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Smart machine tools must be able to recognise the materials they interact with, in order to independently decide on what actions to be taken whenever needed. The purpose of this research is to offer a generic method to automate material identification and classification task utilising image processing and machine learning techniques so as to improve the cognitive capabilities of machine tools. A dataset of four material surfaces; Copper, Aluminium, Stainless Steel, and Bronze is generated and the RGB data are extracted. These colour channels are utilised as input features to train the machine learning algorithms. Convolutional Neural Network (CNN) and other classification methods like Support Vector Machine (SVM), Decision Trees, and k-Nearest Neighbour are also used to classify material images based on the generated dataset. A unique generalised technique, based on CNN, has been proposed to accurately recognise and categorise round surfaced materials during machining. The accuracy achieved by the classifier has reached 100% during training and testing. Test images are used to confirm the proposed methodology's capability to distinguish between materials based on various illumination environments and camera positions. [ABSTRACT FROM AUTHOR]

With the advances in smart sensing and data mining technologies of Industry 4.0, condition monitoring of key equipment in manufacturing has brought transformations in production and maintenance management. However, in practical applications, noise from both the working environment and the sensing devices is inevitable, which causes the low performance of data-driven fault diagnosis. To address this challenge, the paper develops a robust two-stage joint denoising method by integrating ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA), with fuzzy entropy discriminant as a threshold. The developed method can filter noisy components from decomposed modal components and reconstruct a new signal with denoised independent components. Moreover, an improved convolutional neural network (CNN) model based on the VGG structure has been constructed as a classifier to achieve end-to-end fault diagnosis. The experimental results demonstrate the high accuracy and superior anti-interference capability of the proposed method for rolling bearing fault diagnosis under various noise levels. Compared with state-of-the-art denoising methods and fault diagnosis methods, the proposed method achieves higher accuracy and robustness under variable noise interference. The proposed method can be applied to broader fault diagnosis tasks of production equipment in complex practical environments. [ABSTRACT FROM AUTHOR]

ObjectiveFast and accurate deformable image registration (DIR) that include DIR uncertainty estimation are essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds.ApproachIn this study, we introduce a novel probabilistic multi-resolution image registration model utilizing convolutional neural networks (CNNs) to estimate a multivariate normal distributed dense displacement field (DDF) in a multimodal image registration problem. To assess the quality of the DDF distribution predicted by the model, we propose a new metric based on the Kullback-Leibler divergence. The performance of our approach was evaluated against three other DIR algorithms (VoxelMorph, Monte Carlo Drop-Out, and Monte Carlo B-splines) capable of predicting uncertainty. The evaluation of the models included not only the quality of the deformation but also the reliability of the estimated uncertainty. Our application investigated registration of a treatment planning computed tomography (CT) to follow-up cone beam CT for daily adaptive radiotherapy.Main resultsThe hyperparameter tuning of the models showed that there is a trade-off between the reliability of the estimated uncertainty and the accuracy of the deformation. In the optimal trade-off our model excelled in contour propagation and uncertainty estimation (p < 0.01) compared to existing uncertainty estimation models. We obtained an average dice similarity coefficient of 0.89 and a KL-divergence of 0.15.SignificanceBy addressing challenges in DIR uncertainty estimation and evaluation, our work showed that both the DIR and its uncertainty can be reliably predicted paving the way for safe deployment in clinical environment..

Eukaryotic transcription factors activate gene expression with their DNA-binding domains and activation domains. DNA-binding domains bind the genome by recognizing structurally related DNA sequences; they are structured, conserved, and predictable from protein sequences. Activation domains recruit chromatin modifiers, coactivator complexes, or basal transcriptional machinery via structurally diverse protein-protein interactions. Activation domains and DNA-binding domains have been called independent, modular units, but there are many departures from modularity, including interactions between these regions and overlap in function. Compared to DNA-binding domains, activation domains are poorly understood because they are poorly conserved, intrinsically disordered, and difficult to predict from protein sequences. This review, organized around commonly asked questions, describes recent progress that the field has made in understanding the sequence features that control activation domains and predicting them from sequence.

