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Switchgear is a vital component of modern power systems, responsible for regulating the flow of electrical power. Contact issues, irregular loads, and other similar problems can cause switchgear to overheat, leading to unexpected disturbances and potential damage to the power equipment. Thermal imaging shows significant potential and is increasingly employed to detect faults in power equipment. However, the unique characteristics of thermal images often pose challenges to accurate fault detection. This research aims to study the effectiveness of transfer learning architectures for switchgear fault detection. This paper applies eleven transfer learning architectures, including SqueezeNet, GoogLeNet, InceptionV3, DenseNet201, ResNet50, Xception, InceptionResNetV2, ShuffleNet, EfficientNetB0, AlexNet, and VGG19. The results of the testing phase demonstrated that the application of transfer learning by fine-tuning pre-trained convolutional neural network architectures was highly effective in the classification of thermal images captured from switchgear units. The models achieved accuracy rates between 83.87% and 98.38%, and values of F 1 -Scores between 83.11% and 98.34% in the pre-trained architectures. [Display omitted] • Transfer learning is applied to address the unique characteristics of thermal images for switchgear fault detection. • A thermal image dataset is setup and augmented to improve the classification performance. • Establish standard comparisons among eleven transfer learning architectures. • Achieve rapid and precise classification with the proposed transfer learning architectures. [ABSTRACT FROM AUTHOR]

• Automating wildlife monitoring with wireless 4G cameras and end-to-end data streams. • Remote monitoring of sensor performance, API handling and automated task management. • Deep learning for automated identification of focal species and human detection. • Total cost saving of >40 % through automation, AI and less regular site visits. • Enabling technologies allow scaling-up of a cost-efficient biodiversity monitoring. Modern approaches with advanced technology can automate and expand the extent and resolution of biodiversity monitoring. We present the development of an innovative system for automated wildlife monitoring in a coastal Natura 2000 nature reserve of the Netherlands with 65 wireless 4G wildlife cameras which are deployed autonomously in the field with 12 V/2A solar panels, i.e. without the need to replace batteries or manually retrieve SD cards. The cameras transmit images automatically (through a mobile network) to a sensor portal, which contains a PostgreSQL database and functionalities for automated task scheduling and data management, allowing scientists and site managers via a web interface to view images and remotely monitor sensor performance (e.g. number of uploaded files, battery status and SD card storage of cameras). The camera trap sampling design combines a grid-based sampling stratified by major habitats with the camera placement along a traditional monitoring route, and with an experimental set-up inside and outside large herbivore exclosures. This provides opportunities for studying the distribution, habitat use, activity, phenology, population structure and community composition of wildlife species and allows comparison of traditional with novel monitoring approaches. Images are transferred via application programming interfaces to external services for automated species identification and long-term data storage. A deep learning model for species identification was tested and showed promising results for identifying focal species. Furthermore, a detailed cost analysis revealed that establishment costs of the automated system are higher but the annual operating costs much lower than those for traditional camera trapping, resulting in the automated system being >40 % more cost-efficient. The developed end-to-end data pipeline demonstrates that continuous monitoring with automated wildlife camera networks is feasible and cost-efficient, with multiple benefits for extending the current monitoring methods. The system can be applied in open habitats of other nature reserves with mobile network coverage. [ABSTRACT FROM AUTHOR]

[Display omitted] • The suspicious ultrasonography features are related to immune response in thyroid cancer. • The features were also related to epithelial to mesenchymal transition (EMT). • Immune response and EMT were related to aggressive clinical behaviors. • Immune response and EMT reflected in the decision by radiologists and convolutional neural networks. Ultrasonography (US) features of papillary thyroid cancers (PTCs) are used to select nodules for biopsy due to their association with tumor behavior. However, the molecular biological mechanisms that lead to the characteristic US features of PTCs are largely unknown. This study aimed to investigate the molecular biological mechanisms behind US features assessed by radiologists and three convolutional neural networks (CNN) through transcriptome analysis. Transcriptome data from 273 PTC tissue samples were generated and differentially expressed genes (DEGs) were identified according to US feature. Pathway enrichment analyses were also conducted by gene set enrichment analysis (GSEA) and ClusterProfiler according to assessments made by radiologists and three CNNs - CNN1 (ResNet50), CNN2 (ResNet101) and CNN3 (VGG16). Signature gene scores for PTCs were calculated by single-sample GSEA (ssGSEA). Individual suspicious US features consistently suggested an upregulation of genes related to immune response and epithelial-mesenchymal transition (EMT). Likewise, PTCs assessed as positive by radiologists and three CNNs showed the coordinate enrichment of similar gene sets with abundant immune and stromal components. However, PTCs assessed as positive by radiologists had the highest number of DEGs, and those assessed as positive by CNN3 had more diverse DEGs and gene sets compared to CNN1 or CNN2. The percentage of PTCs assessed as positive or negative concordantly by radiologists and three CNNs was 85.6% (231/273) and 7.1% (3/273), respectively. US features assessed by radiologists and CNNs revealed molecular biologic features and tumor microenvironment in PTCs. [ABSTRACT FROM AUTHOR]

In the past few years, the lane detection technique has become a key factor for autonomous driving systems and self-driving cars on the road. Among the various vehicle subsystems, the lane detection module is one of the essential parts of the Advanced Driver Assistance System (ADAS). Conventional lane detection approaches use machine vision algorithms to find straight lines in road scene images. However, it is challenging to identify straight or curved lane markings in complex environments. To deal with this problem, this paper presents a lane detection technique based on deep learning. It is combined with a 3D convolutional network, so the temporal information is added to the network architecture. Using the front camera images, the system can immediately detect the lane marking information ahead. Moreover, we propose an approach for improvement by adding the time axis to the network architecture. In addition to using 3D-ResNet50, the temporal convolution and spatial convolution are separated for processing. The accuracy is 91.34% improved after adding time and space split convolution with LeakyReLU. The experiments carried out using real scene images have demonstrated the feasibility of the proposed technique for applications to various complex scenes. [ABSTRACT FROM AUTHOR]

• A novel optimization algorithm integrating random and grid search mechanisms as function calls. • A cost-effective low inference time deep learning model for detecting 2D ionospheric plasma structures from all-sky airglow images. • The issue of uncertainty quantification has been addressed using the approach of bootstrapping. The ionosphere, part of the Earth's atmosphere, where different plasma irregularities/structures are generated through various electrodynamical processes. Airglow imaging is one of the tools to observe various morphological features of these irregularities. We aim to help all-sky airglow imaging-based ionospheric research by proposing novel deep learning at the edge framework for the detection of different types of mid-latitude ionospheric plasma structures (medium scale traveling ionospheric disturbances with single and multiple bands, and plasma bubbles). The primary challenge was to find an optimal deep neural network by considering the trade-off between accuracy and inference time. In order to address this, a novel hyperparameter optimization technique has been used that integrates the approaches of random and grid search mechanisms as a compact function. The random function generates the subspace for hyperparameters to check the convergence of the model while the grid search creates possible combinations to tune these hyperparameters. Our novel optimization method for deep learning inference at the edge led us to a low-cost, high-accuracy convolutional neural network (CNN) model, which outperformed the complex state-of-the-art deep learning models such as Inception-v3, DenseNet169, VGG16, and VGG19. For airglow image-based plasma structure detection, the proposed model recorded an accuracy of 99.9% with an inference time of 5.8 s. This was an improvement of about 4% in accuracy while 85% reduction in inference time over the best-performing baseline. There was about 93% reduction in the number of model parameters with respect to the best-performing baseline. Uncertainty quantification has also been performed in the present work through bootstrapping to validate the robustness of the proposed model. The findings of our study show the promise of deep learning at the edge for all-sky airglow imaging systems by demonstrating an alternate low-cost, high-accuracy deep neural network. [ABSTRACT FROM AUTHOR]

