scholarly journals Evaluation on NTM based Predictive Analytics on Rainfall and Flood Disaster Management

2020 ◽  
Vol 6 (10) ◽  
pp. 89-95
Author(s):  
Satwik P M and Dr. Meenatchi Sundram
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Research Work ◽  
Evaluation Process ◽  
Machine Learning Algorithms ◽  
New Approach ◽  
Research Article ◽  
The Neural Networks ◽  
Evaluation Parameters

In this Research article, we presented a new approach for predicting the flood through the advanced Machine learning Algorithm which is one among the Neural networks class that outperforms itself in best data operations and predictive analytics. This Research article discusses in detail about the prediction of flood occurrences evaluation process. We interpreted the Research with many algorithms that is existing, and the Research work have been dealing with different research works inculcated and compared with different Research approaches. On Comparing to the Previous Researches its observed that the Neural Turing networks have been performing the prediction of the rainfall and flood-based disasters for the consecutive year counts of 10,15 and 20 with 93.8% accuracy. Here the Research is analyzed with various parameters and Comparing it with the other researches which is implemented with other machine learning algorithms. Comparing with the previous researches the Idea of the research have been described and evaluated with the different evaluation parameters including the number of iterations or Epochs.

Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

2019 ◽  
pp. 191-207
Author(s):  
P. Priyanga ◽  
N. C. Naveen
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
K Nearest Neighbor ◽  
Aged Patients ◽  
Learning Machine

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.

System Model for Prediction Analytics Using K-Nearest Neighbors Algorithm

2019 ◽  
Vol 16 (10) ◽  
pp. 4425-4430 ◽  
Author(s):  
Devendra Prasad ◽  
Sandip Kumar Goyal ◽  
Avinash Sharma ◽  
Amit Bindal ◽  
Virendra Singh Kushwah
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Research Work ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
System Model ◽  
K Nearest Neighbors ◽  
Prediction Analysis ◽  
Pros And Cons

Machine Learning is a growing area in computer science in today’s era. This article is focusing on prediction analysis using K-Nearest Neighbors (KNN) Machine Learning algorithm. Data in the dataset are processed, analyzed and predicated using the specified algorithm. Introduction of various Machine Learning algorithms, its pros and cons have been discussed. The KNN algorithm with detail study is given and it is implemented on the specified data with certain parameters. The research work elucidates prediction analysis and explicates the prediction of quality of restaurants.

scholarly journals Predicting Alert Source Device using Machine Learning Algorithms

2020 ◽  
Vol 9 (9) ◽  
pp. 1-10
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Model Building ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Training Dataset ◽  
Imbalanced Dataset ◽  
Daunting Task

In a large distributed virtualized environment, predicting the alerting source from its text seems to be daunting task. This paper explores the option of using machine learning algorithm to solve this problem. Unfortunately, our training dataset is highly imbalanced. Where 96% of alerting data is reported by 24% of alerting sources. This is the expected dataset in any live distributed virtualized environment, where new version of device will have relatively less alert compared to older devices. Any classification effort with such imbalanced dataset present different set of challenges compared to binary classification. This type of skewed data distribution makes conventional machine learning less effective, especially while predicting the minority device type alerts. Our challenge is to build a robust model which can cope with this imbalanced dataset and achieves relative high level of prediction accuracy. This research work stared with traditional regression and classification algorithms using bag of words model. Then word2vec and doc2vec models are used to represent the words in vector formats, which preserve the sematic meaning of the sentence. With this alerting text with similar message will have same vector form representation. This vectorized alerting text is used with Logistic Regression for model building. This yields better accuracy, but the model is relatively complex and demand more computational resources. Finally, simple neural network is used for this multi-class text classification problem domain by using keras and tensorflow libraries. A simple two layered neural network yielded 99 % accuracy, even though our training dataset was not balanced. This paper goes through the qualitative evaluation of the different machine learning algorithms and their respective result. Finally, two layered deep learning algorithms is selected as final solution, since it takes relatively less resource and time with better accuracy values.

scholarly journals Developing Classification Model for Chickpea Types using Machine Learning Algorithms

2020 ◽  
Vol 10 (1) ◽  
pp. 5-11
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Research Center ◽  
Domain Experts ◽  
Selection Mechanisms ◽  
Type Classification

