An Analysis of Computational Complexity and Accuracy of Two Supervised Machine Learning Algorithms—K-Nearest Neighbor and Support Vector Machine

2020 ◽  
pp. 335-347
Author(s):  
Susmita Ray
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Computational Complexity ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
K Nearest Neighbor

scholarly journals Execution Assessment of Machine Learning Algorithms for Spam Profile Detection on Instagram

2021 ◽  
Vol 10 (3) ◽  
pp. 1889-1894
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Tools ◽  
Learning Models ◽  
K Nearest Neighbor

Witheverypassingsecondsocialnetworkcommunityisgrowingrapidly,becauseofthat,attackershaveshownkeeninterestinthesekindsofplatformsandwanttodistributemischievouscontentsontheseplatforms.Withthefocus on introducing new set of characteristics and features forcounteractivemeasures,agreatdealofstudieshasresearchedthe possibility of lessening the malicious activities on social medianetworks. This research was to highlight features for identifyingspammers on Instagram and additional features were presentedto improve the performance of different machine learning algorithms. Performance of different machine learning algorithmsnamely, Multilayer Perceptron (MLP), Random Forest (RF), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)were evaluated on machine learning tools named, RapidMinerand WEKA. The results from this research tells us that RandomForest (RF) outperformed all other selected machine learningalgorithmsonbothselectedmachinelearningtools.OverallRandom Forest (RF) provided best results on RapidMiner. Theseresultsareusefulfortheresearcherswhoarekeentobuildmachine learning models to find out the spamming activities onsocialnetworkcommunities.

scholarly journals Leveraging Machine Learning Algorithms For Zero-Day Ransomware Attack

2019 ◽  
Vol 8 (6) ◽  
pp. 4104-4107
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Supervised Learning Algorithms ◽  
Microsoft Windows

Current global huge cyber protection attacks resulting from Infected Encryption ransomware structures over all international locations and businesses with millions of greenbacks lost in paying compulsion abundance. This type of malware encrypts consumer files, extracts consumer files, and charges higher ransoms to be paid for decryption of keys. An attacker could use different types of ransomware approach to steal a victim's files. Some of ransomware attacks like Scareware, Mobile ransomware, WannaCry, CryptoLocker, Zero-Day ransomware attack etc. A zero-day vulnerability is a software program security flaw this is regarded to the software seller however doesn’t have patch in vicinity to restore a flaw. Despite the fact that machine learning algorithms are already used to find encryption Ransomware. This is based on the analysis of a large number of PE file data Samples (benign software and ransomware utility) makes use of supervised machine learning algorithms for ascertain Zero-day attacks. This work was done on a Microsoft Windows operating system (the most attacked os through encryption ransomware) and estimated it. We have used four Supervised learning Algorithms, Random Forest Classifier , K-Nearest Neighbor, Support Vector Machine and Logistic Regression. Tests using machine learning algorithms evaluate almost null false positives with a 99.5% accuracy with a random forest algorithm.

scholarly journals A REVIEW ON MACHINE LEARNING TECHNIQUES FOR ADVANCED HEALTH CARE SYSTEMS

2020 ◽  
Vol 9 (11) ◽  
pp. 1-7
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Logistic Regression ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor

Artificial intelligence is the technology that lets a machine mimic the thinking ability of a human being. Machine learning is the subset of AI, that makes this machine exhibit human behavior by making it learn from the known data, without the need of explicitly programming it. The health care sector has adopted this technology, for the development of medical procedures, maintaining huge patient’s records, assist physicians in the prediction, detection, and treatment of diseases and many more. In this paper, a comparative study of six supervised machine learning algorithms namely Logistic Regression(LR),support vector machine(SVM),Decision Tree(DT).Random Forest(RF),k-nearest neighbor(k-NN),Naive Bayes (NB) are made for the classification and prediction of diseases. Result shows out of compared supervised learning algorithms here, logistic regression is performing best with an accuracy of 81.4 % and the least performing is k-NN with just an accuracy of 69.01% in the classification and prediction of diseases.

scholarly journals A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

2021 ◽  
Author(s):  
Dimple Chehal ◽  
Parul Gupta ◽  
Payal Gulati
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Product Reviews ◽  
K Nearest Neighbor ◽  
Customer Reviews ◽  
Percent Accuracy

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

scholarly journals Encrypted DNP3 Traffic Classification Using Supervised Machine Learning Algorithms

2019 ◽  
Vol 1 (1) ◽  
pp. 384-399 ◽  
Author(s):  
Thais de Toledo ◽  
Nunzio Torrisi
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Smart Grids ◽  
Learning Algorithms ◽  
Electric Utility ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Communication Link

The Distributed Network Protocol (DNP3) is predominately used by the electric utility industry and, consequently, in smart grids. The Peekaboo attack was created to compromise DNP3 traffic, in which a man-in-the-middle on a communication link can capture and drop selected encrypted DNP3 messages by using support vector machine learning algorithms. The communication networks of smart grids are a important part of their infrastructure, so it is of critical importance to keep this communication secure and reliable. The main contribution of this paper is to compare the use of machine learning techniques to classify messages of the same protocol exchanged in encrypted tunnels. The study considers four simulated cases of encrypted DNP3 traffic scenarios and four different supervised machine learning algorithms: Decision tree, nearest-neighbor, support vector machine, and naive Bayes. The results obtained show that it is possible to extend a Peekaboo attack over multiple substations, using a decision tree learning algorithm, and to gather significant information from a system that communicates using encrypted DNP3 traffic.

scholarly journals Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 104 ◽  
Author(s):  
Ahmed ◽  
Yigit ◽  
Isik ◽  
Alpkocak
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Leukemia Data

Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multiclass classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other wellknown machine learning algorithms.

scholarly journals A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bhagya M. Patil ◽  
Vishwanath Burkpalli
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Texture Features ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cotton Leaf ◽  
K Nearest Neighbor ◽  
Color Features ◽  
Leaf Image

Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.

scholarly journals Framework for Providing Security in Private Cloud using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 7641-7645
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Learning Algorithms ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cyber Attack ◽  
Learning Techniques

The advancement in cyber-attack technologies have ushered in various new attacks which are difficult to detect using traditional intrusion detection systems (IDS).Existing IDS are trained to detect known patterns because of which newer attacks bypass the current IDS and go undetected. In this paper, a two level framework is proposed which can be used to detect unknown new attacks using machine learning techniques. In the first level the known types of classes for attacks are determined using supervised machine learning algorithms such as Support Vector Machine (SVM) and Neural networks (NN). The second level uses unsupervised machine learning algorithms such as K-means. The experimentation is carried out with four models with NSL- KDD dataset in Openstack cloud environment. The Model with Support Vector Machine for supervised machine learning, Gradual Feature Reduction (GFR) for feature selection and K-means for unsupervised algorithm provided the optimum efficiency of 94.56 %.

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