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Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

As social media and web-based forums have grown in popularity, the fast-spreading trend of fake news has become a major threat to the government and other agencies. With the rise of social media and internet platforms, misinformation may quickly spread over borders and language boundaries. Detecting and neutralizing fake news in several languages can help to protect the integrity of global elections, political discourse, and public opinion. The lack of a robust multilingual database for training the classification models makes detecting fake news a difficult task. This paper looks at it by describing several forms of fake news (like serious fabrications, large-scale hoaxes, stance news, deceptive news, satire news, clickbait, misinformation, rumour). This review paper includes different steps, features, tools for mitigating the scourge of information pollution, and different available datasets. This study presented a taxonomy for detecting fake news, which gives a comprehensive overview and analysis of existing DL-based algorithms focusing on diverse techniques. This paper also includes the monolingual and multilingual fake news detection models. Finally, this paper ends with the technical challenges. [ABSTRACT FROM AUTHOR]

Vehicle identification systems are vital components that enable many aspects of contemporary life, such as safety, trade, transit, and law enforcement. They improve community and individual well-being by increasing vehicle management, security, and transparency. These tasks entail locating and extracting license plates from images or video frames using computer vision and machine learning techniques, followed by recognizing the letters or digits on the plates. This paper proposes a new license plate detection and recognition method based on the deep learning YOLO v8 method, image processing techniques, and the OCR technique for text recognition. For this, the first step was the dataset creation, when gathering 270 images from the internet. Afterward, CVAT (Computer Vision Annotation Tool) was used to annotate the dataset, which is an open-source software platform made to make computer vision tasks easier to annotate and label images and videos. Subsequently, the newly released Yolo version, the Yolo v8, has been employed to detect the number plate area in the input image. Subsequently, after extracting the plate the k-means clustering algorithm, the thresholding techniques, and the opening morphological operation were used to enhance the image and make the characters in the license plate clearer before using OCR. The next step in this process is using the OCR technique to extract the characters. Eventually, a text file containing only the character reflecting the vehicle's country is generated. To ameliorate the efficiency of the proposed approach, several metrics were employed, namely precision, recall, F1-Score, and CLA. In addition, a comparison of the proposed method with existing techniques in the literature has been given. The suggested method obtained convincing results in both detection as well as recognition by obtaining an accuracy of 99% in detection and 98% in character recognition. [ABSTRACT FROM AUTHOR]

The recent adoption of deep learning-based models for the processing and coding of multimedia signals has brought noticeable gains in performance, which have established deep learning-based solutions as the uncontested state-of-the-art both for computer vision tasks, targeting machine consumption, as well as, more recently, coding applications, targeting human visualization. Traditionally, applications requiring both coding and computer vision processing require first decoding the bitstream and then applying the computer vision methods to the decompressed multimedia signals. However, the adoption of deep learning-based solutions enables the use of compressed domain computer vision processing, with gains in performance and computational complexity over the decompressed domain approach. For point clouds (PCs), these gains have been demonstrated in the single available compressed domain computer vision processing solution, named Compressed Domain PC Classifier, which processes JPEG Pleno PC coding (PCC) compressed streams using a PC classifier largely compatible with the state-of-the-art spatial domain PointGrid classifier. However, the available Compressed Domain PC Classifier presents strong limitations by imposing a single, specific input size which is associated to specific JPEG Pleno PCC configurations; this limits the compression performance as these configurations are not ideal for all PCs due to their different characteristics, notably density. To overcome these limitations, this paper proposes the first Adaptive Compressed Domain PC Classifier solution which includes a novel adaptive bridge model that allows to process the JPEG Pleno PCC encoded bit streams using different coding configurations, now maximizing the compression efficiency. Experimental results show that the novel Adaptive Compressed Domain PC Classifier allows JPEG PCC to achieve better compression performance by not imposing a single, specific coding configuration for all PCs, regardless of its different characteristics. Moreover, the added adaptability power can achieve slightly better PC classification performance than the previous Compressed Domain PC Classifier and largely better PC classification performance (and lower number of weights) than the PointGrid PC classifier working in the decompressed domain. [ABSTRACT FROM AUTHOR]

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail surface. It is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing effect. Normally it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes, affected by strong sunlight and visual fatigue. In this case, we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with ease. This paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system, supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions, including the feature extraction performance. Considering the expression capabilities that vary with the computational features in different visual tasks, the paper focuses on five tell tail computing features, which are recoded by an automatic encoder and classified by a SVM classifier. All experimental samples were randomly divided into five groups, and four groups were selected from each group as the training set to train the classifier. The remaining one group was used as the test set for testing. The highest resolution value of the ResNet network was 80.26%. To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the experiments. The method can be used to assist the sailors in making better judgement about the tell tail changes during sailing. [ABSTRACT FROM AUTHOR]

