Wyniki wyszukiwania - Biblioteka Nauki

1

Ganging of Resources via Fuzzy Manhattan Distance Similarity with Priority Tasks Scheduling in Cloud Computing

100%

Priya S. S. , Mehata K. M. , Banu W. A.

Journal of Telecommunications and Information Technology

|

2018

|

tom nr 1

32--41

EN

This paper proposes a fuzzy Manhattan distance-based similarity for gang formation of resources (FMDSGR) method with priority task scheduling in cloud computing. The proposed work decides which processor is to execute the current task in order to achieve efficient resource utilization and effective task scheduling. FMDSGR groups the resources into gangs which rely upon the similarity of resource characteristics in order to use the resources effectively. Then, the tasks are scheduled based on the priority in the gang of processors using gang-based priority scheduling (GPS). This reduces mainly the cost of deciding which processor is to execute the current task. Performance has been evaluated in terms of makespan, scheduling length ratio, speedup, efficiency and load balancing. CloudSim simulator is the toolkit used for simulation and for demonstrating experimental results in cloud computing environments.

2

A rainfall forecasting method using machine learning models and its application to the Fukuoka city case

88%

Sumi S. , Zaman M. , Hirose H.

International Journal of Applied Mathematics and Computer Science

|

2012

|

tom 22

|

nr 4

841-854

EN

In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.

3

Vein Biometric Recognition Methods and Systems: A Review

88%

Al-Khafaji R. S. S. , Al-Tamimi M. S. H.

Advances in Science and Technology. Research Journal

|

2022

|

tom Vol. 16, no 1

36--46

EN

The Finger-vein recognition (FVR) method has received increasing attention in recent years. It is a new method of personal identification and biometric technology that identifies individuals using unique finger-vein patterns, which is the first reliable and suitable area to be recognized. It was discovered for the first time with a home imaging system; it is characterized by high accuracy and high processing speed. Also, the presence of patterns of veins inside one’s body makes it almost difficult to repeat and difficult to steal. Based on the increased focus on protecting privacy, that also produces vein biometrics safer alternatives without forgery, damage, or alteration over time. Fingerprint recognition is beneficial because it includes the use of low-cost, small devices which are difficult to counterfeit. This paper discusses preceding finger-vein recognition approaches systems with the methodologies taken from other researchers’ work about image acquisition, pretreatment, vein extraction, and matching. It is reviewing the latest algorithms; continues to critically review the strengths and weaknesses of these methods, and it states the modern results following a key comparative analysis of methods.

4

A novel adaptive window based technique for T wave detection and delineation in the ECG

88%

Rahul J. , Sora M.

Bio-Algorithms and Med-Systems

|

2020

|

tom Vol. 16, no. 1

art. no. 20190064

EN

The electrocardiogram (ECG) morphology determines the overall activity of the heart and is the most widely used tool in the diagnostic processes. T wave is a crucial wave component that reveals very useful information regarding various cardiac disorders. In this paper we have proposed a novel T wave detection technique based on adaptive window and simple decision rule. The proposed technique uses two-stage median filters followed by the Savitzky-Golay filter at the pre-processing stage to remove the noises in the ECG signal. The QRS complex is detected for locating the T wave as a reference in one ECG cycle. An R-R interval based window is considered for detecting the T wave, and decision logic depends on the iso-electric line value. The proposed technique is tested on the QT database and self-recorded dataset for its performance evaluation. In the present work, the results achieved for T wave detection sensitivity (Se), positive predictivity (+P), detection error rate (DER), and accuracy (Acc) on the QT database are Se = 97.57%, +P = 99.63%, DER = 2.78%, and Acc = 97.22% with an average time error of (3.468 ± 5.732) ms. The proposed technique shows Se = 99.94%, +P = 99.94%, DER = 0.01%, and Acc = 99.89% on the self-recorded dataset. The proposed technique is also capable of detecting both the upward and downward T wave efficiently in the ECG signal.

5

Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images

75%

Głowienka E. , Zembol N.

Geomatics and Environmental Engineering

|

2022

|

tom Vol. 16, no. 4

103--117

EN

The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper ), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer’s accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.

6

Cardiac arrhythmia detection in ECG signals by feature Extraction and support vector machine

75%

Malik K. G. , Kumar Y. , Panda M.

Annals of Computer Science and Information Systems

|

2017

|

tom Vol. 10

241--244

EN

Purpose of this work is to develop an automated physiological signal diagnostic tool that can help us to early determination of arrhythmia for proper medical attention. This paper presents a simple automated approach for classification of normal and abnormal ECG based on arrhythmia. The proposed method validated by the data MIT BIH arrhythmia database. The performance in terms of accuracy for clinical decision must be very high. This method uses fourth order wavelet decomposition, wavelet decomposition used for time frequency representation and feature extraction. For classification support vector machine is used for detection kinds of ECG signals

7

Segmentation of pectoral muscle from digital mammograms with depth-first search algorithm towards breast density classification

63%

Pawar S. D. , Sharma K. K. , Sapate S. G. , Yadav G. Y.

