ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep-learning-enabled antibiotic discovery through molecular de-extinction
Authors: Fangping Wan, Marcelo D. T. Torres, Jacqueline Peng, Cesar de la Fuente-Nunez

Molecular de-extinction aims at resurrecting molecules to solve antibiotic resistance and other present-day biological and biomedical problems. Here we show that deep learning can be used to mine the proteomes of all available extinct organisms for the discovery of antibiotic peptides. We trained ensembles of deep-learning models consisting of a peptide-sequence encoder coupled with neural networks for the prediction of antimicrobial activity and used it to mine 10,311,899 peptides. The models predicted 37,176 sequences with broad-spectrum antimicrobial activity, 11,035 of which were not found in extant organisms. We synthesized 69 peptides and experimentally confirmed their activity against bacterial pathogens. Most peptides killed bacteria by depolarizing their cytoplasmic membrane, contrary to known antimicrobial peptides, which tend to target the outer membrane. Notably, lead compounds (including mammuthusin-2 from the woolly mammoth, elephasin-2 from the straight-tusked elephant, hydrodamin-1 from the ancient sea cow, mylodonin-2 from the giant sloth and megalocerin-1 from the extinct giant elk) showed anti-infective activity in mice with skin abscess or thigh infections. Molecular de-extinction aided by deep learning may accelerate the discovery of therapeutic molecules.
Investigations on machine learning, deep learning, and longitudinal regression methods for global greenhouse gases predictions
Authors: S. D. Yazd, N. Gharib, J. F. Derakhshandeh

Combating climate change is one of the key topics and concerns that our community is currently facing these days. Since a few decades ago, greenhouse gases emissions gradually started to increase. Thus, the researchers attempted to find a permanent solution for this challenge. In this paper, different methods of machine learning and deep learning models are applied to evaluate their effectiveness and accuracy in predicting greenhouse gases emissions. To increase the accuracy of the assessment, the data of 101 countries over a period of 31 years (1991–2021) from the official World Bank sources are considered. In this study, therefore, a range of matrices are analyzed including Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, p value, and correlation coefficient for each model. The results demonstrate that machine learning models typically overtake the deep learning models with the support vector regression polynomial model. Besides, the statistical findings of longitudinal regression analysis reveal that by increasing cereal yield, and permanent cropland areas the greenhouse gas emissions are significantly increase (p value = 0.000) and (p value = 0.06) respectively; however, increasing in renewable energy consumption and forest areas will lead to decreasing in greenhouse gas emissions (p value = 0.000) and (p value = 0.07) respectively.
Deep ensemble transfer learning framework for COVID-19 Arabic text identification via deep active learning and text data augmentation
Authors: Abdullah Y. Muaad, Hanumanthappa Jayappa Davanagere, Jamil Hussain, Mugahed A. Al-antari

Since the declaration of COVID-19 as an epidemic by the World Health Organization in September 2019, the task of monitoring and managing the spread of misinformation related to COVID-19 on social media has become increasingly challenging. Particularly, when it comes to Arabic text recognition, tracking and identifying misleading information regarding COVID-19 on social media platforms presents significant difficulties. The detection of such text is crucial in order to safeguard our communities from the dissemination of false rumors and to establish a reliable framework for text detection. This research paper introduces a novel deep ensemble learning framework that aims to recognize ten distinct categories of Arabic text related to COVID-19, including rumors, restrictions, celebrity news, informational news, plans, requests, advice, personal anecdotes, and others. To build our framework, we leverage a dataset called ArCOVID-19Vac (Dataset1), which consists of 10,000 text samples. In addition, the DAL technique is employed to automatically annotate new text samples acquired for Dataset2. To further expand our datasets, we employ back translation and random insertion augmentation strategies, resulting in Datasets3 and Datasets4, each containing 24,000 text samples. By merging the original and augmented datasets, we create Dataset5, which comprises a total of 39,000 text samples. The final text prediction is carried out using three transformer-based BERT models through ensemble transfer learning. Our proposed ensemble framework is evaluated using each dataset independently, and it demonstrates promising results, particularly when utilizing the largest dataset (Dataset5), achieving an accuracy of 93%, precision of 92%, recall of 93%, and an F1-score of 91%. Furthermore, our proposed model exhibits performance improvements of 27%, 18%, 2%, and 1% when utilizing Datasets2, 3, 4, and 5, respectively. The comprehensive experimental results demonstrate that our ensemble framework outperforms other state-of-the-art AI-based models. The encouraging performance of our framework in accurately identifying Arabic text has the potential to enhance decision-making processes regarding the identification of misleading information and to facilitate the development of strategies to combat such issues in the future.
A comprehensive review of image denoising in deep learning
Authors: Rusul Sabah Jebur, Mohd Hazli Bin Mohamed Zabil, Dalal Adulmohsin Hammood, Lim Kok Cheng

