ADKU/ResearchGPT_model_dataset huggingface dataset:
Are deep learning classification results obtained on CT scans fair and interpretable?
Authors: Mohamad M. A. Ashames, Ahmet Demir, Omer N. Gerek, Mehmet Fidan, M. Bilginer Gulmezoglu, Semih Ergin, Rifat Edizkan, Mehmet Koc, Atalay Barkana, Cuneyt Calisir

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the Computed Tomography (CT) scan of a person to be in the training set, while other images of the same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets.
A Novel Approach Using Transfer Learning Architectural Models Based Deep Learning Techniques for Identification and Classification of Malignant Skin Cancer
Authors: Balambigai Subramanian, Suresh Muthusamy, Kokilavani Thangaraj, Hitesh Panchal, Elavarasi Kasirajan, Abarna Marimuthu, Abinaya Ravi

Melanoma, a form of skin cancer originating in melanocyte cells, poses a significant health risk, although it is less prevalent than other types of skin cancer. Its detection presents challenges, even under expert observation. To enhance the classification accuracy of skin lesions, a Deep Convolutional Neural Network, Visual Geometry Group model has been proposed. However, deep learning methods typically require substantial training time. To mitigate this, transfer learning techniques are employed, reducing training duration. Data sets sourced from the International Skin Imaging Collaboration are utilized to train the model within this proposed approach. Evaluation of classification performance involves metrics such as Accuracy, Positive Predictive Value, Negative Predictive Value, Specificity, and Sensitivity. The classifier’s performance on test data is depicted through a confusion matrix. The introduction of transfer learning techniques into the Deep Convolutional Neural Network has resulted in an improved classification accuracy of 85%, compared to the 81% achieved by a standard Convolutional Neural Network.
Deep contrastive learning of molecular conformation for efficient property prediction
Authors: Yang Jeong Park, HyunGi Kim, Jeonghee Jo, Sungroh Yoon

Data-driven deep learning algorithms provide accurate prediction of high-level quantum-chemical molecular properties. However, their inputs must be constrained to the same quantum-chemical level of geometric relaxation as the training dataset, limiting their flexibility. Adopting alternative cost-effective conformation generative methods introduces domain-shift problems, deteriorating prediction accuracy. Here we propose a deep contrastive learning-based domain-adaptation method called Local Atomic environment Contrastive Learning (LACL). LACL learns to alleviate the disparities in distribution between the two geometric conformations by comparing different conformation-generation methods. We found that LACL forms a domain-agnostic latent space that encapsulates the semantics of an atom’s local atomic environment. LACL achieves quantum-chemical accuracy while circumventing the geometric relaxation bottleneck and could enable future application scenarios such as inverse molecular engineering and large-scale screening. Our approach is also generalizable from small organic molecules to long chains of biological and pharmacological molecules.
Machine and deep learning techniques for the prediction of diabetics: a review
Authors: Sandip Kumar Singh Modak, Vijay Kumar Jha

Diabetes has become one of the significant reasons for public sickness and death in worldwide. By 2019, diabetes had affected more than 463 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 700 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multi factorial metabolic disease, its diagnostic criteria are difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of the medical diagnosis process. With the development of Data mining, researchers find that machine learning and deep learning, playing an important role in diabetes prediction research. This paper is an in-depth study on the application of machine learning and deep learning techniques in the prediction of diabetics. In addition, this paper also discusses the different methodology used in machine and deep learning for prediction of diabetics since last two decades and examines the methods used, to explore their successes and failure. This review would help researchers and practitioners understand the current state-of-the-art methods and identify gaps in the literature.
Deep encoder–decoder-based shared learning for multi-criteria recommendation systems
Authors: Salam Fraihat, Bushra Abu Tahon, Bushra Alhijawi, Arafat Awajan

