scholarly journals Classification of Phonocardiography Signals Using Imbalanced Machine Learning Techniques

2020 ◽  
pp. 103-106
Author(s):  
Mustafa Berkant Selek ◽  
Sude Pehlivan ◽  
Yalcin Isler
Keyword(s):  
Machine Learning ◽  
Frequency Domain ◽  
Cardiovascular Health ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Random Forest Algorithm ◽  
Learning Techniques ◽  
Machine Learning Applications

Cardiovascular diseases, which involve heart and blood vessel dysfunctions, cause a higher number of deaths than any other disease in the world. Throughout history, many approaches have been developed to analyze cardiovascular health by diagnosing such conditions. One of the methodologies is recording and analyzing heart sounds to distinguish normal and abnormal functioning of the heart, which is called Phonocardiography. With the emergence of machine learning applications in healthcare, this process can be automated via the extraction of various features from phonocardiography signals and performing classification with several machine learning algorithms. Many studies have been conducted to extract time and frequency domain features from the phonocardiography signals by segmenting them first into individual heart cycles, and then by classifying them using different machine learning and deep learning approaches. In this study, various time and frequency domain features have been extracted using the complete signal rather than just segments of it. Random Forest algorithm was found to be the most successful algorithm in terms of accuracy as well as recall and precision.

scholarly journals Researching the Research: Applying Machine Learning Techniques to Dissertation Classification

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Suzanna Schmeelk
Keyword(s):  
Machine Learning ◽  
Full Text ◽  
Doctoral Program ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Full Time ◽  
Learning Techniques ◽  
Manual Classification ◽  
Machine Learning Tool

This research examines industry-based dissertation research in a doctoral computing program through the lens of machine learning algorithms to understand topics explored by senior and experienced full-time working professionals (EFWPs).  Our research categorizes dissertation by both their abstracts and by their full-text using the Graplab Create library from Apple’s Turi. We also compare the dissertation categorizations using IBM’s Watson Discovery deep machine learning tool.  Our research provides perspectives on the practicality of the manual classification of technical documents; and, it provides insights into the: (1) categories of academic work created by EFWPs in a Computing doctoral program, (2) viability of automated categorization versus human abstraction, and (3) differences in categorization algorithms.

scholarly journals Machine Learning Algorithms Performance Analysis for VLSI IC Design

2021 ◽  
Vol 3 (2) ◽  
pp. 80-92
Author(s):  
Joy Iong-Zong Chen ◽  
Kong-Long Lai
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Ic Design ◽  
Technical Developments ◽  
Learning Techniques ◽  
Improved Performance ◽  
Automated Tools

The design of an analogue IC layout is a time-consuming and manual process. Despite several studies in the sector, some geometric restrictions have resulted in disadvantages in the process of automated analogue IC layout design. As a result, analogue design has a performance lag when compared to manual design. This prevents the deployment of a large range of automated tools. With the recent technical developments, this challenge is resolved using machine learning techniques. This study investigates performance-driven placement in the VLSI IC design process, as well as analogue IC performance prediction by utilizing various machine learning approaches. Further, several amplifier designs are simulated. From the simulation results, it is evident that, when compared to the manual layout, an improved performance is obtained by using the proposed approach.

scholarly journals Machine Learning:A Review

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Isonkobong Christopher Udousoro
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Data Interpretation ◽  
Relevant Information ◽  
Machine Learning Techniques ◽  
Predictive Analysis ◽  
Learning Approaches ◽  
Processing Data ◽  
Learning Techniques ◽  
Machine Learning Applications

Due to the complexity of data, interpretation of pattern or extraction of information becomes difficult; therefore application of machine learning is used to teach machines how to handle data more efficiently. With the increase of datasets, various organizations now apply machine learning applications and algorithms. Many industries apply machine learning to extract relevant information for analysis purposes. Many scholars, mathematicians and programmers have carried out research and applied several machine learning approaches in order to find solution to problems. In this paper, we focus on general review of machine learning including various machine learning techniques. These techniques can be applied to different fields like image processing, data mining, predictive analysis and so on. The paper aims at reviewing machine learning techniques and algorithms. The research methodology is based on qualitative analysis where various literatures is being reviewed based  on machine learning.

scholarly journals Big Data Analytics to Increase the Agricultural Yield by Using Machine Learning Approaches

2018 ◽  
Vol 7 (S1) ◽  
pp. 82-86
Author(s):  
V. Sudha ◽  
S. Mohan ◽  
S. Arivalagan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Agricultural Research ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Modern Trend ◽  
Cropping Pattern ◽  
Learning Approaches ◽  
Learning Techniques

Agriculture is the backbone of Indian economy. Big data are emerging précised and viable analytical tool in agricultural research field. This review paper facilitates the farmers in selecting the right crops and appropriate cropping pattern for a particular locality. A modern trend in the Agriculture domain has made people realize the importance of big data. It provides a methodology for facing challenges in agricultural production, by applying this Algorithm, using machine learning techniques. The different machine learning techniques survey is presented in this paper to realize enhanced monitory benefits in a particular area. A study of machine learning algorithms for big data Analytic is also done and presented in this paper.

scholarly journals Analysis On Deep Learning Approaches For Timely Detection Of Osteoarthritis

2021 ◽  
Vol 12 (3) ◽  
pp. 4396-4405
Author(s):  
R Kanthavel Et.al
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Elastic Tissue ◽  
Learning Approaches ◽  
X Rays ◽  
Imaging Analysis ◽  
Bone Injury ◽  
Learning Techniques

