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We construct a new numerical measure of earnings announcement surprises, standardized unexpected earnings call text (SUE.txt), that does not explicitly incorporate the reported earnings value. SUE.txt generates a text-based post-earnings-announcement drift (PEAD.txt) larger than the classic PEAD. The magnitude of PEAD.txt is considerable even in recent years when the classic PEAD is close to 0. We explore our text-based empirical model to show that the calls' news content is about details behind the earnings number and the fundamentals of the firm. [ABSTRACT FROM AUTHOR]

High-accuracy prediction of the physical properties of amorphous materials is challenging in condensed-matter physics. A promising method to achieve this is machine-learning potentials, which is an alternative to computationally demanding ab initio calculations. When applying machine-learning potentials, the construction of descriptors to represent atomic configurations is crucial. These descriptors should be invariant to symmetry operations. Handcrafted representations using a smooth overlap of atomic positions and graph neural networks (GNN) are examples of methods used for constructing symmetry-invariant descriptors. In this study, we propose a novel descriptor based on a persistence diagram (PD), a two-dimensional representation of persistent homology (PH). First, we demonstrated that the normalized two-dimensional histogram obtained from PD could predict the average energy per atom of amorphous carbon at various densities, even when using a simple model. Second, an analysis of the dimensional reduction results of the descriptor spaces revealed that PH can be used to construct descriptors with characteristics similar to those of a latent space in a GNN. These results indicate that PH is a promising method for constructing descriptors suitable for machine-learning potentials without hyperparameter tuning and deep-learning techniques. [ABSTRACT FROM AUTHOR]

We report a flexible language-model-based deep learning strategy, applied here to solve complex forward and inverse problems in protein modeling, based on an attention neural network that integrates transformer and graph convolutional architectures in a causal multi-headed graph mechanism, to realize a generative pretrained model. The model is applied to predict the secondary structure content (per-residue level and overall content), protein solubility, and sequencing tasks. Further trained on inverse tasks, the model is rendered capable of designing proteins with these properties as target features. The model is formulated as a general framework, completely prompt-based, and can be adapted for a variety of downstream tasks. We find that adding additional tasks yields emergent synergies that the model exploits in improving overall performance, beyond what would be possible by training a model on each dataset alone. Case studies are presented to validate the method, yielding protein designs specifically focused on structural materials, but also exploring the applicability in the design of soluble, antimicrobial biomaterials. While our model is trained to ultimately perform eight distinct tasks, with available datasets, it can be extended to solve additional problems. In a broader sense, this study illustrates a form of multiscale modeling that relates a set of ultimate building blocks (here, byte-level utf8 characters that define the nature of the physical system at hand) to complex output. This materiomic scheme captures complex emergent relationships between universal building block and resulting properties, via a synergizing learning capacity, to express a set of potentialities embedded in the knowledge used in training via the interplay of universality and diversity. Significance statement: Predicting the properties of materials based on a flexible description of their structure, environment, or process, is a long-standing challenge in multiscale modeling. Our MaterioFormer language model, trained to solve forward and inverse tasks, incorporates a deep learning capacity through attention and graph strategies to yield a multimodal approach to model and design materials. Since our model is prompt-based and information is encoded consistently via byte-level utf8 tokenization, it can process diverse modalities of information, such as sequence data, description of tasks, and numbers, and offers a flexible workflow that integrates human intelligence and artificial intelligence. Autoregressive training, using pre-training against a large unlabeled dataset, allows for straightforward adjustment of specific objectives. [ABSTRACT FROM AUTHOR]

Boron phosphide (BP) is a (super)hard semiconductor constituted of light elements, which is promising for high demand applications at extreme conditions. The behavior of BP at high temperatures and pressures is of special interest but is also poorly understood because both experimental and conventional ab initio methods are restricted to studying refractory covalent materials. The use of machine learning interatomic potentials is a revolutionary trend that gives a unique opportunity for high-temperature study of materials with ab initio accuracy. We develop a deep machine learning potential (DP) for accurate atomistic simulations of the solid and liquid phases of BP as well as their transformations near the melting line. Our DP provides quantitative agreement with experimental and ab initio molecular dynamics data for structural and dynamic properties. DP-based simulations reveal that at ambient pressure, a tetrahedrally bonded cubic BP crystal melts into an open structure consisting of two interpenetrating sub-networks of boron and phosphorous with different structures. Structure transformations of BP melt under compressing are reflected by the evolution of low-pressure tetrahedral coordination to high-pressure octahedral coordination. The main contributions to structural changes at low pressures are made by the evolution of medium-range order in the B-subnetwork and, at high pressures, by the change of short-range order in the P-subnetwork. Such transformations exhibit an anomalous behavior of structural characteristics in the range of 12–15 GPa. DP-based simulations reveal that the Tm(P) curve develops a maximum at P ≈ 13 GPa, whereas experimental studies provide two separate branches of the melting curve, which demonstrate the opposite behavior. Analysis of the results obtained raises open issues in developing machine learning potentials for covalent materials and stimulates further experimental and theoretical studies of melting behavior in BP. [ABSTRACT FROM AUTHOR]