A recommendation system (RS) can help overcome information overload issues by offering personalized predictions for users. Typically, RS considers the overall ratings of users on items to generate recommendations for them. However, users may consider several aspects when evaluating items. Hence, a multi-criteria RS considers n-aspects of items to generate more accurate recommendations than a single-criteria RS. This research paper proposes two deep encoder–decoder models based on shared learning for a multi-criteria RS, multi-modal deep encoder–decoder-based shared learning (MMEDSL) and multi-criteria deep encoder–decoder-based shared learning (MCEDSL). MMEDSL employs the shared learning technique by concentrating on the multi-modality concept in deep learning, while MCEDSL focuses on the training process to apply the shared learning technique. The shared learning captures useful shared information during the learning process since the multi-criteria may have hidden inter-relationships. A set of experiments were conducted to compare the proposed models with recent baseline approaches. The Yahoo! Movies multi-criteria dataset was utilized. The results demonstrate that the proposed models outperform other algorithms. In addition, the results show that integrating the shared learning technique with the RS produces precise recommendation predictions.
A Deep Learning Based Approach for Biomedical Named Entity Recognition Using Multitasking Transfer Learning with BiLSTM, BERT and CRF
Authors: H. Pooja, M. P. Prabhudev Jagadeesh

The named entity recognition (NER) is a method for locating references to rigid designators in text that fall into well-established semantic categories like person, place, organisation, etc., Many natural language e applications, like summarization of text, question–answer models and machine translation, always include NER at their core. Early NER systems were quite successful in reaching high performance at the expense of using human engineers to create features and rules that were particular to a certain domain. Currently, the biomedical data is soaring expeditiously and extracting the useful information can help to facilitate the appropriate diagnosis. Therefore, these systems are widely adopted in biomedical domain. However, the traditional rule-based, dictionary based and machine learning based methods suffer from computational complexity and out-of-vocabulary (OOV)issues Deep learning has recently been used in NER systems, achieving the state-of-the-art outcome. The present work proposes a novel deep learning based approach which uses Bidirectional Long Short Term (BiLSTM), Bidirectional Encoder Representation (BERT) and Conditional Random Field mode (CRF) model along with transfer learning and multi-tasking model to solve the OOV problem in biomedical domain. The transfer learning architecture uses shared and task specific layers to achieve the multi-task transfer learning task. The shared layer consists of lexicon encoder and transformer encoder followed by embedding vectors. Finally, we define a training loss function based on the BERT model. The proposed Multi-task TLBBC approach is compared with numerous prevailing methods. The proposed Multi-task TLBBC approach realizes average accuracy as 97.30%, 97.20%, 96.80% and 97.50% for NCBI, BC5CDR, JNLPBA, and s800 dataset, respectively.
Is deep learning good enough for software defect prediction?
Authors: Sushant Kumar Pandey, Arya Haldar, Anil Kumar Tripathi

Due to high impact of internet technology and rapid change in software systems, it has been a tough challenge for us to detect software defects with high accuracy. Traditional software defect prediction research mainly concentrates on manually designing features (e.g., complexity metrics) and inputting them into machine learning classifiers to distinguish defective code. To gain high prediction accuracy, researchers have developed several deep learning or high computational models for software defect prediction. However, there are several critical conditions and theoretical problems in order to achieve better results. This article explores the investigation of SDP using two deep learning techniques, i.e., SqueezeNet and Bottleneck models. We employed seven different open-source datasets from NASA Repository to perform this comparative study. We use F-Measure as a performance evaluator and found that these methods statistically outperform eight state-of-the-art methods with mean F-Measure of 0.93 ± 0.014 and 0.90 ± 0.013, respectively. We found that these two methods are significantly more effective in terms of F-Measure over large- and moderate-size projects. But they are computationally expensive in terms of training time. As the size of projects is getting immense and sophisticated, such deep learning methods are worth applying.
Forecasting oil price in times of crisis: a new evidence from machine learning versus deep learning models
Authors: Haithem Awijen, Hachmi Ben Ameur, Zied Ftiti, Waël Louhichi