High-frequency signals like vibration and acoustic emission are crucial for condition monitoring, but their high sampling rates challenge data acquisition, especially for online monitoring. Our research developed a novel method for condition identification in undersampled signals using a modified convolutional neural network integrated with a signal enhancement approach. A frequency-domain filtering is applied to suppress similar sidebands and obtain more discriminative features of different conditions, followed by an interpolation-based upsampling in the time domain to restore the signal length and strengthen the low-frequency harmonic information. Enhanced signals are converted into two-dimensional grayscale images for neural network analysis. Tested on bearing datasets and real-world data from regenerative thermal oxidizer lift valve leakage, our method effectively extracts features from low-frequency signals, achieving over 95% fault identification accuracy. • This study introduces a novel 2DCNN-based method for condition identification. It reveals a significant accuracy decline when the sampling frequency falls below the Nyquist rate. • A novel signal enhancement approach is presented, suppressing sidebands to enhance feature discrimination in the frequency domain and bolstering low-frequency harmonic information via cubic interpolation in the time domain. • Tested on the CWRU bearing dataset and real-world regenerative thermal oxidizer data, it achieves over 95% accuracy in condition recognition with undersampled signals. [ABSTRACT FROM AUTHOR]

The large number of near-Earth asteroids (NEAs) has greatly impacted human space activities and Earth security. However, detecting NEAs in astronomical images with complex, varying backgrounds is still extremely challenging. In this paper, we propose a deep segmentation assisted asteroid tracking algorithm, termed DSAT, to construct a possible pipeline for faint NEA tracking in astronomical images. First, the single-frame object detection problem is converted to a segmentation problem, enabling robust extraction of faint potential moving objects. Then, a multiframe motion prior-based moving object tracking algorithm is proposed to find real NEAs. We further propose a distance tolerance criterion to help DSAT achieve effective tracking in practical situations when detection has partially failed. Finally, the pipeline is tested with both simulated and real astronomical images at different SNRs and in crowded fields. The results showed that our pipeline has the potential to detect and track faint NEAs in complex backgrounds. Our code is publicly available at https://github.com/zhenhongdu/DeepSegAsteroidTracker. [ABSTRACT FROM AUTHOR]

Skin lesions refer to changes or abnormalities on the skin, such as moles, freckles, cysts, warts, and more. While not all skin lesions are cancerous, some types can indicate or develop into skin cancer. Skin cancer has been on the rise in recent years, causing many deaths. However, manual examination by dermatologists is expensive and time-consuming. Research in this field has focused on developing an automated system to detect skin cancer and reduce the associated mortality rate. However, the performance of such systems needs improvement. In this study, we propose a Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTEENN) to augment the data, resulting in a more balanced dataset. Then, we used a Convolutional Neural Network (CNN) model on the benchmark HAM10000 dataset. The dataset comprises seven skin diseases, including Actinic Keratoses, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanocytic nevi, Melanoma, and Vascular skin lesions. With a balanced dataset, the proposed model achieves an accuracy, precision, recall, and f1-score of 99.35%, 99.00%, 99.00%, and 99.00%, respectively. The balanced dataset performs 22% better than an unbalanced dataset. [ABSTRACT FROM AUTHOR]

Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification problem. T1-CE, T2WI, and FLAIR 3D-segmented masks of 307 patients (157 GB and 150 BM) were generated post resampling, co-registration normalization and semi-automated 3D-segmentation and used for internal model development. Subsequent external validation was performed on 59 cases (27 GB and 32 BM) from another institution. Four different mask-sequence combinations were evaluated using area under the curve (AUC), precision, recall and F1-scores. Diagnostic performance of a neuroradiologist and a general radiologist, both without and with the model output available, was also assessed. 3D-model using the T1-CE tumor mask (TM) showed the highest performance [AUC 0.93 (95% CI 0.858–0.995)] on the external test set, followed closely by the model using T1-CE TM and FLAIR mask of peri-tumoral region (PTR) [AUC of 0.91 (95% CI 0.834–0.986)]. Models using T2WI masks showed robust performance on the internal dataset but lower performance on the external set. Both neuroradiologist and general radiologist showed improved performance with model output provided [AUC increased from 0.89 to 0.968 (p = 0.06) and from 0.78 to 0.965 (p = 0.007) respectively], the latter being statistically significant. 3D-CNNs showed robust performance for differentiating GB from BMs, with T1-CE TM, either alone or combined with FLAIR-PTR masks. Availability of model output significantly improved the accuracy of the general radiologist. • 3-dimensional CNN models can differentiate GB from BM with high accuracy. • The best performing models used T1-CE images, either alone or combined with FLAIR images. • The model output significantly improved the accuracy of the general radiologist. [ABSTRACT FROM AUTHOR]

Monitoring body condition score (BCS) is a useful management tool to estimate the energy reserves of an individual cow or a group of cows. The aim of this study was to develop and evaluate the performance of a fully automated 2-dimensional imaging system using a machine learning algorithm to generate real-time BCS for dairy cows. Two separate datasets were used for training and testing. The training dataset included 34,150 manual BCS (MAN_BCS) assigned by 5 experienced veterinarians during 35 visits at 7 dairy farms. Ordinal regression methods and deep learning architecture were used when developing the algorithm. Subsequently, the testing dataset was used to evaluate the developed BCS prediction algorithm on 4 of the participating farms. An experienced human assessor (HA1) visited these farms and performed 8 whole-milking-herd BCS sessions. Each farm was visited twice, allowing for 30 d (±2 d) to pass between visits. The MAN_BCS assigned by HA1 were considered the ground truth data. At the end of the validation study, MAN_BCS were merged with the stored automated BCS (AI_BCS), resulting in a testing dataset of 9,657 single BCS. A total of 3,817 cows in the testing dataset were scored twice 30 d (±2 d) apart, and the change in their BCS (ΔBCS) was calculated. A subset of cows at one farm were scored twice on consecutive days to evaluate the within-observer agreement of both the human assessor and the system. The manual BCS of 2 more assessors (HA2 and HA3) were used to assess the interobserver agreement between humans. Finally, we also collected ultrasound measurements of backfat thickness (BFT) from 111 randomly selected cows with available MAN_BCS and AI_BCS. Using the testing dataset, intra- and interobserver agreement for single BCS and ΔBCS were estimated by calculating the simple percentage agreement (PA) at 3 error levels and the weighted kappa (κ w) for the exact agreement. A Bland-Altman plot was constructed to visualize the systematic and proportional bias. The association between MAN_BCS and AI_BCS and the BFT was assessed with Passing-Bablok regressions. The system had an almost perfect repeatability with a κ w of 0.99. The agreement between MAN_BCS and AI_BCS was substantial, with an overall κ w of 0.69. The overall PA at the exact, ± 0.25-unit, and ± 0.50-unit BCS error range between MAN_BCS and AI_BCS was 44.4%, 84.6%, and 94.8%, respectively, and greater than the PA obtained between HA1 and HA3. The Bland-Altman plot revealed a minimal systematic bias of −0.09 with a proportional bias at the extreme scores. Furthermore, despite the low κ w of 0.20, the overall PA at the exact and ± 0.25-unit of BCS error range between MAN_BCS and AI_BCS regarding the ΔBCS was 45.7 and 88.2%, respectively. A strong linear relationship was observed between BFT and AI_BCS (ρ = 0.75), although weaker than that between BFT and MAN_BCS (ρ = 0.91). The system was able to predict single BCS and ΔBCS with satisfactory accuracy, comparable to that obtained between trained human scorers. [ABSTRACT FROM AUTHOR]

