Search

Predictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector. Hence, this study contributes to examine the most efficient pre-processing approaches for predicting sensory data trends based on Gated Recurrent Unit (GRU) neural networks. To validate the model, the data from two paper pulp presses with several pre-processing methods are utilized for predicting the units' conditions. The results of validation criteria show that pre-processing data using a LOWESS in combination with the Elimination of discrepant data filter achieves more stable results, the prediction error decreases, and the predicted values are easier to interpret. The model can anticipate future values with MAPE, RMSE and MAE of 1.2, 0.27 and 0.30 respectively. The errors are below the significance level. Moreover, it is identified that the best hyperparameters found for each paper pulp press must be different. [ABSTRACT FROM AUTHOR]

He explores how Kimberly-Clark, the global consumer goods company, uses this innovation approach to uncover and develop new products In the next 24 hours, about 2 billion people will use a Kimberly-Clark product and put every biological fluid known to man into those products - and then simply throw them away. If we can build the digital IQ of our employees and give them the tools that they need on top of a solid foundation of data science, we can transform our companies from atom-centric companies to bit-centric companies. Redefining Business to Build a Better World: In this paper adapted from his keynote presentation at IRI's Annual Conference in June 2022, Pete Dulcamara discusses the pivotal role of humanity-centric innovation in a rapidly changing world. [Extracted from the article]

Photovoltaic systems (PV) have been extensively used worldwide as a reliable and effective renewable energy resource due to their environmental and economic merits. However, PV systems are prone to several environmental and weather conditions that impact their performance. Amongst these conditions is dust accumulation, which has a significant adversative impact on the solar cells' performance, especially in hot and arid regions. This study provides a comprehensive review of 278 articles focused on the impact of dust on PV panels' performance along with other associated environmental factors, such as temperature, humidity, and wind speed. The review highlights the importance of modelling dust accumulation along with other ecological factors due to their interactive nature, and the differences between cleaning techniques and schedules effectiveness. Moreover, the study provides a review of statistical and artificial intelligence models used to predict PV performance and its prediction accuracies in terms of data size and complexity. Finally, the study draws attention to several research gaps that warrant further investigation. Among these gaps is the need for proper dynamic optimisation models for cleaning schedules and a more advanced machine and deep learning models to predict dust accumulation while considering environmental and ageing factors. [ABSTRACT FROM AUTHOR]

The task of change detection (CD) in optical and SAR images is an ever-evolving and demanding subject within the realm of remote sensing (RS). It holds great significance to identify the target areas by using complementary information between the two. Due to the distinct imaging mechanisms employed by optical and SAR sensors, effectively and accurately identifying changing regions can be challenging. To this end, a novel heterogeneous RS images CD network (Twin-Depthwise Separable Convolution Connect Network, TDSCCNet) is proposed in this paper. Image domain transformation is a front-end task, while the back-end employs a single-branch bilayer depthwise separable convolutionconnected encoder-decoder to accomplish CD work. Specifically, first, the cycle-consistent adversarial network (CycleGAN) serves to integrate the optical and SAR visual domains, and a consistent feature expression is obtained. Second, the single-branch encoder structure of bilayer depthwise separable convolution is employed to realize change feature extraction. Finally, the multiscale connected decoder reconstructed by change map is utilized to reconstruct the original images and solve the local discontinuities and holes in the binary change map. Multiscale loss is designated for optimizing the global and local effects to alleviate the class imbalance problem. It was tested on four representative datasets from Gloucester, Shuguang Village, Italy and WV-3 datasets with an overall accuracy of 97.36%, 97.01%, 97.62%, and 98.01% respectively. By comparing the existing methods, experimental results confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In multi-antenna systems, advanced techniques such as massive multiple-input multiple-output (MIMO), beamforming, and beam selection depend heavily on the accurate acquisition of the channel state. However, pilot contamination (PC) can be a major source of interference which degrades they are performance. Moreover, the severity of PC increases as more pilots are reused between users in the wireless systems. Researchers have shown that PC can be mitigated by using deep learning (DL) approaches. Nevertheless, when minimizing PC, the examination that identifies the applications and factors that distinguish these DL approaches is still limited. This paper reviews these DL approaches and the improvements needed to enhance their performance. Simulation results confirm that DL networks that learn to predict the channels directly have superior performance under PC. [ABSTRACT FROM AUTHOR]

Remote sensing image building segmentation, which is essential in land use and urban planning, is evolving with advancements in deep learning. Conventional methods using convolutional neural networks face limitations in integrating local and global information and establishing long-range dependencies, resulting in suboptimal segmentation in complex scenarios. This paper proposes LMSwin_PNet, a novel segmentation network that addresses the SwinTransformer encoder's deficiency in local information processing through a local feature extraction module. Additionally, it features a multiscale nonparametric merging attention module to enhance feature-channel correlations. The network also incorporates the pyramid large-kernel convolution module, replacing the traditional 3 x 3 convolution in the decoder with multibranch large-kernel convolution, thereby achieving a large receptive field and detailed information capture. Comparative analyses on three public building datasets demonstrated the model's superior segmentation performance and robustness. The results show that LMSwin_PNet produced outputs closely matching labels, showing its potential for broader application in remote sensing image segmentation tasks. It achieved achieving an IoU of 72.35% on the Massachusetts Building Dataset, 91.30% on the WHU Building Dataset, and 78.99% on the Inria aerial-image building dataset. The source code will be freely available at https://github.com/ziyanpeng/pzy. [ABSTRACT FROM AUTHOR]

In modern network environments, the swift recovery of network flow intrusions poses a substantial challenge. Particularly in the context of Software-Defined Networks (SDN), addressing this challenge necessitates the strategic selection of backup paths based on traffic patterns. In response to this critical issue, our paper introduces a groundbreaking approach known as Machine Learning-based Network Intrusion Recovery (MLBNIR) for enhancing intrusion recovery in SDN. We leverage a dedicated SDN dataset to train a flow-based Machine Learning (ML) model, enabling a deeper understanding of traffic dynamics within the SDN framework. Our study, presented in this paper, reveals that the MLBNIR approach significantly reduces intrusion recovery time by up to 90% and concurrently increases network bandwidth consumption by up to 57% when compared to existing methods reviewed in the literature. [ABSTRACT FROM AUTHOR]

