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In recent machine learning applications, promising outcomes have emerged through the integration of Deep Learning (DL) and Extreme Learning Machine (ELM) techniques with wavelet networks (WN), leading to high classification accuracy. Researchers have explored various strategies to enhance classifier performance. Among these methods, Deep Wavelet Autoencoders (AE) and the Deep Wavelet ELM algorithm have been extensively applied in diverse image classification domains. This research paper conducts a comparative analysis between these two wavelet AE‐based techniques. The first approach involves the development of a Deep Stacked Sparse Wavelet Autoencoder (DSSWAE), while the second method focuses on creating a Deep Wavelet ELM Autoencoder (DW‐ELM‐AE). To enable a comprehensive comparison, experiments were conducted using different datasets, specifically MNIST and COIL‐20. The results clearly demonstrate that the Deep Stacked Wavelet AE (DSWAE) and DSSWAE algorithms outperform the State‐of‐the‐Art (SOTA) techniques in terms of accuracy efficiency, emphasizing their significant potential for a wide range of image classification applications. [ABSTRACT FROM AUTHOR]

Introduction: Teleradiology allows distant facilities to electronically transmit images for interpretation, thereby bridging the radiology service gap between urban and rural areas. The technology improves healthcare quality, treatment options, and diagnostic accuracy. However, in low resource settings like Ethiopia, teleradiology services are limited, posing challenges for implementation. Therefore, this study is aimed at exploring the factors that facilitated or hindered the implementation of web‐based teleradiology in the public hospitals of the South Gondar Zone, Northwest Ethiopia. Methods: In this study, a purposive sampling method was employed to select seventeen participants, including hospital managers, physicians, emergency surgeons, and radiologists, for an in‐depth interview (IDI). The interviews were conducted from March to May 2023. A reflexive thematic analysis was conducted using an abductive coding technique at the semantic/explicit level. Data were collected through semistructured interviews conducted face‐to‐face and virtually, with audio recordings transcribed, translated, and analyzed using Open Code version 4.02 software. Trustworthiness was ensured through prolonged engagement, reflective journaling, and review by coauthors. Results: The study examined eight main themes, with barriers to sustainable teleradiology implementation falling into five categories: technological, organizational, environmental, individual, and workflow and communication. Conversely, identified facilitators included improved radiology service efficiency, system accessibility, collaboration opportunities, and user trust in the radiology ecosystem. Within each theme, factors with potential impacts on teleradiology system sustainability were identified, such as the lack of system handover mechanisms, absence of a central image consultation center, and inadequate staffing of full‐time radiologists and technical personnel. Conclusions: The study highlights the positive user perception of a web‐based teleradiology system's user‐friendliness and efficiency. Overcoming challenges and leveraging facilitators are crucial for optimizing teleradiology and improving service delivery and patient outcomes. A centralized consultation center with dedicated radiologists and technical personnel is recommended for maximizing efficiency. [ABSTRACT FROM AUTHOR]

Image segmentation is one important process in image analysis and computer vision and is a valuable tool that can be applied in fields of image processing, health care, remote sensing, and traffic image detection. Given the lack of prior knowledge of the ground truth, unsupervised learning techniques like clustering have been largely adopted. Fuzzy clustering has been widely studied and successfully applied in image segmentation. In situations such as limited spatial resolution, poor contrast, overlapping intensities, and noise and intensity inhomogeneities, fuzzy clustering can retain much more information than the hard clustering technique. Most fuzzy clustering algorithms have originated from fuzzy c‐means (FCM) and have been successfully applied in image segmentation. However, the cluster prototype of the FCM method is hyperspherical or hyperellipsoidal. FCM may not provide the accurate partition in situations where data consists of arbitrary shapes. Therefore, a Fuzzy C‐Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning. Thus, this paper implements FCRM and applies the algorithm to color segmentation using Berkeley's segmentation database. The results show that FCRM obtains more accurate results compared to other fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

The use of hyaluronic acid (HA) fillers in oncology patients undergoing PET-CT scans is a topic of debate due to potential interference with imaging accuracy. A 54-year-old female, postmelanoma metastasectomy in the parotid region with subsequent facial nerve palsy (FNP), received HA filler injections for facial symmetry and functional restoration. Follow-up PET-CT scans showed no interference or artifacts attributable to HA injection, allowing for accurate imaging results. This case suggests that HA fillers administered in oncology patients may not universally pose challenges or disrupt PET-CT imaging interpretation. Due to the possible false positives induced by fillers, the inclusion of aesthetic treatments in patients' anamnesis is a crucial step to accurately interpret PET-CT images. Although maintaining high level of caution in interpreting PET-CT results after filler injection is essential, our case emphasizes the safety of this procedure in oncology patients undergoing follow-up PET-CT scans. [ABSTRACT FROM AUTHOR]

Over the last 100 years, the range of the White Stork Ciconia ciconia asiatica (Linnaeus, 1758) in Kazakhstan has greatly diminished; it has entirely disappeared in its former eastern and northern ranges, while the number of nesting sites has decreased in the remaining part of its habitat. The most recent specialized studies on the distribution of the White Stork here were conducted in the 1980s. Subsequently, ornithologists were only aware of isolated nests, and in the last 15 years, up until recently, in Kazakhstan, there was only one known active nest in the Zhambyl region. To clarify the current distribution of the White Stork in Kazakhstan, we conducted field research during the breeding season in places of its former distribution in the Zhambyl and Turkestan Regions. Nest surveys were conducted by visual observations using binoculars and a camera with a telephoto lens. During the expedition, 43 inhabited nests of the White Stork were found; information about another 4 nests was obtained from data survey and social networks. Analysis of satellite images allowed us to identify 12 nests located on power line pylons in the southern regions of the republic, specifically in the Keles, Zhetisay, and Maktaaral districts. In total, 59 White Stork nests were found in Kazakhstan. In addition, satellite images revealed more than 120 power line pylons with White Stork nests in the territory of Uzbekistan in close proximity to the Kazakhstan border. All literary and electronic sources available to the authors have been meticulously reviewed. By synthesizing these data, two maps were created: one delineating all recorded White Stork nesting sites across Kazakhstan and its neighboring territories, and the other illustrating the known contemporary and historical ranges of the White Stork in Kazakhstan. A cadastral survey accompanies new nest discoveries of the White Stork. Based on the newly acquired data, we conclude that the White Stork population in Kazakhstan is currently on the rise. [ABSTRACT FROM AUTHOR]

