{"results":[{"id":"doaj_10.1002/eng2.70686","title":"Machine Learning–Based Wear Prediction of Recycled Magnesium Matrix Composites Reinforced With Ceramic Fibers","authors":[{"name":"Meenakshi Sudarvizhi Seenipeyathevar"},{"name":"Prasath Palaniappan"},{"name":"Vijayakumar Arumugam"},{"name":"Vivek Sivakumar"},{"name":"Muthupriya Ponnuswamy"},{"name":"Shyamala Guruswamy"},{"name":"Ravindaran Thangavel"},{"name":"Franz Tette Okyere"}],"abstract":"ABSTRACT This study deals with an integrated experimental‐machine learning framework for wear estimation in functionally graded composites made from recycled magnesium machining chips, using low‐cost ceramic fibers as reinforcement with the radial Modeling technique. The primary hurdle that is being addressed is the accurate prediction of wear behavior in spatially graded magnesium matrix composites, while simultaneously avoiding extensive experimental testing. Under varying degrees of applied loads (4.4 to 39 N), sliding speeds (0.45 to 4.5 m/s), and sliding distances (500 to 4500 m), the wear performance was experimentally assessed. Results demonstrate a hardness increment of 26.26% in the outer region compared to the inner region, while resistance to wear was enhanced by 19.8% in the outer zone due to the grading of ceramic fibers. A limited experimental dataset consisting of wear measurements from the inner, middle, and outer zones of the composite was utilized in developing and validating four machine‐learning models for wear rate prediction. The tree‐based ensemble methods significantly outperformed deep‐learning strategies, with the LightGBM model providing the best prediction performance across all zones and achieving optimization with a maximum tree depth of 5, 480 leaves, and a feature fraction of 0.05. Moreover, zone‐specific XGBoost models were also developed, employing customized learning rates and minimal loss reduction parameters in order to elevate prediction accuracy. The proposed machine‐learning framework thus provides a pathway for rapid and reliable wear rate estimation for ceramic fiber‐reinforced magnesium composites, significantly lessening experimental burden. Results highlight that recycled magnesium waste, when combined with ceramic reinforcement, can be effectively employed to produce sustainable and economically viable materials with improved wear resistance, particularly for automotive and industrial applications.","source":"DOAJ","year":2026,"language":"","subjects":["Engineering (General). Civil engineering (General)","Electronic computers. Computer science"],"doi":"10.1002/eng2.70686","url":"https://doi.org/10.1002/eng2.70686","is_open_access":true,"published_at":"","score":70},{"id":"doaj_10.1038/s41598-026-36031-1","title":"A multi-branch network for cooperative spectrum sensing via attention-based and CNN feature fusion","authors":[{"name":"Doi Thi Lan"},{"name":"Quan T. Ngo"},{"name":"Luong Vuong Nguyen"},{"name":"O-Joun Lee"}],"abstract":"Abstract In cognitive radio (CR) systems, the accurate detection of spectrum holes is a cornerstone for efficient spectrum utilization. However, the increasing complexity of CR environments, particularly those with multiple primary users (PUs), has made precise spectrum sensing a paramount challenge. To address this challenge, this study introduces the ATC model, a novel deep learning architecture that integrates a parallel combination of attention mechanism-based networks and a Convolutional Neural Network (CNN). This hybrid design enables the model to capture both spatial and temporal features from the distinct statistics of sensing signals, thereby enhancing the accuracy of spectrum state detection. The model employs a Graph Attention Network (GAT) to extract complex topological features from graph-structured data derived from received signal strength, dynamically highlighting the most relevant information. To complement this, a CNN processes the sample covariance matrix of sensing signals, unlocking localized statistical correlations and hierarchical feature representations by treating the matrix as an image. Temporal dynamics, such as PU activity patterns, are modeled using a Transformer encoder, which leverages a self-attention mechanism to learn sequential features effectively. The proposed model is evaluated using both simulated and real-world datasets. For the simulated datasets, the model is assessed and compared with baseline methods under multi-PU scenarios across different channel models. For the real-world dataset, the experimental setup is configured for a single-PU scenario due to practical data collection limitations. In both cases, the ATC model demonstrates improved performance over the benchmarked spectrum sensing methods, exhibiting higher accuracy and robustness within the respective evaluation settings.","source":"DOAJ","year":2026,"language":"","subjects":["Medicine","Science"],"doi":"10.1038/s41598-026-36031-1","url":"https://doi.org/10.1038/s41598-026-36031-1","is_open_access":true,"published_at":"","score":70},{"id":"arxiv_2603.29261","title":"Monodense Deep Neural Model for Determining Item Price Elasticity","authors":[{"name":"Lakshya Garg"},{"name":"Sai Yaswanth"},{"name":"Deep Narayan Mishra"},{"name":"Karthik Kumaran"},{"name":"Anupriya Sharma"},{"name":"Mayank Uniyal"}],"abstract":"Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newly proposed Monodense deep neural network.   (1) Monodense-DL network -- Hybrid neural network architecture combining embedding, dense, and Monodense layers (2) DML -- Double machine learning setting using regression models (3) LGBM -- Light Gradient Boosting Model   We evaluate our model on multi-category retail data spanning millions of transactions using a back testing framework. Experimental results demonstrate the superiority of our proposed neural network model within the framework compared to other prevalent ML based methods listed above.","source":"arXiv","year":2026,"language":"en","subjects":["cs.LG","cs.AI"],"url":"https://arxiv.org/abs/2603.29261","pdf_url":"https://arxiv.org/pdf/2603.29261","is_open_access":true,"published_at":"2026-03-31T04:50:51Z","score":70},{"id":"doaj_10.1016/j.eng.2024.10.021","title":"LearningEMS: A Unified Framework and Open-Source Benchmark for Learning-Based Energy Management of Electric Vehicles","authors":[{"name":"Yong Wang"},{"name":"Hongwen He"},{"name":"Yuankai Wu"},{"name":"Pei Wang"},{"name":"Haoyu Wang"},{"name":"Renzong Lian"},{"name":"Jingda Wu"},{"name":"Qin Li"},{"name":"Xiangfei Meng"},{"name":"Yingjuan Tang"},{"name":"Fengchun Sun"},{"name":"Amir Khajepour"}],"abstract":"An effective energy management strategy (EMS) is essential to optimize the energy efficiency of electric vehicles (EVs). With the advent of advanced machine learning techniques, the focus on developing sophisticated EMS for EVs is increasing. Here, we introduce LearningEMS: a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS. LearningEMS is distinguished by its ability to support a variety of EV configurations, including hybrid EVs, fuel cell EVs, and plug-in EVs, offering a general platform for the development of EMS. The framework enables detailed comparisons of several EMS algorithms, encompassing imitation learning, deep reinforcement learning (RL), offline RL, model predictive control, and dynamic programming. We rigorously evaluated these algorithms across multiple perspectives: energy efficiency, consistency, adaptability, and practicability. Furthermore, we discuss state, reward, and action settings for RL in EV energy management, introduce a policy extraction and reconstruction method for learning-based EMS deployment, and conduct hardware-in-the-loop experiments. In summary, we offer a unified and comprehensive framework that comes with three distinct EV platforms, over 10  000 km of EMS policy data set, ten state-of-the-art algorithms, and over 160 benchmark tasks, along with three learning libraries. Its flexible design allows easy expansion for additional tasks and applications. The open-source algorithms, models, data sets, and deployment processes foster additional research and innovation in EV and broader engineering domains.","source":"DOAJ","year":2025,"language":"","subjects":["Engineering (General). Civil engineering (General)"],"doi":"10.1016/j.eng.2024.10.021","url":"http://www.sciencedirect.com/science/article/pii/S2095809924007136","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.3390/info17010009","title":"Evaluating Model Resilience to Data Poisoning Attacks: A Comparative Study","authors":[{"name":"Ifiok Udoidiok"},{"name":"Fuhao Li"},{"name":"Jielun Zhang"}],"abstract":"Machine learning (ML) has become a cornerstone of critical applications, but its vulnerability to data poisoning attacks threatens system reliability and trustworthiness. Prior studies have begun to investigate the impact of data poisoning and proposed various defense or evaluation methods; however, most efforts remain limited to quantifying performance degradation, with little systematic comparison of internal behaviors across model architectures under attack and insufficient attention to interpretability for revealing model vulnerabilities. To tackle this issue, we build a reproducible evaluation pipeline and emphasize the importance of integrating robustness with interpretability in the design of secure and trustworthy ML systems. To be specific, we propose a unified poisoning evaluation framework that systematically compares traditional ML models, deep neural networks, and large language models under three representative attack strategies including label flipping, random corruption, and adversarial insertion, at escalating severity levels of 30%, 50%, and 75%, and