Attack behaviour detection of the pig is a valid method to protect the health of pig. Due to the farm conditions and the illumination changes of the piggery, the images of the pig in the videos are often being overlapped, which lead to difficulties in recognizing pig attack behaviour. We propose an improved YOLOX target detection model to overcome these difficulties. The improvements of the proposed model are: (1) the normalization attention mechanism is adopted to gain global information in the last block of the neck network and (2) the loss function IoU in YOLOX is replaced by DIoU to improve the detection accuracy. The pig attack behaviour considered in this paper includes the ear biting, the tail biting, the head to head collision and the head to body collision. The dataset is builded from the artificially observed attack video segments by using the inter-frame difference method. In the pig attack behaviour detection experiments, the improved YOLOX model achieves 93.21% precision which is 5.30% higher than the YOLOX model. The experiment results show that the improved YOLOX can realize pig attack behaviour detection with high precision. [ABSTRACT FROM AUTHOR]

In this paper, hybrid models of Kalman filter and neural network for state estimation are reviewed of their corresponding academic achievements, the creation of which is a noteworthy development in state estimation. This paper aims to provide a summary of research progress on such hybrid models and emphasize their functions and advantages. First of all, the concept and feature are paid attention to about Kalman filter, and its transmutative modes are taken into consideration. Then several popular neural network algorithms are introduced in brief. Subsequently, research results on hybrid models are analysed and discussed comprehensively. Not only Kalman filter and neural network can be adopted in succession, but also can be mixed in structure. The mixed models can also be divided into two types, the equations or parameters of state--space model are trained by neural network for Kalman filter and the parameters of neural network are updated by Kalman filter. It is proved that the hybrid models outperform than single model of Kalman filter or neural network in accuracy and generalization. Last but not least, the effectiveness of state--space equations of Kalman filter can be established by neural network in nonlinear systems is verified. [ABSTRACT FROM AUTHOR]

Many blue and white porcelain are unearthed in Jingdezhen every year. The patterns on the sherds have important research significance. At present, the classification of porcelain shards is mainly based on manual work, which has the disadvantages of large workload. The use of automatic classification methods also faces complex patterns and sample sizes. In order to solve these problems, this paper proposes a new automatic recognition method based on deep learning, including data preprocessing method combined with color segmentation algorithm, a new data augmentation method FCutMix for regions of interest, a new integration strategy and the redesigned deep network model FFCNet that integrates multiple features. After experiments, the data preprocessing method, feature fusion method and integration strategy proposed in the paper can effectively improve the performance of the model by removing redundant information and adding effective features. The FCutMix method can also obtain more accurate mixed samples than the traditional CutMix. The method proposed in this paper improves the accuracy of tasks in 14 categories from 71.7% to 83.2% in a dataset containing only 373 images of porcelain sherds. In the future, this research will further design the network structure and multi-level feature fusion. [ABSTRACT FROM AUTHOR]

Multi-class change detection can make various ground monitoring projects more efficient and convenient. With the development of deep learning, the multi-class change detection methods have introduced Deep Neural Network (DNN) to improve the accuracy and efficiency of traditional methods. The class imbalance in the image will affect the feature extraction effect of DNN. Existing deep learning methods rarely consider the impact of data on DNN. To solve this problem, this paper proposes a class rebalancing algorithm based on data distribution. The algorithm iteratively trains the SSL model, obtains the distribution of classes in the data, then expands the original dataset according to the distribution of classes, and finally trains the baseline SSL model using the expanded dataset. The trained semantic segmentation model is used to detect multi-class changes in two-phase images. This paper is the first time to introduce the image class balancing method in the multi-class change detection task, so a control experiment is designed to verify the effectiveness and superiority of this method for the unbalanced data. The mIoU of the class rebalancing algorithm in this paper reaches 0.4615, which indicates that the proposed method can effectively detect ground changes and accurately distinguish the types of ground changes. [ABSTRACT FROM AUTHOR]

Aim: Bibliometric surveys are time-consuming endeavors, which cannot be scaled up to meet the challenges of ever-expanding fields, such as bone regeneration. Artificial intelligence, however, can provide smart tools to screen massive amounts of literature, and we relied on this technology to automatically identify research topics. Materials & methods: We used the BERTopic algorithm to detect the topics in a corpus of MEDLINE manuscripts, mapping their similarities and highlighting research hotspots. Results: Using BERTopic, we identified 372 topics and were able to assess the growing importance of innovative and recent fields of investigation such as 3D printing and extracellular vescicles. Conclusion: BERTopic appears as a suitable tool to set up automatic screening routines to track the progress in bone regeneration. [ABSTRACT FROM AUTHOR]

In feature based image matching, distinctive features in images are detected and represented by feature descriptors. Matching is then carried out by assessing the similarity of the descriptors of potentially conjugate points. In this paper, we first shortly discuss the general framework. Then, we review feature detection as well as the determination of affine shape and orientation of local features, before analyzing feature description in more detail. In the feature description review, the general framework of local feature description is presented first. Then, the review discusses the evolution from hand-crafted feature descriptors, e.g. SIFT (Scale Invariant Feature Transform), to machine learning and deep learning based descriptors. The machine learning models, the training loss and the respective training data of learning-based algorithms are looked at in more detail; subsequently the various advantages and challenges of the different approaches are discussed. Finally, we present and assess some current research directions before concluding the paper. [ABSTRACT FROM AUTHOR]

This paper focuses on exploring and comparing different machine learning algorithms in the context of diabetes management. The aim is to understand their characteristics, mathematical foundations, and practical implications specifically for predicting blood glucose levels. The study provides an overview of the algorithms, with a particular emphasis on deep learning techniques such as Long Short-Term Memory Networks. Efficiency is a crucial factor in practical machine learning applications, especially in the context of diabetes management. Therefore, the paper investigates the trade-off between accuracy, resource utilisation, time consumption, and computational power requirements, aiming to identify the optimal balance. By analysing these algorithms, the research uncovers their distinct behaviours and highlights their dissimilarities, even when their analytical underpinnings may appear similar. [ABSTRACT FROM AUTHOR]