Ethiopia is the leading producer of chickpea in Africa and among the top ten most important producers of chickpea in the world. Debre Zeit Agriculture Research Center is a research center in Ethiopia which is mandated for the improvement of chickpea and other crops. Genome enabled prediction technologies trying to transform the classification of chickpea types and upgrading the existing identification paradigm.Current state of the identification of chickpea types in Ethiopia still sticks to a manual. Domain experts tried to recognize every chickpea type, the way and efficiency of identifying each chickpea types mainly depend on the skills and experience of experts in the domain area and this frequently causes error and sometimes inaccurate. Most of the classification and identification of crops researches were done outside Ethiopia; for local and emerging varieties, there is a need to design classification model that assists selection mechanisms of chickpea and even accuracy of an existing algorithm should be verified and optimized. The main aim of this study is to design chickpea type classification model using machine learning algorithm that classify chickpea types. This research work has a total of 8303 records with 8 features and 80% for training and 20% for testing were used. Data preprocessing were done to prepare the dataset for experiments. ANN, SVM and DT were used to build the model. For evaluating the performance of the model confusion matrix with Accuracy, Recall and Precision were used. The experimental results show that the best-performed algorithms were decision tree and achieve 97.5% accuracy. After the evaluation of results found in this research work, agriculture research centers and companies have benefited. The model of chickpea type classification will be applied in Debre Zeit agriculture research center in Ethiopia as a base to support the experts during chickpea type identification process. In addition it enables the expertise to save time, effort and cost with the support of the identification model. Moreover, this research can also be used as a corner stone in the area and will be referred by future researchers in the domain area.

Mobile Based Healthcare Tool an Integrated Disease Prediction & Recommendation System

2019 ◽  
Vol 10 (1) ◽  
pp. 38-62
Author(s):  
Megha Rathi ◽  
Vikas Pareek
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Database Analysis ◽  
Statistical Parameters ◽  
Patient Profile ◽  
The Right

Recent advances in mobile technology and machine learning together steer us to create a mobile-based healthcare app for recommending disease. In this study, the authors develop an android-based healthcare app which will detect all kinds of diseases in no time. The authors developed a novel, hybrid machine-learning algorithm in order to provide more accurate results. For the same purpose, the authors have combined two machine-learning algorithms, SVM and GA. The proposed algorithms will enhance the accuracy and at the same time reduce the complexity and count of attributes in the database. Analysis of algorithm is also done using statistical parameters like accuracy, confusion matrix, and roc-curve. The pivotal intent of this research work is to create an android-based healthcare app which will predict disease when provided with certain details. For a disease like cancer, for which a series of tests are required for confirmation, this app will quickly detect cancer and it is helpful to doctors as they can start the right course of treatment right away. Further, this app will also recommend a diet fitting the patient profile.

scholarly journals Predicting Limit-Setting Behavior of Gamblers Using Machine Learning Algorithms: A Real-World Study of Norwegian Gamblers Using Account Data

2019 ◽  
Author(s):  
Michael Auer ◽  
Mark D. Griffiths
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Test Data ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Responsible Gambling ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Training Dataset ◽  
Limit Setting

AbstractPlayer protection and harm minimization have become increasingly important in the gambling industry along with the promotion of responsible gambling (RG). Among the most widespread RG tools that gaming operators provide are limit-setting tools that help players limit the amount of time and/or money they spend gambling. Research suggests that limit-setting significantly reduces the amount of money that players spend. If limit-setting is to be encouraged as a way of facilitating responsible gambling, it is important to know what variables are important in getting individuals to set and change limits in the first place. In the present study, 33 variables assessing the player behavior among Norsk Tipping clientele (N = 70,789) from January to March 2017 were computed. The 33 variables which reflect the players’ behavior were then used to predict the likelihood of gamblers changing their monetary limit between April and June 2017. The 70,789 players were randomly split into a training dataset of 56,532 and an evaluation set of 14,157 players (corresponding to an 80/20 split). The results demonstrated that it is possible to predict future limit-setting based on player behavior. The random forest algorithm appeared to predict limit-changing behavior much better than the other algorithms. However, on the independent test data, the random forest algorithm’s accuracy dropped significantly. The best performance on the test data along with a small decrease in accuracy in comparison to the training data was delivered by the gradient boost machine learning algorithm. The most important variables predicting future limit-setting using the gradient boost machine algorithm were players receiving feedback that they had reached 80% of their personal monthly global loss limit, personal monthly loss limit, the amount bet, theoretical loss, and whether the players had increased their limits in the past. With the help of predictive analytics, players with a high likelihood of changing their limits can be proactively approached.

