Liver Disease Detection Using Grey Wolf Optimization and Random Forest Classification

2021 ◽  
pp. 130-160
Author(s):  
Singaravelan Shanmugasundaram ◽  
Parameswari M.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Cutting Edge ◽  
Learning Approaches ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Metavir Score

Utilizing machine learning approaches as non-obtrusive strategies is an elective technique in organizing perpetual liver infections for staying away from the downsides of biopsy. This chapter assesses diverse machine learning methods in expectation of cutting-edge fibrosis by joining the serum bio-markers and clinical data to build up the order models. An imminent accomplice of patients with incessant hepatitis C was separated into two sets—one classified as gentle to direct fibrosis (F0-F2) and the other ordered as cutting-edge fibrosis (F3-F4) as per METAVIR score. Grey wolf optimization, random forest classifier, and decision tree procedure models for cutting-edge fibrosis chance expectation were created. Recipient working trademark bend investigation was performed to assess the execution of the proposed models.

scholarly journals Analysis Of The Effect Of Knowledge On Entrepreneurship Readiness Using Random Forest Classification Machine Learning

2021 ◽  
Vol 23 ◽  
pp. 134-149
Author(s):  
Ariyono Setiawan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Random Forest ◽  
Rapid Development ◽  
Air Transportation ◽  
Sample Population ◽  
Random Forest Classification ◽  
Main Requirement ◽  
Forest Classification ◽  
Entrepreneurial Readiness

Entrepreneurship is a phenomenon that has an important influence on the progress and welfare of the world, so that entrepreneurship is used as the base of economic development. Psychologically, entrepreneurs are people who have a strong internal drive as an effort to achieve certain goals so that they have a tendency to experiment in showing a character that is free from the control of others. Entrepreneurship can be seen from various points of view. The angle and context in question are views from several fields, namely according to economists, management, business people, psychologists and investors. The main requirement that an entrepreneur must have is entrepreneurial knowledge. entrepreneurial readiness is determined by the knowledge possessed and experience in conducting a business (Kurniawati, 2019). In the midst of the rapid development of artificial intelligence (AI) technology today. Not many people know that artificial intelligence consists of several branches, one of which is machine learning. This machine learning (ML) technology is one of the branches of AI that is very interesting. The sample population in this study was obtained from the air transportation school consisting of 7 populations. Data analysis is done by using . The research location is an air transportation school with Machine Learning Random Forest Classification with a population of cadets, lecturers and the general public

scholarly journals Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

2021 ◽  
Author(s):  
Vittorio A. Gensini ◽  
Cody Converse ◽  
Walker S. Ashley ◽  
Mateusz Taszarek
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Random Forest ◽  
Characteristic Curve ◽  
Grid Point ◽  
Severe Weather ◽  
Theoretic Approach ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Forest Classification

AbstractPrevious studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

scholarly journals A Pinnacle Technique for Detection of COVID-19 Fake News in Social Media

2020 ◽  
Vol 10 (1) ◽  
pp. 256-261
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
General Public ◽  
Classification Model ◽  
Fake News ◽  
Random Forest Classification ◽  
Forest Classification ◽  
The World ◽  
The Impact

Today the world is gripped with fear of the most infectious disease which was caused by a newly discovered virus namely corona and thus termed as COVID-19. This is a large group of viruses which severely affects humans. The world bears testimony to its contagious nature and rapidity of spreading the illness. 50l people got infected and 30l people died due to this pandemic all around the world. This made a wide impact for people to fear the epidemic around them. The death rate of male is more compared to female. This Pandemic news has caught the attention of the world and gained its momentum in almost all the media platforms. There was an array of creating and spreading of true as well as fake news about COVID-19 in the social media, which has become popular and a major concern to the general public who access it. Spreading such hot news in social media has become a new trend in acquiring familiarity and fan base. At the time it is undeniable that spreading of such fake news in and around creates lots of confusion and fear to the public. To stop all such rumors detection of fake news has become utmost important. To effectively detect the fake news in social media the emerging machine learning classification algorithms can be an appropriate method to frame the model. In the context of the COVID-19 pandemic, we investigated and implemented by collecting the training data and trained a machine learning model by using various machine learning algorithms to automatically detect the fake news about the Corona Virus. The machine learning algorithm used in this investigation is Naïve Bayes classifier and Random forest classification algorithm for the best results. A separate model for each classifier is created after the data preparation and feature extraction Techniques. The results obtained are compared and examined accurately to evaluate the accurate model. Our experiments on a benchmark dataset with random forest classification model showed a promising results with an overall accuracy of 94.06%. This experimental evaluation will prevent the general public to keep themselves out of their fear and to know and understand the impact of fast-spreading as well as misleading fake news.