Human age and gender prediction from facial images has garnered significant attention due to its importance in various applications. Traditional models struggle with large-scale variations in unfiltered images. Convolutional Neural Networks (CNNs) have emerged as effective tools for facial analysis due to their robust performance. This paper presents a novel CNN approach for robust age and gender classification using unconstrained real-world images. The CNN architecture includes convolution, pooling, and fully connected layers for feature extraction, dimension reduction, and mapping to output classes. Adience and UTKFace datasets were utilized, with the best training and testing accuracies achieved using an 80% training and 20% testing data split. Robust image pre-processing and data augmentation techniques were applied to handle dataset variations. The proposed approach outperformed existing methods, achieving age prediction accuracies of 86.42% and 81.96%, and gender prediction accuracies of 97.65% and 96.32% on the Adience and UTKFace datasets, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, for controlling crime, surveillance cameras are typically installed in all public places to ensure urban safety and security. However, automating Human Activity Recognition (HAR) using computer vision techniques faces several challenges such as lowlighting, complex spatiotemporal features, clutter backgrounds, and inefficient utilization of surveillance system resources. Existing attempts in HAR designed straightforward networks by analyzing either spatial or motion patterns resulting in limited performance while the dual streams methods are entirely based on Convolutional Neural Networks (CNN) that are inadequate to learning the long-range temporal information for HAR. To overcome the above-mentioned challenges, this paper proposes an optimized dual stream framework for HAR which mainly consists of three steps. First, a shots segmentation module is introduced in the proposed framework to efficiently utilize the surveillance system resources by enhancing the lowlight video stream and then it detects salient video frames that consist of human. This module is trained on our own challenging Lowlight Human Surveillance Dataset (LHSD) which consists of both normal and different levels of lowlighting data to recognize humans in complex uncertain environments. Next, to learn HAR from both contextual and motion information, a dual stream approach is used in the feature extraction. In the first stream, it freezes the learned weights of the backbone Vision Transformer (ViT) B-16 model to select the discriminative contextual information. In the second stream, ViT features are then fused with the intermediate encoder layers of FlowNet2 model for optical flow to extract a robust motion feature vector. Finally, a two stream Parallel Bidirectional Long Short-Term Memory (PBiLSTM) is proposed for sequence learning to capture the global semantics of activities, followed by Dual Stream Multi-Head Attention (DSMHA) with a late fusion strategy to optimize the huge features vector for accurate HAR. To assess the strength of the proposed framework, extensive empirical results are conducted on real-world surveillance scenarios and various benchmark HAR datasets that achieve 78.6285%, 96.0151%, and 98.875% accuracies on HMDB51, UCF101, and YouTube Action, respectively. Our results show that the proposed strategy outperforms State-of-the-Art (SOTA) methods. The proposed framework gives superior performance in HAR, providing accurate and reliable recognition of human activities in surveillance systems. [ABSTRACT FROM AUTHOR]

Exploiting the capability of deep learning-based techniques, this research addresses an important and relevant problem on how to cost effectively detect thyroid cancer. In recent decades, there has been a significant increase in the incidence of thyroid cancer, prompting the need to understand its epidemiology. Existing studies have primarily employed qualitative techniques to investigate a single risk factor correlated with the disease development at a time. However, such an approach is inefficient and tends to ignore the interwoven connections among factors, resulting in a considerable disagreement with the identified risk factors among scholars. Additionally, the use of deep learning techniques in conjunction with medical imaging for computer-aided diagnostic (CAD) systems design has shown promises in detecting the disease, while there are research gaps regarding the detection of subtype and their co-existence. More importantly, existing CAD systems have shortcomings in adapting to different sample groups. To address these challenges, this research aims to shed light on the pathogenesis of thyroid cancer, enhance diagnostic performance, and improve generalisation of deep learning-based decision support systems. By harnessing the power of machine learning, specifically data mining and deep learning techniques, we seek to improve our understanding of the underlying mechanisms of the disease and develop innovative, robust, accurate, and efficient diagnostic tools. Extensive experiments indicate superior performance of the proposed methods than existing works. The systems proposed in this study have great impact to the wider society and contribute to the advancement of thyroid cancer research while enhancing clinical practice in related detection and subsequent management. [ABSTRACT FROM AUTHOR]

Tomato diseases have become a major concern to the tomato production sector around the world. A huge proportion of tomato crops deteriorate yearly during growth or after harvesting due to the infections caused by fungus, viruses and bacteria. Early detection of these diseases plays a crucial role in alleviating overall production loss. Over the past decades, farmers have been using visual observation to identify a crop disease in a field. However, the visual observation method is labour intensive, time consuming, and prone to human error. Currently, intelligent approaches have been widely used to detect and classify these diseases. The objective of this study is to design a convolutional neural network VGG16 net architecture that is able to detect tomato diseases on tomato leaves. A model that can successfully detect 5 tomato leaf diseases was developed: (1) Bacterial spot, (2) Early blight, (3) Late blight, (4) Septoria leaf and (5) Yellow leaf curl with a training accuracy of 0.9328. [ABSTRACT FROM AUTHOR]

The continuous growth of blockchain has brought revolutionary impact on different areas with characteristics like decentralization, trust-worthy and tampering-proof. In the financial field, forecasting stock market had remained a challenging task for researchers. A novel integration of convolution and LSTM along with attention mechanism is proposed for prediction of stock price for the last hour of a day based it's performance in six hours for making prediction in intraday trades. The proposed prediction model uses 4 different stocks of Apple, Facebook, Nike and Uber for data input. The input data is given to the combination of many convolutional and LSTM layers for predicting future value of stocks in short term. Furthermore, the proposed model combines the obtained data with LSTM layers output vectors enhanced for final prediction. The prediction is based on past value of stocks. Performance evaluation is done based on RMSE and computational time. The average values obtained were 0.091 and 11.57 seconds respectively, showing it's superiority from benchmark models. The obtained data is stored on blockchain for enhanced security. The model can further be tuned for predicting other markets and will be more efficient than market specific markets. [ABSTRACT FROM AUTHOR]

The present study uses the benchmark UNSW-NB15 datasets to detect network anomalies using the proposed deep learning-based Convolutional Neural Network (CNN) model. Several studies have already worked on network anomaly detection with some limits. However, earlier research has used old datasets and obtained limited accuracy. In addition, previous studies could not detect the latest malicious activities. Therefore, in the present study, firstly, we implemented the machine learning-based random forest method to diminish the dimensionality of the dataset and pick the most notable features. In this case, the random forest method has set only fifteen features from the forty-one and reduced the complexity of the data. Subsequently, the CNN model was trained and tested on the UNSW-NB15 dataset with 99.00% testing accuracy. The performance of the offered CNN model was assessed using precision, recall and the f1-values. The present study's outcome can be used in real-time malicious activity detection. [ABSTRACT FROM AUTHOR]

