Frontiers in data analysis methods: from causality detection to data driven experimental design

2021 ◽  
Author(s):  
Andrea Murari ◽  
Emmanuele Peluso ◽  
Teddy Craciunescu ◽  
Sebastian Dormido-Canto ◽  
Michele Lungaroni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Empirical Models ◽  
Scientific Investigation ◽  
Data Driven ◽  
Learning Tools ◽  
Detection Techniques ◽  
The Right ◽  
Number Of Discharges ◽  
Commercial Reactor ◽  
Data Analysis Methods

Abstract On the route to the commercial reactor, the experiments in Magnetically Confinement Nuclear Fusion have become increasingly complex and they tend to produce huge amounts of data. New analysis tools have therefore become indispensable, to fully exploit the information generated by the most relevant devices, which are nowadays very expensive to both build and operate. The paper presents a series of innovative tools to cover the main aspects of any scientific investigation. Causality detection techniques can help identifying the right causes of phenomena and can become very useful in the optimisation of synchronisation experiments, such as the pacing of sawteeth instabilities with ICRH modulation. Data driven theory is meant to go beyond traditional machine learning tools, to provide interpretable and physically meaningful models. The application to very severe problems for the Tokamak configuration, such as disruptions, could help not only in understanding the physics but also in extrapolating the solutions to the next generation of devices. A specific methodology has also been developed to support the design of new experiments, proving that the same progress in the derivation of empirical models could be achieved with a significantly reduced number of discharges.

scholarly journals Crop Recommendation using Machine Learning Techniques

2019 ◽  
Vol 9 (2S) ◽  
pp. 658-661
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Climatic Condition ◽  
Recommendation System ◽  
Soil Characteristics ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Traditional Farming ◽  
Learning Techniques ◽  
The Right

Precision agriculture (PA) allows precise utilization of inputs like seed, water, pesticides, and fertilizers at the right time to the crop for maximizing productivity, quality and yields. By deploying sensors and mapping fields, farmers can understand their field in a better way conserve the resources being used and reduce adverse affects on the environment. Most of the Indian farmers practice traditional farming patterns to decide crop to be cultivated in a field. However, the farmers do not perceive crop yield is interdependent on soil characteristics and climatic condition. Thus this paper proposes a crop recommendation system which helps farmers to decide the right crop to sow in their field. Machine learning techniques provide efficient framework for data-driven decision making. This paper provides a review on set of machine learning techniques to support the farmers in making decision about right crop to grow depending on their field’s prominent attributes.

Intelligent Investment Approaches for Mutual Funds

2021 ◽  
pp. 267-281
Author(s):  
Dipankar Majumdar ◽  
Arup Kumar Bhattacharjee ◽  
Soumen Mukherjee
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Stock Market ◽  
Mutual Funds ◽  
Learning Tools ◽  
Stock Trading ◽  
Strategic Investment ◽  
The Past ◽  
The Right ◽  
Selection Of

Investment in the right fund at the right time happens to be the key to success in the stock trading business. Therefore, for strategic investment, the selection of the right opportunity has to be executed crucially so as to reap the maximum returns from the market. Predicting the stock market has always been known to be very critical and needs years of experience as it involves lots of interleaving parameters and constraints. Intelligent investment in mutual funds (MF) can be done when various machine learning tools are used to predict future fund value using the past fund value. In this chapter, an elaborate discussion is presented on the different types of mutual funds and how these data can be used in prediction by machine learning in different literature. In this work, the NAV of a total of 17 different mutual funds have been extracted from the website of AMFI, and thereafter, ANFIS is used to forecast the time series of the NAV of the MF. They have been trained using ANFIS and thereafter tested for prediction with satisfactory results.

scholarly journals Machine Learning for Financial Stability

2021 ◽  
pp. 65-87
Author(s):  
Lucia Alessi ◽  
Roberto Savona
Keyword(s):  
Machine Learning ◽  
Financial Stability ◽  
Financial Risk ◽  
Global Financial Crisis ◽  
Predictive Performance ◽  
Empirical Models ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Learning Approaches

AbstractWhat we learned from the global financial crisis is that to get information about the underlying financial risk dynamics, we need to fully understand the complex, nonlinear, time-varying, and multidimensional nature of the data. A strand of literature has shown that machine learning approaches can make more accurate data-driven predictions than standard empirical models, thus providing more and more timely information about the building up of financial risks. Advanced machine learning techniques provide several advantages over empirical models traditionally used to monitor and predict financial developments. First, they are able to deal with high-dimensional datasets. Second, machine learning algorithms allow to deal with unbalanced datasets and retain all of the information available. Third, these methods are purely data driven. All of these characteristics contribute to their often better predictive performance. However, as “black box” models, they are still much underutilized in financial stability, a field where interpretability and accountability are crucial.

