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In order to accurately estimate the period of X-ray pulsars to improve navigation performance, this paper proposes a pulsar period estimation method based on photon energy using reverse Hilbert coding and double Convolutional Neural Networks (CNNs) discrimination. This method uses photon energy information to perform epoch folding. It converts one-dimensional contour information into two-dimensional image signals through reverse Hilbert curve and recognizes contour image features through CNN models to determine the optimal period. According to the simulation results, the period estimation accuracy of this method is 0.5350 ns, which is 65.71% higher than the χ 2 epoch folding method. The contour phase accuracy is 0.0004767, which is 41.93% higher than the χ 2 epoch folding method. At the same time, the observation time and noise interference that may affect the accuracy of period estimation are also quantitatively analyzed. In addition, in order to simulate real navigation scenarios, this paper also conducts dynamic period estimation experiments on PSR B0531+21 pulsar in a specific period of time. The method proposed in this paper has the advantages of high precision, fast calculation speed, strong anti-interference ability and accurate dynamic period estimation, which can significantly improve the navigation performance of X-ray pulsars. • The proposed period estimation method uses photon energy information for contour weighted folding. • The proposed method uses reverse Hilbert curve to convert one-dimensional contour information into two-dimensional image information. • Convolutional Neural Network models are used to optimize pulsar period estimation. [Display omitted] [ABSTRACT FROM AUTHOR]

An Election is a method of selection of individuals to hold the public office in democracy. Ballot is basically a piece of paper that is used to cast vote during election. In ballot paper voting system each voter uses a ballot paper which is not shared and basically it is a paper printed with the name and symbols of the candidates. The Electronic Voting Machine is basically a memory recorder which records the vote casted by the voters. In this paper, main advantages of E-voting systems for country is highlighted. For constructing E-voting systems, every countries need to do great attention to Verification and Validation requirements. In this research, E-voting scheme with face recognition using deep learning technique is proposed. The process of casting vote is accomplished by blockchain technology and blind signature mechanism. The main objective of the proposed scheme is to explore the positive effects of security and safety in online voting system. • The Election is a method of selection of individuals to hold the public office in democracy. • The Electronic Voting Machine is basically a memory recorder which records the vote casted by the voters. • The aim of the research is to E-voting scheme with face recognition using deep learning technique is proposed. • The main objective of the proposed scheme is to explore the positive effects of security and safety in online voting system. [ABSTRACT FROM AUTHOR]

This paper proposes a high-accuracy EEG-based schizophrenia (SZ) detection approach. Unlike comparable literature studies employing conventional machine learning algorithms, our method autonomously extracts the necessary features for network training from EEG recordings. The proposed model is a ten-layered CNN that contains a max pooling layer, a Global Average Pooling layer, four convolution layers, two dropout layers for overfitting prevention, and two fully connected layers. The efficiency of the suggested method was assessed using the ten-fold-cross validation technique and the EEG records of 14 healthy subjects and 14 SZ patients. The obtained mean accuracy score was 99.18 %. To confirm the high mean accuracy attained, we tested the model on unseen data with a near-perfect accuracy score (almost 100 %). In addition, the results we obtained outperform numerous other comparable works. [ABSTRACT FROM AUTHOR]

In Computer-Controlled Optical Surfacing technology, the precision of the removal function directly affects the accuracy of computer-aided processing software predictions, which in turn influences subsequent polishing machine processing. A key parameter for constructing the removal function is the material removal rate, which is often challenging to obtain accurately. Currently, the Preston equation is widely used to describe the principles of material removal. However, as a linear equation that omits many factors, it struggles to accurately model the removal function in complex machining scenarios. Therefore, this paper proposes a hybrid neural network model combining Convolutional Neural Networks and Bidirectional Long Short-Term Memory to predict the material removal rate. The model's parameters are optimized using an improved Grey Wolf Optimization algorithm, ultimately establishing a removal function closely consistent with an actual removal function. We first tested our method on the PHM2016 Data Challenge dataset, achieving a mean squared error of 6.19 and an R2 of 0.9949, outperforming other mainstream neural network prediction models developed in recent years. Additionally, we further validated the performance of the neural network using a small grinding head polishing dataset, achieving MSE and R2 values of 1.9035 and 0.99902, respectively. Finally, we applied this method to construct the removal function on an actual small grinding head production line. Compared to the traditional Preston equation-based removal function, the predicted residual surface's PV and RMS errors were reduced from 28.24 % to 35.58 %–4.563 % and 4.86 %, respectively. These validation results demonstrate that the proposed method not only facilitates easier acquisition of the removal function model but also significantly enhances the accuracy of computer-aided processing software predictions, thereby better guiding ultra-precision machining processes. • Proposed a CNN-BiLSTM hybrid neural network to predict material removal rates. • First to construct a small grinding head removal function using a BiLSTM Predictor. • Optimized the neural network parameter sets using an improved Grey Wolf algorithm. • Achieved MSE of 6.19 and R2 of 0.9949 on the PHM2016 Data Challenge dataset. • Reduced polishing residual surface PV and RMS prediction errors to 4.563 % and 4.86 %. [ABSTRACT FROM AUTHOR]

Cancer is a condition in which abnormal cells uncontrollably split and damage the body tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical images play an indispensable role in detecting various cancers; however, manual interpretation of these images by radiologists is observer-dependent, time-consuming, and tedious. An automatic decision-making process is thus an essential need for cancer detection and diagnosis. This paper presents a comprehensive survey on automated cancer detection in various human body organs, namely, the breast, lung, liver, prostate, brain, skin, and colon, using convolutional neural networks (CNN) and medical imaging techniques. It also includes a brief discussion about deep learning based on state-of-the-art cancer detection methods, their outcomes, and the possible medical imaging data used. Eventually, the description of the dataset used for cancer detection, the limitations of the existing solutions, future trends, and challenges in this domain are discussed. The utmost goal of this paper is to provide a piece of comprehensive and insightful information to researchers who have a keen interest in developing CNN-based models for cancer detection. [ABSTRACT FROM AUTHOR]

Deep neural networks have achieved outstanding performance in computer vision tasks. Convolutional Neural Networks (CNNs) typically operate in the spatial domain with raw images, but in practice, images are usually stored and transmitted in their compressed representation where JPEG is one of the most widely used encoder. Also, these networks are computationally intensive and slow. This paper proposes performing the learning and inference processes in the compressed domain in order to reduce the computational complexity and improve the speed of popular CNNs. For this purpose, a novel graph-based frequency channel selection method is proposed to identify and select the most important frequency channels. The computational complexity is reduced by retaining the important frequency components and discarding the insignificant ones as well as eliminating the unnecessary layers of the network. Experimental results show that the modified ResNet-50 operating in the compressed domain is up to 70% faster than the spatial-based traditional ResNet-50 while resulting in similar classification accuracy. Moreover, this paper proposes a preprocessing step with partial encoding to improve the resilience to distortions caused by low-quality encoded images. Finally, we show that training a network with highly compressed data can achieve a good classification accuracy with up to 93% reduction in the storage requirements of the training data. [ABSTRACT FROM AUTHOR]

