ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep learning for survival analysis: a review
Authors: Simon Wiegrebe, Philipp Kopper, Raphael Sonabend, Bernd Bischl, Andreas Bender

The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstructured or high-dimensional data such as images, text or omics data. In this work, we conduct a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related attributes. In summary, the reviewed methods often address only a small subset of tasks relevant to time-to-event data—e.g., single-risk right-censored data—and neglect to incorporate more complex settings. Our findings are summarized in an editable, open-source, interactive table: https://survival-org.github.io/DL4Survival. As this research area is advancing rapidly, we encourage community contribution in order to keep this database up to date.
A review on emotion detection by using deep learning techniques
Authors: Tulika Chutia, Nomi Baruah

Along with the growth of Internet with its numerous potential applications and diverse fields, artificial intelligence (AI) and sentiment analysis (SA) have become significant and popular research areas. Additionally, it was a key technology that contributed to the Fourth Industrial Revolution (IR 4.0). The subset of AI known as emotion recognition systems facilitates communication between IR 4.0 and IR 5.0. Nowadays users of social media, digital marketing, and e-commerce sites are increasing day by day resulting in massive amounts of unstructured data. Medical, marketing, public safety, education, human resources, business, and other industries also use the emotion recognition system widely. Hence it provides a large amount of textual data to extract the emotions from them. The paper presents a systematic literature review of the existing literature published between 2013 to 2023 in text-based emotion detection. This review scrupulously summarized 330 research papers from different conferences, journals, workshops, and dissertations. This paper explores different approaches, methods, different deep learning models, key aspects, description of datasets, evaluation techniques, Future prospects of deep learning, challenges in existing studies and presents limitations and practical implications.
Theoretical Assessment for Weather Nowcasting Using Deep Learning Methods
Authors: Abhay B. Upadhyay, Saurin R. Shah, Rajesh A. Thakkar

Weather is influenced by various factors such as temperature, pressure, air movement, moisture/water vapor, and the Earth’s rotating motion. Accurate weather forecasting at a high geographical resolution is a complex and computationally expensive task. This study employs a nowcasting approach using meteorological radar images. Building upon the principles of unsupervised representation in deep learning, we delve into the emerging field of next-frame prediction in computer vision. This research focuses on predicting future images based on prior image data, with applications ranging from robot decision-making to autonomous driving. We present the latest advancements in next-frame prediction networks, categorizing them into two approaches: Machine Learners and deep learners. We discuss the merits and limitations of each approach, comparing them based on various parameters. Finally, we outline potential directions for future research in this field, aiming to make weather forecasting more precise and accessible.
Deep Learning for Stock Market Prediction Using Sentiment and Technical Analysis
Authors: Georgios-Markos Chatziloizos, Dimitrios Gunopulos, Konstantinos Konstantinou

Machine learning and deep learning techniques are applied by researchers with a background in both economics and computer science, to predict stock prices and trends. These techniques are particularly attractive as an alternative to existing models and methodologies because of their ability to extract abstract features from data. Most existing research approaches are based on using either numerical/economical data or textual/sentimental data. In this article, we use cutting-edge deep learning/machine learning approaches on both numerical/economical data and textual/sentimental data in order not only to predict stock market prices and trends based on combined data but also to understand how a stock's Technical Analysis can be strengthened by using Sentiment Analysis. Using the four tickers AAPL, GOOG, NVDA and S&P 500 Information Technology, we collected historical financial data and historical textual data and we used each type of data individually and in unison, to display in which case the results were more accurate and more profitable. We describe in detail how we analyzed each type of data, and how we used it to come up with our results.
IoT-enabled healthcare transformation leveraging deep learning for advanced patient monitoring and diagnosis
Authors: Nawaf Alharbe, Manal Almalki