Bone age is one of the most important evaluation indexes for the growth and development of children and adolescents. The bone age assessment method based on deep learning generally uses the whole left wrist X-ray film or some regions of interest in the left wrist X-ray film. Based on the entire X-ray film, the intelligent evaluation process is simple, but the accuracy is low. Although intelligent evaluation based on regions of interest has high accuracy, it requires prior knowledge and the process is complex. To solve the above problems, this paper proposes a multi-region combined method for bone age assessment.Asmall number of regions of interest in wrist bone X-ray films are extracted, and then the whole X-ray film and these regions of interest were used to evaluate the bone age. The experiment uses the improved Inception-ResNet-V2 convolutional neural network. The results show that compared with other bone age assessment studies on the open data set published by the North American radiological Association, this method can obtain higher accuracy of bone age assessment, with an average absolute error of 7.11 months. This method improves the efficiency and accuracy of bone age assessment while simplifying the assessment process. [ABSTRACT FROM AUTHOR]

As the core technology of precision animal husbandry, efficient and rapid identification of Jinnan cattle individuals can promote the scale, informatization and refinement of breeding, which is very necessary for the development of animal husbandry at this stage. However, the traditional livestock individual recognition method based on ear tag is labour-consuming, time-consuming, inefficient, easy to wear and limited by the recognition distance, and the accuracy is also very low. In order to solve this problem, a new method of Jinnan cattle individual recognition based on deep mutual learning is proposed by using the non-contact image recognition method. Two student networks are designed. They supervise each other and complete the task together. Their efficiency can be higher than that of a strong teacher network. Through this method, the individual recognition performance of Jinnan cattle is also enhanced. The experimental results verify the effectiveness of the method of deep mutual learning. Consult and learn from the peer network are used to improve generalization, so as to improve model recognition performance. Finally, the accuracy is 99.3% on the Jinnan cattle individual dataset established in this paper. The application of the contactless cattle individual recognition method in the farm is of great significance. [ABSTRACT FROM AUTHOR]

The class imbalance problem, in which the distribution of different classes in training data is unequal or skewed, is a prevailing problem. This can lead to classifier algorithms being biased, negatively impacting the performance of the minority class. In this paper, we addressed the class imbalance problem in datasets for aspect-based sentiment classification. Aspect-based Sentiment Classification (AbSC) is a type of fine-grained sentiment analysis in which sentiments about particular aspects of an entity are extracted. In this work, we addressed the issue of class imbalance by creating synthetic data. For synthetic data generation, two techniques have been proposed: paraphrasing using the PEGASUS fine-tuned model and backtranslation using the M2M100 neural machine translation model. We compared these techniques with two other class balancing techniques, such as weighted oversampling and cross-entropy loss with class weight. An extensive experimental study has been conducted on three benchmark datasets for restaurant reviews: SemEval-2014, SemEval-2015, and SemEval-2016. We applied these methods to the BERT-based deep learning model for aspect-based sentiment classification and studied the effect of balancing the data on the performance of these models. Our proposed balancing technique, using synthetic data, yielded better results than the other two existing methods for dealing with multi-class imbalance. [ABSTRACT FROM AUTHOR]

Spatial relation extraction is the process of identifying geographic entities from text and determining their corresponding spatial relations. Traditional spatial relation extraction mainly uses rule-based pattern matching, supervised learning-based or unsupervised learning-based methods. However, these methods suffer from poor time-sensitive, high labor cost and high dependence on large-scale data. With the development of pre-trained language models greatly alleviating the shortcomings of traditional methods, supervised learning methods incorporating pre-trained language models have become the mainstream relation extraction methods. Pipeline extraction and joint extraction, as the two most dominant ideas of relation extraction, both have obtained good performance on different datasets, and whether to share the contextual information of entities and relations is the main differences between the two ideas. In this paper, we compare the performance of two ideas oriented to spatial relation extraction based on Chinese corpus data in the field of geography and verify which method based on pre-trained language models is more suitable for Chinese spatial relation extraction. We fine-tuned the hyperparameters of the two models to optimize the extraction accuracy before the comparison experiments. The results of the comparison experiments show that pipeline extraction performs better than joint extraction of spatial relation extraction for Chinese text data with sentence granularity, because different tasks have different focus on contextual information, and it is difficult to take account into the needs of both tasks by sharing contextual information. In addition, we further compare the performance of the two models with the rule-based template approach in extracting topological, directional and distance relations, summarize the shortcomings of this experiment and provide an outlook for future work. [ABSTRACT FROM AUTHOR]

High Spatial and Spectral Resolution (HSSR) remote-sensing images can provide rich spectral bands and detailed ground information, but there is a relative lack of research on this new type of remote-sensing data. Although there are already some HSSR datasets for deep learning model training and testing, the data volume of these datasets is small, resulting in low classification accuracy and weak generalization ability of the trained models. In this paper, an HSSR dataset Luojia-HSSR is constructed based on aerial hyperspectral imagery of southern Shenyang City of Liaoning Province in China. To our knowledge, it is the largest HSSR dataset to date, with 6438 pairs of 256 × 256 sized samples (including 3480 pairs in the training set, 2209 pairs in the test set, and 749 pairs in the validation set), covering area of 161 km2 with spatial resolution 0.75 m, 249 Visible and Near-Infrared (VNIR) spectral bands, and corresponding to 23 classes of field-validated ground coverage. It is an ideal experimental data for spatial-spectral feature extraction. Furthermore, a new deep learning model 3D-HRNet for interpreting HSSR images is proposed. The conv-neck in HRNet is modified to better mine the spatial information of the images. Then, a 3D convolution module with attention mechanism is designed to capture the global-local fine spectral information simultaneously. Subsequently, the 3D convolution is inserted into the HRNet to optimize the performance. The experiments show that the 3D-HRNet model has good interpreting ability for the Luojia-HSSR dataset with the Frequency Weighted Intersection over Union（FWIoU） reaching 80.54%, indicating that the Luojia-HSSR dataset constructed in this paper and the proposed 3D-HRnet model have good applicable prospects for processing HSSR remote sensing images. [ABSTRACT FROM AUTHOR]