One of the most challenging aspects of satellite remote sensing is image fusion. Image fusion increases the visual interpretation of the image and has many applications such as monitoring water bodies, land cover, urbanisation, agriculture, national defence, and so forth. Remote sensing applications require images with a high spatial and spectral resolution for accurately processing and distinguishing the land cover classes with fine texture and shape details. Due to technical limitations, most satellites cannot take high-resolution multi-spectral images but can get high-resolution panchromatic images and low-resolution multi-spectral satellite images separately. This article proposes a novel fusion method, geospatial data fusion (GDF), to obtain high-resolution multi-spectral images. GDF, along with three well-known fusion methods viz., Brovey Transform (BT), wavelet transform (WT), and Fourier transform (FT), have been comparatively implemented to fuse the Cartosat-2 and Sentinel-2 imageries of the Sangam area of Prayagraj, India. The fusion has been done to extract the earth's surface features from the fused imagery. In this research, the fused image is utilised for river water mapping. Results confirm that the GDF outperforms other existing fusion methods and successfully maps the river water in the study area. [ABSTRACT FROM AUTHOR]

Skeletal muscle of patients with sporadic inclusion body myositis (sIBM) presents with inflammation, including upregulation of inflammatory cytokines such as interferon γ (IFNγ). Non-inflammatory features are also observed, like the sarcoplasmic accumulation of proteins including TDP-43 and p62. This study aimed to investigate the effect of IFNγ and interleukin 1-β (IL-1β) on TDP-43 and p62 aggregation in vitro. Primary human myotubes were treated with IL-1β (20 ng/mL) and IFNγ (750 ng/mL) separately or combined for 48 hr. Sarcoplasmic TDP-43 aggregates and p62 puncta were assessed using image analysis for size, frequency, and colocalization with each other. Total protein expression of TDP-43, p62 and LC3 was assessed using western blotting. The subcellular localization of TDP-43 was also analyzed using image analysis. Combined IL-1β and IFNγ treatment increased puncta size of p62 compared to control (0.49 ± 0.13 µm2 versus 0.28 ± 0.06 µm2), without affecting puncta frequency or p62 expression but with an increased LC3II/LC3I ratio, suggesting autophagic alterations. IL-1β or IFNγ did not alter p62 puncta size or frequency, suggesting a combined insult of multiple inflammatory mediators is necessary to cause p62 alterations. IL-1β increased p62 protein expression in an autophagy-independent manner. None of the cytokine treatments affected TDP-43 protein expression, size, or frequency of TDP-43 aggregates or localization, suggesting IL-1β and IFNγ may influence TDP-43 processing in human skeletal muscle cells. TDP-43 was localized to the sarcoplasm under control conditions, suggesting this may not be a pathological feature. Overall, sIBM-like TDP-43/p62 features were not triggered by IL-1β and/or IFNγ. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has evolved into a powerful tool that has wide-spread application in computer vision such as computer-aided inspection, industrial control systems, and navigation of robots. Monitoring the condition of machineries and mechanical components for the presence of faults with the aid of image-based automated analysis is one major application of computer vision. Diagnosing machinery faults from images can be made feasible with the adoption of deep learning and machine learning techniques. The primary objective of this study is to detect malfunctions in photovoltaic (PV) modules by utilizing a combination of deep learning and machine learning methodologies, with the assistance of RGB images captured via unmanned aerial vehicles. Six test conditions of PV modules such as good panel, snail trail, delamination, glass breakage, discoloration, and burn marks were considered in the study. The overall experimentation was carried out in two phases: (i) deep learning phase and (ii) machine learning phase. In the initial deep learning phase, the final fully connected layer of six pretrained networks, namely, DenseNet-201, VGG19, ResNet-50, GoogLeNet, VGG16, and AlexNet, was utilized to extract PVM image features. During the machine learning phase, feature selection from the extracted features was carried out using the J48 decision tree algorithm. Post selection of features, three families of classifiers such as tree, Bayes, and lazy were applied to determine the best feature extractor-classifier pair. The combination of DenseNet-201 features with k-nearest neighbour (IBK) classifier produced the overall classification accuracy of 100.00% among all other pretrained network features and classifiers considered. [ABSTRACT FROM AUTHOR]

Image edge detection plays a crucial role in image analysis and recognition. However, when dealing with color images captured by unmanned aerial vehicles (UAVs), there are certain limitations, such as large operations, multiple noise sources, easy distortion, and missing information in edge detection. To address these shortcomings, this study proposes a UAV color image edge detection method based on an enhanced fireworks algorithm. In this method, the color image pixels of the UAV are represented using quaternions. The explosion amplitude formula of the fireworks is divided into two categories based on the mean value of the number of fireworks explosions. For each category, an explosion formula is proposed, and the explosion mutation operator of the fireworks algorithm is improved accordingly. By applying the proposed algorithm, the preliminary edges of a UAV color image are obtained. Additionally, a novel approach for color image edge refinement is introduced. This approach involves classifying the edge points based on their degree of attachment, which leads to the formation of the edges in a UAV color image. Experimental results demonstrate that the algorithm proposed in this study offers several advantages, including fast calculation, strong denoising capability, and high-quality edge detection. [ABSTRACT FROM AUTHOR]

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction. [ABSTRACT FROM AUTHOR]

Oral squamous cell carcinoma (OSCC) is one of the deadliest and most common types of cancer. The incidence of OSCC is increasing annually, which requires early diagnosis to receive appropriate treatment. The biopsy technique is one of the most important techniques for analyzing samples, but it takes a long time to get results. Manual diagnosis is still subject to errors and differences in doctors' opinions, especially in the early stages. Thus, automated techniques can help doctors and patients to receive appropriate treatment. This study developed several hybrid models based on the fused CNN features for diagnosing OSCC-100x and OSCC-400x datasets for oral cancer, which have the ability to analyze medical images with a high level of precision and accuracy. They can detect subtle patterns, abnormalities, or indicators of diseases that may be difficult to recognize with the naked eye. The systems have the potential to significantly reduce human error and provide more consistent and reliable results, resulting in improved diagnostic accuracy. The systems also have the potential for early detection of OSCC for treatment success and improved patient outcomes. By detecting diseases at an early stage, clinicians can initiate interventions in a timely manner, potentially preventing OSCC progression and improving the chances of successful treatment. The first strategy was based on GoogLeNet, ResNet101, and VGG16 models pretrained, which did not achieve satisfactory results. The second strategy was based on GoogLeNet, ResNet101, and VGG16 models based on the adaptive region growing (ARG) segmentation algorithm. The third strategy is based on a mixed technique between GoogLeNet, ResNet101, and VGG16 models and ANN and XGBoost networks based on the ARG hashing algorithm. The fourth strategy for oral cancer diagnosis by ANN and XGBoost is based on features fused between CNN models. The ANN with fusion features of GoogLeNet-ResNet101-VGG16 yielded an AUC of 98.85%, accuracy of 99.3%, sensitivity of 98.2%, precision of 99.5%, and specificity of 98.35%. [ABSTRACT FROM AUTHOR]