This study investigates oil price forecasting during a time of crisis, from December 2007 to December 2021. As the oil market has experienced various shocks (exogenous versus endogenous), modelling and forecasting its prices dynamics become more complex based on conventional (econometric and structural) models. A new strand of literature has been attracting more attention during the last decades dealing with artificial intelligence methods. However, this literature is unanimous regarding the performance accuracy between machine learning and deep learning methods. We aim in this study to contribute to this literature by investigating the oil price forecasting based on these two approaches. Based on the stylized facts of oil prices dynamics, we select the support vector machine and long short-term memory approach, as two main models of Machine Learning and deep learning methods, respectively. Our findings support the superiority of the Deep Learning method compared to the Machine Learning approach. Interestingly, our results show that the Deep LSTM-prediction has a close pattern to the observed oil prices, demonstrating robust fitting accuracy at mid-to-long forecast horizons during crisis events. However, our results show that SVM machine learning has poor memory ability to establish a clearer understanding of time-dependent volatility and the dynamic co-movements between actual and predicted data. Moreover, our results show that the power of SVM to learn for long-term predictions is reduced, which potentially lead to distortions of forecasting performance.
A machine learning based deep convective trigger for climate models
Authors: Siddharth Kumar, P Mukhopadhyay, C Balaji

The present study focuses on addressing the issue of too frequent triggers of deep convection in climate models, which are primarily based on physics-based classical trigger functions such as convective available potential energy (CAPE) or cloud work function (CWF). To overcome this problem, the study proposes using machine learning (ML) based deep convective triggers as an alternative. The deep convective trigger is formulated as a binary classification problem, where the goal is to predict whether deep convection will occur or not. Two elementary classification algorithms, namely support vector machines and neural networks, are adopted in this study. Additionally, a novel method is proposed to rank the importance of input variables for the classification problem, which may aid in understanding the underlying mechanisms and factors influencing deep convection. The accuracy of the ML-based methods is compared with the widely used convective available potential energy (CAPE)-based and dynamic generation of CAPE (dCAPE) trigger function found in many convective parameterization schemes. Results demonstrate that the elementary machine learning-based algorithms can outperform the classical CAPE-based triggers, indicating the potential effectiveness of ML-based approaches in dealing with this issue. Furthermore, a method based on the Mahalanobis distance is presented for binary classification, which is easy to interpret and implement. The Mahalanobis distance-based approach shows accuracy comparable to other ML-based methods, suggesting its viability as an alternative method for deep convective triggers. By correcting for deep convective triggers using ML-based approaches, the study proposes a possible solution to improve the probability density of rain in the climate model. This improvement may help overcome the issue of excessive drizzle often observed in many climate models.
Deep learning theory of distribution regression with CNNs
Authors: Zhan Yu, Ding-Xuan Zhou

We establish a deep learning theory for distribution regression with deep convolutional neural networks (DCNNs). Deep learning based on structured deep neural networks has been powerful in practical applications. Generalization analysis for regression with DCNNs has been carried out very recently. However, for the distribution regression problem in which the input variables are probability measures, there is no mathematical model or theoretical analysis of DCNN-based learning theory. One of the difficulties is that the classical neural network structure requires the input variable to be a Euclidean vector. When the input samples are probability distributions, the traditional neural network structure cannot be directly used. A well-defined DCNN framework for distribution regression is desirable. In this paper, we overcome the difficulty and establish a novel DCNN-based learning theory for a two-stage distribution regression model. Firstly, we realize an approximation theory for functionals defined on the set of Borel probability measures with the proposed DCNN framework. Then, we show that the hypothesis space is well-defined by rigorously proving its compactness. Furthermore, in the hypothesis space induced by the general DCNN framework with distribution inputs, by using a two-stage error decomposition technique, we derive a novel DCNN-based two-stage oracle inequality and optimal learning rates (up to a logarithmic factor) for the proposed algorithm for distribution regression.
Deep learning for quadratic hedging in incomplete jump market
Authors: Nacira Agram, Bernt Øksendal, Jan Rems

We propose a deep learning approach to study the minimal variance pricing and hedging problem in an incomplete jump diffusion market. It is based on a rigorous stochastic calculus derivation of the optimal hedging portfolio, optimal option price, and the corresponding equivalent martingale measure through the means of the Stackelberg game approach. A deep learning algorithm based on the combination of the feed-forward and LSTM neural networks is tested on three different market models, two of which are incomplete. In contrast, the complete market Black–Scholes model serves as a benchmark for the algorithm’s performance. The results that indicate the algorithm’s good performance are presented and discussed. In particular, we apply our results to the special incomplete market model studied by Merton and give a detailed comparison between our results based on the minimal variance principle and the results obtained by Merton based on a different pricing principle. Using deep learning, we find that the minimal variance principle leads to typically higher option prices than those deduced from the Merton principle. On the other hand, the minimal variance principle leads to lower losses than the Merton principle.
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Authors: Debby D. Wang, Wenhui Wu, Ran Wang

