Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.
An idiom is a group of words whose meaning put together is different from the meaning of
individual words. English is a rich language when it comes to idioms, they represent variety. For
foreign learners, idioms are problematic because even if they know the meaning of individual
words that compose an idiom the meaning of it might be something completely different.
The present study investigates Iraqi third year college students’ recognition of idioms. To
achieve this, the researchers have conducted a test which comprises three questions. Certain
conclusions are reached here along with some suggestions and recommendations.
In recent years, the number of applications utilizing mobile wireless sensor networks (WSNs) has increased, with the intent of localization for the purposes of monitoring and obtaining data from hazardous areas. Location of the event is very critical in WSN, as sensing data is almost meaningless without the location information. In this paper, two Monte Carlo based localization schemes termed MCL and MSL* are studied. MCL obtains its location through anchor nodes whereas MSL* uses both anchor nodes and normal nodes. The use of normal nodes would increase accuracy and reduce dependency on anchor nodes, but increases communication costs. For this reason, we introduce a new approach called low communication cost schemes to reduce communication
... Show MoreComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes
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