scholarly journals High Performance Classification of Cancer Types with Gene Microarray Datasets: Hybrid Approach

2021 ◽  
Author(s):  
Yılmaz ATAY ◽  
Muhterem Oğuzhan YILDIRIM ◽  
Cuma Umur DOĞAN
Keyword(s):  
High Performance ◽  
Hybrid Approach ◽  
Gene Microarray ◽  
Cancer Types ◽  
Microarray Datasets

Modern opportunities for preparation of ultra-pure water for feeding high-performance boiler plants

2020 ◽  
pp. 74-84
Author(s):  
A.A. Filimonova ◽  
◽  
N.D. Chichirova ◽  
A.A. Chichirov ◽  
A.A. Batalova ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
High Performance ◽  
Waste Heat ◽  
Pure Water ◽  
Combined Cycle ◽  
Feed Water ◽  
Operational Characteristics ◽  
Treatment Equipment

The article provides an overview of modern high-performance combined-cycle plants and gas turbine plants with waste heat boilers. The forecast for the introduction of gas turbine equipment at TPPs in the world and in Russia is presented. The classification of gas turbines according to the degree of energy efficiency and operational characteristics is given. Waste heat boilers are characterized in terms of design and associated performance and efficiency. To achieve high operating parameters of gas turbine and boiler equipment, it is necessary to use, among other things, modern water treatment equipment. The article discusses modern effective technologies, the leading place among which is occupied by membrane, and especially baromembrane methods of preparing feed water-waste heat boilers. At the same time, the ion exchange technology remains one of the most demanded at TPPs in the Russian Federation.

A Study of the Cloud Computing Adoption Issues and Challenges

2020 ◽  
Vol 13 (3) ◽  
pp. 313-318 ◽  
Author(s):  
Dhanapal Angamuthu ◽  
Nithyanandam Pandian
Keyword(s):  
Cloud Computing ◽  
High Performance ◽  
Cloud Service ◽  
Ease Of Use ◽  
Modern Trend ◽  
Security Issues ◽  
Research Areas ◽  
Data Location ◽  
Broad Classification

<P>Background: The cloud computing is the modern trend in high-performance computing. Cloud computing becomes very popular due to its characteristic of available anywhere, elasticity, ease of use, cost-effectiveness, etc. Though the cloud grants various benefits, it has associated issues and challenges to prevent the organizations to adopt the cloud. </P><P> Objective: The objective of this paper is to cover the several perspectives of Cloud Computing. This includes a basic definition of cloud, classification of the cloud based on Delivery and Deployment Model. The broad classification of the issues and challenges faced by the organization to adopt the cloud computing model are explored. Examples for the broad classification are Data Related issues in the cloud, Service availability related issues in cloud, etc. The detailed sub-classifications of each of the issues and challenges discussed. The example sub-classification of the Data Related issues in cloud shall be further classified into Data Security issues, Data Integrity issue, Data location issue, Multitenancy issues, etc. This paper also covers the typical problem of vendor lock-in issue. This article analyzed and described the various possible unique insider attacks in the cloud environment. </P><P> Results: The guideline and recommendations for the different issues and challenges are discussed. The most importantly the potential research areas in the cloud domain are explored. </P><P> Conclusion: This paper discussed the details on cloud computing, classifications and the several issues and challenges faced in adopting the cloud. The guideline and recommendations for issues and challenges are covered. The potential research areas in the cloud domain are captured. This helps the researchers, academicians and industries to focus and address the current challenges faced by the customers.</P>

STIGMERGIC LANDMARK OPTIMIZATION

2012 ◽  
Vol 15 (08) ◽  
pp. 1150025 ◽  
Author(s):  
N. LEMMENS ◽  
K. TUYLS
Keyword(s):  
High Performance ◽  
Nest Site ◽  
Hybrid Approach ◽  
Nest Site Selection ◽  
The Third ◽  
Selection Behavior ◽  
Bee Foraging ◽  
Global Route ◽  
Foraging Task ◽  
Vector Strength