Spatial data analysis provides valuable information to the government as well as companies. The rapid improvement of modern technology with a geographic information system (GIS) can lead to the collection and storage of more spatial data. We developed algorithms to choose optimal locations from those permanently in a space for an efficient spatial data analysis. Distances between neighboring permanent locations are not necessary to be equispaced distances. Robust and sequential methods were used to develop algorithms for design construction. The constructed designs are robust against misspecified regression responses and variance/covariance structures of responses. The proposed method can be extended for future works of image analysis which includes 3 dimensional image analysis. [ABSTRACT FROM AUTHOR]

Building extraction from high-resolution aerial images is critical in geospatial applications such as telecommunications, dynamic urban monitoring, updating geographic databases, urban planning, disaster monitoring, and navigation. Automatic building extraction is a massive task because buildings in various places have varied spectral and geometric qualities. As a result, traditional image processing approaches are insufficient for autonomous building extraction from high-resolution aerial imaging applications. Automatic object extraction from high-resolution images has been achieved using semantic segmentation and deep learning models, which have become increasingly important in recent years. In this study, the U-Net model was used for building extraction, initially designed for biomedical image analysis. The encoder part of the U-Net model has been improved with ResNet50, VGG19, VGG16, DenseNet169, and Xception. However, three other models have been implemented to test the performance of the model studied: PSPNet, FPN, and LinkNet. The performance analysis through the intersection of union method has shown that U-Net with the VGG16 encoder presents the best results compared to the other models with a high IoU score of 83.06%. This research aims to examine the effectiveness of these four approaches for extracting buildings from high-resolution aerial data. [ABSTRACT FROM AUTHOR]

Engineered wood products, such as cross-laminated timber (CLT), are becoming more popular in the designs of modern sustainable buildings. This increased production of CLT requires more robust, yet less labour-intensive means to assess the material characteristics of whole CLT panels. In exploring ways of improving efficiency, this study explores multivariate image analysis (MIA) via partial least squares discriminant analysis (PLS-DA) machine learning as a means to classify CLT material features. CLT panels underwent nondestructive testing using near-infrared (NIR) hyperspectral imaging and X-ray computed tomography (CT) analysis. MIA was performed on these results to build predictive models for wood features, such as fibre alignment and knot type. The models showed that it was possible to classify material features on the surface of CLT using NIR alone; whilst when combined with X-ray data, it enhanced the predictive ability of material features throughout the CLT volume. These first results from such modelling have the potential to help map the chemical and physical material properties of CLT, improving the manufacturing efficiency of the product and allowing greater sustainability of engineered wood products. [ABSTRACT FROM AUTHOR]

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first method used the minimum distance, and the second method used the clustering algorithm called DBSCAN. Both methods were tested with and without reclustering using the self-organizing map (SOM). The result from comparing the images after segmenting them and comparing the time taken to implement the segmentation process shows the effectiveness of these methods when used with SOM. [ABSTRACT FROM AUTHOR]

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms. [ABSTRACT FROM AUTHOR]

In medical image analysis, collecting multiple annotations from different clinical raters is a typical practice to mitigate possible diagnostic errors. For such multirater labels' learning problems, in addition to majority voting, it is a common practice to use soft labels in the form of full-probability distributions obtained by averaging raters as ground truth to train the model, which benefits from uncertainty contained in soft labels. However, the potential information contained in soft labels is rarely studied, which may be the key to improving the performance of medical image segmentation with multirater annotations. In this work, we aim to improve soft label methods by leveraging interpretable information from multiraters. Considering that mis-segmentation occurs in areas with weak supervision of annotations and high difficulty of images, we propose to reduce the reliance on local uncertain soft labels and increase the focus on image features. Therefore, we introduce local self-ensembling learning with consistency regularization, forcing the model to concentrate more on features rather than annotations, especially in regions with high uncertainty measured by the pixelwise interclass variance. Furthermore, we utilize a label smoothing technique to flatten each rater's annotation, alleviating overconfidence of structural edges in annotations. Without introducing additional parameters, our method improves the accuracy of the soft label baseline by 4.2% and 2.7% on a synthetic dataset and a fundus dataset, respectively. In addition, quantitative comparisons show that our method consistently outperforms existing multirater strategies as well as state-of-the-art methods. This work provides a simple yet effective solution for the widespread multirater label segmentation problems in clinical diagnosis. [ABSTRACT FROM AUTHOR]

The goal of this project is to write a program in the C++ language that can recognize motions made by a subject in front of a camera. To do this, in the first place, a sequence of distance images has been obtained using a depth camera. Later, these images are processed through a series of blocks into which the program has been divided; each of them will yield a numerical or logical result, which will be used later by the following blocks. The blocks into which the program has been divided are three; the first detects the subject's hands, the second detects if there has been movement (and therefore a gesture has been made), and the last detects the type of gesture that has been made accomplished. On the other hand, it intends to present to the reader three unique techniques for acquiring 3D images: stereovision, structured light, and flight time, in addition to exposing some of the most used techniques in image processing, such as morphology and segmentation. [ABSTRACT FROM AUTHOR]

Incomplete remote sensing image observation data require proper processing to restore the original data. Nevertheless, the lack of information, due to noise and dead pixels, along with the quality of the acquired images that may be severely degraded, limits the accuracy of later processing. Therefore, this paper proposes a remote sensing image restoration scheme that employs the low-rank structure of the Bayesian probability tensor latent space and a new tensor completion method, which has strong robustness for the model selection process of the following remote sensing image data analysis. Considering the data space, we incorporate an adaptive nuclear norm regularization into the processing of latent factors that performs singular value decomposition on a more accurate scale. The proposed model adopts two algorithms: Augmented Lagrange Multiplier (ALM) and Alternating Least Squares (ALS). Several synthetic and real-data-based experiments illustrate the inherent ability of our method in restoring original information and preventing overfitting problems, even when a vast number of items are missing. In addition, the combination of adaptive nuclear norm regularization and tensor decomposition is stable, settling a high sensitivity of rank selection and low computational efficiency in traditional tensor reconstruction methods. [ABSTRACT FROM AUTHOR]

