scholarly journals Deep Learning Approach for Voice Pathology Detection and Classification

2021 ◽  
Vol 16 (4) ◽  
pp. 1-30
Author(s):  
Vikas Mittal ◽  
R. K. Sharma
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Kappa Index ◽  
Voice Signal ◽  
Classification Score ◽  
Positive Rate ◽  
Voice Pathology Detection ◽  
Sensitivity Specificity

A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current manuscript. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms.The proposed architecture has five different stages.The results are recorded in terms of nine (9) different classification score indices which are – mean average Precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews Correlation Coefficient, and the Cohen’s Kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.

scholarly journals IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2857
Author(s):  
Laura Vigoya ◽  
Diego Fernandez ◽  
Victor Carneiro ◽  
Francisco Nóvoa
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
High Detection Rate ◽  
Security Vulnerabilities ◽  
Smart Systems ◽  
Learning Techniques ◽  
Positive Rate

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

scholarly journals Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 35 ◽  
Author(s):  
Xuan Dau Hoang ◽  
Ngoc Tuong Nguyen
Keyword(s):  
Machine Learning ◽  
Web Applications ◽  
False Positive Rate ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Government Organizations ◽  
Detection Model ◽  
Learning Techniques ◽  
Positive Rate

Defacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of the owner reputation, which may result in huge financial losses. Many solutions have been researched and deployed for monitoring and detection of website defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis, and complicated algorithms. However, some solutions only work on static websites and others demand extensive computing resources. This paper proposes a hybrid defacement detection model based on the combination of the machine learning-based detection and the signature-based detection. The machine learning-based detection first constructs a detection profile using training data of both normal and defaced web pages. Then, it uses the profile to classify monitored web pages into either normal or attacked. The machine learning-based component can effectively detect defacements for both static pages and dynamic pages. On the other hand, the signature-based detection is used to boost the model’s processing performance for common types of defacements. Extensive experiments show that our model produces an overall accuracy of more than 99.26% and a false positive rate of about 0.27%. Moreover, our model is suitable for implementation of a real-time website defacement monitoring system because it does not demand extensive computing resources.

scholarly journals Machine Learning for 5G MIMO Modulation Detection

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1556
Author(s):  
Haithem Ben Chikha ◽  
Ahmad Almadhor ◽  
Waqas Khalid
Keyword(s):  
Machine Learning ◽  
Software Defined Radio ◽  
Mimo Systems ◽  
False Positive Rate ◽  
Cognitive Radios ◽  
Machine Learning Techniques ◽  
Destination Node ◽  
Detection Techniques ◽  
Positive Rate ◽  
Modulation Detection

Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI). At the destination node, we extract the higher-order statistics of the received signals as the discriminating features. After applying the principal component analysis technique, we carry out a comparative study between the random committee and the AdaBoost machine learning techniques (MLTs) at low signal-to-noise ratio. The efficiency metrics, including the true positive rate, false positive rate, precision, recall, F-Measure, and the time taken to build the model, are used for the performance comparison. The simulation results show that the use of the random committee MLT, compared to the AdaBoost MLT, provides gain in terms of both the modulation detection and complexity.

scholarly journals Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 387
Author(s):  
Nahina Islam ◽  
Md Mamunur Rashid ◽  
Santoso Wibowo ◽  
Cheng-Yuan Xu ◽  
Ahsan Morshed ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Weed Detection ◽  
Learning Techniques ◽  
Positive Rate ◽  
Uav Images

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

scholarly journals An Efficient Ensemble Classifier for Heart Disease Diagnosis and early Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 109-114
Keyword(s):  
Machine Learning ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Random Forest ◽  
False Positive Rate ◽  
Ensemble Classifier ◽  
Disease Pattern ◽  
Positive Rate ◽  
Sensitivity Specificity ◽  
Pattern Prediction

Heart Disease is one of the most significant causes of mortality in the world today. Prediction and Diagnosis of Cardiovascular disease is considered as one of the major challenges in the Medical Field especially for Cardiologists. Artificial Intelligence and Machine learning (ML) was popularly employed for pattern prediction and it was noticed that these Intelligent Mechanisms were used in Medical Feld for better Heart Disease Pattern Prediction. Thus more researchers were focusing Machine Learning based Data Mining Classifiers for Heart Disease Pattern Prediction and Diagnosis in the healthcare Industry especially for Cardiologists. This research work identified the recently proposed Hybrid Random Forest with a Linear Model (HRFLM) Classifier for improving the classification accuracy for the cardiovascular disease patterns prediction well in advance and Diagnosis as well. However, it was noticed that for improving the performances better in terms of Accuracy, Sensitivity, Specificity, Precision, FScore and False Positive Rate FPR, needed an efficient classifier. Thus this work developed and implemented an efficient Classifier ensemble Nu-SVC Classifier and Weighted Random Forest Classifier. From the experimental results, it was noticed that the proposed Ensemble Classifier performs better as compared with that of existing Hybrid Classifier in terms of in terms of Accuracy, Sensitivity, Specificity, Precision, FScore and False Positive Rate FPR

scholarly journals An Ensemble-Based Malware Detection Model Using Minimum Feature Set

MENDEL ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Ivan Zelinka ◽  
Eslam Amer
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Detection Model ◽  
Learning Techniques ◽  
Proposed Model ◽  
Positive Rate ◽  
Minimum Number

Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.

