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Many major diseases of the retina often show symptoms of lesions in the fundus of the eye. The extraction of blood vessels from retinal fundus images is essential to assist doctors. Some of the existing methods do not fully extract the detailed features of retinal images or lose some information, making it difficult to accurately segment capillaries located at the edges of the images. In this paper, we propose a multi-scale retinal vessel segmentation network (SCIE_Net) based on skip connection information enhancement. Firstly, the network processes retinal images at multiple scales to achieve network capture of features at different scales. Secondly, the feature aggregation module is proposed to aggregate the rich information of the shallow network. Further, the skip connection information enhancement module is proposed to take into account the detailed features of the shallow layer and the advanced features of the deeper network to avoid the problem of incomplete information interaction between the layers of the network. Finally, SCIE_Net achieves better vessel segmentation performance and results on the publicly available retinal image standard datasets DRIVE, CHASE_DB1, and STARE. [ABSTRACT FROM AUTHOR]

Background: Being an image-based optical technique for full-field deformation measurements, the ultimate purpose of digital image correlation (DIC) is to realize accurate, precise and pixel-wise displacement/strain measurements in a full-automatic manner without users' inputs. Objective: In this work, we propose a task-optimized neural network, called RAFT-DIC, to achieve user-independent, accurate and pixel-wise displacement field measurements. Methods: RAFT-DIC is based on the state-of-the-art optical flow architecture: Recurrent All-Pairs Field Transforms (RAFT). We make two targeted improvements that fundamentally enhanced its measurement accuracy and generalization performance. Firstly, we remove all the down-sampling operations in the encode module to improve the perception of spatial information, and reduce the number of pyramid levels of the correlation layer to increase the small displacement accuracy. By building the correlation layer to compute the similarity of pixel pairs, and iteratively updating the displacement field through a recurrent unit, RAFT-DIC introduces the prior information of DIC measurement to guide the displacement estimation with high accuracy. Secondly, we develop a novel dataset generation method to synthesize customized speckle patterns and diverse displacement fields, which facilitate the construction of a robust and adaptable dataset to improve the network generalization. Results: Both simulated and real experimental results demonstrate that the accuracy of the proposed method is approximately an order of magnitude higher than pervious deep learning-based DIC (DL-DIC). Conclusions: The proposed RAFT-DIC shows higher accuracy as well as stronger practicality and cross-dataset generalization performance over existing DL-DIC methods, and is expected to be a new standard architecture for DL-DIC. [ABSTRACT FROM AUTHOR]

The purpose of mineral prospectivity mapping (MPM) is to discover unknown mineral deposits by means of fusing multisource prospecting information. In recent years, with rapid advancements in artificial intelligence, deep learning algorithms (DLAs) as a groundbreaking technique have exhibited outstanding capabilities in geoscience. However, conventional DLAs for MPM face certain challenges in feature extraction and the fusion of multimodal prospecting data. Moreover, opaque DLAs lead to an insufficient understanding of the predictive results by experts. In this study, a dual-branch convolutional neural network (DBCNN) and its post hoc interpretability were jointly constructed to map gold prospectivity in western Henan Province of China. In particular, channel and spatial attention modules were integrated into two branches to complement the respective advantages of multichannel and high spatial prospecting data for MPM. The Shapley additive explanations (SHAP) framework was then adopted to explain the predictive results by exploring the feature contributions. The comparative experiments illustrated that DBCNN can enhance feature representation and fusion abilities to improve the performance of MPM compared to conventional DLAs. The high-probability areas delineated by the DBCNN model exhibited close spatial relevance with known gold deposits, and the SHAP further confirmed the reliability of the predictive result obtained by the DBCNN model, thereby guiding future gold exploration in this study area. [ABSTRACT FROM AUTHOR]

The aftermath of a natural disaster leaves victims trapped in rubble which is challenging to detect by smart drones due to the victims in low visibility under the adverse disaster environments and victims in various sizes. To overcome the above challenges, a transformer fusion-based scale-aware attention network (TFSANet) is proposed to overcome adverse environmental impacts in disaster areas by robustly integrating the latent interactions between RGB and thermal images and to address the problem of various-sized victim detection. Firstly, a transformer fusion model is developed to incorporate a two-stream backbone network to effectively fuse the complementary characteristics between RGB and thermal images. This aims to solve the problem that the victims cannot be seen clearly due to the adverse disaster area, such as smog and heavy rain. In addition, a scale-aware attention mechanism is designed to be embedded into the head network to adaptively adjust the size of receptive fields aiming to capture victims with different scales. Extensive experiments on two challenging datasets indicate that our TFSANet achieves superior results. The proposed method achieves 86.56% average precision (AP) on the National Institute of Informatics—Chiba University (NII-CU) multispectral aerial person detection dataset, outperforming the state-of-the-art approach by 4.38%. On the drone-captured RGBT person detection (RGBTDronePerson) dataset, the proposed method significantly improves the AP of the state-of-the-art approach by 4.33%. [ABSTRACT FROM AUTHOR]

Time series forecasting is crucial in various domains, ranging from finance and economics to weather prediction and supply chain management. Traditional statistical methods and machine learning models have been widely used for this task. However, they often face limitations in capturing complex temporal dependencies and handling multivariate time series data. In recent years, deep learning models have emerged as a promising solution for overcoming these limitations. This paper investigates how deep learning, specifically hybrid models, can enhance time series forecasting and address the shortcomings of traditional approaches. This dual capability handles intricate variable interdependencies and non-stationarities in multivariate forecasting. Our results show that the hybrid models achieved lower error rates and higher R 2 values, signifying their superior predictive performance and generalization capabilities. These architectures effectively extract spatial features and temporal dynamics in multivariate time series by combining convolutional and recurrent modules. This study evaluates deep learning models, specifically hybrid architectures, for multivariate time series forecasting. On two real-world datasets - Traffic Volume and Air Quality - the TCN-BiLSTM model achieved the best overall performance. For Traffic Volume, the TCN-BiLSTM model achieved an R 2 score of 0.976, and for Air Quality, it reached an R 2 score of 0.94. These results highlight the model's effectiveness in leveraging the strengths of Temporal Convolutional Networks (TCNs) for capturing multi-scale temporal patterns and Bidirectional Long Short-Term Memory (BiLSTMs) for retaining contextual information, thereby enhancing the accuracy of time series forecasting. [ABSTRACT FROM AUTHOR]

Camouflaged object detection (COD) intends to find concealed objects hidden in the surroundings. COD is challenging for it has to discriminate the minor difference between foreground and background. In most existing methods, convolutional neural network (CNN)-based approaches are proposed to overcome this challenge. However, they have limitations in extracting semantic features of input images and learning global contexts. This study presents a novel Transformer and CNN-based Depth Context Aggregation network (call DCA-Net) for concealed object detection and segmentation. This network uses Swin Transformer as backbone to extract globalized semantic features. Dilated Reception (DR) module is designed to connect the encoder and decoder. A hybrid loss function is used for optimizing the model. In particular, to supplement the intersection over union (IOU) loss, Complementary (C) loss is introduced. By analyzing different metrics, comprehensive comparisons with previous methods are conducted on four public datasets, such as CAMO, CHAMELEON, COD10K, and NC4K. Experimental analysis proves that the proposed DCA-Net achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, includes intelligent decision-making tools for evaluation and benchmarking. Rather than just finding a comparable or similar analogy, this framework is intended to help guide future research and pragmatic applications, and to determine which SSVEP-based BCI applications have succeeded at responsibility for what they set out with. [ABSTRACT FROM AUTHOR]