Word spotting in handwritten documents is challenging due to the high intra-class and inter-class variability of handwritten forms. This paper addresses the word spotting problem in the segmentation and the training scenarios. Overall, this paper makes the following three contributions: (1) a new word text representation, called the Pyramid of Bidirectional Character Sequences (PBCS), which can solve both the word spotting problem and the word recognition problem. The use of the PBCS representation allows trained models to identify the character subsequences shared by words. Thus, words that are not seen during training can be represented and spotted. In addition, the PBCS representation encodes word texts redundantly, allowing for word discrimination. (2) A binary classification modeling of the word spotting problem in the difference space between representations, where spotting of non-vocabulary words is more efficient. Finally, (3) a new deep neural network architecture that combines the strengths of convolutional layers and transformers. We evaluated our solution on IAM and RIEMS datasets and showed that it outperforms recent state-of-the-art methods in the query-by-example scenario. • The Pyramid of Bidirectional Character Sequences (PBCS) word text representation. • Word spotting task as a binary classification problem. • Combing the strengths of convolutional layers and transformers. [ABSTRACT FROM AUTHOR]

Thanks to its richness in extractable features, Hyperspectral images (HSI) find an accelerated use in medical, industrial, agricultural, and environmental fields. In this paper, we present a wavelet-based reduction technique that creates a Hypercube containing the most significant features extracted from the original HSI and representing a multi-dimensional array that is utilized for training a Convolutional Neural Network (CNN), which is designed here to classify different types of surfaces or materials. The performance of this approach is tested and proved using two distinct datasets. Then, we compare the same approach with the PCA, a widely used reduction method. The most important contribution of this paper is the implementation of an FPGA-based parallel accelerator to train the same suggested CNN in only 10% of the computational time compared to the classical CPU-based techniques. The Microblaze will be explained and exploited here to play the role of an embedded microprocessor. • A Wavelet-based technique gives a reduced Hypercube rich in pixels spectrum features. • A CNN is designed to classify different types of surfaces or materials. • An FPGA-based parallel design is created to reduce 90% of the CNN training time. • The Microblaze is used to play the role of an embedded microprocessor. [ABSTRACT FROM AUTHOR]

• Compared the performance of three algorithms in classifying the age of documents. • FTIR combine with chemometrics can classify the documents of different years. • CNN are proved to be an effective technique. A non-destructive method based on Fourier Transformed Infrared Spectroscopy (FT-IR) was proposed to estimate the date of paper from different years in this article. For the paper samples, dated from 1940 to 1980, naturally aged and conserved in library. Partial least squares-discriminate analysis (PLS-DA), Logistic regression and convolutional neural network (CNN), were employed to evaluate the date of paper, with the accuracy 60.74%, 95.31% and 98.77%, respectively. Based on the characteristics of CNN model and with the help of network localization, active variables could be recognized in the whole spectrum. Although the localization of active variables showed a discriminative pattern, the selected spectral regions were similar. Most important variables focused on the 1700–1400 cm−1, were corresponding to cellulose crystallinity, which was consisted with the ageing processing. The present work gave the potential of FT-IR combined with chemometric techniques could estimate the dating of unknown paper. Meanwhile, the analysis of active variables obtained further indicated the worthy of CNN model for document dating. [ABSTRACT FROM AUTHOR]

In this paper, the non-uniform illumination enhancement problem of underwater images under the artificial light sources conditions is investigated based on Convolution Neural Network (CNN). First, we propose a trainable end-to-end enhancer called NUIENet, for enhancing the non-uniform illumination of underwater images. The proposed model consists of correction network and fusion layers. The correction network adopts the encoder–decoder structure with skip connections to enhance the features of different channels in the HSV domain, and then these enhanced features are fused by the fusion layers to obtain the desired high-quality images. Second, we built an underwater images dataset using Generative Adversarial Network (GAN) and Gaussian Function. Finally, both qualitative and quantitative experimental results show that the proposed method can produce better performance compared to other state-of-the-art enhancement methods on both real-word and synthetic underwater dataset. • This paper proposes a non-uniform illumination enhancer CNN-based which uses the encoder–decoder structure with skip connections to enhance the underwater images with NUI to the desired high-quality images. • To boost underwater imaging processing, we construct a dataset of the underwater image with NUI based on the GAN and Gaussian function which contains NUI images and their corresponding high-quality reference image. • Compared with other state-of-the-art NUIE methods, the proposed network achieves a nature color correction and superior or equivalent visibility improvement. [Display omitted] [ABSTRACT FROM AUTHOR]

Due to the fast progress of Deep Learning (DL) methods in remote sensing applications, many change detection (CD) algorithms have been recently proposed based on CNN networks and the mechanism of self-attention. These algorithms extract features without focusing on the temporal dependency between features. This shortcoming led to the introduction of the Transformer mechanism. In this paper, we design a convolutional transformer network with a Siamese U-shaped structure and name it CTS-Unet to solve the CD problem. We exploit the ability of the CNN to extract effective semantic features and that of the transformer to extract global information effectively. The Siamese architecture allows using CNN to simultaneously extract effective semantic features from low-resolution bi-temporal images. The transformer part contains an encoder and a decoder, all of which use the Swin transformer module as their basic unit. The encoder processes the features extracted from the CNN using patch merging and the Swin transformer module to produce semantic features. The encoder extracts the detailed information from the features using patch expansion, the Swin transformer module, and convolutional upsampling to create a CD map. The experiments were performed on the widely used LEVIR-CD and DSIFN-CD datasets. Compared with other state-of-the-art CD methods, CTS-Unet provides higher performance with F1-scores of 91.87% and 69.60% for LEVIR-CD and DSIFN-CD datasets, respectively. [ABSTRACT FROM AUTHOR]

• Accuracy of avalanche hazard analysis and prediction rely on avalanche feedback. • Convention methods of avalanche reporting are inefficient and incomplete. • UAV RGB images can provide accurate avalanche occurrence details of an area. • Detected deposits will help in avalanche analysis, characterization and modelling. Avalanche activities in the snow-covered mountainous slopes are very frequent and stochastically distributed over time and geographical extents. Conventional methods of avalanche occurrence reporting in these areas are inefficient and incomplete due to inhospitable weather and inaccessible topographic conditions. This paper presents a novel framework for the detection and analysis of avalanche deposits using UAV (Unmanned Aerial Vehicle) RGB images of snow-bound avalanche-prone areas. In the first part of the proposed framework, an OBIA-CNN (Object Based Image Analysis-Convolution Neural Network) method is applied to UAV images for the detection of avalanche deposits. Initially, the OBIA-based multi-scale multi-resolution segmentation technique is used for UAV image segmentation, then the classification of these image segments was performed using a CNN classifier to detect the avalanche deposits. The CNN-based classifier was created, trained, validated, and applied to generate the class probability maps of different classes present in the images. The post-classification refinements were then applied to the detected deposits. In the second step the detailed accuracy analysis of the detected deposits based on their count, area, size similarity, and shape similarity with respect to reference deposits has been carried out to establish the suitability of the proposed method. The avalanche deposits detected using the proposed method were found in good correlation with manually delineated deposits in terms of all four accuracy criteria. The precision of the model was found 1.0, the value of recall was 0.88 and the F-1 score was 0.93. The overall object shape matching accuracy of the detected deposits was also found high (0.92). The results of the proposed deposit detection method were also compared with the classification results of Support Vector Machine (SVM), Random Forest (RF), and UNET to evaluate the performance of the method. Finally, the surface area and snow volume-based characterization of the detected deposits was performed. The proposed framework will be useful for the automated detection of avalanche deposits and their characterization for the regions of specific interest. The study suggests that regular monitoring of avalanche activities using UAV images provides a good solution for the operational monitoring of important corridors of snowbound regions. [ABSTRACT FROM AUTHOR]

Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. With the introduction of deep networks, the process of activity detection is clustered into two groups known as hand-crafted feature based approach and automatic feature extraction approach. This paper focuses on various approaches used in recent literature based on traditional and automatic approach. Moreover, hierarchy for different approaches under them such as space based, motion based, genetic based, fuzzy based, dictionary based are discussed. With introduction of Convolutional Neural Networks and Recurrent Neural Networks, automatic learning capability from input modality makes them first choice to be implemented for activity recognition. In this paper various approaches have been analyzed according to methodology, accuracy, classifier and datasets. [ABSTRACT FROM AUTHOR]

• The paper fills the gap in the literature by reviewing and analyzing the current studies that apply deep learning for Covid-19 forecasting. • The initial search identified 152 studies of which 53 passed the quality control. • The existing literature is categorized using a model-based taxonomy. • The description of the models along with their performance evaluation is presented. • Recommendations for future improvements are provided. The Covid-19 pandemic has galvanized scientists to apply machine learning methods to help combat the crisis. Despite the significant amount of research there exists no comprehensive survey devoted specifically to examining deep learning methods for Covid-19 forecasting. In this paper, we fill the gap in the literature by reviewing and analyzing the current studies that use deep learning for Covid-19 forecasting. In our review, all published papers and preprints, discoverable through Google Scholar, for the period from Apr 1, 2020 to Feb 20, 2022 which describe deep learning approaches to forecasting Covid-19 were considered. Our search identified 152 studies, of which 53 passed the initial quality screening and were included in our survey. We propose a model-based taxonomy to categorize the literature. We describe each model and highlight its performance. Finally, the deficiencies of the existing approaches are identified and the necessary improvements for future research are elucidated. The study provides a gateway for researchers who are interested in forecasting Covid-19 using deep learning. [ABSTRACT FROM AUTHOR]

Big data, AI, and machine learning have ushered in a transformative era in computer processing and application. AI's capacity to emulate human cognition spans a spectrum of industries, from rudimentary tasks to intricate decision-making processes. Projections indicate that within a short span, AI technology will bestow computers with human-like capabilities, rendering it indispensable. This advancement has substantially bolstered industry autonomy, fortifying security and safety through the integration of electronics, internet connectivity, advanced algorithms, and cutting-edge processing chips. In this context, Automated Guided Vehicles (AGVs) leverage AI-powered systems and sensors to optimize performance and proactively thwart potential failures. Going beyond Advanced Driver Assistance Systems (ADAS), this review scrutinizes the broader spectrum of AI applications. Specifically, it synthesizes pertinent studies encompassing AI's impact on car emissions, predictive maintenance, security, and the realm of connected vehicles. While AGVs can utilize AI algorithms to chart emission-efficient routes and engineer energy-conservative vehicles, AI, through diverse algorithms, proficiently anticipates component failures, substantially enhancing the cost-effectiveness of predictive maintenance and bolstering vehicle reliability. Moreover, AI integrated with sensor technology safeguards vehicles against a spectrum of threats. Neural networks and machine learning further automate security measures for both cargo and passengers. Concurrently, AI and machine learning fortify traffic control and enhance vehicle communication within connected vehicle frameworks, albeit necessitating fortifications in data security, encryption, and system efficiency. This survey paper comprehensively covers the expanse of automotive AI and machine learning applications, extending beyond ADAS. It underscores that the evolving mobility landscape necessitates a thorough evaluation of network architecture, connectivity, and performance metrics, encompassing AGV technology, networking protocols, and swarm dynamics. In this vein, the paper advocates for experts to address existing literature gaps and proactively enhance automotive systems. [ABSTRACT FROM AUTHOR]

• A novel deep learning-based registration network • A novel smooth constraints function • Two-stage skip connections obtain richer local information • Proposed method can be easily transferred to various applications Medical image registration can establish the spatial consistency of the corresponding anatomical structures between different medical images, which is important in medical image analysis. In recent years, with the rapid development of deep learning, the image registration methods based on deep learning greatly improve the speed, accuracy, and robustness of registration. Regrettably, these methods typically do not work well for large deformations and complex deformations in the image, and neglect to preserve the topological properties of the image during deformation. Aiming at these problems, we propose a new network TS-Net that learns deformation from coarse to fine and transmits information of different scales in the two stages. Two-stage network learning deformation from coarse to fine can gradually learn the large and complex deformations in images. In the second stage, the feature maps downsampled in the first stage for skip connection can expand the local receptive field and obtain more local information. The smooth constraints function used in the past is to impose the same restriction on the global, which is not targeted. In this paper, we propose a new smooth constraints function for each voxel deformation, which can better ensure the smoothness of the transformation and maintain the topological properties of the image. The experiments on brain datasets with complex deformations and heart datasets with large deformations show that our proposed method achieves better results while maintaining the topological properties of deformations compared to existing deep learning-based registration methods. [ABSTRACT FROM AUTHOR]

• A new approach for PD detection based on CNN deep features and i-vector features. • The impact of effective use of dynamic features for PD classification. • The significance of CNN deep features and i-vector features in terms of PD detection. Parkinson's disease (PD) corresponds to one of the most common neurological diseases in the world, which is mainly manifested by motor, cognitive, and language disorders. The change in the patient's voice is one of the most striking clinical signs and proves to be an element of interest to support the diagnosis and the assessment of PD. In this research paper, a new approach based on speech signal analysis is set forward to automatically detect Parkinson's disease. The approach evaluates two learning techniques, namely Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), to classify data obtained from speech tasks. Two input data,i.e., the raw speech signal's values and the i-vector features of dimensions 100, 200 and 300 are extracted in this study. Eventually, an evaluation step is undertaken through the use of five evaluation metrics which are accuracy, precision, recall/sensitivity, specificity and f -score. The most pertinent obtained results for a test dataset composed of 28 participants are recorded as follows: an accuracy of 100%, precision of 0.99, recall/sensitivity of 0.98, specificity of 0.96 and f -score of 0.98. These findings corroborate that our approach can help in terms of PD detection. [ABSTRACT FROM AUTHOR]

The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability. [ABSTRACT FROM AUTHOR]

• The improved VMD and DWT are combined to denoise and reconstruct the signal under strong noise without a reference. • The selection of wavelet function and transform level in DWT is optimized by Shannon entropy to denoise the signal without a reference. • The RAPID and the CNN are used to achieve spatial localization and visualization of the composite delamination. In this paper, a damage spatial imaging approach based on novel signal extraction is suggested to reconstruct the Lamb wave response signal under strong noise and realize the spatial localization of damage. First, the Variable Mode Decomposition (VMD) parameters are optimized by the improved Grey Wolf optimization method (IGWO) to decompose the response signal. To rebuild the response signal, the correlation coefficient is used to choose the optimal modal component and the residual. To give the best wavelet function and transform level for adaptive denoising of the reconstructed signal without a reference signal, an enhanced Discrete Wavelet Transform (DWT) based on Shannon entropy is proposed. To achieve damage localization imaging, a damage spatial localization model is built utilizing a reconstruction algorithm for probabilistic inspection of damage (RAPID) approach and a convolutional neural network (CNN). The suggested method may successfully increase the signal-to-noise ratio (SNR) of the reconstructed response signal and lower the error of spatial localization under strong noise through experiments. The spatial localization of composite damage using Lamb wave under strong noise is expanded in this paper. [ABSTRACT FROM AUTHOR]

