ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep Kernel learning for reaction outcome prediction and optimization
Authors: Sukriti Singh, José Miguel Hernández-Lobato

Recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. Deep learning models have gained popularity due to their ability to learn representations directly from the molecular structure. Gaussian processes (GPs), on the other hand, provide reliable uncertainty estimates but are unable to learn representations from the data. We combine the feature learning ability of neural networks (NNs) with uncertainty quantification of GPs in a deep kernel learning (DKL) framework to predict the reaction outcome. The DKL model is observed to obtain very good predictive performance across different input representations. It significantly outperforms standard GPs and provides comparable performance to graph neural networks, but with uncertainty estimation. Additionally, the uncertainty estimates on predictions provided by the DKL model facilitated its incorporation as a surrogate model for Bayesian optimization (BO). The proposed method, therefore, has a great potential towards accelerating reaction discovery by integrating accurate predictive models that provide reliable uncertainty estimates with BO.
Colour fusion effect on deep learning classification of uveal melanoma
Authors: Albert K. Dadzie, Sabrina P. Iddir, Mansour Abtahi, Behrouz Ebrahimi, David Le, Sanjay Ganesh, Taeyoon Son, Michael J. Heiferman, Xincheng Yao

BackgroundReliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures for benign lesions and ensuring timely treatment for potentially malignant cases. The purpose of this study is to validate deep learning classification of uveal melanoma and choroidal nevi, and to evaluate the effect of colour fusion options on the classification performance.MethodsA total of 798 ultra-widefield retinal images of 438 patients were included in this retrospective study, comprising 157 patients diagnosed with UM and 281 patients diagnosed with choroidal naevus. Colour fusion options, including early fusion, intermediate fusion and late fusion, were tested for deep learning image classification with a convolutional neural network (CNN). F1-score, accuracy and the area under the curve (AUC) of a receiver operating characteristic (ROC) were used to evaluate the classification performance.ResultsColour fusion options were observed to affect the deep learning performance significantly. For single-colour learning, the red colour image was observed to have superior performance compared to green and blue channels. For multi-colour learning, the intermediate fusion is better than early and late fusion options.ConclusionDeep learning is a promising approach for automated classification of uveal melanoma and choroidal nevi. Colour fusion options can significantly affect the classification performance.
Predicting DNA structure using a deep learning method
Authors: Jinsen Li, Tsu-Pei Chiu, Remo Rohs

Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based method, Deep DNAshape, that fundamentally changes the current k-mer based high-throughput prediction of DNA shape features by accurately accounting for the influence of extended flanking regions, without the need for extensive molecular simulations or structural biology experiments. By using the Deep DNAshape method, DNA structural features can be predicted for any length and number of DNA sequences in a high-throughput manner, providing an understanding of the effects of flanking regions on DNA structure in a target region of a sequence. The Deep DNAshape method provides access to the influence of distant flanking regions on a region of interest. Our findings reveal that DNA shape readout mechanisms of a core target are quantitatively affected by flanking regions, including extended flanking regions, providing valuable insights into the detailed structural readout mechanisms of protein-DNA binding. Furthermore, when incorporated in machine learning models, the features generated by Deep DNAshape improve the model prediction accuracy. Collectively, Deep DNAshape can serve as versatile and powerful tool for diverse DNA structure-related studies.
Machine learning and deep learning for classifying the justification of brain CT referrals
Authors: Jaka Potočnik, Edel Thomas, Aonghus Lawlor, Dearbhla Kearney, Eric J. Heffernan, Ronan P. Killeen, Shane J. Foley

ObjectivesTo train the machine and deep learning models to automate the justification analysis of radiology referrals in accordance with iGuide categorisation, and to determine if prediction models can generalise across multiple clinical sites and outperform human experts.MethodsAdult brain computed tomography (CT) referrals from scans performed in three CT centres in Ireland in 2020 and 2021 were retrospectively collected. Two radiographers analysed the justification of 3000 randomly selected referrals using iGuide, with two consultant radiologists analysing the referrals with disagreement. Insufficient or duplicate referrals were discarded. The inter-rater agreement among radiographers and consultants was computed. A random split (4:1) was performed to apply machine learning (ML) and deep learning (DL) techniques to unstructured clinical indications to automate retrospective justification auditing with multi-class classification. The accuracy and macro-averaged F1 score of the best-performing classifier of each type on the training set were computed on the test set.Results42 referrals were ignored. 1909 (64.5%) referrals were justified, 811 (27.4%) were potentially justified, and 238 (8.1%) were unjustified. The agreement between radiographers (κ = 0.268) was lower than radiologists (κ = 0.460). The best-performing ML model was the bag-of-words-based gradient-boosting classifier achieving a 94.4% accuracy and a macro F1 of 0.94. DL models were inferior, with bi-directional long short-term memory achieving 92.3% accuracy, a macro F1 of 0.92, and outperforming multilayer perceptrons.ConclusionInterpreting unstructured clinical indications is challenging necessitating clinical decision support. ML and DL can generalise across multiple clinical sites, outperform human experts, and be used as an artificial intelligence-based iGuide interpreter when retrospectively vetting radiology referrals.Clinical relevance statementHealthcare vendors and clinical sites should consider developing and utilising artificial intelligence-enabled systems for justifying medical exposures. This would enable better implementation of imaging referral guidelines in clinical practices and reduce population dose burden, CT waiting lists, and wasteful use of resources.Key Points

