ADKU/ResearchGPT_model_dataset huggingface dataset:
Integrated deep learning approach for automatic coronary artery segmentation and classification on computed tomographic coronary angiography
Authors: Chitra Devi Muthusamy, Ramaswami  Murugesh

The field of coronary artery disease (CAD) has seen a rapid development in coronary computed tomography angiography (CCTA). However, manual coronary artery tree segmentation and reconstruction take time and effort. Deep learning algorithms have been created effectively to analyze large amounts of data for medical image analysis. The primary goal of this research is to create an automated CAD diagnostic model and a deep learning tool for automatic coronary artery reconstruction using a large, single-center retrospective CCTA cohort. We propose an integrated deep learning-based intelligent system for human heart blood vessel position within heart coronary CT angiography images using a multi-class ensemble classification mechanism in this research. The Modified DenseNet201 will segment the cardiac blood vessels in the proposed work. Then, the low-level features are successfully extracted using the ResNet-152 model. Finally, the improved deep residual shrinkage network (IDRSN) model classifies heart blood vessels into four distinct classes: normal, block, narrow, and blood flow-reduced. The CCTA image dataset is used for the experiment analysis. The integrated deep learning-based blood vessel segmentation and classification system (Modified DenseNet201-IDRSN) was developed using the Python tool, and the system’s efficiency was determined using different performance metrics. The experimental results of this research demonstrate that the proposed system is effective and efficient concerning related studies. Compared to the U-Net segmentation model, the segmentation result in the proposed research is smoother and nearest to the segmentation result of the human expert. The proposed integrated deep learning intelligent system improves the efficiency of disease diagnosis, reduces dependence on medical personnel, reduces manual interaction in diagnosis, and offers auxiliary strategies for subsequent medical diagnosis systems based on cardiac coronary angiography.
Survey on deep learning in multimodal medical imaging for cancer detection
Authors: Yan Tian, Zhaocheng Xu, Yujun Ma, Weiping Ding, Ruili Wang, Zhihong Gao, Guohua Cheng, Linyang He, Xuran Zhao

The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection.
A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering
Authors: Elaheh Yaghoubi, Elnaz Yaghoubi, Ahmed Khamees, Amir Hossein Vakili

Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.
A review of deep learning approaches in clinical and healthcare systems based on medical image analysis
Authors: Hadeer A. Helaly, Mahmoud Badawy, Amira Y. Haikal

Healthcare is a high-priority sector where people expect the highest levels of care and service, regardless of cost. That makes it distinct from other sectors. Due to the promising results of deep learning in other practical applications, many deep learning algorithms have been proposed for use in healthcare and to solve traditional artificial intelligence issues. The main objective of this study is to review and analyze current deep learning algorithms in healthcare systems. In addition, it highlights the contributions and limitations of recent research papers. It combines deep learning methods with the interpretability of human healthcare by providing insights into deep learning applications in healthcare solutions. It first provides an overview of several deep learning models and their most recent developments. It then briefly examines how these models are applied in several medical practices. Finally, it summarizes current trends and issues in the design and training of deep neural networks besides the future direction in this field.
Automated multifocus pollen detection using deep learning
Authors: Ramón Gallardo, Carlos J. García-Orellana, Horacio M. González-Velasco, Antonio García-Manso, Rafael Tormo-Molina, Miguel Macías-Macías, Eugenio Abengózar

Pollen-induced allergies affect a significant part of the population in developed countries. Current palynological analysis in Europe is a slow and laborious process which provides pollen information in a weekly-cycle basis. In this paper, we describe a system that allows to locate and classify, in a single step, the pollen grains present in standard glass microscope slides. Besides, processing the samples in the z-axis allows us to increase the probability of detecting grains compared to solutions based on one image per sample. Our system has been trained to recognise 11 pollen types, achieving 97.6 % success rate locating grains, of which 96.3 % are also correctly identified (0.956 macro–F1 score), and with a 2.4 % grains lost. Our results indicate that deep learning provides a robust framework to address automated identification of various pollen types, facilitating their daily measurement.
Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos
Authors: Negin Ghamsarian, Yosuf El-Shabrawi, Sahar Nasirihaghighi, Doris Putzgruber-Adamitsch, Martin Zinkernagel, Sebastian Wolf, Klaus Schoeffmann, Raphael Sznitman

