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The use of Artificial Intelligence (AI) to preserve and promote cultural heritage has experienced significant growth in recent years. Among the various areas of cultural heritage, numismatics have emerged as a particularly promising field where we can develop AI solutions. Numismatics refers to the study of coins, tokens, paper money, and medals, which play a critical role in understanding human history and culture. However, there are still limited resources available to help researchers and collectors in the identification of coins. This is due to the vast number of coins in circulation, which presents a significant challenge in developing smart tools for classification tasks. This paper aims to provide a contribution to this setting. In particular, we start by creating a new dataset called EURO-Coin, which consists of images showing the side of coins with reliefs and is designed to facilitate the training and testing of AI models for euro coin classification. Then, we propose two approaches that leverage Convolutional Neural Networks and self-attention layers to classify the country and value of the coins. In our experiments, we obtain an accuracy of 86.9% for country classification and an accuracy of 96.4% for value classification. Finally, we conduct an ablation study to evaluate the impact of the preprocessing activities and attention layers in our approaches. [ABSTRACT FROM AUTHOR]

This paper presents a convolutional neural network (CNN) to classify between the conventionally and organically cultivated Memecik varieties of green olives. The image forming method called the rising paper chromatography is utilized in preparing the images of Memecik varieties of green olives for CNN. In the rising chromatography method, 20, 30, and 40% sample concentrations were determined as the suitable concentrations for both organic and conventional olives. The concentrations of AgNO3 and FeSO4 were determined as 0.25, 0.5, 0.75 and 1% for both conventional and organic samples. The visual differences used for differentiation of different types of Memecik green olives are usually determined according to the regional color differences, the vivid color occurrence, the width and the frequency of bowl occurrence, the thin line, and the picks at drop zone by the expert assessors. The testing results in this study verified the effectiveness of the CNN methodology in differentiating between the organically and conventionally cultivated Memecik green olives. The newly designed neural network achieved 100% accuracy. Furthermore, this high accuracy achieved by CNN might suggest that it can be effectively used in place of the expert assessors. [ABSTRACT FROM AUTHOR]

This paper aims to craft adversarial queries for image retrieval, which uses image features for similarity measurement. Many commonly used methods are developed in the context of image classification. However, these methods, which attack prediction probabilities, only exert an indirect influence on the image features and are thus found less effective when being applied to the retrieval problem. In designing an attack method specifically for image retrieval, we introduce opposite-direction feature attack (ODFA), a white-box attack approach that directly attacks query image features to generate adversarial queries. As the name implies, the main idea underpinning ODFA is to impel the original image feature to the opposite direction, similar to a U-turn. This simple idea is experimentally evaluated on five retrieval datasets. We show that the adversarial queries generated by ODFA cause true matches no longer to be seen at the top ranks, and the attack success rate is consistently higher than classifier attack methods. In addition, our method of creating adversarial queries can be extended for multi-scale query inputs and is generalizable to other retrieval models without foreknowing their weights, i.e., the black-box setting. [ABSTRACT FROM AUTHOR]

Forest fire is a severe natural disaster, which leads to the destruction of forest ecology. At present, fire detection technology represented by convolutional neural network is widely used in forest resource protection, which can realize rapid analysis. However, in forest flame and smoke detection tasks, due to continuous expansion of the target range, a better detection effect cannot be achieved. This paper proposes an improved YOLOX method for multi-scale forest fire detection. This method proposes a novel feature pyramid model to reduce the information loss of high-level forest fire feature maps and enhance the representation ability of feature pyramids. Moreover, the method applies a small object data augmentation strategy to enrich the forest fire dataset, making it more suitable for the actual forest fire scene. According to the experimental results, the mAP of the model proposed in this paper reaches 79.64%, which is about 4.89% higher than the baseline network YOLOX. The method improves the accuracy of forest fire detection, reduces false alarms, and is suitable for real scenarios of forest fires. [ABSTRACT FROM AUTHOR]

Smart advertising is an important part of the modern information industry. The main problems of smart advertising in Chinese cities at this stage include cluttered settings, single form, lack of coordination and integration between advertising and cities and urban buildings, and inconspicuous visual effects. In order to deal with these problems, it is necessary to optimize the design of smart advertisements to enhance the visual aesthetic effect. Image aesthetics quality assessment has good application prospects in industries such as image recommendation and image editing aesthetics, and can be used to help smart advertisements improve visual aesthetics. This paper proposes an objective quantitative scoring method for image aesthetics based on a multi-scale feature extraction network, which is a computational intelligence approach. The model is mainly composed of multiple multi-scale feature extraction units cascaded, and each unit contains a feature extraction layer composed of 3 different convolution kernels, fusion layer and mapping layer. The feature extraction layer forms the input end of the network by combining the global view and local view of the image. At the output end, the EMD function is used as the loss function, and the output distribution is a probability density mass function of 1–10 points. In order to verify the effectiveness of the method in this paper, the AVA dataset, which is commonly used in the field of image aesthetic evaluation, is selected for testing experiments. In this paper, two parameters, Spearman Rank Correlation Coefficient and accuracy rate, are selected as indicators to evaluate the method in this paper. The experimental results show that the method proposed in this paper is feasible and effective. Outperforms several mainstream models on the AVA dataset. [ABSTRACT FROM AUTHOR]

In this paper, a method based on transfer learning is proposed to recover the three-dimensional shape of cultural relics faces from a single old photo. It can simultaneously reconstruct the three-dimensional facial structure and align the texture of the cultural relics with fewer training samples. The UV position map is used to represent the three-dimensional shape in space and act as the output of the network. A convolutional neural network is used to reconstruct the UV position map from a single 2D image. In the training process, the human face data is used for pre-training, and then a small amount of artifact data is used for fine-tuning. A deep learning model with strong generalization ability is trained with fewer artifact data, and a three-dimensional model of the cultural relic face can be reconstructed from a single old photograph. The methods can train more complex deep networks without a large amount of cultural relic data, and no over-fitting phenomenon occurs, which effectively solves the problem of fewer cultural relic samples. The method is verified by restoring a Chinese Terracotta Warrior with facial cracks and a Buddha with a broken nose. Other applications can be used in the fields such as texture recovery, facial feature extraction, and three-dimensional model estimation of the damaged cultural relics or sculptures in the photos. [ABSTRACT FROM AUTHOR]