In this paper, we present automatic, deep-learning methods for pipeline detection in underwater environments. Seafloor pipelines are critical infrastructure for oil and gas transport. The inspection of those pipelines is required to verify their integrity and determine the need for maintenance. Underwater conditions present a harsh environment that is challenging for image recognition due to light refraction and absorption, poor visibility, scattering, and attenuation, often causing poor image quality. Modern machine-learning object detectors utilize Convolutional Neural Network (CNN), requiring a training dataset of sufficient quality. In the paper, six different deep-learning CNN detectors for underwater object detection were trained and tested: five are based on the You Only Look Once (YOLO) architectures (YOLOv4, YOLOv4-Tiny, CSP-YOLOv4, YOLOv4@Resnet, YOLOv4@Den seNet), and one on the Faster Region-based CNN (RCNN) architecture. The models' performances were evaluated in terms of detection accuracy, mean average precision (mAP), and processing speed measured with the Frames Per Second (FPS) on a custom dataset containing underwater pipeline images. In the study, the YOLOv4 outperformed other models for underwater pipeline object detection resulting in an mAP of 94.21% with the ability to detect objects in real-time. Based on the literature review, this is one of the pioneering works in this field. [ABSTRACT FROM AUTHOR]

Vehicle tracking detection, recognition and counting is an important part of vehicle analysis. Designing such a model with excellent performance is difficult. The traditional target detection algorithm based on artificial features has poor generalization ability and robustness. In order to take use the deep learning method for vehicle tracking detection, recognition and counting, this paper proposes a vehicle detection method based on yolov5. This method uses the deep learning technology, takes the running vehicles video as the research object, analysis the target detection algorithm, proposes a vehicle detection framework and platform. The relevant detection algorithm of the platform designed in this paper has great adaptability, when displayed under various conditions, such as heavy traffic, night environment, multiple vehicles overlap with each other, partial loss of vehicles, etc. it has good performance. The experimental results show that the algorithm can accurately segment and identify vehicles according to the edge contour of vehicles. It can take use the materials includes pictures, videos, and real-time monitoring, and has a high recognition rate in real-time performance. [ABSTRACT FROM AUTHOR]

A virus known as the Coronavirus 2019 (COVID-19) can have serious effects on a variety of body parts. Preventing this spread of the virus requires early detection and proper treatment. Many methods are followed for early detection of this disease based on symptoms and classification for proper treatment. In this paper, classification of COVID-19 using Computed Tomography (CT) scan images with hybrid optimisation-enabled deep learning is researched. Here, a hybrid optimisation algorithm called War Snake Optimisation Algorithm (WSOA) is proposed to train Deep Q-Network (Deep Q-Net). Moreover, the proposed WSOA is formed by integrating War strategy optimisation (WSO) and Snake Optimiser (SO). In this paper, pre-processing is carried out using a median filter, followed by lung lobe segmentation that is done by Generative Adversarial Network (GAN). Furthermore, image augmentation is done by translation, rotation, flipping and scaling. Then, feature extraction is followed after image augmentation, where needed features get extracted to end up with the classification of COVID-19. This classification brings out the patient's health condition of normal and abnormal situations. Moreover, this research work is analysed with three performance metrics, such as accuracy, sensitivity and specificity with values of 93.7%, 94.8% and 89.5%. [ABSTRACT FROM AUTHOR]

Pipelines are crucial for transporting energy sources, yet corrosion especially stress corrosion cracking (SCC) poses a complex and potentially catastrophic form of material degradation. Traditional techniques like finite element analysis (FEA) have been utilized for SCC prediction, but it suffers from high computational cost and limited scalability. Deep learning (DL) with integration of FEA leverages large-scale data and learn complex nonlinear patterns for SCC prediction. Currently, literature on deep learning-enabled finite element analysis for pipelines SCC prediction is scarce, offering limited insights into this emerging approach and lack of comprehensive review. This paper reviews and investigates the current research directions and applications of DL-enabled FEA methodologies for simulation of SCC prediction. The importance of DL, technique type and network are also outlined in this review paper. This paper delves into integration of DL algorithms with FEA and their ability to grab complex interactions between mechanical stress, material properties, and environmental factors. Based on this comprehensive review, it was found that DL and FEA have proven to be strong prediction tools with high accuracy and lower training cost. DL-enabled FEA techniques are also being utilized to replace time-consuming methods and conventional codes. Furthermore, article discusses potential of this integrated approach for enhancing accuracy and efficiency of SCC prediction, leading to improved pipeline integrity management practices. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) images enable high-resolution information collection from complex ground objects and are increasingly utilized in the earth observation research. Recently, RS technologies are continuously enhanced by various characterized platforms and sensors. Simultaneously, artificial intelligence vision algorithms are also developing vigorously and playing a significant role in RS image analysis. In particular, aiming to divide images into different ground elements with specific semantic labels, RS image segmentation could realize the visual acquisition and interpretation. As one of the pioneering methods with the advantages of deep feature extraction ability, deep learning (DL) algorithms have been exploited and proved to be highly beneficial for precise segmentation in recent years. In this paper, a comprehensive review is performed on remote sensing survey systems and various kinds of specially designed deep learning architectures. Meanwhile, DL-based segmentation methods applied on four domains are also illustrated, including geography, precision agriculture, hydrology, and environmental protection issues. In the end, the existing challenges and promising research directions in RS image segmentation are discussed. It is envisioned that this review is able to provide a comprehensive and technical reference, deployment and successful exploitation of DL empowered RS image segmentation approaches. [ABSTRACT FROM AUTHOR]

