Intelligent facial emotion recognition using moth-firefly optimization

In this research, we propose a facial expression recognition system with a variant of evolutionary firefly algorithm for feature optimization. First of all, a modified Local Binary Pattern descriptor is proposed to produce an initial discriminative face representation. A variant of the firefly algorithm is proposed to perform feature optimization. The proposed evolutionary firefly algorithm exploits the spiral search behaviour of moths and attractiveness search actions of fireflies to mitigate premature convergence of the Levy-flight firefly algorithm (LFA) and the moth-flame optimization (MFO) algorithm. Specifically, it employs the logarithmic spiral search capability of the moths to increase local exploitation of the fireflies, whereas in comparison with the flames in MFO, the fireflies not only represent the best solutions identified by the moths but also act as the search agents guided by the attractiveness function to increase global exploration. Simulated Annealing embedded with Levy flights is also used to increase exploitation of the most promising solution. Diverse single and ensemble classifiers are implemented for the recognition of seven expressions. Evaluated with frontal-view images extracted from CK+, JAFFE, and MMI, and 45-degree multi-view and 90-degree side-view images from BU-3DFE and MMI, respectively, our system achieves a superior performance, and outperforms other state-of-the-art feature optimization methods and related facial expression recognition models by a significant margin

Deep learning based melanoma diagnosis using dermoscopic images

The most common malignancies in the world are skin cancers, with melanomas being the most lethal. The emergence of Convolutional Neural Networks (CNNs) has provided a highly compelling method for medical diagnosis. This research therefore conducts transfer learning with grid search based hyper-parameter fine-tuning using six state-of-the-art CNN models for the classification of benign nevus and malignant melanomas, with the models then being exported, implemented, and tested on a proof-of-concept Android application. Evaluated using Dermofit Image Library and PH2 skin lesion data sets, the empirical results indicate that the ResNeXt50 model achieves the highest accuracy rate with fast execution time, and a relatively small model size. It compares favourably with other related methods for melanoma diagnosis reported in the literature

Weather Based Photovoltaic Energy Generation Prediction Using LSTM Networks

Photovoltaic (PV) systems use the sunlight and convert it to electrical power. It is predicted that by 2023, 371,000 PV installations will be embedded in power networks in the UK. This may increase the risk of voltage rise which has adverse impacts on the power network. The balance maintenance is important for high security of the physical electrical systems and the operation economy. Therefore, the prediction of the output of PV systems is of great importance. The output of a PV system highly depends on local environmental conditions. These include sun radiation, temperature, and humidity. In this research, the importance of various weather factors are studied. The weather attributes are subsequently employed for the prediction of the solar panel power generation from a time-series database. Long-Short Term Memory networks are employed for obtaining the dependencies between various elements of the weather conditions and the PV energy metrics. Evaluation results indicate the efficiency of the deep networks for energy generation prediction

Prediction Using LSTM Networks

Photovoltaic (PV) systems use the sunlight and convert it to electrical power. It is predicted that by 2023, 371,000 PV installations will be embedded in power networks in the UK. This may increase the risk of voltage rise which has adverse impacts on the power network. The balance maintenance is important for high security of the physical electrical systems and the operation economy. Therefore, the prediction of the output of PV systems is of great importance. The output of a PV system highly depends on local environmental conditions. These include sun radiation, temperature, and humidity. In this research, the importance of various weather factors are studied. The weather attributes are subsequently employed for the prediction of the solar panel power generation from a time-series database. LongShort Term Memory networks are employed for obtaining the dependencies between various elements of the weather conditions and the PV energy metrics. Evaluation results indicate the efficiency of the deep networks for energy generation prediction

Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification.

In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses

A Multi-Population FA for Automatic Facial Emotion Recognition

Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models

Mask R-CNN Transfer Learning Variants for Multi-Organ Medical Image Segmentation

Medical abdomen image segmentation is a challenging task owing to discernible characteristics of the tumour against other organs. As an effective image segmenter, Mask R-CNN has been employed in many medical imaging applications, e.g. for segmenting nucleus from cytoplasm for leukaemia diagnosis and skin lesion segmentation. Motivated by such existing studies, this research takes advantage of the strengths of Mask R-CNN in leveraging on pre-trained CNN architectures such as ResNet and proposes three variants of Mask R-CNN for multi-organ medical image segmentation. Specifically, we propose three variants of the Mask R-CNN transfer learning model successively, each with a set of configurations modified from the one preceding. To be specific, the three variants are (1) the traditional transfer learning with customized loss functions with comparatively more weightage on the segmentation performance, (2) transfer learning based on Mask R-CNN with deepened re-trained layers instead of only the last two/three layers as in traditional transfer learning, and (3) the fine-tuning of Mask R-CNN with expansion of the Region of Interest pooling sizes. Evaluating using Beyond-the-Cranial-Vault (BTCV) abdominal dataset, a well-established benchmark for multi-organ medical image segmentation, the three proposed variants of Mask R-CNN obtain promising performances. In particular, the empirical results indicate the effectiveness of the proposed adapted loss functions, the deepened transfer learning process, as well as the expansion of the RoI pooling sizes. Such variations account for the great efficiency of the proposed transfer learning variant schemes for undertaking multi-organ image segmentation tasks