The launch of AlphaFold series has brought deep-learning techniques into the molecular structural science. As another crucial problem, structure-based prediction of protein-ligand binding affinity urgently calls for advanced computational techniques. Is deep learning ready to decode this problem? Here we review mainstream structure-based, deep-learning approaches for this problem, focusing on molecular representations, learning architectures and model interpretability. A model taxonomy has been generated. To compensate for the lack of valid comparisons among those models, we realized and evaluated representatives from a uniform basis, with the advantages and shortcomings discussed. This review will potentially benefit structure-based drug discovery and related areas.Graphical Abstract
Deep learning in magnetic resonance enterography for Crohn’s disease assessment: a systematic review
Authors: Ofir Brem, David Elisha, Eli Konen, Michal Amitai, Eyal Klang

Crohn’s disease (CD) poses significant morbidity, underscoring the need for effective, non-invasive inflammatory assessment using magnetic resonance enterography (MRE). This literature review evaluates recent publications on the role of deep learning in improving MRE for CD assessment. We searched MEDLINE/PUBMED for studies that reported the use of deep learning algorithms for assessment of CD activity. The study was conducted according to the PRISMA guidelines. The risk of bias was evaluated using the QUADAS‐2 tool. Five eligible studies, encompassing 468 subjects, were identified. Our study suggests that diverse deep learning applications, including image quality enhancement, bowel segmentation for disease burden quantification, and 3D reconstruction for surgical planning are useful and promising for CD assessment. However, most of the studies are preliminary, retrospective studies, and have a high risk of bias in at least one category. Future research is needed to assess how deep learning can impact CD patient diagnostics, particularly when considering the increasing integration of such models into hospital systems.
English–Vietnamese Machine Translation Using Deep Learning for Chatbot Applications
Authors: Nguyen Minh Tuan, Phayung Meesad, Ha Huy Cuong Nguyen

Recently, artificial intelligence-based machine translation has been much improved over traditional methods. A machine translator is very useful for translating text or speech from one language to another. Machine translators have replaced the word mechanism in one language for words in another with verbatim translations. However, a good translation should be employed as both a sentence and a word that has a completed meaning in accordance with the context of the relevant sentence. In this paper, we studied English–Vietnamese translation using deep learning methods including recurrent neural network, long short-term memory, gated recurrent units, attention, and transformer. The deep learning-based machine translators were compared based on the test accuracy of the result translation. It was found that the best deep learning-based machine translator model was the Attention mechanism, and the Transformer yielded the second rank.
Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review
Authors: Priyadarsini Samal, Mohammad Farukh Hashmi

Emotion is a subjective psychophysiological reaction coming from external stimuli which impacts every aspect of our daily lives. Due to the continuing development of non-invasive and portable sensor technologies, such as brain-computer interfaces (BCI), intellectuals from several fields have been interested in emotion recognition techniques. Human emotions can be recognised using a variety of behavioural cues, including gestures and body language, voice, and physiological markers. The first three, however, might be ineffective because people sometimes conceal their genuine emotions either intentionally or unknowingly. More precise and objective emotion recognition can be accomplished using physiological signals. Among other physiological signals, Electroencephalogram (EEG) is more responsive and sensitive to variation in affective states. Various EEG-based emotion recognition methods have recently been introduced. This study reviews EEG-based BCIs for emotion identification and gives an outline of the progress made in this field. A summary of the datasets and techniques utilised to evoke human emotions and various emotion models is also given. We discuss several EEG feature extractions, feature selection/reduction, machine learning, and deep learning algorithms in accordance with standard emotional identification process. We provide an overview of the human brain's EEG rhythms, which are closely related to emotional states. We also go over a number of EEG-based emotion identification research and compare numerous machine learning and deep learning techniques. In conclusion, this study highlights the applications, challenges and potential areas for future research in identification and classification of human emotional states.