In this paper we present three Swarm Intelligence algorithms which we evaluate on the complex foraging task domain. Each of the algorithms draws inspiration from biologic bee foraging/nest-site selection behavior. The main focus will be on the third algorithm, namely STIGMERGIC LANDMARK FORAGING which is a novel hybrid approach. It combines the high performance of bee-inspired navigation with ant-inspired recruitment. More precisely, navigation is based on Path Integration which results in vectors indicating the distance and direction to a destination. Recruitment only occurs at key locations (i.e., landmarks) inside of the environment. Each landmark contains a collection of vectors with which visiting agents can find their way to a certain goal or to another landmark in an unknown environment. Each vector represents a local segment of a global route. In contrast to ant-inspired recruitment, no attracting or repelling pheromone is used to indicate where to go and how worthwhile a route is in comparison to other routes. Instead, each vector in a landmark has a certain strength indicating how worthwhile it is. In analogy to ant-inspired recruitment, vector strength can be reinforced by visiting agents. Moreover, vector strength decays over time. In the end, this results in optimal routes to destinations. STIGMERGIC LANDMARK FORAGING proves to be very efficient in terms of building and adapting solutions.

scholarly journals Gene Selection and Classification in Microarray Datasets using a Hybrid Approach of PCC-BPSO/GA with Multi Classifiers

2018 ◽  
Vol 14 (6) ◽  
pp. 868-880 ◽  
Author(s):  
Shilan S. Hameed ◽  
Fahmi F. Muhammad ◽  
Rohayanti Hassan ◽  
Faisal Saeed
Keyword(s):  
Gene Selection ◽  
Hybrid Approach ◽  
Microarray Datasets

Wavelet Transform Based Feature Extraction for EEG Signal Classification

2021 ◽  
Vol 20 ◽  
pp. 199-206
Author(s):  
Seda Postalcioglu
Keyword(s):  
Wavelet Transform ◽  
High Performance ◽  
Feature Vector ◽  
Fast Response ◽  
Eeg Signal ◽  
Real Time Control ◽  
Time Control ◽  
Band Power ◽  
Performance Rate

This study focused on the classification of EEG signal. The study aims to make a classification with fast response and high-performance rate. Thus, it could be possible for real-time control applications as Brain-Computer Interface (BCI) systems. The feature vector is created by Wavelet transform and statistical calculations. It is trained and tested with a neural network. The db4 wavelet is used in the study. Pwelch, skewness, kurtosis, band power, median, standard deviation, min, max, energy, entropy are used to make the wavelet coefficients meaningful. The performance is achieved as 99.414% with the running time of 0.0209 seconds

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

scholarly journals Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Atsushi Teramoto ◽  
Tetsuya Tsukamoto ◽  
Yuka Kiriyama ◽  
Hiroshi Fujita
Keyword(s):  
Lung Cancer ◽  
Cell Carcinoma ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Automated Classification ◽  
Deep Convolutional Neural Networks ◽  
Lung Cancers ◽  
Microscopic Images ◽  
Cancer Types

Lung cancer is a leading cause of death worldwide. Currently, in differential diagnosis of lung cancer, accurate classification of cancer types (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma) is required. However, improving the accuracy and stability of diagnosis is challenging. In this study, we developed an automated classification scheme for lung cancers presented in microscopic images using a deep convolutional neural network (DCNN), which is a major deep learning technique. The DCNN used for classification consists of three convolutional layers, three pooling layers, and two fully connected layers. In evaluation experiments conducted, the DCNN was trained using our original database with a graphics processing unit. Microscopic images were first cropped and resampled to obtain images with resolution of 256 × 256 pixels and, to prevent overfitting, collected images were augmented via rotation, flipping, and filtering. The probabilities of three types of cancers were estimated using the developed scheme and its classification accuracy was evaluated using threefold cross validation. In the results obtained, approximately 71% of the images were classified correctly, which is on par with the accuracy of cytotechnologists and pathologists. Thus, the developed scheme is useful for classification of lung cancers from microscopic images.

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