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Roundwood sorting is still a manual process in many Swiss sawmills, requiring employees to visually inspect and categorize thousands of logs per day. The heavy workload can be both physically and mentally taxing and can lead to increased rates of human error. State-of-the-art automation systems like X-ray log scanners are expensive and difficult to integrate into existing process lines. This paper proposes a novel recommendation system that leverages recent advances in image classification to automate roundwood classification by quality and species. The system integrates a camera to capture cross-sectional images of logs and record numerical data, such as length, taper, and diameter. The analysis of the resulting dataset highlights the challenges of data imbalance and noise, which makes classification difficult and, in some cases, impossible. However, by using selected datasets with reduced noise, state-of-the-art Convolutional Neural Networks (CNNs) can extract quality and species features. Quality models learn from a manually selected and simplified dataset, featuring samples that experts can clearly classify based on the image's information. Species models are trained on a label-noise-reduced dataset, reflecting real-world complexity. The accuracy on the selected dataset for three quality classes is 80%. The species determination is less challenging and reaches 91% accuracy on a synchronized dataset for the main species spruce and fir. Overall, this paper highlights the potential of Machine Learning in augmenting the roundwood sorting processes and presents a novel system that can improve the efficiency and accuracy of the process. [Display omitted] • Automation of roundwood sorting: Replaces manual sorting with image-based AI. • Integrated camera system in roundwood sorting to collect labeled dataset. • Species prediction: 91% accuracy in spruce–fir distinction. • Quality prediction on complexity reduced dataset: 80% accuracy between three main quality levels. • Efficient, adaptable & scalable system which is easy to integrate into existing process lines. [ABSTRACT FROM AUTHOR]

Intrusion Detection System (IDS) can ensure the network security by identifying network intrusions according to the abnormal traffic data. However, the intrusion detection data has the problem of high dimensionality and changes with network and attack environments, which leads to the poor performance and poor portability of intrusion detection algorithms. Therefore, this paper proposes an intrusion detection algorithm based on joint symmetric uncertainty and hyperparameter optimized fusion neural network. Firstly, a feature selection method based on symmetric uncertainty and approximate Markov blanket is proposed, which fully considers the correlation and redundancy of features, and also the correlation between combined features and the class label, so as to reduce the data dimensionality. Secondly, the CNN-LSTM classifier fused with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is used to extract the spatial features and temporal features to improve the classification performance. Finally, the Particle Swarm Optimization (PSO) algorithm is improved and used to automatically optimize the hyperparameters of the classifier, so that the classifier can be applied to different intrusion detection datasets with better generalization ability and portability. Experiments have verified the effectiveness and superiority of the proposed algorithm on multiple evaluation indicators. • An effective algorithm for intrusion detection is proposed in this paper. • Feature selection is based on symmetric uncertainty and approximate Markov blanket. • A fusion neural network is constructed to extract the spatial and temporal features. • The PSO algorithm is improved to automatically optimize the hyperparameters. [ABSTRACT FROM AUTHOR]

The detection of underwater fish targets is critical for ecological monitoring and marine biodiversity research. However, underwater fish detection is typically constrained by problems including low image quality and variable underwater surroundings. On behalf of further improving the underwater fish detection accuracy in complex underwater environments, this paper proposes a dual-path (DP) Pyramid Vision Transformer (PVT) feature extraction network named DP-FishNet. The backbone network DP-PVT composed from the PVT Network is made up of two feature extraction paths. The first represents the Vision Transformer path, which extracts global features to enhance the distinction between the foreground and background of underwater images. The second is the convolutional neural network path, which enhances the accuracy of detecting small targets by extracting local features. Additionally, to more effectively utilize the feature information extracted by the network, this paper provides a promising solution to employ the content-aware reassembly of features (Carafe) in the feature pyramid network (FPN). The seesaw loss is utilized as a classification loss to address the problem of unbalanced samples caused by the gap in the number of fish populations. According to the experimental findings, the AP and AP50 of the DP-FishNet are 76.0% and 95.2%, respectively. In comparison to currently available advanced two-stage detection algorithms, the quantity of computation and parameters is reduced by approximately 40%. DP-FishNet strengthens the ability to extract global and local features from underwater images and enhances feature reuse. DP-FishNet can be utilized to detect fish targets in actual and complicated underwater habitats. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new deformable convolutional network with attention mechanism to deal with the Chinese character of various dynasties. These ancient Chinese characters are hieroglyph with special spatial structure and gradually evolving into modern Chinese characters. However, because of the intrinsic limitations of traditional convolutional networks in the model, the geometric structure of the convolutional module is fixed. Therefore, we build a new architecture called attention-based deformable networks for this task. Besides, we construct a new dataset called the Chinese characters from Various Dynasties Dataset (CCDD), which includes the evolution of Chinese characters between major dynasties. Deformable convolution based on attention mechanism can obtain those more important offsets, resulting in better performance than the original deformable convolution. In the experimental stage, extensive experiments validate the performance of our approach. Firstly, the effectiveness of this network was verified on two text datasets, Mnist(99.38%) and notMnist(98.22%) both comparing with several modules. Finally, the best performance was achieved on the CCDD dataset(91.50%) compared to other classical deep learning networks. This is the first paper to apply deep learning methods to recognize and classify the characters of major dynasties in China. And archaeological workers need to be able to recognize Chinese characters from various periods in China, and our algorithm can assist them in completing this task to a certain extent. It can also help cultural relic enthusiasts roughly determine the age of cultural relics. [ABSTRACT FROM AUTHOR]

Marine ecosystems and their fish habitats are becoming increasingly important due to their integral role in providing a valuable food source and conservation outcomes. Due to their remote and difficult to access nature, marine environments and fish habitats are often monitored using underwater cameras to record videos and images for understanding fish life and ecology, as well as for preserve the environment. There are currently many permanent underwater camera systems deployed at different places around the globe. In addition, there exists numerous studies that use temporary cameras to survey fish habitats. These cameras generate a massive volume of digital data, which cannot be efficiently analysed by current manual processing methods, which involve a human observer. Deep Learning (DL) is a cutting-edge Artificial Intelligence (AI) technology that has demonstrated unprecedented performance in analysing visual data. Despite its application to a myriad of domains, its use in underwater fish habitat monitoring remains under explored. In this paper, we provide a tutorial that covers the key concepts of DL, which help the reader grasp a high-level understanding of how DL works. The tutorial also explains a step-by-step procedure on how DL algorithms should be developed for challenging applications such as underwater fish monitoring. In addition, we provide a comprehensive survey of key deep learning techniques for fish habitat monitoring including classification, counting, localisation, and segmentation. Furthermore, we survey publicly available underwater fish datasets, and compare various DL techniques in the underwater fish monitoring domains. We also discuss some challenges and opportunities in the emerging field of deep learning for fish habitat processing. This paper is written to serve as a tutorial for marine scientists who would like to grasp a high-level understanding of DL, develop it for their applications by following our step-by-step tutorial, and see how it is evolving to facilitate their research efforts. At the same time, it is suitable for computer scientists who would like to survey state-of-the-art DL-based methodologies for fish habitat monitoring. • Marine environments and fish habitats are widely surveyed using underwater cameras. • Deep learning (DL) offers an effective solution for processing underwater videos. • We write a tutorial on the key concepts and steps of DL for underwater monitoring. • We provide a survey of key DL-based visual fish monitoring methods/datasets. • We shed light on challenges/opportunities of DL for underwater fish monitoring. [ABSTRACT FROM AUTHOR]