Anomalies such as microaneurysms, exudates, and hemorrhages are diagnosed by retinal vessel segmentation to identify different phases of Diabetic Retinopathy (DR); several typical ways to detect Hard Exudates (HE) in retinal images have been used to determine the severity of diabetes. This work proposes a hybrid approach to better segment such abnormal cells. It optimizes the learning parameters of a Convolutional Neural Network (CNN) with a nature-inspired optimizer, the Self-Adaptive Jaya Optimization Algorithm (SAJOA). The SAJOA helps in avoiding trapping in local minima. It uses the SAJOA to steer the search for obtaining near-optimal parameter values for the CNN training. We choose the learning parameters such as batch size, dropout rate, learning rate, etc. The batch size affects the batch normalization performance, and the dropout rate involves the regularization property of the model. The near-optimal values of these two parameters help improve the generalization ability of the deep learning architecture. Thus, this hybrid approach improves the CNN performance and generalizability, leading to precise segmentation of unseen data, which aids in diagnosing diabetic retinopathy. The results of many trials were evaluated against the DRIVE dataset; the proposed method achieved accuracy and F1 score on average, higher by 7 to 8% than the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In recent days, credit card fraud has emerged as a significant challenge for researchers in the field of detection and prevention. Tackling this challenge holds substantial benefits for both public and private organizations, as it directly impacts economic statistics. Our proposed model introduces crucial advancements to address this issue in real-time scenarios. To achieve this, we harnessed the power of Deep Convolutional Neural Networks (DeepConvNet) in conjunction with various optimization techniques. Optimization algorithms encompass a group of mathematical and computational techniques employed to discover the most suitable solution or set of solutions for a given problem. The primary goal of these algorithms is to optimize, either by maximizing or minimizing, an objective function while ensuring compliance with specific constraints. In this research work, we provide a comparison of highly effective and validated optimization techniques: Stochastic Gradient Descent (Sgd), Adaptive Gradient (Adagrad), Adaptive Moment Estimation (Adam), and Root Mean Squared Propagation (Rmsprop). These optimization algorithms are applied to the Deep Convolutional Neural Network (DeepConvNet) in the subject of our specific problem statement, which involves credit card fraud detection (CCFD). After careful consideration of the problem's nature, objective function characteristics, and computational aspects, we fnd that all four algorithms are suitable for our CCFD task. However, based on experimental results, it is evident that Rmsprop outperforms others, leading to a remarkable 99.93% in accuracy. [ABSTRACT FROM AUTHOR]

Emotion detection has become an important study subject due to the potential uses that it has in many other domains. It incorporates data from various sources, such as facial expressions, voice patterns, physiological indications, and textual signals. To identify human emotions, a real-time bimodal image and audio-based detection-based approach has been proposed. The approach is termed as "bimodal" since images and audio data are considered as inputs to the proposed model. In this study, hybridized CNN-LSTM for the image-based model and Multi-Layer Perceptron is used for the developing audio model. For the image-based model, two combined layers of CNN, and LSTM along with regularization techniques like drop-out, and batch normalization have been adopted for model development. Similarly, for the audio model, the dataset has been fine-tuned using a standard scaler and PCA before training. Based on the combined results of both models, the user's final emotion is determined. Both the audio-based model and the video-based model outperform the existing models discussed in the literature with higher performance accuracy of 79.34% and 77.27% respectively. [ABSTRACT FROM AUTHOR]

The classification of underwater acoustic targets is a challenging task due to the intricacies involved in soundscape, huge amounts of background interference, and the varied features of underwater targets. Various approaches have been explored for classifying underwater targets based on acoustic signatures. The problem with these conventional methods lies in the extensive domain-specific knowledge they demand for effective feature engineering. Alternatively, the deep learning approach is of great appeal for assistance to manual sonar operators who mainly rely on their expertise for the classification of targets. This work leverages the approach of deep neural networks for underwater acoustic target categorization, aiming to analyze whether we can reduce the need for extensive domain knowledge required, by letting deep learning algorithms itself find the deep audio embeddings. This approach implements an audio classifier designed to classify acoustic targets with input as log Mel-spectrograms. To compare the performance of this classifier, we also implement a regular neural network for audio classification with MFCCs as input features. The overall results of the model are promising. [ABSTRACT FROM AUTHOR]

Traffic congestion has become a critical issue in metropolitan areas, inflicting significant hardships on individuals on a daily basis. This adverse situation poses multifaceted challenges, including increased air pollution, heightened fuel consumption, and compromised road safety. Deep learning algorithms, encompassing both Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) have showcased remarkable performance in areas such as computer vision, natural language processing, healthcare, finance, and autonomous systems. This research paper proposes a novel methodology to develop a vehicle detection model - VehiNet, which can subsequently be refined to create an automated traffic signal system. The study explores the effectiveness of various CNN, RNN, and deep learning algorithms and techniques in accomplishing this objective. The dataset picked for the study in addition to being compiled from internet resources, was also painstakingly assembled manually using a variety of methods to collect over 2000 photographs from various angles, lighting conditions, and locations within Bengaluru city. The main motive is to apply CNN, RNN, and other Deep Learning classification techniques for object detection and classification of Indian vehicles and see how well these deep learning algorithms and techniques affect prediction efficiency. This research stands out from others and makes its novelty clear thanks to the carefully collected dataset and the detailed analysis of the methodologies. [ABSTRACT FROM AUTHOR]

The integration of deep learning, computer vision, and advanced algorithms has ushered in a transformative era in the prediction of human driving behavior, consequently revolutionizing road safety. This paper focuses on an innovative convergence of technology that addresses critical issues like driver fatigue and distracted driving by automatically identifying and categorizing diverse driving behaviors. Neural network architectures, such as VGG16, AlexNet, and ResNet are described in this paper that have propelled accuracy in behavior classification to remarkable levels. However, the quest for safer roads remains ongoing, with promising avenues lying ahead. First and foremost, the creation of extensive, diverse, and meticulously annotated datasets is paramount. These datasets serve as the bedrock upon which future models can be trained, enhancing their robustness and generalizability across a spectrum of driving scenarios. Real-time models represent another pivotal frontier. These models hold the potential to provide timely interventions and support systems for drivers, thus preventing accidents proactively. The exploration of hybrid techniques that amalgamate the strengths of various neural network architectures presents an exciting avenue, promising to further push the boundaries of prediction accuracy. Furthermore, this paper also discusses the fusion of multi-modal data, encompassing sensor data from IoT and smartphone devices, that holds immense promise. This holistic approach promises a more comprehensive understanding of driver behavior by integrating diverse data sources, ultimately contributing to the creation of safer road environments.In this research paper, we also explore these cutting-edge developments in deep learning and computer vision, emphasizing technical novelty and innovation. Through an interdisciplinary approach, we envision a future where the synergy of technology, data, and human behavior leads to a substantial reduction in road accidents and improved road safety. [ABSTRACT FROM AUTHOR]

The proliferation of fake images in today's digital landscape poses a significant threat to various domains, including media integrity, social media, and online security. Recognizing the urgent need to distinguish real images from their deceptive counterparts, this paper underscores the importance of developing a robust detection system. While substantial efforts have been made in the realms of computer vision and deep learning, the advent of Generative Adversarial Networks (GANs) has added a new layer of complexity to this challenge. In response to these evolving threats, we present a novel two-step methodology for detecting fake images, with a specific focus on those generated by GANs. Our approach harnesses the combined strengths of GANs and traditional Convolutional Neural Networks (CNNs), offering a comprehensive solution that significantly enhances accuracy in identifying both fake images and machine-generated fake images. The results of our experiments demonstrate the efficacy of our methodology. Using CNNs alone, we achieved a training accuracy of 87%. However, when employing the collaborative power of GANs and CNNs, our model exhibited a remarkable accuracy rate of 94.4%. This substantial improvement underscores the superiority of the GANs+CNN approach, suggesting its potential as a groundbreaking solution in the realm of fake image detection. This research opens up new horizons in fields such as media forensics, social media monitoring, and online security, where the ability to discern genuine content from manipulated or synthetic media is of paramount importance. The promising outcomes of this study not only provide an immediate and effective solution but also pave the way for further exploration and innovation in this critical area of digital security. [ABSTRACT FROM AUTHOR]

