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Smart machine tools must be able to recognise the materials they interact with, in order to independently decide on what actions to be taken whenever needed. The purpose of this research is to offer a generic method to automate material identification and classification task utilising image processing and machine learning techniques so as to improve the cognitive capabilities of machine tools. A dataset of four material surfaces; Copper, Aluminium, Stainless Steel, and Bronze is generated and the RGB data are extracted. These colour channels are utilised as input features to train the machine learning algorithms. Convolutional Neural Network (CNN) and other classification methods like Support Vector Machine (SVM), Decision Trees, and k-Nearest Neighbour are also used to classify material images based on the generated dataset. A unique generalised technique, based on CNN, has been proposed to accurately recognise and categorise round surfaced materials during machining. The accuracy achieved by the classifier has reached 100% during training and testing. Test images are used to confirm the proposed methodology's capability to distinguish between materials based on various illumination environments and camera positions. [ABSTRACT FROM AUTHOR]

ObjectiveFast and accurate deformable image registration (DIR) that include DIR uncertainty estimation are essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds.ApproachIn this study, we introduce a novel probabilistic multi-resolution image registration model utilizing convolutional neural networks (CNNs) to estimate a multivariate normal distributed dense displacement field (DDF) in a multimodal image registration problem. To assess the quality of the DDF distribution predicted by the model, we propose a new metric based on the Kullback-Leibler divergence. The performance of our approach was evaluated against three other DIR algorithms (VoxelMorph, Monte Carlo Drop-Out, and Monte Carlo B-splines) capable of predicting uncertainty. The evaluation of the models included not only the quality of the deformation but also the reliability of the estimated uncertainty. Our application investigated registration of a treatment planning computed tomography (CT) to follow-up cone beam CT for daily adaptive radiotherapy.Main resultsThe hyperparameter tuning of the models showed that there is a trade-off between the reliability of the estimated uncertainty and the accuracy of the deformation. In the optimal trade-off our model excelled in contour propagation and uncertainty estimation (p < 0.01) compared to existing uncertainty estimation models. We obtained an average dice similarity coefficient of 0.89 and a KL-divergence of 0.15.SignificanceBy addressing challenges in DIR uncertainty estimation and evaluation, our work showed that both the DIR and its uncertainty can be reliably predicted paving the way for safe deployment in clinical environment..

Eukaryotic transcription factors activate gene expression with their DNA-binding domains and activation domains. DNA-binding domains bind the genome by recognizing structurally related DNA sequences; they are structured, conserved, and predictable from protein sequences. Activation domains recruit chromatin modifiers, coactivator complexes, or basal transcriptional machinery via structurally diverse protein-protein interactions. Activation domains and DNA-binding domains have been called independent, modular units, but there are many departures from modularity, including interactions between these regions and overlap in function. Compared to DNA-binding domains, activation domains are poorly understood because they are poorly conserved, intrinsically disordered, and difficult to predict from protein sequences. This review, organized around commonly asked questions, describes recent progress that the field has made in understanding the sequence features that control activation domains and predicting them from sequence.

Facial micro-expression (ME), consisting of uncontrollable muscle movements in faces, is an important clue for revealing real people's feelings. Due to the short duration and low intensity, the salient feature representation learning is the main challenge for robust facial ME recognition. To acquire the diverse and spatial relation representation, this paper proposes a simple and yet distinctive micro-expression recognition model based on multiscale convolutional fusion and multi-channel capsule network (MCFMCN). Firstly, the apex frame in a ME clip, located by computing the pixel difference between frames, is filtered by the optical flow transformation. Secondly, a multiscale fusion module is introduced to capture diverse ME related details. Then, to further explore the subtle spatial relations between parts in the ME faces, the multi-channel capsule network is designed to improve the feature representation performance of the traditional single channel capsule network. Finally, the entire ME recognition model is trained and verified on three benchmarks (CASMEII, SAMM, and SMIC) using the associated standard evaluation protocols: unweighted average recall rate (UAR) and unweighted F1 score (UF1). ME recognition experiments indicate that our method based on MCFMCN can improve the UAR (from 75.79% to 83.58%) and UF1(from79.37% to 87.06%) in comparison with the traditional capsule network. Extensive experimental results show the performance of proposed ME recognition is superior to that of works based on pervious single channel capsule network or other state-of-the-art CNN models, which validates the finding that combination of multi-scale analysis and multi-channel capsule network is feasible and effective to improve the ME recognition performance. [ABSTRACT FROM AUTHOR]

Twitter has evolved into a primary platform for sharing opinions and insights on current events, such as the ongoing coronavirus pandemic. It's now regarded as one of the foremost sources for conducting analyses, making predictions, and extracting valuable knowledge for machine learning endeavors. it's crucial for the public and authorities to have access to timely and relevant situational information. This enables them to respond effectively and make informed decisions. This paper categorizes situational information into seven distinct groups and examining the behavioral and emotional shifts that individuals are experiencing amid the Covid-19 pandemic. In this paper, we have done analysis of real-time sentiment in tweets related to the Covid-19 pandemic. We have collected the historical Covid-19 tweets from 01/10/2020 to 30/03/2021. We have proposed an adaptive ensemble learning and Stacked-CNN model and applied the proposed models on the collected datasets for Covid-19 situational information analysis. The experimental results demonstrate the effectiveness of the Adaptive ensemble model and the Stacked CNN model in providing better predictions for real-time sentiment analysis of tweets related to the Covid-19 pandemic. The proposed methods can provide valuable insights into public opinion and emotions and has practical applications in areas like public health communication, crisis management, and understanding community concerns. [ABSTRACT FROM AUTHOR]

Camouflaged object detection (COD) intends to find concealed objects hidden in the surroundings. COD is challenging for it has to discriminate the minor difference between foreground and background. In most existing methods, convolutional neural network (CNN)-based approaches are proposed to overcome this challenge. However, they have limitations in extracting semantic features of input images and learning global contexts. This study presents a novel Transformer and CNN-based Depth Context Aggregation network (call DCA-Net) for concealed object detection and segmentation. This network uses Swin Transformer as backbone to extract globalized semantic features. Dilated Reception (DR) module is designed to connect the encoder and decoder. A hybrid loss function is used for optimizing the model. In particular, to supplement the intersection over union (IOU) loss, Complementary (C) loss is introduced. By analyzing different metrics, comprehensive comparisons with previous methods are conducted on four public datasets, such as CAMO, CHAMELEON, COD10K, and NC4K. Experimental analysis proves that the proposed DCA-Net achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, includes intelligent decision-making tools for evaluation and benchmarking. Rather than just finding a comparable or similar analogy, this framework is intended to help guide future research and pragmatic applications, and to determine which SSVEP-based BCI applications have succeeded at responsibility for what they set out with. [ABSTRACT FROM AUTHOR]

