An interactive evolution strategy based deep convolutional generative adversarial network for 2D video game level procedural content generation.

The generation of desirable video game contents has been a challenge of games level design and production. In this research, we propose a game player flow experience driven interactive latent variable evolution strategy incorporated with a Deep Convolutional Generative Adversarial Network (DCGAN) for undertaking game content generation with respect to a 2D Super Mario video game. Since the Generative Adversarial Network (GAN) models tend to capture the high-level style of the input images by learning the latent vectors, they are used to generate game scenarios and context images in this research. However, as GANs employ arbitrary inputs for game image generation without taking specific features into account, they generate game level images in an incoherent manner without the specific playable game level properties, such as a broken pipe in the Mario game level image. In order to overcome such drawbacks, we propose a game player flow experience driven optimised mechanism with human intervention, to guide the game level content generation process so that only plausible and even enjoyable images will be generated as the candidates for the final game design and production

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

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

3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning From sEMG Sensors

In this paper, we present our work on developing robot arm prosthetic via deep learning. Our work proposes to use transfer learning techniques applied to the Google Inception model to retrain the final layer for surface electromyography (sEMG) classification. Data have been collected using the Thalmic Labs Myo Armband and used to generate graph images comprised of 8 subplots per image containing sEMG data captured from 40 data points per sensor, corresponding to the array of 8 sEMG sensors in the armband. Data captured were then classified into four categories (Fist, Thumbs Up, Open Hand, Rest) via using a deep learning model, Inception-v3, with transfer learning to train the model for accurate prediction of each on real-time input of new data. This trained model was then downloaded to the ARM processor based embedding system to enable the brain-controlled robot-arm prosthetic manufactured from our 3D printer. Testing of the functionality of the method, a robotic arm was produced using a 3D printer and off-the-shelf hardware to control it. SSH communication protocols are employed to execute python files hosted on an embedded Raspberry Pi with ARM processors to trigger movement on the robot arm of the predicted gesture

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

A Deep Learning Based Wearable Healthcare Iot Device for AI-Enabled Hearing Assistance Automation

With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality. This research presents a novel AI-enabled Internet of Things (IoT) device operating from the ESP-8266 platform capable of assisting those who suffer from impairment of hearing or deafness to communicate with others in conversations. In the proposed solution, a server application is created that leverages Google's online speech recognition service to convert the received conversations into texts, then deployed to a micro-display attached to the glasses to display the conversation contents to deaf people, to enable and assist conversation as normal with the general population. Furthermore, in order to raise alert of traffic or dangerous scenarios, an 'urban-emergency' classifier is developed using a deep learning model, Inception-v4, with transfer learning to detect/recognize alerting/alarming sounds, such as a horn sound or a fire alarm, with texts generated to alert the prospective user. The training of Inception-v4 was carried out on a consumer desktop PC and then implemented into the AI-based IoT application. The empirical results indicate that the developed prototype system achieves an accuracy rate of 92% for sound recognition and classification with real-time performance

Intelligent human action recognition using an ensemble model of evolving deep networks with swarm-based optimization.

Automatic interpretation of human actions from realistic videos attracts increasing research attention owing to its growing demand in real-world deployments such as biometrics, intelligent robotics, and surveillance. In this research, we propose an ensemble model of evolving deep networks comprising Convolutional Neural Networks (CNNs) and bidirectional Long Short-Term Memory (BLSTM) networks for human action recognition. A swarm intelligence (SI)-based algorithm is also proposed for identifying the optimal hyper-parameters of the deep networks. The SI algorithm plays a crucial role for determining the BLSTM network and learning configurations such as the learning and dropout rates and the number of hidden neurons, in order to establish effective deep features that accurately represent the temporal dynamics of human actions. The proposed SI algorithm incorporates hybrid crossover operators implemented by sine, cosine, and tanh functions for multiple elite offspring signal generation, as well as geometric search coefficients extracted from a three-dimensional super-ellipse surface. Moreover, it employs a versatile search process led by the yielded promising offspring solutions to overcome stagnation. Diverse CNN–BLSTM networks with distinctive hyper-parameter settings are devised. An ensemble model is subsequently constructed by aggregating a set of three optimized CNN–BLSTM​ networks based on the average prediction probabilities. Evaluated using several publicly available human action data sets, our evolving ensemble deep networks illustrate statistically significant superiority over those with default and optimal settings identified by other search methods. The proposed SI algorithm also shows great superiority over several other methods for solving diverse high-dimensional unimodal and multimodal optimization functions with artificial landscapes

Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models

In this research, we propose Particle Swarm Optimization (PSO)-enhanced ensemble deep neural networks for optic disc (OD) segmentation using retinal images. An improved PSO algorithm with six search mechanisms to diversify the search process is introduced. It consists of an accelerated super-ellipse action, a refined super-ellipse operation, a modified PSO operation, a random leader-based search operation, an average leader-based search operation and a spherical random walk mechanism for swarm leader enhancement. Owing to the superior segmentation capabilities of Mask R-CNN, transfer learning with a PSO-based hyper-parameter identification method is employed to generate the fine-tuned segmenters for OD segmentation. Specifically, we optimize the learning parameters, which include the learning rate and momentum of the transfer learning process, using the proposed PSO algorithm. To overcome the bias of single networks, an ensemble segmentation model is constructed. It incorporates the results of distinctive base segmenters using a pixel-level majority voting mechanism to generate the final segmentation outcome. The proposed ensemble network is evaluated using the Messidor and Drions data sets and is found to significantly outperform other deep ensemble networks and hybrid ensemble clustering models that are incorporated with both the original and state-of-the-art PSO variants. Additionally, the proposed method statistically outperforms existing studies on OD segmentation and other search methods for solving diverse unimodal and multimodal benchmark optimization functions and the detection of Diabetic Macular Edema

Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models

In this research, we propose an intelligent decision support system for skin cancer detection. Since generating an effective lesion representation is a vital step to ensure the success of lesion classification, the discriminative power of different types of features is exploited. Specifically, we combine clinically important asymmetry, border irregularity, colour and dermoscopic structure features with texture features extracted using Grey Level Run Length Matrix, Local Binary Patterns, and Histogram of Oriented Gradients operators for lesion representation. Then, we propose two enhanced Particle Swarm Optimization (PSO) models for feature optimization. The first model employs adaptive acceleration coefficients, multiple remote leaders, in-depth sub-dimension feature search and re-initialization mechanisms to overcome stagnation. The second model uses random acceleration coefficients, instead of adaptive ones, based on non-linear circle, sine and helix functions, respectively, to increase diversification and intensification. Ensemble classifiers are also constructed with each base model trained using each optimized feature subset. A deep convolutional neural network is devised whose hyper-parameters are fine-tuned using the proposed PSO models. Extensive experimental studies using dermoscopic skin lesion data, medical data from the UCI machine learning repository, and ALL-IDB2 image data are conducted to evaluate the model efficiency systematically. The results from empirical evaluations and statistical tests indicate the superiority of the proposed models over other advanced PSO variants and classical search methods pertaining to discriminative feature selection and optimal hyper-parameter identification for deep learning networks in lesion classification as well as other disease diagnosis