pattern. This research work proposed an efficient Cancer Pattern Clas-sifier called An Enhanced Multi-Objective Pswarm (EMOPS) and it is studied thoroughly in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. The results were compared with the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this work considered a few cancer patterns namely Bladder, Breast, Colon, Endometrial, Kidney, Leukemia, Lung, Melanoma, Mom-Hodgkin, Pancreatic, Prostate and Thyroid. From our experimental results, it was noticed that the proposed model outperforms the identified three classifiers in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. To improve the performance of the system further in term of Processing Time, the proposed model Enhanced Multi-Objective Pswarm (EMOPS) is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time decreases considerably which will improve the performance of the Proposed Model.

scholarly journals KGPMH: A hybrid approach for solving a novel facility location problem

2021 ◽  
Author(s):  
Maryam DehghanChenary ◽  
Arman Ferdowsi ◽  
Fariborz Jolai ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Mathematical Model ◽  
Location Problem ◽  
Hybrid Approach ◽  
Facility Location Problem ◽  
Evaluation Function ◽  
Genetic Approach ◽  
Hub Location ◽  
Hub Location Problem ◽  
Proposed Model ◽  
The Impact

<pre>The focus of this paper is to propose a bi-objective mathematical model for a new extension of a multi-period p-mobile hub location problem and then to devise an algorithm for solving it. The developed model considers the impact of the time spent traveling at the hubs' network, the time spent at hubs for processing the flows, and the delay caused by congestion at hubs with specific capacities. Following the unveiled model, a hybrid meta-heuristic algorithm will be devised that simultaneously takes advantage of a novel evaluation function, a clustering technique, and a genetic approach for solving the proposed model.</pre>

Coryphopterus kuna, a new goby (Perciformes: Gobiidae: Gobiinae) from the western Caribbean, with the identification of the late larval stage and  an estimate of the pelagic larval duration

Zootaxa ◽  
2007 ◽  
Vol 1526 (1) ◽  
pp. 51-61 ◽  
Author(s):  
BENJAMIN C. VICTOR
Keyword(s):  
Larval Stage ◽  
Gene Sequence ◽  
Pelagic Larval Duration ◽  
Larval Duration ◽  
Sequence Matching ◽  
Daily Increments ◽  
Black Spots ◽  
Fin Ray ◽  
Western Caribbean ◽  
Pelvic Fin

A new goby, Coryphopterus kuna, is described from the Atlantic coasts of Panama and Mexico. The species is distinguished from other Coryphopterus spp. by the low median fin and pectoral fin ray counts and the morphology of the pelvic fin. The pelvic fins are fully joined with a rounded outline and have branched and longer innermost pelvic fin rays. There is no frenum connecting the two pelvic fin spines and the fin is heavily speckled with black spots in the male holotype. The late larval stage of C. kuna is identified by DNA sequence matching and is morphologically similar to other larval Coryphopterus spp. but has a distinct melanophore pattern. Examination of the otolith microstructure reveals a relatively long pelagic larval duration of 63 days with a narrowing of the later daily increments suggesting delayed metamorphosis. The species is the first vertebrate to include gene sequence barcoding under the Barcode of Life Data System (BOLD) in the species description.

Structured Pyramidal Neural Networks

2018 ◽  
Vol 28 (05) ◽  
pp. 1750021 ◽  
Author(s):  
Alessandra M. Soares ◽  
Bruno J. T. Fernandes ◽  
Carmelo J. A. Bastos-Filho
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Processing Time ◽  
Receptive Fields ◽  
Memory Capacity ◽  
The Other ◽  
Support Vector ◽  
Proposed Model ◽  
Novel Method ◽  
Original Approach

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

scholarly journals An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zongying Liu ◽  
Shaoxi Li ◽  
Jiangling Hao ◽  
Jingfeng Hu ◽  
Mingyang Pan
Keyword(s):  
Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Processing Time ◽  
Kernel Method ◽  
Nearest Neighbors ◽  
Human Activity Recognition ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Proposed Model

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds.

scholarly journals Jaccard Coefficients based Clustering of XML Web Messages for Network Traffic Aggregation

2019 ◽  
Vol 11 (2) ◽  
pp. 82-91
Author(s):  
Dhiah Al-Shammary
Keyword(s):  
Compression Ratio ◽  
Principle Component Analysis ◽  
Network Traffic ◽  
Processing Time ◽  
Model Performance ◽  
Redundant Information ◽  
Principle Component ◽  
Clustering Model ◽  
Proposed Model ◽  
Xml Web

This paper provides static efficient clustering model based simple Jaccard coefficients that supports XML messages aggregator in order to potentially reduce network traffic. The proposed model works by grouping only highly similar messages with the aim to provide messages with high redundancy for web aggregators. Web messages aggregation has become a significant solution to overcome network bottlenecks and congestions by efficiently reducing network volume by aggregating messages together removing their redundant information. The proposed model performance is compared to both K-Means and Principle Component Analysis (PCA) combined with K-Means. Jaccard based clustering model has shown potential performance as it only consumes around %32 and %25 processing time in comparison with K-Means and PCA combined with K-Means respectively. Quality measure (Aggregator Compression Ratio) has overcome both benchmark models

scholarly journals STA/LTA trigger algorithm implementation on a seismological dataset using Hadoop MapReduce

2020 ◽  
Vol 9 (2) ◽  
pp. 269
Author(s):  
Youness Choubik ◽  
Abdelhak Mahmoudi ◽  
Mohammed Majid Himmi ◽  
Lahcen El Moudnib
Keyword(s):  
Processing Time ◽  
Seismic Stations ◽  
Detection Process ◽  
Hadoop Mapreduce ◽  
Seismic Detection ◽  
Trigger Algorithm ◽  
Algorithm Implementation

<span>In this work we implemented STA/LTA trigger algorithm, which is widely used in seismic detection, using Hadoop MapReduce. This<br />implementation allows to find out how effective it is in this type of tasks as well as to accelerate the detection process by reducing the processing time. We tested our implementation on a seismological dataset of 14 broadband seismic stations and compare it with the traditional one. The results show that MapReduce decreased the processing time by 34% compared to the traditional implementation.</span>

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