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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]

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]