High-resolution (HR) MR images provide rich structural detail to assist physicians in clinical diagnosis and treatment plan. However, it is arduous to acquire HR MRI due to equipment limitations, scanning time or patient comfort. Instead, HR MRI could be obtained through a number of computer assisted post-processing methods that have proven to be effective and reliable. This paper aims to develop a convolutional neural network (CNN) based super-resolution reconstruction framework for low-resolution (LR) T2w images. In this paper, we propose a novel multi-modal HR MRI generation framework based on deep learning techniques. Specifically, we construct a CNN based on multi-resolution analysis to learn an end-to-end mapping between LR T2w and HR T2w, where HR T1w is fed into the network to offer detailed a priori information to help generate HR T2w. Furthermore, a low-frequency filtering module is introduced to filter out the interference from HR-T1w during high-frequency information extraction. Based on the idea of multi-resolution analysis, detailed features extracted from HR T1w and LR T2w are fused at two scales in the network and then HR T2w is reconstructed by upsampling and dense connectivity module. Extensive quantitative and qualitative evaluations demonstrate that the proposed method enhances the recovered HR T2w details and outperforms other state-of-the-art methods. In addition, the experimental results also suggest that our network has a lightweight structure and favorable generalization performance. The results show that the proposed method is capable of reconstructing HR T2w with higher accuracy. Meanwhile, the super-resolution reconstruction results on other dataset illustrate the excellent generalization ability of the method. [ABSTRACT FROM AUTHOR]

With the rapid development of blockchain and digital currencies, cybercrimes targeting blockchain transactions are proliferating. Various abnormal transaction behaviors caused by malicious nodes and malicious transactions have emerged in blockchain systems. Therefore, blockchain transaction security research is a hot issue in blockchain research. Deep learning has made remarkable achievements in image and audio. However, deep learning is less effective when processing text-like data than tree-based models. Therefore, for the highly unbalanced textual financial dataset, this paper designs a deep learning anomaly detection method called 1D SA-Inception by combining CNN with Transformer structure on the basis of traditional one-dimensional CNN (1D-CNN), introduces the Inception structure to learn the information of different scales of tabular data, and introduces the Transformer (self-attention) self-attention mechanism for the research of blockchain anomaly transaction detection technology, effectively improving the prediction effect. Finally, by conducting experiments on the extreme class imbalance dataset, the results show that the 1D SA-Inception designed in this paper outperforms the traditional CNN with AUC, G-mean, and F1 indexes of 91.05 %, 84.95 %, and 83.52 %, respectively, on the textual dataset. This method can more efficiently achieve the detection of abnormal transaction behavior, safeguard consumer rights and interests, and ensure the safe development of the financial sector. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN–LSTM were proposed. However, these techniques, when used individually, are not sufficient to effectively learn multiple features from the ECG signal. The fusion of CNN and LSTM overcomes the limitations of CNN in the existing studies as CNN–LSTM hybrids can extract spatiotemporal features. However, LSTMs suffer from long-range dependency issues due to which certain features may be ignored. Hence, to compensate for the drawbacks of the existing models, this paper proposes a more comprehensive feature fusion technique by merging CNN, LSTM, and Transformer models. The fusion of these models facilitates learning spatial, temporal, and long-range dependency features, hence, helping to capture different attributes of the ECG signal. These features are subsequently passed to a majority voting classifier equipped with three traditional base learners. The traditional learners are enriched with deep features instead of handcrafted features. Experiments are performed on the MIT-BIH arrhythmias database and the model performance is compared with that of the state-of-art models. Results reveal that the proposed model performs better than the existing models yielding an accuracy of 99.56%. • This paper proposes a rich feature fusion technique for cardiac arrhythmia detection from ECG by merging three deep learning models, namely, CNN, CNN–LSTM, and Transformer. • Integrating these models combines the advantages of all these individual models, overcomes their limitations, and learns significant features from the input ECG signal. • The deep spatial, temporal, and long-range dependency patterns learned by the aforementioned models are fused via concatenation and fed to a majority voting classifier with three traditional base learners: SVM, LR, and RF. • MIT-BIH Arrhythmia database is used for experimentation. A comparison of the results with the baseline models and state-of-the-art models shows that the proposed model outdoes the existing models achieving an accuracy of 95.56% and an F-score of 99.34%. • The results show that the proposed model outperforms the existing models in terms of precision, recall, F-score, and accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a new proposal of an efficient computational model of face recognition which uses cues from the distributed face recognition mechanism of the brain, and by gathering engineering equivalent of these cues from existing literature. Three distinct and widely used features – Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Principal components (PCs) extracted from target images are used in a manner which is simple, and yet effective. The HOG and LBP features further undergo Principal Component Analysis (PCA) for dimensionality reduction. Our model uses multi-layer perceptrons (MLP) to classify these three features and fuse them to form a sparsely connected model. A computational theory is first developed by using concepts from the information processing mechanism of the brain. Extensive experiments are carried out using eight publicly available face datasets to validate our proposed model's performance in recognizing faces with extreme variation of illumination, pose angle, expression, and background. We also investigate the same mechanism because of reasons discussed later, on object recognition tasks as well. Results obtained are extremely promising when compared with other face and object recognition algorithms including CNN and deep learning-based methods. This highlights that simple computational processes, if clubbed properly, can produce competing performance with best algorithms. [ABSTRACT FROM AUTHOR]

• Deep and handcrafted features are extracted from 2D depth images and 3D scans respectively. • The extracted features are embedded into a covariance pooling for the dimensionality reduction. • Deep covariance learning is carried out using two additional layers to enhance their discrimination power. • The Gaussian kernel is used to generalize the radial basis function to the manifold space for SVM classification. • High classification performance is achieved on two challenging datasets. Facial emotion recognition (FER) methods have been proposed mainly using 2D images. These methods suffer from many problems caused by the difficult conditions of unconstrained environments such as light conditions and view variations. In this paper, we aim to recognize the emotional facial expressions independently of their identity using the 3D data and 2D depth images. Since the 3D FER is a very fine-grained recognition task, mapping the 3D images into 2D depth images may lack some geometric characteristics of the expressive face and decay the FER performance. Convolutional Neural Networks (CNN), however, have been successfully applied to the 2D depth images and improved handcrafted-based methods in computer vision and pattern recognition applications. For this reason, we combine in this paper two types of features; handcrafted and deep learning features and prove their complementarity for 3D FER. Favorably, covariance descriptors have proven a very good ability to combine features from different types into a compact representation. Therefore, we propose to use the covariance matrices of features (handcrafted and deep ones), instead of the features independently. Since covariance matrices belong to one of the manifold space types, formed by SPD (Symmetric Positive Definite) matrices, we mainly focus on the generalization of the RBF kernel to the manifold space for 3D FER using a supervised SVM classification. The achieved performance of the proposed method on the Bosphorus and BU-3DFE datasets outperforms similar state-of-the-arts. [ABSTRACT FROM AUTHOR]