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability. [ABSTRACT FROM AUTHOR]

Dynamic ensemble pruning (DEP) can select the best set of models for an unseen sample to improve a classification system's prediction accuracy and robustness. However, little work has been done to apply DEP to the facial expression recognition (FER) system and reduce DEP's computational burden. This paper proposes a semi-dynamic ensemble pruning algorithm (SDEP) to implement the FER task. First, a subspace-based classifier sequences selection method is proposed to break DEP's neighborhood construction limitation and avoid the exhaustive similarity calculation. Moreover, a pre-prediction mechanism is introduced to calculate the relationship between an unseen sample and data subspaces. Ultimately, the calculated relationship scores and their credibility will be used as the weights for fusion. SDEP reduces the DEP's computational burden while retaining the dynamic characteristics through changing the classifier selection into the subspace-based classifier sequences combination. SDEP demonstrates superior classification accuracy and efficiency to several state-of-the-art dynamic and static EP algorithms in benchmark FER datasets Fer2013, JAFFE, CK+ using a homogeneous ensemble of 799 CNN classifiers. [ABSTRACT FROM AUTHOR]

Speaker recognition is extensively applied in several applications, namely identity verification, electronic voice eavesdropping, surveillance, voice recognition, etc. In an effective speaker recognition system, the extraction and selection of salient and discriminative features is an essential process for accurately identifying the speakers. Therefore, a novel hybrid framework is introduced in this research manuscript. Initially, the input data are acquired from the three-benchmark databases: THUYG-20 SRE corpus, Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), and LibriSpeech. Further, the emotional features are extracted by utilizing hybrid feature extraction techniques which are, amplitude, zero cross rate, energy, Root Mean Square (RMS), statistical moments, autocorrelation, and Mel-Frequency Cepstral Coefficients (MFCC). Then, the feature optimization is carried out using Improved Butterfly Optimization Algorithm (IBOA) that decreases the computational time and complexity of the recognition model. At last, a hybrid classifier: Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) is implemented for speaker recognition, and its performance is analyzed in terms of F1-score, specificity, accuracy, Positive Predictive Value (PPV), and sensitivity. The empirical investigation demonstrated that the IBOA-based hybrid LSTM network achieved 92.65%, 96.97% and 96.98% of recognition accuracy on the LibriSpeech, RAVDESS and THUYG-20 SRE corpus databases. These results are more impressive than the comparative models, Deep Neural Network (DNN), random forest, K-Nearest Neighbor (KNN), LSTM, Multi class Support Vector Machine (MSVM), Deep Convolutional Recurrent Neural Network (DCRNN), Golden Ratio aided Neural Network (GRaNN), deep sequential LSTM, and Probabilistic Neural Network (PNN). [ABSTRACT FROM AUTHOR]

Soil pores can play a significant role in the crucial process related to soil ecological functions. However, it is still challenging on the relationship between pore structure and functional evolution, due to the lack of non-destructive and nonintrusive systems for the spatial structure of multiple types of pores. Among them, accurate segmentation of pore types and ranges was fundamental to the research. In this study, an improved UNet-VAE network was proposed to segment the multiple pores in soil for the first time. Taking the typical black soil as the research object, a Simplified Convolutional Network (SCN) was used to segment the three-dimensional data of soil pores. According to the segmented pore dataset, a combination of automatic segmentation and manual correction was used to obtain four types of soil pore structures as ground truth. A multiscale fusion attention module was proposed to filter out the redundant information generated by convolutional learning using a 3D UNet network. Local attention was used to learn the spatial features of small-scale pores (irregular and spherical pores). Global attention was used to extract the feature information of large-scale pores (cracks and biological pores), in order to fuse the multi-scale features for high segmentation accuracy in the different categories of pores. Meanwhile, the commonly used segmentation networks (such as 3D UNet, Segresnet, VNet, and UNetR network) were used to compare the segmentation of the multiple pores. The experimental results showed that the improved UNet-VAE network accurately determined the range and the category of pores. Specifically, the UNetR network was difficult to learn the features, due to the high requirement of a Transformer for the number of datasets in the large-scale cracks and biological pores. Convolutional networks (such as 3D UNet, Segresnet, and VNet) failed to learn the global and large-scale features, where the cracks with obvious planar features were classified as biological pores. Furthermore, the Segresnet, VNet, and UNetR network misclassified the small-scale irregular pores as cracks. By contrast, the improved UNet VAE network achieved the best performance in the four categories of pores, with the average accuracy, precision, recall, and F1 values reaching 93.83%, 84.75%, 84.88%, and 84.60%, respectively. Compared with the suboptimal VNet, the average accuracy, precision, recall, and F1 value increased by 3.32, 5.06, 8.97, and 8.63 percentage points, respectively. Especially for irregular pores, the accuracy, recall, and F1 values increased by 4.88, 15.46, 15.70, and 15.50 percentage points, respectively. In summary, the improved UNet-VAE network was achieved in the highprecision and three-dimensional segmentation of multiple categories of pores, indicating better feature learning for all four categories of pores. The three-dimensional segmentation was achieved in the better classification of intersecting pores, high segmentation accuracy of single pores, and high automation level. Technical support was offered to explore the relationship between pore structure and ecological function evolution. This finding can also provide a data basis for the precise quantitative characterization of soil pore structure, in order to reveal the role of soil pore evolution in ecosystems. [ABSTRACT FROM AUTHOR]

