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

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

BioMed Research International ◽

10.1155/2021/5546790 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Absalom E. Ezugwu ◽

Ibrahim Abaker Targio Hashem ◽

Olaide N. Oyelade ◽

Mubarak Almutari ◽

Mohammed A. Al-Garadi ◽

...

Keyword(s):

Machine Learning ◽

Smart Cities ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Learning Framework ◽

National Healthcare ◽

Machine Learning Approach ◽

The Internet Of Things ◽

Analyze Data

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 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 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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Toward Generating a new Video Education Lectures Dataset and Performance Comparison with Various Machine Learning Algorithms

10.36295/asro.2019.221230 ◽

2019 ◽

Vol 22 (12) ◽

pp. 279-298

Author(s):

M Maysaa H. Abdulameer ◽

Mahmood Z. Abdullah

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Performance Comparison ◽

Machine Learning Algorithms ◽

And Performance ◽

Video Education

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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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Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

Applied Sciences ◽

10.3390/app11156728 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6728

Author(s):

Muhammad Asfand Hafeez ◽

Muhammad Rashid ◽

Hassan Tariq ◽

Zain Ul Abideen ◽

Saud S. Alotaibi ◽

...

Keyword(s):

Machine Learning ◽

Tabu Search ◽

Decision Tree ◽

Decision Trees ◽

Search Algorithm ◽

Learning Algorithms ◽

Performance Comparison ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

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