Osteoporosis (OP) is the most prevalent and common bone disease, especially knee osteoporosis. It significantly disables sufferers all over the world. Although laborious and prone to user variation, manual diagnosis, segmentation, and annotation of knee joints continue to be the preferred way to diagnose OP in clinical procedures. Therefore, many deep learning algorithms, particularly the convolutional neural network (CNN), have been created to increase clinical workflow efficiency to overcome the shortcomings of the widely used method as above. Medical imaging procedures can show hidden structures in a volumetric view, particularly those that generate three-dimensional (3D) pictures like MRI. We created a dataset of 240 pictures from patients who had knee X-rays and skeletal bone mineral density assessments at the same time. Four convolutional neural networks (CNN) models were used to analyse the X-ray images and deep neural networks for clinical covariances to determine the degree of osteoporosis. Additionally, we investigated ensemble models that included each CNN with a clinical covariance. For every network, scores for accuracy and error rate were computed. ResNet and Alexnet displayed the highest levels of accuracy when the CNN models were tested using knee X-rays with normal, low BMD, and osteoporosis. An ensemble of DNN with Alexnet, ResNet, and both ResNet and Alexnet are employed resulting in improved accuracy. The ensemble of best-performing CNN and DNN is proposed to diagnose osteoporosis more accurately. The proposed method has produced a highly accurate osteoporosis diagnosis. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a prevalent neurodegenerative disorder affecting millions of people globally, with substantial health risks and economic burdens. This study aims to introduce an innovative hybrid approach combining deep learning and machine learning algorithms to improve the diagnosis of PD using handwriting dynamics indicative of Parkinson's symptoms. The proposed approach integrates hybrid feature extraction using nine fine-tuned transfer learning models, i.e., InceptionV3, DenseNet201, EfficientNetB0, ResNet50, MobileNetV2, VGG16, Xception, NASNetMobile, and InceptionResNetV2. Initially, features from these models are used individually or in binary and ternary combinations. Given the limited sample size in PD datasets, some extracted features through fine-tuning may lack significance, and fully connected layers can lead to overfitting. To address this issue, Neighborhood Component Analysis (NCA) is employed to refine these features, retaining only the most informative ones. Finally, the selected features are classified using Support Vector Machines (SVM) maximizing the margin between classes and reducing the risk of overfitting. The proposed hybrid model achieves a state-of-the-art accuracy of 99.39% on the Parkinson Hand Drawings dataset. The combination of features extracted from DenseNet201, Xception, and NASNetMobile models, processed using NCA and SVM methods, has been identified as the most efficient model, balancing high accuracy with computational efficiency. Qualitative assessments further confirm the accuracy and reliability of the approach. [ABSTRACT FROM AUTHOR]

