Nonlinear Classification Models

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying systemâ€™s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.

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Effectiveness of Supervised Classification Models for Hate Speech on Twitter

10.36227/techrxiv.13140281 ◽

2020 ◽

Author(s):

Kunal Srivastava ◽

Ryan Tabrizi ◽

Ayaan Rahim ◽

Lauryn Nakamitsu

Keyword(s):

Supervised Classification ◽

Hate Speech ◽

The Internet ◽

Classification Models ◽

Personal Attack ◽

Political Beliefs ◽

The Many ◽

Primary Medium ◽

Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

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Application of Surface Water Quality Classification Models Using Principal Components Analysis and Cluster Analysis

SSRN Electronic Journal ◽

10.2139/ssrn.3364401 ◽

2019 ◽

Cited By ~ 1

Author(s):

Mohamed Ahmed Reda Hamed

Keyword(s):

Water Quality ◽

Cluster Analysis ◽

Surface Water ◽

Principal Components ◽

Surface Water Quality ◽

Classification Models ◽

Quality Classification ◽

Water Quality Classification ◽

Components Analysis ◽

And Cluster Analysis

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Machine learning based accident prediction in secure IoT enable transportation system

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189743 ◽

2021 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Bhabendu Kumar Mohanta ◽

Debasish Jena ◽

Niva Mohapatra ◽

Somula Ramasubbareddy ◽

Bharat S. Rawal

Keyword(s):

Machine Learning ◽

Smart City ◽

Information Exchange ◽

Secure Communication ◽

Intelligent Transportation System ◽

Transportation System ◽

Information And Communications Technology ◽

Classification Models ◽

Architecture Model ◽

Accident Severity

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

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Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab

Diagnostics ◽

10.3390/diagnostics11071263 ◽

2021 ◽

Vol 11 (7) ◽

pp. 1263

Author(s):

Samy Ammari ◽

Raoul Sallé de Chou ◽

Tarek Assi ◽

Mehdi Touat ◽

Emilie Chouzenoux ◽

...

Keyword(s):

Machine Learning ◽

Therapeutic Option ◽

Binary Classification ◽

Progression Free Survival ◽

Recurrent Glioblastoma ◽

Machine Learning Algorithms ◽

Survival Prediction ◽

Classification Models ◽

Angiogenic Therapy ◽

Recurrent Gbm

Anti-angiogenic therapy with bevacizumab is a widely used therapeutic option for recurrent glioblastoma (GBM). Nevertheless, the therapeutic response remains highly heterogeneous among GBM patients with discordant outcomes. Recent data have shown that radiomics, an advanced recent imaging analysis method, can help to predict both prognosis and therapy in a multitude of solid tumours. The objective of this study was to identify novel biomarkers, extracted from MRI and clinical data, which could predict overall survival (OS) and progression-free survival (PFS) in GBM patients treated with bevacizumab using machine-learning algorithms. In a cohort of 194 recurrent GBM patients (age range 18–80), radiomics data from pre-treatment T2 FLAIR and gadolinium-injected MRI images along with clinical features were analysed. Binary classification models for OS at 9, 12, and 15 months were evaluated. Our classification models successfully stratified the OS. The AUCs were equal to 0.78, 0.85, and 0.76 on the test sets (0.79, 0.82, and 0.87 on the training sets) for the 9-, 12-, and 15-month endpoints, respectively. Regressions yielded a C-index of 0.64 (0.74) for OS and 0.57 (0.69) for PFS. These results suggest that radiomics could assist in the elaboration of a predictive model for treatment selection in recurrent GBM patients.

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gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

Applied Sciences ◽

10.3390/app11062511 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2511

Author(s):

Julian Hatwell ◽

Mohamed Medhat Gaber ◽

R. Muhammad Atif Azad

Keyword(s):

State Of The Art ◽

Heuristic Method ◽

Good Explanation ◽

Classification Rule ◽

Data Sets ◽

Classification Models ◽

Boundary Values ◽

Class Label ◽

Input Space ◽

Boosted Tree

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.

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