ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep learning-based electrocardiographic screening for chronic kidney disease
Authors: Lauri Holmstrom, Matthew Christensen, Neal Yuan, J. Weston Hughes, John Theurer, Melvin Jujjavarapu, Pedram Fatehi, Alan Kwan, Roopinder K. Sandhu, Joseph Ebinger, Susan Cheng, James Zou, Sumeet S. Chugh, David Ouyang

BackgroundUndiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs.MethodsWe collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs between 2005 and 2018.ResultsUsing 12-lead ECG waveforms, our deep learning algorithm achieves discrimination for CKD of any stage with an AUC of 0.767 (95% CI 0.760–0.773) in a held-out test set and an AUC of 0.709 (0.708–0.710) in the external cohort. Our 12-lead ECG-based model performance is consistent across the severity of CKD, with an AUC of 0.753 (0.735–0.770) for mild CKD, AUC of 0.759 (0.750–0.767) for moderate-severe CKD, and an AUC of 0.783 (0.773–0.793) for ESRD. In patients under 60 years old, our model achieves high performance in detecting any stage CKD with both 12-lead (AUC 0.843 [0.836–0.852]) and 1-lead ECG waveform (0.824 [0.815–0.832]).ConclusionsOur deep learning algorithm is able to detect CKD using ECG waveforms, with stronger performance in younger patients and more severe CKD stages. This ECG algorithm has the potential to augment screening for CKD.
Deep-learning based supervisory monitoring of robotized DE-GMAW process through learning from human welders
Authors: Rui Yu, Yue Cao, Jennifer Martin, Otto Chiang, YuMing Zhang

Double-electrode gas metal arc welding (DE-GMAW) modifies GMAW by adding a second electrode to bypass a portion of the current flowing from the wire. This reduces the current to, and the heat input on, the workpiece. Successful bypassing depends on the relative position of the bypass electrode to the continuously varying wire tip. To ensure proper operation, we propose robotizing the system using a follower robot to carry and adaptively adjust the bypass electrode. The primary information for monitoring this process is the arc image, which directly shows desired and undesired modes. However, developing a robust algorithm for processing the complex arc image is time-consuming and challenging. Employing a deep learning approach requires labeling numerous arc images for the corresponding DE-GMAW modes, which is not practically feasible. To introduce alternative labels, we analyze arc phenomena in various DE-GMAW modes and correlate them with distinct arc systems having varying voltages. These voltages serve as automatically derived labels to train the deep-learning network. The results demonstrated reliable process monitoring.
A Pragmatic Privacy-Preserving Deep Learning Framework Satisfying Differential Privacy
Authors: Tran Khanh Dang, Phat T. Tran-Truong

With the increasing use of technology in our daily lives, data privacy has become a critical issue. It is essential to carefully design technologies to ensure the protection of people’s personal information. In fact, what we need are privacy-enhancing technologies (PETs) rather than solely focusing on technologies themselves. Artificial intelligence (AI) and deep learning technologies, which are considered societal locomotives, are no exception. However, AI practitioners usually design and develop without considering privacy concerns. To address this gap, we propose a pragmatic privacy-preserving deep learning framework that is suitable for AI practitioners. Our proposed framework is designed to satisfy differential privacy, a rigorous standard for preserving privacy. It is based on a setting called Private Aggregation of Teacher Ensembles (PATE), in which we have made several improvements to achieve a better level of accuracy and privacy protection. Specifically, we use a differential private aggregation mechanism called sparse vector technique and combine it with several other improvements such as human-in-the-loop and pre-trained models. Our proposed solution demonstrates the possibility of producing privacy-preserving models that approximate ground-truth models with a fixed privacy budget. These models are capable of handling a large number of training requests, making them suitable for deep learning training processes. Furthermore, our framework can be deployed in both centralized and distributed training settings. We hope that our work will encourage AI practitioners to adopt PETs and build technologies with privacy in mind.
A stacked deep learning approach for multiclass classification of plant diseases
Authors: Aman Sharma, Raghav Dalmia, Aarush Saxena, Rajni Mohana