integrate LIME-based explanations to trace the evolution of model reasoning. Experimental results demonstrate that traditional models collapse rapidly under label noise, whereas Bayesian LSTM hybrids and large language models maintain stronger resilience. Further interpretability analysis uncovers attribution failure patterns, such as over-reliance on neutral tokens or misinterpretation of adversarial cues, providing insights beyond accuracy metrics.","source":"DOAJ","year":2025,"language":"","subjects":["Information technology"],"doi":"10.3390/info17010009","url":"https://www.mdpi.com/2078-2489/17/1/9","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.3389/fpsyt.2024.1538534","title":"Editorial: Deep learning for high-dimensional sense, non-linear signal processing and intelligent diagnosis","authors":[{"name":"Hengjin Ke"},{"name":"Cang Cai"},{"name":"Jia Wu"},{"name":"Dan Chen"}],"abstract":"","source":"DOAJ","year":2025,"language":"","subjects":["Psychiatry"],"doi":"10.3389/fpsyt.2024.1538534","url":"https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1538534/full","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.1186/s12880-025-01643-y","title":"Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery","authors":[{"name":"Hemalatha Kanakarajan"},{"name":"Wouter De Baene"},{"name":"Patrick Hanssens"},{"name":"Margriet Sitskoorn"}],"abstract":"Abstract Background and purpose Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists that could be optimized with deep learning (DL). Previous studies assessed several DL algorithms focusing only on training and testing the models on the planning MRI only. The purpose of this study is to evaluate well-known DL approaches (nnU-Net and MedNeXt) for their performance on both planning and follow-up MRI. Materials and methods Pre-treatment brain MRIs were retrospectively collected for 255 patients at Elisabeth-TweeSteden Hospital (ETZ): 201 for training and 54 for testing, including follow-up MRIs for the test set. To increase heterogeneity, we added the publicly available MRI scans from the Mathematical oncology laboratory of 75 patients to the training data. The performance was compared between the two models, with and without the addition of the public data. To statistically compare the Dice Similarity Coefficient (DSC) of the two models trained on different datasets over multiple time points, we used Linear Mixed Models. Results All models obtained a good DSC (DSC \u003e = 0.93) for planning MRI. MedNeXt trained with combined data provided the best DSC for follow-ups at 6, 15, and 21 months (DSC of 0.74, 0.74, and 0.70 respectively) and jointly the best DSC for follow-ups at three months with MedNeXt trained with ETZ data only (DSC of 0.78) and 12 months with nnU-Net trained with combined data (DSC of 0.71). On the other hand, nnU-Net trained with combined data provided the best sensitivity and FNR for most follow-ups. The statistical analysis showed that MedNeXt provides higher DSC for both datasets and the addition of public data to the training dataset results in a statistically significant increase in performance in both models. Conclusion The models achieved a good performance score for planning MRI. Though the models performed less effectively for follow-ups, the addition of public data enhanced their performance, providing a viable solution to improve their efficacy for the follow-ups. These algorithms hold promise as a valuable tool for clinicians for automated segmentation of planning and follow-up MRI scans during stereotactic radiosurgery treatment planning and response evaluations, respectively. Clinical trial number Not applicable.","source":"DOAJ","year":2025,"language":"","subjects":["Medical technology"],"doi":"10.1186/s12880-025-01643-y","url":"https://doi.org/10.1186/s12880-025-01643-y","is_open_access":true,"published_at":"","score":69},{"id":"arxiv_2512.23753","title":"Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation","authors":[{"name":"Deep Shankar Pandey"},{"name":"Hyomin Choi"},{"name":"Qi Yu"}],"abstract":"Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence. However, the Subjective-Logic framework constrains evidence to be non-negative, requiring specific activation functions whose geometric properties can induce activation-dependent learning-freeze behavior: a regime where gradients become extremely small for samples mapped into low-evidence regions. We theoretically characterize this behavior and analyze how different evidential activations influence learning dynamics. Building on this analysis, we design a general family of activation functions and corresponding evidential regularizers that provide an alternative pathway for consistent evidence updates across activation regimes. Extensive experiments on four benchmark classification problems (MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet), two