Edge detection is of critical importance for middle-level and high-level tasks in computer vision. Existing edge detection methods usually use VGG16 as the encoding network and achieve exceptional performance through transfer learning, which has the characteristics of high parameters and high computational cost. Researchers have implemented edge detection by designing decoding networks. Unlike existing methods, this paper is inspired by the effective mechanisms of edge detection in the parallel pathway of biological vision and proposes a new lightweight encoding–decoding structure for edge detection networks, which we refer to as XYW-Net. In the encoding network, we drew inspiration from the receptive field properties of X-type cells, Y-type cells, and W-type cells involved in the parallel pathway, and developed more meticulous convolutional models. Based on the structure of the parallel pathway, the three cell modules are combined into a novel encoding network and excellent feature extraction performance is obtained. Within the decoding network, this paper draws inspiration from the feature integration ability of the inferotemporal cortex (IT) and introduces a novel feature integration module to constitute the decoding network. Experiments show that the proposed network in this paper obtains the Optimal Dataset Scale (ODS) = 0.812 on the BSDS500 dataset with only 0.79 M parameters. Additionally, the model has exhibited outstanding ODS performance on various publicly available edge detection datasets, including NYUD, BIPEDv1, and Multicue. Moreover, future research could concentrate on implementing more efficient antagonistic mechanisms and devising networks grounded in higher-level visual cortical mechanisms. This could further augment the performance and efficiency of edge detection networks. We encourage researchers to delve into these directions within the field. The codes are available at https://github.com/PXinTao/XYW-Net. [ABSTRACT FROM AUTHOR]

• Pinch force strength is accurately predicted using sEMG. • Different pinch movements are accurately classified using sEMG. • Similarity of EMG features is evaluated. • An EMG feature-to-image method is proposed. The movement of intelligent EMG-driven prosthesis mainly relies on the synergy of different fingers to achieve function of grasping objects. The paper proposes a novel scheme for force prediction and movement classification about pinch between different fingers based on surface electromyography (sEMG) using machine learning. The pinch force and sEMG signals are recorded synchronously by a data acquisition device. Eight features are extracted, which are proven to have better performance in the estimation of sEMG-to-force. We present a novel feature selection method that uses the one-dimensional time series similarity assessment based on Manhattan distance to eliminate the repetitive information between features. Three optimal features carrying less repetitive information are retained. Seven machine learning algorithms are used to predict force strength. The results show that the Long Short Term Memory (LSTM) has the best performance of force prediction, achieving a R 2 of 0.9517 and RMSE of 3.2723. The paper proposes a novel method of converting EMG feature sequences to the normalized gray image in order to classify the finger movement. Five classifiers based on image feature extraction and the Convolutional Neural Network (CNN) are developed respectively. The experimental results indicate that the CNN performs best, achieving an accuracy of 97.66%. In this way, it not only realizes the accurate force prediction, but also realizes the movement classification between different fingers. The proposed methodology has the potential to realize simultaneous force and movement control of prosthetic hand. [ABSTRACT FROM AUTHOR]

Fiber-optic hydrophones based on optical fiber sensing and communication technology have developed rapidly in recent years. With the advantages of high sensitivity, small size, and large dynamic range, they are easy to detect underwater acoustic signals, showing great potential in the field of marine ecological protection. Researchers can monitor the marine ecological environment by recognizing underwater optical fiber sensing signals collected by fiber-optic hydrophones in real-time. Deep learning models can achieve high recognition accuracy, but they usually have large amounts of parameters that makes them difficult to be deployed in mobile terminals of marine survey ships. To solve the key problem, this paper discussed the design of the Deep Light-Weight Attention Residual Network (DLA-ResNet). It introduces the depth-wise separable convolution (DSC) and the convolutional block attention module (CBAM) into ResNet18 to make its performance better. ResNet18 is a two-dimensional convolutional network (CNN) model applying residual blocks. Experiments verify that the accuracy of DLA-ResNet improved from ResNet18 is increased by about 2.04% and its size is reduced by 85.67%. The proposed scheme in this paper realizes an average recognition rate of 96.50% for eight types of underwater optical fiber sensing signals in the application of marine ecological environment monitoring. Moreover, its model size is only 6.12 MB and it only takes 27.90 ms, which is available to meet the needs of reliability and effectiveness. [ABSTRACT FROM AUTHOR]

The 2019 coronavirus disease outbreak, caused by the severe acute respiratory syndrome type-2 virus (SARS-CoV-2), was declared a pandemic in March 2020. Since its emergence to the present day, this disease has brought multiple countries to the brink of health care collapse during several waves of the disease. One of the most common tests performed on patients is chest x-ray imaging. These images show the severity of the patient's illness and whether it is indeed covid or another type of pneumonia. Automated assessment of this type of imaging could alleviate the time required for physicians to treat and diagnose each patient. To this end, in this paper we propose the use of Convolutional Neural Networks (CNNs) to carry out this process. The aim of this paper is twofold. Firstly, we present a pipeline adapted to this problem, covering all steps from the preprocessing of the datasets to the generation of classification models based on CNNs. Secondly, we have focused our study on the modification of the information fusion processes of this type of architectures, in the pooling layers. We propose a number of aggregation theory functions that are suitable to replace classical processes and have shown their benefits in past applications, and study their performance in the context of the x-ray classification problem. We find that replacing the feature reduction processes of CNNs leads to drastically different behaviours of the final model, which can be beneficial when prioritizing certain metrics such as precision or recall. • Convolutional Neural Networks are effective for detecting SARS-CoV-2 on x-ray images. • Intermediate extracted features can be fused through different aggregation functions. • Grouping functions are harder to train but can offer better classification accuracy. • An early restart policy eases training models which make use of grouping functions. [ABSTRACT FROM AUTHOR]

Hard exudate plays an important role in grading diabetic retinopathy (DR) as a critical indicator. Therefore, the accurate segmentation of hard exudates is of clinical importance. However, the percentage of hard exudates in the whole fundus image is relatively small, and their shapes are often irregular and the contrasts are usually not high enough. Hence, they are prone to misclassifications e.g., misclassified as part of the optic disc structure or cotton wool spots, which results in the low segmentation accuracy and efficiency. This paper proposes a novel neural network RMCA U-net to accurately segmentation hard exudate in fundus images. The network features a U-shape framework combined with a residual structure to obtain the subtle features of hard exudate. A multi-scale feature fusion (MSFF) module and an improved channel attention (CA) module are designed and involved to effectively segmentation sparse small lesions. The proposed method in this paper has been trained and evaluated on three data sets: IDRID, Kaggle and one local data set. Experiments are shown and indicate that RMCA U-net of this paper is superior to the other convolutional neural networks. The method in this paper is increased by 6% higher in PR-MAP than U-net on the IDRID dataset, increased by 10% in Recall than U-net on the Kaggle dataset and increased by 20% in F1-score than U-net on the local dataset. • An architecture with U-shape and residual module is proposed to segment exudates. • Multi-scale module is involved to learn the subtle feature from different layers. • Ultra-widefield fundus image dataset is introduced to validate the generalization. [ABSTRACT FROM AUTHOR]

Automatic fault diagnosis in many systems is performed via the analysis of vibration data in the time or frequency domain. In the literature, many approaches for extracting featured from signals for fault diagnosis have been proposed. In this paper, we apply a variety of transform functions including Fourier, Wavelet, etc. to extract features from the vibration data, which includes the statistical features and some features automatically extracted via Convolutional Neural Networks (CNNs). For each of these feature extraction approaches, a learning algorithm is trained to diagnose the faults and their results are aggregated in an ensemble machine learning algorithm. The weights of the base learner algorithms are optimized via an evolutionary algorithm to achieve the best-weighted voting scheme. The architecture of CNNs has a significant effect on the performance of the algorithm, thus, in this paper, an evolutionary algorithm is proposed to find the best architecture for CNNs in fault diagnosis. The CNNs are trained via gradient descent algorithms which suffer from getting stuck in local optima. To manage this, we propose an evolutionary algorithm that benefits from the speed of gradient descent and the global search of evolutionary algorithms. The proposed algorithm is tested on a number of benchmark problems and the experimental results are presented. • An ensemble algorithm is proposed for fault diagnosis. • Different feature extractors are exploited. • An evolutionary training algorithm is proposed. [ABSTRACT FROM AUTHOR]

