ELM ZA KLASIFIKACIJU TUMORA MOZGA KOD 3D MR SNIMAKA

Extreme Learning machine (ELM) a widely adopted algorithm in machine learning field is proposed for the use of pattern classification model using 3D MRI images for identifying tissue abnormalities in brain histology. The four class classification includes gray matter, white matter, cerebrospinal-fluid and tumor. The 3D MRI assessed by a pathologist indicates the ROI and the images are normalized. Texture features for each of the sub-regions is based on the Run-length Matrix, Co-occurence Matrix, Intensity, Euclidean distance, Gradient vector and neighbourhood statistics. Genetic Algorithm is custom designed to extract and sub-select a decisive optimal bank of features which are then used to model the ELM classifier and best selection of ELM algorithm parameters to handle sparse image data. The algorithm is explored using different activation function and the effect of number of neurons in the hidden layer by using different ratios of the number of features in the training and test data. The ELM classification outperformed in terms of accuracy, sensitivity and specificity as 93.20 %, 91.6 %, and 97.98% for discrimination of brain and pathological tumor tissue classification against state-of-the-art feature extraction methods and classifiers in the literature for publicly available SPL dataset.ELM, široko prihvaćen algoritam strojnog učenja se predlaže za korištenje u uzorkovanju pomoću klasifikacijskog modela 3D MRI slika za identifikaciju abnormalnosti tkiva u histologiji mozga. Četiri klase obuhvaćaju sive, bijele tvari, cerebrospinalne tekućine-i tumore. 3D MRI koji ocjenjuje patolog, ukazuje na ROI, a slike su normalizirane. Značajke tekstura za svaku od podregija se temelje na Run-length matrici, ponovnom pojavljivanju matrice, intenzitet, euklidska udaljenost, gradijent vektora i statistike susjedstva. Genetski algoritam je obično dizajniran za izdvajanje i sub-optimalan odabir odlučujući o značajkama koje se onda koriste za model ELM klasifikatora i najbolji izbor ELM parametra algoritama za obradu rijetkih slikovnih podataka. Algoritam se istražuje koristeći različite aktivacijske funkcije i utjecaj broja neurona u skrivenom sloju pomoću različitih omjera broja značajki kod trening i test podataka. ELM klasifikacija je nadmašila u smislu točnosti, osjetljivosti i specifičnosti, kao 93,20%, 91,6% i 97,98% za diskriminaciju mozga i patološki kod tumora i sistematizacije metode za prikupljanje podataka i klasifikatore u literaturi za javno dostupne SPL skup podataka

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.</p

Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol�which is a marker of cardiovascular risk�changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95 credible interval 3.7 million�4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world. © 2020, The Author(s), under exclusive licence to Springer Nature Limited

Introduction to genetic algorithms

Berlinxix, 442 p.; bibl.; 25 c

ELM ZA KLASIFIKACIJU TUMORA MOZGA KOD 3D MR SNIMAKA

Extreme Learning machine (ELM) a widely adopted algorithm in machine learning field is proposed for the use of pattern classification model using 3D MRI images for identifying tissue abnormalities in brain histology. The four class classification includes gray matter, white matter, cerebrospinal-fluid and tumor. The 3D MRI assessed by a pathologist indicates the ROI and the images are normalized. Texture features for each of the sub-regions is based on the Run-length Matrix, Co-occurence Matrix, Intensity, Euclidean distance, Gradient vector and neighbourhood statistics. Genetic Algorithm is custom designed to extract and sub-select a decisive optimal bank of features which are then used to model the ELM classifier and best selection of ELM algorithm parameters to handle sparse image data. The algorithm is explored using different activation function and the effect of number of neurons in the hidden layer by using different ratios of the number of features in the training and test data. The ELM classification outperformed in terms of accuracy, sensitivity and specificity as 93.20 %, 91.6 %, and 97.98% for discrimination of brain and pathological tumor tissue classification against state-of-the-art feature extraction methods and classifiers in the literature for publicly available SPL dataset.ELM, široko prihvaćen algoritam strojnog učenja se predlaže za korištenje u uzorkovanju pomoću klasifikacijskog modela 3D MRI slika za identifikaciju abnormalnosti tkiva u histologiji mozga. Četiri klase obuhvaćaju sive, bijele tvari, cerebrospinalne tekućine-i tumore. 3D MRI koji ocjenjuje patolog, ukazuje na ROI, a slike su normalizirane. Značajke tekstura za svaku od podregija se temelje na Run-length matrici, ponovnom pojavljivanju matrice, intenzitet, euklidska udaljenost, gradijent vektora i statistike susjedstva. Genetski algoritam je obično dizajniran za izdvajanje i sub-optimalan odabir odlučujući o značajkama koje se onda koriste za model ELM klasifikatora i najbolji izbor ELM parametra algoritama za obradu rijetkih slikovnih podataka. Algoritam se istražuje koristeći različite aktivacijske funkcije i utjecaj broja neurona u skrivenom sloju pomoću različitih omjera broja značajki kod trening i test podataka. ELM klasifikacija je nadmašila u smislu točnosti, osjetljivosti i specifičnosti, kao 93,20%, 91,6% i 97,98% za diskriminaciju mozga i patološki kod tumora i sistematizacije metode za prikupljanje podataka i klasifikatore u literaturi za javno dostupne SPL skup podataka

An Intelligent Computing Model for Wind Speed Prediction in Renewable Energy Systems

AbstractThis paper presents an intelligent computing model for wind speed prediction, which uses back propagation algorithm. Wind energy is inexhaustible unlimited clean energy. Wind power in the world has been rapidly growing. The prediction of wind speed has an important role in wind energy. The back-propagation algorithm (BPA) is used in the majority of neural networks application. The objective of this paper is to compute predicted output (wind speed) based on BP algorithm. The results are obtained using back propagation algorithm by training and testing methodologies. Simulation results show the performance of ANN for predicting wind speed in renewable energy system

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