This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R2: 0.93). The optimization clearly works for higher wind speeds, achieving a remarkable improvement of 22.9%. When we look at the monthly performance, all the months presented at least some level of consistent enhancement (RRMSE reductions from 1.6 to 10.2%). These findings highlight the potential of advanced deep learning techniques in enhancing wind energy forecasting accuracy, particularly in challenging desert environments. The hybrid method developed in this study presents a promising direction for improving renewable energy management. This allows for more efficient resource allocation and improves wind resource predictability. [ABSTRACT FROM AUTHOR]

Blockchain technology has unlocked the doors of building decentralized applications, where security plays a vital role. Any transaction ever created in a blockchain is recorded permanently. With the increasing usage of social media modalities today, many non-reputable sources create and publish fake and luring news. Due to the easy availability of the internet and excessive use of social media, fake news can spread like a flash. It causes the need to eliminate fake news posted on social media platforms. The main motive of the paper is to develop an ensemble model that assists in the automated identification of fake news. This paper addresses the identified gaps in detecting multi-modal fake news. We propose Fake News Network (FNNet) as a novel secure blockchain-based deep learning model to detect fake news. It uses blockchain and deep learning to assure data integrity and learn data representations, respectively. The proposed model consists of four layers: the node layer, the deep learning layer, the blockchain layer, and the network layer. This is an ensemble deep-learning model that leverages Bi-LSTM and CNN models, where the Bi-LSTM model captures the sequential data in both directions, and CNN captures the hidden features. We use Pheme, CrisisLexT6, and ISOT datasets to train the model. FNNet achieves an average accuracy of 86.93%, 92.22%, and 98.53% on Pheme, CrisisLexT9, and ISOT datasets, respectively. Our results show that the proposed model is robust and applicable to real-time datasets and social media networks to detect fake news effectively. [ABSTRACT FROM AUTHOR]

The flood-prone city of Zaio in Morocco necessitates a precise, fast, real-time flood depth mapping model due to its recurrent flood history. Whether it's the exclusive prediction of one flood category, relying on hard-to-measure inputs like flood hydrographs, or employing less accurate training methods such as cellular automata models, the existing Convolutional Neural Network (CNN) models face limitations in predicting flood depth in a city whose flood dynamics are influenced by outer watersheds such as Zaio. This study addresses these issues by introducing a CNN tailored for real-time pluvial and fluvial flood depth mapping in Zaio at fine resolution (2 m). Training involved eight rainfall hyetographs, with four used for testing. The model's validation included three "unseen" rainfall distribution patterns. The proposed CNN seamlessly connects rainfall-runoff modeling and hydrodynamic 2D flood depth simulation, with a novelty of predicting both pluvial and fluvial flood depth, and reducing computational time by a significant 99.17%. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field. [ABSTRACT FROM AUTHOR]

Pneumonia is a serious inflammatory disease that causes lung ulcers, and it is one of the leading reasons for pediatric death in the world. Chest X-rays are perhaps the most commonly utilized modalities to recognize pneumonia. Generally, the illness could be analyzed by a specialist radiologist. But for some reason, the diagnosis may be subjective. Thus, the physicians must be guided by computer-aided diagnosis frameworks in this challenging task. In this study, we propose a combined deep learning architecture to identify pneumonia in chest radiography images. We first, use Adaptive Median Filter for images enhancement, then we employ a regularized Convolutional Neural Network for features extraction, and then we use Long Short Term Memory as a classifier. Finally, the attention mechanism is used to direct the network attention to relevant features. The suggested approach was tested on two publicly available pneumonia X-ray datasets provided by Kermany and the Radiological Society of North America. On the Kermany and RSNA datasets, the suggested technique attained accuracy rates of 99.91% and 88.86%, respectively. In the last stage of our experiments, we employed a Grad-CAM-based color visualization technique to precisely interpret the detection of pneumonia in radiological images. The results outperformed those of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The global interest in market prediction has driven the adoption of advanced technologies beyond traditional statistical models. This paper explores the use of machine learning and deep learning techniques for stock market forecasting. We propose a comprehensive approach that includes efficient feature selection, data preprocessing, and classification methodologies. The wavelet transform method is employed for data cleaning and noise reduction. Feature selection is optimized using the Dandelion Optimization Algorithm (DOA), identifying the most relevant input features. A novel hybrid model, 3D-CNN-GRU, integrating a 3D convolutional neural network with a gated recurrent unit, is developed for stock market data analysis. Hyperparameter tuning is facilitated by the Blood Coagulation Algorithm (BCA), enhancing model performance. Our methodology achieves a remarkable prediction accuracy of 99.14%, demonstrating robustness and efficacy in stock market forecasting applications. While our model shows significant promise, it is limited by the scope of the dataset, which includes only the Nifty 50 index. Broader implications of this work suggest that incorporating additional datasets and exploring different market scenarios could further validate and enhance the model's applicability. Future research could focus on implementing this approach in varied financial contexts to ensure robustness and generalizability. [ABSTRACT FROM AUTHOR]

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essential in the fields of biomedical engineering and clinical cardiac disease diagnosis. Intelligent ECG diagnosis techniques encompass Recurrent Neural Networks (RNN), Transformers, and Convolutional Neural Networks (CNN). While CNN is capable of extracting local spatial information from images, it lacks the ability to learn global spatial features and temporal memory features. Conversely, RNN relies on time and can retain significant sequential features. However, they are not proficient in extracting lengthy dependencies of sequence data in practical scenarios. The self-attention mechanism in the Transformer model has the capability of global feature extraction, but it does not adequately prioritize local features and cannot extract spatial and channel features. This paper proposes STFAC-ECGNet, a model that incorporates CAMV-RNN block, CBMV-CNN block, and TSEF block to enhance the performance of the model by integrating the strengths of CNN, RNN, and Transformer. The CAMV-RNN block incorporates a coordinated adaptive simplified self-attention module that adaptively carries out global sequence feature retention and enhances spatial–temporal information. The CBMV-CNN block integrates spatial and channel attentional mechanism modules in a skip connection, enabling the fusion of spatial and channel information. The TSEF block implements enhanced multi-scale fusion of image spatial and sequence temporal features. In this study, comprehensive experiments were conducted using the PTB-XL large publicly available ECG dataset and the China Physiological Signal Challenge 2018 (CPSC2018) database. The results indicate that STFAC-ECGNet surpasses other cutting-edge techniques in multiple tasks, showcasing robustness and generalization. [ABSTRACT FROM AUTHOR]

Skin conditions are becoming increasingly prevalent across the world in current times. With the rise in dermatological disorders, there is a need for computerized techniques that are completely noninvasive to patients' skin. As a result, deep learning models have become standard for the computerized detection of skin diseases. The performance efficiency of these models improves with access to more data with their primary aim being image classification. In this paper, we present a skin disease detection methodology using image processing techniques, non-local means denoising and convolutional neural network (CNN) backed by sparse dictionary learning. Here, the major benefit of using NLM denoising followed by sparse dictionary learning with CNNs in image classification lies in leveraging a multi-stage approach that enhances the quality of input data, extracts meaningful and discriminative features, and improves the overall performance of the classification model. This combined approach addresses challenges such as noise robustness, feature extraction, and classification accuracy, making it particularly effective in complex image analysis tasks. For denoising, the average Peak Signal to Noise Ratio (PSNR) obtained for images from HAM-10000 dataset is 33.59 dB. For the ISIC-2019 dataset, the average PSNR for the train folder is 34.37 dB, and for the test folder it is 34.39 dB. The deep learning network is trained for the analysis of skin cancer images using a CNN model and is achieving acceptable results in classifying skin cancer types. The datasets used contain high-resolution images. After all the tests, the accuracy obtained is 85.61% for the HAM-10000 dataset and 81.23% for the ISIC-2019 dataset, which is on par with existing approaches validated by benchmarking results. [ABSTRACT FROM AUTHOR]