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people's sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people's needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs. [ABSTRACT FROM AUTHOR]

Fault diagnosis of industrial processes plays an important role in avoiding heavy losses and ensuring production safety. Complex industrial processes often have many working conditions, and the actual industrial process often concentrates on certain working conditions. As a result, the running time of some working conditions is shorter, so the data of these conditions are difficult to obtain. However, almost all fault diagnosis methods based on Deep Learning (DL) requires a large amount of data. Therefore, it is a big challenge to realize the fault diagnosis of few-shot industrial processes. In order to solve these problems, this paper proposes a Deep Feature Transfer Fusion (DFTF) framework based on hybrid domain transfer learning. The purpose is to take few-shot working conditions as the target domain and carry out fault diagnosis for them. As the features of industrial process images are more complex, this paper introduces Pyramid Hybrid Vision Transformer (PHVT) model, which have stronger feature extraction capabilities and spatial perception, as feature extraction module. In order to improve the transferability of the model, this paper introduces the In-Cross Domain Hybrid Transfer Learning (ICTL) method. By fusing the general object features from ResNet50 which pre-trained under public dataset of common object and the features extracted from PHVT which pre-trained under industrial dataset of multiple working conditions, the adaptability of the model to different scenes is enhanced. The experimental results based on Pronto dataset of Process System Engineering lab of Cranfield University show that the proposed transfer learning method has excellent performance. [ABSTRACT FROM AUTHOR]

We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph convolutional network (GCN). In recent years, RF, a representative shallow machine learning algorithm, and CNN, a representative deep learning approach, have been proved to be powerful tools for ML-based mapping for mineral exploration. In the future, GCN deserves more attention for ML-based mapping for mineral exploration because of its ability to capture the spatial anisotropy of mineralization and its applicability within irregular study areas. Finally, we summarize the original contributions of the six papers comprising this special issue. [ABSTRACT FROM AUTHOR]

Furnace temperature and oxygen content are important parameters reflecting the combustion inside a circulating fluidized bed (CFB) boiler. Accurately predicting boiler output is a complex task due to the high noise and nonsmoothness of actual boiler input and output data. In this paper, a new hybrid convolutional neural network (CNN), bidirectional long short-term memory (biLSTM) network, and squeezing and excitation (SE) network prediction model is proposed to significantly improve the prediction accuracy of oxygen content and furnace temperature by combining the advantages of multiple deep learning networks. This network can extract spatiotemporal characteristics of input parameters such as coal feed to effectively predict boiler furnace temperature and oxygen content. CNNs can extract complex features such as dynamic and static nonlinearities between multiple variables affecting the furnace temperature and oxygen content, as well as high noise. The biLSTM network layer can efficiently handle the temporal information of irregular trends in modeling time series components; SE can extract the important information between channels through the feature relationships between channels for better overfeature extraction. The CNN-biLSTM-SE model can effectively solve the problem of nonlinear mapping complexity between inputs and outputs. Experiments show that the proposed CNN-biLSTM-SE model outperforms existing methods. The experimental results showed that the average MAPE errors for oxygen content prediction were CNN-biLSTM-SE (0.038), CNN-biLSTM with attention mechanism (AM) (0.043), CNN-biLSTM (0.051), CNN-LSTM (0.051), biLSTM (0.051), RNN (0.051), LSTM(0.0052), and CNN(0.0054). Extensive experiments in CFB boilers with oxygen content and furnace temperature show that the proposed CNN-biLSTM-SE model achieves better results in terms of goodness-of-fit, generalization ability and accuracy. [ABSTRACT FROM AUTHOR]

In identifying crop leaf diseases, Due to the complex nature of the disease symptoms. There may be variations in disease symptoms with similar characteristics and similarities in disease symptoms with different elements. This can make it challenging to differentiate between various diseases. CSASNet is a hybrid classification model proposed in this paper that combines the attention and multiscale feature fusion mechanisms. The model first incorporates the multiscale feature fusion module atrous spatial pyramid pooling (ASPP) into the ShuffleNetV2 network structure. This enriches the network with disease-specific multiscale feature information. Additionally, the model combines the special group-wise enhance (SGE) attention mechanism module to enhance the weight of disease spot feature information. Lastly, the leaky ReLU function replaces the original ReLU activation function. This allows the model to reduce damaging feature loss during training. The paper presents a design of multiple cross-validation experiments for comparison. The experimental results suggest that the improved model was used for disease leaf identification and showed an accuracy improvement on different crops. Compared to Convnext and MobileNetV2, the CSASNet model demonstrates higher recognition accuracy. [ABSTRACT FROM AUTHOR]

To achieve scientific and intelligent hierarchical intervention of violent criminals, this paper proposes a deep feature fusion model called the Convolutional Long Short Term Gated Attention Network (CLGA-Net), for violent crime temperament classification. First, the CNN is reconstructed to improve local feature extraction. We combine eigenvectors by filtering word vectors with multiple kernel sizes and convoluting separately to obtain feature maps with different granularities. Second, a multi-model fusion algorithm with attention mechanism is proposed by reconstructing CNN. After concatenate layer, Bi-LSTM and Bi-GRU are combined with Multi-Head Attention, and parallelly combined LSTM and GRU with Self-Attention to capture various structural patterns effectively in text sequences. Then, global average pooling layer is used to solve the overfitting of the traditional fully connected layer. Finally, after the softmax classifier, the arithmetic average algorithm is used to obtain the final classification result. To verify the performance of the proposed model, CLGA-Net was compared with other baseline models. The results show that CLGA-Net achieves the optimal effect under all evaluation indices with an accuracy of 99.25%. The AUC values under the macro average and micro average are 99.03% and 99.81%, respectively. By analyzing crime facts, CLGA-Net shows good violent crime temperament classification ability. Regulatory departments can develop a personalized correctional education program according to the type of attribution obtained. Therefore, the CLGA-Net model proposed in this paper can provide an accurate and scalable computerized hierarchical intervention service for offenders. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks can effectively improve the performance of single-image super-resolution reconstruction. Deep networks tend to achieve better performance than others. However, the deep CNNs will lead to a dramatic increase in the size of parameters, limiting its application on embedding and resource-constrained devices, such as smart phone. To address the common problems of blurred image edges, inflexible convolution kernel size selection and slow convergence during training procedure due to redundant network structure in image super-resolution algorithms, this paper proposes a lightweight single-image super-resolution network that fuses multi-level features. The components are mainly two-level nested residual blocks. To better extract features and reduce the number of parameters, each residual block adopts an asymmetric structure. Firstly, it expands twice and then compresses the number of channels twice. Secondly, in the residual block, the feature information of different channels is weighted and fused by adding an autocorrelation weight unit. The quality of the reconstructed image of the proposed method is superior to the existing image super-resolution reconstruction methods in both subjective perception and objective evaluation indicators, and the reconstruction performance is better when the factor is large. [ABSTRACT FROM AUTHOR]