Deep learning has gained significant interest in image denoising, but there are notable distinctions in the types of deep learning methods used. Discriminative learning is suitable for handling Gaussian noise, while optimization models are effective in estimating real noise. However, there is limited research that summarizes the different deep learning techniques for image denoising. This paper conducts a comprehensive review of techniques and methods used for image denoising and identifying challenges associated with existing approaches. In this paper, a comparative study of deep techniques is offered in image denoising. The study conducted a comprehensive review of 68 papers on image denoising published between 2018 and 2023, providing a detailed analysis of the field’s progress and methodologies over a period of 5 years. Through its literature review, the paper provides a comprehensive summary of image denoising in deep learning, including machine learning methods for image denoising, CNNs for image denoising, additive white noisy-image denoising, real noisy image denoising, blind denoising, hybrid noisy images, state- of-the-art methods for image denoising with deep learning, salt and pepper noise, non-linear filters for digital color images. The main objective of this paper is to provide a comprehensive overview of various approaches used for image denoising, each of which has been explored and developed based on individual research studies. The paper aims to discuss these approaches in a systematic and organized manner, comparing their strengths and weaknesses to provide insights for future research in the field.
Fisheye freshness detection using common deep learning algorithms and machine learning methods with a developed mobile application
Authors: Muslume Beyza Yildiz, Elham Tahsin Yasin, Murat Koklu

AbstractFish is commonly ingested as a source of protein and essential nutrients for humans. To fully benefit from the proteins and substances in fish it is crucial to ensure its freshness. If fish is stored for an extended period, its freshness deteriorates. Determining the freshness of fish can be done by examining its eyes, smell, skin, and gills. In this study, artificial intelligence techniques are employed to assess fish freshness. The author’s objective is to evaluate the freshness of fish by analyzing its eye characteristics. To achieve this, we have developed a combination of deep and machine learning models that accurately classify the freshness of fish. Furthermore, an application that utilizes both deep learning and machine learning, to instantly detect the freshness of any given fish sample was created. Two deep learning algorithms (SqueezeNet, and VGG19) were implemented to extract features from image data. Additionally, five machine learning models to classify the freshness levels of fish samples were applied. Machine learning models include (k-NN, RF, SVM, LR, and ANN). Based on the results, it can be inferred that employing the VGG19 model for feature selection in conjunction with an Artificial Neural Network (ANN) for classification yields the most favorable success rate of 77.3% for the FFE dataset.Graphical Abstract
Deep learning for transesophageal echocardiography view classification
Authors: Kirsten R. Steffner, Matthew Christensen, George Gill, Michael Bowdish, Justin Rhee, Abirami Kumaresan, Bryan He, James Zou, David Ouyang