Segmentation of whole slide histological images through the classification of tissue types of small fragments is an extremely relevant task in digital pathology, necessary for the development of methods for automatic analysis of whole slide histological images. The extremely large resolution of such images also makes the task of increasing image resolution relevant, which allows storing images at a reduced resolution and increasing it if necessary. Annotating whole slide images by histologists is complex and time-consuming, so it is important to make the most efficient use of the available data, both labeled and unlabeled. In this paper we propose a novel neural network method to simultaneously solve the problems of super-resolution of histological images from ×20 optical magnification to ×40 and classifying image fragments into tissue types at ×20 magnification. The use of a single encoder as well as the proposed neural network training scheme allows to achieve better results on both tasks compared to existing approaches. The PATH-DT-MSU WSS2v2 dataset presented for the first time in this paper was used for training and testing the method. On the test sample, an accuracy value of 0.971 and a balanced accuracy value of 0.916 were achieved in the classification task on 5 tissue types, for the super-resolution task, values of PSNR = 32.26 and SSIM = 0.89 were achieved. The source code of the proposed method is available at: https://github.com/Kukty/WSI_SR_CL. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of detecting blurred regions in high-resolution whole slide histologic images. The proposed method is based on the use of a Fourier neural operator trained on the results of two simultaneously used approaches: blur detection using multiscale analysis of the discrete cosine transform coefficients and estimation of the degree of sharpness of objects edges in the image. The efficiency of the algorithm is confirmed on images from the datasets PATH-DT-MSU [1] and FocusPath [2]. [ABSTRACT FROM AUTHOR]

Lower versions of EfficientDet (such as D0, D1) have smaller network structures and parameter sizes, but lower detection accuracy. Higher versions exhibit higher accuracy, but the increase in network complexity poses challenges for real-time processing and hardware requirements. To meet the higher accuracy requirements under limited computational resources, this paper introduces SpanEffiDet based on the channel adaptive frequency filter (CAFF) and the Span-Path Bidirectional Feature Pyramid structure. Firstly, the CAFF module proposed in this paper realizes the frequency domain transformation of channel information through Fourier transform and effectively extracts the key features through semantic adaptive frequency filtering, thus, eliminating channel redundant information of EfficientNet. Simultaneously, the module has the ability to compute the weights across the channels and at fine granularity, and capture the detailed information of element features. Secondly, a two-way characteristic pyramid network multi-level cross-BIFPN, which can achieve multi-layer and multi-nodes, is proposed to build cross-level information transmission to incorporate both semantic and positional information of the target. This design enables the network to more effectively detect objects with significant size differences in complex environments. Finally, by introducing generalized focal Loss V2, reliable localization quality estimation scores are predicted from the distribution statistics of bounding boxes, thereby improving localization accuracy. The experimental results indicate that on the MS COCO dataset, SpanEffiDet-D0 achieved an AP improvement of 3.3% compared to the original EfficientDet series algorithms. Similarly, on the PASCAL VOC2007 and 2012 datasets, the mAP of SpanEffiDet-D0 is respectively 1.66 and 2.65% higher than that of EfficientDet-D0. [ABSTRACT FROM AUTHOR]

The fault big data sets of many open source software (OSS) are recorded on the bug tracking systems. In the past, we have proposed the effort assessment method under the assumption that the fault detection phenomenon depends on the maintenance effort, because the number of software fault is influenced by the effort expenditure. The past research in terms of the effort assessment method of OSS is based on the effort data sets. On the other hand, we propose the deep learning approach to the OSS fault big data. In the past, the existing method without Wiener process cannot estimate within the range of existing data only. The proposed method assumes that the fault detection process follows the Wiener process such as the imperfect debugging and Markov property. Thereby, the proposed method can estimate the exceeding values by adding the white noise based on the Wiener process. Then, the proposed method make it possible for the OSS managers to assess the values exceeding from the existing data. Then, we show several reliability assessment measures based on the fault modification time based on the deep learning. Moreover, several numerical illustrations based on the proposed deep learning model are shown in this paper. [ABSTRACT FROM AUTHOR]

In genetics and medical practice, structural variants (SV) in the genome are thought to be the root cause of numerous diseases, particularly genetic diseases. Accurate structural variant prediction is the foundation for identifying and screening pathogenic variants and performing medication genomics analysis, which is a challenging task. However, data in the field of genomics is typically massive, high-dimensional, and serialized, and existing variant prediction tools are affected by the range and type of variants, resulting in less accurate results. As a result, an effective method for predicting structural variation is critical. In this paper, a variation prediction model DEL-RESSP based on ResNet and attention mechanism is proposed for predicting deletion structural variants. To begin, the deletion variant feature information is derived from the three alignment data of read depth, split read pair, and discordant read pair, and the comparison data is transformed into artificial images by encoding to provide reliable input for the subsequent network models. Second, attention mechanisms are combined based on convolutional networks to improve image sensitivity to local information to improve prediction accuracy. Three SV prediction tools, CNVnator, BreakDancer, and Pindel, were used in this study to test the predictive effectiveness of DEL-RESSP in predicting large-scale deletion variants. The results show that DEL-RESSP can predict deletion variants with 96.93% accuracy, which is a 5–10% improvement over combining only a single strategy, as well as a comparison to existing deep learning methods. DEL-RESSP fully utilizes deep learning in image processing, providing some reference value in subsequent variant analysis and gene function annotation. Part of the classification model code used in this paper can be found on https://github.com/JQ1209/DEL-RESSP. [ABSTRACT FROM AUTHOR]