Recommender systems have been a kind of powerful tool to improve e-commerce benefits. Existing recommender systems mostly employ explicit features of products, such as description and attributes. However, visual characteristics contain fruitful implicit and intuitive information, and are always ignored by existing works. To deal with this issue, this paper proposes a novel intelligent recommender system via visual Transformer (ViT) model and convolutional neural network (CNN) structure. First, the ViT part is utilized to extract visual feature representation. By learning the visual similarity among products, it is expected to obtain a better scene understanding. Then, an improved CNN part is utilized to extract hidden association information from historical behaviors of users. It is expected to better perceive user preference and purchasing characteristics. The combination of two parts constructs the proposed recommender system. Finally, we make performance evaluation for the proposal on real-world e-commerce dataset. The results indicate that the proposal exhibits high recommendation accuracy and efficiency. Compared with other typical algorithms, our proposal can better understand product images and user behaviors, providing more personalized recommendation results. [ABSTRACT FROM AUTHOR]

Blockchain technology has unlocked the doors of building decentralized applications, where security plays a vital role. Any transaction ever created in a blockchain is recorded permanently. With the increasing usage of social media modalities today, many non-reputable sources create and publish fake and luring news. Due to the easy availability of the internet and excessive use of social media, fake news can spread like a flash. It causes the need to eliminate fake news posted on social media platforms. The main motive of the paper is to develop an ensemble model that assists in the automated identification of fake news. This paper addresses the identified gaps in detecting multi-modal fake news. We propose Fake News Network (FNNet) as a novel secure blockchain-based deep learning model to detect fake news. It uses blockchain and deep learning to assure data integrity and learn data representations, respectively. The proposed model consists of four layers: the node layer, the deep learning layer, the blockchain layer, and the network layer. This is an ensemble deep-learning model that leverages Bi-LSTM and CNN models, where the Bi-LSTM model captures the sequential data in both directions, and CNN captures the hidden features. We use Pheme, CrisisLexT6, and ISOT datasets to train the model. FNNet achieves an average accuracy of 86.93%, 92.22%, and 98.53% on Pheme, CrisisLexT9, and ISOT datasets, respectively. Our results show that the proposed model is robust and applicable to real-time datasets and social media networks to detect fake news effectively. [ABSTRACT FROM AUTHOR]

Advancements in Machine Learning (ML) and Computer Vision have led to notable improvements in the detection of breast cancer. However, the accuracy of the classifier is limited due to imbalanced datasets that cause overfitting. Thus, additional images are needed to improve the classifier's performance. Generative Adversarial Networks (GAN) are used for image augmentation. Still, its limitations, such as the random use of different types of GAN, can lead to a too-good discriminator, causing generator training to fail due to vanishing gradients. Therefore, selecting the appropriate GAN model for the given scenario is crucial for optimal performance. This paper proposes a novel Smart Generative Adversarial Network (Smart GAN) architecture to develop an efficient and computational classification model for a limited imbalanced dataset. Smart GAN uses a three-fold approach. Different types of GAN augment the dataset in the first phase of experimental work, and their evaluation metrics are calculated. In the second phase, the metric scores are used as rewards for the Reinforcement learning model (Q-learning approach). The best augmentation is chosen based on the best Q-values for each metric score. It compares three different Convolutional Neural Networks (CNN) and selects the best-suited network to classify the augmented datasets. The proposed Smart GAN architecture outperforms other existing approaches by giving a better accuracy of 89.62% and 89.91% on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) augmented datasets, respectively, representing an approximately 10% increment in detection rate compared to non-augmented datasets. [ABSTRACT FROM AUTHOR]

The accurate health status evaluation of lithium-ion batteries is crucial for preemptive identification of potential battery failures and averting hazardous incidents, given its essential role in indicating the extent of battery degradation. The challenge in determining the State of Health (SOH) arises from the absence of a precise and standardized definition, as well as the difficulty in measuring essential input variables. Therefore, this paper utilizes current and voltage data during the charge and discharge process as direct inputs for SOH estimation and proposes a deep learning-based lithium-ion battery SOH estimation approach. Specifically, it leverages Bayesian optimized Convolutional Neural Network (CNN) within a data-driven framework. Experimental results demonstrate that the proposed deep learning method achieves a Mean Absolute Error (MAE) of 1% and a Maximum Error (MAX) below 4% in estimation accuracy, highlighting its enhanced precision and robustness. [ABSTRACT FROM AUTHOR]

Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F 1 -Scores between 83.11% and 98.34% in the pre-trained architectures. [Display omitted] • Transfer learning is applied to address the unique characteristics of thermal images for switchgear fault detection. • A thermal image dataset is setup and augmented to improve the classification performance. • Establish standard comparisons among eleven transfer learning architectures. • Achieve rapid and precise classification with the proposed transfer learning architectures. [ABSTRACT FROM AUTHOR]

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The state of health (SOH) of lithium-ion batteries is a critical parameter of the battery management system. An accurate estimation of it can improve the battery's overall life. The data-driven method has the problems of low long-term prediction accuracy and difficult feature recognition. Therefore, this paper proposes a SOH estimation method based on the convolutional gated recurrent neural network with the self-attention mechanism. Firstly, the convolutional neural network (CNN) is applied to the input data, and its convolution operation is used to extract important local features. Then, the self-attention mechanism is added later to give greater weight to the more important features in the training process. Finally, the gated recurrent unit (GRU) recurrent neural network extracts the long-term correlation from the weighted output data. The experimental data of lithium-ion batteries from three sources are used to verify the correctness and effectiveness of this method. Experiments were based on whether the self-attention mechanism is present, different prediction starting points and ambient temperature, and analyzed and compared with the common machine learning methods. The experimental results show that the method exhibits good estimation accuracy, with a maximum mean absolute error below 1.78% and a maximum root mean square error below 2.3%. [ABSTRACT FROM AUTHOR]

The flood-prone city of Zaio in Morocco necessitates a precise, fast, real-time flood depth mapping model due to its recurrent flood history. Whether it's the exclusive prediction of one flood category, relying on hard-to-measure inputs like flood hydrographs, or employing less accurate training methods such as cellular automata models, the existing Convolutional Neural Network (CNN) models face limitations in predicting flood depth in a city whose flood dynamics are influenced by outer watersheds such as Zaio. This study addresses these issues by introducing a CNN tailored for real-time pluvial and fluvial flood depth mapping in Zaio at fine resolution (2 m). Training involved eight rainfall hyetographs, with four used for testing. The model's validation included three "unseen" rainfall distribution patterns. The proposed CNN seamlessly connects rainfall-runoff modeling and hydrodynamic 2D flood depth simulation, with a novelty of predicting both pluvial and fluvial flood depth, and reducing computational time by a significant 99.17%. [ABSTRACT FROM AUTHOR]