few-shot classification problems, and blind face restoration problem empirically validate the developed theory and demonstrate the effectiveness of the proposed generalized regularized evidential models.","source":"arXiv","year":2025,"language":"en","subjects":["cs.LG","cs.AI"],"url":"https://arxiv.org/abs/2512.23753","pdf_url":"https://arxiv.org/pdf/2512.23753","is_open_access":true,"published_at":"2025-12-27T11:26:18Z","score":69},{"id":"doaj_10.3390/app14041386","title":"Integrating Merkle Trees with Transformer Networks for Secure Financial Computation","authors":[{"name":"Xinyue Wang"},{"name":"Weifan Lin"},{"name":"Weiting Zhang"},{"name":"Yiwen Huang"},{"name":"Zeyu Li"},{"name":"Qian Liu"},{"name":"Xinze Yang"},{"name":"Yifan Yao"},{"name":"Chunli Lv"}],"abstract":"In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing.","source":"DOAJ","year":2024,"language":"","subjects":["Technology","Engineering (General). Civil engineering (General)","Biology (General)","Physics","Chemistry"],"doi":"10.3390/app14041386","url":"https://www.mdpi.com/2076-3417/14/4/1386","is_open_access":true,"published_at":"","score":68},{"id":"doaj_10.3390/drones8070341","title":"UAV-Embedded Sensors and Deep Learning for Pathology Identification in Building Façades: A Review","authors":[{"name":"Gabriel de Sousa Meira"},{"name":"João Victor Ferreira Guedes"},{"name":"Edilson de Souza Bias"}],"abstract":"The use of geotechnologies in the field of diagnostic engineering has become ever more present in the identification of pathological manifestations in buildings. The implementation of Unmanned Aerial Vehicles (UAVs) and embedded sensors has stimulated the search for new data processing and validation methods, considering the magnitude of the data collected during fieldwork and the absence of specific methodologies for each type of sensor. Regarding data processing, the use of deep learning techniques has become widespread, especially for the automation of processes that involve a great amount of data. However, just as with the increasing use of embedded sensors, deep learning necessitates the development of studies, particularly those focusing on neural networks that better represent the data to be analyzed. It also requires the enhancement of practices to be used in fieldwork, especially regarding data processing. In this context, the objective of this study is to review the existing literature on the use of embedded technologies in UAVs and deep learning for the identification and characterization of pathological manifestations present in building façades in order to develop a robust knowledge base that is capable of contributing to new investigations in this field of research.","source":"DOAJ","year":2024,"language":"","subjects":["Motor vehicles. Aeronautics. Astronautics"],"doi":"10.3390/drones8070341","url":"https://www.mdpi.com/2504-446X/8/7/341","is_open_access":true,"published_at":"","score":68},{"id":"arxiv_2403.12562","title":"PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis","authors":[{"name":"Raghavendra Selvan"},{"name":"Bob Pepin"},{"name":"Christian Igel"},{"name":"Gabrielle Samuel"},{"name":"Erik B Dam"}],"abstract":"The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry barrier to researchers and practitioners with limited access to resources at such scale, particularly in the Global South. In this work, we take a comprehensive look at the landscape of existing DL models for medical image analysis tasks and demonstrate their usefulness in settings where resources are limited. To account for the resource consumption of DL models, we introduce a novel measure to estimate the performance per resource unit, which we call the PePR score. Using a diverse family of 131 unique DL architectures (spanning 1M to 130M trainable parameters) and three medical image datasets, we capture trends about the performance-resource trade-offs. In applications like medical image analysis, we argue that small-scale, specialized models are better than striving for large-scale models. Furthermore, we show that using existing pretrained models that are fine-tuned on new data can significantly reduce the computational resources and data required compared to training models from scratch. We hope this work will encourage the community to focus on improving AI equity by developing methods and models with smaller resource footprints.","source":"arXiv","year":2024,"language":"en","subjects":["cs.LG","cs.AI","stat.ML"],"url":"https://arxiv.org/abs/2403.12562","pdf_url":"https://arxiv.org/pdf/2403.12562","is_open_access":true,"published_at":"2024-03-19T09:17:18Z","score":68},{"id":"arxiv_2412.03084","title":"Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H\u0026E stained liver histopathology images","authors":[{"name":"Ajinkya Deshpande"},{"name":"Deep Gupta"},{"name":"Ankit