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In recent years, the contributions of deep learning have had a phenomenal impact on electroencephalography-based brain-computer interfaces. While the decoding accuracy of electroencephalography signals has continued to increase, the process has caused deep learning models to continuously expand in terms of size and computational resource requirements. However, due to their increased size and computational requirements, it has become difficult to embed, store, and execute deep learning models for artificial intelligence of things, cloud-based, or edge devices used in rehabilitation. Hence, this article proposes a novel deep learning-based lightweight model based on attention-inception convolutional neural network and long- short-term memory. The proposed model achieves excellent accuracy on public competition datasets while requiring few parameters and low computational time. Using the BCI competition IV 2a dataset and the high gamma dataset, the proposed model achieved 82.8% and 97.1% accuracies, respectively. [ABSTRACT FROM AUTHOR]

Automatically recognizing emotions of patients can be a good facilitator of a connected healthcare framework. It can give automatic feedback to the stakeholders of the healthcare industry about patients' states and satisfaction levels. In this article, we propose an automatic audio-visual emotion recognition system in a connected healthcare framework. The system uses a 2D CNN model for the speech modality and a 3D CNN model for the visual modality. For the speech signal, preprocessing is done to extract the PS-PA feature vector. The features from the two CNN models are blended by two ELM networks. The first ELM is trained with gender-specific data, while the other one is trained with emotion-specific data. The proposed system is evaluated using three databases, and the experiments prove the success of the system. In the healthcare framework, we use edge computing prior to intensive-processing cloud computing. In the edge computing, we realize edge caching, which can store the CNN model parameters and thereby perform the testing fast. [ABSTRACT FROM AUTHOR]

Fruit classification is an important task in many industrial applications. A fruit classification system may be used to help a supermarket cashier identify the fruit species and prices. It may also be used to help people decide whether specific fruit species meet their dietary requirements. In this paper, we propose an efficient framework for fruit classification using deep learning. More specifically, the framework is based on two different deep learning architectures. The first is a proposed light model of six convolutional neural network layers, whereas the second is a fine-tuned visual geometry group-16 pretrained deep learning model. Two color image datasets, one of which is publicly available, are used to evaluate the proposed framework. The first dataset (dataset 1) consists of clear fruit images, whereas the second dataset (dataset 2) contains fruit images that are challenging to classify. Classification accuracies of 99.49% and 99.75% were achieved on dataset 1 for the first and second models, respectively. On dataset 2, the first and second models obtained accuracies of 85.43% and 96.75%, respectively. [ABSTRACT FROM AUTHOR]

With the increased population in urban areas, it has become a challenge to a service provider to manage big data coming from various users and devices. One of the fastest growing devices used by the citizens of urban areas is the smartphone. Today we can find almost nobody without at least one smartphone in their possession. While talking through a smartphone, surrounding environmental noises are added to the speech of the talker. Sometimes these noises are very annoying to the listeners. In this article, we propose a communication framework for urban mobile big data integrating an urban environment classification system. The proposed system utilizes a deep learning approach to classify environmental noises so that an appropriate noise cancellation algorithm can be used to reduce the effect of noise while having a conversation through smartphones. The proposed framework uses mobile edge computing technology to provide low-latency and efficient transmission. Experimental results demonstrate that the proposed system is very efficient in classifying environmental noises. [ABSTRACT FROM AUTHOR]

Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method's promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) remains a challenging yet crucial problem to address in computer vision. HAR is primarily intended to be used with other technologies, such as the Internet of Things, to assist in healthcare and eldercare. With the development of deep learning, automatic high-level feature extraction has become a possibility and has been used to optimize HAR performance. Furthermore, deep-learning techniques have been applied in various fields for sensor-based HAR. This study introduces a new methodology using convolution neural networks (CNN) with varying kernel dimensions along with bi-directional long short-term memory (BiLSTM) to capture features at various resolutions. The novelty of this research lies in the effective selection of the optimal video representation and in the effective extraction of spatial and temporal features from sensor data using traditional CNN and BiLSTM. Wireless sensor data mining (WISDM) and UCI datasets are used for this proposed methodology in which data are collected through diverse methods, including accelerometers, sensors, and gyroscopes. The results indicate that the proposed scheme is efficient in improving HAR. It was thus found that unlike other available methods, the proposed method improved accuracy, attaining a higher score in the WISDM dataset compared to the UCI dataset (98.53% vs. 97.05%). [ABSTRACT FROM AUTHOR]

• Bio-Inspired novel attention-based inception architecture is proposed that can adapt to different types of spatial contexts using convolution filters of different sizes. The characteristics of each dataset are unique, hence the attention mechanism helps focus on those features to improve classification performance. • The shallow inception model is designed with a two-layer attention mechanism with fewer layers but with a large number of convolution filters that can address the overfitting problem caused by small dataset sizes. • LSTM-based recurrent neural network (RNN) module is proposed to extract temporal features after the inception module is applied. • The proposed model is lightweight with fewer parameters and has less processing time. • The proposed model achieves good performance for both dynamic and static signs and gestures. Bio-inspired deep learning models have revolutionized sign language classification, achieving extraordinary accuracy and human-like video understanding. Recognition and classification of sign language videos in real-time are challenging because the duration and speed of each sign vary for different subjects, the background of videos is dynamic in most cases, and the classification result should be produced in real-time. This study proposes a model based on a convolution neural network (CNN) Inception model with an attention mechanism for extracting spatial features and Bi-LSTM (long short-term memory) for temporal feature extraction. The proposed model is tested on datasets with highly variable characteristics such as different clothing, variable lighting, and variable distance from the camera. Real-time classification achieves significant early detections while achieving performance comparable to the offline operation. The proposed model has fewer parameters, fewer deep learning layers, and requires significantly less processing time than state-of-the-art models. The Inception model with an attention mechanism with two attention blocks [Display omitted] [ABSTRACT FROM AUTHOR]