Real-time monitoring and quantitative measurement of molecular exchange between different microdomains are useful to characterize the local dynamics in porous media and biomedical applications of magnetic resonance. Diffusion exchange spectroscopy (DEXSY) is a noninvasive technique for such measurements. However, its application is largely limited by the involved long acquisition time and complex parameter estimation. In this study, we introduce a physics-guided deep neural network that accelerates DEXSY acquisition in a data-driven manner. The proposed method combines sampling pattern optimization and physical parameter estimation into a unified framework. Comprehensive simulations and experiments based on a two-site exchange system are conducted to demonstrate this new sampling optimization method in terms of accuracy, repeatability, and efficiency. This general framework can be adapted for other molecular exchange magnetic resonance measurements. [ABSTRACT FROM AUTHOR]

How do producers' distinctiveness and social structure influence third-party certifications? We argue that producers compete against prior and current competitors, and against their past selves. In the context of 153 artists active during a key period of the emergence of modern art (1905–1916), we utilize a convolutional neural network used in computer vision to extract feature vectors of artworks, and measure quantitative distance of these artists' works from canonical reference points. We find that artists are rewarded for distinctiveness from prior and current competitors and their past selves (up to a point). However, artists' autonomy to differentiate themselves depends on their position in the social structure, which we divide into supply-side artist-to-artist networks, and demand-side artist-to-gallerist networks. Artists with high or low supply-side status receive higher rewards for distinctiveness from current competitors than do artists with middle supply-side status. Artists with higher demand-side status receive higher rewards for distinctiveness from their own past, but lower rewards for distinctiveness from current competitors. These results show that peers strive to constrain each other to conform to positions of gravity within product space, and that market audiences deploy either higher or lower constraints on a producer's identity depending on the reference point. [ABSTRACT FROM AUTHOR]

We present a time-dependent variational method to learn the mechanisms of equilibrium reactive processes and efficiently evaluate their rates within a transition path ensemble. This approach builds off of the variational path sampling methodology by approximating the time-dependent commitment probability within a neural network ansatz. The reaction mechanisms inferred through this approach are elucidated by a novel decomposition of the rate in terms of the components of a stochastic path action conditioned on a transition. This decomposition affords an ability to resolve the typical contribution of each reactive mode and their couplings to the rare event. The associated rate evaluation is variational and systematically improvable through the development of a cumulant expansion. We demonstrate this method in both over- and under-damped stochastic equations of motion, in low-dimensional model systems, and in the isomerization of a solvated alanine dipeptide. In all examples, we find that we can obtain quantitatively accurate estimates of the rates of the reactive events with minimal trajectory statistics and gain unique insights into transitions through the analysis of their commitment probability. [ABSTRACT FROM AUTHOR]

The orders makespan and resources utilisation are considered as the objective function of the model, and the heterogeneous production resources and logistics resources are integrated to autonomously communicate and interact with each other to bidding for the dynamic production-logistics-integrated operation tasks. The aim of this special issue is to encourage original and latest contributions, and to review and survey research and development on industrial intelligence-driven production and operations management, focusing on state-of-the-art and potential future approaches and technologies and providing a good starting point for researchers entering these research areas. Then, to evaluate the tolerance and persistence capabilities of MSC under supply and demand uncertainties, a graph-based operational robustness analysis method of the IIoT platform for MSC is proposed. The 8th paper entitled 'An integrative decision-making model for the Internet of Things-enabled supply chains of fresh agri-product', by Han et al. proposed a mixed-integer programming model to generate integrative decision-making in the Internet of Things (IoT)-enabled fresh agri-products supply chains. [Extracted from the article]

Recently, additive manufacturing (AM) has received increased attention due to its high energy consumption. By extracting hidden information or highly representative features from energy-relevant data, knowledge distillation (KD) reduces predictive model complexity and computational load. By using almost predetermined and fixed models, the distillation process restricts students and teachers from learning from one model to another. To reduce computational costs while maintaining acceptable performance, a teacher assistant (TA) was added to the teacher-student architecture. Firstly, a teacher ensemble was combined with three baseline models to enhance accuracy. In the second step, a teacher ensemble (TA) was formed to bridge the capacity gap between the ensemble and the simplified model. As a result, the complexity of the student model was reduced. Using geometry-based features derived from layer-wise image data, a KD-based predictive model was developed to evaluate the feasibility and effectiveness of two independently trained student models. In comparison with independently trained student models, the performance of the proposed method has the lowest RMSE, MAE, and training time. [ABSTRACT FROM AUTHOR]

Surface defect detection by machine vision has received increased attention concerning the quality control of universal joint bearings (UJB). The defect distribution and counting information are important for product quality optimisation. However, vision defect detection for UJB remains a challenging task due to the diversity of background textures and defect characters on multiple surfaces. In this study, a multi-surface defect detection (MSDD) method consisting of region segmentation, feature extraction and detection is proposed. First, defect regions are accurately localised by the proposed adaptive defect region segmentation algorithm, which suppresses the inference of background variety. Then, a novel defect feature named multimodal fusion shape descriptor that integrates the global information and local information of defects is constructed to generate the discriminative defect representation. Finally, a defect feature extraction ability transfer strategy based on the transfer learning mechanism is proposed to address the problem of insufficient defect samples. The experimental results show that our method achieves the best accuracy of 94.8% macro-F1 and processes 28 defect images per second. Besides, the application effect in the practical production line indicates that our method meets the accuracy and real-time requirements of MSDD for UJB. [ABSTRACT FROM AUTHOR]