The inverse design of nanophotonic devices has been widely concerned by researchers, and on-demand design is the difficulty of inverse design. In inverse design, researchers usually define a target spectrum based on the performance indicators and experiences and then inverse design the structural parameters from the target spectrum. Due to the uncertainty of inverse design and "one-to-many" problem, it is not usually possible to guarantee that the target spectrum is sure to correspond to a real nanostructure. In order to solve these problems, an inverse design method combining generative model and genetic algorithm is proposed in this paper. Before the inverse design, the real spectrum is compressed into a latent space by the generation model, and then, the target spectrum is decoded from the latent space according to the performance index. Finally, the hybrid optimization algorithm combining genetic algorithm and forward prediction network is used to optimize the generated spectrum. The design method follows the process from performance indicators to target spectrum to structural parameter, and we successfully realized the inverse design of multilayer nanofilms on demand by using this method in the experimental part. The inverse design method proposed in this paper provides a possible solution for the inverse design of nanophotonic devices on demand. [ABSTRACT FROM AUTHOR]

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

The feature matching algorithm based on deep learning has achieved superior performance compared to traditional algorithms in terms of both matching quantity and accuracy, but there are still some high-error matching results in complex scenes, which adversely affects the subsequent work. Based on SuperGlue, we propose an accurate feature matching algorithm via outlier filtering. Firstly, DBSCAN real-time superpixel segmentation (RTSS-DBSCAN) is used to divide the image into regions, and then the outlier filtering module is designed according to the local similarity principle of feature matching. On the premise of not affecting the correct matching results, the matching results with high errors will be filtered to improve the matching accuracy. Meanwhile, due to the lag of traditional Exponential Moving Average algorithm (EMA), an adaptive EMA is designed and integrated into the SuperGlue training process to further improve the training speed and matching accuracy. We evaluate the overall performance of the matching method using the AUC of pose error at the thresholds (5°, 10°, 20°), a common evaluation metric, to provide a more detailed and intuitive evaluation of the matching effectiveness using precision and recall. The experimental results show that the method in this paper can effectively filter the matching results with large errors and has high accuracy and robustness. The AUC of pose error at thresholds (5°, 10°, 20°) reaches 36.53, 56.23, and 73.68, and the precision and recall reach 80.07 and 91.52, respectively, which have better matching results compared with other algorithms. [ABSTRACT FROM AUTHOR]

The aim of this special issue is to introduce the burgeoning topic of Autonomous System. Finally, in order to check the conformity of this enforcement with defined multi-cloud SLA, the authors propose an approach for multi-cloud SLA reporting inspired by conformance checking techniques. I In i I the i I paper i I authored i I by i I Jeremy i I Mechouche i , the authors propose a hierarchical representation of multi-cloud SLAs: sub-SLAs associated with a system's components deployed on distinct cloud service providers and global-SLA associated with the whole system. [Extracted from the article]

With the proposed generalized, hitless, and simple control strategy, the delay tunability and delay bandwidth product of the delay line increases with the increasing order of the Mach-Zehnder-based stage. To support end-to-end wireless-wired deterministic communication in these private, professional environments, it is necessary to introduce TSN features to the wireless network segments. The article demonstrates the multi-stage Mach-Zehnder-based integrated continuously tunable optical delay line with an extensive tunability range increases transmission bandwidth and the vast delay-bandwidth product. [Extracted from the article]

In modern deep learning, character recognition in images is a very important field of study due to its has many real life applications. The goal of this paper is to create the state-of-the-art character recognition model using a stacking ensemble of convolution neural networks (CNNs).To develop the proposed ensemble model, we evaluated several CNN models. The models were judged on how well they performed on the Chars74k dataset. The dataset contains 74,103 images divided into 62 classes with labels [A-Z], [a-z], and [0-9]. The accuracy distribution based on the dataset's subgroups (uppercase, lowercase, and digit) is shown in results. The proposed ensemble model achieves state-of-the-art performance with a maximum accuracy of 92.31% on complete dataset, 99.22% on Uppercase alphabets, 98.66% on Lowercase alphabets, 99.77% on Digits, 91.97% on Uppercase+Lowercase alphabets. On the complete and partial datasets, a comparison report between the proposed model and other existing approaches is also displayed. A comparative study of the proposed work and the previous methods is also shown in this paper, in order to demonstrate the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

In many countries, agriculture plays a major role in the economy. The health of the crop is therefore very important, but there are many plant diseases that are difficult to diagnose. A close inspection is necessary in many cases, or an expert's advice is required. As a result, it is important to address diseases in plants. Several attempts have been made to develop programs that detect diseases in plants because of the ever-rising growth of computer vision and deep learning. This paper focuses on detecting diseases in guava fruits. We use a dataset with pictures of 4 common diseases found in the fruit namely Phytopthra, Red Rust, Scab, Styler and Root. With the help of transfer learning and Convolutional Neural Networks (CNN) this paper train various models on the dataset and compare the results. The results we evaluated using accuracy, precision, Recall and F1 score. We had multiple models with test accuracy of 99%, with highest accuracy of 99.62% for DenseNet169. The results of this study are also compared with previous methods. According to the results, the proposed methods achieved better results than the previous approach. [ABSTRACT FROM AUTHOR]

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]