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

A Comparative Study of Different Machine Learning Tools

2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Tools

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

Android Malware Detection Techniques: A Literature Review

2020 ◽  
Vol 14 ◽  
Author(s):  
Meghna Dhalaria ◽  
Ekta Gandotra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Malware Detection ◽  
Future Research ◽  
Android Malware ◽  
Detection Techniques ◽  
Android Malware Detection ◽  
Future Research Directions ◽  
To Come ◽  
Tools And Techniques

Purpose: This paper provides the basics of Android malware, its evolution and tools and techniques for malware analysis. Its main aim is to present a review of the literature on Android malware detection using machine learning and deep learning and identify the research gaps. It provides the insights obtained through literature and future research directions which could help researchers to come up with robust and accurate techniques for classification of Android malware. Design/Methodology/Approach: This paper provides a review of the basics of Android malware, its evolution timeline and detection techniques. It includes the tools and techniques for analyzing the Android malware statically and dynamically for extracting features and finally classifying these using machine learning and deep learning algorithms. Findings: The number of Android users is expanding very fast due to the popularity of Android devices. As a result, there are more risks to Android users due to the exponential growth of Android malware. On-going research aims to overcome the constraints of earlier approaches for malware detection. As the evolving malware are complex and sophisticated, earlier approaches like signature based and machine learning based are not able to identify these timely and accurately. The findings from the review shows various limitations of earlier techniques i.e. requires more detection time, high false positive and false negative rate, low accuracy in detecting sophisticated malware and less flexible. Originality/value: This paper provides a systematic and comprehensive review on the tools and techniques being employed for analysis, classification and identification of Android malicious applications. It includes the timeline of Android malware evolution, tools and techniques for analyzing these statically and dynamically for the purpose of extracting features and finally using these features for their detection and classification using machine learning and deep learning algorithms. On the basis of the detailed literature review, various research gaps are listed. The paper also provides future research directions and insights which could help researchers to come up with innovative and robust techniques for detecting and classifying the Android malware.

A Review on Machine-Learning Based Code Smell Detection Techniquesin Object-Oriented Software System(s)

2020 ◽  
Vol 13 ◽  
Author(s):  
Amandeep Kaur ◽  
Sushma Jain ◽  
Shivani Goel ◽  
Gaurav Dhiman
Keyword(s):  
Machine Learning ◽  
Programming Languages ◽  
Software Quality ◽  
Empirical Studies ◽  
Statistical Testing ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Code Smells ◽  
Detection Techniques ◽  
Code Smell

Context: Code smells are symptoms, that something may be wrong in software systems that can cause complications in maintaining software quality. In literature, there exists many code smells and their identification is far from trivial. Thus, several techniques have also been proposed to automate code smell detection in order to improve software quality. Objective: This paper presents an up-to-date review of simple and hybrid machine learning based code smell detection techniques and tools. Methods: We collected all the relevant research published in this field till 2020. We extracted the data from those articles and classified them into two major categories. In addition, we compared the selected studies based on several aspects like, code smells, machine learning techniques, datasets, programming languages used by datasets, dataset size, evaluation approach, and statistical testing. Results: Majority of empirical studies have proposed machine- learning based code smell detection tools. Support vector machine and decision tree algorithms are frequently used by the researchers. Along with this, a major proportion of research is conducted on Open Source Softwares (OSS) such as, Xerces, Gantt Project and ArgoUml. Furthermore, researchers paid more attention towards Feature Envy and Long Method code smells. Conclusion: We identified several areas of open research like, need of code smell detection techniques using hybrid approaches, need of validation employing industrial datasets, etc.

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