Adaptive Learning Algorithm Convergence in Passive and Reactive Environments

2018 ◽  
Vol 30 (10) ◽  
pp. 2805-2832 ◽  
Author(s):  
Richard M. Golden
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Stochastic Approximation ◽  
Approximation Theory ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Evaluation Process ◽  
Machine Learning Algorithms ◽  
Analysis And Design

Although the number of artificial neural network and machine learning architectures is growing at an exponential pace, more attention needs to be paid to theoretical guarantees of asymptotic convergence for novel, nonlinear, high-dimensional adaptive learning algorithms. When properly understood, such guarantees can guide the algorithm development and evaluation process and provide theoretical validation for a particular algorithm design. For many decades, the machine learning community has widely recognized the importance of stochastic approximation theory as a powerful tool for identifying explicit convergence conditions for adaptive learning machines. However, the verification of such conditions is challenging for multidisciplinary researchers not working in the area of stochastic approximation theory. For this reason, this letter presents a new stochastic approximation theorem for both passive and reactive learning environments with assumptions that are easily verifiable. The theorem is widely applicable to the analysis and design of important machine learning algorithms including deep learning algorithms with multiple strict local minimizers, Monte Carlo expectation-maximization algorithms, contrastive divergence learning in Markov fields, and policy gradient reinforcement learning.

scholarly journals Improving Accuracy of The Sentence-Level Lexicon-Based Sentiment Analysis Using Machine Learning

2021 ◽  
pp. 57-69
Author(s):  
Titya Eng ◽  
Md Rashed Ibn Nawab ◽  
Kazi Md Shahiduzzaman
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Research Focus ◽  
Sentence Level ◽  
People’S Attitudes ◽  
Improving Accuracy ◽  
Negative Sentiment

Sentiment Analysis studies people's attitudes, opinions, evaluations, emotions, sentiments toward some entities such as products, topics, individuals, services, issues and classify them whether the opinion or evaluations inclines to that entities or not. It is getting more research focus in recent years due to its benefits for scientific and commercial purposes. This research aims at developing a better approach for sentiment analysis at the sentence level by using a combination of lexicon resources and a machine learning method. Moreover, as reviews data on the internet is unstructured and has much noise, this research uses different preprocessing techniques to clean the data before processing in different algorithms discussed in subsequent sections. Additionally, the lexicon building processes, how the lexicon is handled and combined with the machine learning algorithm for predicting sentiment is also discussed. In sentiment analysis, sentence's sentiment can be classified into three classes: positive sentiment, negative sentiment, or neutral. However, in this research work, we have excluded neutral sentiment for avoiding ambiguity and unnecessary complexity. The experiment results show that the proposed algorithm outperforms compared to the baseline machine learning algorithms. We have used four distinct datasets and different performance measures to check and validate the proposed method's robustness.

Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

2018 ◽  
Vol 13 (4) ◽  
pp. 82-97
Author(s):  
P Priyanga ◽  
N C Naveen
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Research Work ◽  
Kernel Functions ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Clinical Practices

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.

Software-Based Testing Kit Using Machine Learning for Diagnosis and Predictive Analytics of COVID-19 Patients

2021 ◽  
Vol 12 (2) ◽  
pp. 39-50
Author(s):  
Vishal Kumar Goar ◽  
Jyoti Prabha
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Prime Factor ◽  
Research Work ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Artificial Neural Network Ann ◽  
Lm Algorithm ◽  
Selection Of

Nowadays, the global community is being affected with COVID-19 disease and integrated infections, which are becoming a menace to the whole world. Research is going on to find out the solution, and still, no particular vaccination or solution has been achieved. This research work is focusing on the analytics of dataset extracted, which has assorted attributes, and these attributes are processed in the machine learning algorithm so that the prime factor can be recognized. In this research manuscript, the usage of COVID-19 dataset is done and trained using supervised learning approach of artificial neural network (ANN) on Levenberg-Marquardt (LM) algorithm so that the predictions of the test patients can be done on the key attributes of age, gender, location, and related parameters. The selection of LM-based implementation with ANN is done as it is the faster approach compared to other functions in neural networks.

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