Deep learning and the Internet of Things (IoT) are revolutionizing the healthcare industry. This study explores the potential commercial transformation resulting from IoT-enabled healthcare systems that use deep learning for patient monitoring and diagnosis. Wearables, smart sensors, and internet-connected medical devices allow doctors to monitor patients' vital signs, activities, and physiological traits in real time. However, these devices generate vast and complex data, making analysis and diagnosis challenging. Deep learning models are well-suited to analyze this growing volume of medical data. Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks can automatically recognize complex patterns and relationships in sensor data, electronic health records, and patient-reported information. This capability aids clinical professionals in diagnosing illnesses, identifying warning signs, and tailoring treatments. This paper describes a Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -based IoT-enabled healthcare system that performs feature extraction, classification, prediction, and data preparation. Additionally, it addresses interpretability issues, privacy concerns, and resource limitations of deep learning models in real-time healthcare settings. The study demonstrates the effectiveness of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) -powered IoT-based healthcare solutions, such as real-time patient monitoring, disease detection, risk prediction, and therapy optimization. These techniques can improve the quality, cost, and outcomes of healthcare. Combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) with IoT can significantly enhance healthcare by improving disease detection, personalized treatment, and patient monitoring through connected devices and powerful analytics.
An improved ensemble deep learning framework for glaucoma detection
Authors: K. J. Subha, R. Rajavel, B. Paulchamy

In the realm of biomedical engineering, a significant challenge involves detecting physiological changes within the human body. Currently, these irregularities are assessed manually, which proves to be both laborious and time-consuming due to the intricate nature of the methods used for identification. To address the need for early disease detection, there is growing interest in employing computer-assisted diagnostics. The core goal of this proposed work is to develop a computer-aided system (CAD) aimed at the prompt identification, screening, and treatment of glaucoma. A fundus camera is used to extract structural characteristics from segmented optic discs and optic cups, which assists in the characterization of glaucoma and the evaluation of its severity.. Here, a novel ensemble-based deep learning model is used which consists of three modified pre-trained convolutional neural networks such as ResNet50, VGGNet19, and the Inception-V3. To assess the proposed algorithm's performance, a combined four distinct publicly available datasets which includes RIM-ONE, DRISHTI-GS, DRIONS-DB and HRF were utilized. An accuracy of 98.58%, specificity of 98.17%, sensitivity of 98.80%, Precision of 98.86%, F1 Score of 98.83% and AUC of 0.98 was obtained for the combined dataset and also an outstanding accuracy rates were attained across various datasets: 98.67%, 97.71%, 97.22% and 97.50% for RIM-ONE, DRIONS-DB, HRF, DRISHTI-GS respectively. The ensemble framework exhibits a significant advantage over state-of-the-art techniques, showcasing a performance improvement of up to 1.58% in glaucoma classification than the existing deep learning techniques.
On the use of deep learning for phase recovery
Authors: Kaiqiang Wang, Li Song, Chutian Wang, Zhenbo Ren, Guangyuan Zhao, Jiazhen Dou, Jianglei Di, George Barbastathis, Renjie Zhou, Jianlin Zhao, Edmund Y. Lam

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.
Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning
Authors: Nan Lin, Shaoju Wu, Zheyang Wu, Songbai Ji

The large amount of training samples required to develop a deep learning brain injury model demands enormous computational resources. Here, we study how a transformer neural network (TNN) of high accuracy can be used to efficiently generate pretraining samples for a convolutional neural network (CNN) brain injury model to reduce computational cost. The samples use synthetic impacts emulating real-world events or augmented impacts generated from limited measured impacts. First, we verify that the TNN remains highly accurate for the two impact types (N = 100 each; \({R}^{2}\) of 0.948–0.967 with root mean squared error, RMSE, ~ 0.01, for voxelized peak strains). The TNN-estimated samples (1000–5000 for each data type) are then used to pretrain a CNN, which is further finetuned using directly simulated training samples (250–5000). An independent measured impact dataset considered of complete capture of impact event is used to assess estimation accuracy (N = 191). We find that pretraining can significantly improve CNN accuracy via transfer learning compared to a baseline CNN without pretraining. It is most effective when the finetuning dataset is relatively small (e.g., 2000–4000 pretraining synthetic or augmented samples improves success rate from 0.72 to 0.81 with 500 finetuning samples). When finetuning samples reach 3000 or more, no obvious improvement occurs from pretraining. These results support using the TNN to rapidly generate pretraining samples to facilitate a more efficient training strategy for future deep learning brain models, by limiting the number of costly direct simulations from an alternative baseline model. This study could contribute to a wider adoption of deep learning brain injury models for large-scale predictive modeling and ultimately, enhancing safety protocols and protective equipment.
Curriculum learning for ab initio deep learned refractive optics
Authors: Xinge Yang, Qiang Fu, Wolfgang Heidrich