Bhurane"},{"name":"Nisha Meshram"},{"name":"Sneha Singh"},{"name":"Petia Radeva"}],"abstract":"Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge, mainly due to the manual assessment of hematoxylin and eosin-stained whole slide images, which is a time-consuming process and may lead to variability in decision-making. For accurate detection of HCC, we propose a hybrid deep learning-based architecture that uses transfer learning to extract the features from pre-trained convolutional neural network (CNN) models and a classifier made up of a sequence of fully connected layers. This study uses a publicly available The Cancer Genome Atlas Hepatocellular Carcinoma (TCGA-LIHC)database (n=491) for model development and database of Kasturba Gandhi Medical College (KMC), India for validation. The pre-processing step involves patch extraction, colour normalization, and augmentation that results in 3920 patches for the TCGA dataset. The developed hybrid deep neural network consisting of a CNN-based pre-trained feature extractor and a customized artificial neural network-based classifier is trained using five-fold cross-validation. For this study, eight different state-of-the-art models are trained and tested as feature extractors for the proposed hybrid model. The proposed hybrid model with ResNet50-based feature extractor provided the sensitivity, specificity, F1-score, accuracy, and AUC of 100.00%, 100.00%, 100.00%, 100.00%, and 1.00, respectively on the TCGA database. On the KMC database, EfficientNetb3 resulted in the optimal choice of the feature extractor giving sensitivity, specificity, F1-score, accuracy, and AUC of 96.97, 98.85, 96.71, 96.71, and 0.99, respectively. The proposed hybrid models showed improvement in accuracy of 2% and 4% over the pre-trained models in TCGA-LIHC and KMC databases.","source":"arXiv","year":2024,"language":"en","subjects":["eess.IV","cs.CV","cs.LG","q-bio.QM"],"url":"https://arxiv.org/abs/2412.03084","pdf_url":"https://arxiv.org/pdf/2412.03084","is_open_access":true,"published_at":"2024-12-04T07:26:36Z","score":68},{"id":"doaj_10.1038/s41598-023-38896-y","title":"Prediction of stability coefficient of open-pit mine slope based on artificial intelligence deep learning algorithm","authors":[{"name":"Shuai Wang"},{"name":"Zongbao Zhang"},{"name":"Chao Wang"}],"abstract":"Abstract The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China’s large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development.","source":"DOAJ","year":2023,"language":"","subjects":["Medicine","Science"],"doi":"10.1038/s41598-023-38896-y","url":"https://doi.org/10.1038/s41598-023-38896-y","is_open_access":true,"published_at":"","score":67},{"id":"doaj_10.1109/ACCESS.2023.3271748","title":"Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey","authors":[{"name":"Michela Prunella"},{"name":"Roberto Maria Scardigno"},{"name":"Domenico Buongiorno"},{"name":"Antonio Brunetti"},{"name":"Nicola Longo"},{"name":"Raffaele Carli"},{"name":"Mariagrazia Dotoli"},{"name":"Vitoantonio Bevilacqua"}],"abstract":"Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.","source":"DOAJ","year":2023,"language":"","subjects":["Electrical engineering. Electronics. Nuclear engineering"],"doi":"10.1109/ACCESS.2023.3271748","url":"https://ieeexplore.ieee.org/document/10113226/","is_open_access":true,"published_at":"","score":67},{"id":"arxiv_2306.11113","title":"Learn to Accumulate Evidence from All Training Samples: Theory and Practice","authors":[{"name":"Deep Pandey"},{"name":"Qi Yu"}],"abstract":"Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. The resultant evidential models can quantify fine-grained uncertainty using the learned evidence. To ensure theoretically sound evidential models, the evidence needs to be non-negative, which requires special activation functions for model training and inference. This constraint often leads to inferior predictive performance compared to standard softmax models, making it challenging to extend them to many large-scale datasets. To unveil the real cause of this undesired behavior, we theoretically investigate evidential models and identify a fundamental limitation that explains the inferior performance: existing evidential activation functions create zero evidence regions, which prevent the model to learn from training samples falling into such regions. A deeper analysis of evidential activation functions based on our theoretical underpinning inspires the design of a novel regularizer that effectively alleviates this fundamental limitation. Extensive experiments over many challenging real-world datasets and settings confirm our theoretical findings and demonstrate the effectiveness of our proposed approach.","source":"arXiv","year":2023,"language":"en","subjects":["cs.LG","cs.AI","cs.CV"],"url":"https://arxiv.org/abs/2306.11113","pdf_url":"https://arxiv.org/pdf/2306.11113","is_open_access":true,"published_at":"2023-06-19T18:27:12Z","score":67},{"id":"arxiv_2301.00942","title":"Deep Learning and Computational Physics (Lecture Notes)","authors":[{"name":"Deep Ray"},{"name":"Orazio Pinti"},{"name":"Assad A. Oberai"}],"abstract":"These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics.   