In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons’ skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is profoundly reliant on large-scale datasets and annotations. In particular, surgical scene understanding and phase recognition stand as pivotal pillars within the realm of computer-assisted surgery and post-operative assessment of cataract surgery videos. In this context, we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis and detecting post-operative irregularities in cataract surgery. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures for phase recognition and surgical scene segmentation. Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos. The dataset and annotations are publicly available in Synapse.
A real-time traffic sign detection in intelligent transportation system using YOLOv8-based deep learning approach
Authors: Mingdeng Tang

Intelligent transportation systems rely heavily on accurate traffic sign detection (TSD) to enhance road safety and traffic management. Various methods have been explored in the literature for this purpose, with deep learning methods consistently demonstrating superior accuracy. However, existing research highlights the persistent challenge of achieving high accuracy rates while maintaining non-destructive and real-time requirements. In this study, we propose a deep learning model based on the YOLOv8 architecture to address this challenge. The model is trained and evaluated using a custom dataset, and extensive experiments and performance analysis demonstrate its ability to achieve precise results, thus offering a promising solution to the current research challenge in deep learning-based TSD.
Analyzing and identifying predictable time range for stress prediction based on chaos theory and deep learning
Authors: Ningyun Li, Huijun Zhang, Ling Feng, Yang Ding, Haichuan Li

ProposeStress is a common problem globally. Prediction of stress in advance could help people take effective measures to manage stress before bad consequences occur. Considering the chaotic features of human psychological states, in this study, we integrate deep learning and chaos theory to address the stress prediction problem.MethodsBased on chaos theory, we embed one’s seemingly disordered stress sequence into a high dimensional phase space so as to reveal the underlying dynamics and patterns of the stress system, and meanwhile are able to identify the stress predictable time range. We then conduct deep learning with a two-layer (dimension and temporal) attention mechanism to simulate the nonlinear state of the embedded stress sequence for stress prediction.ResultsWe validate the effectiveness of the proposed method on the public available Tesserae dataset. The experimental results show that the proposed method outperforms the pure deep learning method and Chaos method in both 2-label and 3-label stress prediction.ConclusionIntegrating deep learning and chaos theory for stress prediction is effective, and can improve the prediction accuracy over 2% and 8% more than those of the deep learning and the Chaos method respectively. Implications and further possible improvements are also discussed at the end of the paper.
Arithmetic Circuits, Structured Matrices and (not so) Deep Learning
Authors: Atri Rudra

This survey presents a necessarily incomplete (and biased) overview of results at the intersection of arithmetic circuit complexity, structured matrices and deep learning. Recently there has been some research activity in replacing unstructured weight matrices in neural networks by structured ones (with the aim of reducing the size of the corresponding deep learning models). Most of this work has been experimental and in this survey, we formalize the research question and show how a recent work that combines arithmetic circuit complexity, structured matrices and deep learning essentially answers this question. This survey is targeted at complexity theorists who might enjoy reading about how tools developed in arithmetic circuit complexity helped design (to the best of our knowledge) a new family of structured matrices, which in turn seem well-suited for applications in deep learning. However, we hope that folks primarily interested in deep learning would also appreciate the connections to complexity theory.
Geometric deep learning for molecular property predictions with chemical accuracy across chemical space
Authors: Maarten R. Dobbelaere, István Lengyel, Christian V. Stevens, Kevin M. Van Geem