scholarly journals Comparison of Classification Methods used in Machine Learning for Dysgraphia Identification

2021 ◽  
Vol 12 (11) ◽  
pp. 1886-1891
Author(s):  
Sarthika Dutt, Et. al.
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Feature Selection Technique ◽  
Random Forest Classification ◽  
Visual Spatial ◽  
Forest Classification

Dysgraphia is a disorder that affects writing skills. Dysgraphia Identification at an early age of a child's development is a difficult task.  It can be identified using problematic skills associated with Dysgraphia difficulty. In this study motor ability, space knowledge, copying skill, Visual Spatial Response are some of the features included for Dysgraphia identification. The features that affect Dysgraphia disability are analyzed using a feature selection technique EN (Elastic Net). The significant features are classified using machine learning techniques. The classification models compared are KNN (K-Nearest Neighbors), Naïve Bayes, Decision tree, Random Forest, SVM (Support Vector Machine) on the Dysgraphia dataset. Results indicate the highest performance of the Random forest classification model for Dysgraphia identification.

scholarly journals OSMWatchman: Learning How to Detect Vandalized Contributions in OSM Using a Random Forest Classifier

2020 ◽  
Vol 9 (9) ◽  
pp. 504
Author(s):  
Quy Truong ◽  
Guillaume Touya ◽  
Cyril Runz
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Spatial Data ◽  
Random Forest Classifier ◽  
Experimental Results ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Geographical Regions ◽  
Vandalism Detection

Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in “unfamiliar regions”.

scholarly journals Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis

2020 ◽  
Vol 117 (52) ◽  
pp. 33474-33485
Author(s):  
Vittorio Fortino ◽  
Lukas Wisgrill ◽  
Paulina Werner ◽  
Sari Suomela ◽  
Nina Linder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Contact Dermatitis ◽  
Random Forest ◽  
Diagnostic Tools ◽  
Irritant Contact Dermatitis ◽  
Random Forest Classification ◽  
Positive Patch Test ◽  
Forest Classification ◽  
Potential Biomarkers

Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine-learning approaches, we aimed to decipher disease-related signature genes to find suitable sets of biomarkers. A total of 89 positive patch-test reaction biopsies against four contact allergens and two irritants were analyzed via microarray. Coexpression network analysis and Random Forest classification were used to discover potential biomarkers and selected biomarker models were validated in an independent patient group. Differential gene-expression analysis identified major gene-expression changes depending on the stimulus. Random Forest classification identified CD47, BATF, FASLG, RGS16, SYNPO, SELE, PTPN7, WARS, PRC1, EXO1, RRM2, PBK, RAD54L, KIFC1, SPC25, PKMYT, HISTH1A, TPX2, DLGAP5, TPX2, CH25H, and IL37 as potential biomarkers to distinguish allergic and irritant contact dermatitis in human skin. Validation experiments and prediction performances on external testing datasets demonstrated potential applicability of the identified biomarker models in the clinic. Capitalizing on this knowledge, novel diagnostic tools can be developed to guide clinical diagnosis of contact allergies.

scholarly journals Fiber tractography using machine learning

2017 ◽  
Author(s):  
Peter F. Neher ◽  
Marc-Alexandre Côté ◽  
Jean-Christophe Houde ◽  
Maxime Descoteaux ◽  
Klaus H. Maier-Hein
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Random Forest ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Fiber Tractography ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Signal Intensities

AbstractWe present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom andin vivoexperiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.

