ADKU/ResearchGPT_model_dataset huggingface dataset:
Efficient socket-based data transmission method and implementation in deep learning
Authors: Xin-Jian Wei, Shu-Ping Li, Wu-Yang Yang, Xiang-Yang Zhang, Hai-Shan Li, Xin Xu, Nan Wang, Zhanbao Fu

The deep learning algorithm, which has been increasingly applied in the field of petroleum geophysical prospecting, has achieved good results in improving efficiency and accuracy based on test applications. To play a greater role in actual production, these algorithm modules must be integrated into software systems and used more often in actual production projects. Deep learning frameworks, such as TensorFlow and PyTorch, basically take Python as the core architecture, while the application program mainly uses Java, C#, and other programming languages. During integration, the seismic data read by the Java and C# data interfaces must be transferred to the Python main program module. The data exchange methods between Java, C#, and Python include shared memory, shared directory, and so on. However, these methods have the disadvantages of low transmission efficiency and unsuitability for asynchronous networks. Considering the large volume of seismic data and the need for network support for deep learning, this paper proposes a method of transmitting seismic data based on Socket. By maximizing Socket’s cross-network and efficient longdistance transmission, this approach solves the problem of inefficient transmission of underlying data while integrating the deep learning algorithm module into a software system. Furthermore, the actual production application shows that this method effectively solves the shortage of data transmission in shared memory, shared directory, and other modes while simultaneously improving the transmission efficiency of massive seismic data across modules at the bottom of the software.
Regularization by deep learning in signal processing
Authors: Carlos Ramirez Villamarin, Erwin Suazo, Tamer Oraby

In this paper, we explore a new idea of using deep learning representations as a principle for regularization in inverse problems for digital signal processing. Specifically, we consider the standard variational formulation, where a composite function encodes a fidelity term that quantifies the proximity of the candidate solution to the observations (under a physical process), and a second regularization term that constrains the space of solutions according to some prior knowledge. In this work, we investigate deep learning representations as a means of fulfilling the role of this second (regularization) term. Several numerical examples are presented for signal restoration under different degradation processes, showing successful recovery under the proposed methodology. Moreover, one of these examples uses real data on energy usage by households in London from 2012 to 2014.
Network intrusion detection using feature fusion with deep learning
Authors: Abiodun Ayantayo, Amrit Kaur, Anit Kour, Xavier Schmoor, Fayyaz Shah, Ian Vickers, Paul Kearney, Mohammed M. Abdelsamea

Network intrusion detection systems (NIDSs) are one of the main tools used to defend against cyber-attacks. Deep learning has shown remarkable success in network intrusion detection. However, the effect of feature fusion has yet to be explored in how to boost the performance of the deep learning model and improve its generalisation capability in NIDS. In this paper, we propose novel deep learning architectures with different feature fusion mechanisms aimed at improving the performance of the multi-classification components of NIDS. We propose three different deep learning models, which we call early-fusion, late-fusion, and late-ensemble learning models using feature fusion with fully connected deep networks. Our feature fusion mechanisms were designed to encourage deep learning models to learn relationships between different input features more efficiently and mitigate any potential bias that may occur with a particular feature type. To assess the efficacy of our deep learning solutions and make comparisons with state-of-the-art models, we employ the widely accessible UNSW-NB15 and NSL-KDD datasets specifically designed to enhance the development and evaluation of improved NIDSs. Through quantitative analysis, we demonstrate the resilience of our proposed models in effectively addressing the challenges posed by multi-classification tasks, especially in the presence of class imbalance issues. Moreover, our late-fusion and late-ensemble models showed the best generalisation behaviour (against overfitting) with similar performance on the training and validation sets.
Amplifying document categorization with advanced features and deep learning
Authors: M. Kavitha, K. Akila