The 3D pavement surface images provide a great opportunity for transportation agencies to digitally and effectively assess jointed plain concrete pavement (JPCP) conditions. An automated and accurate JPCP slab joint detection algorithm, which can support slab separation and faulting evaluation, is a critical prerequisite for applying 3D pavement images on JPCP condition assessments. However, there is no existing study on automated slab joint detection except for commercial software, and the detected joints of the software require massive manual revisions due to insufficient accuracy. In this study, a novel slab joint detection algorithm is proposed, which leverages the power of deep learning to achieve accurate slab joint detection on 3D pavement images. The proposed algorithm consists of a convolutional neural network-based joint segmentation model and a joint coordinates extractor. The joint segmentation model is trained to generate pixel-wise joint segmentation masks from the pavement images, which provide preliminary localization information of the slab joints. Then, with the joint segmentation mask, the joint coordinate extractor is designed to eliminate the false positive predictions and accurately extract the joint coordinates. The proposed algorithm is evaluated on large-scale 3D pavement images collected from multiple interstate highways in the state of Georgia with diverse conditions. In the performance evaluation, the proposed algorithm achieved the highest F1 score of 0.956, which largely outperformed the commercial software with a 10% improvement. The evaluation results demonstrate the effectiveness of the proposed algorithm, which can further support effective and efficient assessments of JPCP conditions. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN) is one of the well-known deep learning algorithms that uses convolutional filters to extract the features in the images. The most important issue when training CNN is the overfitting which prevents the model from generalization to unseen data. This paper addressed this issue by proposing a novel hybrid approach that uses bees algorithm (BA) to optimize the regularization parameter and weight regularization factor to adjust the regularization value in each convolutional layer and fully connected layer resulting in a hybrid algorithm called bees algorithm regularized convolutional neural network (BA-RCNN). It was applied to three different datasets for classification or predictions purposes and showed an improvement in the validation and testing accuracy leading to a lower difference with the training accuracy which means the overfitting is reduced comparing to the original CNN. Applying the BA-RCNN algorithm to 'Cifar10DataDir' improved the validation accuracy from 80.34% for the original CNN to 82.80% for the hybrid BA-RCNN algorithm, in the electrocardiogram the improvement was from 87.80 to 90.47% and both datasets were used for classification. Furthermore, the hybrid BA-RCNN algorithm was applied to predict the porosity percentage based on artificial porosity images and the results showed that the validation accuracy was improved from 81.67% for the original CNN to 87.33% for the hybrid BA-RCNN algorithm. [ABSTRACT FROM AUTHOR]

Every year, more than two million Muslim pilgrims from all over the world visit Mecca to perform Hajj worship. The challenge of location identification in dense crowds is significant, leading to dangerous consequences such as injury or loss. Existing works for person localization remain challenged, especially in crowded places like Mecca during Hajj. In this work, we propose a novel location identification method using image pre-processing and different machine learning classifiers, with the creation of a new image dataset for hotspot locations in Mecca. Image pre-processing algorithms are applied to enhance the geographic information present in the images, and the obtained features are classified using CNN, ANN, and SVM classifiers. Extensive evaluations reveal that the proposed pre-processing algorithm with CNN achieves the best localization performance with an accuracy of 90%, followed by ANN with an accuracy of 84%, and SVM with an accuracy of 80.50%. Without pre-processing, the accuracies are significantly lower: 63% for CNN, 73% for ANN, and 71.50% for SVM. In addition, the proposed approach was compared with other deep learning models, VGG16, AlexNet and ResNet50 on our dataset achieving an accuracy of 61%, 65% and 62%, respectively. Results demonstrate the effectiveness of our proposed method in comparison with other deep transfer learning methods on a small dataset, offering promising solutions for crowded place navigation. [ABSTRACT FROM AUTHOR]

In an increasingly complex and volatile environment, government officials, researchers, and investors alike would like to possess models that accurately forecast markets in order to make appropriate decisions. This research investigates the efficacy of Transformer-based deep neural networks in predicting financial market returns compared to traditional models, focusing on ten different market indexes. The study employs a comprehensive methodology that involves iterative dropout tests and batch size optimization to enhance model performance. By leveraging the power of deep learning, the research aims to improve prediction accuracy and capture complex patterns in stock market data. Twelve neural network architectures are compared across ten indexes to measure their performance, finding that the proposed Transformer variants produce significantly better results compared to benchmark models in all cases. The results of ablative experiments reveal the superiority of Transformer models in capturing long-term dependencies and extracting meaningful features from time series data. The findings suggest that Transformer neural networks outperform LSTM networks and other traditional models in forecasting financial market trends. This research contributes to the growing body of literature on deep learning applications in finance and provides valuable insights for government officials, researchers, and investors seeking to make informed decisions in the stock market. The implications of this study extend beyond academia, offering practical implications for enhancing prediction accuracy and optimizing investment strategies in the dynamic and volatile financial market landscape. [ABSTRACT FROM AUTHOR]

The use of medical imaging, which is very popular today, is increasing daily, so a need is felt more than ever to obtain the approaches for the automatic segmentation of these images. In our article, a novel approach is proposed for the improvement of the segmentation results in a way that has good efficiency and good accuracy. Due to uncertainty in many aspects of image processing, the assumptions for these uncertainties are considered. These uncertainties include the additive and non-additive noises at the low level of the image processing and the inaccuracy in the basic assumptions of the algorithm, and the interpretation ambiguities during the high-level image processing. In this paper, a distinct deep neural network, which is entirely basis on self-attention among the patches of the neighbor image sans any convolution operation, is presented. This method can achieve segmentation with more accuracy. The network input is a three-dimensional image block, which divides our network into n 3 3D patches. In this network, n = 3 or n = 5 . Furthermore, for each patch, it computes a one-dimensional embedding. Our network forecasts the segmentation mapping for the block central patch basis on the self-attention among the embeddings of this patch. Also, the approaches for the model pre-training in the big sets of un-tagged images are presented. The results of our tests display that the benefit of our network over the convolutional neural network can be considerable (by using the pre-training) when the data of the tagged training are small. For example, in the Brain Cortical Plate dataset, the proposed method had the values equal to 0.884, 0.921 and 0.232 for DSC, HD95 and ASSD, which performed better than the similar methods. [ABSTRACT FROM AUTHOR]

Agriculture is an expanding area of study. Crop prediction in agriculture is highly dependent on soil and environmental factors, such as rainfall, humidity, and temperature. Previously, farmers had the authority to select the crop to be farmed, oversee its development, and ascertain the optimal harvest time. The farming community is facing challenges in sustaining its practices due to the swift alterations in climatic conditions. Therefore, machine learning algorithms have replaced traditional methods in predicting agricultural productivity in recent years. To guarantee optimal precision through a specific machine learning approach. Authors extend their approach not limited to Machine Learning but also with Deep Learning Techniques. We use machine and deep learning algorithms to predict crop outcomes accurately. In this proposed model, we utilise machine learning algorithms such as Naive Bayes, decision tree, and KNN. It is worth noting that the decision tree algorithm demonstrates superior performance compared to the other algorithms, achieving an accuracy rate of 83%. In order to enhance the precision, we have suggested implementing a deep learning technique, specifically a convolutional neural network, to identify the crops. Achieving an accuracy of 93.54% was made possible by implementing this advanced deep-learning model. [ABSTRACT FROM AUTHOR]