An edge-cloud computing collaborative dust concentration detection architecture is proposed for real-time operation of intelligent algorithms to reduce the warning delay. And, an end-to-end three-channel convolutional neural network (E2E-SCNN) method is proposed in the paper to facilitate intelligent monitoring and management of dust concentration in tobacco production workshops. This model, which includes three sub-networks-a local feature branch, a global feature branch, and a spatial feature branch, learns the detail texture, overall layout, and spatial distribution information of the input image respectively. A fusion of the three complementary features is performed at the end of the network for the final dust concentration regression prediction. The design, when compared with the single network structure that directly regresses the entire image, is shown to more fully represent the overall information of the image and enhance monitoring performance. A richly annotated image dataset of tobacco production workshops is constructed to verify the effectiveness of the proposed method. The prediction error of E2E-SCNN is compared with existing image estimation algorithms, dual-channel networks, and other methods on this dataset using indicators such as Mean Absolute Error (MAE) and R 2 . It is shown by the results that excellent performance is achieved by the E2E-SCNN algorithm, significantly surpassing other comparison methods. The paper demonstrates that the accuracy and robustness of dust concentration prediction can be greatly improved by using a three-channel convolutional neural network spatial information monitoring framework. This achievement provides an effective means for dust supervision and governance during the tobacco production process and offers a technical route that can be referred to for image analysis tasks in other similar fields. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. [ABSTRACT FROM AUTHOR]

In big data era, multi-source heterogeneous data become the biggest obstacle to data sharing due to its high dimension and inconsistent structure. Using text classification to solve the ontology construction and mapping problem of multi-source heterogeneous data can not only reduce manual operation, but also improve the accuracy and efficiency. This paper proposes an ontology construction and mapping scheme based on hybrid neural network and autoencoder. Firstly, the proposed text classification method uses the multi-core convolutional neural network to capture local features and uses the improved Bidirectional Long Short-Term Memory network to compensate for the shortcomings of the convolutional neural network that cannot obtain context-related information. Secondly, a similarity matching method is used for ontology mapping, which integrate autoencoder to improve anti-interference ability. We have carried out several sets of experiments to test the validity of the proposed ontology construction and mapping scheme. [ABSTRACT FROM AUTHOR]

With the advancement of society, ensuring the safety of personnel involved in municipal construction projects, particularly in the context of pandemic control measures, has become a matter of utmost importance. This paper introduces a security measure for municipal engineering, combining deep learning with object detection technology. It proposes a lightweight artificial intelligence (AI) detection method capable of simultaneously identifying individuals wearing masks and safety helmets. The method primarily incorporates the ShuffleNetv2 feature extraction mechanism within the framework of the YOLOv5s network to reduce computational overhead. Additionally, it employs the ECA attention mechanism and optimized loss functions to generate feature maps with more comprehensive information, thereby enhancing the precision of target detection. Experimental results indicate that this algorithm improves the mean average precision (mAP) value by 4.3%. Furthermore, it reduces parameter and computational loads by 54.8% and 53.8%, respectively, effectively striking a balance between lightweight operation and precision. This study serves as a valuable reference for research pertaining to lightweight target detection in the realm of municipal construction safety measures. [ABSTRACT FROM AUTHOR]

In recent years, medical image segmentation methods based on deep learning have been of great importance for disease diagnosis and treatment planning in clinical medicine. U-Net and a series of networks derived from it have led the research trend in medical image segmentation. In this paper, an end-to-end lightweight MLP-based medical image segmentation network G-UNeXt is proposed to address the problems of high computational complexity, large number of model parameters and slow inference in medical image segmentation networks. Firstly, this paper proposes a new skip connection method Ghost path. It reduces the semantic gap between features while combining different levels of features, and exploits the redundancy of features, which helps to improve the segmentation ability of the model for details. Secondly, this paper designs a cheap and effective G-S block, which uses low-cost linear operations to mine potential ghost features outside of intrinsic features. The G-S block reduce the number of parameters and computational complexity compared to traditional convolution. In addition, it can also adaptively calibrate the channel feature response, enhancing the characterization capability of the network and bringing some performance improvement at a lower computational cost. Finally, we build the lightweight MLP-based network G-UNeXt used Ghost path and G-S block for real-time segmentation of medical images. The results tested on the benchmark medical image segmentation datasets BUSI and ISIC2018 show that G-UNeXt reduces the parameters by 33% and the computational complexity by 23.7% compared with UNeXt. In addition, G-UNeXt also obtains faster inference speed and higher segmentation accuracy. [ABSTRACT FROM AUTHOR]

Computer-assisted marking can reduce the work pressure on teachers. This paper briefly introduced the automatic English subjective question marking algorithm combined with the convolutional neural network + long short-term memory (CLSTM) algorithm. An attention mechanism was introduced to improve the CLSTM algorithm. The improved CLSTM-based automatic marking algorithm was simulated and tested, and it was compared with the convolutional recurrent neural network (CRNN)-based automatic marking algorithm and the traditional CLSTM-based automatic marking algorithm. The results indicated that the improved CLSTM-based automatic marking algorithm effectively recognized the text in the handwritten answer images and accurately graded answers to subjective questions. [ABSTRACT FROM AUTHOR]

A scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can provide information that would not otherwise be considered. The usefulness of scholarly recommendations, especially literature recommendations, has been established by the widespread acceptance of web search engines such as CiteSeerX, Google Scholar, and Semantic Scholar. This article discusses different aspects and developments of scholarly recommendation systems. We searched the ACM Digital Library, DBLP, IEEE Explorer, and Scopus for publications in the domain of scholarly recommendations for literature, collaborators, reviewers, conferences and journals, datasets, and grant funding. In total, 225 publications were identified in these areas. We discuss methodologies used to develop scholarly recommender systems. Content-based filtering is the most commonly applied technique, whereas collaborative filtering is more popular among conference recommenders. The implementation of deep learning algorithms in scholarly recommendation systems is rare among the screened publications. We found fewer publications in the areas of the dataset and grant funding recommenders than in other areas. Furthermore, studies analyzing users' feedback to improve scholarly recommendation systems are rare for recommenders. This survey provides background knowledge regarding existing research on scholarly recommenders and aids in developing future recommendation systems in this domain. [ABSTRACT FROM AUTHOR]

This editorial briefly analyses, describes, and provides a short summary of a set of selected papers published in a special issue focused on deep learning methods and architectures and their application to several domains and research areas. The set of selected and published articles covers several aspects related to two basic aspects in deep learning (DL) methods, efficiency of the models and effectiveness of the architectures These papers revolve around different interesting application domains such as health (e.g. cancer, polyps, melanoma, mental health), wearable technologies solar irradiance, social networks, cloud computing, wind turbines, object detection, music, and electricity, among others. This editorial provides a short description of each published article and a brief analysis of their main contributions. [ABSTRACT FROM AUTHOR]

This cannot be achieved only through conventional Machine Learning (ML) frameworks and technologies. The technological advancement in recent years has seen a great shift leading to a greater magnitude of data storage, communication and subsequent processing with reduced size and cost of the systems. The thirteen paper "A Novel Emergency Situation Awareness Machine Learning Approach to Assess Flood Disaster Risk based on Chinese Weibo" is authored by Bai Hua, Yu Hualong, Yu Guang and Huang Xing. [Extracted from the article]

Context: Artificial Intelligence (AI) has rapidly developed over the past several decades. Several related AI approaches, such as Machine Learning (ML), have been applied to research on landscape patterns and ecological processes. Objectives: Our goal was to review the methods of AI, particularly ML, used in studies related to landscape ecology and the main topics addressed. We aimed to assess the trend in the number of ML papers and the methods used therein, and provide a synopsis and prospectus of current use and future applications of ML in landscape ecology. Methods: We conducted a systematic literature search and selected 125 papers for review. These were examined and scored according to multiple criteria regarding methods and topic. We applied quantitative statistical methods, including cluster analysis based on titles, abstracts, and keywords and a non-metric multidimensional scaling based on attributes assigned during the review. We used Random Forests machine learning to describe the differences between identified clusters in terms of the topics and methods they included. Results: The most frequent method found was Random Forests, but it is noteworthy to mention the increasing popularity of tools related to Deep Learning. The topics cover both ecologically oriented issues and the landscape-human interface. There has been a rapid increase in ML and AI methods in landscape ecology research, with Deep Learning and complex multi-step pipeline AI methods emerging in the last several years. Conclusions: The rapid increase in the number of ML papers in landscape ecology research, and the range of methods employed in them, suggest explosive growth in application of these methods in landscape ecology. The increase of Deep Learning approaches in the most recent years suggest a major change in analytical paradigms and methodologies that we feel may transform the field and enable analyses of more complex pattern process relationships across vaster data sets than has been possible previously. [ABSTRACT FROM AUTHOR]

We are pleased to present a special issue of Data Science and Engineering (DSE), which contains a collection of six extended papers from the APWeb-WAIM 2020 conference. In the second paper, "GHSH: Dynamic Hyperspace Hashing on GPU", authors construct a fully concurrent dynamic hyperspace hash table for GPU and design special concurrency control and data sharing strategies to ensure wait-free read operations and high parallel acceleration. The conference program committee selected 68 full research papers, 29 short papers, and 8 demonstration papers to be presented at the conference and published in the conference proceedings [[1]]. [Extracted from the article]

Decision making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions. Therefore, in this paper, they proposed an attention-based hierarchical denoised deep clustering (AHDDC) algorithm to solve the problem, which enables GCN to learn multiple layers of hidden information and uses the attention mechanism to strengthen the information. These accepted articles make contributions and novel aspects of different research in social media data scenarios, traffic data scenarios, data security scenarios, medical data scenarios, knowledge graph learning, and the other graph based deep learning models. [Extracted from the article]

Historical documents contain essential information about the past, including places, people, or events. Many of these valuable cultural artifacts cannot be further examined due to aging or external influences, as they are too fragile to be opened or turned over, so their rich contents remain hidden. Terahertz (THz) imaging is a nondestructive 3D imaging technique that can be used to reveal the hidden contents without damaging the documents. As noise or imaging artifacts are predominantly present in reconstructed images processed by standard THz reconstruction algorithms, this work intends to improve THz image quality with deep learning. To overcome the data scarcity problem in training a supervised deep learning model, an unsupervised deep learning network (CycleGAN) is first applied to generate paired noisy THz images from clean images (clean images are generated by a handwriting generator). With such synthetic noisy-to-clean paired images, a supervised deep learning model using Pix2pixGAN is trained, which is effective to enhance real noisy THz images. After Pix2pixGAN denoising, 99% characters written on one-side of the Xuan paper can be clearly recognized, while 61% characters written on one-side of the standard paper are sufficiently recognized. The average perceptual indices of Pix2pixGAN processed images are 16.83, which is very close to the average perceptual index 16.19 of clean handwriting images. Our work has important value for THz-imaging-based nondestructive historical document analysis. [ABSTRACT FROM AUTHOR]

