Classifying Imbalanced Multi-modal Sensor Data for Human Activity Recognition in a Smart Home using Deep Learning

In smart homes, data generated from real-time sensors for human activity recognition is complex, noisy and imbalanced. It is a significant challenge to create machine learning models that can classify activities which are not as commonly occurring as other activities. Machine learning models designed to classify imbalanced data are biased towards learning the more commonly occurring classes. Such learning bias occurs naturally, since the models better learn classes which contain more records. This paper examines whether fusing real-world imbalanced multi-modal sensor data improves classification results as opposed to using unimodal data; and compares deep learning approaches to dealing with imbalanced multi-modal sensor data when using various resampling methods and deep learning models. Experiments were carried out using a large multi-modal sensor dataset generated from the Sensor Platform for HEalthcare in a Residential Environment (SPHERE). The data comprises 16104 samples, where each sample comprises 5608 features and belongs to one of 20 activities (classes). Experimental results using SPHERE demonstrate the challenges of dealing with imbalanced multi-modal data and highlight the importance of having a suitable number of samples within each class for sufficiently training and testing deep learning models. Furthermore, the results revealed that when fusing the data and using the Synthetic Minority Oversampling Technique (SMOTE) to correct class imbalance, CNN-LSTM achieved the highest classification accuracy of 93.67% followed by CNN, 93.55%, and LSTM, i.e. 92.98%

Enhancing Prediction in Cyclone Separators through Computational Intelligence

Pressure drop prediction is critical to the design and performance of cyclone separators as industrial gas cleaning devices. The complex nonlinear relationship between cyclone Pressure Drop Coefficient (PDC) and geometrical dimensions suffice the need for state-of-the-art predictive modelling methods. Existing solutions have applied theoretical/semi-empirical techniques which fail to generalise well, and some intelligent techniques have also been applied such as the neural network which can still be improved for optimal equipment design. To this end, this paper firstly introduces a fuzzy modelling methodology, then presents an alternative Extended Kalman Filter (EKF) for the learning of a Multi-Layer Neural Network (MLNN). The Lagrange dual formulation of Support Vector Machine (SVM) regression model is deployed as well for comparison purposes. For optimal design of these models, manual and grid search techniques are used in a cross-validation setting subsequent to training. Based on the prediction accuracy of PDC, results show that the Fuzzy System (FS) is highly performing with testing mean squared error (MSE) of 3.97e-04 and correlation coefficient (R) of 99.70%. Furthermore, a significant improvement of EKF-trained network (MSE = 1.62e-04, R = 99.82%) over the traditional Back-Propagation Neural Network (BPNN) (MSE = 4.87e-04, R = 99.53%) is observed. SVM gives better prediction with radial basis kernel (MSE = 2.22e-04, R = 99.75) and provides comparable performance to universal approximators. In comparison to conventional theoretical and semi-empirical models, intelligent approaches can provide far better prediction accuracy over a wide range of cyclone designs, while the EKFMLNN performance is noteworthy

A review of learning in biologically plausible spiking neural networks

Artificial neural networks have been used as a powerful processing tool in various areas such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has encouraged researchers to improve artificial neural networks by investigating the biological brain. Neurological research has significantly progressed in recent years and continues to reveal new characteristics of biological neurons. New technologies can now capture temporal changes in the internal activity of the brain in more detail and help clarify the relationship between brain activity and the perception of a given stimulus. This new knowledge has led to a new type of artificial neural network, the Spiking Neural Network (SNN), that draws more faithfully on biological properties to provide higher processing abilities. A review of recent developments in learning of spiking neurons is presented in this paper. First the biological background of SNN learning algorithms is reviewed. The important elements of a learning algorithm such as the neuron model, synaptic plasticity, information encoding and SNN topologies are then presented. Then, a critical review of the state-of-the-art learning algorithms for SNNs using single and multiple spikes is presented. Additionally, deep spiking neural networks are reviewed, and challenges and opportunities in the SNN field are discussed

A Deep Convolutional Neural Network for Time Series Classification with Intermediate Targets

