Machine learning based KNN classifier: towards robust, efficient DTMF tone detection for a Noisy environment

2021 ◽  
Author(s):  
Arunit Maity ◽  
P. Prakasam ◽  
Sarthak Bhargava
Keyword(s):  
Machine Learning ◽  
Noisy Environment ◽  
Knn Classifier ◽  
Tone Detection

Prospects of Open Source Software for Maximizing the User Expectations in Heterogeneous Network

2021 ◽  
pp. 257-271
Author(s):  
Pushpa Singh ◽  
Rajeev Agrawal
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Research Work ◽  
Software Tool ◽  
Knn Classifier ◽  
User Expectations

This article focuses on the prospects of open source software and tools for maximizing the user expectations in heterogeneous networks. The open source software Python is used as a software tool in this research work for implementing machine learning technique for the categorization of the types of user in a heterogeneous network (HN). The KNN classifier available in Python defines the type of user category in real time to predict the available users in a particular category for maximizing profit for a business organization.

Implementing and analysing FAR and FRR for face and voice recognition (multimodal) using KNN classifier

2019 ◽  
Vol 8 (1) ◽  
pp. 55-67
Author(s):  
Dinesh Kumar D.S. ◽  
P.V. Rao
Keyword(s):  
Local Binary Pattern ◽  
Performance Metrics ◽  
Median Filter ◽  
Voice Recognition ◽  
Noisy Environment ◽  
Mel Frequency Cepstral Coefficients ◽  
Content Type ◽  
Knn Classifier ◽  
Military Applications ◽  
Voice Data

Purpose The purpose of this paper is to incorporate a multimodal biometric system, which plays a major role in improving the accuracy and reducing FAR and FRR performance metrics. Biometrics plays a major role in several areas including military applications because of robustness of the system. Speech and face data are considered as key elements that are commonly used for multimodal biometric applications, as they are simultaneously acquired from camera and microphone. Design/methodology/approach In this proposed work, Viola‒Jones algorithm is used for face detection, and Local Binary Pattern consists of texture operators that perform thresholding operation to extract the features of face. Mel-frequency cepstral coefficients exploit the performances of voice data, and median filter is used for removing noise. KNN classifier is used for fusion of both face and voice. The proposed method produces better results in noisy environment with better accuracy. In this proposed method, from the database, 120 face and voice samples are trained and tested with simulation results using MATLAB tool that improves performance in better recognition and accuracy. Findings The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent. Originality/value The algorithms perform better for both face and voice recognition. The outcome of this work provides better accuracy up to 98 per cent with reduced FAR of 0.5 per cent and FRR of 0.75 per cent.

scholarly journals K-NEAREST NEIGHBOUR CLASSIFIER USAGE FOR PERMISSION BASED MALWARE DETECTION IN ANDROID

2020 ◽  
Vol 4 (2) ◽  
pp. 15-27
Author(s):  
Recep Sinan ARSLAN ◽  
Ahmet Haşim Yurttakal
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Static And Dynamic Analysis ◽  
Machine Learning Methods ◽  
Knn Classifier ◽  
Proposed Model ◽  
Malicious Apps ◽  
Nearest Neighbour Classifier ◽  
Application Developers

ABSTRACT Android application platform is making rapid progress in these days. This development has made it the target of malicious application developers. This situation provides a numerical increase in malware apps, diversity in techniques, and rise of damage. Therefore, it is very critical to detect these software and escalation the security of mobile users. Static and dynamic analysis, behaviour scrutiny, machine learning methods are used to ensure security. In this study, K-nearest Neighbourhood (KNN) classifier, one of the machine learning methods, is used. Thus, it is aimed to detect malignant mobile software successfully and quickly. The tests is conducted with dataset includes 492 malware and 697 benign applications. In the proposed algorithm, neighbour number 5 and distance metric is preferred as Minkowski. 80% of dataset randomly selected is reserved for training and 20% for testing. As a result, while 94.1% accuracy is achieved, precision 91.2%, recall 92.7% recall and f1-measure is 92.4%. The high value obtained in f1-measure shows that the proposed model is successful in detecting both malware and benevolent software. The success of using KNN algorithm in classification of malicious apps in the Android has been demonstrated.

scholarly journals Brain Tumor Classification using Machine Learning

2021 ◽  
pp. 790-797
Author(s):  
N. Pavitha ◽  
Atharva Bakde ◽  
Shantanu Avhad ◽  
Isha Korate ◽  
Shaunak Mahajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Confusion Matrix ◽  
False Negative ◽  
Tumor Classification ◽  
Experimental Simulation ◽  
Accuracy Score ◽  
Research Papers ◽  
Knn Classifier

This paper presents a technical analysis of tumor data with Machine Learning and Classification Approach. Feature parameters which are dependent for classification of tumor are used for analyzing and classifying the class of tumor. In the classification of tumor, KNN-Classifier is implemented with cross validating accuracy score and tuning hyper parameters. Experimental simulation for best average score for K makes it to the cross validation. Approaching the prediction with the best accuracy score, hyper parameters of KNN Classifier states the best score. Using Principal Component Analysis on the data, miss-classification of tumor class in data is visualized. Aims: To declare and analyse tumor data from the source of MRI, CT scan, etc. for medication of tumor. To utilize smart predictions for the upcoming tumor patients using Machine Learning. Study Design:  Tumor classification using K Nearest Neighbor algorithm and analysis of the miss-classification. Methodology: We included 11 different studies and research papers which were relevant with tumor classification. Research papers include classification of tumors with different supervised learning approaches. Our proposed analysis and classification give visualization of two classes of tumor. Results: The Project results in classification of tumor data using Machine Learning and analyzing the miss-classification of tumor. In implementation of KNN Algorithm, the accuracy score after cross validation and tuning K values is 0.97. The confusion matrix shows 4 false positives and 1 false negative value in testing. Conclusion: Less miss-classification of tumor results best accuracy score and more efficient working on testing data. Visualizing the classification with 3-dimensional scatter plots made the analysis accurate.

Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 373-381
Author(s):  
Shiva Shanta Mani B. ◽  
Manikandan V. M.
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Early Stage ◽  
Heart Diseases ◽  
Age Groups ◽  
Disease Prediction ◽  
Learning Approaches ◽  
Health Issues ◽  
Decision Tree Classifier ◽  
Knn Classifier

Heart disease is one of the most common and serious health issues in all the age groups. The food habits, mental stress, smoking, etc. are a few reasons for heart diseases. Diagnosing heart issues at an early stage is very much important to take proper treatment. The treatment of heart disease at the later stage is very expensive and risky. In this chapter, the authors discuss machine learning approaches to predict heart disease from a set of health parameters collected from a person. The heart disease dataset from the UCI machine learning repository is used for the study. This chapter discusses the heart disease prediction capability of four well-known machine learning approaches: naive Bayes classifier, KNN classifier, decision tree classifier, random forest classifier.

Realization of Natural User Interface for Computer Control With KNN Classifier Enhanced Smart Glove

2019 ◽  
Author(s):  
Chieh-Ju Wu ◽  
Kai-Hsiang Lin ◽  
Meng-Lin Hsieh ◽  
Jen-Yuan (James) Chang
Keyword(s):  
Machine Learning ◽  
User Interface ◽  
Learning Algorithm ◽  
Computer Control ◽  
Supervised Machine Learning ◽  
Measurement Unit ◽  
Hand Gesture ◽  
Natural User Interface ◽  
Hand Orientation ◽  
Knn Classifier

Abstract Gesture interaction is a commonly used solution when introducing Natural User Interface (NUI), a kind of user interface where the interaction is direct and consistent with natural heuristic behaviors. In this paper, a smart glove with efficient real-time hand orientation calculation and accurate static hand gesture prediction is proposed. This custom-built wireless glove consists of flex sensors, an Inertial Measurement Unit (IMU) sensor, a microcontroller with multi-channel ADC/AMP (analog to digital converter and amplifier), a Bluetooth module, and an Arduino Micro Pro. K-Nearest-Neighbor (KNN) classifier is implemented to assist static hand gesture prediction with the validated accuracy exceeding 97%. This supervised machine learning algorithm allows a highly customizable smart glove which the input gestures, input number of gestures, and the associated activating functions are all easily changeable by the users any time. To show the benefits of combining the NUI and supervised machine learning, a validation experiments, computer control, were conducted.

scholarly journals Movie Success Rate Prediction Using Robust Classifier

2019 ◽  
Vol 8 (6) ◽  
pp. 3517-3522
Keyword(s):  
Machine Learning ◽  
Success Rate ◽  
Film Industry ◽  
Machine Learning Algorithms ◽  
Running Time ◽  
Box Office ◽  
Knn Classifier ◽  
Nearest Neighbours ◽  
Adaboost Classifier ◽  
Movie Success

Film industry is a multi-billion-dollar industry where each movie earns over billions of dollar. Predicting the success of the movie is a difficult task because the success rate is influenced by various factors like running time, actor, actress, genre etc. In this paper a detailed study of machine learning algorithms such as Adaboost, SVM, and K-Nearest Neighbours (KNN) were done and was implemented on IMDB dataset for predicting box office. Based on the results, Adaboost classifier gives better performance compared to SVM and KNN classifier algorithms

scholarly journals Classification Comparative Analysis for Detection of Brain Tumor Using Neural Network, Logistic Regression & KNN Classifier with VGG19 Convolution Neural Network Feature Extraction

2021 ◽  
Author(s):  
Vijaya Kamble ◽  
Rohin Daruwala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Extraction ◽  
Logistic Regression ◽  
Brain Tumor ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Knn Classifier

In recent years due to advancements in digital imaging machine learning techniques are used in medical image analysis for the prognosis and diagnosis of various abnormalities in the human body. Various Machine learning algorithms, convolution and deep neural networks are used for classification, detection and prediction of various brain tumors. The proposed approach is a different comparative classification analysis approach which is based on three different classification namely KNN classifier,Logistic regression & neural network as classifier. It is based on a deep learning feature extraction technique using VGG19. This VGG 19-layer image recognition model trained on Imgenet. Generally, MRI data sequences are analyzed in terms of different modalities and every modality contains rich tissue information. So, feature exaction from MRI sequences is very important task for brain tumor classification. Our approach demonstrated fair classification on BRATS Benchmarks 2018 data set with different modalities and sizes of images,results are without any human annotations. Based on selected classifiers all the classifiers gives accuracy above 90%. It is good compared to other state of art methods.

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