In the context of the COVID-19 epidemic, periocular recognition has become a more effective identity recognition way to replace face recognition. Although various identity recognition model based on convolutional neural networks (CNNs) has achieved satisfactory results, it is limited to high-resolution images. In the previous periocular recognition work, the effect of image resolution is usually ignored. To further extend the application of periocular recognition in unconstrained environments, this work proposes a deep learning-based architecture, AcRNet, to solve the problem of periocular image recognition in different resolutions. AcRNet uses Asymmetric Convolution Block (ACB), and skip connect to enhance the feature extraction capability of the network, and uses a multi-layer feature fusion mechanism to effectively utilize mid-level features. We validated the performance of the proposed network on three datasets, UBIRIS.v2, CASIA_Iris_Distance, and a periocular dataset cropped from VGGface which is called VGGface_periocular, and verified the cross-database performance of the network using a self-acquired periocular dataset. All the low-resolution images were obtained using a bicubic interpolation algorithm. The experimental results demonstrate that AcRNet has a powerful feature extraction capability when extracting features from periocular images of different resolutions. Compared with the previous algorithms, the proposed network has achieved satisfactory performance on the recognition of both high- and low- resolution periocular images. On dataset UBIRIS.v2, AUC values achieved 0.963 and 0.808 when the image resolutions are 128*128 and 8*8. [ABSTRACT FROM AUTHOR]

Fire is recognized as a destructive disaster in smart environments that causes serious harm to ecosystems and humans. Early and rapid-fire detection in cities and forests can prevent human, economic, and environmental damage. Wireless sensor networks have been used for fire detection, but their deployment is costly and limited to specific locations. On the other hand, cameras are widely deployed in smart cities and interurban areas, as they are cheaper and more pervasive than sensor networks. In this paper, an end-to-end neural network model called EfficientNetB2 (3ENB2) based on transfer learning is proposed for accurate fire detection from images. This model implements an online data augmentation strategy encompassing random rotation and horizontal flip during the data training phase. According to this perspective, the precise count of altered data samples during the training procedure remains unspecified. The results show that the proposed model outperforms the primary 3ENB2 model with an accuracy rate of 99.04% compared to 98.57%. Additionally, the proposed model provides better localization and representation of fire images. [ABSTRACT FROM AUTHOR]

Many advanced models have been proposed for automatic surface defect inspection. Although CNN-based methods have achieved superior performance among these models, it is limited to extracting global semantic details due to the locality of the convolution operation. In addition, global semantic details can achieve high success for detecting surface defects. Recently, inspired by the success of Transformer, which has powerful abilities to model global semantic details with global self-attention mechanisms, some researchers have started to apply Transformer-based methods in many computer-vision challenges. However, as many researchers notice, transformers lose spatial details while extracting semantic features. To alleviate these problems, in this paper, a transformer-based Hybrid Attention Gate (HAG) model is proposed to extract both global semantic features and spatial features. The HAG model consists of Transformer (Trans), channel Squeeze-spatial Excitation (sSE), and merge process. The Trans model extracts global semantic features and the sSE extracts spatial features. The merge process which consists of different versions such as concat, add, max, and mul allows these two different models to be combined effectively. Finally, four versions based on HAG-Feature Fusion Network (HAG-FFN) were developed using the proposed HAG model for the detection of surface defects. The four different datasets were used to test the performance of the proposed HAG-FFN versions. In the experimental studies, the proposed model produced 83.83%, 79.34%, 76.53%, and 81.78% mIoU scores for MT, MVTec-Texture, DAGM, and AITEX datasets. These results show that the proposed HAGmax-FFN model provided better performance than the state-of-the-art models. [ABSTRACT FROM AUTHOR]

Background Methods Results Discussion Deep learning methods are revolutionizing natural science. In this study, we aim to apply such techniques to develop blood type prediction models based on cheap to analyze and easily scalable screening array genotyping platforms.Combining existing blood types from blood banks and imputed screening array genotypes for ~111,000 Danish and 1168 Finnish blood donors, we used deep learning techniques to train and validate blood type prediction models for 36 antigens in 15 blood group systems. To account for missing genotypes a denoising autoencoder initial step was utilized, followed by a convolutional neural network blood type classifier.Two thirds of the trained blood type prediction models demonstrated an F1‐accuracy above 99%. Models for antigens with low or high frequencies like, for example, Cw, low training cohorts like, for example, Cob, or very complicated genetic underpinning like, for example, RhD, proved to be more challenging for high accuracy (>99%) DL modeling. However, in the Danish cohort only 4 out of 36 models (Cob, Cw, D‐weak, Kpa) failed to achieve a prediction F1‐accuracy above 97%. This high predictive performance was replicated in the Finnish cohort.High accuracy in a variety of blood groups proves viability of deep learning‐based blood type prediction using array chip genotypes, even in blood groups with nontrivial genetic underpinnings. These techniques are suitable for aiding in identifying blood donors with rare blood types by greatly narrowing down the potential pool of candidate donors before clinical grade confirmation. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field. [ABSTRACT FROM AUTHOR]

A novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online. Highlights: Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images.Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface.Incorporate adaptive algorithms for streamlined training optimization.An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) require precise node location in order to function properly, and they are essential in many different applications. In this research, we propose a unique method to maximize the accurate estimate of average localization errors (ALEs) in WSNs by utilizing federated learning (FL) approaches. The suggested approach combines the extraction of spatial features with collection of contextual data through integration of hybrid convolutional neural network–bidirectional encoder representations from transformers (CNN‐BERT) model. Effectively applying min–max normalization to input features minimizes data from flowing into test, validation, and training sets. The technique is centered around a collaborative learning architecture, wherein the weights of the model are iteratively assessed and modified on centralized server. The suggested methodology's effectiveness in an array of settings is illustrated by mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) values for node localization in WSN. Network B demonstrated accuracy with an MAE of 0.05, MSE of 0.03, and RMSE of 0.10; Network C demonstrated strong accuracy with an MAE of 0.09, MSE of 0.08, and RMSE of 0.12; and Network A generated an MAE of 0.04, MSE of 0.06, and RMSE of 0.08. Furthermore, the centralized server, which is crucial for collaborative learning, obtained exceptional MAE of 0.01, MSE of 0.02, and RMSE of 0.99 demonstrating the superiority of the FL‐optimized method in improving localization accuracy. The outcomes demonstrate the FL‐optimized hybrid model's superiority over conventional methods in providing accurate node localization in a variety of WSN settings. [ABSTRACT FROM AUTHOR]