Deep Learning and Machine Learning are becoming more and more popular as their algorithms get progressively better, and their use is expected to have the large effect on improving the health care system. Also, the pandemic was a chance to show how adding AI to healthcare infrastructure could help, since infrastructures around the world are overworked and falling apart. These new technologies can be used to fight COVID-19 because they are flexible and can be changed. Based on these facts, we looked at how the ML and DL-based models can be used to deal with the COVID-19 pandemic problem and what the pros and cons of each are. This paper gives a full look at the different ways to find COVID-19. We looked at the COVID-19 issues in a systematic way and then rated the methods and techniques for finding it based on their availability, ease of use, accuracy, and cost. We have also shown in pictures how well each of the detection techniques works. We did a comparison of different detection models based on the above factors. This helps researchers understand the different methods and the pros and cons of using them as the basis for their research. In the last part, we talk about the open challenges and research questions that come with putting these techniques together with other detection methods. [ABSTRACT FROM AUTHOR]

Academic performance of students in most places around the world is determined by their presence in the classroom. Lecture attendance control at a university is a traditional process in which student engagement in classes is fostered by means of an institutional policy. In a conventional way, professors have employed different techniques to monitor the influx of students in the classroom, as well as their punctuality and history log. Nevertheless, conventional (paper-based) and modern attendance registration approaches (some using worksheets and computational information systems) often lack either promptness, non-impersonation ability, simplicity, reliability, effectiveness, scalability, acceptance, mobility, or affordability. The aim of this paper is to present a new proposal based on a common fingerprint process and in a new speaker recognition configuration by using a Raspberry system for student attendance administration purposes in order to provide most of these advantages. Thus, the propounded scheme uses ordinary fingerprint verification (a binarized 8-bit grayscale image processing) and speech recognition in the task of text-independent closed-set speaker identification using convolutional neural networks by means of previous enrollment and training process using as input a new approach of spectrograms images configuration. The presented system obtained an accuracy of 97.5% and 97.49% for the fingerprint and speech-based task respectively. These results are comparable with the corresponding state-of-the-art environments. The main findings obtained show a flexible structure with growth capacity, dynamism and prompt response due to the set of user assistance reports and the student control techniques. [ABSTRACT FROM AUTHOR]

Purpose: The mechanical fault diagnosis method based on deep learning mainly uses single-scale convolution kernels to extract fault features, which is difficult to extract fault feature information comprehensively. Under strong noise conditions, the performance of fault diagnosis using single-scale convolution kernels will decrease sharply. Methods: An intelligent fault diagnosis method based on multi-scale deep convolution neural network (MSD-CNN) model and data enhancement for strong noise is proposed in this paper. By the multi-scale cascade convolution kernels in the MSD-CNN, the multi-scale information of the original fault signal is extracted, and the ELU activation function is used to retain the negative information contained in the multi-scale information. By the data enhancement method for strong noise, the number and diversity of the MSD-CNN model training samples are improved, which enables the model to learn deeper features in the training stage. Results and Conclusion: Compared six common fault diagnosis models, the proposed model achieves the optimal diagnostic accuracy of 99.98% and 99.66% under normal conditions and strong noise conditions, respectively, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

The traditional way of football training could be subject to some factors such as the field, and the problems and scenes in the training can not be recovered fully. Thus, this paper proposes a football training method based on the improved tiny-yolovs3 model and the virtual reality (VR). Firstly, the paper makes use of the improved tiny-yolov3 model to detect the football in motion. Then, the paper uses the binocular camera to get the coordinates of the football. Finally, the paper reproduces the position of football in the virtual reality environment. The experimental results show that the football training method is feasible. [ABSTRACT FROM AUTHOR]

In mobile communication, automatic modulation recognition of mobile signals has always attracted attention. In the more and more complex electronic environment, the researchers are trying their best to deal with the communication signals, the study of this part can be used in the reality of the war environment, and it has the most important significance for the complex environment. The color time-frequency images of one-dimensional signals can be obtained by using the smooth pseudo-Wigner distributed time-frequency algorithm, at the same time, color time-frequency diagram contains rich texture information, so this paper proposes a mobile signal modulation recognition algorithm based on multimodal feature fusion. The features extracted from the two modes of the time-frequency diagram are fused to realize the signal recognition. The results of the simulations show that if compared with the other algorithms which were not consider the connections between different characters, the algorithm of this paper can improve the result in some way at low SNR. In order to solve the problems that they were less consider about the connections between different characters of the signals, this paper used the convolutional neural network to solve this kind of problem. The results of the simulation experiments show that the TF-CNN and TF-Resnet of this paper have better performance at low signal-to-noise ratio, they can achieve the high precision signal automatic modulation recognition and classification. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of CNN (convolutional neural network) in image classification, an enhanced Inception-ResNet-v2 model based on CNN is designed through the comparative study and analysis of the structure of classification model. This paper proposes to use multi-scale depthwise separable convolution to replace the convolution structure in Inception-ResNet-v2 model, which can reduce the amount of model parameters and extract features under different receptive fields. At the same time, this paper establishes channel filtering module based on global information comparison to filter and join channels, which realizes the effective extraction of features. Finally, through data enhancement, batch normalization and learning rate adjustment, the effect of the model used in this paper is better than most other models in each dataset, and the accuracy rate can reach 94.8%. [ABSTRACT FROM AUTHOR]

