A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

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A Machine Learning Approach for One-Stop Learning

Data Mining and Knowledge Discovery Technologies ◽

10.4018/978-1-59904-960-1.ch013 ◽

2008 ◽

pp. 333-357 ◽

Cited By ~ 1

Author(s):

Marco A. Alvarez ◽

SeungJin Lim

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Approach ◽

Internet Users ◽

Machine Learning Approach ◽

Supervised Learning Algorithms ◽

One Stop ◽

The Subject

Current search engines impose an overhead to motivated students and Internet users who employ the Web as a valuable resource for education. The user, searching for good educational materials for a technical subject, often spends extra time to filter irrelevant pages or ends up with commercial advertisements. It would be ideal if, given a technical subject by user who is educationally motivated, suitable materials with respect to the given subject are automatically identified by an affordable machine processing of the recommendation set returned by a search engine for the subject. In this scenario, the user can save a significant amount of time in filtering out less useful Web pages, and subsequently the user’s learning goal on the subject can be achieved more efficiently without clicking through numerous pages. This type of convenient learning is called One-Stop Learning (OSL). In this paper, the contributions made by Lim and Ko in (Lim and Ko, 2006) for OSL are redefined and modeled using machine learning algorithms. Four selected supervised learning algorithms: Support Vector Machine (SVM), AdaBoost, Naive Bayes and Neural Networks are evaluated using the same data used in (Lim and Ko, 2006). The results presented in this paper are promising, where the highest precision (98.9%) and overall accuracy (96.7%) obtained by using SVM is superior to the results presented by Lim and Ko. Furthermore, the machine learning approach presented here, demonstrates that the small set of features used to represent each Web page yields a good solution for the OSL problem.

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A Tree Based Machine Learning Approach for PTB Diagnostic Dataset

Journal of Physics Conference Series ◽

10.1088/1742-6596/2115/1/012042 ◽

2021 ◽

Vol 2115 (1) ◽

pp. 012042

Author(s):

S Premanand ◽

Sathiya Narayanan

Keyword(s):

Machine Learning ◽

Feature Extraction ◽

Deep Learning ◽

Learning Algorithms ◽

Primary Objective ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Related Data ◽

Machine Learning Approach ◽

Health Related

Abstract The primary objective of this particular paper is to classify the health-related data without feature extraction in Machine Learning, which hinder the performance and reliability. The assumption of our work will be like, can we able to get better result for health-related data with the help of Tree based Machine Learning algorithms without extracting features like in Deep Learning. This study performs better classification with Tree based Machine Learning approach for the health-related medical data. After doing pre-processing, without feature extraction, i.e., from raw data signal with the help of Machine Learning algorithms we are able to get better results. The presented paper which has better result even when compared to some of the advanced Deep Learning architecture models. The results demonstrate that overall classification accuracy of Random Forest, XGBoost, LightGBM and CatBoost, Tree-based Machine Learning algorithms for normal and abnormal condition of the datasets was found to be 97.88%, 98.23%, 98.03% and 95.57% respectively.

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A Machine Learning Approach for Micro-Credit Scoring

Risks ◽

10.3390/risks9030050 ◽

2021 ◽

Vol 9 (3) ◽

pp. 50

Author(s):

Apostolos Ampountolas ◽

Titus Nyarko Nde ◽

Paresh Date ◽

Corina Constantinescu

Keyword(s):

Machine Learning ◽

Random Forest ◽

Interest Rates ◽

Credit Scoring ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Machine Learning Approach ◽

Credit History ◽

Lending Institutions

In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various credit categories. We demonstrate that off-the-shelf multi-class classifiers such as random forest algorithms can perform this task very well, using readily available data about customers (such as age, occupation, and location). This presents inexpensive and reliable means to micro-lending institutions around the developing world with which to assess creditworthiness in the absence of credit history or central credit databases.