In the in-situ quality detection of yarn production, image deblurring plays a critical role in the vision-based detection systems to restore a sharp image and provide more accurate input for inspection. However, image deblurring is still challenging since the current methods are mainly based on the pre-defined blur degree. In dynamic yarn production, the relationship between the defocus blur degrees and the poses of the yarn body is highly associated, which can be excavated to prior knowledge in image deblurring to achieve more effective restoration. Thus, a knowledge augmented deep learning model is proposed to adaptively deblur yarn images with variable defocus blur degrees. A pose classification module designed by prior knowledge is embedded into the deep neural network, which classifies the yarn poses and feeds them into multi-scale deblurring channels. In each channel, we incorporate the image gradient prior into the specially designed loss function to attract the attention of the deblurring network on the edge details of the yarn. The experimental results from actual spinning processes demonstrate that the proposed method performs a better effect not only in the variable-scale deblurring of the global image but also in the restoration of the edge details. [ABSTRACT FROM AUTHOR]

The fast warning for financial risk of enterprises has always been a realistic demand for their managers. Currently, this mainly relies on expert experience to make comprehensive analysis from massive business data. Benefitting from the strong computational performance of deep learning, this paper proposes a fuzzy neural network (FNN)-based intelligent warning method for financial risk of enterprises. An improved FNN structure with time-varying coefficients and time-varying time lags is established to extract features of enterprises from complex financial context. The algorithm of fuzzy C-means and fuzzy clustering based on sample data are studied. In this paper, the fuzzy C-means algorithm is used to cluster the samples, the input sample set is preprocessed, a new set of learning samples is formed, and then the neural network is trained. The enterprise financial risk sample and its modular FNN model are established, and the evaluation of the enterprise financial risk sample is simulated. Then, a decision part is added following the FNN part to output the warning results. After that, we have also conducted a case study as simulation experiments to evaluate the proposed technical framework. The obtained results show that it can perform well in the fast warning of financial risk for enterprises. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is rapidly transforming the healthcare, finance and transportation industries. This paper presents a field-programmable gate array (FPGA)-based neural network accelerator (NNA) design for power allocation in downlink nonorthogonal multiple access (NOMA) networks. The proposed hardware accelerator effectively cuts computational costs while delivering performance on par with the highest sum capacity. Numerical results show that this NNA offers a remarkable computational speed increase of up to 99% compared to the conventional exhaustive search method. Furthermore, the deep learning (DL) model achieved high accuracy (0.92 training, 0.93 testing), and the hardware accelerator design for this DL inference model was implemented on the PYNQ-Z2 board-constrained edge device to predict power allocation coefficients in NOMA systems. [ABSTRACT FROM AUTHOR]

Train vibrations are the primary concern in environmental engineering and civil engineering. It is significantly imperative to find new methods for reducing and isolating vibrations. The locally resonant metamaterials (LRMs) propose a novel method and concept for reducing train vibration. However, the accurate and quick design structures of LRMs based on vibration characteristics are still an issue. Thus, this study presents a novel inverse design model of three-component locally resonant metamaterial barriers (LRMBs) for vibration reduction based on deep learning. The bandgap characteristics and vibration modes of the LRMB are investigated by using the improved plane wave expansion (IPWE) and finite element method (FEM). Besides, the gradient-combined LRMBs are proposed based on time–frequency features of measured vibration caused by trains and the novel inverse design model, and a two-dimensional finite element model coupling with infinite element boundaries is established to study the reduction efficiency of the gradient-combined LRMBs. And the performances of different LRMBs are fully analyzed in time and frequency domains. The results show that the novel inverse design model can be successfully used to design the LRMB based on vibration features. Moreover, the gradient-combined LRMBs have better isolation performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel unsupervised pre-training method for point cloud deep learning models using multimodal contrastive learning. Point clouds, which consist of a set of three-dimensional coordinate points acquired from 3D scanners, lidars, depth cameras, etc. play an important role in representing 3D scenes, and understanding them is crucial for implementing autonomous driving or navigation. Deep learning models based on supervised learning for point cloud understanding require a label for each point cloud data that corresponds to the correct answer in training. However, generating these labels is expensive, making it difficult to build large datasets, which is essential for good model performance. Our proposed unsupervised pre-training method, on the other hand, does not require labels and can serve as an initial value for a model that can alleviate the need for such large datasets. The proposed method is characterized as a multimodal approach that utilizes two modalities for point clouds: the point cloud itself and an image rendering of the point cloud. By using images that directly render the point clouds, the shape information of the point clouds from various viewpoints can be obtained from the images without additional data such as meshes. We pre-trained the model with the proposed method and conducted performance comparison on ModelNet40 and ScanObjectNN datasets. The linear classification accuracy of the point cloud feature vector extracted by the pre-trained model was 91.5% and 83.9%, and after fine-tuning for each dataset, the classification accuracy was 93.3% and 86.9%, respectively. [ABSTRACT FROM AUTHOR]