Pneumonia is a serious inflammatory disease that causes lung ulcers, and it is one of the leading reasons for pediatric death in the world. Chest X-rays are perhaps the most commonly utilized modalities to recognize pneumonia. Generally, the illness could be analyzed by a specialist radiologist. But for some reason, the diagnosis may be subjective. Thus, the physicians must be guided by computer-aided diagnosis frameworks in this challenging task. In this study, we propose a combined deep learning architecture to identify pneumonia in chest radiography images. We first, use Adaptive Median Filter for images enhancement, then we employ a regularized Convolutional Neural Network for features extraction, and then we use Long Short Term Memory as a classifier. Finally, the attention mechanism is used to direct the network attention to relevant features. The suggested approach was tested on two publicly available pneumonia X-ray datasets provided by Kermany and the Radiological Society of North America. On the Kermany and RSNA datasets, the suggested technique attained accuracy rates of 99.91% and 88.86%, respectively. In the last stage of our experiments, we employed a Grad-CAM-based color visualization technique to precisely interpret the detection of pneumonia in radiological images. The results outperformed those of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Objective: To analyze primary angle closure suspect (PACS) patients' anatomical characteristics of anterior chamber configuration, and to establish artificial intelligence (AI)-aided diagnostic system for PACS screening. Methods: A total of 1668 scans of 839 patients were included in this cross-sectional study. The subjects were divided into two groups: PACS group and normal group. With anterior segment optical coherence tomography scans, the anatomical diversity between two groups was compared, and anterior segment structure features of PACS were extracted. Then, AI-aided diagnostic system was constructed, which based different algorithms such as classification and regression tree (CART), random forest (RF), logistic regression (LR), VGG-16 and Alexnet. Then the diagnostic efficiencies of different algorithms were evaluated, and compared with junior physicians and experienced ophthalmologists. Results: RF [sensitivity (Se) = 0.84; specificity (Sp) = 0.92; positive predict value (PPV) = 0.82; negative predict value (NPV) = 0.95; area under the curve (AUC) = 0.90] and CART (Se = 0.76, Sp = 0.93, PPV = 0.85, NPV = 0.92, AUC = 0.90) showed better performance than LR (Se = 0.68, Sp = 0.91, PPV = 0.79, NPV = 0.90, AUC = 0.86). In convolutional neural networks (CNN), Alexnet (Se = 0.83, Sp = 0.95, PPV = 0.92, NPV = 0.87, AUC = 0.85) was better than VGG-16 (Se = 0.84, Sp = 0.90, PPV = 0.85, NPV = 0.90, AUC = 0.79). The performance of 2 CNN algorithms was better than 5 junior physicians, and the mean value of diagnostic indicators of 2 CNN algorithm was similar to experienced ophthalmologists. Conclusion: PACS patients have distinct anatomical characteristics compared with health controls. AI models for PACS screening are reliable and powerful, equivalent to experienced ophthalmologists. [ABSTRACT FROM AUTHOR]

The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability. [ABSTRACT FROM AUTHOR]

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essential in the fields of biomedical engineering and clinical cardiac disease diagnosis. Intelligent ECG diagnosis techniques encompass Recurrent Neural Networks (RNN), Transformers, and Convolutional Neural Networks (CNN). While CNN is capable of extracting local spatial information from images, it lacks the ability to learn global spatial features and temporal memory features. Conversely, RNN relies on time and can retain significant sequential features. However, they are not proficient in extracting lengthy dependencies of sequence data in practical scenarios. The self-attention mechanism in the Transformer model has the capability of global feature extraction, but it does not adequately prioritize local features and cannot extract spatial and channel features. This paper proposes STFAC-ECGNet, a model that incorporates CAMV-RNN block, CBMV-CNN block, and TSEF block to enhance the performance of the model by integrating the strengths of CNN, RNN, and Transformer. The CAMV-RNN block incorporates a coordinated adaptive simplified self-attention module that adaptively carries out global sequence feature retention and enhances spatial–temporal information. The CBMV-CNN block integrates spatial and channel attentional mechanism modules in a skip connection, enabling the fusion of spatial and channel information. The TSEF block implements enhanced multi-scale fusion of image spatial and sequence temporal features. In this study, comprehensive experiments were conducted using the PTB-XL large publicly available ECG dataset and the China Physiological Signal Challenge 2018 (CPSC2018) database. The results indicate that STFAC-ECGNet surpasses other cutting-edge techniques in multiple tasks, showcasing robustness and generalization. [ABSTRACT FROM AUTHOR]

Multivariate time series have more complex and high‐dimensional characteristics, which makes it difficult to analyze and predict the data accurately. In this paper, a new multivariate time series prediction method is proposed. This method is a generative adversarial networks (GAN) method based on Fourier transform and bi‐directional gated recurrent unit (Bi‐GRU). First, the Fourier transform is utilized to extend the data features, which helps the GAN to better learn the distributional features of the original data. Second, in order to guide the model to fully learn the distribution of the original time series data, Bi‐GRU is introduced as the generator of GAN. To solve the problems of mode collapse and gradient vanishing that exist in GAN, Wasserstein distance is used as the loss function of GAN. Finally, the proposed method is used for the prediction of air quality, stock price and RMB exchange rate. The experimental results show that the model can effectively predict the trend of the time series compared with the other nine baseline models. It significantly improves the accuracy and flexibility of multivariate time series forecasting and provides new ideas and methods for accurate time series forecasting in industrial, financial and environmental fields. [ABSTRACT FROM AUTHOR]