Research on the wave field evolution law is highly significant to the fields of offshore engineering and marine resource development. Numerical simulations have been conducted for high-precision wave field evolution, thus providing short-term wave field prediction. However, its evolution occurs over a long period of time, and its accuracy is difficult to improve. In recent years, the use of machine learning methods to study the evolution of wave field has received increasing attention from researchers. This paper proposes a wave field evolution method based on deep convolutional neural networks. This method can effectively correlate the spatiotemporal characteristics of wave data via convolution operation and directly obtain the offshore forecast results of the Bohai Sea and the Yellow Sea. The attention mechanism, multi-scale path design, and hard example mining training strategy are introduced to suppress the interference caused by Weibull distributed wave field data and improve the accuracy of the proposed wave field evolution. The 72- and 480-h evolution experiment results in the Bohai Sea and the Yellow Sea show that the proposed method in this paper has excellent forecast accuracy and timeliness. [ABSTRACT FROM AUTHOR]

The paper proposes a new approach for crowd movement type estimation in video by combining convolutional neural network and integral optical flow. At first, main notions of crowd detection and tracking are given. Secondly, crowd movement features and parameters are defined. Three rules are proposed to identify direct crowd motion. Signs are presented for identifying chaotic crowd movement. Region movement indicators are introduced to analyze the movement of a group of people or a crowd. Thirdly, an algorithm of crowd movement types estimation using convolutional neural network and integral optical flow is proposed. We calculate crowd movement trajectories and show how they can be used to analyze behavior and divide crowds into groups of people. Experimental results show that with the help of convolutional neural network and integral optical flow crowd movement parameters can be calculated more accurately and quickly. The algorithm demonstrates stronger robustness to noise and the ability to get more accurate boundaries of moving objects. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a neurodegenerative disorder that affects millions of people worldwide, making early detection essential for effective intervention. This review paper provides a comprehensive analysis of the use of deep learning techniques, specifically convolutional neural networks (CNN) and vision transformers (ViT), for the classification of AD using brain imaging data. While previous reviews have covered similar topics, this paper offers a unique perspective by providing a detailed comparison of CNN and ViT for AD classification, highlighting the strengths and limitations of each approach. Additionally, the review presents an updated and thorough analysis of the most recent studies in the field, including the latest advancements in CNN and ViT architectures, training methods, and performance evaluation metrics. Furthermore, the paper discusses the ethical considerations and challenges associated with the use of deep learning models for AD classification, such as the need for interpretability and the potential for bias. By addressing these issues, this review aims to provide valuable insights for future research and clinical applications, ultimately advancing the field of AD classification using deep learning techniques. [ABSTRACT FROM AUTHOR]

Sleep is essential for human physical and mental health. Sleep disorders are a significant threat to human health, and a large number of people in the world suffer from sleep disorders. Effective detection of sleep disorders is essential for the treatment of sleep disorders. Questionnaires and scale assessments are traditional methods of sleep disorder detection, which are subjective, time-consuming and prone to misdiagnosis. To detect sleep disorders quickly and accurately, a Multimodal Attention-Fusion Convolutional Neural Network is proposed in this paper. The network uses electroencephalography, electrooculography, electrocardiography, and electromyography signals to automatically identify healthy and five sleep disorders, namely insomnia, narcolepsy, periodic leg movement, rapid eye movement behaviour disorder and nocturnal frontal lobe epilepsy. First, multiple convolutional neural network branches are used to extract time-invariant features of multimodal signals. Then, a multi-scale attention module based on dilated convolutional networks and a squeeze and excite block is proposed for further extracting features with different scales and fusing feature information. Finally, a prediction module consisting of fully connected layers is used to detect sleep disorders. The accuracy, F1 score, and Kappa coefficient obtained on the Cyclic Alternating Pattern sleep dataset are 99.56%, 99.49% and 0.9942, respectively. Compared to the existing state-of-the-art studies, the method proposed in this paper has higher performance in sleep disorder detection. [ABSTRACT FROM AUTHOR]

In recent years, the field of bionics has attracted the attention of numerous scholars. Some models combined with biological vision have achieved excellent performance in computer vision and image processing tasks. In this paper, we propose a new bio-inspired lightweight contour detection network (BLCDNet) by combining parallel processing mechanisms of bio-visual information with convolutional neural networks. The backbone network of BLCDNet simulates the parallel pathways of ganglion cell–lateral geniculate nucleus and primary visual cortex (V1) area, realizing parallel processing and step-by-step extraction of input information, effectively extracting local features and detailed features in images, and thus improving the overall performance of the model. In addition, we design a depth feature extraction module combining depth separable convolution and residual connection in the decoding network to integrate the output of the backbone network, which further improves the performance of the model. We conducted a large number of experiments on BSDS500 and NYUD datasets, and the experimental results show that the BLCDNet proposed in this paper achieves the best performance compared with traditional methods and previous biologically inspired contour detection methods. In addition, BLCDNet still outperforms some VGG-based contour detection methods without pre-training and with fewer parameters, and it is competitive among all of them. The research in this paper also provides a new idea for the combination of biological vision and convolutional neural networks. [ABSTRACT FROM AUTHOR]

With the recent advancements in high-performance computing, convolutional neural networks (CNNs) have achieved remarkable success in various vision tasks. However, along with improvements in model accuracy, the size and computational complexity of the models have significantly increased with the increasing number of parameters. Although graphics processing unit (GPU) platforms equipped with high-performance memory and specialized in parallel processing are commonly used for CNN processing, the significant power consumption presents challenges in their utilization on edge devices. To address these issues, research is underway to design CNN models using field-programmable gate arrays (FPGAs) as accelerators. FPGAs provide a high level of flexibility, allowing efficient optimization of convolution operations, which account for a significant portion of the CNN computations. Additionally, FPGAs are known for their low power consumption compared to GPUs, making them a promising energy-efficient platform. In this paper, we review and summarize various approaches and techniques related to the design of FPGA-based CNN accelerators. Specifically, to comprehensively study CNN accelerators, we investigate the advantages and disadvantages of various methods for optimizing CNN accelerators and previously designed efficient accelerator architectures. We expect this paper to serve as an important guideline for future hardware research in artificial intelligence. [ABSTRACT FROM AUTHOR]