Facial reenactment is aimed at animating a source face image into a new place using a driving facial picture. In a few shot scenarios, the present strategies are designed with one or more identities or identity-sustained suffering protection challenges. These current solutions are either developed with one or more identities in mind, or face identity protection issues in one or more shot situations. Multiple pictures from the same entity have been used in previous research to model facial reenactment. In contrast, this paper presents a novel model of one-shot many-to-many facial reenactments that uses only one facial image of a face. The proposed model produces a face that represents the objective representation of the same source identity. The proposed technique can simulate motion from a single image by decomposing an object into two layers. Using bi-layer with Convolutional Neural Network (CNN), we named our model Bi-Layer Graph Convolutional Layers (BGCLN) which utilized to create the latent vector's optical flow representation. This yields the precise structure and shape of the optical stream. Comprehensive studies suggest that our technique can produce high-quality results and outperform most recent techniques in both qualitative and quantitative data comparisons. Our proposed system can perform facial reenactment at 15 fps, which is approximately real time. Our code is publicly available at https://github.com/usaeed786/BGCLN. [ABSTRACT FROM AUTHOR]

Inspired by image-to-curve transformation and multi-exposure fusion, in this paper, we have developed a new method to treat the low light image enhancement tasks as an extended problem with multiple virtual exposures by a non-linear intensity mapping function. Considering that existing image-to-curve methods have difficulty in obtaining the desired detail and brightness recovery in any one iteration without relying on any ground truth, we propose a virtual multi-exposure fusion strategy to merge the outputs from these different iterations. Specifically, a simple CNN is trained to learn a pixel-wise intensity mapping function and accordingly adjust a given image multiple times. Then the results of all iterations are retained together with the original input image for fusion via a WGIF-based Multi-scale pyramid to obtain a final enhanced output. We present experimental results to demonstrate the effectiveness of the new technique and its state-of-the-art performances. [ABSTRACT FROM AUTHOR]

• We propose a rotation invariant Gabor convolutional neural network (RIGCN). • We learn Gabor-guided convolutional features in the Siamese network architecture. • We explore a convolutional fusion operator to obtain rotation invariant features. • We show the effectiveness of RIGCN for rotation invariant image classification. Gabor filters have been recently integrated with deep convolutional neural networks to learn better features with fewer model parameters. However, during feature learning the rotation invariance is not well addressed. In this paper, we propose a rotation invariant Gabor convolutional neural network (RIGCN) for image classification. First, we transform each input image to generate multiple rotated image instances and feed them into a weight-sharing Siamese network architecture to learn Gabor-guided deep convolutional features. Then, we compute the maximum and average feature responses from all the rotated instances of the same input image and send them into a convolutional fusion module to obtain a rotation invariant image representation. Finally, we use the cross-entropy loss for image classification. In our method, the use of Siamese architecture enables us to obtain rotation invariant features from rotated image instances. The use of convolutional fusion operator enables us to obtain richer statistical features with high efficiency. Experimental results on several benchmark datasets demonstrate the effectiveness of RIGCN for rotation invariant image classification. [ABSTRACT FROM AUTHOR]

• Measured and analyzed the 3D fluorescence spectra of four emulsified oils, revealing their unique fluorescence spectral characteristics. • Proposes an emulsified oil species identification method based on the ResNet50 + HPO-SVM model. • Expansion of emulsified oil fluorescence spectra using CVAE to meet the high data volume requirements of ResNet50. • Compared ResNet50 + HPO-SVM, ResNet50 + PLS-DA, and ResNet50 + KNN methods by statistical parameter. Increased pollution from oil emulsions may cause serious ecological damage. The development of a fast and effective emulsified oil species identification method plays a crucial role in dealing with oil spills on the sea surface. Because of this, this paper proposes an emulsified oil species category identification method based on 3D fluorescence spectroscopy and CNN. First, the 3D fluorescence spectra of emulsified oil were measured by an FLS1000 steady-state fluorescence spectrometer, and their fluorescence characteristics were analyzed in depth. Second, the 3D fluorescence spectral data of emulsified oil was extended using a conditional variational autoencoder (CVAE), and the spectral features were extracted using a pre-trained ResNet50. Finally, the spectral features extracted by ResNet50 were used as inputs to the hunter-prey optimized support vector machine (HPO-SVM), and the results show that the recognition accuracy of this method is 97 %, which is better than other classification models. [ABSTRACT FROM AUTHOR]

In this paper, we propose a technique for hierarchical yoga pose classification (YPC) in a multi-stage multi-tasking framework. We propose a three-stage transfer learning based end-to-end training methodology. Novelty lies in (a) proposed supervised contrastive combined loss function for stage-1 training, (b) proposed Encoder–Decoder network architecture with attention mechanism for stage-3 training, (c) proposed spatial context aware multi-tasking combined loss function for stage-3 training. Firstly, for stage-1 training, we propose the usage of linear combination of three loss functions: cross-entropy, self-supervised contrastive loss and supervised contrastive loss in a multi-tasking manner. We introduce radial and cosine margin into the formulation of self-supervised and supervised contrastive loss to pull feature embeddings of same class closer together compared to feature embeddings of different classes. Weights learned over stage-1 training are subsequently fine-tuned over cross-entropy multi-tasking loss in stage-2. These stage-2 weights are transfer learned and are further fine-tuned in stage-3 training. For stage-3 training, we propose the usage of spatial context aware multi-tasking combined loss function. This loss function leverages on the fine-grained spatial features obtained from HiResCAM. These are processed in parallel with features obtained from XGrad-CAM (sensitivity and conservation axioms satisfying features) to further supervise the learning of hierarchical yoga pose classifier. We exemplify our methodology on the publicly available Yoga-82 large-scale dataset. We report peak Top-1 YPC accuracy of 95.89% over 6 pose classes (Yoga-6), 93.85% over 20 pose classes (Yoga-20) and 90.0% over 82 pose classes (Yoga-82). Our proposed method achieves 6.1% improvement over Top-1 classification accuracy in Yoga-6 hierarchy, 9.3% improvement in Yoga-20 hierarchy and 10.9% improvement in Yoga-82 hierarchy in comparison with state-of-the-art (SOTA) methodology. We achieve the current best Top-1 classification accuracies in all the three YPC hierarchies. [Display omitted] • Novel spatial context aware multi-tasking multi-stage end-to-end training method. • Improved spatial context awareness, evident through improved CAM explanations. • Improved inter class margins and intra class compactness. • Achieved SOTA hierarchical yoga pose classification accuracies on Yoga-82 dataset. [ABSTRACT FROM AUTHOR]

The escalating prevalence of gastrointestinal disorders, spanning a spectrum from polyps to tumors, underscores the imperative for advanced diagnostic and interventional methodologies. This paper addresses this exigency by introducing telemedicine into the healthcare landscape, presenting an innovative approach for remote patient surgery. Telemedicine harnesses technological advancements to extend the reach of surgical expertise, transcending geographical limitations and augmenting patient-centric care strategies. Within the domain of healthcare 4.0, the integration of Explainable Artificial Intelligence (X-AI) is paramount in the telemedicine sector. This arises from the need to elucidate the decision-making processes of intricate models, particularly in the realm of gastrointestinal disease detection. We introduce TeleXGI , a sophisticated model leveraging Convolutional Neural Network (CNN) architectures ResNet50 and MobileNetV2 with X-AI techniques for accurate gastrointestinal disease classification using the Kvasir-V2 dataset. Advanced X-AI techniques such as Grad-CAM, saliency maps, integrated gradients, attribution heatmaps, and Local Interpretable Model-Agnostic Explanations (LIME) are employed to enhance interpretability. These methodologies unveil the inner workings of CNNs, highlighting impacted regions and augmenting predictive transparency. Our performance assessment validates TeleXGI 's excellent diagnostic accuracy at 98.8% with ResNet50 and 98.5% with MobileNetV2, faster inference with lightweight models, and precise explanation heatmaps essential to the real-time and high-throughput data-processing requirements of Telesurgery. The credibility of the explanation heatmaps is verified against the ground truth masks of Polyps images from the Kvasir-SEG dataset. This research establishes a benchmark for pivotal healthcare applications, showcasing the potential of X-AI in enhancing the interpretability of CNN models. [ABSTRACT FROM AUTHOR]