Skin conditions are becoming increasingly prevalent across the world in current times. With the rise in dermatological disorders, there is a need for computerized techniques that are completely noninvasive to patients' skin. As a result, deep learning models have become standard for the computerized detection of skin diseases. The performance efficiency of these models improves with access to more data with their primary aim being image classification. In this paper, we present a skin disease detection methodology using image processing techniques, non-local means denoising and convolutional neural network (CNN) backed by sparse dictionary learning. Here, the major benefit of using NLM denoising followed by sparse dictionary learning with CNNs in image classification lies in leveraging a multi-stage approach that enhances the quality of input data, extracts meaningful and discriminative features, and improves the overall performance of the classification model. This combined approach addresses challenges such as noise robustness, feature extraction, and classification accuracy, making it particularly effective in complex image analysis tasks. For denoising, the average Peak Signal to Noise Ratio (PSNR) obtained for images from HAM-10000 dataset is 33.59 dB. For the ISIC-2019 dataset, the average PSNR for the train folder is 34.37 dB, and for the test folder it is 34.39 dB. The deep learning network is trained for the analysis of skin cancer images using a CNN model and is achieving acceptable results in classifying skin cancer types. The datasets used contain high-resolution images. After all the tests, the accuracy obtained is 85.61% for the HAM-10000 dataset and 81.23% for the ISIC-2019 dataset, which is on par with existing approaches validated by benchmarking results. [ABSTRACT FROM AUTHOR]

Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of Q EN , Q NIQE , Q SD , Q SSEQ and Q TMQI . [ABSTRACT FROM AUTHOR]

Mosquito-borne diseases are a major global health threat. Traditional morphological or molecular methods for identifying mosquito species often require specialized expertise or expensive laboratory equipment. The use of convolutional neural networks (CNNs) to identify mosquito species based on images may offer a promising alternative, but their practical implementation often remains limited. This study explores the applicability of CNNs in classifying mosquito species. It compares the efficacy of body and wing depictions across three image collection methods: a smartphone, macro-lens attached to a smartphone and a professional stereomicroscope. The study included 796 specimens of four morphologically similar Aedes species, Aedes aegypti, Ae. albopictus, Ae. koreicus and Ae. japonicus japonicus. The findings of this study indicate that CNN models demonstrate superior performance in wing-based classification 87.6% (95% CI: 84.2–91.0) compared to body-based classification 78.9% (95% CI: 77.7–80.0). Nevertheless, there are notable limitations of CNNs as they perform reliably across multiple devices only when trained specifically on those devices, resulting in an average decline of mean accuracy by 14%, even with extensive image augmentation. Additionally, we also estimate the required training data volume for effective classification, noting a reduced requirement for wing-based classification compared to body-based methods. Our study underscores the viability of both body and wing classification methods for mosquito species identification while emphasizing the need to address practical constraints in developing accessible classification systems. [ABSTRACT FROM AUTHOR]

To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this study introduces an enhanced instance segmentation method built upon mask region-based convolutional neural network. By incorporating squeeze-and-excitation networks, a channel attention mechanism, and concatenated attention neural network, a spatial attention mechanism, the model can more adeptly focus on the critical regions and finer details of brain tumors. Residual network-50 combined attention module and feature pyramid network as the backbone network to effectively capture multi-scale characteristics of brain tumors. At the same time, the region proposal network and region of interest align technology were used to ensure that the segmentation area matched the actual tumor morphology. The originality of the research lies in the deep residual network that combines attention mechanism with feature pyramid network to replace the backbone based on mask region convolutional neural network, achieving an improvement in the efficiency of brain tumor feature extraction. After a series of experiments, the precision of the model is 90.72%, which is 0.76% higher than that of the original model. Recall was 91.68%, an increase of 0.95%; Mean Intersection over Union was 94.56%, an increase of 1.39%. This method achieves precise segmentation of brain tumor magnetic resonance imaging, and doctors can easily and accurately locate the tumor area through the segmentation results, thereby quickly measuring the diameter, area, and other information of the tumor, providing doctors with more comprehensive diagnostic information. [ABSTRACT FROM AUTHOR]

To improve students' ability to recognize and appreciate artworks, and further enhance their academic performance and classroom satisfaction, this study explores the application of the Convolutional Neural Network (CNN) model based on optimization in art teaching. Firstly, the importance and challenges of art teaching are analyzed. Secondly, the principle and structure of CNN and its application in the classification field are expounded, and then the CNN classification model is optimized. Finally, the effectiveness of the optimized model is verified by experiments. Experimental results show that the optimized model's accuracy is up to 95.2% in the performance evaluation. The training time of the optimized model is much lower than that of the traditional model, and this model still maintains 95.2% accuracy under the noise of 14.7%. In addition, the accuracy of the optimized model on the unseen test data is 92%. In comparing teaching experiment results, by introducing the CNN classification model, Class B students' average score of art homework has increased by 4.3 points. The score for class satisfaction is 8.1 points. This indicates that the optimized CNN model has significant advantages in art teaching and can effectively improve students' classroom satisfaction and academic performance. Therefore, this study has specific reference significance for the innovation of the art teaching model. [ABSTRACT FROM AUTHOR]

Background: Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of orphans and other fewer evolution information proteins. (2) The other bottleneck is the method of predicting single-sequence-based proteins mainly focuses on selecting protein sequence features and tuning the neural network architecture, However, while the deeper neural networks improve prediction accuracy, there is still the problem of increasing the computational burden. Compared with other neural networks in the field of protein prediction, the graph neural network has the following advantages: due to the advantage of revealing the topology structure via graph neural network and being able to take advantage of the hierarchical structure and local connectivity of graph neural networks has certain advantages in capturing the features of different levels of abstraction in protein molecules. When using protein sequence and structure information for joint training, the dependencies between the two kinds of information can be better captured. And it can process protein molecular structures of different lengths and shapes, while traditional neural networks need to convert proteins into fixed-size vectors or matrices for processing. Results: Here, we propose a single-sequence-based protein contact map predictor PCP-GC-LM, with dual-level graph neural networks and convolution networks. Our method performs better with other single-sequence-based predictors in different independent tests. In addition, to verify the validity of our method against complex protein structures, we will also compare it with other methods in two homodimers protein test sets (DeepHomo test dataset and CASP-CAPRI target dataset). Furthermore, we also perform ablation experiments to demonstrate the necessity of a dual graph network. In all, our framework presents new modules to accurately predict inter-chain contact maps in protein and it's also useful to analyze interactions in other types of protein complexes. [ABSTRACT FROM AUTHOR]

