ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep learning for higher-order nonparametric spatial autoregressive model
Authors: Zitong Li, Yunquan Song, Ling Jian

Deep learning technology has been successfully applied in more and more fields. In this paper, the application of deep neural networks in higher-order nonparametric spatial autoregressive models is studied. For spatial model, we propose the higher-order nonparametric spatial autoregressive neural network (HNSARNN) to fit the model. This method offers both good interpretability and prediction performance, and solves the black box problem in deep learning models to some degree. In various scenarios of spatial data distribution, the proposed method demonstrates superior performance compared to traditional approaches for handling nonparametric functions (such as the B-spline method). Simulation results show the effectiveness of the proposed model.
A meta-analysis on diabetic retinopathy and deep learning applications
Authors: Abdüssamed Erciyas, Necaattin Barişçi

Diabetic retinopathy is one of the negative effects of diabetes on the eye. Early diagnosis of this disease, which can progress to blindness, is very important in this sense. There are many studies that detect and classify diabetic retinopathy, especially Machine Learning and Deep Learning methods. It is known that Deep Learning has been used more and more on disease detection and classification in recent years. There are three important reasons why deep learning is more successful in disease detection than methods such as image processing or machine learning. The first of these is that it achieves higher accuracies. Secondly, there is no need to develop an algorithm for each disease, that is, the algorithm learns the disease itself. Thirdly, faster results can be achieved with GPU (Graphics Processing Unit) support. For these reasons, in this study, articles written between 2015 and 2022 on the classification of diabetic retinopathy with deep learning were examined, and meta and statistical analysis was performed. Considering the work in the last two years the combined SEN value is 0.97 [95% CI, 0.92, 0.98], and the SPE value is 0.99 [95% CI, 0.98, 1.00]. The results obtained show how effective and necessary deep learning is in the early diagnosis of diabetic retinopathy.
Naturalistic Scene Modelling: Deep Learning with Insights from Biology
Authors: Kofi Appiah, Zhiyong Jin, Lei Shi, Sze Chai Kwok

Advances in machine learning coupled with the abundances of training data has facilitated the deep learning era, which has demonstrated its ability and effectiveness in solving complex detection and recognition problems. In general application areas with elements of machine learning have seen exponential growth with promising new and sophisticated solutions to complex learning problems. In computer vision, the challenge related to the detection of known objects in a scene is a thing of the past. With the tremendous increase in detection accuracies, some close to that of human detection, there are several areas still lagging in computer vision and machine learning where improvements may call for more architectural designs. In this paper, we propose a physiologically inspired model for scene understanding that encodes three key components: object location, size and category. Our aim is to develop an energy efficient artificial intelligent model for naturalistic scene understanding capable of deploying on a low power neuromorphic hardware. We have reviewed recent advances in deep learning architecture that have taken inspiration from human or primate learning systems and provided direct to future advancement on deep learning with inspiration from physiological experiments. Upon a review of areas that have benefitted from deep learning, we provide recommendations for enhancing those areas that might have stalled or grinded to a halt with little or no significant improvement.
Needle tracking in low-resolution ultrasound volumes using deep learning
Authors: Sarah Grube, Sarah Latus, Finn Behrendt, Oleksandra Riabova, Maximilian Neidhardt, Alexander Schlaefer

PurposeClinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes.MethodsWe design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 \(\times \) 16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation.ResultsOur experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning.ConclusionOur study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis.
Deep learning, textual sentiment, and financial market
Authors: Fuwei Jiang, Yumin Liu, Lingchao Meng, Huajing Zhang

In this paper, we apply the BERT model, a cut-edging deep learning model, to construct a novel textual sentiment index in the Chinese stock market. By introducing the stock market returns as sentiment labels, our BERT model effectively extracts textual sentiment-related information useful for asset pricing. We find that the BERT-based sentiment has much greater predictive power for stock market returns than the traditional dictionary method as well as the Baker–Wurgler investor sentiment index both in and out of sample. The BERT-based sentiment shows strong predictive power during economic downturns and can significantly predict future macroeconomic conditions. Overall, our BERT model offers a better measure of textual investor sentiment, highlighting the potentially significant value of deep learning, AI, and FinTech in financial market.
Research progress on intelligent monitoring of tool condition based on deep learning
Authors: Dahu Cao, Wei Liu, Jimin Ge, Shishuai Du, Wang Liu, Zhaohui Deng, Jia Chen