In industry, robots are widely used to solve repetitive or dangerous actions in product production, so that product production can be more efficient. However, the problem that robots are often challenged is the convenience and the efficiency of introducing the production line. Therefore, the intuitive robot guidance method is an important issue; this paper will introduce the concept of human–robot interactions (HRI) and use deep learning methods on the machine vision system to complete the robot-guided assembly operation analysis, and the assembly operation analysis requires semantic segmentation as pre-processing. Therefore, we propose a novel semantic template correlation model architecture based on zero-shot learning (ZSL) to achieve the effect of rapid deployment. The semantic template correlation model is to search for the object area offline learning through the semantic template generated by the physics engine, and when inferring online, we can directly enter the semantic template to obtain the relevant object region. Finally, this paper verifies that the MIoU can be increased by more than 5% through the verification of the general database VOC2012. [ABSTRACT FROM AUTHOR]

Images captured by image acquisition systems in scenes with fog or haze contain missing details, dull color, and reduced brightness. To address this problem, the dual multiscale neural network model based on the AOD theory is proposed in this paper. First, two parameters, namely transmittance and atmospheric layer coefficient, of the atmospheric scattering model are combined into a single parameter. The new neural network model proposed in this paper is then used to train this parameter. The network model proposed in this paper consists of two multiscale modules and a mapping module. In order to extract more perfect image features, this paper designs two multiscale modules for feature extraction. The convolution parameters of Multiscale Module 1 are designed to maintain the size of original images during feature extraction by adding pooling, sampling, etc. After each convolution operation, multiscale module 2 uses multiple small-sized convolution kernels for convolution, in which the concat operation is added to better connect the individual kernels, the mapping module maps the fogged images onto the extracted feature map and is able to extract more detail from the original image to obtain better defogging results after processing. Training is performed to derive a unified parameter model for image defogging, and finally, the defogged image is obtained using this parameter estimation model. The experimental results show that the model proposed this paper not only outperforms the AOD network in terms of peak signal-to-noise ratio, structural similarity, and subjective vision but also outperforms the mainstream deep learning and traditional methods in terms of image defogging; moreover, the defogged images are optimized in terms of detail, color, and brightness. In addition, ablation experiments had demonstrated that all of the structures in this paper were necessary. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease. [ABSTRACT FROM AUTHOR]

Tomato, as an essential food crop, is consumed worldwide, and at the same time, it is susceptible to several diseases that lead to a reduction in tomato yield. Proper diagnosis of tomato diseases is required to increase the output of tomato crops. For this purpose, this paper proposes a tomato plant disease detection algorithm based on Pyramid Scene Parsing Network (PSPNet) and deep learning. First, the training data set is augmented with data to alleviate the data imbalance problem in each category, and then the augmented images are fed into the proposed Mob-PSP network for training. The proposed network utilizes the lightweight MobileNet-V2 model as the feature extraction technique while integrating the PSPNet module to enhance the network’s detection performance. The aim is to effectively extract local and global features from plant disease images, which are being introduced in plant disease detection. This study evaluated the model on the tomato subset of the public data set PlantVillage. The experimental results demonstrate that this algorithm achieves a balance between inference speed and detection accuracy, outperforming other state-of-the-art algorithms. Additionally, compared to the baseline model Inception-V3, the inference speed is improved by 10.73 frames per second, while maintaining an average accuracy of 99.69 % with only 6.5M parameters. [ABSTRACT FROM AUTHOR]

Agriculture holds a crucial position in maintaining livelihoods and securing food sources, particularly in nations such as Ethiopia, where a substantial portion of the population depends on agricultural pursuits. However, meeting the growing demand for food production amidst population growth presents considerable challenges. Recent advancements in technology, particularly in the areas of Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) offer promising solutions to address these challenges. This paper explores the potential of integrating ML, DL, and IoT technologies in agriculture to revolutionize the sector. By harnessing data-driven insights, farmers can make informed decisions regarding crop management, soil health, and weather patterns, leading to optimized resource allocation and increased productivity. Moreover, IoT devices enable the real-time monitoring and control of agricultural operations, enhancing sustainability and productivity. Despite the opportunities presented by these technologies, there are also challenges to overcome, such as data quality, connectivity issues, and the need for farmer education. However, with concerted efforts and investment, Ethiopia and other agricultural regions can unlock the full potential of ML, DL, and IoT technologies to ensure food security, alleviate poverty, and drive economic development. This review paper offers perspectives on the present status, challenges, and future possibilities regarding the integration of ML, DL, and IoT in agriculture. It underscores the transformative potential of these technologies within the sector. [ABSTRACT FROM AUTHOR]