The aim of data science is to catch up with the data-intensive life style as well as the demand for decision support, which becomes common in various domains such as medical, education and other smart solutions. As such, high quality of data analysis is greatly desired for accurate and effective downstreaming exploitations. This is also true for the domain of astronomical survey like GOTO (Gravitational-wave Optical Transient Observer), where large amount of raw data has been collected daily. This is one of recognised projects that search for transient events with the new breed of optical survey telescopes that can detect the sky faster and deeper. This is accomplished by comparing the night-specific data with the reference such that new bright sources are obtained for further study. However, the huge size of data makes it difficult to sift by naked eyes, thus requiring an automated system. Yet, many conventional machine-learning models have been sub-optimal for this task, as true positives can hardly be recognised due to the nature of imbalance data. This motivates the exploration of convolutional neural networks or CNN for this binary classification problem. Based on existing technologies, the paper reports the original application of basic CNN model to a representative data, which has been designed and generated within the GOTO project. In addition to the improvement over those previous works, this empirical study also includes details of parameter analysis, which will be useful for practice and further investigation. [ABSTRACT FROM AUTHOR]

This paper presents a model for sound classification in construction that leverages a unique combination of Mel spectrograms and Mel-Frequency Cepstral Coefficient (MFCC) values. This model combines deep neural networks like Convolution Neural Networks (CNN) and Long short-term memory (LSTM) to create CNN-LSTM and MFCCs-LSTM architectures, enabling the extraction of spectral and temporal features from audio data. The audio data, generated from construction activities in a real-time closed environment is used to evaluate the proposed model and resulted in an overall Precision, Recall, and F1-score of 91%, 89%, and 91%, respectively. This performance surpasses other established models, including Deep Neural Networks (DNN), CNN, and Recurrent Neural Networks (RNN), as well as a combination of these models as CNN-DNN, CNN-RNN, and CNN-LSTM. These results underscore the potential of combining Mel spectrograms and MFCC values to provide a more informative representation of sound data, thereby enhancing sound classification in noisy environments. • Recognition of various construction activities using audio data inputs. • Combining MFCC-LSTM numerical features with CNN-LSTM spatial maps for enhanced audio representation. • Evaluating model performance on 790 audio samples from four different construction activities. • Comparison of model's performance against five commonly used deep learning and sequential methods. [ABSTRACT FROM AUTHOR]

Text-based person search aims to use text descriptions to search for corresponding person images. However, due to the obvious pattern differences in image and text modalities, it is still a challenging problem to align the two modalities. Most existing approaches only consider semantic alignment within a global context or partial parts, lacking consideration of how to match image and text in terms of differences in model information. Therefore, in this paper, we propose an efficient Modality-Aligned Person Search network (MAPS) to address this problem. First, we suppress image-specific information by image feature style normalization to achieve modality knowledge alignment and reduce information differences between text and image. Second, we design a multi-granularity modal feature fusion and optimization method to enrich the modal features. To address the problem of useless and redundant information in the multi-granularity fused features, we propose a Multi-granularity Feature Self-optimization Module (MFSM) to adaptively adjust the corresponding contributions of different granularities in the fused features of the two modalities. Finally, to address the problem of information inconsistency in the training and inference stages, we propose a Cross-instance Feature Alignment (CFA) to help the network enhance category-level generalization ability and improve retrieval performance. Extensive experiments demonstrate that our MAPS achieves state-of-the-art performance on all text-based person search datasets, and significantly outperforms other existing methods. • A novel text-based person search network is proposed by reducing modal differences while learning sufficient modal features. • A multi-granularity feature self-optimization module is designed to optimize the multiscale image modal feature and multi-level semantic text modal feature, so as to learn more discriminative features with suppressing useless and redundant information. • A cross-instance feature alignment is proposed to construct image–text feature pairs with category-level information participating in training. • Extensive experiments in both CUHK-PEDES and ICFG-PEDES datasets show our MAPS obtains the state-of-the-art performance, which significantly outperforms other existing methods. [ABSTRACT FROM AUTHOR]

• A lightweight model for pig face recognition was developed using a fourth-order Runge-Kutta numerical method and a hybrid attention module. Subsequent experiments were conducted to evaluate its performance. • The model effectively enhances training and inference efficiency, reduces the number of parameters and memory usage, and improves accuracy in the extraction of pig face information. • Experimental tests show that the model achieves 99.26 % recognition accuracy and has a size of only 1.52 MB, which makes it suitable for deployment on embedded devices. • The heatmap visualization of the model's attention to the region of interest shows that the model pays more attention to the pig face region compared to other models. • The model can provide technical support for the scientific management of pig farms, thus improving the feasibility of deploying lightweight intelligent devices in smart pig farms. Pig face recognition plays a significant role in intelligent pig farming. Accurate and lightweight methods for recognizing pig faces are crucial for precision pig farming. Generally, under the condition of the same computing resources, the lower the image resolution, the faster the model inference speed. Based on the above idea, this paper proposes a lightweight pig face model called RKNet-HAM, based on the fourth-order Runge-Kutta and hybrid attention mechanism. This model exhibits the most prominent focus on the semantic information of low-resolution(64 × 64) pig face images and achieves high accuracy in recognizing similar pig faces with a low error classification rate. A dataset of 21,742 images of 32 pigs was constructed for individual pig recognition. The proposed RKNet-HAM model and several other deep learning models, e.g., RKNet-CBAM, RKNet, ShuffleNetV2.0, DesNet121, and ResNet50. RKNet-HAM has a size of 1.52 Megabytes. Experimental results show that among these models, the proposed model ofachieved the highest accuracy, precision, recall, and specificity, with an accuracy rate of 99.26 %. RKNet-HAM also exhibits good generalization ability. It provides experimental support for mobile and embedded applications. [ABSTRACT FROM AUTHOR]

This paper demonstrates L-band quantum-dash laser (QDL) based orbital angular momentum (OAM) structured light in a free space optics communication (FSO) system. A 4-ary OAM-shift-keying pattern coding communication system, based on Laguerre Gaussian (LG) and superposition LG (MuxLG) mode families, has been investigated under a foggy FSO channel. In addition, joint mode identification and channel condition estimation have been developed at the receiver side using advanced deep learning (DL) methods. We utilize and compare the performance of the convolutional neural networks (CNN) and UNET algorithms. An experimental setup has been conducted using an in-house controlled foggy chamber which allows an FSO transmission of 3-m length. Furthermore, we propose a data balancing approach to the experimental dataset by data augmentation. Visibility prediction results have shown a measured root mean square error of 17(18) m and 10(10) m for 4-ary LG(MuXLG) using CNN and UNET models, respectively. Moreover, the DL models provide an average mode classification accuracy of 94% under various channel visibility conditions. • OAM mode patterns was generated using QDL source and identified under foggy channel conditions. • Eight modes have been formed from the LG mode family. • A controlled chamber environment to emulate the outdoor fog condition was considered. • CNN and UNET DL methods were used as classifiers and regressors for mode identification and channel condition prediction. • Visibility parameter can be estimated with 17 and 10 m root mean square error (RMSE) using CNN and UNET techniques. [ABSTRACT FROM AUTHOR]

