Wavelet-Based Multiresolution Bispectral Analysis for Detection and Classification of Helicopter Drive-Shaft Problems

2017 ◽  
Vol 140 (6) ◽  
Author(s):  
Mohammed A. Hassan ◽  
Michael R. Habib ◽  
Rania A. Abul Seoud ◽  
Abdel M. Bayoumi
Keyword(s):  
Search Algorithm ◽  
Drive Shaft ◽  
Superior Performance ◽  
Support Vector ◽  
Classification Algorithms ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Safety Level ◽  
Rotor Drive ◽  
Minimum Number

Condition monitoring and fault diagnostics in rotorcraft have significant effect on improving safety level and reducing operational and maintenance costs. In this paper, a new method is proposed for fault detection and diagnoses of AH-64D (Apache helicopter) tail rotor drive-shaft problems. The proposed method depends on decomposing signal into different frequency ranges using mother wavelet. The most informative part of the vibration signal is then determined by calculating Shannon entropy of each part. Bispectrum is calculated for this part to investigate quadratic nonlinearities in this segment. Then, search algorithm is used to extract minimum number of indicative features from the bispectrum, which are then fed to classification algorithms. In order to quantitatively evaluate the proposed method, six classification algorithms are compared against each other such as fine K-nearest neighbor (KNN), cubic KNN, quadratic discriminant analysis, linear support vector machine (SVM), Gaussian SVM, and neural network. Comparison criteria include accuracy, precision, sensitivity, F score, true alarm, recall, and error classification accuracy (ECA). The proposed method is verified using real-world vibration data collected from a dedicated AH-64D helicopter tail rotor drive train (TRDT) research test bed. The proposed algorithm proves its ability in finding minimum number of indicative features and classifying the shaft faults with superior performance.

mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation

2018 ◽  
Vol 35 (16) ◽  
pp. 2757-2765 ◽  
Author(s):  
Balachandran Manavalan ◽  
Shaherin Basith ◽  
Tae Hwan Shin ◽  
Leyi Wei ◽  
Gwang Lee
Keyword(s):  
Nearest Neighbor ◽  
Feature Representation ◽  
Superior Performance ◽  
Supplementary Information ◽  
Gradient Boosting ◽  
Support Vector ◽  
Pharmaceutical Drugs ◽  
K Nearest Neighbor ◽  
Feature Descriptors ◽  
Predicted Probability

AbstractMotivationCardiovascular disease is the primary cause of death globally accounting for approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular diseases and other complications is hypertension. Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there is no comprehensive analysis, assessment of diverse features and implementation of various machine-learning (ML) algorithms applied for antihypertensive peptide (AHTP) model construction.ResultsIn this study, we utilized six different ML algorithms, namely, Adaboost, extremely randomized tree (ERT), gradient boosting (GB), k-nearest neighbor, random forest (RF) and support vector machine (SVM) using 51 feature descriptors derived from eight different feature encodings for the prediction of AHTPs. While ERT-based trained models performed consistently better than other algorithms regardless of various feature descriptors, we treated them as baseline predictors, whose predicted probability of AHTPs was further used as input features separately for four different ML-algorithms (ERT, GB, RF and SVM) and developed their corresponding meta-predictors using a two-step feature selection protocol. Subsequently, the integration of four meta-predictors through an ensemble learning approach improved the balanced prediction performance and model robustness on the independent dataset. Upon comparison with existing methods, mAHTPred showed superior performance with an overall improvement of approximately 6–7% in both benchmarking and independent datasets.Availability and implementationThe user-friendly online prediction tool, mAHTPred is freely accessible at http://thegleelab.org/mAHTPred.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Intelligent security system detects the hidden objects in the smart grid

2020 ◽  
Vol 19 (1) ◽  
pp. 188
Author(s):  
Ammar Wisam Altaher ◽  
Abdullah Hasan Hussein
Keyword(s):  
Image Fusion ◽  
Search Algorithm ◽  
Detection System ◽  
Harmony Search ◽  
Hybrid Approach ◽  
Human Vision ◽  
Superior Performance ◽  
Support Vector ◽  
Accurate Information ◽  
Discrete Wavelet