Lumbar spinal canal stenosis (LSCS) is the most common spinal degenerative disorder in elderly people and usually first seen by primary care physicians or orthopedic surgeons who are not spine surgery specialists. Magnetic resonance imaging (MRI) is useful in the diagnosis of LSCS, but the equipment is often not available or difficult to read. LSCS patients with progressive neurologic deficits have difficulty with recovery if surgical treatment is delayed. So, early diagnosis and determination of appropriate surgical indications are crucial in the treatment of LSCS. Convolutional neural networks (CNNs), a type of deep learning, offers significant advantages for image recognition and classification, and work well with radiographs, which can be easily taken at any facility. Our purpose was to develop an algorithm to diagnose the presence or absence of LSCS requiring surgery from plain radiographs using CNNs. Retrospective analysis of consecutive, nonrandomized series of patients at a single institution. Data of 150 patients who underwent surgery for LSCS, including degenerative spondylolisthesis, at a single institution from January 2022 to August 2022, were collected. Additionally, 25 patients who underwent surgery at 2 other hospitals were included for extra external validation. In annotation 1, the area under the curve (AUC) computed from the receiver operating characteristic (ROC) curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated. In annotation 2, correlation coefficients were used. Four intervertebral levels from L1/2 to L4/5 were extracted as region of interest from lateral plain lumbar spine radiographs totaling 600 images were obtained. Based on the date of surgery, 500 images derived from the first 125 cases were used for internal validation, and 100 images from the subsequent 25 cases used for external validation. Additionally, 100 images from other hospitals were used for extra external validation. In annotation 1, binary classification of operative and nonoperative levels was used, and in annotation 2, the spinal canal area measured on axial MRI was labeled as the output layer. For internal validation, the 500 images were divided into each 5 dataset on per-patient basis and 5-fold cross-validation was performed. Five trained models were registered in the external validation prediction performance. Grad-CAM was used to visualize area with the high features extracted by CNNs. In internal validation, the AUC and accuracy for annotation 1 ranged between 0.85–0.89 and 79–83%, respectively, and the correlation coefficients for annotation 2 ranged between 0.53 and 0.64 (all p<.01). In external validation, the AUC and accuracy for annotation 1 were 0.90 and 82%, respectively, and the correlation coefficient for annotation 2 was 0.69, using 5 trained CNN models. In the extra external validation, the AUC and accuracy for annotation 1 were 0.89 and 84%, respectively, and the correlation coefficient for annotation 2 was 0.56. Grad-CAM showed high feature density in the intervertebral joints and posterior intervertebral discs. This technology automatically detects LSCS from plain lumbar spine radiographs, making it possible for medical facilities without MRI or nonspecialists to diagnose LSCS, suggesting the possibility of eliminating delays in the diagnosis and treatment of LSCS that require early treatment. [ABSTRACT FROM AUTHOR]

Multiple‐island endemics (MIE) are considered ideal natural subjects to study patterns of island colonization that involve recent population‐level genetic processes. Kleinia neriifolia is a Canarian MIE widespread across the archipelago, which exhibits a close phylogenetic relationship with species in northwest Africa and at the other side of the Sahara Desert. Here, we used target sequencing with plastid skimming (Hyb‐Seq), a dense population‐level sampling of K. neriifolia, and representatives of its African–southern Arabian relatives to infer phylogenetic relationships and divergence times at the species and population levels. Using population genetic techniques and machine learning (convolutional neural networks [CNNs]), we reconstructed phylogeographic relationships and patterns of genetic admixture based on a multilocus SNP nuclear dataset. Phylogenomic analysis based on the nuclear dataset identifies the northwestern African Kleinia anteuphorbium as the sister species of K. neriifolia, with divergence starting in the early Pliocene. Divergence from its sister clade, comprising species from the Horn of Africa and southern Arabia, is dated to the arid Messinian period, lending support to the climatic vicariance origin of the Rand Flora. Phylogeographic model testing with CNNs supports an initial colonization of the central island of Tenerife followed by eastward and westward migration across the archipelago, which resulted in the observed east/west phylogeographic split. Subsequent population extinctions linked to aridification events, and recolonization from Tenerife, are proposed to explain the patterns of genetic admixture in the eastern Canary Islands. We demonstrate that CNNs based on SNPs can be used to discriminate among complex scenarios of island migration and colonization. [ABSTRACT FROM AUTHOR]