The image-based automatic plant disease identification has emerged as a prominent research area, driven by recent advancements in machine learning algorithms. Conventional disease detection methods rely on manual feature extraction from acquired images, involving extensive expertise from experts and pathologists. This labor-intensive practice is often time-consuming and impractical. With the recent advancements in digital technologies and computer vision, Deep neural networks address these challenges by learning the features automatically. This paper focuses on the EfficientNet B0 architecture-based deep neural network model integrated with a convolutional block attention mechanism module for identifying major wheat crop diseases. The attention module in the EfficientNet B0 model improves its performance by emphasizing the disease-infected regions in leaf images. To evaluate the proposed methodology, experiments were conducted on the WheatRust21 image dataset, comprising 6556 images of three major fungal diseases, namely, stripe rust, leaf rust, and stem rust, collected in natural field conditions from 2019 to 2021. Our developed EfficientNet B0-CBAM model performed well with a testing accuracy of 98.70% and an impressive F1 score of 98% on the dataset. The Grad-CAM visualization demonstrates that the attention module focuses more on the infected region and plays a crucial role in wheat disease identification. The results are quantitatively substantial, with the model achieving high accuracy and other evaluation metrics scores with an average of 98.5 %. These findings hold promise for practical applications in agriculture and sustainable crop management. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is characterized by low mood, loss of interest and even suicidal ideation. Electroencephalogram based diagnosis of a variety of neurological conditions has been conducted in recent years using modern neurocomputing and deep learning approaches. Privacy is a high concern for medical data over distributed training scenarios. Hence, this paper aims to develop an EEG-based privacy-preserved system to identify MDD through the Federated Learning (FL) approach. In this study, training for 3-channel EEG-based MDD screening is introduced over FL using deep learning (DL) algorithms such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and One Dimensional Convolutional Neural Network (1D-CNN). A privacy-preserving solution for MDD patients utilizing FL is advantageous because clinical data is sensitive and most people do not want to share their personal information. Hence, the analysis of the comparative results for multiple applicable strategies including Independent and Identically Disturbed (IID) data, non-IID data, and algorithms provide proof that FL can be used to train DL models. The MODMA dataset of 3-channel EEG with 26 MDD and 29 non-MDD individuals is used in this paper. Future study on MDD has several potential areas of use and these FL approaches can also be coupled with new technologies. [ABSTRACT FROM AUTHOR]

With diseases and weeds alone accounting for approximately 30% of productivity losses in agriculture worldwide, plant diseases are a significant factor. Farmers frequently consult experts to diagnose their crops in an effort to reduce these losses. On the other hand, conventional visual assessment techniques focused on elements like leaf color, shape, and texture can be inefficient, time-consuming, and expensive. This study suggests using symptom traits like yellow leaves, black lesions, wilting, and plant mortality in crops like apple and grape to anticipate plant diseases, notably black rot. AlexNet, VGGNet, ResNet, and Inception are the four deep learning models examined in the study; VGGNet achieved the greatest accuracy of 98.19%. [ABSTRACT FROM AUTHOR]

Fault diagnosis based on deep learning (DL) has been a research hotspot in recent years. However, the current neural networks are getting larger and larger, with more and more parameters and insufficient noise resistance, making it difficult to effectively apply these methods to real working conditions. To address these issues, we propose a novel deep learning method with fewer parameters and better noise resistance based on transfer adversarial subnetwork (TAS) and channel-wise thresholds (CWT), namely, anti-noise transfer adversarial convolutions (ANTAC). In the proposed method, the original data and feature vectors are mapped to reproducing kernel Hilbert space (RKHS) and processed by maximum mean discrepancy (MMD) and Wasserstein distance (WD), which makes the method more capable to distinguish the similar features without producing any additional training parameters. Furthermore, white Gaussian noise (WGN) and the soft thresholding method with CWT are used to reduce data noise and improve the robustness and noise resistance of the network. Finally, the superiority of the proposed method is validated through experiments on different datasets, network structures and the data with different SNRs. The results show that the proposed method has better feature discrimination ability, noise resistance, and fewer parameters compared with other methods. The highest accuracy of the proposed method is 99.90% on the test set. • In order to enhance the network ability in distinguishing similar types of samples, TAS is proposed. Experiments show that the proposed method can effectively improve the network stability and the feature discrimination ability with fewer parameters. • The anti-interference ability and robustness of the network is enhanced by adding a certain disturbance into it. In addition. The soft threshold function with channel-wise threshold is used to improve the denoising ability of the network. • To enhance the network generalization and sensitivity to similar features, AFV loss is calculated by WD and controlled by scaling factor λ , and finally sent into RMSprop optimizer together with classification loss for optimization. • The method can directly process the original signals and the procedures of denoising and feature extraction are finished automatically in the method, which improves the fault diagnosis efficiency and provides a further practical possibility for the deployment of end-to-end fault diagnosis platforms. [ABSTRACT FROM AUTHOR]

The complex flow of a supersonic jet (Mach number 2.4–2.5) around the blunt aerodynamic body (consisting of a cylinder with a diameter of 16 mm and a truncated cone) was studied using the high-speed shadowgraph technique and computer vision-based visual data processing. The problem considered is directly related to the rocket and space systems stages safe separation problem in a sufficiently dense atmosphere. For systems with a sequential arrangement of stages, their separation often occurs under the rocket engine jet action. The occurrence of lateral forces in violation of axial symmetry can lead to overturning of the discarded stage and undesirable interaction with the outgoing stage. The goals of the paper were to study the pulsation characteristics of the incoming supersonic jet streamlining the body, as well as the pulsations of the bow shock wave in both axisymmetric and asymmetric flows. The recording frame rate in the experiments was up to 775 000 fps. Computer vision and various machine learning approaches were used to process a large number of shadowgraph images. Canny edge detection and Hough transform, as well as a convolutional neural network (CNN) based on YOLOv2 architecture, were used to automatically track the position of the bow shock relative to the body. Jet sound generation was studied - the amplitude dependence on the distance from the nozzle and the evolution in time. The position of the bow shock relative to the streamlined body and flow pulsations over time were analyzed. Bow shock pulsation spectra at zero angle of attack were compared to those at a small angle of attack (α = 2.5°–3°). The power spectral density was also estimated. The slope of the spectra for the zero angle of attack case was found to be horizontal and the spectra for the small angle of attack case had a slope close to −5/3. The strongest of the measured bow shock frequencies were found to be close to those of the jet mixing layer pulsations. The hardware signal spectrum was also measured and this signal can be considered as noise. The described approach makes it possible to obtain the physical characteristics of the flow at any point of interest. It was also shown that the automation of flow visualization experiments using computer vision techniques allows to increase the speed of data processing and obtaining new physical information, which may be important for engineers developing aircraft and spacecraft technologies. • Supersonic jet flow around a blunt body was studied experimentally and also numerically. • Computer vision tools were applied to supersonic flow investigation. • Spectra of jet generated acoustic waves were determined. • Time dependent bow shock positions were automatically measured by the neural network. • Amplitudes of bow shock pulsations and flow fluctuations depend on the angle of the body relative to the jet direction. [ABSTRACT FROM AUTHOR]