PurposePlant diseases are one of the main factors affecting food production and reducing production losses, they must be swiftly identified and treated. Deep learning algorithms in association with computer vision techniques have recently found usage in the diagnosis of plant diseases, offering a potent tool with highly accurate results. The objective of this study is to identify a stacking ensemble-based solution by using several algorithms in the process of classifying and diagnosing plant diseases, describing trends, and emphasizing gaps.MethodThe stacking ensemble is made using top four performing deep learning algorithms and multi-layered perceptron as meta classifier. In this regard, we reviewed more than 15 studies from the previous three years that address problems with disease detection, dataset characteristics, researched crops, and pathogens in various ways.ResultsThe proposed ensemble model achieved a maximum accuracy of 98.13% compared to the conventional architectures. For comparing the results, various performance metrics are used such as accuracy, loss, precision etc. which outperformed the results of the deep learning algorithms run separately for the data as shown in Table 5.ConclusionThe suggested framework can help identify the presence of disease in a sample of plant leaves as a preventative strategy as the results were quite promising compared to the results of existing literature.
Forecasting bitcoin volatility: exploring the potential of deep learning
Authors: Tiago E. Pratas, Filipe R. Ramos, Lihki Rubio

This study aims to evaluate forecasting properties of classic methodologies (ARCH and GARCH models) in comparison with deep learning methodologies (MLP, RNN, and LSTM architectures) for predicting Bitcoin's volatility. As a new asset class with unique characteristics, Bitcoin's high volatility and structural breaks make forecasting challenging. Based on 2753 observations from 08-09-2014 to 01-05-2022, this study focuses on Bitcoin logarithmic returns. Results show that deep learning methodologies have advantages in terms of forecast quality, although significant computational costs are required. Although both MLP and RNN models produce smoother forecasts with less fluctuation, they fail to capture large spikes. The LSTM architecture, on the other hand, reacts strongly to such movements and tries to adjust its forecast accordingly. To compare forecasting accuracy at different horizons MAPE, MAE metrics are used. Diebold–Mariano tests were conducted to compare the forecast, confirming the superiority of deep learning methodologies. Overall, this study suggests that deep learning methodologies could provide a promising tool for forecasting Bitcoin returns (and therefore volatility), especially for short-term horizons.
Presenting a three layer stacking ensemble classifier of deep learning and machine learning for skin cancer classification
Authors: Bahman Jafari Tabaghsar, Reza Tavoli, Mohammad Mahdi Alizadeh Toosi

One of the most common types of cancer in the world is skin cancer. Despite the different types of skin diseases with different shapes, the classification of skin diseases is a very difficult task. As a result, considering such a problem, a combination model of deep learning algorithms and machine has been proposed for skin disease classification.In this paper, a three-layer architecture based on ensemble learning is presented. In the first layer, the training input is given to convolutional neural network and EfficientNET. The output of the first layer is given to the classifiers of the second layer including machine learning classifiers. The output of the best decision of these classifiers is sent to the third layer classifier and the final prediction is made.The reason for using the three-layer architecture based on group learning was the lack of correct recognition of some classes by simple classifications. On the other hand, some diseases with different classes are classified in the same class. This model helps to correctly identify input samples with the correct combination of classifications in different layers.HAM10000 data set has been used to test and validate the proposed method. The mentioned dataset includes 10,015 images of skin lesions in seven different classes and includes different types of skin diseases. The accuracy is 99.97 on the testing set, which was much better than the previous heavy models.
RRS: Review-Based Recommendation System Using Deep Learning for Vietnamese
Authors: Minh Hoang Nguyen, Thuat Thien Nguyen, Minh Nhat Ta, Tien Minh Nguyen, Kiet Van Nguyen

Tourism, which includes sightseeing, relaxation, and discovery, is a fundamental aspect of human life. One of the most critical considerations for traveling is accommodation, mainly hotels. To improve the travel experience, we have presented a solution for building a recommendation model using Vietnamese and user data to suggest travelers choose the ideal hotel. Our data was collected from two well-known websites, Traveloka and Ivivu, and includes information about hotels in Vietnam and users’ feedback history, such as comments, ratings, and the names of users and hotels. We then preprocessed and labeled the inter-annotator agreement for various aspects, including service (0.89), infrastructure (0.84), sanitary (0.83), location (0.89), and attitude (0.83). Our recommendation model is built by using Collaborative Filtering and deep learning techniques. Furthermore, we suggest incorporating context vectors from tourists’ Vietnamese comments in the recommendation process. The context model is developed by using deep learning techniques to extract topics and sentiments from the words effectively. The results of our proposed model, as measured by the MSE, were 0.027, which is significantly better than a context-free model using the same parameters, which had an MSE of 0.061. Additionally, our Deep Learning, created using PhoBERT embedding, had an accuracy of 81% for topic classification and 82% for sentiment classification. The FastText based model had 82% and 81% accuracy for topic and sentiment, respectively. Our research demonstrates that our approach improves the accuracy of the recommendation model and has the potential for further development in the future. This idea can introduce a new Recommendation System that can overcome existing limitations and apply to other areas.
Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features
Authors: Akanksha Maurya, R. Joe Stanley, Hemanth Y. Aradhyula, Norsang Lama, Anand K. Nambisan, Gehana Patel, Daniyal Saeed, Samantha Swinfard, Colin Smith, Sadhika Jagannathan, Jason R. Hagerty, William V. Stoecker