Objectives: To assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate‐specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy). Materials and Methods: This systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: 'artificial intelligence', 'prostate cancer', and 'PSMA PET'. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used. Results: Our search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high‐grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non‐PSMA‐avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume. Conclusion: Although the current state of AI differentiating high‐grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019, i.e., COVID-19, an emerging contagious disease with human-to-human transmission, first appeared at the end of year 2019. The sudden demand for disease diagnostic kits prompted researchers to shift their focus toward developing solutions that could assist in identifying COVID-19 using available resources. Therefore, it is imperative to develop a high-accuracy system that makes use of Artificial Intelligence and its tools considering its contribution to computer vision. The time consumed to diagnose test outcomes is to be taken care of as a crucial aspect of an efficient model. To address the global challenges faced by the COVID-19 pandemic, this study proposed two deep learning models developed for automatic COVID-19 detection and distinguish it from pneumonia, another common lung disease. The proposed designs implement layered convolutional neural networks and are trained on a data set of 1824 chest X-rays for binary classification (COVID-19 and normal) and 2736 chest X-rays for ternary classification (COVID-19, normal, and pneumonia). The input images and hyper-parameters in the convolution layers are fine-tuned during the model training phase. The observations show that the proposed models have achieved a better performance as compared to their earlier contemporaries' approaches, resulting in accuracy, precision, recall, and F-score of 98.91%, 98.5%, 98.5%, and 99% for binary-class and 95.99%, 96.3%, 96%, and 96.33% for ternary-class classifiers, respectively. The presented architectures have been built from scratch, thus with the implemented convolutional layered architecture, they were successful in providing more efficient and early diagnosis of the disease. [ABSTRACT FROM AUTHOR]

In this study, we propose a structural health monitoring and diagnostic method for layered (multi-story) structures using a convolutional neural network (CNN). The proposed method is a primary diagnostic one, and its purpose is to allow quick identification of the location of an abnormality after detecting it. An abnormality is defined as a decrease in the stiffness characteristics (spring constant) of the outer wall of a multi-story structure when it deteriorates or is damaged. The proposed method has the following features. A modal circle is generated by multiplying the frequency response functions (FRFs) simulated by a mathematical model and the FRFs from the actual structure, in frequency space, and then a CNN learns the features of the abnormality from the modal circle and diagnoses it in the actual multi-story structure. We first verified the validity of the proposed method by considering a three-story structure as a numerical example. When the method was applied to three types of abnormal conditions, it was shown that the abnormal diagnosis could be performed correctly. Next, we constructed an experimental model of a three-story structure, and realized three types of abnormal conditions similar to those in the numerical model. We verified the applicability of the proposed method and showed that correct diagnosis of an abnormality was possible. Both the validity and applicability of the proposed method were thus confirmed. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey‐scale US images. Methods: According to the CKD diagnostic criteria, 1049 patients from Tongde Hospital of Zhejiang Province were retrospectively enrolled in the study. A total of 4365 renal US images were collected from these patients. Convolutional neural networks were used for feature extractions and a screening model was constructed by fusing ResNet34 and texture features to identify CKD and its stage. A comparative analysis was performed to compare the diagnosis results of the model with physicians. Results: When diagnosing CKD or non‐CKD, the receiver operating characteristic curve (AUC) of our model was 0.918 and that of the senior physician group was 0.869 (p <.05). For the diagnosis of CKD stage, the AUC of our model for CKD G1–G3 was 0.781, 0.880, and 0.905, respectively, while the AUC of the senior physician group for CKD G1–G3 was 0.506, 0.586, and 0.796, respectively; all differences were statistically significant (p <.05). The diagnostic efficiency of our model for CKD G4 and G5 reached the level of the senior physicians group. Specifically, the AUC of our model for CKD G4‐G5 was 0.867 and 0.931, respectively, while the AUC of the senior physician group for CKD G4‐G5 was 0.838 and 0.963, respectively (all p >.05). Conclusions: Our deep learning radiomics model is more effective than senior physicians in the diagnosis of early CKD. [ABSTRACT FROM AUTHOR]

The slope safety factor is a crucial indicator for assessing slope stability. However, the current methods for calculating safety factors are predominantly based on the search of limit equilibrium theory and the iteration of finite element methods, leading to overly intricate computational procedures. Considering classical mechanics theory and the definition of slope safety factors, there inevitably exists a certain functional relationship between various slope parameters and their safety factors. Thus, we propose an approach utilizing response surface surrogate functions to express this relationship.We studied two types of slopes: soil slopes and rock slopes. For soil slopes, assumed to be single-layer saturated clay, we considered five parameters: soil density, cohesion, friction angle, slope height, and slope angle. For rock slopes, we considered six parameters: rock density, uniaxial compressive strength of the rock, GSI, mi, slope height, and slope angle.we introduce a data sampling technique based on genetic algorithms to enhance the quality of training data. This approach reduces the uncertainty in fitting outcomes while minimizing the volume of sample data, while still meeting precision requirements and generalizability.To address the demands of this study, we establish a convolutional neural network to approximate the response surface. A comparison is made with response surfaces approximated using FCNN and polynomial methods, revealing superior performance of the convolutional neural network. Following training, the surrogate function derived enables rapid and accurate computation of the slope safety factor. [ABSTRACT FROM AUTHOR]