In pharmaceutical warehousing operations, a scientific and reasonable medicine and shelf matching strategy can improve medicine shelving efficiency and manual work efficiency. However, the traditional matching strategy has the problems of static matching and low matching efficiency, so this paper proposes the matching algorithm for drugs and shelves based on the fuzzy analytic hierarchy process and Bayesian convolutional neural network (FAHP-BCNN). First, we propose a drug-shelf matching degree model and discuss the influence of the dynamic matching process on the matching results by studying two parts: the attribute matching degree and the dynamic matching influence degree. Secondly, this paper quantitatively assessed the importance of the two matches by means of the fuzzy analytic hierarchy process. Finally, we mapped the massive matching information onto Bayesian convolutional neural network nodes, constructed a dynamic matching network model, and obtained competitive matching strategies and results. The experimental results show that the FAHP-BCNN algorithm exhibits better matching results in all three scenarios with different quantity ratios of drugs and shelf matching. Compared with the fixed cargo matching strategy of the ABC classification method, the FAHP-BCNN algorithm shows excellent performance in both the manual walking distance index and fatigue index. In summary, the drug-shelf matching algorithm based on FAHP-BCNN is effective and can provide theoretical support for pharmaceutical warehousing enterprises to perform drug-shelf matching. [ABSTRACT FROM AUTHOR]

Fake news refuting is an essential part of online social networks (OSNs) management. Identifying the potential voluntary fake news refuters who can spontaneously repost the fake news refutation messages on the OSNs, and intentionally exposing such messages to them can contribute a lot to fake news regulation. Therefore, to fully utilize available user features and enhance fake news refuter identification, the paper proposes a novel paradigm for refuter classification problem, which leverages the pre-trained techniques and mixture of expert system to unify the structured and unstructured data. Specifically, the unstructured text records are first understood by the pre-trained ERNIE2 model, and then feed into Chi-Merge grounded Convolutional Neural Networks, which are expert classifiers in our framework, along with the structured profile data for further transformation. Besides, features extracted from text data are used for determining weights for multiple expert classifiers. Experiments on near 60 k real-world Weibo user data prove that our framework outperforms the large-scale pre-trained deep learning methods and traditional machine learning classification models. This paper provides a novel perspective of fake news refutation from the refuter's side and sheds light on artificial intelligence-driven OSNs management. [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL). The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and non-motor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools. Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method. The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models. The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment. • Deep learning and wearable technology are used in diagnosis and monitoring. • Growing interest in the use of deep learning and wearables in Parkinson's disease. • The adoption of deep learning in Parkinson's disease is currently under development. • Technical and methodological challenges difficult the technology adoption. [ABSTRACT FROM AUTHOR]

Recent years have witnessed a growing interest in neural network-based medical image classification methods, which have demonstrated remarkable performance in this field. Typically, convolutional neural network (CNN) architectures have been commonly employed to extract local features. However, the transformer, a newly emerged architecture, has gained popularity due to its ability to explore the relevance of remote elements in an image through a self-attention mechanism. Despite this, it is crucial to establish not only local connectivity but also remote relationships between lesion features and capture the overall image structure to improve image classification accuracy. Therefore, to tackle the aforementioned issues, this paper proposes a network based on multilayer perceptrons (MLPs) that can learn the local features of medical images on the one hand and capture the overall feature information in both spatial and channel dimensions on the other hand, thus utilizing image features effectively. This paper has been extensively validated on COVID19-CT dataset and ISIC 2018 dataset, and the results show that the method in this paper is more competitive and has higher performance in medical image classification compared with existing methods. This shows that the use of MLP to capture image features and establish connections between lesions is expected to provide novel ideas for medical image classification tasks in the future. • SCNet learns the comprehensive and important information. • S-MLP divides feature maps into regions for MLP to enhance overall features. • C-MLP groups channels to reduce overfitting and improve generalization. [ABSTRACT FROM AUTHOR]

This paper proposes a robust algorithm using two convolutional neural networks (CNN) to find a lost gamma source in a room despite various unpredictable obstacles. This algorithm is an improved and complementary method to our previous research, which does not limit the use of the algorithm to a specific geometry, and the presence of obstacles does not reduce the performance and inability of the method. In our previous research, an algorithm including a CNN was used to estimate the location of the lost gamma source in known geometry. In this paper, by adding another CNN to the previous algorithm, the algorithm can be used in environments with unknown geometry. The environmental simulations and data generation are performed using the Geant4 simulation program. The method has been tested in a designed scenario to check its effectiveness. The results show that by using these two CNNs, an agent can intelligently find the gamma source place in a room with multiple obstacles without human intervention with 95 percent accuracy. [ABSTRACT FROM AUTHOR]

Feature extraction networks play a crucial role in classification algorithms. However, most convolutional neural networks use the form of residual block stacking to extract downstream features of images, and this simple form is not sufficient to obtain better classification performance. Therefore, this paper proposes a YOLOX detection framework based on an improved ConvNeXt which incorporates many advantages of Swin-Transformer with efficient classification performance. To address the problems of unstable IOU convergence and inaccurate regression coordinates of the algorithm, this paper adopts Manhattan distance as the new penalty term, and the new loss function combines the advantages of Euclidean distance and Manhattan distance, which has no gradient explosion in the early training period and can drop smoothly to a stable point in the late training period. The proposed algorithm is robust to negative effects such as dust, light, shadows, and occlusions, and is well-suited for masked-face detection in industrial and public scenarios. The proposed model is tested on the VOC dataset and the AIZOO open-source masked-face dataset, obtaining 5.58% AP improvement on the VOC dataset and reaching 96.665% AP on the masked-face dataset, which is better than other algorithms. • Designing a deep neural network to use for masked-face detection. • Anchor-free model is used to avoid time-consuming post-processing. • The improved ConvNeXt network improves classification performance. • Adopting Manhattan distance-based localization loss makes training stable. • The Mosaic data augmentation strategy alleviates the overfitting problem. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the vital steps in medical image analysis. The segmentation precision and delicacy of targets affect the diagnosis accuracy to a great extent. Deep learning-based technology is currently a hot research direction in the field of medical image analysis. Compared with manual labeling, deep convolutional neural networks (DCNN) can extract valuable deep features that are difficult for humans to identify. However, some existing DCNN methods tend to only focus on extracting deep features and ignore the semantics loss in the process of continuous pooling and convolution. In addition, medical image datasets are characterized by small-scale, mass noise, and imperceptible differences between foreground and background. How to construct sufficient contextual dependency and feature compensation from multiple perspectives to improve the segmentation performance is still faced with great challenges. In this paper, we propose a deep attention network with multi-perspective feature compensation, called Multi-Perspective Feature Compensation Network (MPFC-Net), to supplement semantics loss during continuous pooling and convolution as much as possible and establish strong contextual dependency. Merits of MPFC-Net are as follows: (1) Serializing Multi-scale Aggregation Blocks (SMAB) are constructed to extract features with different scale receptive fields, capture semantics loss in convolutions, and enhance the nonlinear fitting capability of the network. (2) S pace and C hannel Compound M ultidimensional I nteraction Attention (SCMI Attention) is designed to consider the weights of feature maps of space and channel and build long-term information interactions between different dimensions. (3) Multi-Perspective Feature Compensation (MPFC) is proposed to establish dense context interaction and compensate for semantics loss in continuous pooling and convolution. (4) Deep Feature Reuse (DFR) module is adopted to strengthen the propagation and advanced features reuse. We conduct extensive experiments on the 2018 Data Science Bowl (2018DSB), the ISIC-2018 Challenge (ISIC2018), the JSUAH-Cerebellum, and the DRIVE dataset, and the results show that MPFC-Net achieves competitive performance with simultaneous increases in various metrics. [ABSTRACT FROM AUTHOR]

