Abstract Brain networks provided powerful tools for the analysis and diagnosis of epilepsy. This paper performed a pairwise comparative analysis on the brain networks of Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS): spike group (spike), non‐spike group (non‐spike), and control group (control). In this study, fragments with and without interictal spikes in electroencephalograms of 13 BECTS children during non‐rapid eye movement sleep stage I (NREMI) were selected to construct dynamic brain function networks to explore the functional connectivity (FC). Graph theory and statistical analysis were exploited to investigate changes in FC across different brain regions in different frequency bands. From this study, we can draw the following conclusions: (1) Both spike and non‐spike have lower energy in each brain region on the γ band. (2) With the increase of the frequency band, the FC strength of spike, non‐spike and control groups are all weakened. (3) Spikes are correlated with brain network efficiency and the small‐world property. (4) Spikes increase the FC of temporal, parietal and occipital regions except in the γ band and the absence of spikes weakens the FC of the entire brain region.

Abstract The authors present the investigation of a new hybrid predictive model of Duplex Attention‐based Coupled LSTM Markov Averaged Neural Network, known as DACLMANN. The financial field, particularly the stock market, heavily relies on accurate predictive models. DACLMANN comprises four essential components: two LSTM blocks, an Averagiser and a Markov Neural Network block. The first LSTM block is composed of two hidden layers, each containing 50 neurons and a dense layer with 25 neurons. The second LSTM block consists of two hidden layers, each with 100 neurons, and a dense layer with 50 neurons. The Averagiser plays a crucial role by averaging the closing prices and predicted values from the first LSTM block, resulting in a 90% gain. These averaged values are then fed into the second LSTM block for further prediction. Finally, the predictions undergo evaluation using the Markov model, yielding the final prediction. To assess the performance of DACLMANN, it was tested on 22 years of stock prices for the AMZN index. The evaluation metrics used by the authors include an R2 of 0.76, mean absolute error of 6.81216, root mean square error of 8.6040, Precision of 1, Accuracy of 1, Recall of 1 and F1 of 1. Additionally, DACLMANN achieved a Mean Absolute Percentage Error of less than 0.043% and an RMSPE of less than 2.1%. These results not only demonstrate the effectiveness of the proposed model but also authenticate the prediction outcomes. DACLMANN offers several advantages over traditional predictive models in the stock market. By combining the strengths of Duplex Attention‐based Coupled LSTM, Averagiser, and Markov Neural Network, DACLMANN leverages the power of deep learning, attention mechanisms, and sequential modelling. This hybrid approach enables DACLMANN to capture intricate patterns and dependencies present in stock market data, leading to more accurate and reliable predictions. The robust evaluation metrics further validate the superiority of DACLMANN in predicting stock prices.

Abstract In the music mixing, mixers and music producers often use hardness to describe the feeling of percussion instruments, such as the drum instruments. When dealing with the hardness of drums, the mixer often mixes the sound in terms of frequency and dynamics. One important issue is that it is difficult to define the contribution of adjusting the frequency or dynamics of the drum to the perception of drum tone hardness, and this perception is difficult to be quantified. A subjective user study on the hardness of drum sound is proposed. Taking the audio of the Bass Drum as an example, different audio effects are applied to process the Bass Drum. Twenty‐three participants were recruited to participate the auditory perception test. The result indicates that compression, equalisation, high‐frequency excitation and low‐frequency excitation all have varying degrees of impact on the hardness of the Bass Drum. The high‐frequency excitation and low‐frequency excitation have a statistically significant substantial influence on the sound hardness of the Bass Drum. Taking together, for the first time, the authors’ study comprehensively investigate the impact of audio effect processing on the perception of the hardness of Bass Drum, providing useful guidance for the application of various audio effects on percussion instruments.

Abstract This work considers the problem of target search in marine environment. A perceptual adaptive Gaussian sum cubature Kalman filter is proposed for multi‐target search in non‐linear and non‐Gaussian noise environment. Firstly, the robot collects environmental information, conducts environmental perception, and actively makes decisions. Secondly, the Dynamic Time Warping distance and Frechet distance weighted fusion are used to extract the association knowledge by analysing the trajectories of the underwater and surface targets obtained in the search process. The extracted association knowledge is used in the search process to form an evolving search relationship and increase the search efficiency. Finally, it is applied to realise the search of multiple dynamic targets, including underwater and surface targets, in the non‐Gaussian marine environment.

Abstract Leveraging the power of computational methods, AI can perform effective strategies in intelligent design. Researchers are pushing the boundaries of AI, developing computational systems to solve complex questions. The authors investigate the association of user preference for UI and deep image features, aiming to predict user preference level using deep convolutional neural networks (DCNNs) trained on a UI design image dataset. A total of 12,186 UI design images were collected from UI.cn and DOOOOR.com . Users' views and likes can help understand the implicit user preference level, which is set as the ground‐truth annotation for the dataset. Six DCNNs, including VGG‐19, InceptionNet‐V3, MobileNet, EfficientNet, ResNet‐50 and NASNetLarge were trained to learn the user preference of UI images. The experiment achieves an optimal result with a mean‐squared error of 0.000214 and a mean absolute error of 0.0103 based on EfficientNet, which indicates that the proposed method provides the possibility in learning the pattern of user aesthetics preference for UI design. On the basis of the prediction model, a mobile application named ‘HotUI’ was developed for UI design recommendations.

Abstract Patients with cerebral haemorrhages need to drain haematomas. Fresh blood may appear during the haematoma drainage process, so this needs to be observed and detected in real time. To solve this problem, this paper studies images produced during the haematoma drainage process. A blood image feature selection recognition and classification framework is designed. First, aiming at the characteristics of the small colour differences in blood images, the general RGB colour space feature is not obvious. This study proposes an optimal colour channel selection method. By extracting the colour information from the images, it is recombined into a 3 × 3 matrix. The normalised 4‐neighbourhood contrast and variance are calculated for quantitative comparison. The optimised colour channel is selected to overcome the problem of weak features caused by a single colour space. After that, the effective region in the image is intercepted, and the best colour channel of the image in the region is transformed. The first, second and third moments of the three best colour channels are extracted to form a nine‐dimensional eigenvector. K‐means clustering is used to obtain the image eigenvector, outliers are removed, and the results are then transferred to the hidden Markov model (HMM) and support vector machine (SVM) for classification. After selecting the best color channel, the classification accuracy of HMM‐SVM is greatly improved. Compared with other classification algorithms, the proposed method offers great advantages. Experiments show that the recognition accuracy of this method reaches 98.9%.

Existing bearing fault diagnosis methods have some disadvantages, one being that most methods cannot completely consider all specific fault attributes. Another disadvantage is that the qualitative diagnosis method considers different fault types as a whole, and qualitative diagnosis of a single fault attribute is complicated. A convolutional neural network is proposed for application in the multi-attribute quantitative bearing fault diagnosis. Multiple combinations of convolutional layers are adopted to directly extract features from one-dimensional vibration signals. In addition, a softmax layer is designed to realise the simultaneous recognition of different fault attributes. The advantage of this approach is that it can realise diagnostic results for any combination of fault attributes and corresponding types, which overcomes the disadvantage of single attribute recognition in the traditional method. The method is simple but has strong generalisation ability with average diagnostic accuracy of more than 95%. According to bearing data from Case Western Reserve University and laboratory experiments by the authors, the results verify that the method can accurately and quantitatively diagnose bearing faults.