The field of natural language processing (NLP) plays a pivotal role in discerning unstructured data from diverse origins. This study employs advanced techniques rooted in machine learning and deep learning to effectively categorize news articles. Notably, deep learning models have demonstrated superior performance over traditional machine learning algorithms, rendering them a popular choice for a range of NLP tasks. The research employs feature extraction techniques to identify multiword tokens, negation words, and out-of-vocabulary words and replace them. Additionally, convolutional neural network models leverage embedding, convolutional layers, and max pooling layers to capture intricate features. For tasks requiring an understanding of dependencies among long phrases, long short-term memory models come into play. The evaluation of the proposed model hinges on training it with datasets like AG News, BBC, and 20 Newsgroup, gauging its efficacy. The study delves into the myriad challenges inherent to text classification. These challenges are thoughtfully discussed, shedding light on the intricacies of the process. Furthermore, the research furnishes comprehensive test outcomes for both conventional machine learning and deep learning models. The significance of this proposed model is that it uses a multiword expression lexicon, wordnet synset, and word embedding techniques for feature extraction. The performance of the models is increased when using these feature extraction techniques.
Severity Grading of Ulcerative Colitis Using Endoscopy Images: An Ensembled Deep Learning and Transfer Learning Approach
Authors: Subhashree Mohapatra, Pukhraj Singh Jeji, Girish Kumar Pati, Janmenjoy Nayak, Manohar Mishra, Tripti Swarnkar

Ulcerative colitis (UC) is a persistent condition necessitating prompt treatment to avert potential complications. Detecting UC severity aids treatment decisions. The Mayo-endoscopic subscore is a standard for UC severity grading (UCSG). Deep learning (DL) and transfer learning (TL) have enhanced severity grading, but ensemble learning’s impact remains unexplored. This study designed DL-ensemble and TL-ensemble models for UCSG. Using the HyperKvasir dataset, we classified UCSG into two stages: initial and advanced. Three deep convolutional neural networks were trained from scratch for DL, and three pre-trained networks were trained for TL. UCSG was conducted using a majority voting ensemble scheme. A detailed comparative analysis evaluated individual networks. It is observed that TL models perform better than the DL models, and implementation of ensemble learning enhances the performance of both DL and TL models. Following a comprehensive assessment, it is observed that the TL-ensemble model has delivered the optimal outcome, boasting an accuracy of 90.58% and a MCC of 0.7624. This study highlights the efficacy of our methodology. TL-ensemble models, especially, excelled, providing valuable insights into automatic UCSG systems’ potential enhancement. Ensemble learning offers promise for enhancing accuracy and reliability in UCSG, with implications for future research in this field.
A systematic review on smart waste biomass production using machine learning and deep learning
Authors: Wei Peng, Omid Karimi Sadaghiani

The utilization of waste materials, as an energy resources, requires four main steps of production, pre-treatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the waste biomass production step. By investigating numerous related works, it is concluded that there is a considerable reviewing gap in the surveying and collecting the applications of Machine Learning in the waste biomass. To fill this gap with the current work, the kinds and resources of waste biomass as well as the role of Machine Learning and Deep Learning in their development are reviewed. Moreover, the storage and transportation of the wastes are surveyed followed by the application of Machine Learning and Deep Learning in these areas. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in waste biomass production areas to enhance the waste collecting quality and quality, improve the predictions, diminish the losses, as well as increase storage and transformation conditions.
Learning and predicting the unknown class using evidential deep learning
Authors: Akihito Nagahama

In practical deep-learning applications, such as medical image analysis, autonomous driving, and traffic simulation, the uncertainty of a classification model’s output is critical. Evidential deep learning (EDL) can output this uncertainty for the prediction; however, its accuracy depends on a user-defined threshold, and it cannot handle training data with unknown classes that are unexpectedly contaminated or deliberately mixed for better classification of unknown class. To address these limitations, I propose a classification method called modified-EDL that extends classical EDL such that it outputs a prediction, i.e. an input belongs to a collective unknown class along with a probability. Although other methods handle unknown classes by creating new unknown classes and attempting to learn each class efficiently, the proposed m-EDL outputs, in a natural way, the “uncertainty of the prediction” of classical EDL and uses the output as the probability of an unknown class. Although classical EDL can also classify both known and unknown classes, experiments on three datasets from different domains demonstrated that m-EDL outperformed EDL on known classes when there were instances of unknown classes. Moreover, extensive experiments under different conditions established that m-EDL can predict unknown classes even when the unknown classes in the training and test data have different properties. If unknown class data are to be mixed intentionally during training to increase the discrimination accuracy of unknown classes, it is necessary to mix such data that the characteristics of the mixed data are as close as possible to those of known class data. This ability extends the range of practical applications that can benefit from deep learning-based classification and prediction models.
Optimized model architectures for deep learning on genomic data
Authors: Hüseyin Anil Gündüz, René Mreches, Julia Moosbauer, Gary Robertson, Xiao-Yin To, Eric A. Franzosa, Curtis Huttenhower, Mina Rezaei, Alice C. McHardy, Bernd Bischl, Philipp C. Münch, Martin Binder