Transesophageal echocardiography (TEE) imaging is a vital tool used in the evaluation of complex cardiac pathology and the management of cardiac surgery patients. A key limitation to the application of deep learning strategies to intraoperative and intraprocedural TEE data is the complexity and unstructured nature of these images. In the present study, we developed a deep learning-based, multi-category TEE view classification model that can be used to add structure to intraoperative and intraprocedural TEE imaging data. More specifically, we trained a convolutional neural network (CNN) to predict standardized TEE views using labeled intraoperative and intraprocedural TEE videos from Cedars-Sinai Medical Center (CSMC). We externally validated our model on intraoperative TEE videos from Stanford University Medical Center (SUMC). Accuracy of our model was high across all labeled views. The highest performance was achieved for the Trans-Gastric Left Ventricular Short Axis View (area under the receiver operating curve [AUC] = 0.971 at CSMC, 0.957 at SUMC), the Mid-Esophageal Long Axis View (AUC = 0.954 at CSMC, 0.905 at SUMC), the Mid-Esophageal Aortic Valve Short Axis View (AUC = 0.946 at CSMC, 0.898 at SUMC), and the Mid-Esophageal 4-Chamber View (AUC = 0.939 at CSMC, 0.902 at SUMC). Ultimately, we demonstrate that our deep learning model can accurately classify standardized TEE views, which will facilitate further downstream deep learning analyses for intraoperative and intraprocedural TEE imaging.
Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning
Authors: Chung-Ming Lo, Ting-Yi Lin

The quality inspection of products before delivery plays a critical role in ensuring manufacturing quality. Quick and accurate inspection of samples is realized by highly automated inspection based on pattern recognition in smart manufacturing. Conventional ensemble methods have been demonstrated to be effective for defect detection. This study further proposed synthetic mechanisms based on using various features and learning classifiers. A database of 6000 sample images of printed circuit board (PCB) connectors collected from factories was compiled. A novel confidence synthesis mechanism was proposed to prescreen images using deep learning features. Spatially connected texture features were then used to reclassify images with low reliabilities. The synthetic mechanism was found to outperform a single classifier. In particular, the highest improvement in accuracy (from 96.00 to 97.83%) was obtained using the confidence-based synthesis. The synthetic mechanism can be used to achieve high accuracy in defect detection and make automation in smart manufacturing more practicable.
Context-aware geometric deep learning for protein sequence design
Authors: Lucien F. Krapp, Fernando A. Meireles, Luciano A. Abriata, Jean Devillard, Sarah Vacle, Maria J. Marcaida, Matteo Dal Peraro

Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning approach based solely on a geometric transformer of atomic coordinates and element names that predicts protein sequences from backbone scaffolds aware of the restraints imposed by diverse molecular environments. To validate the method, we show that it can produce highly thermostable, catalytically active enzymes with high success rates. This concept is anticipated to improve the versatility of protein design pipelines for crafting desired functions.
Healthcare predictive analytics using machine learning and deep learning techniques: a survey
Authors: Mohammed Badawy, Nagy Ramadan, Hesham Ahmed Hefny

Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry, and thus comes the role of systems depending on machine learning and deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Predictive analytics for healthcare a critical imperative in the healthcare industry. It can significantly affect the accuracy of disease prediction, which may lead to saving patients' lives in the case of accurate and timely prediction; on the contrary, in the case of an incorrect prediction, it may endanger patients' lives. Therefore, diseases must be accurately predicted and estimated. Hence, reliable and efficient methods for healthcare predictive analysis are essential. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain.
LSNet: a deep learning based method for skin lesion classification using limited samples and transfer learning
Authors: Xiaodan Deng