Disaster relief, police work, and environmental monitoring all benefit from satellite images. Objects and infrastructure in the images must be manually identified for these applications. Due to the large areas that need to be searched and the limited number of accessible analysts, automation is essential. However, the accuracy and dependability of existing object recognition and classification algorithms renders them inadequate for the task. One family of machine learning algorithms called "deep learning" has showed immense potential for automating these kinds of jobs. Convolutional neural networks have been successful in the area of image recognition. Here, convolutional neural networks (CNNs) and a particle swarm optimization classifier is utilized to develop efficient algorithms for classifying satellite images. The results of this classifier model are better than those of existing approaches. [ABSTRACT FROM AUTHOR]

In recent years, the advancement of deep learning technologies has significantly impacted various domains and the field of transportation is no exception. As the demand for robust and accurate traffic sign classification systems continues to rise, this study presents an in-depth exploration and comparison of various techniques employed in the field. Focusing on state-of-the-art methodologies, we assess the effectiveness and performance of multiple classification approaches for traffic sign recognition. The review presents an extensive survey of the literature, encompassing traditional computer vision methods, machine learning algorithms, and the latest advancements in deep learning. The comparative analysis aims to identify the strengths and limitations of each technique, considering factors such as computational efficiency and accuracy. Additionally, the paper implements four models—CNN, ResNet50, VGG19, and EfficientNetB7—for traffic sign classification on the GTSRB dataset, the accuracy results are reported as 99.25%, 99.28%, 98.95%, and 98.24% respectively. [ABSTRACT FROM AUTHOR]

Among women breast cancer is the second leading cause of cancer. Emergence of Artificial Intelligence(AI) in the medical care leads to good survival rate by diagnosing and effective prognosis of the breast cancer patients. Scientific findings show that supervised deep learning model is highly dependent on the size of the training set, which must be manually labeled by experienced radiologists. The freely available biomedical imaging dataset are small in size mostly. In addition, obtaining large medical image files is difficult due to privacy and legal reasons. Thus the deep learning models tend to overfit and fail to provide a general result. Also the recent studies indicate that early detection of breast cancer (BC) is crucial to achieve favorable treatment results and reduce associated mortality. Data augmentation is the most widely used approach to address the aforementioned problem. Our proposed architecture consist of the pectoral muscle removal of the dataset, then the variational auto encoders used for data augmentation and then the U-Net and its varients used for breast cancer classification. The dice similarity score obtained for the model after the pectoral muscle segmentation is 97.53% and the classification rate accuracy is 98.5% by use of the Attention Residual U-Net than the other two models. [ABSTRACT FROM AUTHOR]

This paper identifies the model for classifying the bird sounds using the Convolutional Neural Network (CNN) algorithm. CNN are considered as one of the powerful toolkits developed in the machine learning and that have shown to be more efficient particularly in the field of image processing and sound recognition. The two-step verification process which will participating in the creation of datasets and neural network input. This system uses the collection of labelled and categorized recordings. Spectrograms are generated from the downloaded data which is used as the input for the neural network. The experiments are conducted and it will match with the different configurations, which including the number of bird species and Spectrograms. As a result, we can identify the bird species from its audio recordings. By identifying the bird species on a large scale, it provides a vital information about our ecosystems and biodiversity. The changes in the ecological environment can be reflected by the quantity and composition of bird species. It is one of the major challenges which leads to recognition of many bird species based on their audio. In modern years, the deep learning techniques namely convolutional neural networks (CNN) have played a major role in many areas of science and also successfully provided efficient methodologies, such as pattern recognition, object detection in images, and classification of patterns in audios and speech recognition. [ABSTRACT FROM AUTHOR]

Pulmonary Embolism (PE) is a blood clot in the pulmonary arteries of the lungs. Currently, Computerized Tomography Pulmonary Angiography (CTPA) scans are used to diagnose this condition. However, to manually locate the presence of a PE in the scan is laborious. In this paper, the detection of PE and prediction of its features like location, Right Ventricle to Left Ventricle (RV/LV) ratio, and chronicity are automated. First, for the detection of the embolisms, image features from the RSNA Pulmonary Embolism CT (RSPECT) dataset - containing over 12,000 CTPA studies, are extracted by several pre-trained Convolutional Neural Networks (CNN) - AlexNet, Inception V3, ResNet-18, and ResNet-50. Then, various Machine Learning (ML) classifiers are trained on these features and their performances are compared. Second, for the prediction of labels, multi-label classification is performed by training these networks with Binary Cross Entropy Logits (BCE Logits) loss. The results showed that for the detection task, k-NN classifier trained on ResNet-50 features achieved the highest sensitivity of 0.98 with 1.7 false positives per scan and an AUROC score of 97.4%. This system also improved on the state-of-the-art sensitivity score by 0.0975 and AUROC score by 3.4%. For multi-label classification, ResNet-18 performed comparatively better with the best validation loss of 0.07 and weighted macro-average ROC AUC score of 0.61. [ABSTRACT FROM AUTHOR]

To assess the performance of human observers and convolutional neural networks (CNNs) in detecting periodontal lesions in cone beam computed tomography (CBCT), a total of 38 datasets were examined. Three human readers and a CNN-based solution were employed to evaluate the presence of periodontal pathologies in these datasets. Datasets were acquired with a Veraview X800 L P (JMorita Mfg. Corp., Kyoto, Japan). Three general dentists, previously calibrated by a general principal investigator, read the datasets in 3D MPR mode using Horos(LGPL license at Horosproject.org and sponsored by Nimble Co LLC d/b/a Purview in Annapolis, MD, USA) as a DICOM reader. All pathological changes including vertical bone loss, furcation involvement, and periradicular osteolysis were detected. Furthermore, the same datasets were analyzed automatically by Diagnocat (Diagnocat LLC, Prague, Czech Republic), a deep CNN. Finally, the performance of the dentists and the CNN were compared and evaluated. The CNN's performance was significantly lower compared to the human readers in the search for different types of lesions. The human observers achieved good to very good interobserver agreement, except for the evaluation of the vertical lesions, which resulted in a moderate agreement. The CNN used in this study was found to be ineffective in identifying periodontal lesions and was not adequately trained to offer significant assistance in the automated evaluation of periodontal lesions in CBCT datasets. [ABSTRACT FROM AUTHOR]

• The deep learning model can better extract the deeper features of the multi-point deformation sequence. • MVMD algorithm reduces the complexity of multi-point displacement sequence. • The model considers the spatiotemporal characteristics of dam deformation, the prediction accuracy is obviously improved. It is difficult for single measuring point model to synchronously characterize the spatial correlation of deformation sequence of concrete dam and the similarity of displacements of different measuring points. Considering the limitation of conventional model in dealing with spatial-temporal data structure, In this study, a deep learning technique for multi-point and multi-output deformation prediction of concrete dams is proposed, which is based on Multivariate Variational Mode Decomposition (MVMD) and three-dimensional convolutional neural network (3D-CNN) combined model. Firstly, the multi-point sequence is decomposed into several relatively stable sub-sequences with different frequency scales by MVMD, which reduces the complexity of the multi-point displacement sequence. Then, In order to enhance the model 's ability to fit the time and spatial features of the sequence, the 3D-CNN model is used to predict the decomposed components and reconstruct the displacement data of the measuring points. This study focuses on the principle, process and calculation steps of the proposed model, and verifies the proposed model through the deformation data of three typical measuring points. The test results show that the prediction model proposed in this paper is an effective method for dam deformation prediction. [ABSTRACT FROM AUTHOR]