In recent years, the applications of digital humans have become increasingly widespread. One of the most challenging core technologies is the generation of highly realistic and automated 3D facial animation that combines facial movements and speech. The single-modal 3D facial animation driven by speech typically ignores the weak correlation between speech and upper facial movements as well as head posture. In contrast, the video-driven approach can perfectly solve the posture problem while obtaining natural expressions. However, mapping 2D facial information to 3D facial information may lead to information loss, which make lip synchronization generated by video-driven methods is not as good as the speech-driven methods trained on 4D facial data. Therefore, this paper proposes a dual-modal generation method that uses speech and video information to generate more natural and vivid 3D facial animation. Specifically, the lip movements related to speech are generated by speech-video information, while speech-uncorrelated postures and expressions are generated solely by video information. The speech-driven module is used to extract speech features, and its output lip animation is then used as the foundation for facial animation. The expression and pose module is used to extract temporal visual features for regressing expression and head posture parameters. We fuse speech and video features to obtain chin posture parameters related to lip movements, and use these parameters to fine-tune the lip animation generated form the speech-driven module. This paper introduces multiple consistency losses to enhance the network's capability to generate expressions and postures. Experiments conducted on the LRS3, TCD-TIMIT and MEAD datasets show that the proposed method achieves better performance on evaluation metrics such as CER, WER, VER and VWER than the current state-of-the-art methods. In addition, a perceptual user study show that over 77% and 70% of cases believe that this paper's method is better than the comparative algorithms EMOCA and SPECTRE in terms of realism. In terms of lip synchronization, it received over 79% and 66% of cases support, respectively. Both evaluation methods demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for battery management systems. Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data. However, the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be improved. To address this challenge, this paper proposes a novel deep learning model, the MLP-Mixer and Mixture of Expert (MMMe) model, for RUL prediction. The MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level features. Additionally, we devise an ensemble predictor based on a Mixture-of-Experts (MoE) architecture to generate reliable RUL predictions. The experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods, providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation process. Our code and dataset are available at the website of github. [ABSTRACT FROM AUTHOR]

Automating tasks through human activity recognition in video data has become increasingly vital. Deep learning has yielded versatile activity recognition systems applicable in surveillance, healthcare analysis, sports, and human–computer interaction. Despite various proposed video-based activity recognition techniques over the years, the reliance over RGB frames, accompanied by other modalities like joint locations and depth maps, often proves less effective compared to multimodal methods. In response to this challenge, our paper introduces a competitive approach for identifying human activity in video frames. Leveraging a Convolutional Long Short-Term Memory (Conv-LSTM) network and a novel pre-processing step involving a Human Segmentation network, our method accentuates human subjects in each frame using segmentation maps. These highlighted frames undergo further processing through Convolutional Neural Networks (CNNs) to learn feature vectors, without including other modalities with RGB frames directly. The learned features are then subjected to Long Short-Term Memory (LSTM) units for comprehending sequential video data and drawing meaningful inferences. The proposed methodology undergoes rigorous testing on three publicly available datasets—KARD, MSR Daily Activity, and SBU-Interactions. Remarkably, our approach outperforms similar state-of-the-art methods, achieving benchmark accuracy scores exceeding 98% on MSR Daily Activity and 99% on KARD and SBU-Interactions datasets. In essence, our method not only provides a competitive solution for human activity recognition in video frames but also contributes to advancing the field by integrating Conv-LSTM networks and innovative pre-processing techniques. The comprehensive evaluation on multiple datasets underlines the robustness and superior performance of our proposed approach. [ABSTRACT FROM AUTHOR]

The restoration of blurred images is a crucial topic in the field of machine vision, with far-reaching implications for enhancing information acquisition quality, improving algorithmic accuracy and enriching image texture. Efforts to mitigate the phenomenon of blur have progressed from statistical approaches to those utilizing deep learning techniques. In this paper, we propose a Generative Adversarial Network (GAN)-based image restoration method to address the limitations of existing techniques in restoring color and detail in motion-blurred images. To reduce the computational complexity of generative adversarial networks and the vanishing gradient during learning, an U-net-based generator is used, and it is configured to emphasize the channel and spatial characteristics of the original information through a proposed CSAR(Channel and Spatial Attention Residual) blocks module rather than a simple concatenate operation. To validate the efficacy of the algorithm, comprehensive comparative experiments have been conducted on the GoPro dataset. Experimental results show that the peak signal-to-noise ratio is improved compared to SRN and MPRNet algorithms with good image restoration ability. Objects detection experiments using Yolo V3 showed that the proposed algorithms can generate deblerring images with higher information quality. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative effect on integrating technology into people's daily lives. IoT covers essential areas such as smart cities, smart homes, and health-based industries. However, security and privacy challenges arise with the rapid growth of IoT devices and applications. Vulnerabilities such as node spoofing, unauthorized access to data, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection have emerged as significant concerns. Recently, machine learning (ML) and deep learning (DL) methods have significantly progressed and are robust solutions to address these security issues in IoT devices. This paper comprehensively reviews IoT security research focusing on ML/DL approaches. It also categorizes recent studies on security issues based on ML/DL solutions and highlights their opportunities, advantages, and limitations. These insights provide potential directions for future research challenges. [ABSTRACT FROM AUTHOR]

Neural network applications, as an emerging machine learning technology, are increasingly being integrated into pharmaceutical manufacturing technologies, offering significant improvement opportunities for performance, efficiency and sustainability. This review paper utilizes a systematic methodology to establish key literature trends and themes. The state-of-the-art body of knowledge in this hot research area is analyzed in descriptive (e.g. neural network technologies studied, sustainability indicators considered, manufacturing process addressed) and thematic synthesis components. Process analysis and improvement, quality control and additive manufacturing were identified as the three focal research themes, and research lines within these themes were further studied and discussed. To guide future research, potential paths and research questions are proposed against the gaps identified. The originality of this work lies in its methodology (adoption of a systematic review approach, highly limited in the current literature), its inclusion of sustainability (as an imperative concept for manufacturing technology research) and its specific focus on neural network applications in the context of pharmaceutical manufacturing technologies (a perspective, either has been missing or addressed too widely by extant contributions). [ABSTRACT FROM AUTHOR]