Biocybernetics and Biomedical Engineering

|

2021

|

tom Vol. 41, no. 3

1224--1241

EN

Digital mammography acts as a unique screening technology to protect the lives of females against breast cancer for the past few decades. Mammographic breast density is a well-known biomarker and plays a substantial role in breast cancer prediction and treatments. Breast density is calculated based on the opacity of fibro-glandular tissue reflected on digital mammograms concerning the whole area of the breast. The opacity of pectoral muscle and fibro-glandular tissue is similar to each other; hence, the small presence of the pectoral muscle in the breast area can hamper the accuracy of breast density classification. Successful removal of pectoral muscle is challenging due to changes in shape, size, and texture of pectoral muscle in every MLO and LMO views of mammogram. In this article, the depth-first search (DFS) algorithm is proposed to remove artifacts and pectoral muscle from digital mammograms. In the proposed algorithm, image enhancement is performed to improve the pixel quality of the input image. The whole breast as a single connected component is identified from the background region to remove the artifacts and tags. The depth-first search method with and without the heuristic approach is used to delineate the pectoral muscle, and then final suppression is performed on it. This algorithm is tested on 2675 images of the DDSM dataset, which is further divided into four density classes as per BIRADs classification. Segmentation results are calculated individually on each BIRADs density class of the DDSM dataset. Results are validated subjectively by the expert’s Radiologist’s ground truth with segmentation accuracy and objectively by the Jaccard coefficient and a dice similarity coefficient. This algorithm is found robust on each density class and provides overall segmentation accuracy of 86.18%, a mean value of Jaccard index, and a Dice similarity coefficient of 0.9315 and 0.9548, respectively. The experimental results show that the proposed algorithms applied for pectoral muscle removal follow the ground truth marked by an expert radiologist. The proposed algorithm can be part of the pre-processing unit of breast density measurement and breast cancer detection system used during clinical practice.

8

Urabianie skał w kopalniach odkrywkowych jako problem optymalizacyjny

63%

Malewski J. , Ring P.

Kruszywa : produkcja - transport - zastosowanie

|

2013

|

tom Nr 3

58--60

PL

W artykule przedstawiono sposób i przykłady optymalizacji układu operacji urabiania MW – rozdrabiania wtórnego – załadunku – transportu – kruszenia wstępnego w kopalniach odkrywkowych. Parametrem sterującym jest jednostkowe użycie MW. Efekt rozdrabiania ma wpływ na wydajność i koszty realizacji operacji technologicznych oraz koszty produkcji górniczej. Na przykładzie analizy rzeczywistego układu kopalni granitu pokazano wartość takich analiz na etapie negocjacji ceny usługi robót wiertniczo-strzałowych.

EN

The method and numerical examples of typical quarry system optimization has been presented in the paper. The powder factor defined as amount of blast powder per 1 cubic meter of the rock body has been taken as controlling parameter of the quarrying. In such process results of blasting operation effects on the system capacity and its running costs. Some numerical examples of the real quarry system are presented in the paper.

9

Automated lung nodule detection in CT images by optimized CNN: impact of improved whale optimization algorithm

51%

Kumar M. K. , Amalanathan A.

Computer Assisted Methods in Engineering and Science

|

2022

|

tom Vol. 29, no. 1-2 spec.

7--31

EN

Lung cancer is one of the leading causes of cancer-related deaths among individuals.It should be diagnosed at the early stages, otherwise it may lead to fatality due to itsmalicious nature. Early detection of the disease is very significant for patients’ survival, andit is a challenging issue. Therefore, a new model including the following stages: (1) imagepre-processing, (2) segmentation, (3) proposed feature extraction and (4) classificationis proposed. Initially, pre-processing takes place, where the input image undergoes specificpre-processing. The pre-processed images are then subjected to segmentation, which iscarried out using the Otsu thresholding model. The third phase is feature extraction, wherethe major contribution is obtained. Specifically, 4D global local binary pattern (LBP)features are extracted. After their extracting, the features are subjected to classification,where the optimized convolutional neural network (CNN) model is exploited. For a moreprecise detection of a lung nodule, the filter size of a convolution layer, hidden unit inthe fully connected layer and the activation function in CNN are tuned optimally byan improved whale optimization algorithm (WOA) called the whale with tri-level enhancedencircling behavior (WTEEB) model.