In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explainability. To dermatologists, the presence of telangiectasia, or narrow blood vessels that typically appear serpiginous or arborizing, is a critical indicator of basal cell carcinoma (BCC). Exploiting the feature information present in telangiectasia through a combination of DL-based techniques could create a pathway for both, improving DL results as well as aiding dermatologists in BCC diagnosis. This study demonstrates a novel “fusion” technique for BCC vs non-BCC classification using ensemble learning on a combination of (a) handcrafted features from semantically segmented telangiectasia (U-Net-based) and (b) deep learning features generated from whole lesion images (EfficientNet-B5-based). This fusion method achieves a binary classification accuracy of 97.2%, with a 1.3% improvement over the corresponding DL-only model, on a holdout test set of 395 images. An increase of 3.7% in sensitivity, 1.5% in specificity, and 1.5% in precision along with an AUC of 0.99 was also achieved. Metric improvements were demonstrated in three stages: (1) the addition of handcrafted telangiectasia features to deep learning features, (2) including areas near telangiectasia (surround areas), (3) discarding the noisy lower-importance features through feature importance. Another novel approach to feature finding with weak annotations through the examination of the surrounding areas of telangiectasia is offered in this study. The experimental results show state-of-the-art accuracy and precision in the diagnosis of BCC, compared to three benchmark techniques. Further exploration of deep learning techniques for individual dermoscopy feature detection is warranted.
Detection of Image Tampering Using Deep Learning, Error Levels and Noise Residuals
Authors: Sunen Chakraborty, Kingshuk Chatterjee, Paramita Dey

Images once were considered a reliable source of information. However, when photo-editing software started to get noticed it gave rise to illegal activities which is called image tampering. These days we can come across innumerable tampered images across the internet. Software such as Photoshop, GNU Image Manipulation Program, etc. are applied to form tampered images from real ones in just a few minutes. To discover hidden signs of tampering in an image deep learning models are an effective tool than any other methods. Models used in deep learning are capable of extracting intricate features from an image automatically. Here we proposed a combination of traditional handcrafted features along with a deep learning model to differentiate between authentic and tampered images. We have presented a dual-branch Convolutional Neural Network in conjunction with Error Level Analysis and noise residuals from Spatial Rich Model. For our experiment, we utilized the freely accessible CASIA dataset. After training the dual-branch network for 16 epochs, it generated an accuracy of 98.55%. We have also provided a comparative analysis with other previously proposed work in the field of image forgery detection. This hybrid approach proves that deep learning models along with some well-known traditional approaches can provide better results for detecting tampered images.
Repetition Dynamics-based Deep Learning Model for Next Basket Recommendation
Authors: Kaushlendra Kumar Sinha, Somaraju Suvvari

Next Basket Recommendation system analyzes users’ past interactions to provide personalized recommendations. For a better understanding of the complex relations between users and objects and to handle large datasets, most recommendation system researchers present deep learning-based models. These deep learning based models considers users short-term preferences and long-term preferences for better performance, but these existing deep learning-based models neglected the repetition behavior of the user, as some researchers have highlighted the importance of repetition behavior in the literature. Towards this objective, we proposed a deep learning model that considers the user’s repetition behavior, and also our model included the correlation dynamics at the embedding level. This means that the generated embeddings represent a combination of a user’s repetition behavior and the correlation-based dynamics of items. Further, to extract the short-term preferences of the user, we fed these embeddings to an LSTM architecture, and finally, it generates suitable personalized recommendations. To evaluate the effectiveness of the proposed model, we tested our model on different real-world datasets by using extensive performance metrics, and the results are compared against state-of-the-art models. There was been considerable improvement in recall ranging from 42 to 99% over various values of k in Ta Feng and Dunnhumby dataset and considering precision there has been an improvement of 221–400%.
A technique to forecast Pakistan’s news using deep hybrid learning model
Authors: Rukhshanda Ihsan, Syed Khaldoon Khurshid, Muhammad Shoaib, Sadia Ali, Sana Mahnoor, Syed Muhammad Hamza