Potholes are a significant hazard, causing severe damage to vehicles and potentially leading to fatal accidents. The automatic detection of potholes is crucial for timely maintenance and reducing these risks. This research evaluates the performance of three pre-trained Convolutional Neural Network models—ResNet 50, ResNet 18, and MobileNet—in classifying pavement images. Initially, the models distinguish between images containing potholes and those without (Potholes vs. Normal). Subsequently, they categorize pavement images into three classes: Small Pothole, Large Pothole, and Normal. Pavement images were captured from two different heights: 3.5 feet (waist height) and 2 feet. Among the models, MobileNet v2 demonstrated the highest accuracy of 98% for detecting potholes. For images taken at 2 feet, the classification accuracies for large potholes, small potholes, and normal pavement were 87.33%, 88.67%, and 92%, respectively. For images taken from waist height, the accuracies increased to 98.67%, 98.67%, and 100%, respectively. The study highlights the potential of these models in enhancing road safety through reliable and automated pothole detection, providing valuable insights for infrastructure maintenance. Article Highlights: Potholes are dangerous and can cause serious vehicle damage. Train CNN models for the classification of potholes These CNNs, for different sizes of potholes can be used for the rehabilitation of potholes. Among all CNN modes, MobileNet v2 achieves 98% accuracy in detecting the presence of a pothole. [ABSTRACT FROM AUTHOR]

Alphonso, a renowned mango variety cultivated in India, is commercially valuable for its delightful taste, vibrant saffron color, texture, and prolonged shelf life, attracts global attention. Spongy Tissue (ST) disorder is prevalent in mangoes, especially in the Alphonso variety, leading to spongy and corky texture in the fruit's pulp. This issue has been identified in as much as 30% of mangoes in a single lot, resulting in the entire lot being rejected during export due to the delayed detection of the disorder. Current mango quality assessment involves destructive cutting, causing fruit damage, leading to significant loss. Limited physical checks don't ensure overall batch quality. By considering above challenges present study demonstrates on use of X-ray imaging technique for detecting internal quality of mango non-destructively along with machine learning algorithms for automatic binary classification (Non spongy vs. Spongy). For evaluation 648 original X-ray images of mangoes were captured and augmented to 3888 images using different augmentation techniques. For automated classification purposes deep learning (DL) models were used. For automated classification, DL models were employed and fine-tuned through grid search hyperparameter optimization within a 5-fold cross-validation framework. Among the DL models, namely CNN, ResNet50, AlexNet, VGG16, and VGG19, they achieved notable overall accuracy rates of 87.27%, 89.18%, 90.21%, 91.75% and 95.82%, respectively, on the test dataset. Significantly, the VGG19 model demonstrated superior performance with highest classification accuracy by TukeyHSD test. DL in X-ray imaging shows promise for detecting internal issues in fruits, enhancing inspection capabilities for diverse purposes, such as identifying diseases or damages. Highlights: •'Alphonso' is variety of mango that has suffered from spongy tissue malady. •Potential of X-ray imaging was tested for identifying internal spongy tissue defects. •CNN, AlexNet, ResNet, VGG16 and VGG19 models were used for automatic classification. •Models were optimized by hyperparameter tuning and cross-validation technique. •Deep transfer learning models achieved >90% mango binary classification accuracy. [ABSTRACT FROM AUTHOR]

Baishuihe Landslide is a large active landslide that threatens shipping transportation in the Three Gorges Reservoir (China). A manual monitoring system has been implemented since 2003. However, after some intervention works in 2018–2019, new automatic instruments providing continuous data on displacements, rainfall, reservoir water level, and groundwater table were installed. The data recorded by the new system show that interventions led to an effective stabilization improvement since the present displacement rate is smaller than that before interventions. However, the relevance of the Three Gorges basin and the potential hazard of a possible collapse requires a reliable forecast of the landslide evolution in a time scale from a few hours to a few days. To this aim, a two-step procedure is proposed here. In the first step, after a preliminary preprocessing-denoising of data, carried out by means of Discrete Wavelet Transform (DWT), a Continuous Wavelet Transform (CWT) procedure is used to provide scalograms of the time series of three quantities, e.g., landslide displacement rate, rainfall and the difference of water level between one piezometer and reservoir water level (RWL). In the second step, to evaluate the relationships among the velocity trend and the other significant quantities and obtain a reliable velocity forecast, the images given by binding together two or three scalograms of the mentioned quantities were analyzed using Convolutional Neural Network (CNN) tool. Several trials with different combinations of input time series of 2 or 3 quantities were carried out in order to recognize the factors which mainly affect the current displacement evolution. The results show that, after the interventions, rainfall is an important factor inducing deformation acceleration. The hydrodynamic pressure induced by the difference between the groundwater pressure and reservoir water level also plays a dominant role in accelerating the Baishuihe landslide. Furthermore, the coupling of rainfall and hydrodynamic pressure produces displacement velocities higher than what the quantities singularly do. These results provide valuable indications for optimizing the monitoring configuration on the landslide and obtaining velocity forecasts in a few hours/days. [ABSTRACT FROM AUTHOR]

Since their first applications, Convolutional Neural Networks (CNNs) have solved problems that have advanced the state-of-the-art in several domains. CNNs represent information using real numbers. Despite encouraging results, theoretical analysis shows that representations such as hyper-complex numbers can achieve richer representational capacities than real numbers, and that Hamilton products can capture intrinsic interchannel relationships. Moreover, in the last few years, experimental research has shown that Quaternion-valued CNNs (QCNNs) can achieve similar performance with fewer parameters than their real-valued counterparts. This paper condenses research in the development of QCNNs from its very beginnings. We propose a conceptual organization of current trends and analyze the main building blocks used in the design of QCNN models. Based on this conceptual organization, we propose future directions of research. [ABSTRACT FROM AUTHOR]

Furnace temperature and oxygen content are important parameters reflecting the combustion inside a circulating fluidized bed (CFB) boiler. Accurately predicting boiler output is a complex task due to the high noise and nonsmoothness of actual boiler input and output data. In this paper, a new hybrid convolutional neural network (CNN), bidirectional long short-term memory (biLSTM) network, and squeezing and excitation (SE) network prediction model is proposed to significantly improve the prediction accuracy of oxygen content and furnace temperature by combining the advantages of multiple deep learning networks. This network can extract spatiotemporal characteristics of input parameters such as coal feed to effectively predict boiler furnace temperature and oxygen content. CNNs can extract complex features such as dynamic and static nonlinearities between multiple variables affecting the furnace temperature and oxygen content, as well as high noise. The biLSTM network layer can efficiently handle the temporal information of irregular trends in modeling time series components; SE can extract the important information between channels through the feature relationships between channels for better overfeature extraction. The CNN-biLSTM-SE model can effectively solve the problem of nonlinear mapping complexity between inputs and outputs. Experiments show that the proposed CNN-biLSTM-SE model outperforms existing methods. The experimental results showed that the average MAPE errors for oxygen content prediction were CNN-biLSTM-SE (0.038), CNN-biLSTM with attention mechanism (AM) (0.043), CNN-biLSTM (0.051), CNN-LSTM (0.051), biLSTM (0.051), RNN (0.051), LSTM(0.0052), and CNN(0.0054). Extensive experiments in CFB boilers with oxygen content and furnace temperature show that the proposed CNN-biLSTM-SE model achieves better results in terms of goodness-of-fit, generalization ability and accuracy. [ABSTRACT FROM AUTHOR]