Human pose and motion detection is an important area of computer vision research, covering the interplay of different fields such as image processing, pattern recognition, and artificial intelligence. Due to the complexity of human motion, existing 3D recognition and pose detection methods based on low-quality depth images are not very accurate and reliable. Due to the time-sensitive nature of features, a single feature cannot adapt to the dynamic changes of the scene, so it is difficult for the target tracking algorithm based on a single feature to achieve robust tracking results. If multiple features are fused and applied in the tracking algorithm, the complementarity between different features can be used to better adapt to the scene changes and achieve robust tracking results. In order to solve the problem of human pose and human motion recognition in low-quality depth images, this paper uses the Kinect somatosensory camera to obtain 20 human skeleton joint points through the Kinect skeleton tracking technology. This paper studies the typical human posture and interactive action recognition technology in daily life. On the basis of understanding the characteristics of skeletal data, this paper proposes a distance feature and angle feature model combined with human body structure. Through the experimental results, it is found that the distance feature and the angle feature value are basically not affected by the distance change. When the subjects turned 45° to the left and 45° to the right, the distance characteristics changed, which was different from the characteristic data when the subjects turned around. [ABSTRACT FROM AUTHOR]

We develop effective medical image classification techniques, with an emphasis on histopathology and magnetic resonance imaging (MRI). The trainer utilized the curriculum as a starting point for a set of data and a restricted number of samples, and we used it as a starting point for a set of data. As calibrating a machine learning model is difficult, we used alternative methods as unsupervised feature extracts or weight-conditioning factors for identifying pathological histology pictures. As a result, the pretrained models will be trained on 3-channel RGB pictures, while the MRI sample has more slices. To alter the working model using the MRI data, the convolutional neural network (CNN) must be fine-tuned. Pretrained models are placed and then used as feature snippets. However, there is a scarcity of well-done medical photos, making training machine learning models a difficult endeavor to begin with. In any case, data augmentation aids in the generation of sufficient training samples; however, it is unclear if data augmentation aids in the prediction of unknown data samples. As a result, we fine-tuned machine learning models without using any additional data. Furthermore, rather than utilizing a standard machine learning classifier for the MRI data, we created a unique CNN that uses both 3D shear descriptors and deep features as input. This custom network identifies the MRI sample after processing our representation of the characteristics from beginning to end. On the hidden MRI dataset, our bespoke CNN outperforms traditional machine learning. Our CNN model is less prone to overfitting as a result of this. Furthermore, we have given cutting-edge outcomes employing machine learning. [ABSTRACT FROM AUTHOR]

Image sentiment recognition has attracted considerable attention from academia and industry due to the increasing tendency of expressing opinions via images and videos online. Previous studies focus on multilevel representation from global and local views to improve recognition performance. However, it is insufficient to research the importance and relationship of visual regions for image sentiment recognition. This paper proposes an attention-based sentiment region importance and relationship (ASRIR) analysis method, including important attention and relation attention for image sentiment recognition. First, we extract spatial region features using a multilevel pyramid network from the image. Second, we design important attention to exploring the sentiment semantic-related regions and relation attention to investigating the relationship between regions. In order to release the excessive concentration of attention, we employ a unimodal function as the objective function for regularization. Finally, the region features weighted by the attention mechanism are fused and input into a fully connected layer for classification. Extensive experiments on various commonly used image sentiment datasets demonstrate that our proposed method outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

This study aims to develop a nondestructive methodology using image analysis to classify papayas (Carica papaya L.) based on their skin color, volume, and mass. First, papayas were classified visually according to their skin color, and the volume and mass were determined experimentally. In sequence, images from papayas were acquired and processed using image analysis. Papayas were classified according to their maturity using a computational methodology based on the CIELab scale. Furthermore, the volume and mass were assessed using a Python code developed to analyze the fruit images. The prediction of the maturity stage had a global estimate of 95.0%, and the estimated volume and mass did not differ statistically from the experimental values. This research reports a novel image analysis methodology focused on papaya classification during its postharvest. Practical applications: Image analysis can be used to classify fruits during their postharvest. In the current research, a nondestructive methodology using image analysis was developed and then used to classify papayas according to their skin color, volume, and mass. Results suggest that papayas can be classified based on their maturity (unripe, partially ripe, and ripe), volume, and mass. The developed methodology could be used to replace the manual sorting of papayas during their classification. [ABSTRACT FROM AUTHOR]

The medical images and reference intervals, in the diagnosis and treatment process of diseases, are undoubtedly very important. Clear and easy interpretation of images and reference intervals derived from medical devices and statistical methods, respectively, are of great importance for doctors in the diagnosis and treatment process. In this article, for systemic lupus erythematosus disease, we have transformed the reference intervals into fuzzy sets and calculated the entropy values to find the uncertainty contained in the reference interval of systemic lupus erythematosus disease, so the doctors can keep in view patient treatment using entropy values. Finally, for better observations of patients, with the help of the entropy functions, new types of medical images were given for some trabecular bone diseases using Wolfram Mathematica 7.0. We should note that this type of medical images for bone mineral density is not in medicine. [ABSTRACT FROM AUTHOR]

In order to design an environmentally friendly gold furnace that considers both traditional cultural elements and modern decorative elements, this paper puts forward the application of factor analysis in the image modeling design of an environmentally friendly gold furnace. Firstly, the elements of lines, colors, and patterns were extracted by factor analysis. By using the hierarchical structure model and factor analysis, the synchronous parameter fusion model of image modeling design factors of the environmental protection gold furnace is constructed to fuse the output reliability parameters of image modeling of the environmental protection gold furnace and adjust them adaptively. The factor analysis and process control of image modeling of environmental protection golden stoves are carried out using the method of differential feature analysis. Then, the Atlas and image Kanban are analyzed, and the image design factors of the environmental protection golden stove are excavated. Finally, the concepts of cultural space and cultural time are introduced. Based on the study of historical and material materials, the overall comparative analysis is carried out from three aspects: shape, color, and pattern. Combined with the extracted target image and the excavated design factors, the image modeling design of the environmental protection gold furnace is effectively carried out, providing design methods and sample references for the innovative design of the environmental protection gold furnace. The experimental results show that the mining design factors can effectively analyze the problems existing in the modeling design of the current environmental protection gold furnace, with a low bit error rate and strong practicability. [ABSTRACT FROM AUTHOR]

Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods. [ABSTRACT FROM AUTHOR]