The recommendation algorithm is an important means to alleviate the information explosion in the era of big data. There has been a great deal of research into the use of knowledge graphs as auxiliary information in recommender systems, which can be used to alleviate data sparsity and cold start problems. However, most knowledge graph-based recommendation methods only use rating data to capture the user's potential interest, and the rating is only a comprehensive evaluation of the item by the user, which cannot intuitively and accurately express the user's personalized preference. In addition, existing recommendation strategies that blend ratings and reviews cannot simultaneously model the aspect fine-grained sentiment preferences of users in reviews as well as the personalized characteristics of items from the user's perspective. To this end, in this paper, we propose Reviews Sentiment-Aware Knowledge Graph Convolutional Neural Network (RAKCR), a generic review and knowledge graph-based framework that provides better recommendations by fully mining the fine-grained personalization features in user reviews. In contrast to existing correlation recommendation methods, we designed a new reviews sentiment perception feature and knowledge graph alignment module to characterize user preferences for specific features of items in the knowledge graph. To better represent the personalized feature distribution of users and items, we use the proposed RAKCR to aggregate sentiment relationship weight-aware neighborhood information in the knowledge graph to capture personalized feature representations of both users and items, and to better learn user and item embeddings for more accurate personalized recommendations. Experimental results demonstrate that the proposed RAKCR model outperforms the benchmark model significantly in click-through rate prediction for recommendation scenarios. Across the three datasets, Movielens-20 m, Amazon-book, and Yelp, the AUC values show an average improvement of 6.4%, 6.0%, and 3.4%, respectively. Additionally, the F1 values exhibit an average improvement of 7.2%, 6.2%, and 4.1%, respectively, when compared to existing state-of-the-art methods. • Sentiment analysis of reviews to extract aspect features and sentiment polarity. • Aligning sentiment features with knowledge graphs to build sentiment dictionaries. • Knowledge graph convolutional networks incorporating sentiment weights. • Aggregate user fine-grained and item personalized feature representations. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach that merges a lightweight parallel depth-wise separable convolutional neural network (LPDCNN) with a ridge regression extreme learning machine (Ridge-ELM) for precise classification of three lung cancer types alongside normal lung tissue (adenocarcinoma, large cell carcinoma, normal, and squamous cell carcinoma) using CT images. The proposed methodology combines contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur to enhance image quality, reduce noise, and improve visual clarity. The LPDCNN extracts discriminant features while minimizing computational complexity (0.53 million parameters and 9 layers). The Ridge-ELM model was developed to enhance classification performance, replacing the traditional pseudoinverse in the ELM approach. Through comprehensive evaluation against state-of-the-art models, the framework achieves remarkable average recall and accuracy values of 98.25 ± 1.031 % and 98.40 ± 0.822 %, respectively, through rigorous five-fold cross-validation for four-class classifications. In binary classifications, outstanding results are obtained with recall and accuracy values of 99.70 ± 0.671 % and 99.70 ± 0.447 %%, respectively. Notably, the framework exhibits exceptional efficiency, with a testing time of only 0.003 s. Additionally, integrating the SHAP (Shapley Additive Explanations) in the proposed framework enhances Explain-ability, providing insights into decision-making and boosting confidence in real-world lung cancer diagnoses. [ABSTRACT FROM AUTHOR]

Large indoor cluttered environment representation is still a challenging task when non-uniform triangle cell-based or quadrangle cell-based decomposition is used to build the map. This paper aims at proposing a new method to represent a large indoor environment for efficient and global robot path planning using a trade-off among three criteria: path length, distance to obstacles, and path search complexity. In this regard, three steps are involved: (i) the design of a tiled map that represents several regular sub-regions of the given large environment; (ii) the selection of the best representation between non-uniform triangle cell-based submap and regular quadrangle cell-based submap for each tile using the predefined efficient path planning criteria. Hence, the entire large environment is represented by a hybrid cell-based map; (iii) the path search using the well-known A* algorithm. Moreover, to find the best representation, firstly, a generative method based on cellular automata is used to build a large synthetic database of maps of the same size as the tiles. Each map of the database is associated with the representation which provides the most efficient path planning. Given a tile, the corresponding image is used to find the closed tile image from the database, and the associated representation is selected as the best representation. Extensive simulations, as well as experiments, suggest that for a given large, cluttered environment, the hybrid representation with a mix of triangle-based cells and quadrangles-based cells can provide a more efficient global path compared to the traditional regular quadrangle or triangle representations. [ABSTRACT FROM AUTHOR]

Telco players are constantly evaluating their loss in customer revenue or customer churn, especially on the prevention, control, and risk assessment of churn. In this paper, a domain-order based shallow fusion deep learning model, the Fully Connected Layer Convolutional Neural Network - Long Short-Term Memory (FCLCNN-LSTM) is designed and applied to a decision support system to assess the risk of customer churn. First, a majority least absolute shrinkage and selection operator (Maj-LASSO) algorithm is proposed to identify churn predictors that have an important impact on classification. The proposed Maj-LASSO algorithm is different from previous studies that solved the study of unbalanced data and feature selection separately, but solved both as one problem to give the feature selection method under unbalanced data. Then, a fully connected layer based on multiple ReLU neurons is used to balance the importance of the features. Doing so addresses the problem where some key features are overlooked due to their unequal contribution to the risk assessment of telco customer churn. Third, a 2D convolutional neural network based on domain information and an LSTM model based on ordinal information are combined to learn the complex mapping between the features and labels, to improve the accuracy and generalizability of the predictive model. The proposed FCLCNN-LSTM model is different from the previous single spatial model or temporal model, but improves the network classification feature extraction capability from both temporal and spatial dimensions, thus innovatively solving the problem of model development for large-scale and high-dimensional data, and applying it in the field of telecommunication customer churn prediction. Finally, using three public customer churn datasets from the Kaggle and UCI platform, the FCLCNN-LSTM model is compared against other classification models, namely, Logistic Regression, Support Vector Machine, Random Forest, eXtreme Gradient Boosting (XGBoost), Convolutional Neural Network, and Long Short-Term Memory. The experimental results inform that the accuracy of the FCLCNN-LSTM is 3.43% higher than that of the other comparison models, while the AUC is 4.84% higher. Thus, the FCLCNN-LSTM model provides a better decision-making reference for telco players to identify potential churners. [ABSTRACT FROM AUTHOR]

• To develop a hybrid deep learning model for student behavior analysis. • To develop an Opposition-based Food Foraging-Pelican Optimizer (O-FFPO) algorithm. • To carry out optimal feature selection by utilizing developed O-FFPO algorithm. • To develop an O-FFPO to enhance the classification performance of developed model. • To adopt a Radian Basis-Recurrent Neural Networks (RB-RNN) deep learning model. With the development of the internet, e-learning is rapidly growing in worldwide, which pave more attention to the students. Further, the learning materials like video lectures, shared assignments as well as lecture slides are performed during the online courses. Moreover, these activities among the students exhibit different behaviors. Hence, the analysis of student behavior becomes complicated in the existing models. This research focuses on developing a deep learning model for analyzing student behaviour in an accurate manner. This paper aims to develop a new student behavior prediction model for E-learning by evaluating brain signals and chatbot interaction. This suggested model collects data from both chatbots and the Brain waves of students. The collected textual data from the chatbot are forwarded to the feature extraction phase by word2vector and bag-of-n grams. Then, the optimal features are selected using an Opposition-based Food Foraging-Pelican Optimizer (O-FFPO). Similarly, the second set of Brain waves of students are forwarded to the phase feature extraction stage by Short-time Fourier transforms (STFT), and then, deep features are extracted using Convolutional Neural Network (CNN). These gathered data can also be forwarded to the optimal feature selection process using the same O-FFPO. Finally, these fused features are fed to Radian Basis-Recurrent Neural Networks (RB-RNN) to get student behaviors, where the constraints are optimized by the same O-FFPO. Various performance metrics are compared and validated with the existing approaches. Moreover, the developed model shows 95% and 93% concerning accuracy and F1-score. Finally, experimental evaluation was carried out with the estimation of both brain waves and chatbot queries of students. [ABSTRACT FROM AUTHOR]

Currently, deep learning-based video inpainting algorithms can fill in a specified video region with visually plausible content, usually leaving imperceptible traces. Since deep video inpainting methods can be used to maliciously manipulate video content, there is an urgent need for an effective method to detect and localize deep video inpainting. In this paper, we propose a dual-stream video inpainting detection network, which includes a ConvNeXt dual-stream encoder and a multi-scale feature cross-fusion decoder. To further explore the spatial and temporal traces left by deep inpainting, we extract motion residuals and enhance them using 3D convolution and SRM filtering. Furthermore, we extract filtered residuals using LoG and Laplacian filtering. These residuals are then entered into ConvNeXt, thereby learning discriminative inpainting features. To enhance detection accuracy, we design a top-down pyramid decoder that aims at deep fusion of multi-dimensional multi-scale features to fully exploit the information of different dimensions and levels in detail. We created two datasets containing state-of-the-art video inpainting algorithms and conducted various experiments to evaluate our approach. The experimental results demonstrate that our approach outperforms existing methods and attains a competitive performance despite encountering unseen inpainting algorithms. • End-to-end video inpainting detection and localization method. • Two effective residual extraction module to enhance the inpainting artifacts. • A top-down structure for fusing multi-dimensional and multi-scale features. [ABSTRACT FROM AUTHOR]