Handwritten character recognition (HCR) remains a challenging pattern recognition problem despite decades of research, and lacks research on script independent recognition techniques. This is mainly because of similar character structures, different handwriting styles, diverse scripts, handcrafted feature extraction techniques, unavailability of data and code, and the development of script-specific deep learning techniques. To address these limitations, we have proposed a script independent deep learning network for HCR research, called HCR-Net, that sets a new research direction for the field. HCR-Net is based on a novel transfer learning approach for HCR, which partly utilizes feature extraction layers of a pre-trained network. Due to transfer learning and image augmentation, HCR-Net provides faster and computationally efficient training, better performance and generalizations, and can work with small datasets. HCR-Net is extensively evaluated on 40 publicly available datasets of Bangla, Punjabi, Hindi, English, Swedish, Urdu, Farsi, Tibetan, Kannada, Malayalam, Telugu, Marathi, Nepali and Arabic languages, and established 26 new benchmark results while performed close to the best results in the rest cases. HCR-Net showed performance improvements up to 11% against the existing results and achieved a fast convergence rate showing up to 99% of final performance in the very first epoch. HCR-Net significantly outperformed the state-of-the-art transfer learning techniques and also reduced the number of trainable parameters by 34% as compared with the corresponding pre-trained network. To facilitate reproducibility and further advancements of HCR research, the complete code is publicly released at https://github.com/jmdvinodjmd/HCR-Net. [ABSTRACT FROM AUTHOR]

Renal Cell Carcinoma (RCC) is the most common malignant tumor (85%) of kidney cancer and has a complex histological pattern and nuclear structure. The manual diagnosis of kidney cancer or any other cancer from histopathology image depends on the knowledge and experience of pathologists, and the pathologist's experience influences the results. According to studies, the kind of histology in kidney cancer is related to the prognosis and course of treatment. Since the kind of histology, molecular profile, and stage of the disease all affect how the disease is treated, there is an essential need to develop an automated system that can precisely analyze the histopathological images of the disease. This work demonstrates how a deep learning framework can be used to predict and classify associated grades of RCC from provided haematoxylin and eosin (H &E) images. The proposed model focuses on two important tasks- First to capture and extract associated features from the H &E images of five different grades. Second, to classify the new set of unseen H &E images into five separate grades using the obtained features. The proposed architecture has been tested and experimented on two independent datasets containing H &E stained histopathology images. The proposed architecture has been examined using the following performance metrics namely precision, recall, F1 - score, accuracy, Floating-point operations (FLOPs), and the total number of parameters. The obtained results show that the proposed architecture attains better results over seven state-of-the-art deep learning architectures on two different H &E stained histopathology image datasets. [ABSTRACT FROM AUTHOR]

Nutrient deficiency identification in plants is crucial for precision agriculture. Over the past decade, the application of digital imagery and deep learning has been utilized to tackle challenges related to this task. Nevertheless, the constraints inherent in current models have significantly heightened the difficulty of this task. To overcome these limitations, this study proposes an ensemble transfer learning method for nutrient deficiency identification and yield-loss prediction in crops using plant-leaf images. The proposed method is evaluated through two experiments. The initial experiment canters on the classification of images according to their nutrient deficiency, while the second experiment determines the yield loss based on the severity of the deficient nutrient. The proposed ensemble transfer learning methodology achieves remarkable accuracy rates of 99% and 94% for the groundnut and rice datasets, respectively. Furthermore, the severity-based crop yield prediction method predicts yield loss of 32.5% and 28.34% for the rice and groundnut datasets, respectively. The study utilizes two different crop datasets that represent actual environmental conditions. The first dataset contains 4399 leaf images of the rice plant, while the second dataset contains 4550 leaf images of the groundnut plant. The proposed method outperforms existing models and demonstrates that digital imagery and Deep learning can play a significant role in addressing agricultural challenges within the context of precision agriculture. [ABSTRACT FROM AUTHOR]

Otitis media is a medical concept that represents a range of inflammatory middle ear disorders. The high costs of medical devices utilized by field experts to diagnose the disease relevant to otitis media prevent the widespread use of these devices. This makes it difficult for field experts to make an accurate diagnosis and increases subjectivity in diagnosing the disease. To solve these problems, there is a need to develop computer-aided middle ear disease diagnosis systems. In this study, a deep learning-based approach is proposed for the detection of OM disease to meet this emerging need. This approach is the first that addresses the performance of a voting ensemble framework that uses Inception V3, DenseNet 121, VGG16, MobileNet, and EfficientNet B0 pre-trained DL models. All pre-trained CNN models used in the proposed approach were trained using the Public Ear Imagery dataset, which has a total of 880 otoscopy images, including different eardrum cases such as normal, earwax plug, myringosclerosis, and chronic otitis media. The prediction results of these models were evaluated with voting approaches to increase the overall prediction accuracy. In this context, the performances of both soft and hard voting ensembles were examined. Soft voting ensemble framework achieved highest performance in experiments with 98.8% accuracy, 97.5% sensitivity, and 99.1% specificity. Our proposed model achieved the highest classification performance so far in the current dataset. The results reveal that our voting ensemble-based DL approach showed quite high performance for the diagnosis of middle ear disease. In clinical applications, this approach can provide a preliminary diagnosis of the patient's condition just before field experts make a diagnosis on otoscopic images. Thus, our proposed approach can help field experts to diagnose the disease quickly and accurately. In this way, clinicians can make the final diagnosis by integrating automatic diagnostic prediction with their experience. [ABSTRACT FROM AUTHOR]