At present, the segmentation method for the heart is one of the most difficult problems and a hot topic in medical imaging segmentation and image analysis. In order to achieve high-precision recognition of heart and blood vessel image, a single target auxiliary blood vessel image recognition algorithm, based on Markov Random Field (MRF) and ash fusion residual network information, is proposed in this paper. The corresponding features are extracted by the residual network depth, and the heart and blood vessels are segmented with high precision through MRF gray information to assist blood vessel image recognition. Thus, an algorithm combining Markov Random Field and Grayscale Information can complete such auxiliary recognition for single target ship image. The paper will also discuss auxiliary recognition techniques for identifying images of the heart and blood vessels. Added to that, it presents current medical image algorithms with a particular emphasis on cardiac image algorithms. Through a series of analysis, better algorithms are proposed. Thus, the research contents of a single target present the vascular image recognition algorithm. Therefore, a blood vessel image recognition algorithm is proposed for the research content of a single target. A series of validation experiments are conducted on the DRIVE and star datasets. The experimental results show that the method proposed in this paper has high segmentation accuracy, can achieve high-precision segmentation, and can achieve high-precision single target blood vessel image recognition. By observing and analyzing two-dimensional cardiac images, such as diabetic arterioles, hemorrhages, hard exudates, and so on, the difficulty of diagnosing cardiovascular disease characteristics can be reduced, and the diagnosis and treatment can be facilitated. [ABSTRACT FROM AUTHOR]

With the aggravation of human sub-health problems, people's demand for medical assistance is increasing. In the face of an endless stream of diseases, doctors use medical image analysis to intuitively obtain the morphological information of the affected part of the disease, which is convenient for doctors to make a more accurate assessment of the disease. The processing of medical images is essential for the treatment of people's diseases and subsequent observation and recovery. Therefore, it is necessary to pay attention to the development and innovation of image processing. With the continuous increase of diseases and the innovation and development of technology, the existing medical image processing technology still has problems such as noise and low image contrast. The application of fuzzy genetic clustering algorithms and artificial neural networks can help image processing be more accurate and perfect. In view of the above problems, this paper has carried out data analysis and research on image processing methods based on fuzzy genetic clustering algorithm (FGCA) and artificial neural network (ANN). The research results have shown that in the case of no noise and 5% salt and pepper noise, the FGCA segmentation coefficient is the largest, at 0.9756 and 0.9758, respectively, and the segmentation entropy is the smallest, at 0.0885 and 0.0925, respectively. The liver CT (Computer Tomography) image segmentation method based on DeepLab V3+ has the highest PA, Mlou value, and Dice coefficient, which are 88%, 95%, and 94%, respectively, which has laid the foundation for the innovation and development of image processing methods. [ABSTRACT FROM AUTHOR]

In this paper, the initial parameters of C-V level set image recognition are optimized by using the global optimization characteristics of cultural algorithm, and a cultural algorithm C-V level set image recognition model is proposed, which is abbreviated as the CC-V model. The initial population space of the cultural algorithm is used to set the initial recognition parameters in a large range, and the population evolution is continuously optimized and guided by the situation knowledge and normative knowledge of the reliability space, so as to realize the global optimization of the recognition parameters and to timely terminate by judging the change of the image entropy fitness value. Through the analysis and comparison of the experimental results, the CC-V model has a better recognition effect than the C–V model. The partial differential equation image recognition model is applied to the video image sequence for moving target recognition. The background model is constructed by the block statistical histogram. The background difference method is used to locate the video moving target, and the minimum circumscribed rectangle of the multitarget positioning is used as the initial outline of the model recognition. The research results show that the nonintellectual factors of each group of ethnic traditional sports have an obvious effect on the application value of people, and the application effect of nonintellectual factors produced by different groups will also vary. All ethnic traditional sports groups show higher application value to nonintellectual character factors. The comprehensive application value of nonintelligence factors of the combat confrontation item group, accuracy item group, and endurance item group is relatively higher than the comprehensive application value of nonintelligence factors of other item groups. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has fascinated the present study assigned to multiple areas such as distress detection on the pavement, pothole detection, and healthcare. The distress detection on pavement and roads and delivering healthcare and medical services need to be monitored through state-of-the-art technology, i.e., drone technology. Improvement in construction sites and healthcare delivery are of serious concern. Nowadays, computer vision techniques are commonly used in this area utilizing images and videos of construction sites. Due to confined data, researchers are using Unmanned Aerial Vehicles (UAVs) or Drone to get maximum information through 360° monitoring. This review article presents the useful monitoring techniques using AI-enabled drones for scholars around the world. In this comprehensive review, initially, the image acquisition equipment along with the perks and limitations has been presented. Second, the main constraints related to different computer vision techniques are highlighted for detecting distress in the pavement. Then, the possible research solution to some of the distress issues such that detection of pavement texture, cracks or potholes, joint faulting, temperature segregation, and rutting issues are predicted. Finally, the application of AI-enhanced drones in the healthcare field is elucidated which showed their significance. Moreover, in this research, the comparative image analysis of pavement and path hole detection was presented for the collection of detailed information and accurate detection. In the future, the work can also be enhanced to monitor the live pavement distress detection, especially for busy roads and highways. Moreover, an analysis to determine and reduce the costs in the healthcare sectors and organizations is required for future work. [ABSTRACT FROM AUTHOR]

The robust segmentation of organs from the medical image is the key technique in medical image analysis for disease diagnosis. U-Net is a robust structure for medical image segmentation. However, U-Net adopts consecutive downsampling encoders to capture multiscale features, resulting in the loss of contextual information and insufficient recovery of high-level semantic features. In this paper, we present a new multibranch hybrid attention network (MHA-Net) to capture more contextual information and high-level semantic features. The main idea of our proposed MHA-Net is to use the multibranch hybrid attention feature decoder to recover more high-level semantic features. The lightweight pyramid split attention (PSA) module is used to connect the encoder and decoder subnetwork to obtain a richer multiscale feature map. We compare the proposed MHA-Net to state-of-art approaches on the DRIVE dataset, the fluoroscopic roentgenographic stereophotogrammetric analysis X-ray dataset, and the polyp dataset. The experimental results on different modal images reveal that our proposed MHA-Net provides better segmentation results than other segmentation approaches. [ABSTRACT FROM AUTHOR]

The effect of electrohydrodynamic drying on the drying kinetics, energy consumption, and quality was studied for apple and strawberry slices with different thicknesses from 1 to 4 mm. Effective diffusivity and energy consumption were quantified via direct measurements, whereas visual quality attributes, such as color and shrinkage, were determined through image analysis. It was found that effective diffusivity was independent of thickness, around 0.5 × 10−11 m2/s for apples and 0.24 × 10−11 m2/s for strawberries. The average specific energy consumption increased with the fruit thickness from 601.13 kJ/kg (1 mm) to 1433.83 kJ/kg (4 mm) for apple slices and from 1324.17 (3 mm) to 1756.71 kJ/kg (4 mm) for strawberry slices. The effect of EHD drying on apple color was significantly smaller than conventional hot air drying. The impact of EHD drying on the shrinkage of apple slices was about 85%, and for strawberry slices, about 90%, which was higher than in hot air drying. It can be concluded that EHD is effective for drying thin fruit slices due to the low energy consumption and better quality. Novelty impact statement: The main advantage of EHD drying over conventional drying techniques is its low energy consumption and better product quality, which is beneficial for the commercialization of the technique. In this study, the effect of fruit thickness on drying kinetics, energy consumption, and product quality were evaluated for apple and strawberry slices. The specific energy consumption increased with thickness and was very low compared to thermal drying. EHD dried fruit slices had better visual quality than thermal drying. [ABSTRACT FROM AUTHOR]