Vehicle-based crack detection can serve as a highly effective method for assessing pavement damage. This paper proposes an automated crack detection, measurement and mapping method based on GPS tagged images acquired from a camera installed on the license plate of a vehicle. An attention-based mechanism is added to the multiscale feature fusion RetinaNet convolution neural network to detect cracks. This enables the neural network to focus on key features at different scales enhancing the model's detection capabilities. Adding the attention block effectively increases the model's performance when compared with the original network. This is followed by a lightweight categorization algorithm, a high-precision edge detection algorithm, a crack width measurement algorithm, and a mapping algorithm. The results show that the crack detection model is a well-represented model that exhibits high performance on crack-induced pavements. The process of estimating crack width is validated by achieving a low mean average relative error. The crack width is further used to assign severity to the crack. The crack detection algorithm and the crack width estimation algorithm are tested using real-world data in order to assess their effectiveness. The results of the study suggest that by integrating crack detection and width estimation, a comprehensive engineering solution for crack monitoring can be achieved. This solution can be effectively applied to conduct pavement surveys, resulting in the creation of a GPS-based map that identifies various distresses. This framework provides a holistic approach to infrastructure maintenance, allowing improved transportation infrastructure management. [ABSTRACT FROM AUTHOR]

Fluid accumulation in gas wells lead to a sudden drop in production and is a problem. This paper proposes a prediction system for water-producing gas wells using edge intelligence. The method is characterized by use of edge intelligence to realize automatic collection of gas well data, effusion analysis, data backup, upload, production dynamic monitoring, and other functions. The Pearson correlation coefficient was employed to analyze the relevant parameters of liquid accumulation in gas wells. A collaborative discrimination approach utilizing convolutional neural networks and ensemble learning models was implemented to predict liquid accumulation state of gas wells. The model achieved an accuracy rate as high as 98.2%, which aligns well with actual liquid accumulation state of gas wells compared to traditional physical models. In addition, suggestions for extraction and drainage plans are provided based on the degree of liquid accumulation in gas wells, thereby realizing automated and refined management of liquid accumulation wells in the field. This method provides effective theoretical support and reasonable guidance for the efficient development and overall decision-making in gas fields. [ABSTRACT FROM AUTHOR]

Ion adsorption rare earth mining activities have caused serious ecological problems. It is critical to monitor mining activities through precise object detection. However, in the special scenes of mining sites captured by high-resolution remote sensing images, the large amount of complex background information intensifies the semantic confusion among geographic entities. Therefore, the accurate detection of ground objects in mining areas remains a challenging task. This paper proposes an innovative multispectral class-constraint attentional neural network, referred to as MCCANet, which can use multispectral channel information to derive intra-class and inter-class implicit semantic relations. It forms attention with class information constraints for imposing stronger class supervision in order to suppress the background information and highlight the feature differences of the foreground object in complex scenes. Furthermore, a dataset dedicated to rare earth mining areas is constructed to validate the efficiency of the proposed method. The obtained results show that MCCANet has high accuracy in targeting potential objects within mining scenes while significantly mitigating the high false alarm rates resulting from complex backgrounds. Notably, when compared to current popular deep learning models, MCCANet outperforms others by achieving the highest level of detection accuracy. [ABSTRACT FROM AUTHOR]

The recent technological advances and their applications to agriculture provide leverage for the new paradigm of smart agriculture. Remote sensing applications can help optimise resources, making agriculture more ecological, increasing productivity and helping farmers to anticipate events that could not otherwise be avoided. Considering that losses caused by anomalies such as diseases, weeds and pests account for 20–40 % of overall agricultural productivity, a successful research effort in this area would be a breakthrough for agriculture. In this paper, we propose a methodology with which to discover and classify anomalies in images of crops, taken from a wide range of distances, using different Convolutional Neural Network architectures. This methodology also deals with several difficulties that usually appear in this kind of problems, such as class imbalance, the insufficient and small variety of images, overtraining or lack of models generalisation. We have implemented four convolutional neural network architectures in a high-performance computing environment, and propose a methodology based on data augmentation with the addition of Gaussian noise to the images to solve the above problems. Our approach was tested using two well-established open datasets that are unalike: DeepWeeds, which provides a classification of 8 weed species native to Australia using images that were taken at a distance of 1 m, and Agriculture-Vision, which classifies 6 types of crop anomalies using multispectral satellite imagery. Our methodology attained accuracies of 98 % and 95.3% respectively, improving the state-of-the-art by several points. In order to ease reproducibility and model selection, we have provided a comparison in terms of computational time and other metrics, thus enabling the choice between architectures to be made according to the resources available. The complete code is available in an open repository in order to encourage reproducibility and promote scientific advances in sustainable agriculture. • A convolutional deep learning model was developed for anomaly detection in agricultural images. • High and low-resolution imagery can be used as input for the model. • Data transformation and augmentation are part of our methodology. • Extensive experiments comparing 4 architectures and 2 datasets are shown. [ABSTRACT FROM AUTHOR]

Surveillance video can capture a variety of realistic scenarios for providing security in both private and public spaces. The prediction of anomalies promptly is essential. However, existing methods are time-consuming due to marking conflicting clips in training videos. Therefore, this paper proposes a CBVR (Content-Based Video Retrieval) framework for detecting anomalies in surveillance videos. The proposed method leverages deep learning techniques and the HNSW (Hierarchical Navigable Small World) index to achieve efficient and accurate video retrieval. To preprocess the video data, frames are resized and normalized, and the dataset is augmented with data transformations such as rotations, translations, and flips to improve model generalization. The best results are offered while evaluating with the UCSD Ped2 dataset which produces the MAP score of 94.25%, and recall, precision, and F1-scores of 96.15%, 98.03%, and 92.63% respectively. The results proved that maximizing the number of layers in the HNSW index enhances the MAP score, suggesting that deeper layers are more effective at retrieving relevant results. Overall, the proposed CBVR framework provides an efficient and accurate method for anomaly detection in surveillance videos. It has potential applications in various domains, including video summarization, recommendation, and surveillance, among others. [ABSTRACT FROM AUTHOR]

Chronic Kidney Disease (CKD) and liver diseases are progressive diseases with high morbidity and large fatal rates occurring generally in the adult population exclusively those people affected with hypertension and diabetes. Thus this paper develops a deep learning method named snake optimization-based BiGRU model for predicting kidney and liver disease earlier and precisely. Gathered medical data of the patients using the various IoT sensor devices stored under the patient log in the cloud. These data are abundant in quantity. The pre-processing is initially carried out to refill the missing details of the patient. The Convolutional Neural Network ResNet and snake optimization (SO) are included for extracting features and selecting optimal features. The disease prediction is performed using the BiGRU neural network which learns data from two various sources such as forward and backward for providing extremely precise prediction output. For evaluating the performance we compared the recall, MCC, accuracy, specificity, and Kappa score values of the method with the four existing disease prediction methods of LDA-GA-SVM, CNN-RF, Decision Tree, and XGBoost. The proposed method achieved a higher accuracy of 98.9 %, recall of 98.9 %, sensitivity of 98.9 %, and an F1-score of 98.2 %. [ABSTRACT FROM AUTHOR]

With the proliferation of cloud computing and the Internet of Things(IoT), ensuring privacy and robust data protection has become increasingly critical. Reversible data hiding in encrypted images (RDHEI) methods emerge as a promising solution to address these challenges. In this paper, we propose an asymmetric CNN-based predictor (ACNNP) that significantly outperforms other predictors in terms of prediction accuracy. Then, an adaptive mean predictor is proposed to cooperate with ACNNP. According to these two predictors, a two-stage prediction and embedding model is designed to further enhance the embedding capacity and make data embedding more flexible. Experimental results demonstrate that the proposed method exhibits a high embedding capacity and reversibility when compared to state-of-the-art methods. On the BOWS-2, BOSSBase, and UCID datasets, it obtains average embedding rates of 3.302, 3.461, and 2.69 bpp, respectively. Therefore, this method provides an effective solution for applications such as data integrity verification, privacy protection, and copyright preservation. • Apply convolutional neural network to reversible data hiding in encrypted images • Design a novel asymmetric CNN-based predictor • Design an adaptive mean predictor • Propose a two-stage prediction and embedding model. [ABSTRACT FROM AUTHOR]