Ocular diseases can have debilitating consequences on visual acuity if left untreated, necessitating early and accurate diagnosis to improve patients' quality of life. Although the contemporary clinical prognosis involving fundus screening is a cost-effective method for detecting ocular abnormalities, however, it is time-intensive due to limited resources and expert ophthalmologists. While computer-aided detection, including traditional machine learning and deep learning, has been employed for enhanced prognosis from fundus images, conventional deep learning models often face challenges due to limited global modeling ability, inducing bias and suboptimal performance on unbalanced datasets. Presently, most studies on ocular disease detection focus on cataract detection or diabetic retinopathy severity prediction, leaving a myriad of vision-impairing conditions unexplored. Minimal research has been conducted utilizing deep models for identifying diverse ocular abnormalities from fundus images, with limited success. The study leveraged the capabilities of four Swin Transformer models (Swin-T, Swin-S, Swin-B, and Swin-L) for detecting various significant ocular diseases (including Cataracts, Hypertensive Retinopathy, Diabetic Retinopathy, Myopia, and Age-Related Macular Degeneration) from fundus images of the ODIR dataset. Swin Transformer models, confining self-attention to local windows while enabling cross-window interactions, demonstrated superior performance and computational efficiency. Upon assessment across three specific ODIR test sets, utilizing metrics such as AUC, F1-score, Kappa score, and a composite metric representing an average of these three (referred to as the final score), all Swin models exhibited superior performance metric scores than those documented in contemporary studies. The Swin-L model, in particular, achieved final scores of 0.8501, 0.8211, and 0.8616 on the Off-site, On-site, and Balanced ODIR test sets, respectively. An external validation on a Retina dataset further substantiated the generalizability of Swin models, with the models reporting final scores of 0.9058 (Swin-T), 0.92907 (Swin-S), 0.95917 (Swin-B), and 0.97042 (Swin-L). The results, corroborated by statistical analysis, underline the consistent and stable performance of Swin models across varied datasets, emphasizing their potential as reliable tools for multi-ocular disease detection from fundus images, thereby aiding in the early diagnosis and intervention of ocular abnormalities. [ABSTRACT FROM AUTHOR]

A question-answering (QA) system is a key application in the field of natural language processing (NLP) that provides relevant answers to user queries written in natural language. In factoid QA using knowledge bases, predicting the semantic category of answers, such as location, person, or numerical values, helps to reduce the search spaces and is an essential step in formal query construction for answer retrieval. However, finding the semantics in sequence data like questions is challenging. In this regard, Recursive neural networks based deep learning methods have been applied. But, they are inefficient in handling long-term dependencies. Recently, pre-trained language models employing transformers are proven effective and can generate context-dependent embedding for words and sentences from their encoders with attention mechanisms. However, to train an efficient transformer model for semantic category prediction requires a large dataset and high computational resources. Therefore, in this work, we employ a transfer learning approach using pre-trained transformer models by efficiently adapting them to predict the semantic category of answers from input questions. Here, embeddings from the encoders of the text-to-text transfer transformer (T5) model have been leveraged to obtain an efficient question representation and to train the classification model which is named as QcT5. Along with QcT5, an extensive experimental study on other recent transformer models - BERT, RoBERTa, DeBERTa, and XLNet, is conducted, and their performance is analyzed in various fine-tuning settings. Experimental results indicate that the QcT5 model significantly improves the performance compared to the selected state-of-the-art methods by achieving an f1-score of 98.7%, 89.9% on TREC-6, and TREC-50 datasets respectively. [ABSTRACT FROM AUTHOR]

One of the most severe cancers worldwide is Colorectal Cancer (CRC), which has the third-highest incidence of cancer cases and the second-highest rate of cancer mortality. Early diagnosis and treatment are receiving much attention globally due to the increasing incidence and death rates. Colonoscopy is acknowledged as the gold standard for screening CRC. Despite early screening, doctors miss approximately 25% of polyps during a colonoscopy examination because the diagnosis varies from expert to expert. After a few years, this missing polyp may develop into cancer. This study is focused on addressing such diagnostic challenges, aiming to minimize the risk of misdiagnosis and enhance the overall accuracy of diagnostic procedures. Thus, we propose an efficient deep learning method, DeepCPD, combining transformer architecture and Linear Multihead Self-Attention (LMSA) mechanism with data augmentation to classify colonoscopy images into two categories: polyp versus non-polyp and hyperplastic versus adenoma based on the dataset. The experiments are conducted on four benchmark datasets: PolypsSet, CP-CHILD-A, CP-CHILD-B, and Kvasir V2. The proposed model demonstrated superior performance compared to the existing state-of-the-art methods with an accuracy above 98.05%, precision above 97.71%, and recall above 98.10%. Notably, the model exhibited a training time improvement of over 1.2x across all datasets. The strong performance of the recall metric shows the ability of DeepCPD to detect polyps by minimizing the false negative rate. These results indicate that this model can be used effectively to create a diagnostic tool with computer assistance that can be highly helpful to clinicians during the diagnosing process. [ABSTRACT FROM AUTHOR]