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A Machine Learning Approach to Career Path Choice for Information Technology Graduates

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.3821 ◽

2020 ◽

Vol 10 (6) ◽

pp. 6589-6596

Author(s):

H. Al-Dossari ◽

F. A. Nughaymish ◽

Z. Al-Qahtani ◽

M. Alkahlifah ◽

A. Alqahtani

Keyword(s):

Machine Learning ◽

Information Technology ◽

Recommendation System ◽

Learning Algorithms ◽

Career Path ◽

Performance Comparison ◽

Machine Learning Algorithms ◽

Learning Approach ◽

It Professionals ◽

Machine Learning Approach

Enterprises rely more and more on well-qualified and highly specialized IT professionals. Although the increasing availability of IT jobs is a good indicator for IT graduates, they nonetheless may find themselves confused about the most appropriate career for their future. In this paper, a recommendation system called CareerRec is proposed, which uses machine learning algorithms to help IT graduates select a career path based on their skills. CareerRec was trained and tested using a dataset of 2255 employees in the IT sector in Saudi Arabia. We conducted a performance comparison between five machine learning algorithms to assess their accuracy for predicting the best-suited career path among 3 classes. Our experiments demonstrate that the XGBoost algorithm outperforms other models and gives the highest accuracy (70.47%).

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A Review of Machine Learning Approach for Twitter Sentiment Analysis

Al-Nahrain Journal of Science ◽

10.22401/anjs.24.4.08 ◽

2021 ◽

Vol 24 (4) ◽

pp. 52-58

Author(s):

Mohammed W. Habib ◽

◽

Zainab N. Sultani ◽

Keyword(s):

Machine Learning ◽

Social Media ◽

Comparative Study ◽

Sentiment Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Textual Data ◽

Machine Learning Approach ◽

Social Media Platforms

One of the active sciences or studies whose importance is rising is the science of sentiment analysis. The reason is due to the increasing sources of data that require investigation. Among the most valuable sources is Twitter, in addition to Facebook and other social media platforms. The objective of sentiment analysis is to classify sentiment/opinions of users as positive, negative, or neutral from textual data. This analysis is valuable for many applications that require understanding people's or users' opinions and emotions about a particular topic, product, or service. Several researchers tackle the problem of sentiment analysis using machine learning algorithms. In this paper, a comparative study is presented of various researches conducted a sentiment analysis on social media and especially on Tweets. The survey carried out in this paper provides an overview of preprocessing steps, machine learning algorithms, and approaches used for sentiment classification during the period 2015-2020.

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Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽

10.31854/2307-1303-2019-7-3-50-55 ◽

2019 ◽

Vol 7 (3) ◽

pp. 50-55

Author(s):

D. Saharov ◽

D. Kozlov

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Mobile Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Abnormal Behavior ◽

Traffic Model ◽

The Internet ◽

Wide Spread ◽

The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

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A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

Microorganisms ◽

10.3390/microorganisms9051034 ◽

2021 ◽

Vol 9 (5) ◽

pp. 1034

Author(s):

Carlos Sabater ◽

Lorena Ruiz ◽

Abelardo Margolles

Keyword(s):

Machine Learning ◽

Supervised Classification ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Human Milk Oligosaccharides ◽

Future Studies ◽

High Fiber ◽

Machine Learning Approach ◽

Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

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Machine learning algorithms for smart data analysis in the Internet of things: an overview

Intelligent Wireless Communications ◽

10.1049/pbte094e_ch12 ◽

2021 ◽

pp. 307-327

Author(s):

Mohammed H. Alsharif ◽

Anabi Hilary Kelechi ◽

Imran Khan ◽

Mahmoud A. Albreem ◽

Abu Jahid ◽

...

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The Internet ◽

Smart Data ◽

The Internet Of Things

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Comparing Machine Learning Models for the Predictions of Speed in Smart Transportation Systems

10.4018/978-1-7998-7685-4.ch002 ◽

2022 ◽

pp. 34-46

Author(s):

Amtul Waheed ◽

Jana Shafi ◽

Saritha V.

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Smart Cities ◽

Learning Algorithms ◽

High Capacity ◽

Transportation Systems ◽

Machine Learning Algorithms ◽

Smart Transportation ◽

Data Propagation ◽

Machine Learning Models

In today's world of advanced technologies in IoT and ITS in smart cities scenarios, there are many different projections such as improved data propagation in smart roads and cooperative transportation networks, autonomous and continuously connected vehicles, and low latency applications in high capacity environments and heterogeneous connectivity and speed. This chapter presents the performance of the speed of vehicles on roadways employing machine learning methods. Input variable for each learning algorithm is the density that is measured as vehicle per mile and volume that is measured as vehicle per hour. And the result shows that the output variable is the speed that is measured as miles per hour represent the performance of each algorithm. The performance of machine learning algorithms is calculated by comparing the result of predictions made by different machine learning algorithms with true speed using the histogram. A result recommends that speed is varying according to the histogram.

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