Multiple tasks within the field of geographical information retrieval and geographical information sciences necessitate toponym matching, which involves the challenge of aligning toponyms that share a common referent. The multiple string similarity approaches struggle when confronted with the complexities associated with unofficial and/ or historical variants of identical toponyms. Also, current state-of-the-art approaches/tools to supervised machine learning rely on labeled samples, and they do not adequately address the intricacies of character replacements either from transliterations or historical shifts in linguistic and cultural norms. To address these issues, this paper proposes a novel matching approach that leverages a deep neural network model empowered by geographic language representation model, known as GeoBERT, which stands for geographic Bidirectional Encoder Representations from Transformers (BERT). This model harnesses the groundbreaking capabilities of the GeoBERT framework by extending a generalized Enhanced Sequential Inference Model architecture and integrating multiple features to enhance the accuracy and robustness of the toponym matching. We present a comprehensive evaluation of the proposed method's performance using three extensive datasets. The findings clearly illustrate that our approach outperforms the individual similarity metrics used in previous studies. [ABSTRACT FROM AUTHOR]

The endoscopic Hill classification of the gastroesophageal flap valve (GEFV) is of great importance for understanding the functional status of the esophagogastric junction (EGJ). Deep learning (DL) methods have been extensively employed in the area of digestive endoscopy. To improve the efficiency and accuracy of the endoscopist's Hill classification and assist in incorporating it into routine endoscopy reports and GERD assessment examinations, this study first employed DL to establish a four-category model based on the Hill classification. A dataset consisting of 3256 GEFV endoscopic images has been constructed for training and evaluation. Furthermore, a new attention mechanism module has been provided to improve the performance of the DL model. Combined with the attention mechanism module, numerous experiments were conducted on the GEFV endoscopic image dataset, and 12 mainstream DL models were tested and evaluated. The classification accuracy of the DL model and endoscopists with different experience levels was compared. 12 mainstream backbone networks were trained and tested, and four outstanding feature extraction backbone networks (ResNet-50, VGG-16, VGG-19, and Xception) were selected for further DL model development. The ResNet-50 showed the best Hill classification performance; its area under the curve (AUC) reached 0.989, and the classification accuracy (93.39%) was significantly higher than that of junior (74.83%) and senior (78.00%) endoscopists. The DL model combined with the attention mechanism module in this paper demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists. A new attention mechanism module has been proposed and integrated into the DL model. According to our knowledge, this is the first study to establish a four-category DL model based on the Hill grading. The DL model demonstrated outstanding classification performance based on the Hill grading and has great potential for improving the accuracy of the Hill classification by endoscopists. [ABSTRACT FROM AUTHOR]

Black and odorous water (BOW) is a common issue in rapidly urbanizaing developing countries. Existing methods for extracting BOW from remote sensing images focus mainly on spectral information and ignores important spatial characteristics like texture, context and orientation. Deep learning has emerged as a powerful approach for BOW extraction, but its effectiveness is hindered by limited amount of labeled data and a small proportion of objects. In this paper, we proposed a fully convolutional adversarial network (FANet) for end-to-end pixel-level semantic segmentation of BOW.. FANet combines a fully convolutional network (FCN) with a larger receptive field and perceptual loss, and employs adversarial learning to enhance stability in the absence of sufficient data labels. The Normalized Difference BOW Index, which can reflect the higher spectral reflectance of BOW in the near-infrared band, is used as the input of FANet together with RGB. In addition, we create a standard BOW dataset containing 5100 Gaofen-2 of 224 × 224 pixels. Evaluation of FANet on BOW dataset using intersection over union and F1-score demonstrates its superiority over popular models like FCN, U-net, and Segnet. FANet successfully preserves the integrity, continuity, and boundaries of BOW, achieving superior performance in both quantity and quality of BOW extraction. [ABSTRACT FROM AUTHOR]

In recent years, deep learning methods have been widely used for the classification of hyperspectral images. However, their limited availability under the condition of small samples remains a serious issue. Moreover, the current mainstream approaches based on convolutional neural networks do well in local feature extraction but are also restricted by its limited receptive field. Hence, these models are unable to capture long-distance dependencies both on spatial and spectral dimension. To address above issues, this paper proposes a global-local U-Net augmented by transformers (TrmGLU-Net). First, whole hyperspectral images are input to the model for end-to-end training to capture the contextual information. Then, a transformer-augmented U-Net is designed with alternating transformers and convolutional layers to perceive both global and local information. Finally, a superpixel-based label expansion method is proposed to expand the labels and improve the performance under the condition of small samples. Extensive experiments on four hyperspectral scenes demonstrate that TrmGLU-Net has better performance than other advanced patch-level and image-level methods with limited training samples. The relevant code will be opened at [ABSTRACT FROM AUTHOR]

Remote sensing image segmentation plays an important role in many industrial-grade image processing applications. However, the problem of uncertainty caused by intraclass heterogeneity and interclass blurring is prevalent in high-resolution remote sensing images. Moreover, the complexity of information in high-resolution remote sensing images leads to a large amount of background information around objects. To solve this problem, a new fuzzy convolutional neural network is proposed in this paper. This network resolves the ambiguity and uncertainty of feature information by introducing a fuzzy neighbourhood module in the deep learning network structure. In addition, it adds a multi-attention gating module to highlight small object features and separate them from the complex background information to achieve fine segmentation of high-resolution remote sensing images. Experimental results on three different segmentation datasets suggest that the proposed method has higher segmentation accuracy and better performance than other deep learning networks, especially for complicated shadow information. Code will be provided in (). [ABSTRACT FROM AUTHOR]

Reliable evaluation of damage in vehicles is a primary concern in the insurance industry. Consequently, solutions enhanced with Artificial Intelligence (AI) have become the norm. During the assessment, precise damage segmentation plays a crucial role. Dent is a type of damage that can commonly occur in vehicles. It is difficult to pinpoint and tends to blend in with the background. This paper proposes a novel loss function to improve dent segmentation accuracy in vehicle insurance claims. Centre and Pixel-based Weighted DICE (CPW-DICE) is a loss function that performs pixel-based weighting. The CPW-DICE aims to concentrate on the centre of the dent damage to lessen faulty segmentations. CPW-DICE generates a weight mask during training by employing ground truth (GT) and prediction masks. Simultaneously, the weight mask is incorporated into DICE loss. Experiments conducted on our comprehensive internal dataset show a 3% improvement in Intersection over Union (IoU) score for three state-of-the-art (SOTA) approaches compared to DICE loss. Finally, CPW-DICE is evaluated in similar tasks to demonstrate its benefits beyond car damage segmentation. [ABSTRACT FROM AUTHOR]

