Machine Learning and Deep Learning

Chahal, Ayushi; Gulia, Preeti

doi:10.35940/ijitee.l3550.1081219

Cited by 29 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…There are multiple hidden layers, as well as input and output layers. Voice synthesis, handwriting identification, image analysis, predictive analytics, object recognition, and decision making are just few of the many applications [34]. The term "Deep Learning" refers to a set of learning methods that utilize artificial neural networks with many layers to acquire hierarchical representations in deep architectures.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Coronary Artery Disease using Artificial Intelligence – A Systematic Literature Review

Ramanathan

2023

IJHSP

View full text Add to dashboard Cite

Purpose: Coronary heart disease and the risk of having a heart attack have both risen in recent years. Angioplasty, lifestyle changes, stent implantation, and medications are only some of the methods used to diagnose and treat various diseases. In this study, we will gather and analyze a variety of health indicators in order to identify heart-related illnesses via Machine Learning and Deep Learning prediction models. The best way to improve treatment and mortality prevention is to identify the relevant critical parameters and use Machine Learning or Deep Learning algorithms to achieve optimum accuracy. Design/Methodology/Approach: Secondary sources were used for this investigation. These included periodicals, papers presented at conferences, online sources, and scholarly books and articles. In order to analyze and present the data gathered from academic journals, websites, and other sources, the SWOT analysis is being used. Findings/Results: Predicting heart problems and their severity with a handful of crucial characteristics can save lives. Machine Learning algorithms such as Linear Regression, Deep Learning algorithms such as Neural Networks, and many others can all be applied to those medical parameters for this goal. Originality/Value: This literature study utilizes secondary data collected from diverse sources. Understanding the many types of coronary artery disease and evaluating the most recent advances in predicting the same using Machine Learning approaches will be facilitated by the learned knowledge. This knowledge will aid in the development of a new model or the enhancement of an existing model for predicting coronary artery disease in an individual. Included are tables detailing the forms of coronary artery disease, a variety of recently published research publications on the topic, and standard datasets. Paper Type: Literature Review

show abstract

Section: Deep Learning (Dl)mentioning

confidence: 99%

“…When one layer receives data from another, it can use that data to trigger a chain reaction that is not linear. The functionality of Deep Learning is replicated from the mechanics of human brain and neurons for processing of inputs [34].…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Prediction of Coronary Artery Disease using Artificial Intelligence – A Systematic Literature Review

Ramanathan

2023

IJHSP

View full text Add to dashboard Cite

show abstract

“…Dengan tingkat akurasi yang mencapai nilai lebih dari 83%, metode ini sering dimanfaatkan untuk masalah klasifikasi dengan data yang kompleks dan berjumlah sangat besar [2]. JST memiliki dua kategori, yaitu shallow learning yang hanya menggunakan jaringan syaraf dengan satu layer, dan deep learning yang menggunakan jaringan syaraf lebih dari satu layer [3].…”

Section: Pendahuluanunclassified

Kajian Literatur Multi Layer Perceptron Seberapa Baik Performa Algoritma Ini

Pardede¹,

Hayadi²,

Iskandar

2022

JICTAS

View full text Add to dashboard Cite

Multi Layer Perceptron (MLP), one of the deep learning algorithms, has been widely used in classification problem research because it has advantages over other conventional classification methods. This study takes 15 articles that have been published in research journals regarding the application of the multi-layer perceptron algorithm to prediction and classification problems. From the results of the analysis carried out, the results show that the lowest performance value of this algorithm is 62.89%, the highest performance value of this algorithm is 100% and the average performance value of this algorithm is 91.98%. From these values, it can be concluded that the multi layer perceptron algorithm is very good and feasible to be used in solving prediction and classification problems

show abstract

“…As a fundamental component of cognitive systems, the wide applications of machine learning represent an innovative method for data mining of large databases and for creating accurate predictive models. Machine learning is based on the idea that a system can learn from data, recognize patterns on its own, and make decisions with minimal human intervention (Chahal and Gulia, 2019). Fast and precise the automated models of machine learning enable a more detailed analysis of the aforementioned huge databases and the surrounding big data characterized by the 5Vs (Cui, Wong and Lui, 2006).…”

Section: Literature Review On Cognitive Computingmentioning

confidence: 99%

Genders’ Fascinations and Fears Towards the Use of Cognitive Computing

Barbul¹,

Pelău²,

Pop³

2022

New Trends in Sustainable Business and Consumption

View full text Add to dashboard Cite

Cognitive computing are intelligent systems that have been designed by companies in order to enhance customer experiences by providing personal advice and guidance for the everyday life buying decisions. The ability of cognitive computing systems to analyze huge amount of consumer data and their ability to learn from experience by machine learning systems, will revolutionize the interaction between consumers and companies. Therefore it is important to determine the major factors that impact the consumers' willingness to use cognitive computing systems. The objective of our research is to determine the perception and preferences of consumers regarding cognitive computing systems based on gender. The results of an online survey show that the two genders have more similarities than differences in the perception of cognitive computing. However women tend to be more reluctant to cognitive computing systems, as they don't know the processes behind the system and they don't have a control over the information shared with the intelligent system. These results are in accordance to previous research and they have important implications for the implementation of cognitive computing systems and for future research.

show abstract

Machine Learning and Deep Learning

Cited by 29 publications

References 5 publications

Prediction of Coronary Artery Disease using Artificial Intelligence – A Systematic Literature Review

Prediction of Coronary Artery Disease using Artificial Intelligence – A Systematic Literature Review

Kajian Literatur Multi Layer Perceptron Seberapa Baik Performa Algoritma Ini

Genders’ Fascinations and Fears Towards the Use of Cognitive Computing

Contact Info

Product

Resources

About