Deep learning has brought essential improvement to image inpainting technology. Conventional deep-learning methods primarily focus on creating visually appealing content in the missing parts of images. However, these methods usually generate edge variations and blurry structures in the filled images, which lead to imbalances in quantitative metrics PSNR/SSIM and LPIPS/FID. In this work, we introduce a pioneering model called PTG-Fill, which utilizes a coarse-to-fine architecture to achieve smooth-variation image inpainting. Our approach adopts the novel Stable-Partial Convolution to construct the coarse network, which integrates a smooth mask-update process to ensure its long-term operation. Meanwhile, we propose the novel Distinctive-Gated Convolution to construct the refined network, which diminishes pixel-level variations by the distinctive attention. Additionally, we build up a novel Transformer bridger to preserve the in-depth features for image refinement and facilitate the operation of the two-stage network. Our extensive experiments demonstrate that PTG-Fill outperforms previous state-of-the-art methods both quantitatively and qualitatively under various mask ratios on four benchmark datasets: CelebA-HQ, FFHQ, Paris StreetView, and Places2. Code and pre-trained weights are available at https://github.com/zeyuwang-zju/PTG-Fill. [ABSTRACT FROM AUTHOR]

Precise and automatic plant disease detection represent a fundamental research topic. Indeed, traditional or manual disease detection can be laborious, inaccurate and time-consuming. In this work, a particular attention is given to automatic potato late blight disease detection. In fact, this devastating pathogen leads every year to a significant reduction in potato yields. In addition, since potatoes are a major food source for many nations, decreased production generate a real food insecurity. Therefore, considering these challenges, using advanced techniques in computer vision and machine learning allowed farmers to swiftly and accurately identify disease. The main objective of this research work is to generate a super-resolved labeled dataset (SRD) and evaluate its impact on plant disease detection. Three states of the art object detection methods (Faster-RCNN, Detr and Yolo V8) have been used to conduct an exhaustive evaluation on the effect of using a super-resolved dataset to perform detection. The obtained results show that the detection of potato late blight disease is enhanced using SRD. Training Yolo V8 model on SRD outperform other trained models in detecting very small lesions. In fact, training Yolo V8 on SRD reached higher mAP, lower Loss values and a reasonable inference time, making it suitable for real time applications. [ABSTRACT FROM AUTHOR]

Temporal action localization (TAL) is one of the most important tasks in video understanding. Weakly supervised temporal action localization (WTAL) involves classifying and localizing all the action instances in untrimmed videos under the supervision of only video-level category labels, which is a challenging task because of the absence of frame-level annotations. In this study, first, we review the development process of the WTAL task in recent years, summarize and analyze the main problems of WTAL. Second, we classify and compare the research approaches of existing models and thoroughly discuss methods based on multiple instance learning (MIL), feature erasing, the attention mechanism, similarity propagation, pseudo-ground truth generation, contrastive learning, and adversarial learning. Then, we present the datasets and evaluation criteria for the WTAL task. Finally, we discuss the main application areas and further developments in WTAL. [ABSTRACT FROM AUTHOR]

In recent times, deep learning-based steganalysis classifiers have become popular due to their state-of-the-art performance. Most deep steganalysis classifiers usually extract noise residuals using high-pass filters as preprocessing steps and feed them to their deep model for classification. It is observed that recent steganographic embedding does not always restrict their embedding in the high-frequency zone; instead, they distribute it as per embedding policy. Therefore, besides noise residual, learning the embedding zone is another challenging task. In this work, unlike the conventional approaches, the proposed model first extracts the noise residual using learned denoising kernels to boost the signal-to-noise ratio. After preprocessing, the sparse noise residuals are fed to a novel Multi-Contextual Convolutional Neural Network (M-CNET) that uses heterogeneous context size to learn the sparse and low-amplitude representation of noise residuals. The model performance is further improved by incorporating the Self-Attention module to focus on the areas prone to steganalytic embedding. A set of comprehensive experiments is performed to show the proposed scheme's efficacy over the prior arts. Besides, an ablation study is given to justify the contribution of various modules of the proposed architecture. [ABSTRACT FROM AUTHOR]

Reconstruction of multi-view 3-dimensional images is essential in robotics and computer vision to obtain an accurate 3-dimensional representation of objects by analyzing the 2-dimensional input data. For reconstructing the 3-dimensional image, it is mandatory to analyze the 3-dimensional geometry features from multiple viewpoints. It includes feature extraction and transformation from 2-dimensional features to 3-dimensional volumetric meshes. However, the existing research cannot produce consistent reconstruction results for the same input images with different orders. Therefore, the deep learning-based Residual Network-50 model is developed for 3-dimensional image reconstruction from multi-view images in the present work. The proposed system model comprises a 2-dimensional and 3-dimensional network and a backpropagation layer. From the input image, 2-dimensional features are computed using a 2-dimensional network. Then, the metaheuristic Adaptive School of Fish Optimization is used to improve the neural network's output, determining the optimal weight that gives less classification error. Then, the testing process uses the deep autoencoder, which decodes the output of the training model. Residual Network-50 is used to reconstruct 2-dimensional images to 3-dimensional using single or multi-views. Finally, the experimental analysis is performed in Python. The experiment is performed on the ShapeNet dataset and compared with the existing works. The proposed model yields better accuracy, F-score and Intersection-over-Union values of 99.3%, 0.893 and 0.734, respectively, which is more efficient than other existing models. [ABSTRACT FROM AUTHOR]

