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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

The existing machine learning (ML) models for vibration-based damage assessments often use highly compressed features or are restricted to preprocessed signals with fixed durations and sampling rates. Additionally, the learning capacities of ML models and computational resources are limited, which restricts using raw signals as direct input. This paper studied Mel filter banks (MFBs) for seismic signal processing, inspired by speech recognition technology. It is argued that the same filter designs in audio engineering may not be appropriate for seismic records, and therefore, a customized filter bank formulation was developed. Hybrid deep learning models for rapid assessments (HyDRA) were introduced as multibranch neural network architectures that enable end-to-end training for different types of processed vibration data structures. Moreover, the performance-based earthquake engineering (PBEE) equation was adjusted to integrate ML model uncertainties for probabilistic assessments. The proposed concepts were validated in a case study based on a data set of 32,400 nonlinear time-history analyses of a highway bridge in California. Insights and guidelines are provided for optimum filter design based on 5,184 experiments. Several HyDRA architectures were compared with benchmark models. The optimized MFB feature type outperformed features obtained from continuous wavelet transform and a stacked vector of conventional earthquake engineering indexes. A Bayesian variant of HyDRA was investigated to showcase its integration in the modified PBEE equation. Adopting custom filter banks with the HyDRA architecture enables effective feature extraction from raw vibration records by diversifying feature space and preserving information in the time and frequency domains. [ABSTRACT FROM AUTHOR]

As an effective way to ensure the economic benefits of enterprises, process operating performance assessment has attracted more and more attention from industry and academia in recent years. In this paper, a stacked performance‐relevant enhanced denoising autoencoder (SPEDAE) network is designed for the operating performance assessment of industrial processes. Compared to the original denoising auto‐encoder (DAE), each performance‐relevant enhanced denoising auto‐encoder (PEDAE) not only reconstructs the input features in the output layer, but also strives to reconstruct the original input data and the performance grade labels simultaneously. Then the SPEDAE is formed by stacking multiple PEDAEs layer by layer. Through this improved training strategy, SPEDAE can avoid accumulated information loss during the deep feature extraction process, improve the robustness of the network, and extract features closely related to the operating performance, thereby better completing the assessment task. The effectiveness of the proposed assessment method is validated on the case of gold cyanide leaching process. Compared with several methods, the proposed SPEDAE has the highest accuracy and reaches 99.85%, which demonstrates its superiority in operating performance assessment. [ABSTRACT FROM AUTHOR]

The process operating performance assessment is critical for the smelting process of electro-fused magnesium furnaces to improve quality of the magnesia product and pursue optimal comprehensive economic benefit. This paper proposes a new method of multi-source heterogeneous information deep feature fusion (MSHIDFF) to achieve higher accuracy operating performance assessment in the electro-fused magnesium smelting process. Firstly, we utilize convolutional neural network, bidirectional long short-term memory network and stacked auto-encoder to extract deep features from raw image, sound and current of different performance grades. Furthermore, those multi-source deep features are fused and the softmax regression with attention mechanism is employed to train a neural network classifier for the fused deep features of different performance grades. The simulation results show that the proposed MSHIDFF method obtains the superior assessment accuracy. • CNN, Bi-LSTM and SAE are used to extract the features of image, sound and current. • The depth features of different information are fused to form comprehensive features. • Softmax with AM is trained to assess the comprehensive features of different grades. • The proposed MSHIDFF method is applied to the EFMF and obtains the superior accuracy. [ABSTRACT FROM AUTHOR]

Image quality assessment of authentically distorted images constitutes a indispensable part of numerous computer vision tasks. Despite the substantial progress in recent years, accuracy and generalization performance is still unsatisfactory. These challenges are primarily attributed to the scarcity of labeled images. In order to increase the amount of images for training, we use semi-supervised learning to combine labeled images and specifically selected unlabeled images. In our new training paradigm, denominated Selected Data Retrain under Regularization, the selection criteria of unlabeled images is based on the supposition that an image and a certain of its patches ought to have approximate image quality scores. Unlabeled images that meets the aforementioned criteria, named as Highly Credible Unlabeled Images, mitigate the problem of scarcity, thus, improve accuracy. However generalization may be compromised due to selection procedure's reliance on labeled images and presence of coherent variance existed between labeled images and unlabeled images. Therefore we incorporate a sorting loss function to reduce variation within the new dataset of labeled images and specifically selected unlabeled images, and thus achieve better generalization. The effectiveness of our proposed paradigm is empirically validated using public datasets. Codes are available at https://github.com/dvstter/SDRR_IQA. [ABSTRACT FROM AUTHOR]

Background: The prediction of protein secondary structures is a crucial and significant step for ab initio tertiary structure prediction which delivers the information about proteins activity and functions. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. Currently, most of the top methods use evolutionary-based input features produced by PSSM and HHblits software, although quite recently the embeddings—the new description of protein sequences generated by language models (LM) have appeared that could be leveraged as input features. Apart from input features calculation, the top models usually need extensive computational resources for training and prediction and are barely possible to run on a regular PC. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman–Pearson approach is not appropriate. Results: We present a lightweight deep network ProteinUnet2 for SS prediction which is based on U-Net convolutional architecture and evolutionary-based input features (from PSSM and HHblits) as well as SPOT-Contact features. Through an extensive evaluation study, we report the performance of ProteinUnet2 in comparison with top SS prediction methods based on evolutionary information (SAINT and SPOT-1D). We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher–Pitman permutation tests accompanied by practical significance measured by Cohen's effect size. Conclusions: Our results suggest that ProteinUnet2 architecture has much shorter training and inference times while maintaining results similar to SAINT and SPOT-1D predictors. Taking into account the relatively long times of calculating evolutionary-based features (from PSSM in particular), it would be worth conducting the predictive ability tests on embeddings as input features in the future. We strongly believe that our proposed here statistical methodology for the evaluation of SS prediction results will be adopted and used (and even expanded) by the research community. [ABSTRACT FROM AUTHOR]