<p>Monitoring the general public gathered in large numbers is one of the most challenging tasks faced by the law and order enforcement team. There is swiftly demand to that have inbuilt sensors which can detect the concealed weapon, from a standoff distance the system can locate the weapon with very high accuracy. Objects that are obscure and invisible from human vision can be seen vividly from enhanced artificial vision systems. Image Fusion is a computer vision technique that fuses images from multiple sensors to give accurate information. Image fusion using visual and infrared images has been employed for a safe, non-invasive standoff threat detection system. The fused imagery is further processed for specific identification of weapons. The unique approach to discover concealed weapon based on DWT in conjunction with Meta heuristic algorithm Harmony Search Algorithm and SVM classification is presented. It firstly uses the traditional discrete wavelet transform along with the hybrid Hoteling transform to obtain a fused imagery. Then a heuristic search algorithm is applied to search the best optimal harmony to generate the new principal components of the registered input images which is later classified using the K means support vector machines to build better classifiers for concealed weapon detection. Experimental results demonstrate the hybrid approach which shows the superior performance.</p>

scholarly journals Plant disease prediction using classification algorithms

2021 ◽  
Vol 10 (1) ◽  
pp. 257
Author(s):  
Maria Morgan ◽  
Carla Blank ◽  
Raed Seetan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nearest Neighbor ◽  
Disease Classification ◽  
Future Research ◽  
Support Vector ◽  
Classification Algorithms ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Artificial Neural

<p>This paper investigates the capability of six existing classification algorithms (Artificial Neural Network, Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Decision Tree and Random Forest) in classifying and predicting diseases in soybean and mushroom datasets using datasets with numerical or categorical attributes. While many similar studies have been conducted on datasets of images to predict plant diseases, the main objective of this study is to suggest classification methods that can be used for disease classification and prediction in datasets that contain raw measurements instead of images. A fungus and a plant dataset, which had many differences, were chosen so that the findings in this paper could be applied to future research for disease prediction and classification in a variety of datasets which contain raw measurements. A key difference between the two datasets, other than one being a fungus and one being a plant, is that the mushroom dataset is balanced and only contained two classes while the soybean dataset is imbalanced and contained eighteen classes. All six algorithms performed well on the mushroom dataset, while the Artificial Neural Network and k-Nearest Neighbor algorithms performed best on the soybean dataset. The findings of this paper can be applied to future research on disease classification and prediction in a variety of dataset types such as fungi, plants, humans, and animals.</p>

scholarly journals COMPARATIVE STUDY OF CLASSIFICATION ALGORITHMS: HOLDOUTS AS ACCURACY ESTIMATION

2016 ◽  
Vol 1 (1) ◽  
pp. 13 ◽  
Author(s):  
Debby Erce Sondakh
Keyword(s):  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Decision Rules ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Accuracy Estimation ◽  
F Measure

Penelitian ini bertujuan untuk mengukur dan membandingkan kinerja lima algoritma klasifikasi teks berbasis pembelajaran mesin, yaitu decision rules, decision tree, k-nearest neighbor (k-NN), naïve Bayes, dan Support Vector Machine (SVM), menggunakan dokumen teks multi-class. Perbandingan dilakukan pada efektifiatas algoritma, yaitu kemampuan untuk mengklasifikasi dokumen pada kategori yang tepat, menggunakan metode holdout atau percentage split. Ukuran efektifitas yang digunakan adalah precision, recall, F-measure, dan akurasi. Hasil eksperimen menunjukkan bahwa untuk algoritma naïve Bayes, semakin besar persentase dokumen pelatihan semakin tinggi akurasi model yang dihasilkan. Akurasi tertinggi naïve Bayes pada persentase 90/10, SVM pada 80/20, dan decision tree pada 70/30. Hasil eksperimen juga menunjukkan, algoritma naïve Bayes memiliki nilai efektifitas tertinggi di antara lima algoritma yang diuji, dan waktu membangun model klasiifikasi yang tercepat, yaitu 0.02 detik. Algoritma decision tree dapat mengklasifikasi dokumen teks dengan nilai akurasi yang lebih tinggi dibanding SVM, namun waktu membangun modelnya lebih lambat. Dalam hal waktu membangun model, k-NN adalah yang tercepat namun nilai akurasinya kurang.

scholarly journals Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 775 ◽  
Author(s):  
Yuliang Ma ◽  
Songjie Zhang ◽  
Donglian Qi ◽  
Zhizeng Luo ◽  
Rihui Li ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Swarm Optimization ◽  
Eeg Data ◽  
Learning Machine

Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject’s drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers’ drowsiness, compared to the other techniques.

Service complaint identification in hotel social media: A two-step classification approach

2020 ◽  
pp. 002072092092846
Author(s):  
Jiahua Jin ◽  
Lu Lu
Keyword(s):  
Social Media ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Classification Algorithms ◽  
Construction Process ◽  
K Nearest Neighbor ◽  
Classification Approach ◽  
Consumer Complaint ◽  
Training Samples ◽  
Binary Classifiers

Hotel social media provides access to dissatisfied customers and their experiences with services. However, due to massive topics and posts in social media, and the sparse distribution of complaint-related posts and, manually identifying complaints is inefficient and time-consuming. In this study, we propose a supervised learning method including training samples enlargement and classifier construction. We first identified reliable complaint and noncomplaint samples from the unlabeled dataset by using small labeled samples as training samples. Combining the labeled samples and enlarged samples, classification algorithms support vector machine and k-nearest neighbor were then adopted to build binary classifiers during the classifier construction process. Experimental results indicate the proposed method can identify complaints from social media efficiently, especially when the amount of labeled training samples is small. This study provides an efficient approach for hotel companies to distinguish a certain kind of consumer complaint information from large number of unrelated information in hotel social media.

Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

2020 ◽  
pp. 1577-1597
Author(s):  
Kusuma Mohanchandra ◽  
Snehanshu Saha
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Broad Perspective ◽  
Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

scholarly journals Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor

2021 ◽  
Vol 11 (8) ◽  
pp. 3662
Author(s):  
Cosmas Ifeanyi Nwakanma ◽  
Fabliha Bushra Islam ◽  
Mareska Pratiwi Maharani ◽  
Jae-Min Lee ◽  
Dong-Seong Kim
Keyword(s):  
Internet Of Things ◽  
Human Activity ◽  
Real Life ◽  
Vibration Sensor ◽  
Smart Factory ◽  
Occupational Accidents ◽  
Shop Floor ◽  
Support Vector ◽  
Test Bed ◽  
K Nearest Neighbor

Factory shop floor workers are exposed to threats and accidents due to their encounters with tools, equipment, and toxic materials. There are cases of occupational accidents resulting in injuries to workers and precipitating lawsuits, which on the other hand affect company’s operational cost. To ensure the safety of workers within the shop floor, there is a need for proactive activity monitoring. Such activities include detection of falling objects, abnormal vibration, and movement of humans within an acceptable area of the factory floor. Breathing sensor-based monitoring of workers in the smart factory shop floor can also be implemented. This is for the detection of human activity, especially in cases where workers are in isolation with no available emergency assistance. Internet of Things (IoT), Industrial Internet of Things (IIoT), and machine learning (ML) have enabled so many possibilities in this area. In this study, we present a simple test-bed, which is made up of a vibration sensor, a breathing and movement sensor, and a Light Detection and Ranging (LIDAR) sensor. These sensors were used to gather normal and abnormal data of human activities at the factory. We developed a dataset based on possible real-life situations and it is made up of about 10,000 data points. The data was split with a ratio of 75:25 for training and testing the model. We investigated the performance of different ML algorithms, including support vector machine (SVM), linear regression, naive Bayes (NB), K-nearest neighbor (KNN), and convolutional neural network (CNN). From our experiments, the CNN model outperformed other algorithms with an accuracy of 99.45%, 99.78%,100%, and 100%, respectively, for vibration, movement, breathing, and distance. We have also successfully developed a dataset to assist the research community in this field.

scholarly journals Heart Rate Variability and Clinical Features as Predictors of Atrial Fibrillation Recurrence After Catheter Ablation: A Pilot Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Javier Saiz-Vivo ◽  
Valentina D. A. Corino ◽  
Robert Hatala ◽  
Mirko de Melis ◽  
Luca T. Mainardi
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Catheter Ablation ◽  
Success Rate ◽  
Search Algorithm ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
K Nearest Neighbor ◽  
Af Recurrence

Single-procedure catheter ablation success rate is as low as 52% in atrial fibrillation (AF) patients. This study evaluated the feasibility of using clinical data and heart rate variability (HRV) features extracted from an implantable cardiac monitor (ICM) to predict recurrences in patients prior to undergoing catheter ablation for AF. HRV-derived features were extracted from the 500 beats preceding the AF onset and from the first 2 min of the last AF episode recorded by an ICM of 74 patients (67% male; 57 ± 12 years; 26% non-paroxysmal AF; 57% AF recurrence) before undergoing their first AF catheter ablation. Two types of classification algorithm were studied to predict AF recurrence: single classifiers including support vector machines, classification and regression trees, and K-nearest neighbor classifiers as well as ensemble classifiers. The sequential forward floating search algorithm was used to select the optimum feature set for each classification method. The optimum weighted voting method, which used an optimum combination of the single classifiers, was the best overall classifier (accuracy = 0.82, sensitivity = 0.76, and specificity = 0.87). Clinical and HRV features can be used to predict rhythm outcome using an ensemble classifier which would enable a more effective pre-ablation patient triage that could reduce the economic and personal burden of the procedure by increasing the success rate of first catheter ablation.

scholarly journals Diabetes Prediction Using Machine Learning Techniques

2021 ◽  
pp. 150-152
Author(s):  
Seyma Kiziltas Koc ◽  
Mustafa Yeniad
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
High Performance ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Machine Learning Classification

Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.

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