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype‐based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post‐Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC‐based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease‐risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases. [ABSTRACT FROM AUTHOR]

Recent years have seen an unprecedented increase in fire incidents, resulting in severe damage to forest regions, loss of human and animal lives, and unwarranted displacement of people. Owing to these issues, artificial intelligence-based fire detection systems have been developed. These systems mostly work with camera-based inputs for fire detection and alarm generation. As fire-smoke can be seen from a distance, methods for smoke detection have also been deployed for identifying fire incidents. But detecting smoke in an outdoor environment can be challenging due to weather conditions such as haze, fog, or clouds. These challenges have led to advancements in the field, and various deep learning architectures have been suggested over the years for fire-smoke detection in normal and foggy or cloudy weather conditions. Many existing methods are either computationally expensive and demand high memory, or if they are lightweight, they suffer from lower smoke detection accuracy and high false alarms. In the present paper, a dual-channel vision transformer model 'SmokeViT' is proposed for detecting fire-smoke in an outdoor environment. These channels are interlaced with a convolutional neural network to improve the feature learning capability of the model. 'SmokeViT' shows a remarkable accuracy of more than 99% and false alarm rate below 0.20% on two publicly available smoke datasets, outperforming six state-of-the-art methods. The model architecture has only 0.4 million parameters and 0.17 giga floating-point operations, making it suitable for deployment on resource-constrained surveillance devices. [ABSTRACT FROM AUTHOR]

Radioisotope identification presents challenges that can be effectively addressed through pattern recognition and machine learning (ML) techniques. However, further investigation is necessary to assess the accuracy of these algorithms in quantifying mixtures of radioisotopes. The novelty of the study focuses on a hybrid convolutional neural network (CNN) architecture, called Arch, which utilizes numerical values to predict the presence of radioisotopes based on their signals. The feature extraction methods are employed to analyze small-isotope libraries using area-of-interest techniques and low-resolution spectrometers, with fully calibrated detectors ensuring accurate identification complexity. Additionally, this study explores the use of two sets of machine learning approaches for the automated identification of radioisotopes, focusing specifically on the feature extraction method. The Hybrid CNN Arc model, as proposed, achieved a test data accuracy of 95%. Additionally, a recurrent neural network model achieved an accuracy of 92%, while a GBDT model achieved an accuracy of 86%. The precision, recall, and f1-score metrics have been computed for the Hybrid CNN Arch approach, yielding values of 95%, 95%, and 95%, respectively. Similarly, the RNN model achieved precision, recall, and f1-score scores of 89%, 82%, and 81.5%, respectively. Lastly, the GBDT model attained precision, recall, and f1-score values of 84%, 81%, and 74.6%, respectively. [ABSTRACT FROM AUTHOR]

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model's performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) is an age-related, persistent, painless eye disease that impairs central vision. The central area (macula) of the retina, located at the back of the eye, sustains damage that is the cause of loss of vision. The early detection of AMD can increase the probability of treatment and prevent vision loss. The AMD can be classified into dry and wet AMD based on the absence of neovascularization. This study introduces a new methodology for the classification of AMD using optical coherence tomography (OCT) retinal images. The proposed methodology is based on three stages. The first stage is the data preparation stage for resizing and normalizing the used images. The second stage is the image processing stage for enhancing the image quality as contrast and resolution these enhancements have been checked by the weighted peak signal-to-noise ratio (WPSNR) methodology. The third stage is the deep feature extraction and classification stage, which consists of two sub-models. The first model is MobileNet V1 which has been used as a deep feature extractor. The second model is LSTM (long short-term memory), fed with deep features to classify the AMD stages. A multi-classification with six separate trials has been employed with the proposed methodology, and compared with other models like DenseNet201 and InceptionV3. The proposed model has been tested on a sample of benchmark data with 4005 grayscale images labeled into three classes. The proposed methodology has achieved an accuracy of 98.85%, a sensitivity of 99.09%, and a specificity of 99.1%. To ensure the effectiveness of the proposed methodology, a comparative analysis has been established with previous approaches in the related field, and the results demonstrated the superiority of the proposed system in AMD multi-classification. [ABSTRACT FROM AUTHOR]