A tunnel boring machine (TBM) generates rock chips during excavation, which are crucial for assessing surrounding rock integrity, enhancing excavation efficiency, and evaluating cutter wear. However, traditional methods struggle to identify small rock chips, chips submerged in soil or water, and chips in stacked states. This paper proposes a convolutional neural network (CNN)-based method for directly recognizing the particle size distribution from rock chip images. A dataset of 2520 rock chip images representing 84 particle-size distributions was collected in a laboratory environment. By comparing various CNN architectures and hyperparameters, an optimal model was obtained with a mean absolute error (MAE) of 1.66 × 10−2 and R 2 of 0.923 on the test set. The results demonstrate that the proposed method enables the real-time recognition of particle size distribution using rock chip images, which has the potential to significantly improve intelligent auxiliary excavation technology in TBMs. • A dataset of 2520 rock chip images representing 84 particle size distributions was collected. • A CNN-based method for directly recognizing particle size distribution from rock chip images is proposed. • An optimal model was obtained with a MAE of 1.66 × 10−2 and R 2 of 0.923 on the test set. [ABSTRACT FROM AUTHOR]

In today's digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification. [Display omitted] • An innovative hybrid feature extraction method for training of DDoS attack detection model. • We propose a robust CNN model integrated with an Inception mechanism designed to classify various types of DDoS attacks. • Evaluated the network performance of the proposed system in terms of various parameters and compared the results with existing studies and baseline models. • The proposed model outperforms more than 96% in all performance metrics (Precision, Recall, Accuracy, and F1 Score). • The proposed model achieves the fastest prediction time of 0.189 ms among existing models in the literature. [ABSTRACT FROM AUTHOR]

With the development of modern cities, urban rail transit has become an indispensable part of residents' travelling mode, and accurate prediction of urban rail transit passenger flow is particularly important. However, due to the non-linearity and non-stability of passenger flow, the low quality of big data and the lack of data make it more and more difficult to predict the passenger flow of urban rail transit. In this paper, we propose a unique model structure that ingeniously integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN) with fused residual network connections, and a self-attention mechanism. The design of this model is primarily to effectively handle complex and low-quality data, which is often prevalent in passenger flow prediction scenarios. To validate the effectiveness of the proposed model, we used data from the Beijing urban rail transit in 2016 for prediction. In the experiments, we performed comparison study and ablation study. The experimental results show that the model proposed in this paper has significantly improved prediction accuracy in both horizontal and vertical comparisons. This outcome substantiates that the proposed model can not only effectively handle complex and low-quality data but also extract short-term features and long-term dependency features of passenger flow well, thereby achieving more accurate predictions. • Integration of GRU, CNN with fused residual connections, and self-attention mechanism constitutes a novel model. • The model is capable of handling complex and lower-quality data, significantly improving the accuracy of predictions. • The model can simultaneously extract short-term features and long-term dependency features from time series. [ABSTRACT FROM AUTHOR]

The task of accurately classifying defect types and pinpointing their locations in the domain of industrial product defect detection remains a formidable challenge. This paper introduces an advanced industrial defect detection framework, named YOLOv7-SiamFF, which utilizes the YOLOv7 as a feature extraction and detection backbone with three feature reinforcement modules. Firstly, we employ a parallel Siamese network, facilitating differential feature extraction through dual-stream feature extraction channels, aimed at better highlighting defect features and suppressing background interference. Additionally, we introduce a depth information feature fusion module, which effectively integrates high and low-level features in the Siamese network, thus enhancing the model's detection accuracy for small target defects. Finally, an attention mechanism is integrated into the feature extraction network, further enhancing the model's precision in identifying defect-specific features. In the simulation experiment, a specialized visual dataset was created for object detection tasks focusing on industrial defects, dubbed the BC-DD dataset. Additionally, the effectiveness of the proposed model has been validated in this paper using the aforementioned dataset. [ABSTRACT FROM AUTHOR]

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN. [ABSTRACT FROM AUTHOR]

In this paper, a clustering method for KMedoids based on dynamic time warping (DTW-KMedoids) is designed to analyze multi-channel signals, and a lightweight network, clustered blueprint separable convolutional neural network (CBS-CNN), is established to perform fault diagnosis of high-speed train (HST) bogie. The motivation for proposing the novel method is to address the problems of large network size and high training cost in deep learning. First, DTW-KMedoids is adopted to cluster the channels of multi-channel signals. Second, based on the principles of blueprint separable convolution (BSConv) and mixed depthwise convolution (MixConv), a lightweight convolution model construction strategy called clustered blueprint separable convolution (CBS-Conv) is proposed, which uses the same blueprint to convolute the data of the channels in a cluster. Third, CBS-CNN is established, with multiple branches to process data from different clusters, and the computational result of each branch is connected by the proposed Connect layer. Finally, by virtue of the learned features from training, the model completes the end-to-end HST bogie fault diagnosis task, where the usefulness of CBS-CNN in detecting bogie failures including component performance degradation, component failures, and composite failures is validated. Further experiments show that CBS-CNN has a remarkable ability to adapt itself to different task environments and objectives. [ABSTRACT FROM AUTHOR]

• An automatic energy leakage detection for district heating systems based on UAV Infrared (IR) images is presented. • A deep learning-based model and 8 different ML classifiers have been developed and evaluated for detection of leakages. • A region extraction algorithm has been incorporated to extract potential leakages enabling the ML analysis. • The proposed ML methods have been applied to ca. 13.4 million image patches and achieved an actual detection rate of 98.6%. In this paper, we propose automatic energy leakage detection in underground pipes of district heating systems based on Infrared (IR) images, captured by an Unmanned Aerial Vehicle (UAV). Hot water or steam is distributed to homes and industries through underground pipes from a central power plan. Leakages in underground pipes pose a very common problem, which can occur for many reasons, e.g. unprofessional installation and end of service life. Potentially, a leakage remains undiscovered for a very long period of time. Therefore, it is of great interest for power supply companies to monitor district heating networks to identify leakages. In this paper, the original IR images are captured in a 16 bit format by a UAV. On ground, potential leakages are extracted using a region extraction algorithm. Thereafter a Convolutional Neural Network (CNN) as well as eight conventional Machine Learning (ML) classifiers are applied on these regions to classify whether or not it is a leakage. In total, twelve UAV sequences are captured at different cities in Denmark. Based on these, around 13.4 million samples of image patches of district heating systems are extracted. Eleven sequences are used for training and the remaining one for testing. This was performed on all splits in the leave-one-out testing. The deep learning CNN achieved an average weighted accuracy of 0.872 with a false positive and negative rate of 12.7 % and 10.4 %, respectively. This CNN model detected around 98.6 % of the true leakages. In comparison, conventional ML classifiers, i.e. Adaboost (AB), Random Forest (RF), etc. provide lower average weighted accuracy, but on the other hand they require less computational resources. We have compared our method with a state-of-art method and the result shows that the proposed method is very competitive. [ABSTRACT FROM AUTHOR]

