scholarly journals A Comprehensive Overview of Machine Learning Algorithms and their Applications

2021 ◽  
pp. 12-23
Author(s):  
G. Dheepak ◽  
Dr. D. Vaishali
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Basic Structure ◽  
Machine Learning Algorithms ◽  
Comprehensive Overview ◽  
New Knowledge ◽  
Machine Learning Applications ◽  
Definition Of ◽  
Over Time ◽  
Do So

Machine learning (ML) utilises data and algorithms to simulate the way people learn and improve their accuracy over time and it’s also a subdivision of artificial intelligence (AI) and computer science. In AI, ML is a relatively recent domain that involves studying computational methods for discovering new knowledge and managing existing knowledge. Methods of machine learning have been applied to a diversity of application domains. . However, in recent years, as a result of various technological advancements and research efforts, new data has become available, resulting in new domains in which machine learning can be applied. This paper introduces the definition of machine learning and its basic structure. These algorithms are used for various purposes, including data mining, image processing, predictive analytics, and so on. The primary benefit of using machine learning is that once an algorithm learns what to do with data, it can do so automatically. This survey replenishes a brief outline and outlook on numerous machine learning applications.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

scholarly journals Smooth input preparation for quantum and quantum-inspired machine learning

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhikuan Zhao ◽  
Jack K. Fitzsimons ◽  
Patrick Rebentrost ◽  
Vedran Dunjko ◽  
Joseph F. Fitzsimons
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Machine Learning Algorithms ◽  
Low Rank ◽  
Smoothed Analysis ◽  
Machine Learning Applications ◽  
Data Points ◽  
Input Model ◽  
The Cost ◽  
Input Perturbation

AbstractMachine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

scholarly journals Clinician checklist for assessing suitability of machine learning applications in healthcare

2021 ◽  
Vol 28 (1) ◽  
pp. e100251
Author(s):  
Ian Scott ◽  
Stacey Carter ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Routine Care ◽  
Machine Learning Algorithms ◽  
Clinical Settings ◽  
Machine Learning Applications ◽  
Key Issues

Machine learning algorithms are being used to screen and diagnose disease, prognosticate and predict therapeutic responses. Hundreds of new algorithms are being developed, but whether they improve clinical decision making and patient outcomes remains uncertain. If clinicians are to use algorithms, they need to be reassured that key issues relating to their validity, utility, feasibility, safety and ethical use have been addressed. We propose a checklist of 10 questions that clinicians can ask of those advocating for the use of a particular algorithm, but which do not expect clinicians, as non-experts, to demonstrate mastery over what can be highly complex statistical and computational concepts. The questions are: (1) What is the purpose and context of the algorithm? (2) How good were the data used to train the algorithm? (3) Were there sufficient data to train the algorithm? (4) How well does the algorithm perform? (5) Is the algorithm transferable to new clinical settings? (6) Are the outputs of the algorithm clinically intelligible? (7) How will this algorithm fit into and complement current workflows? (8) Has use of the algorithm been shown to improve patient care and outcomes? (9) Could the algorithm cause patient harm? and (10) Does use of the algorithm raise ethical, legal or social concerns? We provide examples where an algorithm may raise concerns and apply the checklist to a recent review of diagnostic imaging applications. This checklist aims to assist clinicians in assessing algorithm readiness for routine care and identify situations where further refinement and evaluation is required prior to large-scale use.

A new generic method to improve machine learning applications in official statistics

2021 ◽  
pp. 1-16
Author(s):  
Kevin Kloos
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Statistical Properties ◽  
Machine Learning Algorithms ◽  
Official Statistics ◽  
Academic Literature ◽  
Misclassification Bias ◽  
Squared Error ◽  
Machine Learning Applications ◽  
Applications Of Machine Learning

The use of machine learning algorithms at national statistical institutes has increased significantly over the past few years. Applications range from new imputation schemes to new statistical output based entirely on machine learning. The results are promising, but recent studies have shown that the use of machine learning in official statistics always introduces a bias, known as misclassification bias. Misclassification bias does not occur in traditional applications of machine learning and therefore it has received little attention in the academic literature. In earlier work, we have collected existing methods that are able to correct misclassification bias. We have compared their statistical properties, including bias, variance and mean squared error. In this paper, we present a new generic method to correct misclassification bias for time series and we derive its statistical properties. Moreover, we show numerically that it has a lower mean squared error than the existing alternatives in a wide variety of settings. We believe that our new method may improve machine learning applications in official statistics and we aspire that our work will stimulate further methodological research in this area.

scholarly journals Stock Market Prediction using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 3628-3632
Author(s):  
Prof. Gowrishankar B S
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Price ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Stock Market Prediction ◽  
The Future ◽  
Complicated Model

Stock market is one of the most complicated and sophisticated ways to do business. Small ownerships, brokerage corporations, banking sectors, all depend on this very body to make revenue and divide risks; a very complicated model. However, this paper proposes to use machine learning algorithms to predict the future stock price for exchange by using pre-existing algorithms to help make this unpredictable format of business a little more predictable. The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The data has to be cleansed before it can be used for predictions. This paper focuses on categorizing various methods used for predictive analytics in different domains to date, their shortcomings.

scholarly journals An Application of Predictive Analytics in Manufacturing Sector for Price Prediction and Demand Prediction

2020 ◽  
Vol 9 (9) ◽  
pp. 196-199
Keyword(s):  
Machine Learning ◽  
Historical Data ◽  
Predictive Analytics ◽  
Manufacturing Sector ◽  
Regression Tree ◽  
Machine Learning Algorithms ◽  
Demand Prediction ◽  
Price Prediction ◽  
Near Future ◽  
Overall Functioning