Chemical engineers heavily rely on precise knowledge of physicochemical properties to model chemical processes. Despite the growing popularity of deep learning, it is only rarely applied for property prediction due to data scarcity and limited accuracy for compounds in industrially-relevant areas of the chemical space. Herein, we present a geometric deep learning framework for predicting gas- and liquid-phase properties based on novel quantum chemical datasets comprising 124,000 molecules. Our findings reveal that the necessity for quantum-chemical information in deep learning models varies significantly depending on the modeled physicochemical property. Specifically, our top-performing geometric model meets the most stringent criteria for “chemically accurate” thermochemistry predictions. We also show that by carefully selecting the appropriate model featurization and evaluating prediction uncertainties, the reliability of the predictions can be strongly enhanced. These insights represent a crucial step towards establishing deep learning as the standard property prediction workflow in both industry and academia.Scientific contributionWe propose a flexible property prediction tool that can handle two-dimensional and three-dimensional molecular information. A thermochemistry prediction methodology that achieves high-level quantum chemistry accuracy for a broad application range is presented. Trained deep learning models and large novel molecular databases of real-world molecules are provided to offer a directly usable and fast property prediction solution to practitioners.
Deep learning-enabled segmentation of ambiguous bioimages with deepflash2
Authors: Matthias Griebel, Dennis Segebarth, Nikolai Stein, Nina Schukraft, Philip Tovote, Robert Blum, Christoph M. Flath

Bioimages frequently exhibit low signal-to-noise ratios due to experimental conditions, specimen characteristics, and imaging trade-offs. Reliable segmentation of such ambiguous images is difficult and laborious. Here we introduce deepflash2, a deep learning-enabled segmentation tool for bioimage analysis. The tool addresses typical challenges that may arise during the training, evaluation, and application of deep learning models on ambiguous data. The tool’s training and evaluation pipeline uses multiple expert annotations and deep model ensembles to achieve accurate results. The application pipeline supports various use-cases for expert annotations and includes a quality assurance mechanism in the form of uncertainty measures. Benchmarked against other tools, deepflash2 offers both high predictive accuracy and efficient computational resource usage. The tool is built upon established deep learning libraries and enables sharing of trained model ensembles with the research community. deepflash2 aims to simplify the integration of deep learning into bioimage analysis projects while improving accuracy and reliability.
Deep Multi-task Learning for Animal Chest Circumference Estimation from Monocular Images
Authors: Hongtao Zhang, Dongbing Gu

The applications of deep learning algorithms with images to various scenarios have attracted significant research attention. However, application scenarios in animal breeding managements are still limited. In this paper we propose a new deep learning framework to estimate the chest circumference of domestic animals from images. This parameter is a key metric for breeding and monitoring the quality of animal in animal husbandry. We design a set of feature extraction methods based on a multi-task learning framework to address the challenging issues in the main estimation task. The multiple tasks in our proposed framework include object segmentation, keypoint estimation, and depth estimation of cow from monocular images. The domain-specific features extracted from these tasks improve upon our main estimation task. In addition, we also attempt to reduce unnecessary computations during the framework design to reduce the cost of subsequent practical implementation of the developed system. Our proposed framework is tested on our own collected dataset to evaluate its performance.
White-box inference attack: compromising the security of deep learning-based COVID-19 diagnosis systems
Authors: Burhan Ul Haque Sheikh, Aasim Zafar

The COVID-19 pandemic has necessitated the exploration of innovative diagnostic approaches, including the utilization of machine learning (ML) and deep learning (DL) technologies. However, recent findings shed light on the susceptibility of deep learning-based models to adversarial attacks, leading to erroneous predictions. This study investigates the vulnerability of a deep COVID-19 diagnosis model to the Fast Gradient Sign Method (FGSM) adversarial attack. Leveraging transfer learning of EfficientNet-B2 on a publicly available dataset, a deep learning-based COVID-19 diagnosis model is developed, achieving an impressive average accuracy of 94.56% on clean test data. However, when subjected to an untargeted FGSM attack with varying epsilon values, the model’s accuracy is severely compromised, plummeting to 21.72% at epsilon 0.008. Notably, the attack successfully misclassifies adversarial COVID-19 images as normal with 100% confidence. This study underscores the critical need for further research and development to address these vulnerabilities and ensure the reliability and accuracy of deep learning models in the diagnosis of COVID-19 patients.
Detection of vulnerabilities in blockchain smart contracts using deep learning
Authors: Namya Aankur Gupta, Mansi Bansal, Seema Sharma, Deepti Mehrotra, Misha Kakkar

