scholarly journals Theme Identification using Machine Learning Techniques

2021 ◽  
Vol 1 (2) ◽  
pp. 123-134
Author(s):  
Siti Hajar Jayady ◽  
Hasmawati Antong
Keyword(s):  
Machine Learning ◽  
Confusion Matrix ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Text Documents ◽  
Search Optimization ◽  
Automatic Text Classification ◽  
Theme Identification ◽  
Linear Svm ◽  
Time And Energy

With the abundance of online research platforms, much information presented in PDF files, such as articles and journals, can be obtained easily. In this case, students completing research projects would have many downloaded PDF articles on their laptops. However, identifying the target articles manually within the collection can be tiring as most articles consist of several pages that need to be analyzed. Reading each article to determine if the article relates theme and organizing the articles based on themes is time and energy-consuming. Referring to this problem, a PDF files organizer that implemented a theme identifier is necessary. Thus, work will focus on automatic text classification using the machine learning methods to build a theme identifier employed in the PDF files organizer to classify articles into augmented reality and machine learning. A total of 1000 text documents for both themes were used to build the classification model. Moreover, the pre-preprocessing step for data cleaning and TF-IDF feature extraction for text vectorization and to reduce sparse vectors were performed. 80% of the dataset were used for training, and the remaining were used to validate the trained models. The classification models proposed in this work are Linear SVM and Multinomial Naïve Bayes. The accuracy of the models was evaluated using a confusion matrix. For the Linear SVM model, grid-search optimization was performed to determine the optimal value of the Cost parameter.

scholarly journals Detecting Suspicious Texts Using Machine Learning Techniques

2020 ◽  
Vol 10 (18) ◽  
pp. 6527 ◽  
Author(s):  
Omar Sharif ◽  
Mohammed Moshiul Hoque ◽  
A. S. M. Kayes ◽  
Raza Nowrozy ◽  
Iqbal H. Sarker
Keyword(s):  
Machine Learning ◽  
Instant Messaging ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Text Documents ◽  
Digital Platforms ◽  
Malicious Activity ◽  
Internet Users ◽  
Learning Techniques ◽  
The Web

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents has been growing enormously in recent years through instant messaging, social networking posts, blogs, online portals and other digital platforms. Unfortunately, the misapplication of technologies has increased with this rapid growth of online content, which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio, or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier ‘tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

scholarly journals Detecting Suspicious Texts Using Machine Learning Techniques

2020 ◽  
Author(s):  
Omar Sharif ◽  
Mohammed Moshiul Hoque ◽  
A. S. M. Kayes ◽  
Raza Nowrozy ◽  
Iqbal H. Sarker
Keyword(s):  
Machine Learning ◽  
Instant Messaging ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Text Documents ◽  
Digital Platforms ◽  
Malicious Activity ◽  
Internet Users ◽  
Learning Techniques ◽  
The Web

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

Selecting optimal SpMV realizations for GPUs via machine learning

2021 ◽  
pp. 109434202199073
Author(s):  
Ernesto Dufrechou ◽  
Pablo Ezzatti ◽  
Enrique S Quintana-Ortí
Keyword(s):  
Machine Learning ◽  
Sparse Matrix ◽  
Machine Learning Techniques ◽  
Optimal Method ◽  
Learning Techniques ◽  
General Rules ◽  
Machine Learning Approach ◽  
The Matrix ◽  
Time And Energy ◽  
Matrix Vector

More than 10 years of research related to the development of efficient GPU routines for the sparse matrix-vector product (SpMV) have led to several realizations, each with its own strengths and weaknesses. In this work, we review some of the most relevant efforts on the subject, evaluate a few prominent routines that are publicly available using more than 3000 matrices from different applications, and apply machine learning techniques to anticipate which SpMV realization will perform best for each sparse matrix on a given parallel platform. Our numerical experiments confirm the methods offer such varied behaviors depending on the matrix structure that the identification of general rules to select the optimal method for a given matrix becomes extremely difficult, though some useful strategies (heuristics) can be defined. Using a machine learning approach, we show that it is possible to obtain unexpensive classifiers that predict the best method for a given sparse matrix with over 80% accuracy, demonstrating that this approach can deliver important reductions in both execution time and energy consumption.

scholarly journals A Soft Voting Ensemble-Based Model for the Early Prediction of Idiopathic Pulmonary Fibrosis (IPF) Disease Severity in Lungs Disease Patients