scholarly journals A Random Forest Classifier for Protein-Protein Docking Models

2021 ◽  
Author(s):  
Didier Barradas-Bautista ◽  
Zhen Cao ◽  
Anna Vangone ◽  
Romina Oliva ◽  
Luigi Cavallo
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Protein Complexes ◽  
Protein Docking ◽  
Random Forest Classifier ◽  
Features Selection ◽  
Learning Approaches ◽  
Scoring Functions ◽  
Comparative Performance ◽  
Protein Protein Interaction

Herein, we present the results of a machine learning approach we developed to single out correct 3D docking models of protein-protein complexes obtained by popular docking software. To this aim, we generated a set of ~7xE06 docking models with three different docking programs (HADDOCK, FTDock and ZDOCK) for the 230 complexes in the protein-protein interaction benchmark, version 5 (BM5). Three different machine-learning approaches (Random Forest, Supported Vector Machine and Perceptron) were used to train classifiers with 158 different scoring functions (features). The Random Forest algorithm outperformed the other two algorithms and was selected for further optimization. Using a features selection algorithm, and optimizing the random forest hyperparameters, allowed us to train and validate a random forest classifier, named CoDES (COnservation Driven Expert System). Testing of CoDES on independent datasets, as well as results of its comparative performance with machine-learning methods recently developed in the field for the scoring of docking decoys, confirm its state-of-the-art ability to discriminate correct from incorrect decoys both in terms of global parameters and in terms of decoys ranked at the top positions.

scholarly journals Comparative Analysis of Random Forest Classification Over SVM Classifier to Detect Cyber Thefts in Credit Card to Reduce False Rate

2021 ◽  
Vol 11 (2) ◽  
pp. 1339-1348
Author(s):  
Ruthvik K.R.
Keyword(s):  
Comparative Analysis ◽  
Random Forest ◽  
Credit Card ◽  
Random Forest Classifier ◽  
Svm Classifier ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Random State

Aim: To reduce the false rate of cyber thefts in credit card attacks based on binary selection Random Forest classifier and SVM classifier. Materials and Methods: Classification is performed by Random forest classifier (N=28) over SVM classifier (N=28) is for false rate detection. Results and Discussion: The values obtained in terms of accuracy is identified by random state in Random forest (94.4%) over SVM (91.4%) Conclusion: The reduction of false rate with sigma value 0.126 appears to be better in Random Forest classifier than SVM classifier.

scholarly journals Development of anti-phishing browser based on random forest and rule of extraction framework

Cybersecurity ◽  
2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohith Gowda HR ◽  
Adithya MV ◽  
Gunesh Prasad S ◽  
Vinay S
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Time ◽  
Social Engineering ◽  
Detection Methods ◽  
Sensitive Information ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Client Side ◽  
Phishing Detection

Abstract Phishing is a technique under Social Engineering attacks which is most widely used to get user sensitive information, such as login credentials and credit and debit card information, etc. It is carried out by a person masquerading as an authentic individual. To protect web users from these attacks, various anti-phishing techniques are developed, but they fail to protect the user from these attacks in various ways. In this paper, we propose a novel technique to identify phishing websites effortlessly on the client side by proposing a novel browser architecture. In this system, we use the rule of extraction framework to extract the properties or features of a website using the URL only. This list consists of 30 different properties of a URL, which will later be used by the Random Forest Classification machine learning model to detect the authenticity of the website. A dataset consisting of 11,055 tuples is used to train the model. These processes are carried out on the client-side with the help of a redesigned browser architecture. Today Researches have come up with machine learning frameworks to detect phishing sites, but they are not in a state to be used by individuals having no technical knowledge. To make sure that these tools are accessible to every individual, we have improvised and introduced detection methods into the browser architecture named as ‘Embedded Phishing Detection Browser’ (EPDB), which is a novel method to preserve the existing user experience while improving the security. The newly designed browser architecture introduces a special segment to perform phishing detection operations in real-time. We have prototyped this technique to ensure maximum security, better accuracy of 99.36% in the identification of phishing websites in real-time.

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