Significant variations exist among human experts in interpreting unstructured clinical indications/patient presentations.


Machine and deep learning can automate the justification analysis of radiology referrals according to iGuide categorisation.


Machine and deep learning can improve retrospective and prospective justification auditing for better implementation of imaging referral guidelines.
Applications of deep learning method of artificial intelligence in education
Authors: Fan Zhang, Xiangyu Wang, Xinhong Zhang

Intersection of education and deep learning method of artificial intelligence (AI) is gradually becoming a hot research field. Education will be profoundly transformed by AI. The purpose of this review is to help education practitioners understand the research frontiers and directions of AI applications in education. This paper reviews the applications of deep learning in education and provides a visualized bibliometric analysis. The data of this paper come from Education Resources Information Center (ERIC) and Web-of-Science (WOS). These two data bases are searched to identify research of deep learning-based education applications from 2015 to 2023. CiteSpace is used to analyze the number of publications, authors, institutions, and keywords of articles that are related to deep learning and education. This paper reviews and systematically analyzes the educational applications of deep learning in the following six aspects: learning effect prediction, educational game, learning recommendation, automatic scoring, assisted teaching and medical education. Based on the visualized bibliometric analysis, the path and inflection point of research evolution can be inferred, the potential motivation of research can be analyzed, and the frontier of research can be explored. At present, AI can enable teachers to focus more on teaching and personalized interactions with students, enhancing rather than replacing human instructions.
A systematic literature review of visual feature learning: deep learning techniques, applications, challenges and future directions
Authors: Mohammed Abdullahi, Olaide Nathaniel Oyelade, Armand Florentin Donfack Kana, Mustapha Aminu Bagiwa, Fatimah Binta Abdullahi, Sahalu Balarabe Junaidu, Ibrahim Iliyasu, Ajayi Ore-ofe, Haruna Chiroma

Visual Feature Learning (VFL) is a critical area of research in computer vision that involves the automatic extraction of features and patterns from images and videos. The applications of VFL are vast, including object detection and recognition, facial recognition, scene understanding, medical image analysis, and autonomous vehicles. In this paper, we propose to conduct extensive systematic literature review (SLR) on VFL based on deep learning algorithms. The paper conducted an SLR covering deep learning algorithms such as Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs) including their variants. The review highlights the importance of VFL in computer vision and the limitations of traditional feature extraction techniques. Furthermore, it provides an in-depth analysis of the strengths and weaknesses of various deep learning algorithms for solving problems in VFL. The discussion of the applications of VFL provides an insight into the impact of VFL on various industries and domains. The review also analyzed the challenges faced by VFL, such as data scarcity and quality, overfitting, generalization, interpretability, and explainability. The discussion of future directions for VFL includes hybrid techniques, unsupervised feature learning, continual learning, attention-based models, and explainable AI. These techniques aim to address the challenges faced by VFL and improve the performance of the models. The systematic literature review concludes that VFL is a rapidly evolving field with the potential to transform many industries and domains. The review highlights the need for further research in VFL and emphasizes the importance of responsible use of VFL models in various applications. The review provides valuable insights for researchers and practitioners in the field of computer vision, who can use these insights to enhance their work and ensure the responsible use of VFL models.
Deep Learning Challenges and Prospects in Wireless Sensor Network Deployment
Authors: Yaner Qiu, Liyun Ma, Rahul Priyadarshi