Life ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1092
Author(s):  
Sikandar Ali ◽  
Ali Hussain ◽  
Satyabrata Aich ◽  
Moo Suk Park ◽  
Man Pyo Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Lung Disease ◽  
Lung Diseases ◽  
Early Stage ◽  
Confusion Matrix ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Proposed Model

Idiopathic pulmonary fibrosis, which is one of the lung diseases, is quite rare but fatal in nature. The disease is progressive, and detection of severity takes a long time as well as being quite tedious. With the advent of intelligent machine learning techniques, and also the effectiveness of these techniques, it was possible to detect many lung diseases. So, in this paper, we have proposed a model that could be able to detect the severity of IPF at the early stage so that fatal situations can be controlled. For the development of this model, we used the IPF dataset of the Korean interstitial lung disease cohort data. First, we preprocessed the data while applying different preprocessing techniques and selected 26 highly relevant features from a total of 502 features for 2424 subjects. Second, we split the data into 80% training and 20% testing sets and applied oversampling on the training dataset. Third, we trained three state-of-the-art machine learning models and combined the results to develop a new soft voting ensemble-based model for the prediction of severity of IPF disease in patients with this chronic lung disease. Hyperparameter tuning was also performed to get the optimal performance of the model. Fourth, the performance of the proposed model was evaluated by calculating the accuracy, AUC, confusion matrix, precision, recall, and F1-score. Lastly, our proposed soft voting ensemble-based model achieved the accuracy of 0.7100, precision 0.6400, recall 0.7100, and F1-scores 0.6600. This proposed model will help the doctors, IPF patients, and physicians to diagnose the severity of the IPF disease in its early stages and assist them to take proactive measures to overcome this disease by enabling the doctors to take necessary decisions pertaining to the treatment of IPF disease.

scholarly journals Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning

2020 ◽  
Vol 21 (15) ◽  
pp. 5280
Author(s):  
Irini Furxhi ◽  
Finbarr Murphy
Keyword(s):  
Machine Learning ◽  
Goodness Of Fit ◽  
Information Gain ◽  
Sampling Technique ◽  
Exposure Dose ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Tissue Specific

The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with encouraging results. A neurotoxicity classification model for diverse nanoparticles is presented in this study. A data set created from multiple literature sources consisting of nanoparticles physicochemical properties, exposure conditions and in vitro characteristics is compiled to predict cell viability. Pre-processing techniques were applied such as normalization methods and two supervised instance methods, a synthetic minority over-sampling technique to address biased predictions and production of subsamples via bootstrapping. The classification model was developed using random forest and goodness-of-fit with additional robustness and predictability metrics were used to evaluate the performance. Information gain analysis identified the exposure dose and duration, toxicological assay, cell type, and zeta potential as the five most important attributes to predict neurotoxicity in vitro. This is the first tissue-specific machine learning tool for neurotoxicity prediction caused by nanoparticles in in vitro systems. The model performs better than non-tissue specific models.

Twitter sentiment analysis for the estimation of voting intention in the 2017 Chilean elections

2020 ◽  
Vol 24 (5) ◽  
pp. 1141-1160
Author(s):  
Tomás Alegre Sepúlveda ◽  
Brian Keith Norambuena
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Sentiment Analysis ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Traditional Methods ◽  
Actual Result ◽  
Learning Techniques ◽  
Vector Machines

In this paper, we apply sentiment analysis methods in the context of the first round of the 2017 Chilean elections. The purpose of this work is to estimate the voting intention associated with each candidate in order to contrast this with the results from classical methods (e.g., polls and surveys). The data are collected from Twitter, because of its high usage in Chile and in the sentiment analysis literature. We obtained tweets associated with the three main candidates: Sebastián Piñera (SP), Alejandro Guillier (AG) and Beatriz Sánchez (BS). For each candidate, we estimated the voting intention and compared it to the traditional methods. To do this, we first acquired the data and labeled the tweets as positive or negative. Afterward, we built a model using machine learning techniques. The classification model had an accuracy of 76.45% using support vector machines, which yielded the best model for our case. Finally, we use a formula to estimate the voting intention from the number of positive and negative tweets for each candidate. For the last period, we obtained a voting intention of 35.84% for SP, compared to a range of 34–44% according to traditional polls and 36% in the actual elections. For AG we obtained an estimate of 37%, compared with a range of 15.40% to 30.00% for traditional polls and 20.27% in the elections. For BS we obtained an estimate of 27.77%, compared with the range of 8.50% to 11.00% given by traditional polls and an actual result of 22.70% in the elections. These results are promising, in some cases providing an estimate closer to reality than traditional polls. Some differences can be explained due to the fact that some candidates have been omitted, even though they held a significant number of votes.

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