The paper introduces an integrated methodology for license plate character recognition, combining YOLOv8 for segmentation and a CSPBottleneck-based CNN classifier for character recognition. The proposed approach incorporates pre-processing techniques to enhance the recognition of partial plates and augmentation methods to address challenges arising from colour diversity. Performance analysis demonstrates YOLOv8’s high segmentation accuracy and fast processing time, complemented by precise character recognition and efficient processing by the CNN classifier. The integrated system achieves an overall accuracy of 99.02% with a total processing time of 9.9 ms, offering a robust solution for automated license plate recognition (ALPR) systems. The integrated approach presented in the paper holds promise for the practical implementation of ALPR technology and further development in the field of license plate recognition systems. [ABSTRACT FROM AUTHOR]

As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets. [ABSTRACT FROM AUTHOR]

This paper focuses on how regulatory bodies respond to artificial intelligence (AI)-enabled medical devices. To achieve this, we present a comparative overview of the United States (USA), European Union (EU), and China. Our search in the governmental database identified 59 AI medical devices approved in China as of July 2023. In comparison to the rules-based regulatory approach in China, the approaches in the USA and EU are more standards-oriented. [ABSTRACT FROM AUTHOR]

Even though accurate detection of dangerous malignancies from mammogram images is mostly dependent on radiologists' experience, specialists occasionally differ in their assessments. Computer-aided diagnosis provides a better solution for image diagnosis that can help experts make more reliable decisions. In medical applications for diagnosing cancerous growths from mammogram images, computerized and accurate classification of breast cancer mammogram images is critical. The deep learning approach has been widely applied in medical image processing and has had considerable success in biological image classification. The Convolutional Neural Network (CNN), Inception, and EfficientNet are proposed in this paper. The proposed models attain better performance compared to the conventional CNN. The models are used to automatically classify breast cancer mammogram images from Kaggle into benign and malignant. Simulation results demonstrated that EfficientNet, with an accuracy between 97.13 and 99.27%, and overall accuracy of 98.29%, perform better than the other models in this paper. [ABSTRACT FROM AUTHOR]

To solve the problem of mobile robots needing to adjust their pose for accurate operation after reaching the target point in the indoor environment, a localization method based on scene modeling and recognition has been designed. Firstly, the offline scene model is created by both handcrafted feature and semantic feature. Then, the scene recognition and location calculation are performed online based on the offline scene model. To improve the accuracy of recognition and location calculation, this paper proposes a method that integrates both semantic features matching and handcrafted features matching. Based on the results of scene recognition, the accurate location is obtained through metric calculation with 3D information. The experimental results show that the accuracy of scene recognition is over 90%, and the average localization error is less than 1 meter. Experimental results demonstrate that the localization has a better performance after using the proposed improved method. [ABSTRACT FROM AUTHOR]

In current convolution-based image dehazing networks, increasing the depth and width of convolutional layers is a common strategy to improve network performance. However, this approach significantly increases the complexity and computational cost of the dehazing network. To address this issue, this paper proposes a U-shaped multi-scale adaptive selection network (UMA-Net). Without introducing additional parameters and computational costs, the network leverages the influence of different scales of convolutional kernels on the receptive field. It combines standard and dilated convolutions into the feed-forward network (FFN) to propose a multi-scale adaptive (MA) dehazing module, further expanding the receptive field and focusing on important spatial and channel information within the FFN. To fully exploit the multi-scale features of the MA module, a lightweight channel attention-guided fusion (CAGF) module is proposed, which achieves the restoration of high-quality dehazed images from hazy images. Extensive experiments demonstrate the effectiveness of the proposed modules. On the Reside SOTS dataset, it achieves state-of-the-art performance with only 0.816M parameters and 8.794G FLOPs. [ABSTRACT FROM AUTHOR]

Fabric pilling can significantly affect the usability and appearance of fabrics; making the evaluation of pilling grades a crucial aspect of textile quality inspection. Currently, manual visual inspection is the mainstream strategy in the industry for assessing pilling, but this method suffers from low efficiency and inconsistent accuracy. Therefore, there is a need for a more precise and efficient method to evaluate textile pilling grades. In this paper, we propose an objective system for assessing fabric pilling grades using a deep learning model in conjunction with a gradient boosting decision tree classification algorithm. The system comprises a multi-light source fabric image acquisition setup, an Attention-enhanced UNet model, and a gradient boosting decision tree classifier. The process begins with capturing fabric images, which are then segmented using the Attention-enhanced UNet model. This model effectively extracts pilling features, including the number of pills, the pilling area, and the pilling coverage ratio. Our experimental results show that this segmentation method outperforms traditional filtering and edge detection algorithms in terms of efficiency. The attention mechanism integrated into the UNet model significantly enhances the segmentation performance, particularly for fabric pilling. By combining the extracted features as input for the fabric evaluation algorithm, our proposed method achieves an impressive classification accuracy of 98.47% in objectively evaluating fabric pilling grades. This approach offers a substantial improvement in both the accuracy and efficiency of fabric pilling grade assessment. [ABSTRACT FROM AUTHOR]