Visual perception plays a vital role in autonomous driving systems, demanding high accuracy and real-time inference speed to ensure safety. In this paper, we propose a multi-task framework that simultaneously performs object detection, drivable area segmentation, and lane line identification, addressing the requirements of accurate and efficient visual perception. Our approach utilizes a shared-encoder architecture with three separate decoders, targeting each specific task. We investigate three configurations for the shared encoder: a Convolutional Neural Network (CNN), a Polyp Vision Transformer (PVT), and a hybrid CNN+PVT model. Through extensive experimentation and comparative analysis on the challenging BD100K dataset, we evaluate the performance of these shared-encoder models and provide valuable insights into their strengths and weaknesses. Our research contributes to the advancement of multi-task visual perception for autonomous driving systems by achieving competitive results in terms of accuracy and efficiency. The source code is publicly available on GitHub to facilitate further research in this domain. • Resolve the issue of latency and computational cost. • Facilitate multiple tasks by adopting an inter-correlated feature sharing approach. • Explore and evaluate the use of both CNN and PVT as shared encoders. • Design an enhanced CNN-based shared encoder. [ABSTRACT FROM AUTHOR]

Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analyzed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilized in real-time. [ABSTRACT FROM AUTHOR]

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have proposed a novel method with a new deep learning-based model and the enhanced data augmentation pipeline to train field images alone and subsequently predict both field images and UAV images for weed segmentation and mapping. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90 % when the spraying resolution is at 1.78 × 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results. To achieve this performance, it is crucial to perform preprocessing techniques that reduce dataset differences between two distinct domains. [ABSTRACT FROM AUTHOR]

• To design an automatic multi-disease detection in CXR images by ensemble model. • To adopt ODeepLabv3 to perform segmentation process for multi-disease detection. • To adopt a new ensemble model named Optimized Ensemble Transfer Learning (OETL). • To design a Mutation Rate-based Lion Algorithm (MR-LA) to obtain high accuracy. • To optimize the epochs in ensemble classifier with the help of MR-LA algorithm. Chest radiography is a comparatively low-cost, most probably applied in a medical process that brings crucial information to discover diagnostic conclusions. Chest X-rays (CXR) are used in the prognosis of chest diseases that includes asthma, lung cancer, pneumonia, and COVID-19. Artificial intelligence detects the multi-disease automatically to enhance its proficiency and performance by solving image detection complications with various machine learning and deep learning approaches. Convolutional Neural Network (CNN) has been designed for the advancement of computerized recognition systems. For the classification of medical images, texture, shape, size, and tissue constitution are essential features for detecting diseases. Hence, huge input features are merged with deep CNN validated to increase the effectiveness of CXR analysis. In addition, multi-scale features are visualized in CNN for the detection of variable sizes of thoracic diseases. This paper has implemented a novel multi-disease diagnosis model using chest X-ray images through deep learning approaches, and thus, it helps to minimize the computational cost. The standard CXR images are gathered from the standard datasets. After, the pre-processing is performed for cleaning and contrast the images. Further, the image segmentation is carried out using Optimized DeepLabv3 (ODeepLabv3), where the optimization of parameters in DeepLabv3 is done by a new Mutation Rate-based Lion Algorithm (MR-LA). Then, multi-disease classification is carried out through Optimized Ensemble Transfer Learning (OETL), where the OETL is carried out through VGG16, ResNet, ImageNet, MobileNet, GoogleNet, Inception, and Xception. Here, the parameter optimization of all models is done by the same MR-LA. The proposed model improved the effectiveness and performance in expressions of sensitivity, precision, and specificity factors and get higher classification and detection accuracy. The outcome of the recommended method over other models is identified by the relative analysis conducted. [ABSTRACT FROM AUTHOR]

• The proposed method had fused multi-level information compensation and U-Net. • The module designed for multi-level information compensation and compression. • The method had fused compressed features and correlation features. Recently, Deep Convolutional Neural Networks have demonstrated high-quality reconstruction in image super-resolution procedure. In this paper, we propose improved image super-resolution reconstruction via multi-level information compensation and U-Net network to address the problem that the image super-resolution reconstruction algorithm based on deep neural networks tends to lose feature information in the feature extraction process, resulting in the lack of texture and edge details in the reconstructed image. Firstly, we design the U-net like network for image super-resolution reconstruction, which performs multi-level feature extraction and channel compression for the input features through the down-channel branch. It fuses the compressed features and extracts the correlation features of different channels through the bottom module, and performs multi-level feature extraction and channel recovery for the compressed correlation features through the up-channel branch. The multi-level information compensation model is then designed to compensate for the information lost in the channel compression process and the information that is difficult to recover in the channel recovery process of U-net like network. The experimental results can show that the proposed algorithm achieves a significant improvement in Peak Signal-to-Noise Ratio and Structure Similarity Index and visual effect compared with the state-of-arts algorithms. The average experimental results of PSNR from proposed method had improved by 1.63 dB, 1.53 dB, 0.97 dB and 0.94 dB compared to SRCNN, HAT, DAT and CARN, respectively. [ABSTRACT FROM AUTHOR]

The vulnerability of RGB-based human action recognition in complex environment and variational scenes can be compensated by skeleton modality. Therefore, action recognition methods fusing RGB and skeleton modalities have received increasing attention. However, the recognition performance of the existing methods is still not satisfactory due to the insufficiently optimized sampling, modeling and fusion strategy, even the computational cost is heavy. In this paper, we propose a Dense-Sparse Complementary Network (DSCNet), which aims to leverage the complementary information of the RGB and skeleton modalities at light computational cost to obtain the competitive action recognition performance. Specifically, we first adopt dense and sparse sampling strategies according to the advantages of RGB and skeleton modalities, respectively. And then, we use the skeleton as guiding information to crop the key active region of the persons in the RGB frame, which largely eliminates the interference of the background. Moreover, a Short-Term Motion Extraction Module (STMEM) is proposed to compress the densely sampled RGB frames to fewer frames before feeding them into the backbone network, which avoids a surge in computational cost. And a Sparse Multi-Scale Spatial–Temporal convolutional neural Network (Sparse-MSSTNet) is designed to modeling sparse skeleton. Extensive experiments show that our method effectively combines complementary information of RGB and skeleton modalities to improve recognition accuracy. The DSCNet achieves competitive performance on NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, UAV-human, IKEA ASM and Northwest-UCLA datasets with much less computational cost than exiting methods. The code is available at https://github.com/Maxchengqin/DSCNet. • Targeted sampling strategies for RGB and skeleton. • A network for sparse skeleton-based action recognition is proposed. • Segmentally aggregate the motion information in dense RGB frames. • Achieves state-of-the-art performance with much less computational cost. [ABSTRACT FROM AUTHOR]

Seamless positioning ability has become an essential requirement in large-scale smart city scenes with the development of Artificial Intelligence of Things technology. The performance of seamless positioning is limited by the inaccurate crowdsourced navigation database, cumulative error of built-in sensors, and changeable measurement errors of different location sources. In order to solve these problems, this paper presents the CrowdLOC-S framework, which provides a concrete and accurate indoor/outdoor localization performance using the combination of crowdsourced Wi-Fi fingerprinting, Global Navigation Satellite System (GNSS), and low-cost sensors. A data and model dual-driven based trajectory estimator is developed for improving the long-term positioning performance of built-in sensors, and a hybrid one-dimensional convolutional neural network (1D-CNN), Bi-directional Long Short-Term Memory (Bi-LSTM), and Multilayer Perceptron (MLP) enhanced quality indicator is proposed for quality evaluation of crowdsourced trajectories and further Wi-Fi fingerprinting database construction. Besides, the transfer learning approach is applied in the quality indicator for autonomously predicting the location errors towards different indoor and outdoor location sources and realizing seamless scenes switching. Finally, a unified extended Kalman filter is developed to realize multi-source integration-based seamless localization using the positioning information provided by indoor and outdoor location sources and corresponding quality indicator results. Comprehensive experiments demonstrate that the presented CrowdLOC-S system is proven to realize precise and efficient indoor and outdoor positioning performance in complex and large-scale urban environments. [ABSTRACT FROM AUTHOR]

