scholarly journals Cascaded Machine-Learning Technique for Debris Classification in Floor-Cleaning Robot Application

2018 ◽  
Vol 8 (12) ◽  
pp. 2649 ◽  
Author(s):  
Balakrishnan Ramalingam ◽  
Anirudh Lakshmanan ◽  
Muhammad Ilyas ◽  
Anh Le ◽  
Mohan Elara
Keyword(s):  
Binary Classification ◽  
Support Vector ◽  
Single Shot ◽  
Machine Learning Technique ◽  
Cleaning Robot ◽  
Svm Model ◽  
Learning Technique ◽  
Essential Function ◽  
Cleaning Robots

Debris detection and classification is an essential function for autonomous floor-cleaning robots. It enables floor-cleaning robots to identify and avoid hard-to-clean debris, specifically large liquid spillage debris. This paper proposes a debris-detection and classification scheme for an autonomous floor-cleaning robot using a deep Convolutional Neural Network (CNN) and Support Vector Machine (SVM) cascaded technique. The SSD (Single-Shot MultiBox Detector) MobileNet CNN architecture is used for classifying the solid and liquid spill debris on the floor through the captured image. Then, the SVM model is employed for binary classification of liquid spillage regions based on size, which helps floor-cleaning devices to identify the larger liquid spillage debris regions, considered as hard-to-clean debris in this work. The experimental results prove that the proposed technique can efficiently detect and classify the debris on the floor and achieves 95.5% percent classification accuracy. The cascaded approach takes approximately 71 milliseconds for the entire process of debris detection and classification, which implies that the proposed technique is suitable for deploying in real-time selective floor-cleaning applications.

scholarly journals An Ensemble Machine Learning Technique for Functional Requirement Classification

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1601
Author(s):  
Nouf Rahimi ◽  
Fathy Eassa ◽  
Lamiaa Elrefaei
Keyword(s):  
Machine Learning ◽  
Requirement Engineering ◽  
Support Vector ◽  
Functional Requirement ◽  
Machine Learning Technique ◽  
Engineering Software ◽  
Ensemble Machine Learning ◽  
Learning Technique ◽  
Combined Models

In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.

scholarly journals A machine learning approach for estimation of shallow water depths from optical satellite images and sonar measurements

2013 ◽  
Vol 15 (4) ◽  
pp. 1408-1424 ◽  
Author(s):  
Z. Vojinovic ◽  
Y. A. Abebe ◽  
R. Ranasinghe ◽  
A. Vacher ◽  
P. Martens ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Shallow Water ◽  
Satellite Images ◽  
Image Data ◽  
Support Vector ◽  
Machine Learning Technique ◽  
High Resolution Satellite Images ◽  
Svm Model ◽  
Learning Technique

There has been a rapid growth in the field of remote sensing and its various applications in the area of water management. Nowadays, there are several remote sensing techniques that can be used as a source to derive bathymetry data along coastal areas. The key techniques are: sonar (sound navigating and ranging), LiDAR (light detection and ranging) and high-resolution satellite images. The present paper describes a method which was developed and used to create a shallow water bathymetry data along the Dutch side of Sint Maarten Island by combining sonar measurements and satellite images in a nonlinear machine learning technique. The purpose of this work is to develop a bathymetry dataset that can be used to set up physically-based models for coastal flood modelling work. The nonlinear machine learning technique used in the work is a support vector machine (SVM) model. The sonar data were used as an output whereas image data were used as an input into the SVM model. The results were analysed for three depth ranges and the findings are promising. It remains to further verify the capacity of the new method on a dataset with higher resolution satellite imagery.

scholarly journals Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7417
Author(s):  
Alex J. Hope ◽  
Utkarsh Vashisth ◽  
Matthew J. Parker ◽  
Andreas B. Ralston ◽  
Joshua M. Roper ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Random Forest ◽  
Binary Classification ◽  
Classification Performance ◽  
Support Vector ◽  
Use Case ◽  
Signal Features ◽  
Test Population

Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological impairments and longer-term health risks to be identified earlier, better quantified, and more easily monitored over time. We address this challenge by combining a head-mounted wearable inertial motion unit (IMU)-based physiological vibration acceleration (“phybrata”) sensor and several candidate machine learning (ML) models. The performance of this solution is assessed for both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments. Results are compared with previously reported approaches to ML-based concussion diagnostics. Using phybrata data from a previously reported concussion study population, four different machine learning models (Support Vector Machine, Random Forest Classifier, Extreme Gradient Boost, and Convolutional Neural Network) are first investigated for binary classification of the test population as healthy vs. concussion (Use Case 1). Results are compared for two different data preprocessing pipelines, Time-Series Averaging (TSA) and Non-Time-Series Feature Extraction (NTS). Next, the three best-performing NTS models are compared in terms of their multiclass prediction performance for specific concussion-related impairments: vestibular, neurological, both (Use Case 2). For Use Case 1, the NTS model approach outperformed the TSA approach, with the two best algorithms achieving an F1 score of 0.94. For Use Case 2, the NTS Random Forest model achieved the best performance in the testing set, with an F1 score of 0.90, and identified a wider range of relevant phybrata signal features that contributed to impairment classification compared with manual feature inspection and statistical data analysis. The overall classification performance achieved in the present work exceeds previously reported approaches to ML-based concussion diagnostics using other data sources and ML models. This study also demonstrates the first combination of a wearable IMU-based sensor and ML model that enables both binary classification of concussion patients and multiclass predictions of specific concussion-related neurophysiological impairments.

scholarly journals Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1502
Author(s):  
Ben Wilkes ◽  
Igor Vatolkin ◽  
Heinrich Müller
Keyword(s):  
Visual Analysis ◽  
Binary Classification ◽  
Statistical Significance ◽  
Image Features ◽  
Support Vector ◽  
Audio Signals ◽  
Text And Image ◽  
The Impact ◽  
Music Track

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.

Polarity-based Classification of Subjective Micro-posts using Machine Learning Technique

2018 ◽  
Vol 9 (7) ◽  
pp. 373
Author(s):  
Arshad Arain ◽  
Rajesh kumar ◽  
Nudra Siddiquie ◽  
Komal Naz ◽  
Sabeen gul ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique

Classification of Automobile Lubricant by Near-Infrared Spectroscopy Combined with Machine Classification

2011 ◽  
Vol 460-461 ◽  
pp. 667-672
Author(s):  
Yun Zhao ◽  
Xing Xu ◽  
Yong He
Keyword(s):  
Near Infrared ◽  
Prediction Models ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Svm Model

The main objective of this paper is to classify four kinds of automobile lubricant by near-infrared (NIR) spectral technology and to observe whether NIR spectroscopy could be used for predicting water content. Principle component analysis (PCA) was applied to reduce the information from the spectral data and first two PCs were used to cluster the samples. Partial least square (PLS), least square support vector machine (LS-SVM), and Gaussian processes classification (GPC) were employed to develop prediction models. There were 120 samples for training set and test set. Two LS-SVM models with first five PCs and first six PCs were built, respectively, and accuracy of the model with five PCs is adequate with less calculation. The results from the experiment indicate that the LS-SVM model outperforms the PLS model and GPC model outperforms the LS-SVM model.

scholarly journals Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

2009 ◽  
Vol 8 ◽  
pp. S59-S67 ◽  
Author(s):  
H Kimura ◽  
H Kawashima ◽  
H Kusaka ◽  
H Kitagawa
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique
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