Background: Seed vigor identification is critical to guaranteeing the quality and yield of maize. Although seeds with impaired vigor may germinate under normal conditions, planting under unfavorable conditions makes it difficult to produce healthy plants. Therefore, non-destructive and rapid detection of seed vigor using hyperspectral imaging (HSI) technology is crucial for improving crop production efficiency. Methods: Hyperspectral images of maize seeds were acquired employing the HSI system, the original spectra were preprocessed using Savitzky–Golay smoothing and multiplicative scatter correction, and the feature wavelengths were extracted using the successive projections algorithm (SPA). Discriminant models were constructed based on support vector machine (SVM), random forest, artificial neural network (ANN), and convolutional neural network (CNN-DC). Results: The results showed that SVM, ANN, and CNN-DC could discriminate well between maize seeds with different vigor levels, and their accuracy rate was over 70%. The SPA algorithm showed that the RMSE value achieved a minimum of 0.3406, while the number of variables was 49. The CNN-DC model outperformed the other models, which reached the highest accuracy of 92.06%. This study demonstrates that DL combined with HSI has excellent potential for identifying seed vigor. Conclusions: This study shows that the proposed method has excellent results for hyperspectral image data processing and can accurately identify maize seed vigor. [ABSTRACT FROM AUTHOR]

As the globe struggles to recover from COVID-19, the monkeypox virus has emerged as a new global pandemic threat. Monkeypox cases are still being reported daily from different nations despite the virus not being as harmful or contagious as COVID-19. As a result, the possibility of another worldwide pandemic occurring directly due to a lack of adequate preventative measures will not come as a complete shock to everyone. Diagnosing Monkeypox in its early stages may be challenging because it resembles chickenpox and measles. When confirmatory Polymerase Chain Reaction assays are not readily available, monitoring suspected cases and swiftly detecting them may be possible with computer-assisted detection of monkeypox lesions. Recent research has shown that deep learning models have significant promise for image-based diagnostics, including cancer diagnosis, identifying tumor cells, and detecting COVID-19 patients. To address these challenges, we built a deep learning model based on transfer learning that can assist medical professionals and other individuals in determining whether they are suffering from Monkeypox. The InceptionV3 model utilized in this study was trained with the publicly accessible Monkeypox dataset. During the studies, the model attained an accuracy of 98%. [ABSTRACT FROM AUTHOR]

Considering the noise impact in the bearing operating environment and the time-consuming and non-universal design of traditional diagnostic algorithms, this paper proposes a new model for rolling bearing fault diagnosis, which uses convolutional pooling group (CPG) to extract features from data, At the same time, expanding the dual convolutional kernel to obtain a larger receptive field obtained the WCPGCNN (A CPG Convolutional Neural Network with Wide Convolutional Kernel as the First Lay) model based on the CPG network architecture. Firstly, the fault features of the input signal are automatically extracted through four convolutional pooling groups; Next, fault features are further extracted using the fully connected layer, and finally input into the Softmax layer for fault identification. By utilizing algorithms such as Adam, dropout, and batch normalization, the model performs well in terms of accuracy, noise resistance, and timeliness, while also possessing good cross dataset high generalization ability. This article uses the rolling bearing fault standard data from Case Western Reserve University (CWRU) and the American Society for Mechanical Fault Prevention Technology (MFPT), and verifies through multiple controlled experiments that the model established in this article has high accuracy and good generalization characteristics. [ABSTRACT FROM AUTHOR]

Female mortality is frequently caused by ovarian cancer (OC). Because of its late detection, ovarian cancer seems to have a low survival rate, and new techniques are required for its early identification. One of the more prevalent gynecologic cancers is ovarian cancer. The various diagnoses of ovarian cancer depend on the efficient classification of the various forms. Patients with ovarian tumours require accurate diagnosis. When compared to a deep convolutional neural network, previous neural networks are an outmoded technology that offers fewer characteristics, which demonstrates that deep convolutional layers supply essential and healthy features. To get over these limitations, ovarian tumours are identified using the krill herd optimization-based convolutional neural network (KHO-CNN) mechanism, a novel optimized deep neural network approach. The system analyses datasets related to ultrasound-detected ovarian cancer. The obtained real-world ultrasound images of ovarian cancer also contain additional noise, which is removed using a Wavelet Transform. An enhanced KHO model has been used in the segmentation process. Features were extracted by use of a local binary pattern. Ovarian tumours are classified as benign, malignant, or normal by the KHO-CNN. To identify ovarian cancers using deep learning techniques that utilize optimised convolutional neural networks, this model was developed and utilised with a set. [ABSTRACT FROM AUTHOR]

Breast cancer poses a threat to women's health and contributes to an increase in mortality rates. Mammography has proven to be an effective tool for the early detection of breast cancer. However, it faces many challenges in early breast cancer detection due to poor image quality, traditional segmentation, and feature extraction. Therefore, this work addresses these issues and proposes an attention-based backpropagation convolutional neural network (ABB-CNN) to detect breast cancer from mammogram images more accurately. The proposed work includes image enhancement, reinforcement learning-based semantic segmentation (RLSS), and multiview feature extraction and classification. The image enhancement is performed by removing noise and artefacts through a hybrid filter (HF), image scaling through a pixel-based bilinear interpolation (PBI), and contrast enhancement through an election-based optimization (EO) algorithm. In addition, the RLSS introduces intelligent segmentation by utilizing a deep Q network (DQN) to segment the region of interest (ROI) strategically. Moreover, the proposed ABB-CNN facilitates multiview feature extraction from the segmented region to classify the mammograms into normal, malignant, and benign classes. The proposed framework is evaluated on the collected and the digital database for screening mammography (DDSM) datasets. The proposed framework provides better outcomes in terms of accuracy, sensitivity, specificity, precision, f-measure, false-negative rate (FNR) and area under the curve (AUC). This work achieved (99.20%, 99.35%), (99.56%, 99.66%), (98.96%, 98.99%), (99.05%, 99.12%), (0.44%, 0.34%), (99.31%, 99.39%) and (99.27%, 99.32%) of accuracy, sensitivity, specificity, precision, FNR, f-measure and AUC on (collected, DDSM datasets), respectively. This research addresses the prevalent challenges in breast cancer identification and offers a robust and highly accurate solution by integrating advanced deep-learning techniques. The evaluated results reveal the proposed framework's potential in early breast cancer detection. [ABSTRACT FROM AUTHOR]