Predictive analytics is the examination of concerned data so that we can recognize the problem that may arise in the near future. Manufacturers are interested in quality control, and making sure that the whole factory is functioning at the best possible efficiency. Hence, it’s feasible to increase manufacturing quality, and expect needs throughout the factory with predictive analytics. Hence, we have proposed an application of predictive analytics in manufacturing sector especially focused on price prediction and demand prediction of various products that get manufactured on regular basis. We have trained and tested different machine learning algorithms that can be used to predict price as well as demand of a particular product using historical data about that product’s sales and other transactions. Out of these different tested algorithms, we have selected the regression tree algorithm which gives accuracy of 95.66% for demand prediction and 88.85% for price prediction. Therefore, Regression Tree is best suited for use in manufacturing sector as long as price prediction and demand prediction of a product is concerned. Thus, the proposed application can help the manufacturing sector to improve its overall functioning and efficiency using the price prediction and demand prediction of products.

Towards a better understanding of river dynamics in semi-urbanised areas: a machine learning analysis on time-series satellite images

2021 ◽  
Author(s):  
Alessio Cislaghi ◽  
Paolo Fogliata ◽  
Emanuele Morlotti ◽  
Gian Battista Bischetti
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Riparian Vegetation ◽  
Satellite Images ◽  
River Management ◽  
Machine Learning Algorithms ◽  
Flood Events ◽  
River Dynamics ◽  
Clear Cutting ◽  
Over Time

<p>River channels and floodplains have been highly modified over the last 70 years to mitigate flood risk and to gain lands for agricultural activities, settlements and soft infrastructures (e.g., cycle paths). River engineering measures simplified the geomorphologic complexity of river system, usually from braided or wandering channels to highly-confined single-thread channel. Meanwhile, rivers naturally adjust and self-organise the geomorphologic function as response of all the disturbances (e.g., flood events, river-bed degradation, narrowing, control works) altering sediment and water transfer, exacerbating bank erosion processes and streambank failures, and exposing bare sediment that can be subsequently colonized by pioneer species. In this context, river management has to address river dynamics planning sustainable practices with the aim to combine hydraulic safety, river functionality, and ecological/environmental quality. These actions require the detection of river processes by monitoring the geomorphological changes over time, both over the active riverbank and the close floodplains. Thus, remote sensing technology combined with machine learning algorithms offers a viable decision-making instrument (Piégay et al., 2020).</p><p>This study proposes a procedure that consists in applying image segmentation and classification algorithms (i.e., Random Forest and dendrogram-based method) over time-series high resolution RGB-NIR satellite-images, to identify the fluvial forms (bars and islands), the vegetation patches and the active riverbed. The study focuses on three different reaches of Oglio River (Valcamonica, North Italy), representative of the most common geomorphic changes in Alpine rivers.</p><p>The results clearly show the temporal evolution/dynamics of vegetated and non-vegetated bars and islands, as consequence of human and natural disturbances (flood events, riparian vegetation clear-cutting, and bank-protection works). Moreover, the procedure allows to distinguish two stages of riparian vegetation (i.e., pioneer and mature vegetated areas) and to quantify the timing of colonization and growth. Finally, the study proposes a practical application of the described methodology for river managers indicating which river management activity (including timing, intensity and economic costs) is more appropriate and sustainable for each studied reach.</p><p> </p><p>References: Piégay, H., Arnaud, F., Belletti, B., Bertrand, M., Bizzi, S., Carbonneau, P., Dufour, S., Liébault, F., Ruiz‐Villanueva, V. and Slater, L.: Remotely sensed rivers in the Anthropocene: state of the art and prospects, Earth Surf. Process. Landf., 45(1), 157–188, https://doi.org/10.1002/esp.4787, 2020.</p>

Machine Learning Applications in Breast Cancer Diagnosis

2017 ◽  
pp. 465-490
Author(s):  
Syed Jamal Safdar Gardezi ◽  
Mohamed Meselhy Eltoukhy ◽  
Ibrahima Faye
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Extraction ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Extraction Techniques ◽  
Cad Systems ◽  
Cad System ◽  
Machine Learning Applications ◽  
Detection And Diagnosis

Breast cancer is one of the leading causes of death in women worldwide. Early detection is the key to reduce the mortality rates. Mammography screening has proven to be one of the effective tools for diagnosis of breast cancer. Computer aided diagnosis (CAD) system is a fast, reliable, and cost-effective tool in assisting the radiologists/physicians for diagnosis of breast cancer. CAD systems play an increasingly important role in the clinics by providing a second opinion. Clinical trials have shown that CAD systems have improved the accuracy of breast cancer detection. A typical CAD system involves three major steps i.e. segmentation of suspected lesions, feature extraction and classification of these regions into normal or abnormal class and further into benign or malignant stages. The diagnostics ability of any CAD system is dependent on accurate segmentation, feature extraction techniques and most importantly classification tools that have ability to discriminate the normal tissues from the abnormal tissues. In this chapter we discuss the application of machine learning algorithms e.g. ANN, binary tree, SVM, etc. together with segmentation and feature extraction techniques in a CAD system development. Various methods used in the detection and diagnosis of breast lesions in mammography are reviewed. A brief introduction of machine learning tools, used in diagnosis and their classification performance on various segmentation and feature extraction techniques is presented.

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