Compact representation and identification of important regions of metal microstructures using complex-step convolutional autoencoders

Arumugam, Dharanidharan; Kiran, Ravi

doi:10.1016/j.matdes.2022.111236

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…The dataset analyzed contained 67,000 unique quaternary oxides and more than 80,000 unique quinary oxide compositions, making it the largest set of experimental materials utilized in machine learning investigations. Arumugam and Jiran [132] proposed the use of complex-step convolutional auto-encoders (Figure 17) to identify regions of importance in metal microstructures for secure sharing. The ANN developed was capable of reconstructing the original microstructural images from just 3.5% of the original data.…”

Section: Auto-encoders (Aes)mentioning

confidence: 99%

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Kibrete,

Trzepieciński,

Gebremedhen

et al. 2023

J. Compos. Sci.

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The determination of mechanical properties plays a crucial role in utilizing composite materials across multiple engineering disciplines. Recently, there has been substantial interest in employing artificial intelligence, particularly machine learning and deep learning, to accurately predict the mechanical properties of composite materials. This comprehensive review paper examines the applications of artificial intelligence in forecasting the mechanical properties of different types of composites. The review begins with an overview of artificial intelligence and then outlines the process of predicting material properties. The primary focus of this review lies in exploring various machine learning and deep learning techniques employed in predicting the mechanical properties of composites. Furthermore, the review highlights the theoretical foundations, strengths, and weaknesses of each method used for predicting different mechanical properties of composites. Finally, based on the findings, the review discusses key challenges and suggests future research directions in the field of material properties prediction, offering valuable insights for further exploration. This review is intended to serve as a significant reference for researchers engaging in future studies within this domain.

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Section: Auto-encoders (Aes)mentioning

confidence: 99%

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Kibrete,

Trzepieciński,

Gebremedhen

et al. 2023

J. Compos. Sci.

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“…In this work, the CAE model is utilized for the identification and classification of vehicles. As a variant of AE, CAE integrates the capability of autoencoder (AE) to represent the input dataset and the capability of CNNs to effectively extract image features [23]. An encoder and decoder are two NN blocks used to recreate the input.…”

Section: Vehicle Classification: Cae Modelmentioning

confidence: 99%

Improved Deep Learning-Based Vehicle Detection for Urban Applications Using Remote Sensing Imagery

Ragab,

Abdushkour,

Khadidos

et al. 2023

Remote Sensing

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Remote sensing (RS) data can be attained from different sources, such as drones, satellites, aerial platforms, or street-level cameras. Each source has its own characteristics, including the spectral bands, spatial resolution, and temporal coverage, which may affect the performance of the vehicle detection algorithm. Vehicle detection for urban applications using remote sensing imagery (RSI) is a difficult but significant task with many real-time applications. Due to its potential in different sectors, including traffic management, urban planning, environmental monitoring, and defense, the detection of vehicles from RS data, such as aerial or satellite imagery, has received greater emphasis. Machine learning (ML), especially deep learning (DL), has proven to be effective in vehicle detection tasks. A convolutional neural network (CNN) is widely utilized to detect vehicles and automatically learn features from the input images. This study develops the Improved Deep Learning-Based Vehicle Detection for Urban Applications using Remote Sensing Imagery (IDLVD-UARSI) technique. The major aim of the IDLVD-UARSI method emphasizes the recognition and classification of vehicle targets on RSI using a hyperparameter-tuned DL model. To achieve this, the IDLVD-UARSI algorithm utilizes an improved RefineDet model for the vehicle detection and classification process. Once the vehicles are detected, the classification process takes place using the convolutional autoencoder (CAE) model. Finally, a Quantum-Based Dwarf Mongoose Optimization (QDMO) algorithm is applied to ensure an optimal hyperparameter tuning process, demonstrating the novelty of the work. The simulation results of the IDLVD-UARSI technique are obtained on a benchmark vehicle database. The simulation values indicate that the IDLVD-UARSI technique outperforms the other recent DL models, with maximum accuracy of 97.89% and 98.69% on the VEDAI and ISPRS Potsdam databases, respectively.

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“…For crop type classification, the CAE model is used. CAE is a specific kind of autoencoder (AE) that integrates the capability of CNNs to effectively extract image features and the capability of AE to represent the input dataset [21]. In the DODTL-CTDC approach, the CAE roles a vital play in crop classification.…”

Section: B Image Classificationmentioning

confidence: 99%

Exploiting Hyperspectral Imaging and Optimal Deep Learning for Crop Type Detection and Classification

Alajmi,

Mengash,

Eltahir

et al. 2023

IEEE Access

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Hyperspectral imaging (HSI) plays a major role in agricultural remote sensing applications. Its data unit is the hyperspectral cube that contains spatial data in 2D but spectral band data of all the pixels in 3D. The classification accuracy of HSI was significantly enhanced by deploying either spatial or spectral features. HSIs are developed as a significant approach to achieve growth data monitoring and distinguish crop classes for precision agriculture, based on the reasonable spectral response to the crop features. The latest developments in deep learning (DL) and computer vision (CV) approaches permit the effectual detection and classification of distinct crop varieties on HSIs. At the same time, the hyperparameter tuning process plays a vital role in accomplishing effectual classification performance. The study introduces a dandelion optimizer with deep transfer learning-based crop type detection and classification (DODTL-CTDC) technique on HSI. The DODTL-CTDC technique makes use of the Xception model for the extraction of features from the HSI. In addition, the hyperparameter selection of the Xception model takes place using the DO algorithm. Moreover, the convolutional autoencoder (CAE) model is applied for the classification of crops into distinct classes. Furthermore, an arithmetic optimization algorithm (AOA) can be employed for optimal hyperparameter selection of the CAE model. The performance analysis of the DODTL-CTDC technique is assessed on the benchmark data set. The experimental outcomes demonstrate the betterment of the DODTL-CTDC method in the crop classification process.

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Compact representation and identification of important regions of metal microstructures using complex-step convolutional autoencoders

Cited by 6 publications

References 46 publications

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Artificial Intelligence in Predicting Mechanical Properties of Composite Materials

Improved Deep Learning-Based Vehicle Detection for Urban Applications Using Remote Sensing Imagery

Exploiting Hyperspectral Imaging and Optimal Deep Learning for Crop Type Detection and Classification

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