Artificial intelligence algorithms, with roots extending into the past but experiencing a resurgence and evolution in recent years due to their superiority over traditional methods and contributions to human capabilities, have begun to make their presence felt in the field of pediatric rheumatology. In the ever-evolving realm of pediatric rheumatology, there have been incremental advancements supported by artificial intelligence in understanding and stratifying diseases, developing biomarkers, refining visual analyses, and facilitating individualized treatment approaches. However, like in many other domains, these strides have yet to gain clinical applicability and validation, and ethical issues remain unresolved. Furthermore, mastering different and novel terminologies appears challenging for clinicians. This review aims to provide a comprehensive overview of the current literature, categorizing algorithms and their applications, thus offering a fresh perspective on the nascent relationship between pediatric rheumatology and artificial intelligence, highlighting both its advancements and constraints. [ABSTRACT FROM AUTHOR]

Myocardial contrast echocardiography (MCE) plays a crucial role in diagnosing ischemia, infarction, masses and other cardiac conditions. In the realm of MCE image analysis, accurate and consistent myocardial segmentation results are essential for enabling automated analysis of various heart diseases. However, current manual diagnostic methods in MCE suffer from poor repeatability and limited clinical applicability. MCE images often exhibit low quality and high noise due to the instability of ultrasound signals, while interference structures can further disrupt segmentation consistency. To overcome these challenges, we proposed a deep-learning network for the segmentation of MCE. This architecture leverages dilated convolutions to capture high-scale information without sacrificing positional accuracy and modifies multi-head self-attention to enhance global context and ensure consistency, effectively overcoming issues related to low image quality and interference. Furthermore, we also adapted the cascade application of transformers with convolutional neural networks for improved segmentation in MCE. In our experiments, our architecture achieved the best Dice score of 84.35% for standard MCE views compared with that of several state-of-the-art segmentation models. For non-standard views and frames with interfering structures (mass), our models also attained the best Dice scores of 83.33% and 83.97%, respectively. These studies proved that our architecture is of excellent shape consistency and robustness, which allows it to deal with segmentation of various types of MCE. Our relatively precise and consistent myocardial segmentation results provide fundamental conditions for the automated analysis of various heart diseases, with the potential to discover underlying pathological features and reduce healthcare costs. [ABSTRACT FROM AUTHOR]

Productivity assessment plays a key role in successful earthwork projects and is primarily achieved by monitoring key construction equipment such as excavators and dump trucks. Vision-based methods are widely adopted to analyze the productivity of construction equipment in earthwork projects. These methods are inexpensive and easy to implement and maintain. However, previous studies on vision-based productivity analysis of earthmoving equipment have predominantly demonstrated its effectiveness under favorable conditions characterized by stable lighting, areas devoid of shadows, and high-visibility environments free from significant occlusion by other objects or terrain. There is a significant illumination difference and limited visibility under harsh low-visibility conditions at earthwork construction sites, which makes it difficult to achieve reliable identification accuracy using these already developed methods. To address this problem, this study proposes an automatic vision-based dump truck productivity measurement method based on a deep learning illumination enhancement algorithm combined with transfer learning for low-visibility, harsh conditions at earthwork construction sites. This method uses an internet of things (IoT) system equipped with a camera to capture the image and a deep learning illumination enhancement algorithm, trainable deep hybrid network (TDHN), to enhance the image quality under low-light conditions at earthwork sites. Then, a deep convolutional neural network image recognition algorithm, ResNet50, was combined with a transfer learning technique to extract information from the image. Through the IoT, this processed information is sent to the earthwork platform to perform productivity analysis and make timely decisions regarding equipment allocation schemes. To validate the effectiveness of the proposed methodology, it was implemented in a real-time earthwork project. This study results show that image recognition accuracy of 99.6%, 95.67%, and 94.77% under normal lighting, low lighting, and extremely low lighting conditions, respectively. The dump truck recognition accuracy increased by 1.10%, 3.62%, and 21.19%, leading to a significant improvement in productivity measurement of 1.08%, 3.54%, and 20.71% for the above-mentioned lighting conditions, respectively. [ABSTRACT FROM AUTHOR]

