Classifying Images of Drought-Affected Area Using Deep Belief Network, kNN, and Random Forest Learning Techniques

2018 ◽  
pp. 102-119 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Pulkit Kulshrestha ◽  
Pijush Samui
Keyword(s):  
Random Forest ◽  
Performance Metrics ◽  
Deep Belief Network ◽  
Machine Learning Algorithms ◽  
Nearest Neighbour ◽  
Data Set ◽  
Belief Network ◽  
Learning Techniques ◽  
Severe Shortage

Drought is a condition of land in which the ground water faces a severe shortage. This condition affects the survival of plants and animals. Drought can impact ecosystem and agricultural productivity, severely. Hence, the economy also gets affected by this situation. This paper proposes Deep Belief Network (DBN) learning technique, which is one of the state of the art machine learning algorithms. This proposed work uses DBN, for classification of drought and non-drought images. Also, k nearest neighbour (kNN) and random forest learning methods have been proposed for the classification of the same drought images. The performance of the Deep Belief Network(DBN) has been compared with k nearest neighbour (kNN) and random forest. The data set has been split into 80:20, 70:30 and 60:40 as train and test. Finally, the effectiveness of the three proposed models have been measured by various performance metrics.

scholarly journals Classification of hazelnut cultivars: comparison of DL4J and ensemble learning algorithms

2020 ◽  
Vol 48 (4) ◽  
pp. 2316-2327
Author(s):  
Caner KOC ◽  
Dilara GERDAN ◽  
Maksut B. EMİNOĞLU ◽  
Uğur YEGÜL ◽  
Bulent KOC ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Random Forest ◽  
Ensemble Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Performance Criteria ◽  
Gradient Boosting ◽  
Data Set

Classification of hazelnuts is one of the values adding processes that increase the marketability and profitability of its production. While traditional classification methods are used commonly, machine learning and deep learning can be implemented to enhance the hazelnut classification processes. This paper presents the results of a comparative study of machine learning frameworks to classify hazelnut (Corylus avellana L.) cultivars (‘Sivri’, ‘Kara’, ‘Tombul’) using DL4J and ensemble learning algorithms. For each cultivar, 50 samples were used for evaluations. Maximum length, width, compression strength, and weight of hazelnuts were measured using a caliper and a force transducer. Gradient boosting machine (Boosting), random forest (Bagging), and DL4J feedforward (Deep Learning) algorithms were applied in traditional machine learning algorithms. The data set was partitioned into a 10-fold-cross validation method. The classifier performance criteria of accuracy (%), error percentage (%), F-Measure, Cohen’s Kappa, recall, precision, true positive (TP), false positive (FP), true negative (TN), false negative (FN) values are provided in the results section. The results showed classification accuracies of 94% for Gradient Boosting, 100% for Random Forest, and 94% for DL4J Feedforward algorithms.

scholarly journals On the classification of Microsoft-Windows ransomware using hardware profile

2021 ◽  
Vol 7 ◽  
pp. e361
Author(s):  
Sana Aurangzeb ◽  
Rao Naveed Bin Rais ◽  
Muhammad Aleem ◽  
Muhammad Arshad Islam ◽  
Muhammad Azhar Iqbal
Keyword(s):  
Random Forest ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Substantial Part ◽  
Data Set ◽  
Performance Counters ◽  
Extreme Gradient Boosting ◽  
Hardware Performance Counters ◽  
Microsoft Windows

Due to the expeditious inclination of online services usage, the incidents of ransomware proliferation being reported are on the rise. Ransomware is a more hazardous threat than other malware as the victim of ransomware cannot regain access to the hijacked device until some form of compensation is paid. In the literature, several dynamic analysis techniques have been employed for the detection of malware including ransomware; however, to the best of our knowledge, hardware execution profile for ransomware analysis has not been investigated for this purpose, as of today. In this study, we show that the true execution picture obtained via a hardware execution profile is beneficial to identify the obfuscated ransomware too. We evaluate the features obtained from hardware performance counters to classify malicious applications into ransomware and non-ransomware categories using several machine learning algorithms such as Random Forest, Decision Tree, Gradient Boosting, and Extreme Gradient Boosting. The employed data set comprises 80 ransomware and 80 non-ransomware applications, which are collected using the VirusShare platform. The results revealed that extracted hardware features play a substantial part in the identification and detection of ransomware with F-measure score of 0.97 achieved by Random Forest and Extreme Gradient Boosting.