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Deep Convolutional Neural Networks (CNNs) have been successfully used in different applications, including image recognition. Time series data, which are generated in many applications, such as tasks using sensor data, have different characteristics compared to image data and accordingly there is a need for specific CNN structures to address their processing. This paper proposes a new CNN for classifying time series data. It is proposed to have new intermediate outputs extracted from different hidden layers instead of having a single output to control weight adjustment in the hidden layers during training. Intermediate targets are used to act as labels for the intermediate outputs to improve the performance of the method. The intermediate targets are different from the main target. Additionally, the proposed method artificially increases the number of training instances using the original training samples and the intermediate targets. The proposed approach converts a classification task with original training samples to a new (but equivalent) classification task that contains two classes with a high number of training instances. The proposed CNN for Time Series classification, called CNN-TS, extracts features depending the distance of two time series. CNN-TS was evaluated on various benchmark time series datasets. The proposed CNN-TS achieved 3.5% higher overall accuracy compared to the CNN base method (without an intermediate layer). Additionally, CNN-TS achieved 21.1% higher average accuracy compared to classical machine learning methods, i.e. linear SVM, RBF SVM, and RF. Moreover, CNN-TS was on average 8.43 times faster in training time compared to the ResNet method

A deep convolutional neural network for time series classification with intermediate targets

Deep Convolutional Neural Networks (CNNs) have been successfully used in different applications, including image recognition. Time series data, which are generated in many applications, such as tasks using sensor data, have different characteristics compared to image data, and accordingly, there is a need for specific CNN structures to address their processing. This paper proposes a new CNN for classifying time series data. It is proposed to have new intermediate outputs extracted from different hidden layers instead of having a single output to control weight adjustment in the hidden layers during training. Intermediate targets are used to act as labels for the intermediate outputs to improve the performance of the method. The intermediate targets are different from the main target. Additionally, the proposed method artificially increases the number of training instances using the original training samples and the intermediate targets. The proposed approach converts a classification task with original training samples to a new (but equivalent) classification task that contains two classes with a high number of training instances. The proposed CNN for Time Series classification, called CNN-TS, extracts features depending the distance of two time series. CNN-TS was evaluated on various benchmark time series datasets. The proposed CNN-TS achieved 5.1% higher overall accuracy compared to the CNN base method (without an intermediate layer). Additionally, CNN-TS achieved 21.1% higher average accuracy compared to classical machine-learning methods, i.e., linear SVM, RBF SVM, and RF. Moreover, CNN-TS was on average 8.43 times faster in training time compared to the ResNet method.</p

Enhancing prediction in cyclone separators through computational intelligence

Pressure drop prediction is critical to the design and performance of cyclone separators as industrial gas cleaning devices. The complex non-linear relationship between cyclone Pressure Drop Coefficient (PDC) and geometrical dimensions suffice the need for state-of-the-art predictive modelling methods. Existing solutions have applied theoretical/semi-empirical techniques which fail to generalise well, and the suitability of intelligent techniques has not been widely explored for the task of pressure drop prediction in cyclone separators. To this end, this paper firstly introduces a fuzzy modelling methodology, then presents an alternative version of the Extended Kalman Filter (EKF) to train a Multi-Layer Neural Network (MLNN). The Lagrange dual formulation of Support Vector Machine (SVM) regression model is also deployed for comparison purposes. For optimal design of these models, manual and grid search techniques are used in a cross-validation setting subsequent to training. Based on the prediction accuracy of PDC, results show that the Fuzzy System (FS) is highly performing with testing mean squared error (MSE) of 3.97e-04 and correlation coefficient (R) of 99.70%. Furthermore, a significant improvement of EKF-trained network (MSE = 1.62e-04, R = 99.82%) over the traditional Back-Propagation Neural Network (BPNN) (MSE = 4.87e-04, R = 99.53%) is observed. SVM gives better prediction with radial basis kernel (MSE = 2.22e-04, R = 99.75%) and provides comparable performance to universal approximators. Of the conventional models considered, the model of Shepherd and Lapple ( MSE = 7.3e-03, R = 97.88%) gives the best result which is still inferior to the intelligent models

Graph Convolutional Networks for Predicting Mechanical Characteristics of 3D Lattice Structures

Recent advancements in deep learning methods encouraged researchers to apply them to process 3D objects. Initially, convolutional neural networks which have shown their ability in the processing of 2D images were used for 3D object processing. These methods need a complex process to convert 3D objects to 2D images. This conversion leads to increased computation cost and possible information loss during the transformation. This research introduces a Graph Convolutional Network approach for predicting mechanical properties of custom-designed 3D lattice structures for tissue engineering applications. Seventeen scaffold geometrics were generated for training while eight were used for testing. Unlike traditional preprocessing into images, this methodology reduces preprocessing by leveraging GCNs to directly process 3D geometrics in graph form. The experimental results show the efficiency of our proposed method in predicting 3D lattice structures.</p