YouTube video recommendation algorithm plays an important role in enhancing user engagement and profitability (or monetization), yet it struggles to deal with disturbing visual content. Real-time analysis using deep learning techniques can play a vital role to identify and filter the disturbing content in online videos. In this paper, we propose an end-to-end trainable two-stream deep learning framework that analyzes and classifies the different categories of disturbing content embedded in child-friendly cartoon videos on YouTube and YouTube Kids platforms. At first, the model extracts the static and motion features from videos through an individual pretrained convolutional neural network (CNN) i.e., EfficientNet-B7. In the next phase, the extracted features are processed by spatio-temporal bidirectional long short-term memory (BiLSTM) network to capture the long-term global temporal dependencies of videos. The learned video representations are forwarded to the classifier for multiclass classification into six categories. Three different types of fusion strategies are investigated to combine the spatial and temporal streams. The evaluation of these methods is performed through extensive experiments on a customized large-scale dataset–YouTube cartoon content filtering (YT-C2F) dataset. The spatio-temporal EfficientNet-BiLSTM network with feature-level fusion displays the best results (f1-score = 0.9316) and shows the efficiency of using a two-stream network compared to single-stream baseline methods. This paper makes two-fold contributions. First, using only static information from videos in a deep learning framework is less effective than using both static and motion information. Second, the feature-level fusion of two streams of networks generates better classification results than early and late fusion techniques. The performance comparison of the proposed model with existing state-of-the-art techniques confirmed the competitive or even better results of our framework. [ABSTRACT FROM AUTHOR]

Time series analysis is widely applied in action recognition, anomaly detection, and weather forecasting. Time series forecasting remains a key challenge due to the complexity of temporal patterns, overlapping changes within sequences, and the need for advanced predictive models to forecast longer sequences in many scenarios. In this study, a model named the decomposed dimension time-domain convolutional neural network (DDTCN) is proposed. This model is specifically designed to address the challenges associated with long time series data. This paper presents a dimension temporal convolutional network (DTCN) module, which has a strong ability to capture variable correlations, and an adaptive strategy is introduced. Specifically, the model proposed in this paper first decomposes time series trends and is combined with the DTCN to extract variable correlations, thus achieving accurate predictions for complex time series and providing a powerful solution for long-term series forecasting. Experiments are conducted on multiple long-term series datasets covering five practical applications: energy, transportation, economics, weather, and health care. The proposed model is extensively evaluated and compared with traditional time series prediction methods and several benchmarks. The experimental results demonstrate that the proposed model outperforms state-of-the-art methods in most tasks involving multiple long-term series forecasting. Additionally, a pig price dataset is generated to predict agricultural economic trends, where compared to that of state-of-the-art methods, the DDTCN achieves a reduction in prediction error of 25.36%. Hence, this model holds promising prospects for wide-ranging applications. [ABSTRACT FROM AUTHOR]

Recognizing familiar faces holds great value in various fields such as medicine, criminal investigation, and lie detection. In this paper, we designed a Complex Trial Protocol-based familiar and unfamiliar face recognition experiment that using self-face information, and collected EEG data from 147 subjects. A novel neural network-based method, the EEG-based Face Recognition Model (EEG-FRM), is proposed in this paper for cross-subject familiar/unfamiliar face recognition, which combines a multi-scale convolutional classification network with the maximum probability mechanism to realize individual face recognition. The multi-scale convolutional neural network extracts temporal information and spatial features from the EEG data, the attention module and supervised contrastive learning module are employed to promote the classification performance. Experimental results on the dataset reveal that familiar face stimuli could evoke significant P300 responses, mainly concentrated in the parietal lobe and nearby regions. Our proposed model achieved impressive results, with a balanced accuracy of 85.64%, a true positive rate of 73.23%, and a false positive rate of 1.96% on the collected dataset, outperforming other compared methods. The experimental results demonstrate the effectiveness and superiority of our proposed model. [ABSTRACT FROM AUTHOR]

The research field of this paper is unsupervised learning in machine learning, aiming to address the problem of how to simultaneously resist feature attacks and improve model classification performance in unsupervised learning. For this purpose, this paper proposes a method to add an optimized loss function after the graph encoding and representation stage. When the samples are relatively balanced, we choose the cross-entropy loss function for classification. When difficult-to-classify samples appear, an optimized Focal Loss*() function is used to adjust the weights of these samples, to solve the problem of imbalanced positive and negative samples during training. The developed method achieved superior performance accuracy with the values of 0.721 on the Cora dataset, 0.598 on the Citeseer dataset,0.862 on the Polblogs dataset. Moreover, the testing accuracy value achieved by optimized model is 0.745, 0.627, 0.892 on the three benchmark datasets, respectively. Experimental results show that the proposed method effectively improves the robustness of adversarial training models in downstream tasks and reduces potential interference with original data. All the test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. [ABSTRACT FROM AUTHOR]

Accurate and stable estimation of the state of health (SOH), which is one of the critical indicators to characterize the ability of lithium-ion (Li-ion) batteries to store and release energy, is critical in the stable driving of electric vehicles. In this paper, a novel SOH estimation method based on the aging factors of battery, which combines convolutional neural network (CNN), wavelet neural network (WNN), and wavelet long short-term memory (WLSTM) named CNN–WNN–WLSTM, is designed. The proposed CNN–WNN–WLSTM estimation scheme inherits both the fast convergence and robust stability of the WNN, as well as the ability of long short-term memory neural network (LSTM) to extract the time series features of the data; moreover, using CNN can make the proposed algorithm extract the data features from the original battery data automatically, and the WNN–WLSTM is then adopted to produce the final SOH estimation by exploiting the features from the CNN. To further speed and achieve global optimization, the RMSprop optimizer, instead of the usually used Adagrad optimizer, is chosen as the solver of the CNN–WNN–WLSTM network. Experimental results on data set from the NASA Ames Prognostics Center of Excellence show that the proposed algorithm can be commendably used for Li-ion battery health management by quantitative comparison with other commonly used machine learning methods, such as back-propagation neural network, WNN, LSTM, WLSTM, convolutional neural network–long short-term memory neural network (CNN–LSTM), and Gaussian process regression. [ABSTRACT FROM AUTHOR]