Deep visual data analysis from social network has become an increasingly important area of research. In fact, this form of assessment makes it viable to recognize new information on social users which incorporates emotions. In order to recognize users' emotions and other latent attributes, most of the existing approaches have used textual data and have obtained accurate results. However, little attention has been paid to visual data that have become increasingly popular in recent years.This work describes how to develop a conceptual representation model for social network analysis and social emotion analysis based on the integration of fuzzy logic and ontological modeling. The primary aim is to create an ontology that can extract new information about a social user's mood, which can be classified as panic, no-panic, or neutral. Fuzzy logic is necessary to deal with subjective data, as users share imprecise and vague data on their social profiles. Fuzzy logic has been observed as a successful method to capture the expression of emotions due to the fuzzy nature of emotions and the ambiguous definitions of emotion words. The proposed work investigate the role of fuzzy logic in social network analysis. This study simulate a fuzzy deep system integrated with ontology for classifying social visual data (shared images) into panic, no-panic or neutral classes in order to determine the social users' stress intensity. The Social distancing and the huge amount of shared data in Tunisia were calculated to demonstrate this classification. The experiments performed in this paper aim to create not only a novel annotated visual database named visual panic database, but also a new semantic model for modeling users' profiles, in social network, based on the combination between ontology and deep learning techniques. In this context, we will work in the future on the combination between a user's visual and textual data in order to improve the performance of this recognition. The proposed fuzzy system reflected the viral proliferation in stressed users and achieved an accuracy of 87%. [ABSTRACT FROM AUTHOR]

Objective: The research aims to develop an advanced and precise lung cancer screening model based on Convolutional Neural Networks (CNN). Methods: Based on the health medical big data platform of Shandong University, we developed a VGG16-Based CNN lung cancer screening model. This model was trained using the Computed Tomography scans data of patients from Pingyi Traditional Chinese Medicine Hospital in Shandong Province, from January to February 2023. Data augmentation techniques, including random resizing, cropping, horizontal flipping, color jitter, random rotation and normalization, were applied to improve model generalization. We used five-fold cross-validation to robustly assess performance. The model was fine-tuned with an SGD optimizer (learning rate 0.001, momentum 0.9, and L2 regularization) and a learning rate scheduler. Dropout layers were added to prevent themodel from relying too heavily on specific neurons, enhancing its ability to generalize. Early stopping was implemented when validation loss did not decrease over 10 epochs. In addition, we evaluated the model's performance with Area Under the Curve (AUC), Classification accuracy, Positive Predictive Value (PPV), and Negative Predictive Value (NPV), Sensitivity, Specificity and F1 score. External validation used an independent dataset from the same hospital, covering January to February 2022. Results: The training and validation loss and accuracy over iterations show that both accuracy metrics peak at over 0.9 by iteration 15, prompting early stopping to prevent overfitting. Based on five-fold cross-validation, the ROC curves for the VGG16-Based CNN model, demonstrate an AUC of 0.963 ± 0.004, highlighting its excellent diagnostic capability. Confusion matrices provide average metrics with a classification accuracy of 0.917 ± 0.004, PPV of 0.868 ± 0.015, NPV of 0.931 ± 0.003, Sensitivity of 0.776 ± 0.01, Specificity of 0.962 ± 0.005 and F1 score of 0.819 ± 0.008, respectively. External validation confirmed the model's robustness across different patient populations and imaging conditions. Conclusion: The VGG16-Based CNN lung screening model constructed in this study can effectively identify lung tumors, demonstrating reliability and effectiveness in real-world medical settings, and providing strong theoretical and empirical support for its use in lung cancer screening. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one‐dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state‐of‐the‐art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT‐BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT‐BIH Arrhythmia dataset. [ABSTRACT FROM AUTHOR]

Various types of sensors are needed to monitor the health state of smart deep-space habitats. However, measured data can be affected by sensor faults, which influence the health management system and consequently the decision-making. In this paper, an unsupervised learning approach based on convolutional autoencoders (CAEs) is developed to detect anomalies in temperature and pressure sensors. The proposed method is systematically investigated using a habitat simulator (HabSim). Several illustrative examples are demonstrated in the nominal and hazardous states of the habitat, including micrometeorite impact and fire scenarios. The performance of the proposed method using CAEs is compared with that of existing methods using auto-associative neural networks (AANNs) and variational autoencoders. This comparison is based on typical evaluation metrics, including precision, recall, F1 score, training time, and testing time. The effect of temperature-pressure coupling on the detection performance of CAEs and AANNs is explored by training different data-driven models, including one with temperature sensors, one with pressure sensors, and one with both temperature and pressure sensors. The effect of the number of faulty sensors on the performance of CAEs is studied, as with an increase in the number of faulty sensors, redundant information among the sensors is reduced. The capability of CAEs to change the number of sensors without redesigning the network architecture and retraining the neural network is investigated and demonstrated. The capabilities and limitations of the proposed solution are discussed. [ABSTRACT FROM AUTHOR]

Indoor positioning is the key enabling technology for many location-aware applications. As GPS does not work indoors, various solutions are proposed for navigating devices. Among these solutions, Bluetooth low energy (BLE) technology has gained significant attention due to its affordability, low power consumption, and rapid data transmission capabilities, making it highly suitable for indoor positioning. Received signal strength (RSS)-based positioning has been studied intensively for a long time. However, the accuracy of RSS-based positioning can fluctuate due to signal attenuation and environmental factors like crowd density. Angle of arrival (AoA)-based positioning uses angle measurement technology for location devices and can achieve higher precision, but the accuracy may also be affected by radio reflections, diffractions, etc. In this study, a dual-branch convolutional neural network (CNN)-based BLE indoor positioning algorithm integrating RSS and AoA is proposed, which exploits both RSS and AoA to estimate the position of a target. Given the absence of publicly available datasets, we generated our own dataset for this study. Data were collected from each receiver in three different directions, resulting in a total of 2675 records, which included both RSS and AoA measurements. Of these, 1295 records were designated for training purposes. Subsequently, we evaluated our algorithm using the remaining 1380 unseen test records. Our RSS and AoA fusion algorithm yielded a sub-meter accuracy of 0.79 m, which was significantly better than the 1.06 m and 1.67 m obtained when using only the RSS or the AoA method. Compared with the RSS-only and AoA-only solutions, the accuracy was improved by 25.47% and 52.69%, respectively. These results are even close to the latest commercial proprietary system, which represents the state-of-the-art indoor positioning technology. [ABSTRACT FROM AUTHOR]