With the development of highways, there has been an increase in toll evasion by unscrupulous individuals who employ various means to avoid paying fees. This has made it essential for highway management to focus on effective methods to prevent and manage toll evasion, as well as regulate toll-related operations. However, traditional auditing techniques are heavily reliant on manual toll data and lack sufficient auxiliary data such as video and pictures, making big data analysis and in-depth data analysis difficult. Consequently, these methods can only identify a portion of evasion behavior, with a significant number of potential evasion cases remaining unidentified. To address this issue, we propose utilizing vehicle re-identification to enable cross-camera identification and continuous tracking of vehicles, allowing for accurate calculation of their operating mileage. However, vehicle re-identification using images captured by highway cameras is a challenging task, primarily due to the low resolution of the images and the lack of relevant datasets. To tackle these problems, we construct a large-scale, low-resolution dataset based on images captured by cameras on highways. We also propose a Part-guided Feature-combination Network (PFNet) to analyze the dataset. PFNet adopts a three-stage approach, wherein each stage of the network requires independent training. In the first stage, the Pyramid Scene Parsing Network (PSPNet) is improved to segment the entire vehicle into parts. In the second stage, based on a comparative learning approach, the Siamese Feature Extraction Module (SFEM) module was created to extract features of pairs of vehicle parts. To train the SFEM, pairs of positive samples, which consist of the same vehicle component pairs captured by different cameras, and pairs of negative samples, which consist of different vehicle component pairs are needed. To ensure that the positive sample pairs are closer to each other in the embedding space, and the negative samples are as far away as possible from each other, contrast loss is utilized. Moreover, The SFEM incorporates VGG16 and a Multi-period Convolutional Block Attention Module (MCBAM), which enhances the learning ability of the conventional Convolutional Block Attention Module (CBAM). In the third stage, information propagation among the vehicle parts is achieved using a Multi-layer Perceptron (MLP), which calculates the similarity after combining the features of all parts. Our self-built test set demonstrates that PFNet is effective for continuous monitoring of vehicles on highways under multiple cameras, resulting in improved accuracy of vehicle re-identification, with Top1 accuracy of 93.61% and Top5 accuracy of 99.44%. [ABSTRACT FROM AUTHOR]

Deep learning methods are leveraged in various computer science and artificial intelligence areas, including image classification. Convolutional neural network (CNN) is one of the most widely used deep neural networks for which, several highly effective architectures for image classification have been presented. In this paper, an improved version of the recently introduced CondenseNet is provided as a new network architecture. On the other hand, due to the necessity of reducing the dependence on labeled data in the training process of neural networks, a self-supervised learning method is also proposed for labeling unlabeled images. The results of the experiments show the proper performance of the proposed self-supervised CondenseNet method compared to the basic version of CondenseNet. The experiments are conducted on CIFAR_10 and CIFAR-100 datasets and show better accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

A fricative sound is produced by the close proximity of two articulators, resulting in a partially obstructed airstream and turbulent airflow. The frequency spectrum of the majority of fricatives is similar to noise. This peculiarity presents a significant challenge in their numerical processing. Nonetheless, the literature contains a large number of works for fricatives, particularly those of Latin languages. However, very few are related to the Arabic language. The objective of this article is to create and validate a dataset of 14 Arabic fricatives using an acoustic characterization approach. We recorded a speech corpus from thirty speakers for this purpose, yielding 25 200 units of analysis. The data was then analyzed and validated using deep learning-based classification methods. As a result, two distinct approaches were proposed. The first approach is based on the use of three deep neural networks (Convolutional Recurrent Neural Network (CRNN), Vision Transformers (ViT), and ResNet50) whereas their input is one of three feature extraction techniques (spectrogram, Mel-spectrogram, and Mel-frequency Cepstral Coefficient (MFCC)) from speech units /Vowel-Consonant-Vowel/ (or /VCV/). The best classification rate observed when using the Mel spectrogram with the CRNN model was 94.07%, while the ViT model surpassed this with a rate of 95.64%. This rate was improved to 97.5% by proposing a second approach based on a hierarchical H-CRNN model with fricatives organized into categories (sibilance, voicing, and place of articulation). The obtained results surpassed the traditional approaches proposed in the literature for the classification of Arabic fricatives due to the hierarchical approach. [ABSTRACT FROM AUTHOR]