Orbit maneuver detection is one of the major concerns for the work of Space Situation Awareness of GEO protected region. In this study, we propose a non-cooperative GEO satellites station-keeping maneuver detection approach based on the two-dimensional Convolutional Neural Network (CNN) and the public Two-Line Element (TLE). The proposed method focuses on identifying "station-keeping categories" using the time series segments of TLE data. The time series TLE of the selected objects are converted into several equal-sized segments using a window slicing method. These segments are then used to train a two-dimensional CNN classification model that can divide the input data into three categories of maneuver. The applicability of the proposed method on satellites in the same orbital region were further tested using other objects that had not been included during the modeling process. Experimental results demonstrated the feasibility and robustness of the proposed method, with a classification macro F1-score of 94.45%, macro precision of 94.48%, and macro recall of 94.46%. The results suggest that the proposed method provides a promising solution for detecting GEO station-keeping maneuvers. [ABSTRACT FROM AUTHOR]

Recently, deep learning (DL) has become increasingly important in the field of image processing, and its application in dental radiology has become particularly relevant. DL-based image recognition has become a key component of image interpretation in dental radiology, making it possible to automate diagnosis and treatment planning. These techniques can be applied to areas such as caries, tooth detection, periodontal disease, oral lesions, age determination, gender estimation, and osteoporosis. In addition, recent studies have shown that DL algorithms in these areas have high accuracy, with some studies achieving accuracy rates of over 90%. Such high accuracy can significantly improve the efficiency and accuracy of dental diagnostics and treatment planning. Thus, using DL in dental imaging may become an everyday tool in dentistry with the current advances in applications. In this paper, recent applications and potential challenges of using deep convolutional neural network architectures in dental image analysis will be reviewed. [ABSTRACT FROM AUTHOR]

This research focuses on developing a Convolutional Neural Network (CNN)-based machine learning algorithm for detecting anomalies in Fused Deposition Modelling (FDM) 3D printers. To develop the CNN algorithm, the training data was obtained from the experiments. Therefore, process parameters such as; material flow, printing speed, and vibration of the print bed were adjusted to create infill pattern defects such as over-extrusion and layer shift (misalignment). A high-resolution camera was used to take the images during the printing. The developed CNN-based machine learning algorithm is capable of detecting and distinguishing types of defects providing a systematic quality assessment of a product. The main advantage is that it can be used to detect different types of defects related to infill patterns, and it can reduce the calibration error. Therefore, the application of machine learning in defect detection in 3D printing illustrates the prospects of integrating machine learning algorithms into manufacturing processes. [ABSTRACT FROM AUTHOR]

Recently, hyperspectral image (HSI) classification methods based on deep-learning have attracted widespread attention. Convolutional neural networks, as a crucial deep-learning technique, have exhibited outstanding performance in HSI classification. However, there are still some challenges, such as limited labeled samples, and feature extraction of complex land cover objects. To address these challenges, in this paper, we propose a multiscale cross-fusion network for HSI classification. It consists of three components: a spectral signatures extraction network, a spatial features extraction network and a classification network, which are utilized to extract spectral signatures, extract spatial contextual information and generate classification results, respectively. Specifically, the cross-branch multiscale convolutional block and the channel global contextual attention are integrated to extract spectral signatures, and the cross-hierarchy multiscale convolutional blocks and the spatial global contextual attention are combined to extract spatial features. Furthermore, special fusion strategies are proposed in these blocks to promote the interaction between features and achieve better feature connectivity. A series of experiments are conducted on three public HCI datasets, and the results show that the overall accuracy of the proposed network is 0.57%, 0.61%, and 0.3% higher than that of the state-of-the-art method on the PU, SV, and HH datasets, respectively. [ABSTRACT FROM AUTHOR]

Human pose estimation and action recognition have important applications in the areas of security, medicine and sports. In this work, we propose an improved YOLOv7-Pose algorithm to solve the problem of human posture and action recognition for fitness movements in various scenarios. For the algorithm based on YOLOv7-Pose, we add the function of classification to the original network. And we introduce Coordinate Attention to the network to improve the model's ability to identify important features of human skeletal joints and action classifications. The improved ConvNeXt network structure is introduced to replace the CBS convolution kernel of the original model to improve the accuracy of human key point detection and action classification of the model. We optimise the spatial pyramid pool structure, which can reduce the loss function and accelerate the convergence rate of the model. We adopt EIOU as the regression function of the target detection frame to improve the accuracy of the coordinate regression. Experiments show that the improved YOLOv7-Pose has an mAP of 95.9% on a homemade test set of fitness actions, which is 5.4% higher than HRNet, with a 4.2% improvement over the original YOLOv7 algorithm, suggesting that the accuracy of action recognition and key point estimation has significantly improved. [Display omitted] [ABSTRACT FROM AUTHOR]

Recently, anomaly detection in time-series data has received great attention from researchers due to its importance in problem-solving and predicting future system events. The majority of researchers combine convolutional neural networks (CNN) with image-encoding methods, including Gramian Angular Field (GAF) and Markov Transition Field (MTF). The selection of the right image-encoding method typically involves an iterative evaluation process that requires extensive computational time. This study proposes descriptive statistical analysis and Pearson correlation to find the correlation between statistical features in the encoded data from each image encoding and CNN accuracy. This information is essential for determining the image encoding method without image conversion processes and CNN trials, thus accelerating the computational time. The method tested on the initial dataset showed that the standard-deviation difference has the highest correlation with CNN accuracy (with a Pearson correlation coefficient of r = 0.944), followed by the mean difference (r = 0.705) and the count difference (r = 0.455), also resulting in a 95.35 % reduction in the computation time. The validity of this result was confirmed through the evaluation of several new datasets with different anomaly patterns, making this proposed approach suitable for selecting an appropriate image-encoding method. [ABSTRACT FROM AUTHOR]

Early detection and treatment of COVID-19 patients is crucial. Convolutional neural networks have been proven to accurately extract features in medical images, which accelerates time required for testing and increases the effectiveness of COVID-19 diagnosis. This study proposes two classification models for multiple chest diseases including COVID-19. The first is Stacking-ensemble model, which stacks six pretrained models including EfficientNetV2-B0, EfficientNetV2-B1, EfficientNetV2-B2, EfficientNetV2-B3, EfficientNetV2-S and EfficientNetV2-M. The second model is self-designed model ECA-EfficientNetV2 based on ECA-Net and EfficientNetV2. Ten-fold cross validation was performed for each model on chest X-ray and CT images. One more dataset, COVID-CT dataset, was tested to verify the performance of the proposed Stacking-ensemble and ECA-EfficientNetV2 models. The best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.21%, Precision of 99.23%, Recall of 99.25%, F1-score of 99.20%, and (area under the curve) AUC of 99.51% on chest X-ray dataset; the best performance comes from the proposed ECA-EfficientNetV2 model with the highest Accuracy of 99.81%, Precision of 99.80%, Recall of 99.80%, F1-score of 99.81%, and AUC of 99.87% on chest CT dataset. The differences for five metrics between Stacking-ensemble and ECA-EfficientNetV2 models are not significant. Ensemble model achieves better performance than single pretrained models. Compared to the SOTA, Stacking-ensemble and ECA-EfficientNetV2 models proposed in this study demonstrate promising performance on classification of multiple chest diseases including COVID-19. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) plays a crucial role in communication and the Internet of Things (IoT), by enabling vision sensors to understand and respond to human behavior more intelligently and efficiently. Existing deep learning models are complex to deal with the low illumination, diverse viewpoints, and cluttered backgrounds, which require substantial computing resources and are not appropriate for edge devices. Furthermore, without an effective video analysis technique it processes entire frames, resulting inadequate performance. To address these key challenges, a cloud-assisted IoT computing framework is proposed for HAR in uncertain low-lighting environments, which is mainly composed of two tiers: edge and cloud computing. Initially, a lightweight Convolutional Neural Network (CNN) model is developed which is responsible to enhance the low-light frames, followed by the human detection algorithm to process the selective frames, thus enabling efficient resource utilization. Next, these refined frames are then transmitted to the cloud for accurate HAR, where dual stream CNN and transformer fusion network extract both short-and long-range spatiotemporal discriminative features followed by proposed Optimized Parallel Sequential Temporal Network (OPSTN) with squeeze and excitation attention to efficiently learn HAR in complex scenarios. Finally, extensive experiments are conducted over three challenging HAR datasets to deeply examine the proposed framework from various perspectives such as complex activity recognition, lowlighting, etc., where the results are outperformed compared with the state-of-art methods. [ABSTRACT FROM AUTHOR]