scholarly journals Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification

2020 ◽  
pp. 1-9
Author(s):  
Supun Nakandala ◽  
Marta M. Jankowska ◽  
Fatima Tuz-Zahra ◽  
John Bellettiere ◽  
Jordan A. Carlson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Logistic Regression ◽  
Random Forest ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Feature Engineering ◽  
Free Living ◽  
Data Set

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

scholarly journals Classification of Sentiment on Business Data for Decision Making using Supervised Machine Learning Methods

2020 ◽  
Vol 9 (3) ◽  
pp. 3595-3600
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Product Review ◽  
Data Set ◽  
Random Forest Classification

Sentiment analysis is deals with the classification of sentiments expressed in a particular document. The analysis of user generated data by using sentiment analysis is very useful for knowing the opinion of a crowd. This paper is mainly aimed to tackle the problem of polarity categorization of sentiment analysis. A Detailed description of the sentiment analysis process is also given. Product review data set from UCI repository is used for analysis. This paper is giving a comparative analysis of four supervised machine learning algorithms namely Naive Bayes, Support Vector Machine, Decision Tree and Random Forest which are used for product review analysis. The result shows that, Random Forest classification algorithm provides better accuracy than other three algorithms

scholarly journals Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Faizan Ullah ◽  
Qaisar Javaid ◽  
Abdu Salam ◽  
Masood Ahmad ◽  
Nadeem Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Feature Vector ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Proposed Model ◽  
Testing Accuracy ◽  
Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

scholarly journals Researching the Research: Applying Machine Learning Techniques to Dissertation Classification

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Suzanna Schmeelk
Keyword(s):  
Machine Learning ◽  
Full Text ◽  
Doctoral Program ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Full Time ◽  
Learning Techniques ◽  
Manual Classification ◽  
Machine Learning Tool

This research examines industry-based dissertation research in a doctoral computing program through the lens of machine learning algorithms to understand topics explored by senior and experienced full-time working professionals (EFWPs).  Our research categorizes dissertation by both their abstracts and by their full-text using the Graplab Create library from Apple’s Turi. We also compare the dissertation categorizations using IBM’s Watson Discovery deep machine learning tool.  Our research provides perspectives on the practicality of the manual classification of technical documents; and, it provides insights into the: (1) categories of academic work created by EFWPs in a Computing doctoral program, (2) viability of automated categorization versus human abstraction, and (3) differences in categorization algorithms.

Screening and Diagnosis of Autism Spectrum Disorder via Assistive Tools

2022 ◽  
pp. 1-21
Author(s):  
Gurkan Tuna ◽  
Ayşe Tuna
Keyword(s):  
Autism Spectrum Disorder ◽  
Random Forest ◽  
Performance Metrics ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
K Nearest Neighbors ◽  
Medical Test ◽  
And Behavior

Autism spectrum disorder (ASD) is a challenging developmental condition that involves restricted and/or repetitive behaviors and persistent challenges in social interaction and speech and nonverbal communication. There is not a standard medical test used to diagnose ASD; therefore, diagnosis is made by looking at the child's developmental history and behavior. In recent years, due to the increase in diagnosed cases of ASD, researchers proposed software-based tools to aid in and expedite the diagnosis. Considering the fact that most of these tools rely on the use of classifiers, in study, random forest, decision tree, k-nearest neighbors, and zero rule algorithms are used as classifiers, and their performances are compared using well-known performance metrics. As proven in the study, random forest algorithm can provide higher accuracy than the others in the classification of ASD and can be integrated into a computer- or humanoid-robot-based system for automated prescreening and diagnosis of ASD in preschool children groups.

Classification of ECG beats using deep belief network and active learning

2018 ◽  
Vol 56 (10) ◽  
pp. 1887-1898 ◽  
Author(s):  
Sayantan G. ◽  
Kien P. T. ◽  
Kadambari K. V.
Keyword(s):  
Active Learning ◽  
Deep Belief Network ◽  
Belief Network
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