Automatic detection of deteriorated inverted-T patching using 3D laser imaging system based on a true story indiana

Indian traffic sign detection and recognition using deep learning

Water quality prediction (WQP) plays an essential role in water quality management for aquaculture to make aquaculture production profitable and sustainable. In this work, we propose hybrid deep learning (DL) models, convolutional neural network (CNN) with the long short-term memory (LSTM) and gated recurrent unit (GRU) for aquaculture WQP. CNN can effectively fetch the aquaculture water quality characteristics, whereas GRU and LSTM can learn long-term dependencies in the time series data. We conduct experiments using the two different water quality datasets and present an extensive study on the impact of hyperparameters on the performance of the proposed hybrid DL models. Furthermore, the performance of hybrid CNN-LSTM and CNN-GRU models are compared with different baseline LSTM, GRU and CNN DL models and also with attention-based LSTM and attention-based GRU DL models. The results show that the hybrid CNN-LSTM outperformed all other models in terms of prediction accuracy and computation time.

Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks

Black ice has recently been identified as a major cause of transportation accidents due to detecting difficulties on the road surface. It is crucial to provide traffic users with black ice warnings beforehand to sustain commuting safety. The identification of black ice, however, is a difficult initiative, since it necessitates the installation of sophisticated monitoring stations and demands frequently manual inspection. In order to build an economical automatic black ice detection technique, the datasets are built upon a variety of weather conditions, including clear, snowy, rainy, and foggy conditions, as well as two distinct forms of pavement: asphalt and concrete pavement. The Mask R-CNN model was performed to construct the black ice detection via image segmentation. The deep learning architecture was constructed based on pre-trained convolutional neural network models (ResNetV2) for black ice detection purposes. Different pretrained models and architecture (Yolov4) were then compared to determine which is superior for image segmentation of black ice. Afterward, through the retrieved bounding box data, the degree of danger area is determined based on the number of segmentation pixels. In general, the training results confirm the feasibility of the black ice detection method via the deep learning technique. Within “Clear” weather conditions, the detecting precision can be achieved up to 92.5%. The results also show that the increase in the number of weather types leads to a noticeable reduction in the training precision. Overall, the proposed image segmentation method is capable of real-time detection and can caution commuters of black ice in advance.

An analysis of machine learning models for sentiment analysis of Tamil code-mixed data

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin.

In computer vision, significant advances have been made in recent years on object recognition and detection with the rapid development of deep learning, especially deep convolutional neural networks (CNN). The majority of deep learning methods for object detection have been developed for large objects and their performances on small-object detection are not very good. This paper contributes to research in low-resolution small-object detection by evaluating the performances of leading deep learning methods for object detection using a common dataset, which is a new dataset for bird detection, called Little Birds in Aerial Imagery (LBAI), created from real-life aerial imagery data. LBAI contains birds with sizes ranging from 10px to 40px. In our experiments, some of the best deep learning architectures were implemented and applied to LBAI, which include object detection techniques such as YOLOv2, SSH, and Tiny Face, in addition to small instance segmentation techniques including U-Net and Mask R-CNN. Among the object detection methods, experimental results demonstrated that SSH performed the best for easy cases, whereas Tiny Face performed the best for hard cases, i.e. where a cluttered background makes detecting birds difficult. Among small instance segmentation methods, experimental results revealed U-Net achieved slightly better performance than Mask R-CNN.

Identify suitable artificial groundwater recharge zones using hybrid deep learning models

Globally, cardiovascular diseases (CVDs) remain the major cause of death among citizens. With echocardiography, doctors are able to diagnose and determine vital parameters for the evaluation of these diseases. Segmentation of left ventricular (LV) from echocardiography is a significant tool for cardiovascular medical analysis. Besides calculating important clinical indices (e.g. ejection fraction), segmentation also can be useful for the investigation of the basic structure of ventricle. Automatic segmentation of the LV has become a valuable means in echocardiography as we can achieve fast and accurate results and a large number of cases can be handled with limited availability of experts. The Convolutional Neural Networks (CNN) have shown outstanding outcomes for image classification, detection, and segmentation in numerous fields. Recently Mask Regions Convolutional Neural Network (Mask R-CNN) has emerged as a very good segmentation model. In this work, Mask R-CNN is proposed for the segmentation of LV. The Mask R-CNN model is first fine-tuned with Common Object in Context (COCO) weights and then the model is trained with our own data. The model first finds out the region of interest (ROI) in the image that contains the desired object i.e. LV. In the ROI, the model segment LV by generating the mask around it. The results demonstrated by the proposed method segments the LV accurately and efficiently with limited training data.

Steering angle prediction plays a crucial role in the control of Autonomous Vehicles (AVs) and has garnered the interest of researchers, manufacturers, and insurance companies within the automotive industry. Various Deep Learning (DL) architectures have been utilized to forecast the steering angle of AVs across different scenarios. An examination focusing on steering angle prediction through deep learning algorithms can assist seasoned researchers in pinpointing areas that necessitate further advancement. Additionally, newcomers in the field can utilize this study as a foundation. This paper offers an extensive analysis of the recent progress achieved in DL architectures concerning the steering angle prediction of AVs. A comprehensive taxonomy outlining the application of DL in steering angle prediction of AVs has been developed. The survey provides a succinct synthesis, summary, and analysis of research findings. It is evident that Convolutional Neural Network (CNN) is the preferred choice among researchers for predicting the steering angle of autonomous vehicles, compared to other DL architectures. Furthermore, existing research challenges have been identified. The primary obstacle encountered in DL-based steering angle prediction of AVs is the scarcity of real-world datasets, leading researchers to heavily rely on data generated from simulated environments. Lastly, potential alternative approaches to address the identified research challenges have been suggested, indicating promising avenues for future research endeavours.