With image analysis as the core for multitarget detection and intelligent tracking, mostly applying the Faster R-CNN or YOLO framework, the MOTA score for multitarget tracking is low in the face of complex working environments. Therefore, further research into computer vision techniques is carried out to design new multitarget detection and intelligent tracking methods. Based on the small-aperture imaging model, the principle of lens distortion was analyzed, and a camera calibration and image calibration scheme was designed to obtain effective environmental images. The attention mechanism is introduced to optimise the structure of deep learning networks, and a computer vision detection algorithm based on this is applied to complete regional multitarget detection. The distance between each target and the body is then measured in combination with binocular vision principles. Finally, the spatiotemporal context algorithm is applied to perform simulation calculations to obtain the multitarget intelligent tracking results. The experimental results show that the mean MOTA score of the proposed technique is 0.87 in the night environment, which is 24.14% and 28.374% better than the neural network-based and machine vision-based tracking methods, respectively; in the daytime environment, the mean MOTA score of the multitarget tracking results of the technique is 0.94, which is 28.72%, and the mean MOTA score of 0.94 for the multitarget tracking results in the daytime environment was 28.72% and 22.34% higher than the other two methods. [ABSTRACT FROM AUTHOR]

Edge detection is a boundary-based segmentation method to extract important information from an image, and it is a research hotspot in the fields of computer vision and image analysis. Especially feature extraction is also the basis of image segmentation, target detection, and recognition. In recent years, in order to solve the problems of edge detection refinement and low detection accuracy, the industry has proposed multiscale fusion wavelet edge, spectral clustering, network reconstruction, and other edge detection algorithms based on deep learning. In order to enable researchers to understand the current research status of edge detection, this paper first introduces the classic algorithm of traditional edge detection, compare with advantages and disadvantages of different edge detection algorithms. Then, it summarizes the main edge detection methods based on deep learning in recent years and classifies and compares them according to the implementation technology. Finally, it shows the development direction of edge detection algorithm research. [ABSTRACT FROM AUTHOR]

The rare earth (RE = Eu, Sm, Dy, and Tb) ions doped La10Si6O27 nanophosphor was synthesized by a simple solution combustion method. The prepared La10Si6O27:RE3+ nanophosphors (LNPs) were subjected to diverse technical tools for exploring their structural, optical, morphological, and electrochemical features. The structural analysis using powder X-ray diffraction (PXRD) patterns revealed the hexagonal oxy apatite phase for LNPs with a crystallite size in the range of 25–50 nm, and the equivalent was affirmed by image analysis via transmission electron microscopy (TEM). Utilizing DRS data, the bandgap energy (Eg) values were recorded for LNPs. Cyclic voltametric (CV), electrochemical impedance spectroscopy (EIS), and sensor studies were performed using a modified carbon paste electrode of LNPs. The modified LNP electrodes were found to be highly effective in sensing paracetamol in acidic medium with a quick response time of 3 secs for sensing the drugs at 1 mM concentration. All the RE ions Eu3+, Sm3+, Dy3+, and Tb3+ (5 mol%)-doped LNPs exhibited the most promising electrochemical sensing characteristics. [ABSTRACT FROM AUTHOR]

The whip leg technique is an indispensable offensive action for high-level Sanda athletes in the competition. In order to improve the effective guidance of the action of Wushu Sanda whip leg, based on wavelet transform, the three-dimensional image analysis of Sanda whip leg technical action characteristics, to explore the movement speed of each link of the attacking leg, the contraction form, and force sequence of major muscles or muscle groups and put forward correct and reasonable movement techniques, is expected to provide theoretical basis and reference for coaches and athletes in the training of Sanda whip leg movement. Based on this, this paper attempts to use the three-dimensional image analysis system and surface electromyography acquisition system from the perspective of wavelet transform to collect the speed of the attacking leg movement and the parameters related to muscle discharge and to quantitatively analyze and evaluate its movements. The sequence of activities of the whip leg strike is hip joint-knee joint-hip joint-knee joint-ankle joint. It is suggested to strictly follow the principle of link movement in practice, and the completion of the whole movement should be relaxed and natural. [ABSTRACT FROM AUTHOR]

Art design major is a relatively important course in college teaching. It involves a wide range of directions. Advertising art, landscape art, interior design, etc. are closely related to people's lives. Art design has appeared in all aspects of people's lives. However, a new art design program is time-consuming and human resources for art design. Different art designs will contain relatively similar characteristics, which can alleviate many difficulties for art designers. However, it is also a relatively difficult task to discover the relationship between the characteristics of the art and design only by artificial means. Image recognition technology can assist designers to discover and find the relationship between artworks, and these related features can assist designers to design. In this study, an intelligent image recognition method for intelligent art design is designed using the VB-CNN-GRU method. It can identify patterns, shapes, color matching, and text features of artistic design products. The research results show that the VB-CNN-GRU method can accurately complete the intelligent image recognition task of art design major. The VB-CNN-GRU method has specifically higher accuracy in art design image recognition than the single VB-CNN method. The maximum prediction error of VB-CNN-GRU in art design image recognition is only 2.37%. For the four characteristics of art design, it can better assist designers to complete related designs. [ABSTRACT FROM AUTHOR]

With people's attention to physical health and health preservation, more and more people practice Taijiquan to achieve the purpose of physical fitness and self-cultivation. However, many people do not fully understand the principles of Taijiquan in the early stage of practicing Taijiquan, rely on imitation to practice, and ignore the action essentials and characteristics of Taijiquan, resulting in joint pain. At the same time, many beginners ignore the causes of these situations, resulting in joint damage. Therefore, this paper proposes the research on the joint injury of Taijiquan based on computer image analysis and carries out the dependent research and analysis through the joint image processing model based on the improved Robert's edge detection algorithm. The experimental results show that the action cycle time of wrong action is longer than the total time of correct action due to the increase of action displacement, and the long displacement distance increases the pressure on the knee joint and improves the probability of knee joint damage. In addition, there are obvious differences between wrong action and correct action in knee adduction and abduction, flexion and extension, internal rotation, and external angle. Wrong action knee joint movement does not conform to the natural movement law of human body, which increases the pressure or tension to the knee joint and surrounding related tissues and improves the probability of knee joint damage. [ABSTRACT FROM AUTHOR]

Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature's extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%. [ABSTRACT FROM AUTHOR]

This paper contains the specification of functional and nonfunctional requirements of a CNN (Convolutional Neural Network) model to identify masses in liver ultrasounds. A neural network model based on CNN was implemented, which performs the analysis of ultrasound images of the liver, identifying the presence of masses or not in them. CNN can be executed either on a machine with a CPU (Central Processing Unit) or in a high processing capacity environment that uses GPU (Graphics Processing Unit). The validation of the accuracy of the obtained results happens through the statistical technique of k-fold cross-validation using IoT. During the neural network training process, dense data necessitates a lot of computational resources. Because the number of network entries is precisely proportional to the number of pixels in the image, parallel processing for image processing is justified. It is important to remember that medical photos cannot be saved in a lossy format and must be identified with exceptional accuracy using IoT. [ABSTRACT FROM AUTHOR]

Concrete's binder can be substituted with rice husk ash, metakaolin, kaolin clay, GGBS, and other agricultural and industrial byproducts and waste materials, which lowers greenhouse gas emissions, namely carbon dioxide (CO2). When employed in the manufacturing of concrete, the aforementioned items reduce the amount of water required and have no discernible impact on the material's long-term performance. Combination of metakaolin and class-F fly ash is taken for this experimental study to create geopolymer concrete, as both the materials are very good sources of alumino-silicates, which is the foremost requirement for the material to be the binder in geopolymer concrete. Also, the percentage replacement of crushed aggregates with crushed E-waste plastic (EWP) is are created for making the concrete eco-friendlier as the EWP is a big problem for us to dispose of it. To activate raw ingredients and mixing of concrete, alkaline solution is used, made from sodium hydroxide (NaOH) and water glass solution, i.e., sodium silicate (Na2SiO3). Low molarity metakaolin geopolymer concrete (MGPC) was created to have benefits over standard cement concrete in terms of performance, durability, and mechanical properties. This study describes the findings of a comprehensive experimental test and analysis to investigate the characteristics of MGPC-EWP with a change in different factors of the mix design. The study's findings demonstrated that MGPC performance is closely correlated with its molarity, as the molarity increases, the mechanical strengths of the MGPC also increases. Also, the optimum percentages of the EWP are to be incorporated in the MGPC. This study also uses the petrographic image analysis to validate the geopolymerization products forms in the MGPC and check the microstructure growth of geopolymerization of particles with age. The petrographic image analysis provides photographs of the microstructure of hydration products of the samples with good details. As the petrographic microscope is very affordable as compared to the SEM setup and requires less skilled worker, it comes out to be a good approach for the microstructural analysis. [ABSTRACT FROM AUTHOR]

Focusing on the performance improvement of glass fiber-modified loess, a series of laboratory tests were carried out, and the macroscopic mechanical properties of glass fiber-modified loess were studied by conventional triaxial shear tests. With the help of scanning electron microscopy (SEM), the pores and cracks image analysis system (PCAS) and nuclear magnetic resonance (NMR) tests, the microscopic properties of the improved loess were studied qualitatively and quantitatively from three aspects: mode of structural contact, pore morphological characteristics and pore structure arrangement characteristics. Finally, the interface zone bond-slip model was used to analyze the internal mechanism of glass fiber and loess interface zone slip. The macroscopic test results show with the same glass fiber length, the shear strength, cohesion and internal friction angle of the modified loess all increased first and then decreased with increasing incorporation of glass fibers. And the optimal ratio of glass fibers is 0.6%. Under the same fiber ratio, the shear strength and cohesion of the improved loess continued to increase with increasing fiber length. The optimal fiber length used in the project was 9 mm. Qualitative analysis of the SEM images show that with the increase in the glass fiber incorporation ratio, the fiber increasingly sutures the cracks in the soil and tightens the soil at both ends of the cracks. Quantitative analysis shows that the proportion of pore area is the largest at the 0.8% mixing ratio, followed by the 0% mixing ratio, and the smallest at the 0.6% mixing ratio. It can be seen from the NMR test results that the T2 spectral distribution curve under the fiber ratios of 0.2%–0.6% is shifted to the left compared with the ratios of 0% and 0.8%. The peak-top signal intensity of the main spectrum peak when the fiber ratio is 0.6% is the weakest. [ABSTRACT FROM AUTHOR]

Deep learning is a new research direction in the field of machine learning, which was introduced into machine learning to bring it closer to its original goal. Accurate dish recognition becomes increasingly important in the multimedia community since it can help cuisine recommendation, calorie management, service improvement, and other food computing tasks. Many novel approaches have been developed on web recipes and menu pictures, while few are concerned real-life dish image analysis. In this study, a deep learning-based prototype system is deployed in a Chinese canteen, and 28 dish types, 16,904 images, and 45,061 instances have been collected. Specifically, in the prototype system, three practical issues are explored, including the backbone network selection, the training strategy determination, and the minimum number of samples for model upgrading. Experimental results suggest that fine-tuned Faster-RCNN can serve as the backbone network of the prototype system since it outperforms the other four fine-tuned networks on dish recognition (accuracy, 98.10%; recall, 97.20%; MAP (mean average precession), 98.30%) and satisfies real-time requirement (0.15 second per image). Meanwhile, the transferred backbone network achieves superior results (MAP, 96.48%) over the same architecture trained from image scratches (MAP, 87.84%). On model upgrading, a good (MAP, 91.34%) to better (MAP, 96.48%) outcome is obtained when the training size is increased from 50 to 200 samples per dish type, and 150 and more instances should be annotated if a new dish type is added to the system's recognition list. Conclusively, the real-life deployment and evaluation of the prototype system indicate that deep learning is full of potential to enhance customer experience through accurate daily dish recognition. [ABSTRACT FROM AUTHOR]

This study was aimed to explore the effect of CT image feature extraction of pulmonary nodules based on an artificial intelligence algorithm and the image performance of benign and malignant pulmonary nodules. In this study, the CT images of pulmonary nodules were collected as the research object, and the lung nodule feature extraction model based on expectation maximization (EM) was used to extract the image features. The Dice similarity coefficient, accuracy, benign and malignant nodule edges, internal signs, and adjacent structures were compared and analyzed to obtain the extraction effect of this feature extraction model and the image performance of benign and malignant pulmonary nodules. The results showed that the detection sensitivity of pulmonary nodules in this model was 0.955, and the pulmonary nodules and blood vessels were well preserved in the image. The probability of burr sign detection in the malignant group was 73.09% and that in the benign group was 8.41%. The difference was statistically significant (P < 0.05). The probability of malignant component leaf sign (69.96%) was higher than that of a benign component leaf sign (0), and the difference was statistically significant (P < 0.05). The probability of cavitation signs in the malignant group (59.19%) was higher than that in the benign group (3.74%), and the probability of blood vessel collection signs in the malignant group (74.89%) was higher than that in the benign group (11.21%), with statistical significance (P < 0.05). The probability of the pleural traction sign in the malignant group was 17.49% higher than that in the benign group (4.67%), and the difference was statistically significant (P < 0.05). In summary, the feature extraction effect of CT images based on the EM algorithm was ideal. Imaging findings, such as the burr sign, lobulation sign, vacuole sign, vascular bundle sign, and pleural traction sign, can be used as indicators to distinguish benign and malignant nodules. [ABSTRACT FROM AUTHOR]

