Predicting Demands of COVID-19 Prevention and Control Materials via Co-Evolutionary Transfer Learning.

The novel coronavirus pneumonia (COVID-19) has created great demands for medical resources. Determining these demands timely and accurately is critically important for the prevention and control of the pandemic. However, even if the infection rate has been estimated, the demands of many medical materials are still difficult to estimate due to their complex relationships with the infection rate and insufficient historical data. To alleviate the difficulties, we propose a co-evolutionary transfer learning (CETL) method for predicting the demands of a set of medical materials, which is important in COVID-19 prevention and control. CETL reuses material demand knowledge not only from other epidemics, such as severe acute respiratory syndrome (SARS) and bird flu but also from natural and manmade disasters. The knowledge or data of these related tasks can also be relatively few and imbalanced. In CETL, each prediction task is implemented by a fuzzy deep contractive autoencoder (CAE), and all prediction networks are cooperatively evolved, simultaneously using intrapopulation evolution to learn task-specific knowledge in each domain and using interpopulation evolution to learn common knowledge shared across the domains. Experimental results show that CETL achieves high prediction accuracies compared to selected state-of-the-art transfer learning and multitask learning models on datasets during two stages of COVID-19 spreading in China.

Real-time neural network scheduling of emergency medical mask production during COVID-19.

During the outbreak of the novel coronavirus pneumonia (COVID-19), there is a huge demand for medical masks. A mask manufacturer often receives a large amount of orders that must be processed within a short response time. It is of critical importance for the manufacturer to schedule and reschedule mask production tasks as efficiently as possible. However, when the number of tasks is large, most existing scheduling algorithms require very long computational time and, therefore, cannot meet the needs of emergency response. In this paper, we propose an end-to-end neural network, which takes a sequence of production tasks as inputs and produces a schedule of tasks in a real-time manner. The network is trained by reinforcement learning using the negative total tardiness as the reward signal. We applied the proposed approach to schedule emergency production tasks for a medical mask manufacturer during the peak of COVID-19 in China. Computational results show that the neural network scheduler can solve problem instances with hundreds of tasks within seconds. The objective function value obtained by the neural network scheduler is significantly better than those of existing constructive heuristics, and is close to those of the state-of-the-art metaheuristics whose computational time is unaffordable in practice.

An automatic measurement method of spinal curvature on ultrasound coronal images in adolescent idiopathic scoliosis.

This study proposed a new automatic measurement method of spinal curvature on ultrasound coronal images in adolescent idiopathic scoliosis (AIS). After preprocessing of Gaussian enhancement, the symmetric information of the image was extracted using the phase congruency. Then bony features were segmented from the soft tissues and background using the greyscale polarity. The morphological methods of image erosion and top-bottom-hat transformation, and geometric moment were utilized to identify the spinous column profile from the transverse processes. Finally, the spine deformity curve was obtained using robust regression. In-vivo experiments based on AIS patients were performed to evaluate the performance of the developed method. The comparison results revealed there was a significant correlation (y=0.81x, r=0.86) and good agreement between the new automatic method and the manual measurement method. It can be expected that this novel method may help to provide effective and objective deformity assessment method during the ultrasound scanning for AIS patients.

Generative adversarial network based telecom fraud detection at the receiving bank.

Recently telecom fraud has become a serious problem especially in developing countries such as China. At present, it can be very difficult to coordinate different agencies to prevent fraud completely. In this paper we study how to detect large transfers that are sent from victims deceived by fraudsters at the receiving bank. We propose a new generative adversarial network (GAN) based model to calculate for each large transfer a probability that it is fraudulent, such that the bank can take appropriate measures to prevent potential fraudsters to take the money if the probability exceeds a threshold. The inference model uses a deep denoising autoencoder to effectively learn the complex probabilistic relationship among the input features, and employs adversarial training that establishes a minimax game between a discriminator and a generator to accurately discriminate between positive samples and negative samples in the data distribution. We show that the model outperforms a set of well-known classification methods in experiments, and its applications in two commercial banks have reduced losses of about 10 million RMB in twelve weeks and significantly improved their business reputation.

Fireworks Algorithm with Enhanced Fireworks Interaction.

As a relatively new metaheuristic in swarm intelligence, fireworks algorithm (FWA) has exhibited promising performance on a wide range of optimization problems. This paper aims to improve FWA by enhancing fireworks interaction in three aspects: 1) Developing a new Gaussian mutation operator to make sparks learn from more exemplars; 2) Integrating the regular explosion operator of FWA with the migration operator of biogeography-based optimization (BBO) to increase information sharing; 3) Adopting a new population selection strategy that enables high-quality solutions to have high probabilities of entering the next generation without incurring high computational cost. The combination of the three strategies can significantly enhance fireworks interaction and thus improve solution diversity and suppress premature convergence. Numerical experiments on the CEC 2015 single-objective optimization test problems show the effectiveness of the proposed algorithm. The application to a high-speed train scheduling problem also demonstrates its feasibility in real-world optimization problems.

Airline Passenger Profiling Based on Fuzzy Deep Machine Learning.

Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

Game theory for Wireless Sensor Networks: a survey.

Game theory (GT) is a mathematical method that describes the phenomenon of conflict and cooperation between intelligent rational decision-makers. In particular, the theory has been proven very useful in the design of wireless sensor networks (WSNs). This article surveys the recent developments and findings of GT, its applications in WSNs, and provides the community a general view of this vibrant research area. We first introduce the typical formulation of GT in the WSN application domain. The roles of GT are described that include routing protocol design, topology control, power control and energy saving, packet forwarding, data collection, spectrum allocation, bandwidth allocation, quality of service control, coverage optimization, WSN security, and other sensor management tasks. Then, three variations of game theory are described, namely, the cooperative, non-cooperative, and repeated schemes. Finally, existing problems and future trends are identified for researchers and engineers in the field.

Parametric shape representation by a deformable NURBS model for cardiac functional measurements.

This paper proposes a method of parametric representation and functional measurement of 3-D cardiac shapes in a deformable nonuniform rational B-splines (NURBS) model. This representation makes it very easy to automatically evaluate the functional parameters and myocardial kinetics of the heart, since quantitative analysis can be followed in a simple way. In the model, local deformation and motion on the cardiac shape are expressed in adjustable parameters. Especially, an effective integral algorithm is used for volumetric measurement of a NURBS shape since the volume is the most basic parameter in cardiac functional analysis. This method promises the numerical computation to be very convenient, efficient, and accurate, in comparison with traditional methods. Practical experiments are carried out, and results show that the algorithm can get satisfactory measurement accuracy and efficiency. The parametric NURBS model in cylindrical coordinates is not only very suitable to fit the anatomical surfaces of a cardiac shape, but also easy for geometric transformation and nonrigid registration, and able to represent local dynamics and kinetics, and thus, can easily be applied for quantitative and functional analysis of the heart.