Recommender System of Final Project Topic Using Rule-based and Machine Learning Techniques

2021 ◽  
Author(s):  
Cut Fiarni ◽  
Herastia Maharani ◽  
Billy Lukito
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Machine Learning Techniques ◽  
Rule Based ◽  
Final Project ◽  
Learning Techniques

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

A recommender system for component-based applications using machine learning techniques

2019 ◽  
Vol 164 ◽  
pp. 68-84 ◽  
Author(s):  
Antonio Jesús Fernández-García ◽  
Luis Iribarne ◽  
Antonio Corral ◽  
Javier Criado ◽  
James Z. Wang
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Applying Artificial Intelligence for Operating System Fingerprinting

2021 ◽  
Vol 7 (1) ◽  
pp. 51
Author(s):  
Rubén Pérez-Jove ◽  
Cristian R. Munteanu ◽  
Alejandro Pazos Sierra ◽  
José M. Vázquez-Naya
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Computer Security ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Reference Database ◽  
Penetration Test ◽  
Rule Based ◽  
Learning Techniques ◽  
Rule Based Approach

In the field of computer security, the possibility of knowing which specific version of an operating system is running behind a machine can be useful, to assist in a penetration test or monitor the devices connected to a specific network. One of the most widespread tools that better provides this functionality is Nmap, which follows a rule-based approach for this process. In this context, applying machine learning techniques seems to be a good option for addressing this task. The present work explores the strengths of different machine learning algorithms to perform operating system fingerprinting, using for that, the Nmap reference database. Moreover, some optimizations were applied to the method which brought the best results, random forest, obtaining an accuracy higher than 96%.

scholarly journals Voice Disorder Detection via an m-Health System: Design and Results of a Clinical Study to Evaluate Vox4Health

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ugo Cesari ◽  
Giuseppe De Pietro ◽  
Elio Marciano ◽  
Ciro Niri ◽  
Giovanna Sannino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Clinical Practice ◽  
Clinical Study ◽  
Health System ◽  
Acoustic Analysis ◽  
Machine Learning Techniques ◽  
Voice Disorder ◽  
Rule Based ◽  
Learning Techniques ◽  
The Voice

Objectives. The current study presents a clinical evaluation of Vox4Health, an m-health system able to estimate the possible presence of a voice disorder by calculating and analyzing the main acoustic measures required for the acoustic analysis, namely, the Fundamental Frequency, jitter, shimmer, and Harmonic to Noise Ratio. The acoustic analysis is an objective, effective, and noninvasive tool used in clinical practice to perform a quantitative evaluation of voice quality. Materials and Methods. A clinical study was carried out in collaboration with medical staff of the University of Naples Federico II. 208 volunteers were recruited (mean age, 44.2 ± 13.9 years), 58 healthy subjects (mean age, 36.7 ± 13.3 years) and 150 pathological ones (mean age, 47 ± 13.1 years). The evaluation of Vox4Health was made in terms of classification performance, i.e., sensitivity, specificity, and accuracy, by using a rule-based algorithm that considers the most characteristic acoustic parameters to classify if the voice is healthy or pathological. The performance has been compared with that achieved by using Praat, one of the most commonly used tools in clinical practice. Results. Using a rule-based algorithm, the best accuracy in the detection of voice disorders, 72.6%, was obtained by using the jitter or shimmer value. Moreover, the best sensitivity is about 96% and it was always obtained by using jitter. Finally, the best specificity was achieved by using the Fundamental Frequency and it is equal to 56.9%. Additionally, in order to improve the classification accuracy of the next version of the Vox4Health app, an evaluation by using machine learning techniques was conducted. We performed some preliminary tests adopting different machine learning techniques able to classify the voice as healthy or pathological. The best accuracy (77.4%) was obtained by the Logistic Model Tree algorithm, while the best sensitivity (99.3%) was achieved using the Support Vector Machine. Finally, Instance-based Learning performed the best specificity (36.2%). Conclusions. Considering the achieved accuracy, Vox4Health has been considered by the medical experts as a “good screening tool” for the detection of voice disorders in its current version. However, this accuracy is improved when machine learning classifiers are considered rather than the rule-based algorithm.

scholarly journals COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 932
Author(s):  
Rathimala Kannan ◽  
Ivan Zhi Wei Wang ◽  
Hway Boon Ong ◽  
Kannan Ramakrishnan ◽  
Andry Alamsyah
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Low Income ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Cash Assistance ◽  
Learning Techniques ◽  
Boosted Tree ◽  
Machine Learning Models ◽  
Stimulus Package

Background: The Malaysian government reacted to the pandemic’s economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled “Effects of COVID-19 on the Economy and Individual - Round 2,” collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques—Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes—were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance.  Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

scholarly journals COVID-19 impact: Customised economic stimulus package recommender system using machine learning techniques

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 932
Author(s):  
Rathimala Kannan ◽  
Ivan Zhi Wei Wang ◽  
Hway Boon Ong ◽  
Kannan Ramakrishnan ◽  
Andry Alamsyah
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Low Income ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Cash Assistance ◽  
Learning Techniques ◽  
Boosted Tree ◽  
Machine Learning Models ◽  
Stimulus Package

Background: The Malaysian government reacted to the pandemic’s economic effect with the Prihatin Rakyat Economic Stimulus Package (ESP) to cushion the novel coronavirus 2019 (COVID-19) impact on households. The ESP consists of cash assistance, utility discount, moratorium, Employee Provident Fund (EPF) cash withdrawals, credit guarantee scheme and wage subsidies. A survey carried out by the Department of Statistics Malaysia (DOSM) shows that households prefer different types of financial assistance. These preferences forge the need to effectively customise ESPs to manage the economic burden among low-income households. In this study, a recommender system for such ESPs was designed by leveraging data analytics and machine learning techniques. Methods: This study used a dataset from DOSM titled “Effects of COVID-19 on the Economy and Individual - Round 2,” collected from April 10 to April 24, 2020. Cross-Industry Standard Process for Data Mining was followed to develop machine learning models to classify ESP receivers according to their preferred subsidies types. Four machine learning techniques—Decision Tree, Gradient Boosted Tree, Random Forest and Naïve Bayes—were used to build the predictive models for each moratorium, utility discount and EPF and Private Remuneration Scheme (PRS) cash withdrawals subsidies. The best predictive model was selected based on F-score metrics. Results: Among the four machine learning techniques, Gradient Boosted Tree outperformed the rest. This technique predicted the following: moratorium preferences with 93.8% sensitivity, 82.1% precision and 87.6% F-score; utilities discount with 86% sensitivity, 82.1% precision and 84% F-score; and EPF and PRS with 83.6% sensitivity, 81.2% precision and 82.4% F-score. Households that prefer moratorium subsidies did not favour other financial aids except for cash assistance.  Conclusion: Findings present machine learning models that can predict individual household preferences from ESP. These models can be used to design customised ESPs that can effectively manage the financial burden of low-income households.

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