Most studies on privacy in machine learning have primarily focused on supervised learning, with little research on privacy concerns in reinforcement learning. However, our study has demonstrated that observation information can be extracted through trajectory analysis. In this paper, we propose a variable information inference attack targeting the observation space of policy models, which is categorised into two types: observed value inference attack and observed variable inference. Our algorithm has demonstrated a high success rate in privacy inference attacks for both types of observation information. [ABSTRACT FROM AUTHOR]

Industry 4.0 refers to a new generation of connected and intelligent factories that is driven by the emergence of new technologies such as artificial intelligence, Cloud computing, Big Data and industrial control systems (ICS) in order to automate all phases of industrial operations. The presence of connected systems in industrial environments poses a considerable security challenge, moreover with the huge amount of data generated daily, there are complex attacks that occur in seconds and target production lines and their integrity. But, until now, factories do not have all the necessary tools to protect themselves, they mainly use traditional protection. In order to improve industrial control systems in terms of efficiency and response time, the present paper propose a new distributed intrusion detection approach using artificial intelligence methods, Big Data techniques and deployed in a cloud environment. A variety of Machine Learning and Deep Learning algorithms, basically convolutional neural networks (CNN), have been tested to compare performance and choose the most suitable model for the classification. We test the performance of our model by using the industrial dataset SWat. [ABSTRACT FROM AUTHOR]

The 2019 new coronavirus illness (COVID-19) is an international public health emergency. Our social and healthcare systems are under a great deal of strain as a result of the daily increase in infection rates and fatalities. Doctors typically perform a chest Xray to identify the diseased areas of the lungs since pneumonia is a common type of infection that spreads in the lungs. In this paper, we propose a Convolution Neural Network via the li regularization model to detect COVID-19 patients using chest X-Ray images. Due to the lack of the COVID-19 benchmark dataset, we use deep learning techniques to identify the best pre-trained CNN model for this job by comparing 15 models. The suggested algorithm was tested on 1316 photos (116 COVID-19 cases, 328 healthy controls, and 872 pneumonia cases), with 66% for training, 17% for validation, and 17% for testing. The classification accuracy, loss, valueaccuracy, and value-loss values obtained by the suggested technique are 0.9912, 0.0187, 0.1119, and 0.9506 respectively. Additionally, the model effectively decreases training loss while boosting accuracy. The results show that proposed procedures are more effective than existing ones at identifying COVID-19 cases from chest X-ray pictures. [ABSTRACT FROM AUTHOR]

This paper proposes a new framework for leak diagnosis in pipelines using leak-augmented scalograms and deep learning. Acoustic emission (AE) scalogram images obtained from the continuous wavelet transform have been useful for pipeline health diagnosis, particularly when combined with deep learning. However, background noise has a significant impact on AE signals, which can reduce the accuracy of pipeline health identification using classification models. To address this issue, a new type of scalograms called leak-augmented scalogram is introduced, which enhances the variation in colour intensities of AE scalogram images. The leak-augmented scalograms are obtained by pre-processing them using image-enhancing Gaussian and Laplacian filters. The proposed method utilizes convolutional neural networks (CNNs) and convolutional autoencoders (CAEs) for feature extraction. The CNN extracts patterns specific to local changes, while the CAE extracts holistic patterns from the leak-augmented scalograms. The resulting leak susceptible and leak holistic indicators are merged into a single feature pool and provided as input to a shallow artificial neural network (ANN) to evaluate pipeline health conditions. The proposed method achieves high classification as well as accuracy, precision, F-1 Score and recall, compared to existing state of the art methods. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an essential part of the intelligent transport system. This is the accurate estimation of traffic flow in a given region at a particular interval of time in the future. The study of traffic forecasting is useful in mitigating congestion and make safer and cost-efficient travel. While traditional models use shallow networks, there has been an exponential growth in the number of vehicles in recent times and these traditional machine learning models fail to work in current scenarios. In our paper, we review some of the latest works in deep learning for traffic flow prediction. Many deep learning architectures include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long ShortTerm Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto Encoder (SAE). These deep learning models use multiple layers to extract higher level of features from raw input progressively. The latest deep learning models developed to tackle this very problem are reviewed and due to the complexity of transport networks, this review gives the reader information about how various factors influence these models and what models work best in different scenarios. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) has been widely used in emergency response, self-driving and city-scale 3D mapping and navigation. Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges (e.g. extreme lighting) while the traditional extractors are struggling. In this paper, we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework. In this thread, our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device. The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively. The scale estimation of the monocular visual SLAM is also significantly improved (0.01 versus 0.98). In addition, a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping. This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors. [ABSTRACT FROM AUTHOR]

Numerous news or event pictures are taken and shared on the internet every day that have abundant information worth being mined, but only a small fraction of them are geotagged. The visual content of the news image hints at clues of the geographical location because they are usually taken at the site of the incident, which provides a prerequisite for geo-localization. This paper proposes an automated pipeline based on deep learning for the geo-localization of news pictures in a large-scale urban environment using geotagged street view images as a reference dataset. The approach obtains location information by constructing an attention-based feature extraction network. Then, the image features are aggregated, and the candidate street view image results are retrieved by the selective matching kernel function. Finally, the coordinates of the news images are estimated by the kernel density prediction method. The pipeline is tested in the news pictures in Hong Kong. In the comparison experiments, the proposed pipeline shows stable performance and generalizability in the large-scale urban environment. In addition, the performance analysis of components in the pipeline shows the ability to recognize localization features of partial areas in pictures and the effectiveness of the proposed solution in news picture geo-localization. [ABSTRACT FROM AUTHOR]