Blockchain helps to give a sense of security as there is only one history of transactions visible to all the involved parties. Smart contracts enable users to manage significant asset amounts of finances on the blockchain without the involvement of any intermediaries. The conditions and checks that have been written in smart contract and executed to the application cannot be changed again. However, these unique features pose some other risks to the smart contract. Smart contracts have several flaws in its programmable language and methods of execution, despite being a developing technology. To build smart contracts and implement numerous complicated business logics, high-level languages are used by the developers to code smart contracts. Thus, blockchain smart contract is the most important element of any decentralized application, posing the risk for it to be attacked. So, the presence of vulnerabilities are to be taken care of on a priority basis. It is important for detection of vulnerabilities in a smart contract and only then implement and connect it with applications to ensure security of funds. The motive of the paper is to discuss how deep learning may be utilized to deliver bug-free secure smart contracts. Objective of the paper is to detect three kinds of vulnerabilities- reentrancy, timestamp and infinite loop. A deep learning model has been created for detection of smart contract vulnerabilities using graph neural networks. The performance of this model has been compared to the present automated tools and other independent methods. It has been shown that this model has greater accuracy than other methods while comparing the prediction of smart contract vulnerabilities in existing models.
Integrating Deep Learning and Reinforcement Learning for Enhanced Financial Risk Forecasting in Supply Chain Management
Authors: Yuanfei Cui, Fengtong Yao

In today’s dynamic business landscape, the integration of supply chain management and financial risk forecasting is imperative for sustained success. This research paper introduces a groundbreaking approach that seamlessly merges deep autoencoder (DAE) models with reinforcement learning (RL) techniques to enhance financial risk forecasting within the realm of supply chain management. The primary objective of this research is to optimize financial decision-making processes by extracting key feature representations from financial data and leveraging RL for decision optimization. To achieve this, the paper presents the PSO-SDAE model, a novel and sophisticated approach to financial risk forecasting. By incorporating advanced noise reduction features and optimization algorithms, the PSO-SDAE model significantly enhances the accuracy and reliability of financial risk predictions. Notably, the PSO-SDAE model goes beyond traditional forecasting methods by addressing the need for real-time decision-making in the rapidly evolving landscape of financial risk management. This is achieved through the utilization of a distributed RL algorithm, which expedites the processing of supply chain data while maintaining both efficiency and accuracy. The results of our study showcase the exceptional precision of the PSO-SDAE model in predicting financial risks, underscoring its efficacy for proactive risk management within supply chain operations. Moreover, the augmented processing speed of the model enables real-time analysis and decision-making — a critical capability in today’s fast-paced business environment.
ZS-DML: Zero-Shot Deep Metric Learning approach for plant leaf disease classification
Authors: Davood Zabihzadeh, Mina Masoudifar

Automatic plant disease detection plays an important role in food security. Deep learning methods are able to detect precisely various types of plant diseases but at the expense of using huge amounts of resources (processors and data). Therefore, employing few-shot or zero-shot learning methods is unavoidable. Deep Metric Learning (DML) is a widely used technique for few/zero shot learning. Existing DML methods extract features from the last hidden layer of a pre-trained deep network, which increases the dependence of the specific features on the observed classes. In this paper, the general discriminative feature learning method is used to learn general features of plant leaves. Moreover, a proxy-based loss is utilized that learns the embedding without sampling phase while having a higher convergence rate. The network is trained on the Plant Village dataset where the images are split into 32 and 6 classes as source and target, respectively. The knowledge learned from the source domain is transferred to the target in a zero-shot setting. A few samples of the target domain are presented to the network as a gallery. The network is then evaluated on the target domain. The experimental results show that by presenting few or even only one sample of new classes to the network without fine-tuning step, our method can achieve a classification accuracy of 99%/80.64% for few/one image(s) per class.