Epileptic seizure (ES) detection is an active research area, that aims at patient-specific ES detection with high accuracy from electroencephalogram (EEG) signals. The early detection of seizure is crucial for timely medical intervention and prevention of further injuries of the patients. This work proposes a robust deep learning framework called HyEpiSeiD that extracts self-trained features from the pre-processed EEG signals using a hybrid combination of convolutional neural network followed by two gated recurrent unit layers and performs prediction based on those extracted features. The proposed HyEpiSeiD framework is evaluated on two public datasets, the UCI Epilepsy and Mendeley datasets. The proposed HyEpiSeiD model achieved 99.01% and 97.50% classification accuracy, respectively, outperforming most of the state-of-the-art methods in epilepsy detection domain. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a neurodegenerative disease that is well-known for causing continuous loss of memory, cognition, and other higher brain functions. AD is not a single disease, but rather a group of related diseases with similar characteristics. The use of deep neural network-based pattern classification techniques, such as convolutional neural networks, is effective in classifying patients into different sub-types of AD and in distinguishing the different stages of severity of the disease. in the medical field, early detection of its start can be quite beneficial. This article focuses on the early detection of various stages of cognitive aging and AD using neuroimaging and transfer learning (TL). Images of imagery via resonance magnetic (IRM) obtained from a Kaggle database called Alzheimer's Dataset (4 class of Images) with several classes of non-dementia (NONDEM), very mild dementia(VERDEM), mild dementia(MILDEM), moderate dementia(MODDEM) are classified using a transfer learning approach. In this work, we compare the classification performance of six pre-trained networks, which are VGG-19, VGG-16, ResNet-50, InceptionV3, Xception, and DenseNet169. They were enthralled and tested using 6400 images from the Kaggle data pool. The confusion matrix and its parameters are used to assess the classification performance of these six networks. VGG-19, VGG-16, Inception-V3, Xception, ResNet-50, and DenseNet169 all have 92.86%, 92.83%, 91.04%, 90.57%, 85.99%, and 88.64% overall precision in MA detection, respectively. [ABSTRACT FROM AUTHOR]

This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular disorder and whether the diagnostic accuracy of the model improved when patients' clinical information was provided in addition to MRI images. The sagittal MR images of 2948 TMJs were collected from 1017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances (p < 0.05). The fine-tuning model had excellent specificity compared to the human experts (87.25% vs. 58.17%). However, the human experts were superior in sensitivity (80.00% vs. 57.43%) (all p < 0.001). In gradient-weighted class activation mapping (Grad-CAM) visualizations, the fine-tuning scheme focused more on effusion than on other structures of the TMJ, and the sparsity was higher than that of the from-scratch scheme (82.40% vs. 49.83%, p < 0.05). The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. Two fine-tuning models on PD and T2-weighted images showed that the diagnostic performance did not improve compared with using PD alone (p < 0.05). Diverse AUCs were observed across each group when the patients were divided according to age (0.7083–0.8375) and sex (male:0.7576, female:0.7083). The prediction accuracy of the ensemble model was higher than that of the human experts when all the data were used (74.21% vs. 67.71%, p < 0.05). A deep neural network (DNN) was developed to process multimodal data, including MRI and patient clinical data. Analysis of four age groups with the DNN model showed that the 41–60 age group had the best performance (AUC = 0.8258). The fine-tuning model and DNN were optimal for judging TMJ effusion and may be used to prevent true negative cases and aid in human diagnostic performance. Assistive automated diagnostic methods have the potential to increase clinicians' diagnostic accuracy. [ABSTRACT FROM AUTHOR]

This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing pipelines, and (3) rigorously validate the model's transferability on independent, external test-datasets. A convolutional neural network was trained on 235 TOF-MRAs acquired on local scanners from a single vendor to segment intracranial aneurysms. Different pre-processing pipelines including bias field correction, resampling, cropping and intensity-normalization were compared regarding their effect on model performance. The models were tested on independent, external same-vendor and other-vendor test-datasets, each comprised of 70 TOF-MRAs, including patients with and without aneurysms. The best-performing model achieved excellent results on the external same-vendor test-dataset, surpassing the results of the previous publication with an improved sensitivity (0.97 vs. ~ 0.86), a higher Dice score coefficient (DSC, 0.60 ± 0.25 vs. 0.53 ± 0.31), and an improved false-positive rate (0.87 ± 1.35 vs. ~ 2.7 FPs/case). The model further showed excellent performance in the external other-vendor test-datasets (DSC 0.65 ± 0.26; sensitivity 0.92, 0.96 ± 2.38 FPs/case). Specificity was 0.38 and 0.53, respectively. Raising the voxel-size from 0.5 × 0.5×0.5 mm to 1 × 1×1 mm reduced the false-positive rate seven-fold. This study successfully replicated core principles of a previous approach for detecting and segmenting cerebral aneurysms in TOF-MRAs with a robust, fully automatable pre-processing pipeline. The model demonstrated robust transferability on two independent external datasets using TOF-MRAs from the same scanner vendor as the training dataset and from other vendors. These findings are very encouraging regarding the clinical application of such an approach. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing applications have facilitated the distribution of heterogeneous, unstructured digitized data among users' social networks of varying opinions. Text processing at a large scale requires high-precision computational techniques, which increases the computational burden. The advent of big data analytics along with Natural Language Processing is a powerful factor in improving the efficiency of processing large-scale text data, using the kernel of the MapReduce big data analytics frameworks that allow parallelization of large computational operations. In this paper, we propose an intelligent sentiment prediction approach based on deep learning, batch, and streaming big data analytics. In fact, our main objective is to take advantages of the powerful tools provided by the distributed platforms, such as, Hadoop and Spark to preprocess streaming data. This involves various tasks such as cleaning the data, reducing its size, minimizing access time, and decreasing storage volume. This step prepares the big streaming data to be fully exploited by Deep Learning models. This work includes a research study on processing big data related to short volume scripts based on batch and streaming distributed frameworks as well as deep learning approaches in Natural Language Processing. We detail our idea for analyzing short texts to determine their semantic context and categorize them into pros and cons poles. There were different stages in building this model, the first involving data reduction and refinement using selected features and big data analysis tools. In the second stage, words are embedded by global vector to be computed in layers of convolutional and recurrent neural networks. The experimental study and the analysis of the results confirm the usefulness of our proposed model and its superiority over the main approaches studied in the literature. Our model achieved a performance of 96% in terms of accuracy. [ABSTRACT FROM AUTHOR]

Land subsidence (LS) and pipe collapse (PC) as the major types of geomorphologic hazards lead to noticeable changes in landscape alterations, land damage, loss of soil and water, surface erosion, and sediment buildup in affected areas. To overcome this, the susceptibility to LS and CP was investigated using three deep learning convolutional neural network (DL-CNN) architectures, including Res-Net, AlexNet, and VGG-Network. We used various predictor variables, and then, trained and tested our DL-CNN models using ReLu, Cross-Entropy, and Adam as activation, loss, and optimization functions, respectively. Our findings showed that DL-CNN models achieved an overall accuracy of 0.9836, 0.9721, and 0.9642 for the Res-Net, AlexNet, and VGG-Network, respectively, for CP sensitivity detection. In addition, the Res-Net, AlexNet, and VGG-Network with an overall accuracy of 0.9698, 0.9654, and 0.9519, respectively, showed satisfying performances for LS detection. We also applied univariate summary statistics, including L(r), the pair correlation function (g(r)), and the O-ring function (O(r)), to investigate the spatial pattern and distribution of CP and LS. The L(r) function graph showed that the spatial patterns of CP and LS were clustered across all the investigated distance scales. The value of this function fell outside the Monte Carlo range, indicating that the accumulation of CP and LS at the mentioned distance scale was statistically significant. The results of the O(r) function for the distribution pattern of CP in the study area indicated that this phenomenon was mostly distributed next to each other, implying the facilitating effect of CP on the creation and expansion of each other across all the investigated distance scales. Similarly, the univariate function g(r) also showed the dispersed distribution of subsidence LS at all distances next to each other. In summary, the results of this research revealed that much of the study area was susceptible to CP and LS. The proposed methodology and findings of this study would be useful for land managers, stakeholders, and researchers. [ABSTRACT FROM AUTHOR]