When analyzing skin lesion image data using deep learning, the lack of a sufficient amount of effective training data poses a challenge. Although transfer learning can alleviate the problem of a small amount of data, the difference between the source data and the target data makes the tranfer learning missing some key knowledge. It is important to find the key knowledge neglected by transfer learning. This paper argues that this key knowledge is contained in challenging samples. We propose a novel method named as Limited Samples Network (LSNet) to search challenging samples and strengthen the learning of them. Specifically, LSNet utilizes patch-based structured input and employs pseudoinverse learning autoencoder to quickly obtain position-sensitive loss. Challenging samples can be obtained by searching for position-sensitive loss. Subsequently, challenging samples-augmented transfer learning is employed to enhance the classification performance of deep learning models on skin lesion datasets with limited samples. We carry comparison experiment with the existing state-of-the-art method. Experiments are carried out on the ISIC 2017, ISIC 2018 and ISIC 2019 skin lesion datasets. The results demonstrate that our training strategy significantly improves the vanilla transfer learning procedure for different types of pre-trained DCNNs. In particular, our method achieves state-of-the-art performance on different skin lesion datasets without using any extra training data.
Classical learning or deep learning: a study on food photo aesthetic assessment
Authors: Zhaotong Li, Zeru Zhang, Song Gao

Food photo aesthetic assessment has gained increasing attention in both commercial activity and social life. However, there has been little research dedicated to the quality classification of food photos. This paper presents a study on food photo aesthetic evaluation, covering dataset collection and evaluation methods. First, a dataset of food photos was collected by web crawler from food-sharing websites, and the appropriate images were selected and labeled using a WeChat applet for binary classification. Then, food photo aesthetic assessment was evaluated using classical machine learning and deep learning methods. Different hand-crafted features, including layout, texture, color, local, and deep features, were manually extracted. Two classifiers, support vector machine and random forest, were used to establish the classical learning models. Meanwhile, three convolutional neural networks (AlexNet, VGGNet, ResNet) were applied to compare with former methods by fine-tuning the model parameters. Four quantitative metrics (accuracy, recall, precision, and f1-score) were used to evaluate the performance of food photo aesthetic assessment, with the accuracy of classical and deep learning methods being 91.09% vs 94.70%, respectively. This demonstrates that classical learning with good enough hand-crafted features is capable of producing performance close to that of CNNs. The dataset for food photo aesthetic assessment can be used as a preliminary exploration of food image aesthetics assessment from both classical learning and deep learning.
Deep learning in terrestrial conservation biology
Authors: Zoltán Barta

Biodiversity is being lost at an unprecedented rate on Earth. As a first step to more effectively combat this process we need efficient methods to monitor biodiversity changes. Recent technological advance can provide powerful tools (e.g. camera traps, digital acoustic recorders, satellite imagery, social media records) that can speed up the collection of biological data. Nevertheless, the processing steps of the raw data served by these tools are still painstakingly slow. A new computer technology, deep learning based artificial intelligence, might, however, help. In this short and subjective review I oversee recent technological advances used in conservation biology, highlight problems of processing their data, shortly describe deep learning technology and show case studies of its use in conservation biology. Some of the limitations of the technology are also highlighted.
Medical images classification using deep learning: a survey
Authors: Rakesh Kumar, Pooja Kumbharkar, Sandeep Vanam, Sanjeev Sharma

Deep learning has made significant advancements in recent years. The technology is rapidly evolving and has been used in numerous automated applications with minimal loss. With these deep learning methods, medical image analysis for disease detection can be performed with minimal errors and losses. A survey of deep learning-based medical image classification is presented in this paper. As a result of their automatic feature representations, these methods have high accuracy and precision. This paper reviews various models like CNN, Transfer learning, Long short term memory, Generative adversarial networks, and Autoencoders and their combinations for various purposes in medical image classification. The total number of papers reviewed is 158. In the study, we discussed the advantages and limitations of the methods. A discussion is provided on the various applications of medical imaging, the available datasets for medical imaging, and the evaluation metrics. We also discuss the future trends in medical imaging using artificial intelligence.
Super-resolution Deep Learning Reconstruction Cervical Spine 1.5T MRI: Improved Interobserver Agreement in Evaluations of Neuroforaminal Stenosis Compared to Conventional Deep Learning Reconstruction
Authors: Koichiro Yasaka, Shunichi Uehara, Shimpei Kato, Yusuke Watanabe, Taku Tajima, Hiroyuki Akai, Naoki Yoshioka, Masaaki Akahane, Kuni Ohtomo, Osamu Abe, Shigeru Kiryu

