A Macrocause Classification Model for Violent Crime Analysis in the Field of Public Safety Based on Machine Learning Techniques

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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Detecting Suspicious Texts Using Machine Learning Techniques

Applied Sciences ◽

10.3390/app10186527 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6527 ◽

Cited By ~ 1

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%.

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Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

Journal of Environmental Geography ◽

10.2478/jengeo-2020-0005 ◽

2020 ◽

Vol 13 (1-2) ◽

pp. 43-52

Author(s):

Boudewijn van Leeuwen ◽

Zalán Tobak ◽

Ferenc Kovács

Keyword(s):

Neural Network ◽

Machine Learning ◽

Land Use ◽

Land Cover ◽

Classification Model ◽

Machine Learning Techniques ◽

Support Vector ◽

Land Use Land Cover ◽

Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

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Classification Model for Water Quality using Machine Learning Techniques

International Journal of Software Engineering and Its Applications ◽

10.14257/ijseia.2015.9.6.05 ◽

2015 ◽

Vol 9 (6) ◽

pp. 45-52 ◽

Cited By ~ 10

Author(s):

Salisu Yusuf Muhammad ◽

Mokhairi Makhtar ◽

Azilawati Rozaimee ◽

Azwa Abdul Aziz ◽

Azrul Amri Jamal

Keyword(s):

Machine Learning ◽

Water Quality ◽

Classification Model ◽

Machine Learning Techniques ◽

Learning Techniques

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Multi-layer Hybrid Classification Model of COVID-19 Chest X-ray Images v1

10.17504/protocols.io.bzdzp276 ◽

2021 ◽

Author(s):

Thanakorn Poomkur ◽

Thakerng Wongsirichot

Keyword(s):

Machine Learning ◽

Classification Model ◽

Machine Learning Techniques ◽

Computational Time ◽

Support Vector ◽

X Ray ◽

Learning Techniques ◽

Hybrid Classification ◽

Chest X Ray ◽

Segmentation Image

The coronavirus disease of 2019 (COVID-19) has been declared a pandemic and has raised worldwide concern. Lung inflammation and respiratory failure are commonly observed in moderate-to-severe cases. Chest X-ray imaging is compulsory for diagnosis, and interpretation is commonly performed by skilled medical specialists. Many studies have been conducted using machine learning approaches such as Deep Learning (DL) with acceptable accuracy. However, other dimensions such as computational time were less discussed. Thus, our work is motivated to design anew computer-aided diagnosis (CADx) tool for identifying chest X-ray images of COVID-19 infection using machine learning techniques including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs). Our work is designed with the concept of multi-layer classification architecture and performs with minimal computational time and acceptable classification results. First, image segmentation, image enhancement and feature extraction techniques are performed. Second, machine learning techniques are selected based on classification performance. Finally, selected machine learning techniques are assembled into a multi-layer hybrid classification model for COVID-19 (MLHC-COVID-19). Specifically, the MLHC-COVID-19 consists of two layers, Layer I: Healthy and Unhealthy; Layer II: COVID-19 and non-COVID-19.

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Intelligent Vision‐Based Systems for Public Safety and Protection via Machine Learning Techniques

Machine Learning for Healthcare Applications ◽

10.1002/9781119792611.ch6 ◽

2021 ◽

pp. 89-102

Author(s):

B. Rajitha

Keyword(s):

Machine Learning ◽

Public Safety ◽

Machine Learning Techniques ◽

Learning Techniques

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Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Sustainability ◽

10.3390/su13105406 ◽

2021 ◽

Vol 13 (10) ◽

pp. 5406

Author(s):

Mohd Khanapi Abd Ghani ◽

Nasir G. Noma ◽

Mazin Abed Mohammed ◽

Karrar Hameed Abdulkareem ◽

Begonya Garcia-Zapirain ◽

...

Keyword(s):

Machine Learning ◽

Hearing Loss ◽

Error Rate ◽

Naive Bayes ◽

Naïve Bayes ◽

Classification Model ◽

Machine Learning Techniques ◽

Feature Transformation ◽

Learning Techniques ◽

Multivariate Bernoulli

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.

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An ensemble machine learning model based on multiple filtering and supervised attribute clustering algorithm for classifying cancer samples

PeerJ Computer Science ◽

10.7717/peerj-cs.671 ◽

2021 ◽

Vol 7 ◽

pp. e671

Author(s):

Shilpi Bose ◽

Chandra Das ◽

Abhik Banerjee ◽

Kuntal Ghosh ◽

Matangini Chattopadhyay ◽

...

Keyword(s):

Machine Learning ◽

Clustering Algorithm ◽

Class Imbalance ◽

Classification Model ◽

Machine Learning Techniques ◽

Class Imbalance Problem ◽

Imbalance Problem ◽

Ensemble Machine Learning ◽

Learning Techniques ◽

Attribute Clustering

Background Machine learning is one kind of machine intelligence technique that learns from data and detects inherent patterns from large, complex datasets. Due to this capability, machine learning techniques are widely used in medical applications, especially where large-scale genomic and proteomic data are used. Cancer classification based on bio-molecular profiling data is a very important topic for medical applications since it improves the diagnostic accuracy of cancer and enables a successful culmination of cancer treatments. Hence, machine learning techniques are widely used in cancer detection and prognosis. Methods In this article, a new ensemble machine learning classification model named Multiple Filtering and Supervised Attribute Clustering algorithm based Ensemble Classification model (MFSAC-EC) is proposed which can handle class imbalance problem and high dimensionality of microarray datasets. This model first generates a number of bootstrapped datasets from the original training data where the oversampling procedure is applied to handle the class imbalance problem. The proposed MFSAC method is then applied to each of these bootstrapped datasets to generate sub-datasets, each of which contains a subset of the most relevant/informative attributes of the original dataset. The MFSAC method is a feature selection technique combining multiple filters with a new supervised attribute clustering algorithm. Then for every sub-dataset, a base classifier is constructed separately, and finally, the predictive accuracy of these base classifiers is combined using the majority voting technique forming the MFSAC-based ensemble classifier. Also, a number of most informative attributes are selected as important features based on their frequency of occurrence in these sub-datasets. Results To assess the performance of the proposed MFSAC-EC model, it is applied on different high-dimensional microarray gene expression datasets for cancer sample classification. The proposed model is compared with well-known existing models to establish its effectiveness with respect to other models. From the experimental results, it has been found that the generalization performance/testing accuracy of the proposed classifier is significantly better compared to other well-known existing models. Apart from that, it has been also found that the proposed model can identify many important attributes/biomarker genes.

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