Intelligent monitoring of tool condition is the key to ensuring workshop manufacturing efficiency, product machining quality, and accuracy, and is also an indispensable part of intelligent processing. In the face of complex and massive, multi-source heterogeneous, and low-value density machining process data, the monitoring method based on traditional machine learning is challenging to meet the development needs of intelligent manufacturing. In contrast, with its powerful data processing and automatic feature extraction capabilities, deep learning shows broad application prospects in tool condition intelligent monitoring. Given this, this paper first systematically introduces the components of tool condition intelligent monitoring framework based on deep learning. Subsequently, the basic principles, modeling process, and application status of the four most widely used deep learning models (deep belief network, stacked auto-encoder network, convolutional neural network, and recurrent neural network) in the field of tool condition monitoring are detailed, and the advantages and disadvantages of different models are comparably discussed. Finally, the challenges and prospects of the current tool condition intelligent monitoring based on deep learning are summarized.
Prospective de novo drug design with deep interactome learning
Authors: Kenneth Atz, Leandro Cotos, Clemens Isert, Maria Håkansson, Dorota Focht, Mattis Hilleke, David F. Nippa, Michael Iff, Jann Ledergerber, Carl C. G. Schiebroek, Valentina Romeo, Jan A. Hiss, Daniel Merk, Petra Schneider, Bernd Kuhn, Uwe Grether, Gisbert Schneider

De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the “zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
Handloomed fabrics recognition with deep learning
Authors: Lipi B. Mahanta, Deva Raj Mahanta, Taibur Rahman, Chandan Chakraborty

Every nation treasures its handloom heritage, and in India, the handloom industry safeguards cultural traditions, sustains millions of artisans, and preserves ancient weaving techniques. To protect this legacy, a critical need arises to distinguish genuine handloom products, exemplified by the renowned “gamucha” from India’s northeast, from counterfeit powerloom imitations. Our study’s objective is to create an AI tool for effortless detection of authentic handloom items amidst a sea of fakes. Six deep learning architectures—VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, and DenseNet201—were trained on annotated image repositories of handloom and powerloom towels (17,484 images in total, with 14,020 for training and 3464 for validation). A novel deep learning model was also proposed. Despite respectable training accuracies, the pre-trained models exhibited lower performance on the validation dataset compared to our novel model. The proposed model outperformed pre-trained models, demonstrating superior validation accuracy, lower validation loss, computational efficiency, and adaptability to the specific classification problem. Notably, the existing models showed challenges in generalizing to unseen data and raised concerns about practical deployment due to computational expenses. This study pioneers a computer-assisted approach for automated differentiation between authentic handwoven “gamucha”s and counterfeit powerloom imitations—a groundbreaking recognition method. The methodology presented not only holds scalability potential and opportunities for accuracy improvement but also suggests broader applications across diverse fabric products.
Deep fake detection using an optimal deep learning model with multi head attention-based feature extraction scheme
Authors: R. Raja Sekar, T. Dhiliphan Rajkumar, Koteswara Rao Anne

Face forgery, or deep fake, is a frequently used method to produce fake face images, network pornography, blackmail, and other illegal activities. Researchers developed several detection approaches based on the changing traces presented by deep forgery to limit the damage caused by deep fake methods. They obtain limited performance when evaluating cross-datum scenarios. This paper proposes an optimal deep learning approach with an attention-based feature learning scheme to perform DFD more accurately. The proposed system mainly comprises ‘5’ phases: face detection, preprocessing, texture feature extraction, spatial feature extraction, and classification. The face regions are initially detected from the collected data using the Viola–Jones (VJ) algorithm. Then, preprocessing is carried out, which resizes and normalizes the detected face regions to improve their quality for detection purposes. Next, texture features are learned using the Butterfly Optimized Gabor Filter to get information about the local features of objects in an image. Then, the spatial features are extracted using Residual Network-50 with Multi Head Attention (RN50MHA) to represent the data globally. Finally, classification is done using the Optimal Long Short-Term Memory (OLSTM), which classifies the data as fake or real, in which optimization of network is done using Enhanced Archimedes Optimization Algorithm. The proposed system is evaluated on four benchmark datasets such as Face Forensics +  + (FF + +), Deepfake Detection Challenge, Celebrity Deepfake (CDF), and Wild Deepfake. The experimental results show that DFD using OLSTM and RN50MHA achieves a higher inter and intra-dataset detection rate than existing state-of-the-art methods.
Imaging-based deep learning in kidney diseases: recent progress and future prospects
Authors: Meng Zhang, Zheng Ye, Enyu Yuan, Xinyang Lv, Yiteng Zhang, Yuqi Tan, Chunchao Xia, Jing Tang, Jin Huang, Zhenlin Li