Deep learning algorithms show good prospects for remote sensing flood monitoring. They mostly rely on huge amounts of labeled data. However, there is a lack of available labeled data in actual needs. In this paper, we propose a high-resolution multi-source remote sensing dataset for flood area extraction: GF-FloodNet. GF-FloodNet contains 13388 samples from Gaofen-3 (GF-3) and Gaofen-2 (GF-2) images. We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it. Compare with other flood-related datasets, GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels, but also consists of multi-source remote sensing data. We thoroughly validate and evaluate the dataset using several deep learning models, including quantitative analysis, qualitative analysis, and validation on large-scale remote sensing data in real scenes. Experimental results reveal that GF-FloodNet has significant advantages by multi-source data. It can support different deep learning models for training to extract flood areas. There should be a potential optimal boundary for model training in any deep learning dataset. The boundary seems close to 4824 samples in GF-FloodNet. We provide GF-FloodNet at and . [ABSTRACT FROM AUTHOR]

Application of deep learning technology in remote sensing image target detection(RSITD)has made remarkable progress in recent years. However, existing methods usually fuse spatial context information at the pixel level while ignoring the mining of spatial relationships between ground objects. This paper proposes a high-resolution remote sensing object detection network fusion of road-adjacent relationships (HRRSODN) that integrates road proximity relations. Taking artificial features such as toll stations adjacent to roads as an example, the spatial relationship of road proximity is used to assist visual information and improve the extraction accuracy. The method optimizes detection by: Using the spatial relationship as prior information; fusing coordinate attention that captures the spatial relationship between objects; and adding the attention generated by the spatial relationship to the attention generated in the original X and Y directions. This enables the model to understand the spatial layout of objects in the image. The distance loss function concretizes the spatial relationship into a distance index. The distance loss is incorporated into the training process by measuring the spatial distance between feature targets. Thus, during training, the network focuses not only on the positional information of the targets but also pays more attention to the spatial layout of the features. Experimental results show that, compared with conventional methods, the proposed method has achieved significant improvements in detection performance and mining spatial relationships. This verifies the importance of considering the spatial relationship of ground objects in remote sensing target detection. Further, this paper proposes a new method for target detection in remote sensing images (RSIs), which effectively utilizes the spatial relationship information between targets, and provides a new idea for further improving target detection in RSI. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding LSM's developmental drivers. Utilizing SciMAT, a scientometric tool, we analyzed 1661 papers on LSM from the Web of Science core collection database, spanning 1993 to 2022. We employed cluster and thematic evolution analysis in SciMAT to unveil trends. The results indicate a consistent upward trend over the past three decades. LSM modeling methods, geological data, and contributing factors are major focal points. Notably, the evolution of LSM models, with a rising adoption of machine learning and deep learning for risk assessment, emerges as a central knowledge pathway. This study offers valuable insights to scholars by identifying literature gaps and highlighting crucial research directions, facilitating informed decision-making in the realm of LSM. SciMAT tool initially employs cluster analysis and thematic evolution analysis to pinpoint the current popular themes and evolutionary trends of landslide susceptibility. The main trajectory of knowledge lies in the progression of landslide modeling methods. The latest trend in landslide susceptibility research is centered on the use of deep learning techniques, particularly convolutional neural networks, to achieve precise risk zonation. The study aims to highlight critical research directions, predict the future of LSM technology, and provide guidance for scholars. [ABSTRACT FROM AUTHOR]

Land Cover or Land Use (LCLU) classification is an important, challenging problem in remote sensing (RS) images. RS image classification is a recent technology used to extract hidden information from remotely sensed images in the observed earth environment. This classification is essential for sustainable development in agricultural decisions and urban planning using deep learning (DL) methods. DL gets more attention for accuracy and performance improvements in large datasets. This paper is aimed to apply one of the DL methods called transfer learning (TL). TL is the recent research problem in machine learning and DL approaches for image classification. DL consumes much time for training when starting from scratch. This problem could be overcome in the TL modeling technique, which uses pre-trained models to build deep TL models efficiently. We applied the TL model using bottleneck feature extraction from the pre-trained models: InceptionV3, Resnet50V2, and VGG19 to LCLU classification in the UC Merced dataset. With these experiments, the TL model has been built the outdate performance of 92.46, 94.38, and 99.64 in Resnet50V2, InceptionV3, and VGG19, respectively. [ABSTRACT FROM AUTHOR]

In recent years, contactless fraud crimes via telecommunication and Internet have grown rapidly. Meanwhile, the rate of solved criminal cases is much lower, which is mainly due to two reasons. Firstly, the definition of risk factors in the field of new Internet and telecommunication fraud crime is not comprehensive, resulting in the problem not being well defined. Secondly, Internet fraud crime information is mostly recorded using natural language with huge volume, and there is a lack of auto-mated and intelligent way to deeply analyze and extract the risk factor. To better analyze the Internet and telecommunication fraud crime to help solve more cases, in this paper, we propose a new Internet and telecommunication fraud crime risk factor extraction system. After studying the existing related research, we propose a novel risk factor extraction technology based on BERT. This novel technology can gracefully deal with multi- sources and heterogeneous data problems during the extraction of risk factors in multiple dimensions; meanwhile, it can significantly reduce the need for computation resources and improve the online serving performance. After experimentation, this technique can significantly reduce training time by 60%-70%, and meanwhile, it can reduce the computation resources by 80% and improve serving performance by 5 times during serving. In our approach, we propose a novel approach to set sample weight and loss weight based on data characteristics and data distribution during model training, which can significantly improve extraction precision. With adjusting the sample weight during model training, we can get 1.56% precision improved. Moreover, setting the loss weight during model training, the precision can be improved by 1.63% compared to base-line mode. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) is a type of machine learning used to model big data to extract complex relationship as it has the advantage of automatic feature extraction. This paper presents a review on DL showing all its network topologies along with their advantages, limitations, and applications. The most popular Deep Neural Network (DNN) is called a Convolutional Neural Network (CNN), the review found that the most important issue is designing better CNN topology, which needs to be addressed to improve CNN performance further. This paper addresses this problem by proposing a novel nature inspired hybrid algorithm that combines the Bees Algorithm (BA), which is known to mimic the behavior of honey bees, with Bayesian Optimization (BO) in order to increase the overall performance of CNN, which is referred to as BA-BO-CNN. Applying the hybrid algorithm on Cifar10DataDir benchmark image data yielded an increase in the validation accuracy from 80.72% to 82.22%, while applying it on digits datasets showed the same accuracy as the existing original CNN and BO-CNN, but with an improvement in the computational time by 3 min and 12 s reduction, and finally applying it on concrete cracks images produced almost similar results to existing algorithms. [ABSTRACT FROM AUTHOR]