Learning effective feature spaces for KNN (K-Nearest Neighbor) classifiers is critical for their performance. Existing KNN loss functions designed to optimize CNNs in R n feature spaces for specific KNN classifiers greatly boost the performance. However, these loss functions need to compute the pairwise distances within each batch, which requires large computational resource, GPU and memory. This paper aims to exploit lightweight KNN loss functions in order to reduce the computational cost while achieving comparable to or even better performance than existing KNN loss functions. To this end, an anchor loss function is proposed that assigns each category an anchor vector in KNN feature spaces and introduces the distances between training samples and anchor vectors in the NCA (Neighborhood Component Analysis) function. The proposed anchor loss function largely reduces the required computation by existing KNN loss functions. In addition, instead of optimizing CNNs in R n feature spaces, this paper proposed to optimize them in hypersphere feature spaces for faster convergence and better performance. The proposed anchor loss optimized in the hypersphere feature space is called HAL (Hypersphere Anchor Loss). Experiments on various image classification benchmarks show that HAL reduces the computational cost and achieves better performance: on CIFAR-10 and Fashion-MNIST datasets, compared with existing KNN loss functions, HAL improves the accuracy by over 1 % , and the computational cost decreases to less than 10 % . [ABSTRACT FROM AUTHOR]

Technological improvements have increased the number of people who use online social networking sites, resulting in an increase in cyberbullying. Bullies can attack victims through a large network of online social networking platforms. Cyberbullying is an umbrella term encompassing a wide range of online abuse, including but not limited to harassment, doxing, and reputation attacks. These attacks frequently leave the victim(s) with persistent mental scars, leading to desperate measures such as depression, self-harm, and suicidal thoughts. Given the effects of cyberbullying, there is an urgent need to prosecute and prevent such crimes. This paper gives a comprehensive review as well the empirical analysis of the machine learning, ensemble based and transformer-based models for the cyberbullying detection. This paper proposes two architectures to efficiently detect cyberbullying pattern. The proposed ensemble model makes use of CNN to extract the relevant features and the classification is performed by the SVM. Another proposed architecture utilizes the pre-trained model BERT to detect cyberbullying behavior on online platforms. Both the proposed models were tested on two separate datasets and achieved maximum accuracy of 96.88 and 97.34% for ensemble and BERT models, respectively. This paper provides a thorough examination of the various methodologies used for cyberbullying detection and conducts an empirical and comparative analysis of the presented models with traditional and current algorithms. [ABSTRACT FROM AUTHOR]

Distance calculation is a critical link in the research fields of object trajectory prediction, automatic driving obstacle avoidance, and so on. However, the research on distance using deep learning methods has yet to attract wide attention. The accuracy of traditional distance estimation algorithms based on the optical principle and mathematical modeling is low in practical applications, mainly the curve or slope of the road surface. This paper addresses the challenging distance estimation problem by developing an end-to-end structured model to directly predict the distance for objects in a given image. Besides, the traditional mathematical modeling process is replaced by this learning-based method. To facilitate the research on this task, we construct the extended distance datasets by KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) and NYU(Nathan Silberman, Pushmeet Kohli, Derek Hoiem, Rob Fergus) Depth V2 distance datasets. Experimental results demonstrate that the structured learning model has higher accuracy than the traditional algorithm in different distance ranges and better performance for curves and ramps. Moreover, improving neural network performance will be the direction of improving the model in the future. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used for object recognition and grasping posture planning in intelligent robotic grasping (IRG). Compared with the traditional usage of CNNs in image recognition, IRGs require high recognition accuracy and computational efficiency. However, the existing methodologies for CNN architecture design often rely on human experience and numerous trial-and-error attempts, which make it a very challenging task to obtain an optimal CNN for IRGs. To tackle this challenge, this paper develops a new differentiable architecture search (DARTS) method considering the floating-point operations (FLOPs) of CNNs, named the DARTS-F method, which converts the discrete CNN architecture search to a gradient-based continuous optimization problem and considers both the prediction accuracy and the computational cost of the CNN during the optimization. To efficiently identify the optimal neural network, this paper adopts a bilevel optimization, which first trains the neural network weights in the inner level and then optimizes the neural network architecture by fine-tuning the operational variables in the outer level. In addition, a new digital twin (DT) of IRG is developed considering the physics of realistic robotic grasping in the DT's virtual space, which could not only improve the IRG accuracy but also avoid the expensive training time. In the experiments, the proposed DARTS-F method could generate an optimized CNN with higher prediction accuracy and lower FLOPs than those obtained by the original DARTS method. The DT framework improves the accuracy of real robotic grasping from 61 to 71%. [ABSTRACT FROM AUTHOR]

Human measures many things consciously or subconsciously by touching and sensing without any measurable challenges. However, measuring intangible features like human behavior is a challenging task. Human behavior analysis is an important computer vision technique with a lot of attention which includes human–computer interaction and assisted living. It is essential not only for a wide range of applications but also to understand the abnormal behavior of humans. This survey paper reviews various methods used for the analysis of human behavior by dermatoglyphics fingerprint pattern classifications. This survey studies 50 research papers based on fingerprint pattern classification and presents techniques related overviews such as deep learning (DL)-based methods, convolutional neural network (CNN)-based DL methods, machine learning (ML)-based methods, Support vector machine (SVM)-based ML methods, and optimization methods. The overview of this survey comprised of classification of research methods, year of publication, evaluation metrics, employed datasets, and toolsets for human behavior analysis. The analysis demonstrates that accuracy is the most commonly used evaluation parameter in fingerprint classification which is used by 33 research papers. Finally, the research gap of analyzed methods is explained, which encourages researchers to develop new effective methods for human behavior analysis using fingerprint pattern classification. [ABSTRACT FROM AUTHOR]

