Highlights Automatic classification of harvester sounds. Final obtained using three convolutional neural networks. The results the networks were combined via stacking and voting to achieve 100% accuracy. Abstract. use deep learning in agricultural tasks has recently become popular. Deep have been used for analyzing images crops, identifying paddy areas, distinguishing sick plants from healthy o...

In this work, we propose a new algorithm for boosting Deep Convolutional Neural Networks (BoostCNN) to combine the merits of boosting and modern neural networks. To learn this new model, we propose a novel algorithm to incorporate boosting weights into the deep learning architecture based on least squares objective function. We also show that it is possible to use networks of different structur...

There has been significant recent interest towards achieving highly efficient deep neural network architectures that preserve strong modeling capabilities. A particular promising paradigm for achieving such deep neural networks is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over success...

Constructing a targeted wavelet neural network is an effective method to enhance network performances and recognition effect. By introducing heart sounds wavelet of neural network hidden layer as the activation function, heart sound targeted learning and recognition technology are integrated deeply, to obtain a new heart sounds wavelet neural network. Selecting normal heart sounds and premature...

The alarmingly high mortality rate and increasing global prevalence of cardiovascular diseases signify the crucial need for early detection schemes. Phonocardiogram (PCG) signals have been historically applied in this domain owing to its simplicity cost-effectiveness. In paper, we propose CardioXNet, a novel lightweight end-to-end CRNN architecture automatic five classes cardiac auscultation na...

The automatic sound event classification (SEC) has attracted a growing attention in recent years. Feature extraction is a critical factor in SEC system, and the deep neural network (DNN) algorithms have achieved the state-of-the-art performance for SEC. The extreme learning machine-based auto-encoder (ELM-AE) is a new deep learning algorithm, which has both an excellent representation performan...

Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the mo...

Deep learning has been proven be very effective for various image recognition tasks, e.g., image classification, semantic segmentation, image retrieval, shape classification etc. However, existing works on deep learning for image recognition mainly focus on either natural image data or binary shape data. In this paper, we show that deep convolutional neural networks (DCNN) is also suitable for ...

In machine learning, error back-propagation in multi-layer neural networks (deep learning) has been impressively successful in supervised and reinforcement learning tasks. As a model for learning in the brain, however, deep learning has long been regarded as implausible, since it relies in its basic form on a non-local plasticity rule. To overcome this problem, energy-based models with local co...

Despite the promise of brain-inspired machine learning, deep neural networks (DNN) have frustratingly failed to bridge the deceptively large gap between learning and memory. Here, we introduce a Perpetual Learning Machine; a new type of DNN that is capable of brain-like dynamic ‘on the fly’ learning because it exists in a self-supervised state of Perpetual Stochastic Gradient Descent. Thus, we ...