From past few years, the Internet of things (IoT) is an emerging and encouraging technology that has gained prominence in the industries. Due to its increasing usages, a huge amount of data are exchanged within IoT architecture using the internet, which is why privacy and cyber-security are major issues. The heterogeneous nature of various technologies that are combined using IoT makes it problematic to provide security using prescriptive networking. The future of secure IoT depends on privacy issues. The research intends to improve security mechanisms based on intrusion and anomaly detection for IoT using deep learning. In this context, a systematic literature review (SLR) is conducted to identify 'How to perform data transformation analysis of IoT dataset to detect anomaly detection for cyber IoT attacks? The SLR result found 24 datasets used for IoT analysis, 35 performance metrics to evaluate IoT problems, 6--42 features identified for detection, 42 preprocessing techniques have been used for transforming data, and 26 different methods and models were used to process the given problem. The SLR highlights further enhancement for the issue and identification of cyber-security in IoT. Anomaly detection can be done based on reinforcement deep learning after a thorough analysis of SLR. [ABSTRACT FROM AUTHOR]

Different types of research have been done on video data using Artificial Intelligence (AI) deep learning techniques. Most of them are behavior analysis, scene understanding, scene labeling, human activity recognition (HAR), object localization, and event recognition. Among all these, HAR is one of the challenging tasks and thrust areas of video data processing research. HAR is applicable in different areas, such as video surveillance systems, human-computer interaction, human behavior characterization, and robotics. This paper aims to present a comparative review of vision-based human activity recognition with the main focus on deep learning techniques on various benchmark video datasets comprehensively. We propose a new taxonomy for categorizing the literature as CNN and RNN-based approaches. We further divide these approaches into four sub-categories and present various methodologies with their experimental datasets and efficiency. A short comparison is also made with the handcrafted feature-based approach and its fusion with deep learning to show the evolution of HAR methods. Finally, we discuss future research directions and some open challenges on human activity recognition. The objective of this survey is to give the current progress of vision-based deep learning HAR methods with the up-to-date study of literature. [ABSTRACT FROM AUTHOR]

Malaria fever is a potentially fatal disease caused by the Plasmodium parasite. Identifying Plasmodium parasites in blood smear images can help diagnose malaria fever rapidly and precisely. According to the World Health Organization (WHO), there were 241 million malaria cases and 627 000 deaths worldwide in 2020, while 95% of malaria cases and 96% of malaria deaths occurred in Africa. Also in Africa, children that are less than five years old accounted for an estimated 80% of all malaria deaths. To address the menace of malaria, this paper proposes a novel deep learning model, called a data augmentation convolutional neural network (DACNN), trained by reinforcement learning to tackle this problem. The performance of the proposed DACNN model is compared with CNN and directed acyclic graph convolutional neural network (DAGCNN) models. Results show that DACNN outperforms previous studies in processing and classification images. It achieved 94.79% classification accuracy in malaria blood sample images of balanced class dataset obtained from the Kaggle dataset. The proposed model can serve as an effective tool for the detection of malaria parasites in blood smear images. [ABSTRACT FROM AUTHOR]

In recent years, the wide use of deep learning based methods has greatly improved the classification performance of hyperspectral image (HSI). As an effective method to improve the performance of deep convolution networks, attention mechanism is also widely used for HSI classification tasks. However, the majority of the existing attention mechanisms for HSI classification are based on the convolution layer, and the classification accuracy still has margins for improvement. Motivated by the latest self attention mechanism in natural language processing, a deep transformer is proposed for HSI classification in this paper. Specifically, deep transformer along the spectral dimension and the spatial dimension are explored respectively. Then, a deep spatial-spectral transformer (DSS-TRM) is proposed to improve the classification performance of HSI. The contribution of this paper is to make full use of self attention mechanism, that is to use transformer layer instead of convolution layer. More importantly, a DSS-TRM is proposed to realize end-to-end HSI classification. Extensive experiments are conducted on three HSI data sets. The experimental results demonstrates that the proposed DSS-TRM could outperform the traditional convolutional neural networks and attention based methods. [ABSTRACT FROM AUTHOR]

Lane detection is an indispensable technology for environmental perception and is a function of autonomous vehicles. Although many researchers have applied deep learning to lane detection and achieved good results, their application scenarios were relatively simple. When a lane is blocked, lost, or met with other challenges, the accuracy of lane detection decreases tremendously. This paper proposes a novel semantic segmentation network for lane detection that includes IBN-Net, an attention module, and an encoder-decoder structure called IAED. IBN-Net improves the modelling and generalisation capability with little computational complexity. The attention module can better capture context information and improve the performance of the network. We evaluated the performance of the proposed method, which improved by 6.3% over ResNet34 on the TuSimple datasets. Tests on the CULane datasets showed that our method is at least 3% better than existing methods. Experimental results showed that the proposed method has strong robustness under complex conditions such as insufficient illumination and shadow occlusion. [ABSTRACT FROM AUTHOR]

Cross-project defect prediction (CPDP) technology can effectively ensure software quality, which plays an important role in software engineering. When encountering a newly developed project with insufficient training data, CPDP can be used to build defect predictors using other projects. However, CPDP does not take into account the prior knowledge of the target items and the class imbalance in the source item data. In this paper, we design an active learning selection algorithm for cross-project defect prediction to alleviate the above problems. First, we use clustering and active learning algorithms to filter and label some representative data from the target items and use these data as prior knowledge to guide the selection of source items. Then, the active learning algorithm is used to filter representative data from the source items. Finally, the balanced cross-item dataset is constructed using the active learning algorithm, and the defect prediction model is built. In this article, we selected 10 open-source projects by using common defect prediction models, active learning algorithms, and common evaluation metrics. The results show that the proposed algorithm can effectively filter the data, solve the class imbalance problem in cross-project data, and improve the defect prediction performance. [ABSTRACT FROM AUTHOR]