In order to meet the needs of digital development of power system and ensure the safety of power communication equipment, an intelligent patrol inspection method for power communication equipment with low code enabling based on digital transformation is proposed. Build an intelligent patrol platform for low code enabled power communication equipment based on digital transformation. The data acquisition module of the infrastructure layer scans the RFID tags on the communication equipment through the patrol personnel's handheld devices to obtain the specific information of the current equipment. The UDT data transmission protocol of the data transmission module transmits the equipment information collected by the patrol personnel to the patrol application layer; The fault diagnosis module of patrol application layer uses convolutional neural network to analyze patrol data and complete fault detection of power communication equipment; The integrated component layer uses the application components developed by visual tools to enable the platform with low code through the low threshold platform development technology; The front-end service layer, supported by the technology of the integrated component layer, implements the simplification and configurable processing of front-end display results such as patrol results display, and views and manages the patrol results of the platform through the user terminal layer. The experiment shows that the method is accurate in data acquisition, fast in data transmission and high in fault diagnosis accuracy. It is a highly practical intelligent patrol method for power communication equipment. [ABSTRACT FROM AUTHOR]

Infrared thermal imaging presents a promising avenue for detecting physiological phenomena such as hot flushes in animals, presenting a non-invasive and efficient approach for monitoring health and behavior. However, thermal images often suffer from various types of noise, which can impede the accuracy of analysis. In this study, the efficacy of different noise filtering algorithms is investigated utilizing filtering algorithms as preprocessing steps to enhance U-Net convolutional neural network (CNN) performance in processing animal skin infrared image sets for hot flush recognition. This study compares the performance of four commonly used filtering methods: mean, median, Gaussian, and bilateral filters. The impact of each filtering technique on noise reduction and preservation of features critical for hot flush detection is evaluated in a machine learning hot flush detection algorithm. The optimal filtering for skin thermal imaging was a median filter which significantly improved the U-Net CNN's ability to accurately identify hot flush patterns, achieving an Intersection over Union score of 92.6% compared to 90.4% without filters. This research contributes to the advancement of thermal image processing methodologies for animal health monitoring applications, providing valuable insights for researchers and practitioners in the field of veterinary medicine and animal behavior studies utilizing autonomous thermal image segmentation. [ABSTRACT FROM AUTHOR]

Colorectal cancer ranks as the second most prevalent cancer worldwide, with a high mortality rate. Colonoscopy stands as the preferred procedure for diagnosing colorectal cancer. Detecting polyps at an early stage is critical for effective prevention and diagnosis. However, challenges in colonoscopic procedures often lead medical practitioners to seek support from alternative techniques for timely polyp identification. Polyp segmentation emerges as a promising approach to identify polyps in colonoscopy images. In this paper, we propose an advanced method, PolySegNet, that leverages both Vision Transformer and Swin Transformer, coupled with a Convolutional Neural Network (CNN) decoder. The fusion of these models facilitates a comprehensive analysis of various modules in our proposed architecture.To assess the performance of PolySegNet, we evaluate it on three colonoscopy datasets, a combined dataset, and their augmented versions. The experimental results demonstrate that PolySegNet achieves competitive results in terms of polyp segmentation accuracy and efficacy, achieving a mean Dice score of 0.92 and a mean Intersection over Union (IoU) of 0.86. These metrics highlight the superior performance of PolySegNet in accurately delineating polyp boundaries compared to existing methods. PolySegNet has shown great promise in accurately and efficiently segmenting polyps in medical images. The proposed method could be the foundation for a new class of transformer-based segmentation models in medical image analysis. [ABSTRACT FROM AUTHOR]

