scholarly journals Human Behavior Prediction and Analysis Using Machine Learning-A Review

2021 ◽  
Vol 12 (5) ◽  
pp. 870-876
Author(s):  
Monali Gulhane, T.Sajana
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Social Behavior ◽  
Human Behavior ◽  
Technical Difficulty ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
Classification Techniques

Nowadays many trends are being in the area of medicine to predict the human behaviour and analysis of patient behaviour is being studied but the technical difficulty of cost efficient method to predict the behaviour of user is overcome in the proposed researched methodology .The mental health of the used can lead to good immunity system to be healthy in this pandemic of COVID-19. Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different human parameters. CNN is the most optimum choice of classification of diseases. Feature extraction and feature selection is automatically managed by the CNN layers, which reduces the training speed. Techniques like sensor-based feature extraction like EEG, ECG, etc. will be further explored using machine learning algorithms for detection of early detections of diseases from human behavior on different platforms in this research. Social behavior and eating habits play a vital role in disease detection. A system that combines such a wide variety of features with effective classification techniques at each stage is needed. The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. The main objective of research is to analysis theses different area parameters to predict the early signs of the diseases.

scholarly journals Survey on machine learning algorithms for liver disease diagnosis and prediction

2018 ◽  
Vol 7 (1.8) ◽  
pp. 99 ◽  
Author(s):  
M Kiran Kumar ◽  
M Sreedevi ◽  
Y C. A. Padmanabha Reddy
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Learning Algorithms ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis

Machine learning plays a vital role in health care industry. It is very important in Computer Aided Diagnosis. Computer Aided Diagnosis is a quickly developing dynamic region of research in medicinal industry. The current specialists in machine learning guarantee the enhanced precision of discernment and analysis of diseases. The computers are empowered to think by creating knowledge by learning. This procedure enables the computers to self-learn individually without being explicitly programed by the programmer .There are numerous sorts of Machine Learning Techniques and which are utilized to classify the data sets. They are Supervised, Unsupervised and Semi-Supervised, Reinforcement, deep learning algorithms. The principle point of this paper is to give comparative analysis of supervised learning algorithms in medicinal area and few of the techniques utilized as a part of liver disease prediction.

scholarly journals Automated Tongue Feature Extraction for ZHENG Classification in Traditional Chinese Medicine

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Ratchadaporn Kanawong ◽  
Tayo Obafemi-Ajayi ◽  
Tao Ma ◽  
Dong Xu ◽  
Shao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Color Space ◽  
Image Features ◽  
Machine Learning Algorithms ◽  
Disease Classification ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Control Group

ZHENG, Traditional Chinese Medicine syndrome, is an integral and essential part of Traditional Chinese Medicine theory. It defines the theoretical abstraction of the symptom profiles of individual patients and thus, used as a guideline in disease classification in Chinese medicine. For example, patients suffering from gastritis may be classified as Cold or Hot ZHENG, whereas patients with different diseases may be classified under the same ZHENG. Tongue appearance is a valuable diagnostic tool for determining ZHENG in patients. In this paper, we explore new modalities for the clinical characterization of ZHENG using various supervised machine learning algorithms. We propose a novel-color-space-based feature set, which can be extracted from tongue images of clinical patients to build an automated ZHENG classification system. Given that Chinese medical practitioners usually observe the tongue color and coating to determine a ZHENG type and to diagnose different stomach disorders including gastritis, we propose using machine-learning techniques to establish the relationship between the tongue image features and ZHENG by learning through examples. The experimental results obtained over a set of 263 gastritis patients, most of whom suffering Cold Zheng or Hot ZHENG, and a control group of 48 healthy volunteers demonstrate an excellent performance of our proposed system.

Smart Meter Data Analysis Using Big Data Tools

2019 ◽  
Vol 16 (8) ◽  
pp. 3629-3636
Author(s):  
S. M. Sulaiman ◽  
P. Aruna Jeyanthy ◽  
D. Devaraj
Keyword(s):  
Machine Learning ◽  
Electrical Energy ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Consumption Pattern ◽  
Smart Meter ◽  
Consumption Data ◽  
Short Time ◽  
Electrical Consumption

In recent years, the problem of electrical load forecasting gained attention due to the arrival of new measurement technologies that produce electrical energy consumption data at very short intervals of time. Such short term measurements become voluminous in very short time. The availability of big electrical consumption data allows machine learning techniques to be employed to analyze consumption behavior of every consumer on a greater detail. Predicting the consumption of a residential customer is crucial at this point of time because tailor-made consumer-specific tariffs will play a vital role in load balancing process of Utilities. This paper analyzes the electrical consumption of a single residential customer measured using a smart meter that is capable of measuring electrical consumption at circuit level. The issues and challenges in collecting the data and pre-processing required for making them suitable for data analytics are discussed in detail. A comparison of the performance of different machine learning algorithms implemented using Python’s Scikit-learn module gives an insight on the consumption pattern.