Forecasting future events is a challenging task that can have a significant impact on decision-making and policy-making. In this research, we focus on forecasting news related to Pakistan. Despite the importance of accurate predictions in this field, there currently exists no dataset for forecasting Pakistani news, specifically with regards to politics. Unlike numerical time series data, textual data includes information about the event's potential causes in addition to its impact. Better forecasts are thus anticipated as a result of this greater information. In order to address this gap, our research aims to create a first Pakistani news dataset for forecasting of Pakistan news that is mostly related to politics of Pakistan. This dataset was collected from various sources, including Pakistani news websites and social media platforms, as well as frequently asked questions about Pakistani politics. We develop a forecasting model using this dataset and evaluate the effectiveness of cutting-edge deep hybrid learning techniques incorporating neural networks, random forest, Word2vec, Natural language processing (NLP), and Naive Bayes. To the best of our understanding, no research has been done on the application of a deep hybrid learning model—a blend of deep learning and machine learning—for news forecasting. The accuracy for forecasting model is 97%. According to our findings, the model's performance is adequate when compared to that of other forecasting models. Our research not only fills the gap in the current literature but also presents a new challenge for large language models and has the potential to bring significant practical advantages in the field of forecasting. The unique contribution of this study lies in the intelligent modeling of the prediction challenge, allowing for the utilization of text rich in content for forecasting objectives.
A Transfer Learning-Based CNN Deep Learning Model for Unfavorable Driving State Recognition
Authors: Jichi Chen, Hong Wang, Enqiu He

The detection of unfavorable driving states (UDS) of drivers based on electroencephalogram (EEG) measures has received continuous attention from extensive scholars on account of directly reflecting brain neural activity with high temporal resolution and low risk of being deceived. However, the existing EEG-based driver UDS detection methods involve limited exploration of the functional connectivity patterns and interaction relationships within the brain network. Therefore, there is still room for improvement in the accuracy of detection. In this project, we propose three pretrained convolutional neural network (CNN)-based automatic detection frameworks for UDS of drivers with 30-channel EEG signals. The frameworks are investigated by adjusting the learning rate and choosing the optimization solver, etc. Two different conditions of driving experiments are performed, collecting EEG signals from sixteen subjects. The acquired 1-dimensional 30-channel EEG signals are converted into 2-dimensional matrices by the Granger causality (GC) method to form the functional connectivity graphs of the brain (FCGB). Then, the FCGB are fed into pretrained deep learning models that employed transfer learning strategy for feature extraction and judgment of different EEG signal types. Furthermore, we adopt two visualization interpretability techniques, named, activation visualization and gradient-weighted class activation mapping (Grad-CAM) for better visualizing and understanding the predictions of the pretrained models after fine-tuning. The experimental outcomes show that Resnet 18 model yields the highest average recognition accuracy of 90% using the rmsprop optimizer with a learning rate of 1e − 3. The overall outcomes suggest that cooperating of biologically inspired functional connectivity graphs of the brain and pretrained transfer learning algorithms is a prospective approach in reducing the rate of major traffic accidents caused by driver unfavorable driving states.
Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study
Authors: Hui Tang, Ming Hong, Lu Yu, Yang Song, Mengqiu Cao, Lei Xiang, Yan Zhou, Shiteng Suo

BackgroundWe compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance of common degenerative pathologies.MethodsThis prospective, single-center study included 31 patients (15 males and 16 females; aged 51 ± 16 years (mean ± standard deviation)) who underwent lumbar spine exams with both TSE-SD and TSE-DL acquisitions for degenerative spine diseases. Images were analyzed by two radiologists and assessed for qualitative image quality using a 4-point Likert scale, quantitative signal-to-noise ratio (SNR) of anatomic landmarks, and detection of common pathologies. Paired-sample t, Wilcoxon, and McNemar tests, unweighted/linearly weighted Cohen κ statistics, and intraclass correlation coefficients were used.ResultsScan time for TSE-DL and TSE-SD protocols was 2:55 and 5:17 min:s, respectively. The overall image quality was either significantly higher for TSE-DL or not significantly different between TSE-SD and TSE-DL. TSE-DL demonstrated higher SNR and subject noise scores than TSE-SD. For pathology detection, the interreader agreement was substantial to almost perfect for TSE-DL, with κ values ranging from 0.61 to 1.00; the interprotocol agreement was almost perfect for both readers, with κ values ranging from 0.84 to 1.00. There was no significant difference in the diagnostic confidence or detection rate of common pathologies between the two sequences (p ≥ 0.081).ConclusionsTSE-DL allowed for a 45% reduction in scan time over TSE-SD in lumbar spine MRI without compromising the overall image quality and showed comparable detection performance of common pathologies in the evaluation of degenerative lumbar spine changes.Relevance statementDeep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies.Key points• Lumbar spine MRI with deep learning reconstruction has broad application prospects.• Deep learning reconstruction of lumbar spine MRI saved 45% scan time without compromising overall image quality.• When compared with standard sequences, deep learning reconstruction showed similar detection performance of common degenerative lumbar spine pathologies.Graphical Abstract