The success of deep learning in various applications depends on task-specific architecture design choices, including the types, hyperparameters, and number of layers. In computational biology, there is no consensus on the optimal architecture design, and decisions are often made using insights from more well-established fields such as computer vision. These may not consider the domain-specific characteristics of genome sequences, potentially limiting performance. Here, we present GenomeNet-Architect, a neural architecture design framework that automatically optimizes deep learning models for genome sequence data. It optimizes the overall layout of the architecture, with a search space specifically designed for genomics. Additionally, it optimizes hyperparameters of individual layers and the model training procedure. On a viral classification task, GenomeNet-Architect reduced the read-level misclassification rate by 19%, with 67% faster inference and 83% fewer parameters, and achieved similar contig-level accuracy with ~100 times fewer parameters compared to the best-performing deep learning baselines.
Stroke detection in the brain using MRI and deep learning models
Authors: Subba Rao Polamuri

When it comes to finding solutions to issues, deep learning models are pretty much everywhere. Medical image data is best analysed using models based on Convolutional Neural Networks (CNNs). Better methods for early detection are crucial due to the concerning increase in the number of people suffering from brain stroke. Among the several medical imaging modalities used for brain imaging, magnetic resonance imaging (MRI) stands out. When it comes to analysing medical photos, the deep learning models currently utilised with MRI have showed good outcomes. To improve the efficacy of brain stroke diagnosis, we suggested several upgrades to deep learning models in this work, including DenseNet121, ResNet50, and VGG16. Since these models are not purpose-built to solve any particular issue, they are modified according to the present situation involving the detection of brain strokes. To make use of all of these cutting-edge deep learning models in a pipeline, we proposed a strategy based on supervised learning. Results from the experiments showed that optimised models outperformed baseline models.
Combining Deep Learning with Good Old-Fashioned Machine Learning
Authors: Moshe Sipper

We present a comprehensive, stacking-based framework for combining deep learning with good old-fashioned machine learning, called Deep GOld. Our framework involves ensemble selection from 51 retrained pretrained deep networks as first-level models, and 10 machine-learning algorithms as second-level models. Enabled by today’s state-of-the-art software tools and hardware platforms, Deep GOld delivers consistent improvement when tested on four image-classification datasets: Fashion MNIST, CIFAR10, CIFAR100, and Tiny ImageNet. Of 120 experiments, in all but 10 Deep GOld improved the original networks’ performance.
Deep learning evaluation of echocardiograms to identify occult atrial fibrillation
Authors: Neal Yuan, Nathan R. Stein, Grant Duffy, Roopinder K. Sandhu, Sumeet S. Chugh, Peng-Sheng Chen, Carine Rosenberg, Christine M. Albert, Susan Cheng, Robert J. Siegel, David Ouyang

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95–0.96), AUPRC 0.91 (0.90–0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71–0.77), AUPRC 0.19 (0.16–0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67–0.70), AUPRC 0.34 (0.31–0.36)). Performance held across patients who were women (AUC 0.76 (0.72–0.81)), older than 65 years (0.73 (0.69–0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79–0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62–0.67)), TTE measurements (0.64 (0.62–0.67)), left atrial size (0.63 (0.62–0.64)), or CHA2DS2VASc (0.61 (0.60–0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.
Relationship constraint deep metric learning
Authors: Yanbing Zhang, Ting Xiao, Zhe Wang, Xinru Wang, Wenyi Feng, Zhiling Fu, Hai Yang

AbstractDeep metric learning (DML) models aim to learn semantically meaningful representations in which similar samples are pulled together and dissimilar samples are pushed apart. However, the classification effect is limited due to the high time complexity of previous models and their poor performance in extracting data relationships. This paper presents a novel relationship constraint deep metric learning (RCDML) approach, including proxy relationship constraint (PRC) and sample relationship constraint (SRC) for inter-class separability and intra-class compactness, to solve the above problems and improve the classification effect. The PRC combines the proxy-to-proxy relationship loss term with the proxy-to-sample relationship loss function to maximize the proxy features, hence enhancing inter-class separability by decreasing proxy similarity. Additionally, the SRC combines the sample-to-sample relationship loss term with the proxy-to-sample relationship loss function to maximize the sample features, which promotes intra-class compactness by increasing the similarity between the most different samples of the same class. Unlike existing proxy-based and pair-based methods, the relationship constraint framework uses a diverse range of proxy and sample data relationships. In addition, the proxy correction (PC) module is used to optimize the proxy. Extensive tests conducted on the widely popular CUB-200-2011, CARS-196, and SOP datasets show that the framework is effective and attains state-of-the-art performance.Graphical abstract
Deep structure-level N-glycan identification using feature-induced structure diagnosis integrated with a deep learning model
Authors: Suideng Qin, Zhixin Tian