This paper proposes a novel drone detection method based on a convolutional neural network (CNN) utilizing range–Doppler map images from a frequency-modulated continuous-wave (FMCW) radar. The existing drone detection and identification techniques, which rely on the micro-Doppler signature (MDS), face challenges when a drone is small or located far away, leading to performance degradation due to signal attenuation and faint (MDS). In order to address these issues, this paper suggests a method where multiple time-series range–Doppler images from an FMCW radar are overlaid onto a single image and fed to a CNN. The experimental results, using actual data for three different drone sizes, show significant performance improvements in drone detection accuracy compared to conventional methods. [ABSTRACT FROM AUTHOR]

Emotion recognition through speech is a technique employed in various scenarios of Human–Computer Interaction (HCI). Existing approaches have achieved significant results; however, limitations persist, with the quantity and diversity of data being more notable when deep learning techniques are used. The lack of a standard in feature selection leads to continuous development and experimentation. Choosing and designing the appropriate network architecture constitutes another challenge. This study addresses the challenge of recognizing emotions in the human voice using deep learning techniques, proposing a comprehensive approach, and developing preprocessing and feature selection stages while constructing a dataset called EmoDSc as a result of combining several available databases. The synergy between spectral features and spectrogram images is investigated. Independently, the weighted accuracy obtained using only spectral features was 89%, while using only spectrogram images, the weighted accuracy reached 90%. These results, although surpassing previous research, highlight the strengths and limitations when operating in isolation. Based on this exploration, a neural network architecture composed of a CNN1D, a CNN2D, and an MLP that fuses spectral features and spectogram images is proposed. The model, supported by the unified dataset EmoDSc, demonstrates a remarkable accuracy of 96%. [ABSTRACT FROM AUTHOR]

The fault diagnosis of rolling bearings is faced with the problem of a lack of fault data. Currently, fault diagnosis based on traditional convolutional neural networks decreases the diagnosis rate. In this paper, the developed adaptive residual shrinkage network model is combined with transfer learning to solve the above problems. The model is trained on the Case Western Reserve dataset, and then the trained model is migrated to a small-sample dataset with a scaled-down sample size and the Jiangnan University bearing dataset to conduct the experiments. The experimental results show that the proposed method can efficiently learn from small-sample datasets, improving the accuracy of the fault diagnosis of bearings under variable loads and variable speeds. The adaptive parameter-rectified linear unit is utilized to adapt the nonlinear transformation. When rolling bearings are in operation, noise production is inevitable. In this paper, soft thresholding and an attention mechanism are added to the model, which can effectively process vibration signals with strong noise. In this paper, the real noise is simulated by adding Gaussian white noise in migration task experiments on small-sample datasets. The experimental results show that the algorithm has noise resistance. [ABSTRACT FROM AUTHOR]

The early detection of liver fibrosis is of significant importance. Deep learning analysis of ultrasound backscattered radiofrequency (RF) signals is emerging for tissue characterization as the RF signals carry abundant information related to tissue microstructures. However, the existing methods only used the time-domain information of the RF signals for liver fibrosis assessment, and the liver region of interest (ROI) is outlined manually. In this study, we proposed an approach for liver fibrosis assessment using deep learning models on ultrasound RF signals. The proposed method consisted of two-dimensional (2D) convolutional neural networks (CNNs) for automatic liver ROI segmentation from reconstructed B-mode ultrasound images and one-dimensional (1D) CNNs for liver fibrosis stage classification based on the frequency spectra (amplitude, phase, and power) of the segmented ROI signals. The Fourier transform was used to obtain the three kinds of frequency spectra. Two classical 2D CNNs were employed for liver ROI segmentation: U-Net and Attention U-Net. ROI spectrum signals were normalized and augmented using a sliding window technique. Ultrasound RF signals collected (with a 3-MHz transducer) from 613 participants (Group A) were included for liver ROI segmentation and those from 237 participants (Group B) for liver fibrosis stage classification, with a liver biopsy as the reference standard (Fibrosis stage: F0 = 27, F1 = 49, F2 = 51, F3 = 49, F4 = 61). In the test set of Group A, U-Net and Attention U-Net yielded Dice similarity coefficients of 95.05% and 94.68%, respectively. In the test set of Group B, the 1D CNN performed the best when using ROI phase spectrum signals to evaluate liver fibrosis stages ≥F1 (area under the receive operating characteristic curve, AUC: 0.957; accuracy: 89.19%; sensitivity: 85.17%; specificity: 93.75%), ≥F2 (AUC: 0.808; accuracy: 83.34%; sensitivity: 87.50%; specificity: 78.57%), and ≥F4 (AUC: 0.876; accuracy: 85.71%; sensitivity: 77.78%; specificity: 94.12%), and when using the power spectrum signals to evaluate ≥F3 (AUC: 0.729; accuracy: 77.14%; sensitivity: 77.27%; specificity: 76.92%). The experimental results demonstrated the feasibility of both the 2D and 1D CNNs in liver parenchyma detection and liver fibrosis characterization. The proposed methods have provided a new strategy for liver fibrosis assessment based on ultrasound RF signals, especially for early fibrosis detection. The findings of this study shed light on deep learning analysis of ultrasound RF signals in the frequency domain with automatic ROI segmentation. [ABSTRACT FROM AUTHOR]

Precise measurement of fiber diameter in animal and synthetic textiles is crucial for quality assessment and pricing; however, traditional methods often struggle with accuracy, particularly when fibers are densely packed or overlapping. Current computer vision techniques, while useful, have limitations in addressing these challenges. This paper introduces a novel deep-learning-based method to automatically generate distance maps of fiber micrographs, enabling more accurate fiber segmentation and diameter calculation. Our approach utilizes a modified U-Net architecture, trained on both real and simulated micrographs, to regress distance maps. This allows for the effective separation of individual fibers, even in complex scenarios. The model achieves a mean absolute error (MAE) of 0.1094 and a mean square error (MSE) of 0.0711 , demonstrating its effectiveness in accurately measuring fiber diameters. This research highlights the potential of deep learning to revolutionize fiber analysis in the textile industry, offering a more precise and automated solution for quality control and pricing. [ABSTRACT FROM AUTHOR]