The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools.","source":"arXiv","year":2023,"language":"en","subjects":["cs.LG","math-ph"],"url":"https://arxiv.org/abs/2301.00942","pdf_url":"https://arxiv.org/pdf/2301.00942","is_open_access":true,"published_at":"2023-01-03T03:56:19Z","score":67},{"id":"arxiv_2302.04143","title":"Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models","authors":[{"name":"Haoyue Zhang"},{"name":"Jennifer S. Polson"},{"name":"Eric J. Yang"},{"name":"Kambiz Nael"},{"name":"William Speier"},{"name":"Corey W. Arnold"}],"abstract":"For acute ischemic stroke (AIS) patients with large vessel occlusions, clinicians must decide if the benefit of mechanical thrombectomy (MTB) outweighs the risks and potential complications following an invasive procedure. Pre-treatment computed tomography (CT) and angiography (CTA) are widely used to characterize occlusions in the brain vasculature. If a patient is deemed eligible, a modified treatment in cerebral ischemia (mTICI) score will be used to grade how well blood flow is reestablished throughout and following the MTB procedure. An estimation of the likelihood of successful recanalization can support treatment decision-making. In this study, we proposed a fully automated prediction of a patient's recanalization score using pre-treatment CT and CTA imaging. We designed a spatial cross attention network (SCANet) that utilizes vision transformers to localize to pertinent slices and brain regions. Our top model achieved an average cross-validated ROC-AUC of 77.33 $\\pm$ 3.9\\%. This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.","source":"arXiv","year":2023,"language":"en","subjects":["eess.IV","cs.CV"],"url":"https://arxiv.org/abs/2302.04143","pdf_url":"https://arxiv.org/pdf/2302.04143","is_open_access":true,"published_at":"2023-02-08T15:41:21Z","score":67},{"id":"crossref_10.1017/9781108891530.017","title":"Deep Reinforcement Learning","authors":null,"abstract":"","source":"CrossRef","year":2022,"language":"en","subjects":null,"doi":"10.1017/9781108891530.017","url":"https://doi.org/10.1017/9781108891530.017","is_open_access":true,"citations":2,"published_at":"","score":66.06},{"id":"doaj_10.3390/s22041663","title":"Evaluation of 3D Vulnerable Objects’ Detection Using a Multi-Sensors System for Autonomous Vehicles","authors":[{"name":"Esraa Khatab"},{"name":"Ahmed Onsy"},{"name":"Ahmed Abouelfarag"}],"abstract":"One of the primary tasks undertaken by autonomous vehicles (AVs) is object detection, which comes ahead of object tracking, trajectory estimation, and collision avoidance. Vulnerable road objects (e.g., pedestrians, cyclists, etc.) pose a greater challenge to the reliability of object detection operations due to their continuously changing behavior. The majority of commercially available AVs, and research into them, depends on employing expensive sensors. However, this hinders the development of further research on the operations of AVs. In this paper, therefore, we focus on the use of a lower-cost single-beam LiDAR in addition to a monocular camera to achieve multiple 3D vulnerable object detection in real driving scenarios, all the while maintaining real-time performance. This research also addresses the problems faced during object detection, such as the complex interaction between objects where occlusion and truncation occur, and the dynamic changes in the perspective and scale of bounding boxes. The video-processing module works upon a deep-learning detector (YOLOv3), while the LiDAR measurements are pre-processed and grouped into clusters. The output of the proposed system is objects classification and localization by having bounding boxes accompanied by a third depth dimension acquired by the LiDAR. Real-time tests show that the system can efficiently detect the 3D location of vulnerable objects in real-time scenarios.","source":"DOAJ","year":2022,"language":"","subjects":["Chemical technology"],"doi":"10.3390/s22041663","url":"https://www.mdpi.com/1424-8220/22/4/1663","is_open_access":true,"published_at":"","score":66},{"id":"crossref_10.1017/9781108955652.016","title":"Reinforcement Learning and Deep Reinforcement Learning","authors":null,"abstract":"","source":"CrossRef","year":2021,"language":"en","subjects":null,"doi":"10.1017/9781108955652.016","url":"https://doi.org/10.1017/9781108955652.016","is_open_access":true,"citations":4,"published_at":"","score":65.12}],"total":3042368,"page":1,"page_size":20,"sources":["DOAJ","arXiv","CrossRef"],"query":"deep learning"}