Numerical simulation of multiphase flow in porous media is an important step in understanding the dynamic behavior of geologic CO 2 storage (GCS). Scaling up GCS requires fast and accurate high-resolution modeling of the storage reservoir pressure and saturation plume migration; however, such modeling is challenging due to the high computational costs of traditional physics-based simulations. Deep learning models trained with numerical simulation data can provide a fast and reliable alternative to expensive physics-based numerical simulations. We propose a Stochastic pix2vid neural network architecture for solving multiphase fluid flow problems with significant speed, accuracy, and efficiency. The Stochastic pix2vid model is designed based on the principles of computer vision and video synthesis and is able to generate dynamic spatiotemporal predictions of fluid flow from static reservoir models, closely mimicking the performance of traditional numerical simulation. We apply the Stochastic pix2vid model to a highly-complex CO 2 -water multiphase problem with a wide range of reservoir models in terms of porosity and permeability heterogeneity, facies distribution, and injection configurations. The Stochastic pix2vid method is first-of-its-kind in static-to-dynamic prediction of reservoir behavior, where a single static input is mapped to its dynamic response with a fixed number of timesteps. The Stochastic pix2vid method provides notable performance in highly heterogeneous geologic formations and complex estimation such as CO 2 saturation and pressure buildup plume determination. The trained model can serve as a general-purpose, static-to-dynamic (image-to-video) alternative to traditional numerical reservoir simulation of 2D CO 2 injection problems with up to 6,500 × speedup compared to traditional numerical simulation using the MATLAB Reservoir Simulation Toolbox. [ABSTRACT FROM AUTHOR]

The vast majority of online media rely heavily on the revenues generated by their readers' views, and due to the abundance of such outlets, they must compete for reader attention. It is a common practise for publishers to employ attention-grabbing headlines as a means to entice users to visit their websites. These headlines, commonly referred to as clickbaits, strategically leverage the curiosity gap experienced by users, enticing them to click on hyperlinks that frequently fail to meet their expectations. Therefore, the identification of clickbaits is a significant NLP application. Previous studies have demonstrated that language models can effectively detect clickbaits. Deep learning models have attained great success in text-based assignments, but these are vulnerable to adversarial modifications. These attacks involve making undetectable alterations to a small number of words or characters in order to create a deceptive text that misleads the machine into making incorrect predictions. The present work introduces "Non-Alpha-Num", a newly proposed textual adversarial assault that functions in a black box setting, operating at the character level. The primary goal is to manipulate a certain NLP model in a manner that the alterations made to the input data are undetectable by human observers. A series of comprehensive tests were conducted to evaluate the efficacy of the suggested attack approach on several widely-used models, including Word-CNN, BERT, DistilBERT, ALBERTA, RoBERTa, and XLNet. These models were fine-tuned using the clickbait dataset, which is commonly employed for clickbait detection purposes. The empirical evidence suggests that the attack model being offered routinely achieves much higher attack success rates (ASR) and produces high-quality adversarial instances in comparison to traditional adversarial manipulations. The findings suggest that the clickbait detection system has the potential to be circumvented, which might have significant implications for current policy efforts. [ABSTRACT FROM AUTHOR]

To equip convolutional neural networks (CNNs) with explainability, it is essential to interpret how opaque models make specific decisions, understand what causes the errors, improve the architecture design, and identify unethical biases in the classifiers. This paper introduces ADVISE, a new explainability method that quantifies and leverages the relevance of each unit of the feature map to provide better visual explanations. To this end, we propose using adaptive bandwidth kernel density estimation to assign a relevance score to each unit of the feature map with respect to the predicted class. We also propose an evaluation protocol to quantitatively assess the visual explainability of CNN models. Our extensive evaluation of ADVISE in image classification tasks using pretrained AlexNet, VGG16, ResNet50, and Xception models on ImageNet shows that our method outperforms other visual explainable methods in quantifying feature-relevance and visual explainability while maintaining competitive time complexity. Our experiments further show that ADVISE fulfils the sensitivity and implementation independence axioms while passing the sanity checks. The implementation is accessible for reproducibility purposes on https://github.com/dehshibi/ADVISE. [ABSTRACT FROM AUTHOR]

Multicomponent seismic data can be used to predict unconventional reservoirs; however, this is a challenging task. Although machine learning (ML), particularly deep learning, can be used in this regard, its accuracy in reservoir prediction depends largely on the amount of data available for training and the complexity of the architecture. This study attempted to address this problem using transfer learning (TL) and a compact convolutional neural network with a self-attention mechanism (SACNN). We developed a framework for unconventional reservoir prediction by expanding the data samples and optimizing model performance. First, the synthetic data for both oil and gas reservoirs were used as the source data; their effectiveness was tested using the SACNN model. Subsequently, a real dataset was obtained by optimizing the real multicomponent seismic attributes. The TL dataset was constructed by transferring synthetic gas reservoir data to real dataset. Finally, the constructed SACNN model was used to predict the gas-bearing distribution in tight sandstone gas reservoirs. The results showed the superiority of the proposed model over conventional ML models, with lower error in the unconventional reservoir distribution prediction. Moreover, the proposed model exhibited superior prediction performance (R2 = 0.9731) on the testing dataset compared to models trained solely on synthetic (R2 = 0.9389) and real (R2 = 0.9627) data. Moreover, uncertainty analysis showed that the proposed model is robust and efficient. The proposed framework provides a basis for constructing data-driven models for energy conversion and utilization. [ABSTRACT FROM AUTHOR]

Analyzing the microscopic dynamics of pushing behavior within crowds can offer valuable insights into crowd patterns and interactions. By identifying instances of pushing in crowd videos, a deeper understanding of when, where, and why such behavior occurs can be achieved. This knowledge is crucial to creating more effective crowd management strategies, optimizing crowd flow, and enhancing overall crowd experiences. However, manually identifying pushing behavior at the microscopic level is challenging, and the existing automatic approaches cannot detect such microscopic behavior. Thus, this article introduces a novel automatic framework for identifying pushing in videos of crowds on a microscopic level. The framework comprises two main components: (i) feature extraction and (ii) video detection. In the feature extraction component, a new Voronoi-based method is developed for determining the local regions associated with each person in the input video. Subsequently, these regions are fed into EfficientNetV1B0 Convolutional Neural Network to extract the deep features of each person over time. In the second component, a combination of a fully connected layer with a Sigmoid activation function is employed to analyze these deep features and annotate the individuals involved in pushing within the video. The framework is trained and evaluated on a new dataset created using six real-world experiments, including their corresponding ground truths. The experimental findings demonstrate that the proposed framework outperforms state-of-the-art approaches, as well as seven baseline methods used for comparative analysis. [ABSTRACT FROM AUTHOR]

The integration of convolutional neural network (CNN) and transformer enhances the network's capacity for concurrent modeling of texture details and global structures. However, training challenges with transformer limit their effectiveness to low-resolution images, leading to increased artifacts in slightly larger images. In this paper, we propose a single-stage network utilizing large kernel attention (LKA) to address high-resolution damaged images. LKA enables the capture of both global and local details, akin to transformer and CNN networks, resulting in high-quality inpainting. Our method excels in: (1) reducing parameters, improving inference speed, and enabling direct training on 1024 × 1024 resolution images; (2) utilizing LKA for enhanced extraction of global high-frequency and local details; (3) demonstrating excellent generalization on irregular mask models and common datasets such as Places2, Celeba-HQ, FFHQ, and the random irregular mask dataset Pconv from NVIDIA. [ABSTRACT FROM AUTHOR]