This paper explores the transformative integration of deep learning applications in the deployment of Wireless Sensor Networks (WSNs). As WSNs continue to play a pivotal role in diverse domains, the infusion of deep learning techniques offers unprecedented opportunities for enhanced data processing, analysis, and decision-making. The research problem addressed in this paper revolves around navigating the challenges associated with incorporating deep learning into WSN deployment. The methodology involves an extensive literature review, highlighting the increasing role of deep learning in addressing WSN challenges. Key findings underscore the potential improvements in energy efficiency, data processing speed, and accuracy achieved through deep learning-empowered WSNs. The implications of this research extend to diverse applications, including environmental monitoring, healthcare, industrial systems, and smart agriculture. As we delve into the future research agenda, the paper identifies the need for further exploration in areas such as adaptability to dynamic environments, privacy-preserving optimizations, and scalable deep learning models tailored to the unique constraints of WSNs.
Wheat crop classification using deep learning
Authors: Harmandeep Singh Gill, Bikramjit Singh Bath, Rajanbir Singh, Amarinder Singh Riar

Crop yield forecasting is becoming more essential in the present environment, when food security must be maintained despite climate, population, and climate change concerns. Machine learning is a useful decision-making tool for predicting agricultural yields, as well as for deciding what crops to plant and what to do throughout the crop’s growth season. To aid agricultural production prediction studies, a number of machine learning methods have been used. Wheat is a significant food source in India, particularly in the north. The wheat crop is categorised using deep learning techniques in the proposed research. The suggested system uses deep learning CNN, RNN, and LSTM applications to classify wheat crops. The results showed that the test accuracy ranged from \(85\%\) to 95.68\(\%\) for varietal level classification. Hence, the proposed approach results are accurate and reliable, encouraging the deployment of such an approach in practice.
HARNet in deep learning approach—a systematic survey
Authors: Neelam Sanjeev Kumar, G. Deepika, V. Goutham, B. Buvaneswari, R. Vijaya Kumar Reddy, Sanjeevkumar Angadi, C. Dhanamjayulu, Ravikumar Chinthaginjala, Faruq Mohammad, Baseem Khan

A comprehensive examination of human action recognition (HAR) methodologies situated at the convergence of deep learning and computer vision is the subject of this article. We examine the progression from handcrafted feature-based approaches to end-to-end learning, with a particular focus on the significance of large-scale datasets. By classifying research paradigms, such as temporal modelling and spatial features, our proposed taxonomy illuminates the merits and drawbacks of each. We specifically present HARNet, an architecture for Multi-Model Deep Learning that integrates recurrent and convolutional neural networks while utilizing attention mechanisms to improve accuracy and robustness. The VideoMAE v2 method (https://github.com/OpenGVLab/VideoMAEv2) has been utilized as a case study to illustrate practical implementations and obstacles. For researchers and practitioners interested in gaining a comprehensive understanding of the most recent advancements in HAR as they relate to computer vision and deep learning, this survey is an invaluable resource.
Deep learning bulk spacetime from boundary optical conductivity
Authors: Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim, Kwan Yun

We employ a deep learning method to deduce the bulk spacetime from boundary optical conductivity. We apply the neural ordinary differential equation technique, tailored for continuous functions such as the metric, to the typical class of holographic condensed matter models featuring broken translations: linear-axion models. We successfully extract the bulk metric from the boundary holographic optical conductivity. Furthermore, as an example for real material, we use experimental optical conductivity of UPd2Al3, a representative of heavy fermion metals in strongly correlated electron systems, and construct the corresponding bulk metric. To our knowledge, our work is the first illustration of deep learning bulk spacetime from boundary holographic or experimental conductivity data.
Deep learning: systematic review, models, challenges, and research directions
Authors: Tala Talaei Khoei, Hadjar Ould Slimane, Naima Kaabouch

The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the data by several studies, while it may lead to time-consuming and costly models. Thus, to address these challenges, several studies have been conducted to investigate deep learning techniques; however, they mostly focused on specific learning approaches, such as supervised deep learning. In addition, these studies did not comprehensively investigate other deep learning techniques, such as deep unsupervised and deep reinforcement learning techniques. Moreover, the majority of these studies neglect to discuss some main methodologies in deep learning, such as transfer learning, federated learning, and online learning. Therefore, motivated by the limitations of the existing studies, this study summarizes the deep learning techniques into supervised, unsupervised, reinforcement, and hybrid learning-based models. In addition to address each category, a brief description of these categories and their models is provided. Some of the critical topics in deep learning, namely, transfer, federated, and online learning models, are explored and discussed in detail. Finally, challenges and future directions are outlined to provide wider outlooks for future researchers.
General deep learning framework for emissivity engineering
Authors: Shilv Yu, Peng Zhou, Wang Xi, Zihe Chen, Yuheng Deng, Xiaobing Luo, Wangnan Li, Junichiro Shiomi, Run Hu