Seq2Feature: a comprehensive web-based feature extraction tool

2019 ◽  
Vol 35 (22) ◽  
pp. 4797-4799 ◽  
Author(s):  
Rahul Nikam ◽  
M Michael Gromiha
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Dna Sequence ◽  
Nucleotide Composition ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Supplementary Information ◽  
Web Based ◽  
Conformational Properties ◽  
Neutral Mutations

Abstract Motivation Machine learning techniques require various descriptors from protein and nucleic acid sequences to understand/predict their structure and function as well as distinguishing between disease and neutral mutations. Hence, availability of a feature extraction tool is necessary to bridge the gap. Results We developed a comprehensive web-based tool, Seq2Feature, which computes 252 protein and 41 DNA sequence-based descriptors. These features include physicochemical, energetic and conformational properties of proteins, mutation matrices and contact potentials as well as nucleotide composition, physicochemical and conformational properties of DNA. We propose that Seq2Feature could serve as an effective tool for extracting protein and DNA sequence-based features as applicable inputs to machine learning algorithms. Availability and implementation https://www.iitm.ac.in/bioinfo/SBFE/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Classification Comparative Analysis for Detection of Brain Tumor Using Neural Network, Logistic Regression & KNN Classifier with VGG19 Convolution Neural Network Feature Extraction

2021 ◽  
Author(s):  
Vijaya Kamble ◽  
Rohin Daruwala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Logistic Regression ◽  
Brain Tumor ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Knn Classifier

In recent years due to advancements in digital imaging machine learning techniques are used in medical image analysis for the prognosis and diagnosis of various abnormalities in the human body. Various Machine learning algorithms, convolution and deep neural networks are used for classification, detection and prediction of various brain tumors. The proposed approach is a different comparative classification analysis approach which is based on three different classification namely KNN classifier,Logistic regression & neural network as classifier. It is based on a deep learning feature extraction technique using VGG19. This VGG 19-layer image recognition model trained on Imgenet. Generally, MRI data sequences are analyzed in terms of different modalities and every modality contains rich tissue information. So, feature exaction from MRI sequences is very important task for brain tumor classification. Our approach demonstrated fair classification on BRATS Benchmarks 2018 data set with different modalities and sizes of images,results are without any human annotations. Based on selected classifiers all the classifiers gives accuracy above 90%. It is good compared to other state of art methods.

scholarly journals Constructing a System for Analysis of Machine Learning Techniques for Early Detection of Thyroid

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1978-1981
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Strategies ◽  
Clinical Decision Making ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Classification Techniques ◽  
Safe Condition

Thyroid is an unending and complex infection caused by unedifying levels of TSH (Thyroid Simulation Hormone) or by thyroid organ problems themselves. Hashimoto's thyroid is the most widely recognized cause of hypothyroidism. The body makes anticorps that pulverize the thyroid organ in an auto-safe condition. It offers machine learning algorithms in the system proposed to predict thyroid disease in disease-intensive societies effectively. This is a serious concern for public health even though it is massively increasing in many countries. This shows that the problem must be predicted as urgently as possible to overcome the shortcomings of previously existing clinical decision-making tools with low precision. This paper examines numerous machine learning strategies for osteoporosis prediction. The paper examines and assesses the use of the strategy of feature selection combined with classification techniques. WEKA's classification techniques are used to measure an osteoporosis data set. The results are calculated by means of various test options, including 10-fold cross-validation, training sets and the percentage divided with and without the selection method. The results are compared with correctly classified instances, runtime, kappa and absolute mean values for experiments with and without feature selection techniques.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

scholarly journals A Comparative Survey of Feature Extraction and Machine Learning Methods in Diverse Acoustic Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1274
Author(s):  
Daniel Bonet-Solà ◽  
Rosa Ma Alsina-Pagès
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Best Practice ◽  
Nearest Neighbor ◽  
Gaussian Mixture ◽  
Machine Learning Algorithms ◽  
Multimedia Retrieval ◽  
Natural Environments ◽  
K Nearest Neighbor ◽  
Acoustic Environments

Acoustic event detection and analysis has been widely developed in the last few years for its valuable application in monitoring elderly or dependant people, for surveillance issues, for multimedia retrieval, or even for biodiversity metrics in natural environments. For this purpose, sound source identification is a key issue to give a smart technological answer to all the aforementioned applications. Diverse types of sounds and variate environments, together with a number of challenges in terms of application, widen the choice of artificial intelligence algorithm proposal. This paper presents a comparative study on combining several feature extraction algorithms (Mel Frequency Cepstrum Coefficients (MFCC), Gammatone Cepstrum Coefficients (GTCC), and Narrow Band (NB)) with a group of machine learning algorithms (k-Nearest Neighbor (kNN), Neural Networks (NN), and Gaussian Mixture Model (GMM)), tested over five different acoustic environments. This work has the goal of detailing a best practice method and evaluate the reliability of this general-purpose algorithm for all the classes. Preliminary results show that most of the combinations of feature extraction and machine learning present acceptable results in most of the described corpora. Nevertheless, there is a combination that outperforms the others: the use of GTCC together with kNN, and its results are further analyzed for all the corpora.

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