The recognition of different activities in sports has gained attention in recent years for its applications in various athletic events, including soccer and cricket. Cricket, in particular, presents a challenging task for automatic activity recognition methods due to its closely overlapped activities such as cover drive, and pull short, to name a few. Existing methods often rely on hand-crafted features as the limited availability of public data has restricted the scope of research to only the significant categories of cricket activities. To this end, we proposed a cricket activities dataset and an intuitive end-to-end deep learning model for cricket activity recognition. The data is collected from online sources and pre-processed through cleaning, resizing, and organizing. Similarly, an intuitive deep model is designed with a combination of time-distributed 2D CNN layers and LSTM cells for extracting and learning the spatiotemporal information from the input sequences. For benchmarking, we evaluated the model on our cricket datasets and four standard datasets namely UCF101, HMDB51, YouTube action, and Kinetics. The quantitative results show that the proposed model outperforms different variants of recurrent neural networks and achieved an accuracy of 92%, recall of 91%, and F1 score of 91%. Our code and dataset is publicly available for further research on https://drive.google.com/file/d/1c9qcAz4q00qvx4yFA3pSudWFczm1cWUL/view?usp=sharing. [ABSTRACT FROM AUTHOR]

With the continuous development of social economy, the road traffic system as one of the basis of national economy is also increasingly developed, which is followed by the traffic safety problems are increasingly prominent. Among them, driving violations such as not obeying traffic signs are the main cause of traffic accidents. In this research, Traffic Sign Recognition (TSR) has become a hot research topic. The research status and application field of traffic sign detection and recognition technology are introduced and analyzed in detail, and the improvement of traffic sign database is introduced. After that, the application of CNN in image processing and traffic sign recognition is introduced, and the existing problems in traffic sign recognition are analyzed. The CNN deep learning network used in this paper can conduct training while identifying targets, effectively improve the recognition accuracy of traffic signs under complex natural conditions and reduce the time consumption. [ABSTRACT FROM AUTHOR]

Cephalometry is the study of skull measurements for clinical evaluation, diagnosis, and surgical planning. Machine learning (ML) algorithms have been used to accurately identify cephalometric landmarks and detect irregularities related to orthodontics and dentistry. ML-based cephalometric imaging reduces errors, improves accuracy, and saves time. In this study, we conducted a meta -analysis and systematic review to evaluate the accuracy of ML software for detecting and predicting anatomical landmarks on two-dimensional (2D) lateral cephalometric images. The meta -analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for selecting and screening research articles. The eligibility criteria were established based on the diagnostic accuracy and prediction of ML combined with 2D lateral cephalometric imagery. The search was conducted among English articles in five databases, and data were managed using Review Manager software (v. 5.0). Quality assessment was performed using the diagnostic accuracy studies (QUADAS-2) tool. Summary measurements included the mean departure from the 1–4-mm threshold or the percentage of landmarks identified within this threshold with a 95% confidence interval (CI). This meta -analysis included 21 of 577 articles initially collected on the accuracy of ML algorithms for detecting and predicting anatomical landmarks. The studies were conducted in various regions of the world, and 20 of the studies employed convolutional neural networks (CNNs) for detecting cephalometric landmarks. The pooled successful detection rates for the 1-mm, 2-mm, 2.5-mm, 3-mm, and 4-mm ranges were 65%, 81%, 86%, 91%, and 96%, respectively. Heterogeneity was determined using the random effect model. In conclusion, ML has shown promise for landmark detection in 2D cephalometric imagery, although the accuracy has varied among studies and clinicians. Consequently, more research is required to determine its effectiveness and reliability in clinical settings. [ABSTRACT FROM AUTHOR]

Although the parking lot is equipped with CCTV surveillance, car theft incidents still occur occasionally. These thieves exploit a loophole in the system that prevents the identification of the car's brand and model upon entering and exiting the parking lot using the same ticket. Therefore, the aim of this research is to develop a system capable of detecting the brand and model of cars. The study utilizes convolutional neural network technology and specifically focuses on the EfficientNet, MobileNet, and MobileNetV2 architectures due to their low resource requirements. Training images for the study are sourced from an online marketplace for used cars, with each of the 20 car brand and model variations consisting of 100 photographs. creates a model after being processed. For the prototype, the approach is put into practice through a mobile application. This study compares EfficientNet, MobileNetV1, and MobileNetV2 as the end result. EfficientNet scored 74%, MobileNetV1 74%, and MobileNetV2 66% on the test of accuracy. The accuracy increases to 80% after the EfficientNet has been tuned. [ABSTRACT FROM AUTHOR]

One of the most promising architectures for generative models is the variational autoencoder (VAE). To reconstruct Batik patterns for this work, we used a deep convolutional VAE architecture. Reconstruction outcomes from various batik motifs are mapped and contrasted using some criteria. As another crucial component of the convolutional network, batch normalization's impact on the model's performance was also examined. The dataset is used to study some learned latent space features. Through these findings, we laid the framework for next research on Batik generation utilizing VAE. [ABSTRACT FROM AUTHOR]

In image classification task, imbalanced dataset is a problem that often occurs. Batik pattern data also suffers this problem, mainly because of the poor quality of available images and rarity of certain patterns. In this research, we employed a novel ad- vanced augmentation and oversampling techniques on the imbalanced dataset to address this issue. This approach enhanced the diversity of the images, encompassing variations in color, contrast, wrinkles, and warps that may be present in batik garments. We employed two CNN models, DenseNet169 and VGG-16, along with three different training methods for our study. These methods included training without oversampling and advanced augmentation, training with oversampling, and training with both oversampling and advanced augmentation. The results showed that the best accuracy was achieved with DenseNet169 with our oversampled and augmented dataset, with an accuracy of 84.62%. Additionally, VGG-16 also performed well on said dataset, achieving an accuracy of 82.56%.Our results suggested that by using our oversampling & advanced augmentation on the dataset,the model performance improved compared to plain data and oversampled data. [ABSTRACT FROM AUTHOR]

Age estimation from facial images is a challenging topic in computer vision since it can automatically label the human face with an exact age according to various physical or biological characteristics, such as facial structure, spots, and wrinkles. Additionally, it has substantial applications in many fields, including healthcare, security, entertainment, and education. There are a lot of techniques to estimate age, but the most popular one is the convolutional neural network (CNN), which offers high accuracy but needs a lot of training time and much more labeled data to achieve it. Another popular technique is transfer learning with feature extraction, which can provide higher accuracy with faster training time because it uses a pre-trained model rather than building one from scratch but has limited flexibility and a substantial risk of overfitting. Due to the advantages and disadvantages of both methods, this study analyzes and compares the effectiveness of transfer learning and CNN methods for age estimation from facial images. Using R-squared and RMSE as test metrics, the results show that the CNN method offers a better process and accuracy than the transfer learning method, with a higher R-squared value and a lower RMSE value. [ABSTRACT FROM AUTHOR]