There is an ever-increasing number of public users of the internet just to remain learned and updated as well as distribute millions of posts, articles, and videos across platforms such as Twitter, Facebook and YouTube etc. The speedy acceptance of social media has forced to an explosive growth in information-distribution among users, with fake news becoming a fundamental intrusion of our digital day-to-day life. The spread of disinformation is in part associated to social media failing to verify the correctness of a news item. Although it is technically possible, it is challenging for a deep neural network to "attend to" the false alone portions of each news piece because the linguistics of fake news will approximate that of true news. In this paper, a Simple Shallow Perceptron Network (SSPN) has been proposed and will reduce the complications such as the curse of high dimensionality, high training time of Deep Neural Networks, over-fitting and over-thinking problem during network training. The effectiveness of the proposed SSPN model for false news identification using the benchmark LIAR dataset and the more recent FakeNewsNet dataset have also been examined. The proposed model outperformed all other existing models, with an F1-score of 0.73 for the LIAR dataset and 0.90 for the FakeNewsNet dataset. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing images (RSI) refer to images captured from a distance, usually from an aircraft or satellite that provide details about the Earth's surface. It can be used in several application areas like environmental monitoring, urban planning, agriculture, and disaster response. In urban planning, high-resolution imagery is used to monitor the growth of urban areas or to recognize the area that requires infrastructure improvement. Vehicle detection is a significant way of understanding high-resolution RSIs. Vehicle detection and classification on high-resolution RSI is a difficult task that needs a group of computer vision (CV), image processing, and machine learning (ML) algorithms. Deep convolutional neural network (DCNN) based techniques have attained recent outcomes in many object detection datasets and have enriched several CV tasks. This article designed and developed a sparrow search optimization algorithm with deep learning for vehicle type detection and classification (SSOADL-VTDC) technique on high-resolution remote sensing images. The presented SSOADL-VTDC technique examines the high-quality RSIs for the accurate detection and classification of vehicles. To accomplish this, the SSOADL-VTDC technique employs a YOLOv5 object detector with a Residual Network as a backbone approach. In addition, the SSOADL-VTDC technique uses SSOA based hyperparameter optimizer designed for the parameter tuning of the YOLOv5 model. For the vehicle classification process, the SSOADL-VTDC technique exploits the softmax classifier. The simulation validation of the SSOADL-VTDC approach was validated on a high-resolution RSI dataset and the outcomes demonstrated the greater of the SSOADL-VTDC methodology in terms of different measures. [ABSTRACT FROM AUTHOR]

Glaucoma is an eye disease that damages the optic nerve (or retina) and impairs vision. This disease can be prevented with regular checkups, but this increases the workload for professionals and the time it takes to get results. So an automated method using deep learning would be helpful for detection of disease. In order to shorten the diagnosis time for glaucoma, this paper proposed a deep learning based method for automatic glaucoma detection. The experiments are conducted on glaucoma datasets available on Kaggle. This paper used transfer learning based pretrained models namely DenseNet169, MobileNet, InceptionV3, Xception, ReseNet152V2,and VGG19. Among all models DenseNet169 gives best result with accuracy 0.993590 and precision and recall of 0.993671 and 0.9935895 respectively. A comparison of the best model results with existing work shows that the proposed model provides better results. [ABSTRACT FROM AUTHOR]

Deep learning (DL) and big data analytics are powerful tools when combined for disease detection and healthcare applications. They enable the processing of vast amounts of medical data to uncover patterns, make predictions, and assist healthcare professionals in diagnosing diseases and improving patient care. Existing disease detection model often faces two challenging issues namely feature selection and insufficient hyperparameter tuning, which considerabley affects the performance and generalization abilities of DL models in disease detection. To resolve these issues, this article focuses on the design of a new Chaotic Red Deer Optimizer with Deep Learning based big data analytics for Disease Classification (CRDODL-BDADC) technique in the healthcare environment. The CRDODL-BDADC technique resolves the high dimensionality problem by the use of CRDO based feature selection approach. For disease detection process, convolutional bidirectional gated recurrent unit (CBGRU) is applied for disease diagnosis and its hyperparameters are selected by using Seeker Optimization Algorithm (SOA). Furthermore, Map Reduce is applied to manage healthcare BD. A widespread set of simulation outcomes occur on the PCOS database, from the Kaggle repository to illustrate the greater performance of the CRDODL-BDADC technique. The simulation values highlighted the enhanced efficiency of the CRDODL-BDADC method over other recent approaches under various measures. [ABSTRACT FROM AUTHOR]

The world population is growing very fast and the lifestyle of human beings is changing with time and place. So, there is a need for disease management which includes disease diagnosis, its detection and classification, cure and lastly for future disease prevention. The outermost protective layer of a human body is the skin. Skin not only impacts a person's health but also psychologically impacts one's life. Computer-aided systems are very helpful in skin disease detection and classification and their application is growing rapidly in healthcare. This literature review paper aims to help the researchers to get a synthesized and appropriate information for the same. We have included papers from 2021 to 2023 for the review from the Scopus database. 45 studies are selected for the review of which 32 studies use deep learning techniques, 11 use machine learning techniques and 2 studies use a hybrid approach. The studies are compared on various parameters like models, datasets, and performance metrics. The work also identified some of the challenges like dealing with noise and also explained disease symptoms. [ABSTRACT FROM AUTHOR]