Deep optical optimization has recently emerged as a new paradigm for designing computational imaging systems using only the output image as the objective. However, it has been limited to either simple optical systems consisting of a single element such as a diffractive optical element or metalens, or the fine-tuning of compound lenses from good initial designs. Here we present a DeepLens design method based on curriculum learning, which is able to learn optical designs of compound lenses ab initio from randomly initialized surfaces without human intervention, therefore overcoming the need for a good initial design. We demonstrate the effectiveness of our approach by fully automatically designing both classical imaging lenses and a large field-of-view extended depth-of-field computational lens in a cellphone-style form factor, with highly aspheric surfaces and a short back focal length.
Verifying the Generalization of Deep Learning to Out-of-Distribution Domains
Authors: Guy Amir, Osher Maayan, Tom Zelazny, Guy Katz, Michael Schapira

Deep neural networks (DNNs) play a crucial role in the field of machine learning, demonstrating state-of-the-art performance across various application domains. However, despite their success, DNN-based models may occasionally exhibit challenges with generalization, i.e., may fail to handle inputs that were not encountered during training. This limitation is a significant challenge when it comes to deploying deep learning for safety-critical tasks, as well as in real-world settings characterized by substantial variability. We introduce a novel approach for harnessing DNN verification technology to identify DNN-driven decision rules that exhibit robust generalization to previously unencountered input domains. Our method assesses generalization within an input domain by measuring the level of agreement between independently trained deep neural networks for inputs in this domain. We also efficiently realize our approach by using off-the-shelf DNN verification engines, and extensively evaluate it on both supervised and unsupervised DNN benchmarks, including a deep reinforcement learning (DRL) system for Internet congestion control—demonstrating the applicability of our approach for real-world settings. Moreover, our research introduces a fresh objective for formal verification, offering the prospect of mitigating the challenges linked to deploying DNN-driven systems in real-world scenarios.
A multimodal deep learning model for predicting severe hemorrhage in placenta previa
Authors: Munetoshi Akazawa, Kazunori Hashimoto

Placenta previa causes life-threatening bleeding and accurate prediction of severe hemorrhage leads to risk stratification and optimum allocation of interventions. We aimed to use a multimodal deep learning model to predict severe hemorrhage. Using MRI T2-weighted image of the placenta and tabular data consisting of patient demographics and preoperative blood examination data, a multimodal deep learning model was constructed to predict cases of intraoperative blood loss > 2000 ml. We evaluated the prediction performance of the model by comparing it with that of two machine learning methods using only tabular data and MRI images, as well as with that of two human expert obstetricians. Among the enrolled 48 patients, 26 (54.2%) lost > 2000 ml of blood and 22 (45.8%) lost < 2000 ml of blood. Multimodal deep learning model showed the best accuracy of 0.68 and AUC of 0.74, whereas the machine learning model using tabular data and MRI images had a class accuracy of 0.61 and 0.53, respectively. The human experts had median accuracies of 0.61. Multimodal deep learning models could integrate the two types of information and predict severe hemorrhage cases. The model might assist human expert in the prediction of intraoperative hemorrhage in the case of placenta previa.
Deep Reinforcement Learning for Mineral  Prospectivity  Mapping
Authors: Zixian Shi, Renguang Zuo, Bao Zhou

Machine learning algorithms, including supervised and unsupervised learning ones, have been widely used in mineral prospectivity mapping. Supervised learning algorithms require the use of numerous known mineral deposits to ensure the reliability of the training results. Unsupervised learning algorithms can be applied to areas with rare or no known deposits. Reinforcement learning (RL) is a type of machine learning algorithm that differs from supervised and unsupervised learning models in that the learning process is performed interactively by the agent and environment. The environment feeds the agent with reward signals and states, and the agent synthetically evaluates the mineralization potential of each state based on these rewards. In this study, a deep RL framework was constructed for mineral prospectivity mapping, and a case study for mapping gold prospectivity in northwest Hubei Province, China, was used to test the framework. The deep RL agent extracted the information of known mineralization by automatically interacting with the environment while simultaneously mining potential mineralization information from the unlabeled dataset. Its comparison with random forest and isolation forest models demonstrates that deep RL performs better regardless of the number of known mineral deposits because of its unique reward and feedback mechanism. The delineated high-potential areas show a strong spatial correlation with known gold deposits and can therefore provide significant clues for future prospecting in the study area.
A deep learning based approach for image retrieval extraction in mobile edge computing
Authors: Jamal Alasadi, Ghassan F. Bati, Ahmed Al Hilli