The use of machine and deep learning models for the automated detection of eye disorders has gained significant popularity. Glaucoma, cataracts, and diabetic retinopathy are prevalent ocular conditions that can result in substantial harm. Prompt identification of ocular disorders is crucial for optimal therapeutic intervention. A novel idea of implementing weighted ensemble deep learning, which is integrated with Two-Dimensional Discrete Wavelet Transform (2D DWT) and 2D Principal Component Analysis (2D PCA) for optimum feature selection for classification, has been proposed in this research. This work includes evaluating the performance of pre-trained deep-learning models in detecting eye problems using the Efficientb6 and Densnet169 models. Then, the impact of applying weighted ensemble learning on improving these pre-trained deep-learning models' eye disease classification performance is investigated. The proposed methodology comprises three essential stages: pre-processing, feature extraction, and classification. Various methods are utilized during the pre-processing stage, including implementing Data Augmentation, which involves modifying the training data. Different techniques are employed in the feature extraction stage to extract the useful features from the input data. Each of these techniques achieved different dimensionality reduction. Finally, both models of Efficientb6 and Densnet169 are employed for the extracted features to achieve exceptional performance. The proposed system accomplished a recognition rate of 96.1 with 0% dimensionality reduction when the ensemble learning model was implemented directly on the pre-processed data set, skipping the feature extraction stage. Furthermore, the system proposed achieved an accuracy of 94.9 with 67.3% dimensionality reduction when only one level of 2D DWT decompositions is used. Moreover, the system proposed obtained a comparable accuracy of 92.9 with 93.75% data reduction when the 2-level of 2D DWT decompositions is integrated with 2D PCA. The experiments undertaken in this research successfully achieved its aims of obtaining favorable outcomes of eye disease classification, reducing the data dimensions to decrease the deep learning model trainable parameters, and implementing complexity requirements. The findings of the suggested approach surpassed those shown in prior studies utilizing the same Kaggle datasets. [ABSTRACT FROM AUTHOR]

The diversity and scarcity of the medical information makes it difficult to create precise global classification approach for the healthcare applications. The main motive is the privacy issue that restricts the data exchanging scope between healthcare institutions. On the contrary, an information from single source is not adequate for developing the worldwide diagnosis approach. The Federated Learning (FL) is a promising solution for privacy and data multiplicity issues, an appropriate aggregation model for multi class and dissimilar medical information is still challenging task in the recognition. Moreover, the FL approaches does not effectively analyzes the each participant execution in the local model and secures the user data. In order to overcome this issue, the Zero-Knowledge Proof (ZKP) based FL approach is developed over blockchain (BC) for performing the COVID-19 classification. The global model of FL uses the two layer Long Short Term Memory (2LLSTM) with federated proximal term (FedProx) namely 2LLSTMFP while the Convolutional Neural Network (CNN) is used in the local model. The integration ZKP and BS is used to improve the data confidentiality while the immutability of BC helps to prevent unauthorized variations for the ledger. The developed FLBC-ZKP is analyzed with two datasets such as COVID-19 Radiography, and CXR images pneumonia and COVID-19. The FLBC-ZKP is evaluated using accuracy, recall, precision, specificity, F1-score, False Negative Rate (FNR) and False Positive Rate (FPR). The existing researches such as WMT, MCCF, 3SFDL and TOTL are used to compare the FLBC-ZKP method. The FLBC-ZKP achieves improved accuracy of 98.34 % for COVID-19 Radiography dataset that is better than the MCCF and 3SFDL. [ABSTRACT FROM AUTHOR]