Artificial intelligence for visually impaired

The eyes are an essential tool for human observation and perception of the world, helping people to perform their tasks. Visual impairment causes many inconveniences in the lives of visually impaired people. Therefore, it is necessary to focus on the needs of the visually impaired community. Researchers work from different angles to help visually impaired people live normal lives. The advent of the digital age has profoundly changed the lives of the visually impaired community, making life more convenient. Deep learning, as a promising technology, is also expected to improve the lives of visually impaired people. It is increasingly being used in the diagnosis of eye diseases and the development of visual aids. The earlier accurate diagnosis of the eye disease by the doctor, the sooner the patient can receive the appropriate treatment and the better chances of a cure. This paper summarises recent research on the development of artificial intelligence-based eye disease diagnosis and visual aids. The research is divided according to the purpose of the study into deep learning methods applied in diagnosing eye diseases and smart devices to help visually impaired people in their daily lives. Finally, a summary is given of the directions in which artificial intelligence may be able to assist the visually impaired in the future. In addition, this overview provides some knowledge about deep learning for beginners. We hope this paper will inspire future work on the subjects

An Evolutionary Hyper-Heuristic for Airport Slot Allocation

A large number of airports across Europe are resource constrained. With long-term growth in air transportation forecast to rise, more airports are expected to feel the imbalance between increased demand and limited resource capacity. This imbalance will lead to increasingly constrained scenarios, which require sophisticated solution methods to produce viable schedules. The use of ‘slots’ is one of the core mechanisms for managing access to constrained resources at airports. This paper presents a genetic algorithm-based hyper-heuristic approach to construct feasible solutions to the single airport slot allocation problem. To evaluate the proposed approach, we compare the solutions found by a number of previously developed and newly proposed constructive heuristics, over a range of real-world data sets. Our results show that the hyper-heuristic outperforms any individual constructive heuristic in all test instances, overcoming the drawbacks that a single heuristic faces when required to solve instances with different problem features.</p

ROENet: A ResNet-Based Output Ensemble for Malaria Parasite Classification

(1) Background: People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. (2) Methods: In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pre-trained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. (3) Results: We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. (4) Conclusions: The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods

A Hybrid Framework for Lung Cancer Classification

Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84% ± 0.156% (accuracy), 99.84% ± 0.153% (precision), 99.84% ± 0.156% (sensitivity), and 99.84% ± 0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods

Enhanced Feature Pyramid Network with Deep Semantic Embedding for Remote Sensing Scene Classification

Recent progress on remote sensing scene classification is substantial, benefiting mostly from the explosive development of convolutional neural networks (CNNs). However, different from the natural images in which the objects occupy most of the space, objects in remote sensing images are usually small and separated. Therefore, there is still a large room for improvement of the vanilla CNNs that extract global image-level features for remote sensing scene classification, ignoring local object-level features. In this paper, we propose a novel remote sensing scene classification method via enhanced feature pyramid network with deep semantic embedding. Our proposed framework extracts multi-scale multi-level features using an enhanced feature pyramid network (EFPN). Then, to leverage the complementary advantages of the multi-level and multi-scale features, we design a deep semantic embedding (DSE) module to generate discriminative features. Third, a feature fusion module, called two-branch deep feature fusion (TDFF), is introduced to aggregate the features at different levels in an effective way. Our method produces state-of-the-art results on two widely used remote sensing scene classification benchmarks, with better effectiveness and accuracy than the existing algorithms. Beyond that, we conduct an exhaustive analysis on the role of each module in the proposed architecture, and the experimental results further verify the merits of the proposed method

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For the aged: A novel PM2.5 concentration forecasting method based on spatial-temporal graph ordinary differential equation networks in home-based care parks