Kidney diseases result from various causes, which can generally be divided into neoplastic and non-neoplastic diseases. Deep learning based on medical imaging is an established methodology for further data mining and an evolving field of expertise, which provides the possibility for precise management of kidney diseases. Recently, imaging-based deep learning has been widely applied to many clinical scenarios of kidney diseases including organ segmentation, lesion detection, differential diagnosis, surgical planning, and prognosis prediction, which can provide support for disease diagnosis and management. In this review, we will introduce the basic methodology of imaging-based deep learning and its recent clinical applications in neoplastic and non-neoplastic kidney diseases. Additionally, we further discuss its current challenges and future prospects and conclude that achieving data balance, addressing heterogeneity, and managing data size remain challenges for imaging-based deep learning. Meanwhile, the interpretability of algorithms, ethical risks, and barriers of bias assessment are also issues that require consideration in future development. We hope to provide urologists, nephrologists, and radiologists with clear ideas about imaging-based deep learning and reveal its great potential in clinical practice.Critical relevance statement The wide clinical applications of imaging-based deep learning in kidney diseases can help doctors to diagnose, treat, and manage patients with neoplastic or non-neoplastic renal diseases.Key points• Imaging-based deep learning is widely applied to neoplastic and non-neoplastic renal diseases.• Imaging-based deep learning improves the accuracy of the delineation, diagnosis, and evaluation of kidney diseases.• The small dataset, various lesion sizes, and so on are still challenges for deep learning.Graphical Abstract
Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology
Authors: Tatsuhiko Naito, Yukinori Okada

The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibility complex, referred to as HLA imputation. Despite their various advantages, the current deep learning-based genotype imputation methods do have certain limitations and have not yet become standard. These limitations include the modest accuracy improvement over statistical and conventional machine learning-based methods. However, their benefits include other aspects, such as their “reference-free” nature, which ensures complete privacy protection, and their higher computational efficiency. Furthermore, the continuing evolution of deep learning technologies is expected to contribute to further improvements in prediction accuracy and usability in the future.
Deep learning-assisted medical image compression challenges and opportunities: systematic review
Authors: Nour El Houda Bourai, Hayet Farida Merouani, Akila Djebbar

Over the preceding decade, there has been a discernible surge in the prominence of artificial intelligence, marked by the development of various methodologies, among which deep learning emerges as a particularly auspicious technique. The captivating attribute of deep learning, characterised by its capacity to glean intricate feature representations from data, has served as a catalyst for pioneering approaches and methodologies spanning a multitude of domains. In the face of the burgeoning exponential growth in digital medical image data, the exigency for adept image compression methodologies has become increasingly pronounced. These methodologies are designed to preserve bandwidth and storage resources, thereby ensuring the seamless and efficient transmission of data within medical applications. The critical nature of medical image compression accentuates the imperative to confront the challenges precipitated by the escalating deluge of medical image data. This review paper undertakes a comprehensive examination of medical image compression, with a predominant focus on sophisticated, research-driven deep learning techniques. It delves into a spectrum of approaches, encompassing the amalgamation of deep learning with conventional compression algorithms and the application of deep learning to enhance compression quality. Additionally, the review endeavours to explicate these fundamental concepts, elucidating their inherent characteristics, merits, and limitations.
Open and reusable deep learning for pathology with WSInfer and QuPath
Authors: Jakub R. Kaczmarzyk, Alan O’Callaghan, Fiona Inglis, Swarad Gat, Tahsin Kurc, Rajarsi Gupta, Erich Bremer, Peter Bankhead, Joel H. Saltz