This study proposes a compact deep learning (DL) architecture and a highly parallelized computing hardware platform to reconstruct the blood flow index (BFi) in diffuse correlation spectroscopy (DCS). We leveraged a rigorous analytical model to generate autocorrelation functions (ACFs) to train the DL network. We assessed the accuracy of the proposed DL using simulated and milk phantom data. Compared to convolutional neural networks (CNN), our lightweight DL architecture achieves 66.7% and 18.5% improvement in MSE for BFi and the coherence factor β, using synthetic data evaluation. The accuracy of rBFi over different algorithms was also investigated. We further simplified the DL computing primitives using subtraction for feature extraction, considering further hardware implementation. We extensively explored computing parallelism and fixed-point quantization within the DL architecture. With the DL model's compact size, we employed unrolling and pipelining optimizations for computation-intensive for-loops in the DL model while storing all learned parameters in on-chip BRAMs. We also achieved pixel-wise parallelism, enabling simultaneous, real-time processing of 10 and 15 autocorrelation functions on Zynq-7000 and Zynq-UltraScale+ field programmable gate array (FPGA), respectively. Unlike existing FPGA accelerators that produce BFi and the β from autocorrelation functions on standalone hardware, our approach is an encapsulated, end-to-end on-chip conversion process from intensity photon data to the temporal intensity ACF and subsequently reconstructing BFi and β. This hardware platform achieves an on-chip solution to replace post-processing and miniaturize modern DCS systems that use single-photon cameras. We also comprehensively compared the computational efficiency of our FPGA accelerator to CPU and GPU solutions.

Various deep learning architectures have been created to cater to the diverse nature of time-series datasets across various domains. This article examines prevalent designs for both encoder and decoder components used in time-series forecasting, covering both one-step-ahead and multi-horizon approaches. It delves into the incorporation of temporal information into predictions by each model. Additionally, recent advancements in hybrid deep learning models are highlighted, showcasing the integration of well-established statistical models with neural network elements to enhance methods in both categories. Lastly, the article outlines how deep learning can contribute to decision support in the context of time-series data. This piece is part of the theme issue on 'Machine learning for weather and climate modeling.

The accuracy of computed tomography angiography (CTA) image interpretation depends on the radiologist. This study aims to develop a new method for automatically detecting intracranial aneurysms from CTA images using deep learning, based on a convolutional neural network (CNN) implemented on the DeepMedic platform. Ninety CTA scans of patients with intracranial aneurysms are collected and divided into two datasets: training (80 subjects) and test (10 subjects) datasets. Subsequently, a deep learning architecture with a three-dimensional (3D) CNN model is implemented on the DeepMedic platform for the automatic segmentation and detection of intracranial aneurysms from the CTA images. The samples in the training dataset are used to train the CNN model, and those in the test dataset are used to assess the performance of the established system. Sensitivity, positive predictive value (PPV), and false positives are evaluated. The overall sensitivity and PPV of this system for detecting intracranial aneurysms from CTA images are 92.3% and 100%, respectively, and the segmentation sensitivity is 92.3%. The performance of the system in the detection of intracranial aneurysms is closely related to their size. The detection sensitivity for small intracranial aneurysms (≤ 3mm) is 66.7%, whereas the sensitivity of detection for large (> 10mm) and medium-sized (3-10mm) intracranial aneurysms is 100%. The deep learning architecture with a 3D CNN model on the DeepMedic platform can reliably segment and detect intracranial aneurysms from CTA images with high sensitivity.

The international roughness index (IRI) is typically obtained using an inertial profiler (IP) to measure pavement elevation. However, the IP has several limitations: it can only measure wheelpaths, requires a constant speed, and displays problems with reproducibility. To overcome these constraints, we developed an image-based roughness inspection system (IPRIS), capable of capturing pavement images at 1-m intervals at speeds of up to 100 km/h. Utilizing photogrammetry, IPRIS reconstructs image positions and orientations to generate three-dimensional (3D) point clouds, producing a high-resolution digital surface model (DSM) and orthophoto. This enables flexible profile selection on the orthophoto, allowing IRI calculations directly from DSM elevation data. To assess the accuracy of the IPRIS, we established a reference elevation profile using a high-precision total station and leveling and then compared the IPRIS with a terrestrial light detection and ranging system (TLS). Results showed that the IPRIS achieved IRI Class 1 specification, with an elevation standard deviation of approximately 1.2 mm and an IRI difference of less than 0.200 m/km. By contrast, the TLS exhibited significant point cloud noise, creating a two-fold increase in elevation errors compared to the IPRIS and an IRI difference of 0.419 m/km, meeting only IRI Class 2 specification. Furthermore, repeated IPRIS data collection on a highway yielded consistent results, demonstrating its reliability for high-speed IRI inspections. However, a key limitation of the IPRIS is its reliance on clear pavement images for 3D reconstruction, making it unsuitable for nighttime or rainy conditions.