When the gearbox fails, the impulsive fault feature is usually submerged in other frequency component. The recognition accuracy of the network model will be seriously affected when directly using the vibration signal as the sample input of Convolutional Neural Network (CNN). To solve this problem, a state recognition method based on vibration signal fusion feature convolutional learning network (FF-CNN) is proposed to conduct the gear fault diagnosis. This method uses the Resonance-based Sparse Signal Decomposition (RSSD) algorithm to separate the periodic and impulsive components that characterize different fault characteristics of the vibration signal. The impulsive feature is amplified by the Teager energy operator. And then the signal periodic and impulsive components are input into CNN to perform the targeted deep learning. Finally, the efficiency of this method is validated using the gear power transmission failure simulation experimental setup. The research in this paper improves the effectiveness of the feature learning, reduces the complexity of deep learning model, and ensures the accuracy of state recognition. Through experimental research, it is found that the recognition accuracy of the recognition model based on the method in this paper reaches more than 95.6%. Highlights: The experiments were carried out in the dynamic engineering laboratory. An FF-CNN method is proposed to conduct the fault diagnosis. T-SNE tool in the manifold learning method was used to visualize the results. FF-CNN can improve the accuracy of fault diagnosis by separating fault features. [ABSTRACT FROM AUTHOR]

To solve the problem that existing traffic signs are not easily detected leading to low detection performance due to their small sizes and external factors such as weather conditions, this paper proposes a traffic sign detection method, MTSDet (Multi-scale Traffic Sign Detection with attention and path aggregation), which focuses on the multi-scale detection problem and effectively improves the detection performance. First, the method efficiently extracts semantic features by introducing the Attention Mechanism Network(AMNet), and then feeds the multi-scale semantic features into Path Aggregation Feature Pyramid Network(PAFPN) for multi-scale feature fusion to obtain multi-scale advanced semantic features. Finally, the multi-scale advanced semantic feature map is deformable interest pooled to effectively enhance the multi-scale object detection modeling capability. In this paper, the above method is validated by two classical datasets, German traffic sign detection dataset and Chinese traffic sign detection dataset, which achieve 92.9% and 94.3% mAP, respectively, and have obvious detection accuracy improvement when compared with other classical advanced algorithms, effectively proving the superiority and generalization of the algorithm in this paper. Code is available at https://github.com/why529913/MTSDet [ABSTRACT FROM AUTHOR]

In recent years, there have been frequent jellyfish outbreaks in many offshore areas worldwide, which have severely affected marine fishery production, coastal tourism, coastal industrial cooling water systems, and marine ecology. Achieving the monitoring of jellyfish plays a vital role in solving the problems mentioned above. However, the research on jellyfish is still in the primary stage. Jellyfish detection technology based on deep learning is gradually being applied to jellyfish detection due to its high efficiency and accuracy, but it is not systematic enough and can identify few jellyfish species. So this paper studies a jellyfish detection algorithm based on deep learning. Based on convolution neural network theory and digital image processing technology, 10 species of jellyfish and fish are detected. Because the quality of underwater images affects the detection accuracy, to further improve the accuracy of the detection algorithm, this paper studies the underwater image processing algorithm. Experimental results show that the image quality is better after applying the three algorithms of dark channel prior algorithm, quadratic combining gray world and perfect reflection algorithm, and contrast-limited adaptive histogram equalization algorithm, which is more conducive to detection. Then, deep learning theory is applied to classify jellyfish. By comparing the AlexNet and GoogLeNet backbone networks' classification results, the accuracy of the jellyfish classification task based on the GoogLeNet backbone network is 96.21%, which is better than AlexNet. Finally, the Faster R-CNN algorithm is used to detect jellyfish, and its detection performance is analyzed based on the two backbone networks mentioned above. The results show that the Faster R-CNN algorithm based on GoogLeNet has a higher detection accuracy in the jellyfish detection task, with an average detection accuracy of 74.96%. In addition, we set up a new jellyfish data set, which includes 25,344 images. The images were divided into 11 species, including 10 species of jellyfish and one fish species. The paper's research lays a theoretical and technical foundation for the subsequent construction of a real-time monitoring system for underwater jellyfish optical imaging, plays an important role in the development of jellyfish monitoring technology, and provides valuable information for marine biologists. [ABSTRACT FROM AUTHOR]

In order to reduce damages caused by slope instability and landslide disasters, it is of great significance to find an efficient, accurate and time-saving method for slope stability analyses. This paper proposes a convolutional neural network based on digital twin models to predict the safety factor of a slope and be evaluate its stability state. In order to solve the problem of lack of the CNN training samples, the digital twin method is resorted to generate 4000 slope models from 10 real slopes by fine-tuning the geometric coordinates and material parameters of their soil layers. The finite element computation of the safety factor of these 4000 slope models were realized by using the parametric analysis of ABAQUS platform and 4000 slope datasets were obtained to serve as the CNN training samples. With the geometric coordinates and material parameters of the slopes as the CNN input and the slope safety factor as the CNN output, the slope safety factor can be effectively predicted. The results show that the prediction accuracy for the testing set reaches 96% and the root mean square error is 0.079. Compared with the finite element modeling time, the prediction time is greatly shortened. The evaluation accuracy of stability states for the 10 real slopes has reached 100%, which indicates that the CNN model has good generalization ability and prediction effect and has practical significance in engineering applications. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

The communication between the human brain and the external devices can be established using Electroencephalograms (EEG)-based Brain–Computer Interface by converting the neural activities of the brain into electric signals. The EEG signals were isolated into an energy–frequency–time spectrum with Hilbert Huang transform that was used by the Deep Learning (DL)-based model to learn discriminative spectro-temporal patterns of the raw EEG signals of ten digits. This paper has two major contributions: first, create a novel dataset known as BrainDigiData of EEG signals of ten digits from (0–9) using a multi-channel EEG device. Second to propose a DL-based one-dimensional Convolutional neural network model BrainDigiCNN to classify the BrainDigiData of EEG signals of digits. The publicly available Mind Big Dataset (MBD) of digits was also used to evaluate the performance of the proposed model. The research done in this paper showed that the band-wise analysis of EEG signals in a complex scenario resulted in improved results as compared to the scenario used in the previously existing work for digit classification using EEG signals. The proposed BrainDigiCNN model achieved the highest average accuracy of 96.99%. The average classification accuracy of 98.27% was achieved for the MBD dataset of 14 channel device EMOTIV EPOC+ and 89.62% on the MBD dataset of 5-channel EMOTIV Insight. The statistical analysis of the proposed model on traditional Machine Learning (ML) classifiers using paired t-test resulted in a p-value less than 0.05 which shows the significant difference between the proposed model and ML classifiers. [ABSTRACT FROM AUTHOR]

