A Comprehensive Study of Contemporary IoT Technologies and Varied Machine Learning (ML) Schemes

2020 ◽  
pp. 623-634
Author(s):  
Mahendra Prasad Nath ◽  
Sushree Bibhuprada B. Priyadarshini ◽  
Debahuti Mishra ◽  
Samarjeet Borah
Keyword(s):  
Machine Learning ◽  
Comprehensive Study

Plant leaf recognition with shallow and deep learning: A comprehensive study

2020 ◽  
Vol 24 (6) ◽  
pp. 1311-1328
Author(s):  
Jozsef Suto
Keyword(s):  
Machine Learning ◽  
Plant Species ◽  
Research Area ◽  
Research Papers ◽  
Plant Leaf ◽  
Leaf Characteristics ◽  
The Past ◽  
The Earth ◽  
Test Sets ◽  
Comprehensive Study

Nowadays there are hundreds of thousands known plant species on the Earth and many are still unknown yet. The process of plant classification can be performed using different ways but the most popular approach is based on plant leaf characteristics. Most types of plants have unique leaf characteristics such as shape, color, and texture. Since machine learning and vision considerably developed in the past decade, automatic plant species (or leaf) recognition has become possible. Recently, the automated leaf classification is a standalone research area inside machine learning and several shallow and deep methods were proposed to recognize leaf types. From 2007 to present days several research papers have been published in this topic. In older studies the classifier was a shallow method while in current works many researchers applied deep networks for classification. During the overview of plant leaf classification literature, we found an interesting deficiency (lack of hyper-parameter search) and a key difference between studies (different test sets). This work gives an overall review about the efficiency of shallow and deep methods under different test conditions. It can be a basis to further research.

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

scholarly journals Investigation of reliability assessement in power electronics circuits using machine learning

2021 ◽  
Vol 12 (1) ◽  
pp. 558
Author(s):  
Soumya Rani Mestha ◽  
Pinto Pius A.J
Keyword(s):  
Machine Learning ◽  
Power Electronics ◽  
New Technologies ◽  
Remaining Useful Life ◽  
Power Electronic ◽  
Electronic Components ◽  
Recent Advances ◽  
Useful Life ◽  
Comprehensive Study

<p>Recent advances in power electronics (PE) and machine learning (ML) have prompted the technologists to adapt these new technologies to improve the reliability of PE systems. During the process, a lot of investigations on the performance and reliability of PE systems is carried out. The intention of this paper is to present a comprehensive study of advances in the field of reliability of PE systems using machine learning. Recent publications in this regard are analysed and findings are tabulated. In addition to this, literatures published in the prediction of remaining useful life (RUL) of power electronic components is discussed with emphasis on its limitations.</p>

scholarly journals A large-scale comparative study on peptide encodings for biomedical classification

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Sebastian Spänig ◽  
Siba Mohsen ◽  
Georges Hattab ◽  
Anne-Christin Hauschild ◽  
Dominik Heider
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
State Of The Art ◽  
Multiple Datasets ◽  
Wide Range ◽  
Fixed Parameter ◽  
Additional Sequence ◽  
Automated Machine Learning ◽  
Distinct Peptide ◽  
Comprehensive Study

Abstract Owing to the great variety of distinct peptide encodings, working on a biomedical classification task at hand is challenging. Researchers have to determine encodings capable to represent underlying patterns as numerical input for the subsequent machine learning. A general guideline is lacking in the literature, thus, we present here the first large-scale comprehensive study to investigate the performance of a wide range of encodings on multiple datasets from different biomedical domains. For the sake of completeness, we added additional sequence- and structure-based encodings. In particular, we collected 50 biomedical datasets and defined a fixed parameter space for 48 encoding groups, leading to a total of 397 700 encoded datasets. Our results demonstrate that none of the encodings are superior for all biomedical domains. Nevertheless, some encodings often outperform others, thus reducing the initial encoding selection substantially. Our work offers researchers to objectively compare novel encodings to the state of the art. Our findings pave the way for a more sophisticated encoding optimization, for example, as part of automated machine learning pipelines. The work presented here is implemented as a large-scale, end-to-end workflow designed for easy reproducibility and extensibility. All standardized datasets and results are available for download to comply with FAIR standards.

A Comprehensive Study on Diabetes Detection Using Various Machine Learning Algorithms

2021 ◽  
pp. 523-534
Author(s):  
Shweta Sharma ◽  
Suraj Tiwari ◽  
Shahid Alam ◽  
Rewa Sharma
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Comprehensive Study

scholarly journals Multicriteria Decision and Machine Learning Algorithms for Component Security Evaluation: Library-Based Overview

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jibin Zhang ◽  
Shah Nazir ◽  
Ansheng Huang ◽  
Abdullah Alharbi
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Security Evaluation ◽  
Fundamental Issue ◽  
Reusable Components ◽  
Multicriteria Decision ◽  
System Components ◽  
New System ◽  
Comprehensive Study

Components are the significant part of a system which plays an important role in the functionality of the system. Components are the reusable part of a system which are already tested, debugged, and experienced based on the previous practices. A new system is developed based on the reusable components, as reusability of components is recommended to save time, effort, and resources as such components are already made. Security of components is a significant constituent of the system to maintain the existence of the component as well as the system to function smoothly. Component security can protect a component from illegal access and changing its contents. Considering the developments in information security, protecting the components becomes a fundamental issue. In order to tackle such issues, a comprehensive study report is needed which can help practitioners to protect their system. The current study is an endeavor to report some of the existing studies regarding component security evaluation based on multicriteria decision and machine learning algorithms in the popular searching libraries.

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