[Display omitted] • Artificially determining the degree of fermentation of black tea was arbitrary. • Compare the experimental results and select the student model and teacher model. • Focal Loss was introduced to improve the discriminative performance of the model. • The best result was achieved with the MGD method at distillation loss rate of 0.8. Black tea is the second most common type of tea in China. Fermentation is one of the most critical processes in its production, and it affects the quality of the finished product, whether it is insufficient or excessive. At present, the determination of black tea fermentation degree completely relies on artificial experience. It leads to inconsistent quality of black tea. To solve this problem, we use machine vision technology to distinguish the degree of fermentation of black tea based on images, this paper proposes a lightweight convolutional neural network (CNN) combined with knowledge distillation to discriminate the degree of fermentation of black tea. After comparing 12 kinds of CNN models, taking into account the size of the model and the performance of discrimination, as well as the selection principle of teacher models, Shufflenet_v2_x1.0 is selected as the student model, and Efficientnet_v2 is selected as the teacher model. Then, CrossEntropy Loss is replaced by Focal Loss. Finally, for Distillation Loss ratios of 0.6, 0.7, 0.8, 0.9, Soft Target Knowledge Distillation (ST), Masked Generative Distillation (MGD), Similarity-Preserving Knowledge Distillation (SPKD), and Attention Transfer (AT) four knowledge distillation methods are tested for their performance in distilling knowledge from the Shufflenet_v2_x1.0 model. The results show that the model discrimination performance after distillation is the best when the Distillation Loss ratio is 0.8 and the MGD method is used. This setup effectively improves the discrimination performance without increasing the number of parameters and computation volume. The model's P, R and F1 values reach 0.9208, 0.9190 and 0.9192, respectively. It achieves precise discrimination of the fermentation degree of black tea. This meets the requirements of objective black tea fermentation judgment and provides technical support for the intelligent processing of black tea. [ABSTRACT FROM AUTHOR]

• Now a days the major oral health issues of man is crucial an area of research. • This paper traces classification, segmentation and pre-processing of dental images. • Histogram based adaptive approach is used in the radiographic X-ray 2d dental Image. • This is useful to distinguish the foreground teeth & the regions of background bones. In the present scenario the major oral health issues of man is crucial an area of research. The Data mining techniques, image processing, and Computational intelligence techniques are playing a vital role in biomedical research. Dental image processing helps to improve the early detection and classification of the diagnostic process to make accurate decisions. The radiographic 2d dental image is widely utilized for analytic thinking of several dental disorders. In this paper traces the complete steps such as classification and segmentation as well as pre-processing of dental images has been carried out. In the pre-processing, histogram based on adaptive approach is used to stretch the contrast and equalize the brightness throughout the radiographic X-ray 2d dental Image. This operation is useful to distinguish the foreground teeth and the regions of background bones. Separation of dental 2d images into regions corresponding to the objects is a fundamental step of segmentation. The hybrid graph cut segmentation is used to segment the oral cavity and its tissues. In this research deep learning based convolution neural network (CNN) has been used to process the dental image and shows promising outcomes with 97.07% accuracy. [ABSTRACT FROM AUTHOR]

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage systems and reserve. In this paper, a hybrid deep learning model and a convolutional neural network with memory is proposed, to provide intraday (2 h) solar irradiance forecasts using sequentially-collected images from public webcams. The performance of the proposed model is compared to those of a standard time-series forecast models, a linear regression as well as state-of-the-art neural networks. All models are trained and tested using images collected from two webcams on EPFL's campus for just over a year. The results show that the proposed method outperforms all other models and matches the state-of-the-art methodology while providing simplicity of implementation and efficient computation. • A novel method for estimating global horizontal irradiance is proposed. • The method uses images public webcams instead of all-sky cameras. • The method combines images from different cameras to improve forecasts' accuracy. • The method can be extended to include other types of data (meteorological data). • This simple method matches state-of-the-art methods. [ABSTRACT FROM AUTHOR]

With the increasing impact of climate change and relative sea level rise, low-lying coastal communities face growing risks from recurrent nuisance flooding and storm tides. Thus, timely and reliable predictions of coastal water levels are critical to resilience in vulnerable coastal areas. Over the past decade, there has been increasing interest in utilizing machine learning (ML) based models for emulation and prediction of coastal water levels. However, flood advisory systems still rely on running computationally demanding hydrodynamic models. To alleviate the computational burden, these physics-based models are either run at small scales with high resolution or at large scales with low resolution. While ML-based models are very fast, they face challenges in terms of ensuring reliability and ability to capture any surge levels. In this paper, we develop a deep neural network for spatiotemporal prediction of water levels in coastal areas of the Chesapeake Bay in the U.S. Our model relies on data from numerical weather prediction models as the atmospheric input and astronomical tide levels, while its outputs are time series of predicted water levels at several tide gauge locations across the Chesapeake Bay. We utilized a CNN-LSTM setting as the architecture of the model. The CNN part extracts the features from a sequence of gridded wind fields and fuses its output to several independent LSTM units. The LSTM units concatenate the atmospheric features with respective astronomical tide levels and produce water level time series. The novel contribution of the present work is in spatiotemporality and in prioritization of the physical relationships in the model to maintain a high analogy to hydrodynamic modeling, either in the network architecture or in the selection of predictors and predictands. The results show that this setting yields a strong performance in predicting coastal water levels that cause flooding from minor to major levels. We also show that the model stands up successfully to the rigorous comparison with a high-fidelity ADCIRC model, yielding mean RMSE and correlation coefficient of 14.3 cm and 0.94, respectively, in two extreme cases, versus 12.30 cm and 0.96 for the ADCIRC model. The results highlight the practical feasibility of employing fast yet inexpensive data-driven models for resilient coastal management. • A deep learning model is developed to predict a wide range of coastal water levels using wind and tides as input. • CNN layers extract features from the wind field and fuse them to independent LSTM units, creating spatiotemporality. • The model compares well with the data for different storm scenarios. • The model is comparable to or surpasses a high-fidelity hydrodynamic model while taking a fraction of a second to run. [ABSTRACT FROM AUTHOR]

As solar photovoltaic (PV) has emerged as a dominant player in the energy market, there has been an exponential surge in solar deployment and investment within this sector. With the rapid growth of solar energy adoption, accurate and efficient detection of PV panels has become crucial for effective solar energy mapping and planning. This paper presents the application of the Mask2Former model for segmenting PV panels from a diverse, multi-resolution dataset of satellite and aerial imagery. Our primary objective is to harness Mask2Former's deep learning capabilities to achieve precise segmentation of PV panels in real-world scenarios. We fine-tune the pre-existing Mask2Former model on a carefully curated multi-resolution dataset and a crowdsourced dataset of satellite and aerial images, showcasing its superiority over other deep learning models like U-Net and DeepLabv3+. Most notably, Mask2Former establishes a new state-of-the-art in semantic segmentation by achieving over 95% IoU scores. Our research contributes significantly to the advancement solar energy mapping and sets a benchmark for future studies in this field. • Transformer based deep learning model is introduced for PV panel segmentation in multi-resolution imagery. • Currently available open-source datasets for PV segmentation are summarized, including diverse multi-resolution datasets of satellite and aerial imagery. • The performance of state-of-art segmentation models, such as U-Net, DeepLabv3 and Mask2Former are evaluated in detail. [ABSTRACT FROM AUTHOR]