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as a typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures, thus lacking of a general design framework for emissivity engineering across different applications. Moreover, previous designs fail to tackle the simultaneous design of both materials and structures, as they either fix materials to design structures or fix structures to select suitable materials. Herein, we employ the deep Q-learning network algorithm, a reinforcement learning method based on deep learning framework, to design multilayer WS-TEs. To demonstrate the general validity, three WS-TEs are designed for various applications, including thermal camouflage, radiative cooling and gas sensing, which are then fabricated and measured. The merits of the deep Q-learning algorithm include that it can (1) offer a general design framework for WS-TEs beyond one-dimensional multilayer structures; (2) autonomously select suitable materials from a self-built material library and (3) autonomously optimize structural parameters for the target emissivity spectra. The present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications, and the design parameters are highly scalable in materials, structures, dimensions, and the target functions, offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials.
Forecasting VIX using Bayesian deep learning
Authors: Héctor J. Hortúa, Andrés Mora-Valencia

Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. In this paper, we leverage probabilistic deep learning for inferring the volatility index VIX. We employ the probabilistic counterpart of WaveNet, Temporal Convolutional Network (TCN), and Transformers. We show that TCN outperforms all models with an RMSE around 0.189. In addition, it has been well known that modern neural networks provide inaccurate uncertainty estimates. For solving this problem, we use the standard deviation scaling to calibrate the networks. Furthermore, we found out that MNF with Gaussian prior outperforms Reparameterization Trick and Flipout models in terms of precision and uncertainty predictions. Finally, we claim that MNF with Cauchy and LogUniform prior distributions yield well-calibrated TCN, and Transformer and WaveNet networks being the former that best infer the VIX values for one and five-step-ahead forecasting, and the probabilistic Transformer model yields an adequate forecasting for the COVID-19 pandemic period.
Review of Deep Learning Techniques for Neurological Disorders Detection
Authors: Akhilesh Kumar Tripathi, Rafeeq Ahmed, Arvind Kumar Tiwari

Neurological disease is one of the most common types of dementia that predominantly concerns the elderly. In clinical approaches, identifying its premature stages is complicated, and no biomarker is comprehended to be thorough in witnessing neurological disorders in their earlier stages. Deep learning approaches have attracted much attention in the scientific community using scanned images. They differ from simple machine learning (ML) algorithms in that they study the most favorable depiction of untreated images. Deep learning is helpful in the neuroimaging analysis of neurological diseases with subtle and dispersed changes because it can discover abstract and complicated patterns. The current study discusses a vital part of deep learning and looks at past work that has been used to switch between different ML algorithms that can predict neurological diseases. Convolution Neural Networks, Generative Adversarial Network, Recurrent Neural Network, Deep Belief Network, Auto Encoder, and other algorithms for Alzheimer’s illness prediction have been considered. Many publications on preprocessing methods, such as scaling, correction, stripping, and normalizing, have been evaluated.
Enhancing Cardiovascular Health Monitoring Through IoT and Deep Learning Technologies
Authors: Huu-Hoa Nguyen, Tri-Thuc Vo

Monitoring cardiovascular conditions is crucial in healthcare due to their significant impact on overall wellness and their role in mitigating heart-related diseases. To address this pressing issue, the research community has introduced various methodologies, among which deep learning approaches have shown notable effectiveness. Despite this potential, creating effective deep learning models tailored to time-series health data remains challenging. These challenges include processing vast amounts of data from IoT devices, building and selecting optimal deep learning models with appropriate parameters, and designing and implementing reliable systems for cardiovascular health monitoring. In response, our research introduces an advanced cardiovascular health monitoring system that takes advantage of wearable IoT and deep learning technologies to enhance healthcare. It features a multi-layered architecture, where each layer serves a specific function and integrates closely with the others. This integration enhances the system’s overall functionality and reliability. The system efficiently integrates processes from health data collection through deep learning analysis to the delivery of timely health alerts. A critical feature of this system is the targeted deep learning model, selected from six potential algorithms based on experiments with data from IoT-enabled smartwatches. The selection process involves an in-depth evaluation of the models’ performance, leading to the choice of the most effective model for system implementation. Our results highlight the system’s effectiveness in monitoring cardiovascular health, underscoring its potential to enhance personalized healthcare, particularly for individuals with cardiovascular conditions, through advanced monitoring technologies.