The aim of this study was to investigate whether super-resolution deep learning reconstruction (SR-DLR) is superior to conventional deep learning reconstruction (DLR) with respect to interobserver agreement in the evaluation of neuroforaminal stenosis using 1.5T cervical spine MRI. This retrospective study included 39 patients who underwent 1.5T cervical spine MRI. T2-weighted sagittal images were reconstructed with SR-DLR and DLR. Three blinded radiologists independently evaluated the images in terms of the degree of neuroforaminal stenosis, depictions of the vertebrae, spinal cord and neural foramina, sharpness, noise, artefacts and diagnostic acceptability. In quantitative image analyses, a fourth radiologist evaluated the signal-to-noise ratio (SNR) by placing a circular or ovoid region of interest on the spinal cord, and the edge slope based on a linear region of interest placed across the surface of the spinal cord. Interobserver agreement in the evaluations of neuroforaminal stenosis using SR-DLR and DLR was 0.422–0.571 and 0.410–0.542, respectively. The kappa values between reader 1 vs. reader 2 and reader 2 vs. reader 3 significantly differed. Two of the three readers rated depictions of the spinal cord, sharpness, and diagnostic acceptability as significantly better with SR-DLR than with DLR. Both SNR and edge slope (/mm) were also significantly better with SR-DLR (12.9 and 6031, respectively) than with DLR (11.5 and 3741, respectively) (p < 0.001 for both). In conclusion, compared to DLR, SR-DLR improved interobserver agreement in the evaluations of neuroforaminal stenosis using 1.5T cervical spine MRI.
Accelerating three-dimensional phase-field simulations via deep learning approaches
Authors: Xuewei Zhou, Sheng Sun, Songlin Cai, Gongyu Chen, Honghui Wu, Jie Xiong, Jiaming Zhu

Phase-field modeling (PFM) is a powerful but computationally expensive technique for simulating three-dimensional (3D) microstructure evolutions. Very recently, integrating machine learning into phase-field simulations provides a promising way to reduce calculation time remarkably. In this study, we propose a deep learning model that combines a convolutional autoencoder with a deep operator network to predict 3D microstructure evolution by using 2D slices of the 3D system. It is found that the deep learning model can shorten the calculation time from 37 min to 3 s after the initial training, while skipping 5-time steps, and reduce the phase-field simulation time by 31% in entire calculation of the evolution process. Interestingly, this model achieves good accuracy in predicting 3D microstructures by utilizing only 2D information. This work demonstrates the efficiency of machine learning in accelerating phase-field simulations while maintaining high accuracy and promotes the application of PFM in fundamental studies.
Fostering success in online English education: Exploring the effects of ICT literacy, online learning self-efficacy, and motivation on deep learning
Authors: Wei Sun, Hong Shi

This research explores how motivation and online learning self-efficacy (OLSE) act as mediators in the association between information and communication technology (ICT) literacy and deep learning within the context of online English as a foreign language (EFL) education. A sample of 372 participants were recruited on a voluntary and anonymous basis from a public university in northern China for this study. Confirmatory factor analysis (CFA) was employed to evaluate the reliability and validity of the questionnaires. Subsequently, structural equation modeling (SEM) was conducted to examine the hypothesized model, with both CFA and SEM conducted with AMOS 29.0. The results reveal that ICT literacy, motivation, and OLSE positively and directly predict deep learning in online education. Additionally, ICT literacy also positively predicts deep learning indirectly with motivation and OLSE being significant mediators. The findings underscore the importance of ICT literacy, motivation, and OLSE for EFL learners to achieve deep learning in online EFL education. Drawn from these findings, pedagogical implications for alleviating EFL learners’ online deep learning were provided.