Digital pathology has seen a proliferation of deep learning models in recent years, but many models are not readily reusable. To address this challenge, we developed WSInfer: an open-source software ecosystem designed to streamline the sharing and reuse of deep learning models for digital pathology. The increased access to trained models can augment research on the diagnostic, prognostic, and predictive capabilities of digital pathology.
Adversarial robustness of deep reinforcement learning-based intrusion detection
Authors: Mohamed Amine Merzouk, Christopher Neal, Joséphine Delas, Reda Yaich, Nora Boulahia-Cuppens, Frédéric Cuppens

Machine learning techniques, including Deep Reinforcement Learning (DRL), enhance intrusion detection systems by adapting to new threats. However, DRL’s reliance on vulnerable deep neural networks leads to susceptibility to adversarial examples-perturbations designed to evade detection. While adversarial examples are well-studied in deep learning, their impact on DRL-based intrusion detection remains underexplored, particularly in critical domains. This article conducts a thorough analysis of DRL-based intrusion detection’s vulnerability to adversarial examples. It systematically evaluates key hyperparameters such as DRL algorithms, neural network depth, and width, impacting agents’ robustness. The study extends to black-box attacks, demonstrating adversarial transferability across DRL algorithms. Findings emphasize neural network architecture’s critical role in DRL agent robustness, addressing underfitting and overfitting challenges. Practical implications include insights for optimizing DRL-based intrusion detection agents to enhance performance and resilience. Experiments encompass multiple DRL algorithms tested on three datasets: NSL-KDD, UNSW-NB15, and CICIoV2024, against gradient-based adversarial attacks, with publicly available implementation code.
Yoga with Deep Learning: Linking Mind and Machine
Authors: Sakshi, Sandeep Saini

Health and fitness play a crucial role in every aspect of an individual’s life. In an era where well-being is an absolute target, Yoga is one of the most straightforward ways to remain healthy and achieve mental focus, breath control and body balance without undergoing useless expenditures. The motive of this study is to combine the knowledge of Yoga with the continuously developing power of deep learning. In this study, we observe yoga pose estimation along with breath control and focus instructions for 10 different poses with the help of deep learning and Mediapipe. The proposed work predicts pose and instructions through model matching with landmark extraction done by obtaining keypoints on the pose by initializing the Mediapipe pose model. Here, a hybrid model is created using ANN with existing state-of-the-art models to achieve the pose estimation. The maximum accuracy achieved during the model implementation was around 93.43%. Later, when the model was deployed on Raspberry Pi-4 hardware, the maximum accuracy achieved was around 90.9%. The user also gets guidance on breath and focus control during a particular pose. Integration with deep learning and data science is seen to have created a whole new personalized Yoga experience with on-demand posture monitoring, customized feedback, and global access while creating future scopes for risk analysis and injury prevention.
A review of deep learning and machine learning techniques for hydrological inflow forecasting
Authors: Sarmad Dashti Latif, Ali Najah Ahmed

Conventional machine learning models have been widely used for reservoir inflow and rainfall prediction. Nowadays, researchers focus on a new computing architecture in the area of AI, namely, deep learning for hydrological forecasting parameters. This review paper tends to broadcast more of the intriguing interest in reservoir inflow prediction utilizing deep learning and machine learning algorithms. The AI models utilized for different hydrology sectors, as well as the most prevalent machine learning techniques, will be explored in this thorough study, which divides AI techniques into two primary categories: deep learning and machine learning. In this study, we look at the long short-term memory deep learning method as well as three traditional machine learning algorithms: support vector machine, random forest, and boosted regression tree. Under each part, a summary of the findings is provided. For convenience of reference, some of the benefits and drawbacks discovered through literature reviews have been listed. Finally, future recommendations and overall conclusions based on research findings are given. This review focuses on papers from high-impact factor periodicals published over a 4 years period beginning in 2018 onwards.