TISSUE CLASSIFICATION FOR COLORECTAL CANCER UTILIZING TECHNIQUES OF DEEP LEARNING AND MACHINE LEARNING

2021 ◽  
pp. 2150022
Author(s):  
Kasikrit Damkliang ◽  
Thakerng Wongsirichot ◽  
Paramee Thongsuksai
Keyword(s):  
Colorectal Cancer ◽  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Texture Classification ◽  
False Positive Rate ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Cancer Tissues

Since the introduction of image pattern recognition and computer vision processing, the classification of cancer tissues has been a challenge at pixel-level, slide-level, and patient-level. Conventional machine learning techniques have given way to Deep Learning (DL), a contemporary, state-of-the-art approach to texture classification and localization of cancer tissues. Colorectal Cancer (CRC) is the third ranked cause of death from cancer worldwide. This paper proposes image-level texture classification of a CRC dataset by deep convolutional neural networks (CNN). Simple DL techniques consisting of transfer learning and fine-tuning were exploited. VGG-16, a Keras pre-trained model with initial weights by ImageNet, was applied. The transfer learning architecture and methods responding to VGG-16 are proposed. The training, validation, and testing sets included 5000 images of 150 × 150 pixels. The application set for detection and localization contained 10 large original images of 5000 × 5000 pixels. The model achieved F1-score and accuracy of 0.96 and 0.99, respectively, and produced a false positive rate of 0.01. AUC-based evaluation was also measured. The model classified ten large previously unseen images from the application set represented in false color maps. The reported results show the satisfactory performance of the model. The simplicity of the architecture, configuration, and implementation also contributes to the outcome this work.

Machine Learning for Detecting Scallops in AUV Benthic Images

2015 ◽  
pp. 22-40
Author(s):  
Prasanna Kannappan ◽  
Herbert G. Tanner ◽  
Arthur C. Trembanis ◽  
Justin H. Walker
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Template Matching ◽  
False Positive Rate ◽  
Image Data ◽  
Machine Learning Techniques ◽  
Detection And Counting ◽  
Attractive Option ◽  
Positive Rate ◽  
Histogram Of Gradients

A large volume of image data, in the order of thousands to millions of images, can be generated by robotic marine surveys aimed at assessment of organism populations. Manual processing and annotation of individual images in such large datasets is not an attractive option. It would seem that computer vision and machine learning techniques can be used to automate this process, yet to this date, available automated detection and counting tools for scallops do not work well with noisy low-resolution images and are bound to produce very high false positive rates. In this chapter, we hone a recently developed method for automated scallop detection and counting for the purpose of drastically reducing its false positive rate. In the process, we compare the performance of two customized false positive filtering alternatives, histogram of gradients and weighted correlation template matching.

scholarly journals Intrusion Detection using Machine Learning and Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 9704-9719
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection Rate ◽  
False Positive Rate ◽  
Future Research ◽  
Different Types ◽  
Security Problem ◽  
Positive Rate ◽  
Networking Technology

With the increase in usage of networking technology and the Internet, Intrusion detection becomes important and challenging security problem. A number of techniques came into existence to detect the intrusions on the basis of machine learning and deep learning procedures. This paper will give inspiration to the use of ML and DL systems to IP traffic and gives a concise depiction of every one of the ML and DL strategies. This paper gives an audit of 40 noteworthy works that covers the period from 2015 to 2019. ML and DL methods are compared with regard to their accuracy and detection potential to detect different types of intrusions. Future Research includes ML and DL methods to find the intrusions so as to improve the detection rate, accuracy and to minimize the false positive rate.

scholarly journals Challenges for Tractogram Filtering

2021 ◽  
pp. 149-168
Author(s):  
Daniel Jörgens ◽  
Maxime Descoteaux ◽  
Rodrigo Moreno
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Post Processing ◽  
Learning Techniques ◽  
Processing Step ◽  
Positive Rate ◽  
Neural Fiber ◽  
Modern Machine

AbstractTractography aims at describing the most likely neural fiber paths in white matter. A general issue of current tractography methods is their large false-positive rate. An approach to deal with this problem is tractogram filtering in which anatomically implausible streamlines are discarded as a post-processing step after tractography. In this chapter, we review the main approaches and methods from literature that are relevant for the application of tractogram filtering. Moreover, we give a perspective on the central challenges for the development of new methods, including modern machine learning techniques, in this field in the next few years.

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