Highlights • This paper suggests two methods based on AI tools for medical image segmentation. • The first method uses CBR to guide a region-growing process for segmentation. • The second method based on Deep Learning performs segmentation with a small data set. • Both methods were tested on CT-scans images from children with tumoral kidneys. • They provided accurate enough results to consider an automatic. segmentation Abstract Most often, image segmentation is not fully automated and a user is required to lead the process in order to obtain correct results. In a medical context, segmentation can furnish much information to surgeons, but this task is rarely executed. Artificial Intelligence (AI) is a powerful approach for devising a viable solution to fully automated treatment. In this paper, we have focused on kidneys deformed by nephroblastoma. However, a frequent medical constraint is encountered which is a lack of sufficient data with which to train our system. In function of this constraint, two AI approaches were used to segment these structures. First, a Case Based Reasoning (CBR) approach was defined which can enhance the growth of regions for segmentation of deformed kidneys using an adaptation phase to modify coordinates of recovered seeds. This CBR approach was confronted with manual region growing and a Convolutional Neural Network (CNN). The CBR system succeeded in performing the best segmentation for the kidney with a mean Dice of 0.83. Deep Learning was then examined as a possible solution, using the latest performing networks for image segmentation. However, for relevant efficiency, this method requires a large data set. An option would be to manually segment only certain representative slices from a patient and then use them to train a Convolutional Neural Network (CNN) how to segment. In this article the authors propose an evaluation of a CNN for medical image segmentation following different training sets with a variable number of manual segmentations. To choose slices to train the CNN, an Overlearning Vector for Valid Sparse SegmentatIONs (OV 2 ASSION) was used, with the notion of gap between two slices from the training set. This protocol made it possible to obtain reliable segmentations of per patient with a small data set and to determine that only 26% of initial segmented slices are required to obtain a complete segmentation of a patient with a mean Dice of 0.897. [ABSTRACT FROM AUTHOR]

Highlights • An activation function with data-range-segment-wise nonlinear behavior is proposed. • Values of slope coefficients for the proposed function are evaluated empirically. • Proposed activation function is presented in adaptive and non-adaptive variants. • Performance of proposed function is evaluated against state-of-the-art. • Internal activation behavior and convergence characteristics are analyzed. Abstract Activation functions play a crucial role in discriminative capabilities of the deep neural networks. They are also one of the main reasons for revival of the neural networks. Although, recent activation functions provide solution to vanishing and exploding gradient problems, but they do not have sufficient capacity to model non-linear data. This paper proposes a novel activation function, which imparts neural networks with capability to model non-linear dependencies in data. Behavior of proposed activation function remains non-saturating even with non-linear structure. The activation function provides distinct activation behaviors to different data range segments. The paper presents two different variants of proposed function. The first, "linear sigmoidal activation" function is a fixed structure activation function with function coefficients defined at the start of model design. Whereas second, "adaptive linear sigmoidal activation" function is a trainable function which can adapt itself according to the complexity of the given data. Both of the proposed models are tested against the state of the art activation functions on benchmark datasets (CIFAR-10, MNIST, SVHN, FER-2013). The proposed activation function is able to outperforms every known activation functions in all the tests. [ABSTRACT FROM AUTHOR]

This paper proposes a distance-based index to evaluate the input data and the data flowing through layers of a convolutional neural network in classification problems. This shape-less index is proposed based on the class separability and data complexity concepts which are explained in a classification problem. A dataset is complex if many of its samples are located in overlapping regions belonging to different classes, making the classification challenging. In addition, a class is separable if its samples are not located in the overlapped area. The proposed index counts the number of samples in the overlapped area normalized by the number of all samples, measuring the complexity of the dataset. The paper explains the idea of dividing features into exclusive and common features, and identifies the conditions for separability based on this concept. After that, probability evidence shows why the Separation index can be included in complexity measurement indices. The benefits of using the introduced index are shown through many case studies. In the first case study, the proposed index ranks some known supervised datasets from less to more challenging. In the second case study, the separability of Dataflow is evaluated through layers of two pre-trained VGG-16 networks in the classification of CIFAR-10, CIFAR-100, and Caltech-101. The evaluation of Dataflow is also shown through a trained ResNet-18 model for the Fashion-MNIST dataset in the subsequent case study. Finally, it is shown that the correct classification rate and the separation index are almost equivalent through layers, particularly when the separation index is increased. [ABSTRACT FROM AUTHOR]

Dysarthria is a motor speech disability caused by weak muscles and organs involved in the articulation process, thereby affecting the speech intelligibility of individuals. Because this condition is linked to physical exhaustion disabilities, individuals not only have communication difficulties, but also have difficulty interacting with digital devices. Automatic speech recognition (ASR) makes an important difference for individuals with dysarthria since modern digital devices offer a better interaction medium that enables them to interact with their community and computers. Still, the performance of ASR technologies is poor in recognizing dysarthric speech, particularly for acute dysarthria. Multiple challenges, including dysarthric phoneme inaccuracy and labeling imperfection, are facing dysarthric ASR technologies. This paper proposes a spatio-temporal dysarthric ASR (DASR) system using Spatial Convolutional Neural Network (SCNN) and Multi-Head Attention Transformer (MHAT) to visually extract the speech features, and DASR learns the shapes of phonemes pronounced by dysarthric individuals. This visual DASR feature modeling eliminates phoneme-related challenges. The UA-Speech database is utilized in this paper, including different speakers with different speech intelligibility levels. However, because the proportion of usable speech data to the number of distinctive classes in the UA-speech database was small, the proposed DASR system leverages transfer learning to generate synthetic leverage and visuals. In benchmarking with other DASRs examined in this study, the proposed DASR system outperformed and improved the recognition accuracy for 20.72% of the UA-Speech database. The largest improvements were achieved for very-low (25.75%) and low intelligibility (33.67%). [ABSTRACT FROM AUTHOR]

Infrared small and dim target detection benefits from the exploration of correlations among targets, neighboring regions, and the background. However, existing methods that rely on convolutional neural networks and vision transformers cannot effectively capture long-range information correlations within images. To overcome this limitation, this paper proposes CS-ViG-UNet, a framework that introduces vision graph convolution for infrared small and dim target detection. Our framework employs a cyclic shift sparse graph attention mechanism to address the issue of reduced expressive power. Meanwhile, the CS-ViG module is designed to construct an effective graph structure using image patches, thereby capturing feature information relevant to target recognition. On the public datasets Sirst AUG and IRSTD-1K, our method obtained F1 scores of 0.8561 and 0.745, respectively, showing an improvement of 3.15 % and 4.1 % compared to the state-of-the-art methods. On the RTX3090 with TensorRT acceleration, CS-ViG-UNet can process approximately 357 images of size 256 × 256 pixels per second at FP16 precision. For detailed information, please visit our homepage: https://linaom1214.github.io/CSViG-UNet. • A framework is proposed for infrared small and dim target detection (IRSDT). • This framework extends vision graph convolution to IRSTD for the first time. • An IR feature learning module which is based on cyclic shift sparse graph convolution. • Outperforms state-of-the-art methods in both quantitative and qualitative evaluation. [ABSTRACT FROM AUTHOR]