The immune ability of the elderly is not strong, and the functions of the body are in a stage of degeneration, the ability to clear PM2.5 is reduced, and the cardiopulmonary system is easily affected. Accurate prediction of PM2.5 can provide guidance for the travel of the elderly, thereby reducing the harm of PM2.5 to the elderly. In PM2.5 prediction, existing works usually used shallow graph neural network (GNN) and temporal extraction module to model spatial and temporal dependencies, respectively, and do not uniformly model temporal and spatial dependencies. In addition, shallow GNN cannot capture long-range spatial correlations. External characteristics such as air humidity are also not considered. We propose a spatial-temporal graph ordinary differential equation network (STGODE-M) to tackle these problems. We capture spatial-temporal dynamics through tensor-based ordinary differential equation, so we can build deeper networks and exploit spatial-temporal features simultaneously. In addition, in the construction of the adjacency matrix, we not only used the Euclidean distance between the stations, but also used the wind direction data. Besides, we propose an external feature fusion strategy that uses air humidity as an auxiliary feature for feature fusion, since air humidity is also an important factor affecting PM2.5 concentration. Finally, our model is evaluated on the home-based care parks atmospheric dataset, and the experimental results show that our STGODE-M can more fully capture the spatial-temporal characteristics of PM2.5, achieving superior performance compared to the baseline. Therefore, it can provide better guarantee for the healthy travel of the elderly

WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections

Background and objective: The COVID-19 pandemic is a major global health crisis of this century. The use of neural networks with CT imaging can potentially improve clinicians’ efficiency in diagnosis. Previous studies in this field have primarily focused on classifying the disease on CT images, while few studies targeted the localisation of disease regions. Developing neural networks for automating the latter task is impeded by limited CT images with pixel-level annotations available to the research community. Methods: This paper proposes a weakly-supervised framework named “Weak Variational Autoencoder for Localisation and Enhancement” (WVALE) to address this challenge for COVID-19 CT images. This framework includes two components: anomaly localisation with a novel WVAE model and enhancement of supervised segmentation models with WVALE. Results: The WVAE model have been shown to produce high-quality post-hoc attention maps with fine borders around infection regions, while weak supervision segmentation shows results comparable to conventional supervised segmentation models. The WVALE framework can enhance the performance of a range of supervised segmentation models, including state-of-art models for the segmentation of COVID-19 lung infection. Conclusions: Our study provides a proof-of-concept for weakly supervised segmentation and an alternative approach to alleviate the lack of annotation, while its independence from classification & segmentation frameworks makes it easily integrable with existing systems

Classification of Alzheimer’s Disease Based on Weakly Supervised Learning and Attention Mechanism

The brain lesions images of Alzheimer’s disease (AD) patients are slightly different from the Magnetic Resonance Imaging of normal people, and the classification effect of general image recognition technology is not ideal. Alzheimer’s datasets are small, making it difficult to train large-scale neural networks. In this paper, we propose a network model (WS-AMN) that fuses weak supervision and an attention mechanism. The weakly supervised data augmentation network is used as the basic model, the attention map generated by weakly supervised learning is used to guide the data augmentation, and an attention module with channel domain and spatial domain is embedded in the residual network to focus on the distinctive channels and spaces of images respectively. The location information enhances the corresponding features of related features and suppresses the influence of irrelevant features.The results show that the F1-score is 99.63%, the accuracy is 99.61%. Our model provides a high-performance solution for accurate classification of AD

Numerical analysis on the buckling behaviour of curved-crease origami pipelines

External pressure loadings in sub-sea pipelines can generate catastrophic structural instabilities such as propagation buckling. This failure mode is typified by a pipe collapse (snap-through phenomenon) that occurs at an initiation pressure PI and a subsequent propagation of the collapse to pipe ends that occurs at a propagation pressure PP. Recent studies have shown that pipelines with a textured geometry, corresponding to the post-buckled shape of a thin-walled cylindrical pipe under axial compression, are able to substantially increase PI, PP, and thus resistance to propagation buckling, compared to conventional smooth pipelines. This study investigates the performance of alternative post-buckled shapes observed in thin-walled pipelines under hydrostatic loading. These shapes correspond closely to a geometric family known as curved-crease origami and so a geometric definition is developed to map geometric parameters from origami to pipelines. A numerical analysis is then conducted on two curved-crease forms and comparative smooth and textured forms. Textured and smooth numerical models show good correspondence with previously reported post-buckling behaviour. One curved crease form is shown to have an increase in PP that is 10.8% greater than the textured pipeline and 131.8% greater than smooth