Being a widely occurring protein post-translational modification, N-glycosylation features unique multi-dimensional structures including sequence and linkage isomers. There have been successful bioinformatics efforts in N-glycan structure identification using N-glycoproteomics data; however, symmetric “mirror” branch isomers and linkage isomers are largely unresolved. Here, we report deep structure-level N-glycan identification using feature-induced structure diagnosis (FISD) integrated with a deep learning model. A neural network model is integrated to conduct the identification of featured N-glycan motifs and boosts the process of structure diagnosis and distinction for linkage isomers. By adopting publicly available N-glycoproteomics datasets of five mouse tissues (17,136 intact N-glycopeptide spectrum matches) and a consideration of 23 motif features, a deep learning model integrated with a convolutional autoencoder and a multilayer perceptron was trained to be capable of predicting N-glycan featured motifs in the MS/MS spectra with previously identified compositions. In the test of the trained model, a prediction accuracy of 0.8 and AUC value of 0.95 were achieved; 5701 previously unresolved N-glycan structures were assigned by matched structure-diagnostic ions; and by using an explainable learning algorithm, two new fragmentation features of m/z = 674.25 and m/z = 835.28 were found to be significant to three N-glycan structure motifs with fucose, NeuAc, and NeuGc, proving the capability of FISD to discover new features in the MS/MS spectra.Graphical Abstract
An empirical study of sentiment analysis utilizing machine learning and deep learning algorithms
Authors: Betul Erkantarci, Gokhan Bakal

Among text-mining studies, one of the most studied topics is the text classification task applied in various domains, including medicine, social media, and academia. As a sub-problem in text classification, sentiment analysis has been widely investigated to classify often opinion-based textual elements. Specifically, user reviews and experiential feedback for products or services have been employed as fundamental data sources for sentiment analysis efforts. As a result of rapidly emerging technological advancements, social media platforms such as Twitter, Facebook, and Reddit, have become central opinion-sharing mediums since the early 2000s. In this sense, we build various machine-learning models to solve the sentiment analysis problem on the Reddit comments dataset in this work. The experimental models we constructed achieve F1 scores within intervals of 73–76%. Consequently, we present comparative performance scores obtained by traditional machine learning and deep learning models and discuss the results.
Deep learning in neglected vector-borne diseases: a systematic review
Authors: Atmika Mishra, Arya Pandey, Ruchika Malhotra

This study explores the application of Deep Learning in combating neglected vector-borne Diseases, a significant global health concern, particularly in resource-limited areas. It examines areas where Deep Learning has proven effective, compares popular Deep Learning techniques, focuses on interdisciplinary approaches with translational impact, and finds untapped potential for deep learning application. Thorough searches across multiple databases yielded 64 pertinent studies, from which 16 were selected based on inclusion criteria and quality assessment. Deep Learning applications in disease transmission risk prediction, vector detection, parasite classification, and treatment procedure optimization were investigated and focused on diseases such as Schistosomiasis, Chagas disease, Leishmaniasis, Echinococcosis, and Trachoma. Convolutional neural networks, artificial neural networks, multilayer perceptrons, and AutoML algorithms surpassed traditional methods for disease prediction, species identification, and diagnosis. The interdisciplinary integration of Deep Learning with public health, entomology, and epidemiology provides prospects for improved disease control and understanding. Deep Learning models automate disease surveillance, simplify epidemiological data processing, and enable early detection, particularly in resource-constrained settings. Smartphone apps driven by deep learning allow for rapid disease diagnosis and identification, boosting healthcare accessibility and global health outcomes. Improved algorithms, broadening the scope of applications to areas such as one health approach, and community engagement, and expanding deep learning applications to diseases such as lymphatic filariasis, hydatidosis, and onchocerciasis hold promise for improving global health outcomes.