Osteoarthritis is mainly a familiar kind of arthritis when an elastic tissue named Cartilage that softens the tops of the bones, cracks down. The Person with osteoarthritis can encompass joint pain, inflexibility, or inflammation and there is no particular examination for osteoarthritis and physicians take the amalgamation of both medical cum clinical record and X-rays imaging analysis to make a diagnosis of the state. Osteoarthritis is generally only detected following ache and bone scratch and in advance, analysis could permit for ultimate involvement to avoid cartilage worsening and bone injury. Through machine-learning algorithms, the system can be trained to automatically distinguish among people who would develop osteoarthritis and persons who would not with the detection of exact biochemical variances in the midpoint of the knee’s cartilage. The outcome of the Machine learning Techniques will give the persons who are pre-symptomatic by the occasion of the baseline imaging and also the reduction in liquid concentration. In this study, we present the analysis of various deep learning techniques for timely detection of osteoarthritis disease. Several subsets of machine learning called deep learning techniques have been in use for the timely detection of osteoarthritis disease; and therefore analysis is needed highly to choose the best as far as accuracy and reliability are concerned.

scholarly journals Robust Skin lesion Classification via Machine Intelligence

2020 ◽  
Vol 8 (6) ◽  
pp. 4496-4500
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Skin Cancer ◽  
Texture Features ◽  
Machine Intelligence ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Image Size ◽  
Learning Techniques

Skin cancer is typically growth and spread of cells or lesion on the uppermost part or layer of skin known as the epidermis. It is one of rarest and deadliest found type of cancer, if undetected or untreated at early stages may lead in patient’s demise. Dermatologists use dermatoscopic images to identify the type of skin cancer by identification of asymmetry, border, colour, texture & size mole or a lesion. This method of detection can also be applied using machine learning techniques for classification these images into respective of cancer. There have been various studies and techniques which have been proposed various researchers across the globe in order to improve the classification of these dermatoscopic images. The proposed studies primarily focus on classification of dermatoscopic images based on lesion’s colour and texture features followed by intelligent machine learning approaches. Advances in these machine intelligent approaches such as deep neural networks and convolutional neural networks can be applied on dermatoscopic images to identify their features. A CNN based approach provides a additional accuracy over feature extraction as the algorithm is applied on pixel in overall image size. CNN also provides the ability to perform huge chunk of mathematical operations which is basic requirement in case of image processing and machine learning. The CNN based algorithm can be used to classify the dermatoscopic images with better efficiency and overall accuracy with having power of artificial-neural-network.

scholarly journals Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Yijun Zhao ◽  
◽  
Tong Wang ◽  
Riley Bove ◽  
Bruce Cree ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
San Francisco ◽  
Ensemble Learning ◽  
Disease Onset ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Disease Course ◽  
Learning Techniques

AbstractThe rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women’s Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in Expanded Disability Status Scale (EDSS) ≥ 1.5 (worsening) or not (non-worsening) at up to 5 years after the baseline visit. Classification models were built using the CLIMB dataset with patients’ clinical and MRI longitudinal observations in first 2 years, and further validated using the EPIC dataset. We compared the performance of three popular machine learning algorithms (SVM, Logistic Regression, and Random Forest) and three ensemble learning approaches (XGBoost, LightGBM, and a Meta-learner L). A “threshold” was established to trade-off the performance between the two classes. Predictive features were identified and compared among different models. Machine learning models achieved 0.79 and 0.83 AUC scores for the CLIMB and EPIC datasets, respectively, shortly after disease onset. Ensemble learning methods were more effective and robust compared to standalone algorithms. Two ensemble models, XGBoost and LightGBM were superior to the other four models evaluated in our study. Of variables evaluated, EDSS, Pyramidal Function, and Ambulatory Index were the top common predictors in forecasting the MS disease course. Machine learning techniques, in particular ensemble methods offer increased accuracy for the prediction of MS disease course.

Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

2017 ◽  
Vol 4 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Abinash Tripathy ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Performance Parameters ◽  
Linear Discriminant ◽  
Learning Techniques

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

scholarly journals Discovering pathway and cell-type signatures in transcriptomic compendia with machine learning

2018 ◽  
Author(s):  
Gregory P Way ◽  
Casey S Greene
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Specific Cell ◽  
Biological Processes ◽  
Learning Approaches ◽  
Transcriptome Data ◽  
Cell Type ◽  
Learning Techniques ◽  
Pathway Expression ◽  
Machine Learning Applications

Pathway and cell-type signatures are patterns present in transcriptome data that are associated with biological processes or phenotypic consequences. These signatures result from specific cell-type and pathway expression, but can require large transcriptomic compendia to detect. Machine learning techniques can be powerful tools in a practitioner’s toolkit for signature discovery through their ability to provide accurate and interpretable results. In the following review, we discuss various machine learning applications to extract pathway and cell-type signatures from transcriptomic compendia. We focus on the biological motivations and interpretation for both supervised and unsupervised learning approaches in this setting. We consider recent advances, including deep learning, and their applications to expanding bulk and single cell RNA data. As data and compute resources increase, opportunities for machine learning to aid in revealing biological signatures will continue to grow.

scholarly journals Visual Social Data Clustersfor Effective Topics Tendnecy with Hybrid Machine Learning Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 4100-4104
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Social Data ◽  
Learning Techniques ◽  
Social Classification ◽  
Data Clusters ◽  
Hybrid Machine

The machine learning is an emerging field in social classification of data, which enable the learning of social data patterns and classify the data by unsupervised approaches. Majorly, k-means and graph-based machine learning algorithms are used for discovering of social data clusters based on similarity features of user views, opinions. This paper presents the sentimental analysis of social users for the topics using the cluster tendency of derived clusters. The experimental of social data clusters and the cluster tendency are visualized for effective sentiment of topics analysis.

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