Improving the segmentation accuracy of cells from microscopy images directly benefits medical practitioners for differential cell counting, disease diagnosis, and treatment. However, separating each cell continues to be challenging, especially when the cells appear in clumps. Recent advancements in deep learning have demonstrated success in automating cell segmentation and counting, but they necessitate large volumes of annotated data for training. Creating precise ground truth for cell segmentation is laborious and demands expertise due to the intricate and varied characteristics of microscopy images. Consequently, this complexity poses a challenge to the practicality of implementing deep neural network-based solutions for microscopy cell segmentation. This paper addresses a significant research gap by exploring the underutilized traditional image features to build a lightweight deep-learning framework for cell boundary segmentation that can be effectively trained with a limited amount of labeled data. We have developed Saliency Guided Attention Module Network (SGAM-net), which uses perceptual features inherent along the cell boundaries and poses cell segmentation as a thick boundary prediction problem with the aim to separate the cells. SGAM-net uses a specialized attention mechanism to combine the perceptual feature maps with the learned features. When tested on the three public bench-mark microscopy datasets, with respect to the base Unet model, there is an increase of 1.36%, 1.29%, and 6.35% in Dice score and 2.39%, 2.31% and 6.99% in Intersection Over Union (IOU) score. The model requires only 23.07% less parameters than Unet++ model. The potential of SGAM-net in segmenting boundaries semantically has been demonstrated, and it is further processed to compute cell counts. The model outperformed the state-of-the-art instance separation method 'Stardist' in cell counting by giving a reduction ratio of 2.34, 1.89, and 2.03 on the metric 'mean absolute error'. • Propose a novel deep learning method for boundary semantic segmentation of cells. • Utilize both perceptual and learned features of the images for segmentation. • Employs lightweight model with specialized attention gate for better segmentation. • Applicable to estimate cell counts in cancer diagnosis and drug discovery systems. [ABSTRACT FROM AUTHOR]

Multivariate time series classification is a significant research topic in the realm of data mining, which encompasses a wide array of practical applications in domains such as healthcare, energy systems, and traffic. The complex temporal and spatial dependencies inherent in multivariate time series pose challenges to classification tasks. Previous studies have usually focused on the time-domain information of multivariate time series. However, achieving accurate classification using only the time-domain information may be difficult. To address this challenge, a time and frequency-domain feature fusion network (TF-Net) for multivariate time series classification is proposed in this paper. Our model contains two modules, the time-domain module and the frequency-domain module. The time-domain module is used to capture the time-domain features of multivariate time series. It is constructed using CNNs and GCNs, enabling the capture of both temporal and spatial dependencies within the time-domain. The frequency-domain module is used to capture the frequency-domain features of multivariate time series data. In this module, we treat the frequency-domain features as images and innovatively transform the multivariate time series classification task into an image classification task. Our method is able to classify multivariate time series from both the time-domain and frequency-domain, which provides a new perspective for multivariate time series analysis. We conduct extensive experiments on the UAE archive, and the experimental results show that our model achieves the best performance compared to the ten state-of-the-art methods. • We proposed a deep model that captures the spatiotemporal dependence of time series. • We transform the time series classification task into an image classification task. • Our method can classify time series from both time and frequency-domain perspectives. [ABSTRACT FROM AUTHOR]

Although the accurate potential for growth prediction is very important for Government grants and contributions programs to better support Small and Medium-sized Enterprises (SMEs), it is a challenging task due to the data heterogeneity (both structured data and free text data bilingual in English and French), the class imbalance issue, and the difficulties in efficient feature learning. To address these challenges, this paper presents a novel BERT-TCN model for portfolio predictions in government funding programs, with the following key contributions. First, we describe the application of a novel architecture to a prediction task involving sequential, structured, partially quantitative input data and free text input data. Specifically, our novel model predicts the growth of firms receiving government funding for innovation. Our model also deals with class imbalance in the data and the difficulties in efficient feature learning. Our model integrates a Transformer model, i.e., BERT, for text modeling with a Temporal Convolutional Network (TCN) for sequential prediction. Second, we also developed various performance evaluation criteria in Section 4.3, allowing comprehensive assessments of the proposed approach from both the machine learning perspective and funding program-specific perspective. Third, the importance of features (both text and numerical features) is quantified and evaluated, allowing insights into how different features contribute to the prediction and explainability of the proposed model. The proposed approach is trained and tested on a large dataset from a rich database, demonstrating that the proposed approach can greatly help individual human experts improve their results. • Proposing a novel BERT-TCN model for text modeling and temporal prediction. • Designing various evaluation criteria for comprehensive assessments of the model. • Exploring explainability of the model by ablation studies of various input features. [ABSTRACT FROM AUTHOR]

In recent years, phishing websites have constantly evolved, causing traditional URL or HTML-based detection methods less effective. This limitation motivated the development of visual similarity-based detection methods. These methods can identify phishing websites in a rapidly changing phishing landscape since the key factor in deceiving users is the high visual similarity between phishing pages and their corresponding legitimate ones. However, existing similarity-based methods often suffer from high false positive rates. To alleviate the problem, this paper proposed a new webpage similarity phishing detection framework. Using a deep learning model, the framework locates a website logo and integrates the logo area with the full webpage to detect phishing pages. Experimental evaluations on multiple datasets demonstrate the model's ability to achieve a precision of 91% and a recall of 88% in detecting phishing websites. Comparative experiments against existing methods reveal that the proposed model enhances precision and recall by 17% and 21%, respectively, while reducing the false positive rate by 1%. • A visual similarity method combining full webpages with logos is proposed. • The method obtains better performance than prior anti-phishing methods. • The method can be used to detect partially similar phishing webpages. • The method can be used to detect zero-day phishing webpages. • The method is robust to the adversarial samples. [ABSTRACT FROM AUTHOR]

This study purpose of recording pedestrians' activities in all surveillance videos and then convert these records into structured descriptions to analyze a large number of surveillance videos. This paper proposes a four-stage processing method. The first stage uses convolutional neural network (CNN) to detect objects in the surveillance video and classify them. The second stage finds the correlation of pedestrians in continuous images and describes multiple objects' trajectories in the surveillance video. The third stage calculates the similarity of the pedestrian object sets of the surveillance video at different times as the basis for pedestrian re-identification. In the fourth stage, extract the walking path of the pedestrian in each surveillance video. Each pedestrian's actions in each surveillance video, including whether to stand, walking path, direction, clothing color and walking time. To construct a correlation table of pedestrian motion in multiple surveillance videos. This experiment uses two image datasets for experimentation and analysis to evaluate this method's effectiveness for pedestrian movement in multiple surveillance videos. The image datasets are the Multi-Camera Object Tracking (MCT) Challenge and PETS 2009 Benchmark Data. This experiment includes pedestrian detection, pedestrian identification, and multi-pedestrian tracking of pedestrians in single and multiple surveillance cameras. The results show that in the pedestrian detection experiment, the convolutional neural network can effectively solve the problems caused by static objects, light changes, similar colors, crowded pedestrians, and incomplete objects. In the pedestrian recognition and re-recognition experiments, the accuracy is reduced due to the influence of low-resolution surveillance videos. This detection method of the pedestrian is more accurate than traditional dynamic object detection methods. After the pedestrian tracking and re-identification are completed, the pedestrian relationships in multiple surveillance cameras are integrated, and a pedestrian behavior association table is built for surveillance videos. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding has found widespread application across various fields due to its inherent structured data. However, some non-graph-based models necessitate improvement in accurately describing complex hierarchical structures and connecting intricate relational paths. Graph neural networks embedding methods provide a promising direction for exploring complex hierarchical modeling, facilitating the flow of information from nodes along relational paths. Nevertheless, the deficient interpretable characterization by the saliency estimation in entities, which contributes to the efficient performance of the model, is difficult to comprehend. This paper proposes a novel model to mitigate the described phenomenon by intensifying the original structure of knowledge graphs. Specifically, it integrates the Axiomatic Fuzzy Set entity semantic extraction framework with a heterogeneous relationship self-attention mechanism to bolster node representation. Next, this framework incorporates a convolutional neural network model to increase the interaction capabilities of entities and relations. This interpretable entity-level attention mechanism complements the structural information of the knowledge graphs, enabling more comprehensive neighbor information capture and improving the ability to identify the correct entity location. Experimental validation on two relationally complex datasets demonstrates superior performance compared to current methods, obtaining a new Hit@1 value of 45.6% on WN18RR. • Propose a hierarchical graph neural network knowledge representation framework. • Employ the Axiomatic Fuzzy Set to capture underlying semantics between entities. • The model has interpretable entity-level and heterogeneous relational attention. • Estimates of proximity values between nodes are yielded by semantic descriptions. • Produce promising performance compared to other knowledge graph embedding models. [ABSTRACT FROM AUTHOR]