In order to realize the evaluation of regional comprehensive disaster reduction capacity in a complex environment, an evaluation model of regional comprehensive disaster reduction capacity in a complex environment based on remote sensing monitoring and data image feature analysis is proposed. According to the geographical location and scale of disaster spots and the parameter analysis of the model of disaster-bearing bodies around the disaster spots, the remote sensing monitoring method is adopted to extract the geographical remote sensing images of regional disaster spots in a complex environment. The collected geographical remote sensing images of regional disaster points under the complex environmental background are filtered and preprocessed, and the texture parameters of the geographical remote sensing images of regional disaster points under the complex environmental background are recognized by combining the method of image texture feature extraction. Based on the method of tone mapping, the rapid filtering and feature analysis of the geographical remote sensing images of regional disaster points under the complex environmental background are carried out, and the time, position, damage, and so on in the geographical remote sensing images of regional disaster points under the complex environmental background are analyzed. By using the method of parameter analysis and gradient operator operation, a comparison model of geographical remote sensing images of regional disaster points under the complex environmental background is established, and the reliability evaluation of regional comprehensive disaster reduction ability under the complex environmental background is realized according to the method of contrast and detail significance enhancement. The test shows that this method has high accuracy in evaluating regional comprehensive disaster reduction capability under a complex environment, high accuracy in marking the geographical location of regional disaster points under a complex environment, and good fusion performance and reliability of regional comprehensive disaster reduction capability evaluation parameters. [ABSTRACT FROM AUTHOR]

In the field of computer vision, it is a very challenging task to use artificial intelligence deep learning method to realize the programming and creation of NFT artwork. With the continuous development and improvement of deep learning technology, this task has become a reality. The generative adversarial network model used in deep learning can generate new images based on the extraction and analysis of image data features and has become an important tool for NFT artwork image generation. In order to better realize the NFT artwork programming, this paper analyzes the working principle of the traditional adversarial generation method and then uses the StyleGAN model to edit the higher-level attributes of the image, which can effectively control the generated style and style of the NFT artwork image. Finally, in order to improve the quality of the generated images, this paper introduces a channel attention mechanism and a spatial attention mechanism to ensure that the generated images are more reasonable and realistic. Finally, through a large number of experiments, it is proved that the NFT artwork transmission programming algorithm based on artificial intelligence deep learning proposed in this paper can control the overall style of image generation according to the needs of the transmission, and the generated image features have good details and high visual quality. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence technology not only realizes the transformation of people's production and lifestyle but also promotes the transformation of the clothing industry. At present, the proportion of artificial intelligence in the clothing industry is becoming larger and larger. Using its advanced methods and technologies of information display and data analysis, this study is worth transforming and optimizing the clothing graphic design system under the artificial intelligence environment, so as to promote the intelligent development of the clothing industry. This study proposes to use three-dimensional image analysis algorithm technology to guide the integration of garment graphic design process and computer network, which is helpful to analyze and solve the imbalance between the singularity of traditional garment pattern design and people's aesthetic appreciation at present. Using the technology and principle of artificial intelligence for reference, the intelligent clothing graphic design system is constructed and analyzed. In the analysis of the clothing graphic design system of a group, the group applied artificial intelligence technology to the customized clothing in the production workshop of the company. Although the efficiency of the adjustment system is not as good as that of mass production, it eliminates inventory, and its net profit can reach 20–30%, which is much higher than that of traditional clothing. In contrast, the industry reported a net profit of 12% of the highest level. Therefore, it is urgent to analyze the intelligent garment graphic design system through artificial intelligence technology. [ABSTRACT FROM AUTHOR]

Convolution neural network (CNN) is one of the most popular machine learning techniques that is being used in many applications like image classification, image analysis, textile archives, object recognition, and many more. In the textile industry, the classification of defective and nondefective fabric is an essential and necessary step to control the quality of fabric. Traditionally, a user physically inspects and classifies the fabric, which is an ineffective and tedious activity. Therefore, it is desirable to have an automated system for detecting defects in the fabric. To address these issues, this research proposes a solution for classifying defective and nondefective fabric using deep learning-based framework. Therefore, in this research, an image processing technique with CNN-based GoogleNet is presented to classify defective and nondefective fabric. To achieve the purpose, the system is trained using different kinds of fabric defects. The performance of the proposed approach was evaluated on the textile texture TILDA dataset, and achieved a classification accuracy of 94.46%. The classification results show that the proposed approach for classifying defective and nondefective fabric is better as compared to other state-of-the-art approaches such as Bayesian, BPNN, and SVM. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence technology, image editing technology has evolved from relying on software such as Photoshop and GIMP for manual modification to using artificial intelligence technology to achieve intelligent and automated tampering of images. Editing, falsifying, and disseminating digital images have become simple and easy, leading to a crisis of confidence in digital images and reducing their reliability as judicial evidence. Therefore, how to identify falsified images, improve their trustworthiness, and avoid judicial injustice has become a problem that must be overcome in the information age. In this paper, we propose a target detection and adaptive feature analysis in image forensics based on a dual-flow layer CNN model, which can effectively perform image forensics. The results show that our algorithm has a clear theoretical basis, a small operational complexity, and a high detection accuracy. [ABSTRACT FROM AUTHOR]

Due to the continuous development of computer technology to promote the continuous progress of substation automation technology, the current substation equipment is diverse and there are many interferences, making the accuracy of the image processing algorithm to be low, and there is a lack of a complete automatic processing system. Convolutional neural networks (CNNs) are one of the most important breakthroughs in artificial intelligence in the last decade, especially in the field of image recognition, and have made important research achievements. In this study, we apply CNNs to substation equipment image processing, a method that performs feature extraction for recognition through substation equipment images. The research focuses on the expansion of the image sample set, the automatic training method based on recognition rate, and the voting strategy based on integrated learning, which not only improves the training efficiency of the model but also increases the recognition rate, and the proposed method is of high practicality. [ABSTRACT FROM AUTHOR]