Deep learning has been widely explored in 5G applications, including computer vision, the Internet of Things (IoT), and intermedia classification. However, applying the deep learning approach in limited-resource mobile devices is one of the most challenging issues. At the same time, users’ experience in terms of Quality of Service (QoS) (e.g., service latency, outcome accuracy, and achievable data rate) performs poorly while interacting with machine learning applications. Mobile edge computing (MEC) has been introduced as a cooperative approach to bring computation resources in proximity to end-user devices to overcome these limitations. This article aims to design a novel image reiterative extraction algorithm based on convolution neural network (CNN) learning and computational task offloading to support machine learning-based mobile applications in resource-limited and uncertain environments. Accordingly, we leverage the framework of image retrieval extraction and introduce three approaches. First, privacy preservation is strict and aims to protect personal data. Second, network traffic reduction. Third, minimizing feature matching time. Our simulation results associated with real-time experiments on a small-scale MEC server have shown the effectiveness of the proposed deep learning-based approach over existing schemes. The source code is available here: https://github.com/jamalalasadi/CNN_Image_retrieval.
Deep learning-based automated angle measurement for flatfoot diagnosis in weight-bearing lateral radiographs
Authors: Won-Jun Noh, Mu Sook Lee, Byoung-Dai Lee

This study aimed to develop and evaluate a deep learning-based system for the automatic measurement of angles (specifically, Meary’s angle and calcaneal pitch) in weight-bearing lateral radiographs of the foot for flatfoot diagnosis. We utilized 3960 lateral radiographs, either from the left or right foot, sourced from a pool of 4000 patients to construct and evaluate a deep learning-based model. These radiographs were captured between June and November 2021, and patients who had undergone total ankle replacement surgery or ankle arthrodesis surgery were excluded. Various methods, including correlation analysis, Bland–Altman plots, and paired T-tests, were employed to assess the concordance between the angles automatically measured using the system and those assessed by clinical experts. The evaluation dataset comprised 150 weight-bearing radiographs from 150 patients. In all test cases, the angles automatically computed using the deep learning-based system were in good agreement with the reference standards (Meary’s angle: Pearson correlation coefficient (PCC) = 0.964, intraclass correlation coefficient (ICC) = 0.963, concordance correlation coefficient (CCC) = 0.963, p-value = 0.632, mean absolute error (MAE) = 1.59°; calcaneal pitch: PCC = 0.988, ICC = 0.987, CCC = 0.987, p-value = 0.055, MAE = 0.63°). The average time required for angle measurement using only the CPU to execute the deep learning-based system was 11 ± 1 s. The deep learning-based automatic angle measurement system, a tool for diagnosing flatfoot, demonstrated comparable accuracy and reliability with the results obtained by medical professionals for patients without internal fixation devices.
Visual sentiment analysis using data-augmented deep transfer learning techniques
Authors: Zhiguo Jiang, Waneeza Zaheer, Aamir Wali, S. A. M. Gilani

There has been a growing trend among users of social media platforms to express their emotions using visual content. Visual sentiment analysis is the process of understanding the emotional polarity of images or videos and is still considered a challenging problem in artificial intelligence. Most of the existing models are based on robust machine learning or deep learning techniques. The idea of using deep transfer learning techniques for visual sentiment analysis is fairly new. In this paper, we propose a new approach using data-augmented-transfer learning architecture consisting of a pre-trained VGG16 model that is fine-tuned using SVM with augmented training data. For fine-tuning and evaluation, we initially use two Twitter image datasets. We further validated the proposed model on a third dataset. The commonly used geometric augmentation methods such as rotation, zoom range, width shift, height shift, shear range and horizontal flip were are used. We compare our proposed VGG16-SVM model with 3 other state-of-the-art deep models commonly used for transfer learning and 4 machine learning models (besides SVM) used for fine-tuning. The results show that VGG16-SVM produces the overall best accuracy (94%) and recall (96%) among all transfer learning and machine learning pairs. We also show that our proposed model outperforms all previous studies that use the same dataset.