The existence of defective drilled lotus seeds will lead to problems such as plumule residue and lotus seed appearance damage, which decreases the quality of lotus seed food products. Therefore, it is vital to sort drilled lotus seeds. Since the drilled holes on defective seeds are not coaxial with the axis of the lotus seeds, blowing an air jet along the axial direction towards the end face of defective seeds will generate a different acoustic response than that of qualified ones. Based on this characteristic, this study proposes an intelligent sorting method for drilled lotus seeds based on air jet impact acoustic signal and one-dimensional convolutional neural network (1D-CNN) acoustic classification. The method directly extracts features from 1D acoustic signals and achieves automatic classification through the constructed 1D-CNN. First, the sorting principle, acoustic signal data acquisition and preprocessing, and dataset preparation methods were introduced. Then, the effect of hyper-parameters, including the number of convolutional layers, convolution kernel size, learning rate, and training epochs, on the performance of the 1D-CNN model was investigated. On this basis, the parameters were optimized to form the final 1D-CNN model – AudioLS (Audio of Lotus Seed). The accuracy, detection time, and parameters achieved by AudioLS were 98.04%, 25.12 ms, and 0.79 M, respectively. Compared with five classic 2D-CNN models, i.e., Residual Network (ResNet) 50, Visual Geometry Group (VGG) 16, VGG19, DenseNet121, and Extreme Inception (Xception), AudioLS achieved better performance. The accuracy increased by 1.82%, 1.30%, 1.28%, 1.83%, and 2.05%, respectively, and the detection time was shortened by 16.77%, 2.71%, 7.85%, 28.11%, and 11.92%, respectively. The research results verify the effectiveness of the proposed intelligent sorting method. [ABSTRACT FROM AUTHOR]

The presence of road cracks significantly impacts both traffic safety and road maintenance. Therefore, accurate detection of road cracks plays a crucial role in road maintenance and management. This study focused on addressing the critical challenge of accurate road crack detection in images through the development of a novel network architecture (PMENet) based on deep learning semantic segmentation algorithms. The proposed PMENet algorithm combined parallel contextual squeeze and excitation(PCSE), multiscale feature fusion(MFF) and double residual efficient attention(DREA) modules based on the UNet structure, which improves the ability to extract global local information and crack boundary information from the model. We thoroughly evaluate the performance of the PMENet algorithm on the publicly available Crack500 dataset by the ablation experiment. We compared our method with existing approaches, demonstrating its superiority in terms of accuracy, robustness, and generalization ability. The PMENet algorithm achieves an accuracy of 96.14%, an F1-score of 84.22%, and an IoU of 62.00%. The proposed model achieves a significant improvement of 5.9% in F1-score and 5.49% in IoU compared to the UNet model. [ABSTRACT FROM AUTHOR]

Driver drowsiness and alcohol consumption leads for the majority of road accidents. Intoxicated or drugged drivers are less likely to make timely judgments, react quickly, and operate their vehicles safely. It also affect vision, coordination, and balance, making it difficult to maintain control of the vehicle. To address these primary causes, a real-time, cost-effective, and non-intrusive technology has been developed using powerful deep learning technology. In this research paper a novel Smart AVDNet has been proposed for alcohol detection in the face of car drivers using IMDB-WIKI dataset. The input images are taken from the dashboard camera, the captured images are pre-processed using adaptive unsharp mask guided filter. In the first level of checking, fuzzy based HOG-SSD approach is utilized whether the driver drunk based on the analysis of the eye and mouth. After analysis, drivers face based on the fuzzy decision is send as input to the second level. In this level ROI based CNN techniques by analysing the chin and cheek features of the car drivers. If the driver drunk the car stops automatically and whether the driver is not drunk the camera is keep monitoring. The Smart AVDNet demonstrated an average classification accuracy of 99.39%. When compared with Two-Stage DNN, CNN, and DNN, Smart AVDNet achieved accuracies of 5.39, 17.73, and 0.42%, respectively. [ABSTRACT FROM AUTHOR]

The stock market which is a critical instrument in the modern financial system consistently attracts a significant number of individuals and financial institutions. The amalgamation of machine learning with stock forecasting has seen increased interest due to the rising prominence of machine learning. Among numerous machine learning models, the long short-term memory (LSTM) is favored by many researchers for its superior performance in long time series. This paper presents an innovative approach that integrates LSTM with versatile sliding window techniques to enhance prediction results and training performance. Moreover, the attached experiments incorporate convolutional filters and combined bivariate performance measures, which are invaluable methodologies for enhancing stock forecasting systems. These significant contributions are expected to be beneficial for researchers operating within this domain. [ABSTRACT FROM AUTHOR]

Virus‒host protein‒lncRNA interaction (VHPLI) predictions are critical for decoding the molecular mechanisms of viral pathogens and host immune processes. Although VHPLI interactions have been predicted in both plants and animals, they have not been extensively studied in viruses. For the first time, we propose a new deep learning-based approach that consists mainly of a convolutional neural network and bidirectional long and short-term memory network modules in combination with transfer learning named CBIL‒VHPLI to predict viral–host protein‒lncRNA interactions. The models were first trained on large and diverse datasets (including plants, animals, etc.). Protein sequence features were extracted using a k-mer method combined with the one-hot encoding and composition–transition–distribution (CTD) methods, and lncRNA sequence features were extracted using a k-mer method combined with the one-hot encoding and Z curve methods. The results obtained on three independent external validation datasets showed that the pre-trained CBIL‒VHPLI model performed the best with an accuracy of approximately 0.9. Pretraining was followed by conducting transfer learning on a viral protein–human lncRNA dataset, and the fine-tuning results showed that the accuracy of CBIL‒VHPLI was 0.946, which was significantly greater than that of the previous models. The final case study results showed that CBIL‒VHPLI achieved a prediction reproducibility rate of 91.6% for the RIP-Seq experimental screening results. This model was then used to predict the interactions between human lncRNA PIK3CD-AS2 and the nonstructural protein 1 (NS1) of the H5N1 virus, and RNA pull-down experiments were used to prove the prediction readiness of the model in terms of prediction. The source code of CBIL‒VHPLI and the datasets used in this work are available at https://github.com/Liu-Lab-Lnu/CBIL-VHPLI for academic usage. [ABSTRACT FROM AUTHOR]

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists' expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks' feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet. [ABSTRACT FROM AUTHOR]

Purpose objective(s): Manual contouring of the prostate region in planning computed tomography (CT) images is a challenging task due to factors such as low contrast in soft tissues, inter- and intra-observer variability, and variations in organ size and shape. Consequently, the use of automated contouring methods can offer significant advantages. In this study, we aimed to investigate automated male pelvic multi-organ contouring in multi-center planning CT images using a hybrid convolutional neural network-vision transformer (CNN-ViT) that combines convolutional and ViT techniques. Materials/methods: We used retrospective data from 104 localized prostate cancer patients, with delineations of the clinical target volume (CTV) and critical organs at risk (OAR) for external beam radiotherapy. We introduced a novel attention-based fusion module that merges detailed features extracted through convolution with the global features obtained through the ViT. Results: The average dice similarity coefficients (DSCs) achieved by VGG16-UNet-ViT for the prostate, bladder, rectum, right femoral head (RFH), and left femoral head (LFH) were 91.75%, 95.32%, 87.00%, 96.30%, and 96.34%, respectively. Experiments conducted on multi-center planning CT images indicate that combining the ViT structure with the CNN network resulted in superior performance for all organs compared to pure CNN and transformer architectures. Furthermore, the proposed method achieves more precise contours compared to state-of-the-art techniques. Conclusion: Results demonstrate that integrating ViT into CNN architectures significantly improves segmentation performance. These results show promise as a reliable and efficient tool to facilitate prostate radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]