At present, in end-to-end network combined with deep learning in stereo matching, how to perceive ample image details within an acceptable range of computational cost, so as to improve the final matching accuracy, has become a hot research topic. In this paper, we design a cascaded disparity correction network based on DispNet, which can refine the initial disparity image by combining the process output results in the trunk network. The method proposed in this paper is named ETE–DC–CNN (end to end–disparity correction–convolutional neural network). In the trunk network, the mixed dilated convolution module is employed to extract the features of the original image, and the feature image is constructed into a 3D disparity space volume through depth-separable convolution, after which the initial disparity image can be obtained through 2D encoder–decoder module. In the correction network, the gradient information of the initial feature image is combined to reconstruct the modified matching cost body guided by the initial disparity image and then a smaller encoder–decoder module is trained and processed to make the final result at the sub-pixel level. The algorithm in this paper has been verified in SceneFlow and KITTI 2012/2015 data sets. The method proposed in this article achieves 3.46, 2.12, 1.19, and 0.6 results in the non-occluded area and 4.04, 2.55, 1.58, and 0.7 results in the global area on the Er (> 2 px), Er (> 3 px), Er (> 5 px), and Epe indicators in the KITTI 2012 test set, which are higher than those of the backbone network DispNet. [ABSTRACT FROM AUTHOR]

Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs. [ABSTRACT FROM AUTHOR]

With the rapid development of convolutional neural networks (CNNs), higher accuracy is usually accompanied by huge parameters and calculations. In embedded devices with limited computing resources, huge network models are often difficult to deploy. It has been shown in previous researches that there are a large number of invalid filters in the CNNs network, and removing these invalid filters has little effect on network accuracy. Therefore, compressing CNNs by pruning these redundant filters can be done while maintaining accuracy. Model pruning is one of the main compression techniques for models. In the existing model pruning work, the filter with a small ℓ 1 -norm value is pruned by calculating the ℓ 1 -norm of the filter, but some researches have pointed out that the ℓ 1 -norm is not always effective. This paper proposes a kernel density estimation-based entropy algorithm to prune filters with low information content. We experimentally demonstrate the effectiveness of the algorithm. Pruning VGG-16 model on CIFAR-10 datasets achieves an accuracy of 93.36% even after removing 91.97% of the original parameter count and 64.87% of the FLOPs. After pruning 75.84% of the network parameters and 52.35% of the FLOPs of the ResNet-110 model on the CIFAR-100 datasets, the accuracy remains almost the same. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive survey of publicly available neural network models specifically designed for detecting wildfires using regular visible-range cameras positioned on hilltops or forest lookout towers. The surveyed models are first pre-trained on the ImageNet-1K dataset and then fine-tuned on a custom wildfire dataset to enhance their performance. Evaluations are conducted on diverse wildfire images, enabling a thorough assessment of their capabilities. The survey findings provide valuable insights for individuals interested in leveraging transfer learning techniques for wildfire detection. Among the examined models, Swin Transformer-tiny achieves the highest area under the curve value, indicating strong overall performance in distinguishing wildfire events. However, ConvNext-tiny stands out for its exceptional ability to detect all instances of wildfires while maintaining the lowest false alarm rate within our dataset. These results highlight the varying strengths of different neural network models and offer valuable guidance for selecting an appropriate model based on specific detection requirements and priorities. [ABSTRACT FROM AUTHOR]

The occurrence of apple diseases has dramatically affected the quality and yield of apples. Disease monitoring is an important measure to ensure the healthy development of the apple industry. Based on a residual network (ResNet50), this paper proposes an MSO-ResNet (multistep optimization ResNet) apple leaf disease recognition model. By decomposing the convolution kernel, updating the identity mapping method, reducing the number of residual modules, and replacing the batch normalization layer, the identification accuracy and speed of the model are improved, and the number of model parameters is reduced. The experimental results show that the average precision, recall, and F1-score of the proposed model for leaf disease identification are 0.957, 0.958, and 0.957, respectively. The parameter memory is 14.77 MB, and the recognition time of each image is only 25.84 ms. The overall performance of the proposed model was better than that of the other models. The proposed model in this paper has high recognition performance and strong robustness and can provide critical technical support for the automatic recognition of apple leaf diseases. [ABSTRACT FROM AUTHOR]

With the development of high-resolution camera technology, the shooting scene coverage has reached the square kilometer level, thousands of people can be observed at the same time, and the faces of people from a hundred meters away are clearly recognizable. The images captured by high-resolution cameras are very different from those captured by conventional cameras. In the face of many detection targets in high-resolution images, large differences in target scales due to spatial position, as well as difficulties in extracting features and poor detection results caused by target overlap and concealment phenomena, this paper proposes a multi-target detection method SARNet that combined with spatial attention optimization feature extraction. Use spatial attention to optimize the backbone network, expand the local receptive field, thereby enhance the representation ability, and enhance the feature extraction ability of small targets; the different scale features of the dilated feature pyramid network are subjected to the deformable region of interest pooling operation, which effectively improves the different scales detection accuracy. The experimental results show that the method proposed in this paper can get 51.9% mAP on the PANDA dataset, which is superior to the existing detection algorithms. At the same time, experimental verification of pedestrians and vehicles on the COCO2017 dataset fully proves the feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

Today, governments tackle the science of science policy with quantitative analysis, especially in the USA. This trend promotes the use of objective data by decision-makers in the fields of innovation policy and technology management. These decision-makers seek more reliable research and development at an earlier stage to contribute to the national economy. Under this worldwide trend, there is a high demand for quantitative analysis accompanying complex IT skills from governmental officers and non-IT researchers. The purpose of this research is to extract the destinations of currently growing fields of science by means of tracking citations and to capture signs of state of the art studies. The analysis data are bibliographic data of academic papers retrieved from Web of Science. I provide results for 16 growing topics as of July 2016. I deepen the understanding of the 'convolutional neural network' among a lot of topics, and found future application candidates. I provide a methodology to discover future seeds of research and development. Additionally, the analysis system used in this research automatically publishes its results, and always provides the results. This research will contribute to improving strategies around research and development. [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]