ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep learning-assisted characterization of nanoparticle growth processes: unveiling SAXS structure evolution
Authors: Yikun Li, Lunyang Liu, Xiaoning Zhao, Shuming Zhou, Xuehui Wu, Yuecheng Lai, Zhongjun Chen, Jizhong Chen, Xueqing Xing

PurposeThe purpose of this study is to explore deep learning methods for processing high-throughput small-angle X-ray scattering (SAXS) experimental data.MethodsThe deep learning algorithm was trained and validated using simulated SAXS data, which were generated in batches based on the theoretical SAXS formula using Python code. Our self-developed SAXSNET, a convolutional neural network based on PyTorch, was employed to classify SAXS data for various shapes of nanoparticles. Additionally, we conducted comparative analysis of classification algorithms including ResNet-18, ResNet-34 and Vision Transformer. Random Forest and XGboost regression algorithms were used for the nanoparticle size prediction. Finally, we evaluated the aforementioned shape classification and numerical regression methods using actual experimental data. A pipeline segment is established for the processing of SAXS data, incorporating deep learning classification algorithms and numerical regression algorithms.ResultsAfter being trained with simulated data, the four deep learning algorithms achieved a prediction accuracy of over 96% on the validation set. The fine-tuned deep learning model demonstrated robust generalization capabilities for predicting the shapes of experimental data, enabling rapid and accurate identification of morphological changes in nanoparticles during experiments. The Random Forest and XGboost regression algorithms can simultaneously provide faster and more accurate predictions of nanoparticle size.ConclusionThe pipeline segment constructed in this study, integrating deep learning classification and regression algorithms, enables real-time processing of high-throughput SAXS data. It aims to effectively mitigates the impact of human factors on data processing results and enhances the standardization, automation, and intelligence of synchrotron radiation experiments.
A survey: evolutionary deep learning
Authors: Yifan Li, Jing Liu

Deep learning (DL) has made remarkable progress on various real-world tasks, but its construction pipeline strongly relies on human scientists. Furthermore, evolutionary computing (EC), as an optimization tool based on the biological evolution mechanism, has good performance on complex optimization problems. It provides a new way to construct DL models and has generated many sparks in the DL field, especially in automatic machine learning (AutoML). Although many reviews have been conducted on AutoML, in recent years, few comprehensive works have studied on the application of EC in DL, which is called evolutionary deep learning (EDL). After a thorough investigation, we think that EDL can be divided into four parts: (1) learning rule optimization, (2) hyperparameter optimization, (3) neural architecture search, and (4) other EDL-related works. In this work, we introduce the classic optimization methods and the challenges of EDL with respect to these four parts, review the related work, and then present the future research prospects. This work clearly and comprehensively reviews the concept and research content of EDL, which can help readers quickly find the intersection between EC and DL and seek their inspiration.
Impact of the Preprocessing Steps in Deep Learning-Based Image Classifications
Authors: H. James Deva Koresh

Deep learning softwares are designed using artificial neural networks for various applications by training and testing them with an appropriate dataset. The raw image samples available in the dataset may contain noisy and unclear information due to radiation, heat and poor lighting conditions. Therefore, the researchers are trying to filter and enhance such noisy images through preprocessing steps for providing a valid feature information to the neural network layers included in the deep learning software. However, there are certain claims that roam around the researchers such as an image may lose some useful information when it is not preprocessed with an appropriate filter or enhancement technique. Hence, the work reviews the efficacy of the methodologies that are designed with and without a preprocessing step. Also, the work summarizes the common reasons and statements highlighted by the researchers for using and avoiding the preprocessing steps on designing a deep learning approach. The study is conducted to provide a clarity toward the requirement and non-requirement of preprocessing step in a deep learning software.
Deep learning applications in games: a survey from a data perspective
Authors: Zhipeng Hu, Yu Ding, Runze Wu, Lincheng Li, Rongsheng Zhang, Yujing Hu, Feng Qiu, Zhimeng Zhang, Kai Wang, Shiwei Zhao, Yongqiang Zhang, Ji Jiang, Yadong Xi, Jiashu Pu, Wei Zhang, Suzhen Wang, Ke Chen, Tianze Zhou, Jiarui Chen, Yan Song, Tangjie Lv, Changjie Fan

This paper presents a comprehensive review of deep learning applications in the video game industry, focusing on how these techniques can be utilized in game development, experience, and operation. As relying on computation techniques, the game world can be viewed as an integration of various complex data. This examines the use of deep learning in processing various types of game data. The paper classifies the game data into asset data, interaction data, and player data, according to their utilization in game development, experience, and operation, respectively. Specifically, this paper discusses deep learning applications in generating asset data such as object images, 3D scenes, avatar models, and facial animations; enhancing interaction data through improved text-based conversations and decision-making behaviors; and analyzing player data for cheat detection and match-making purposes. Although this review may not cover all existing applications of deep learning, it aims to provide a thorough presentation of the current state of deep learning in the gaming industry and its potential to revolutionize game production by reducing costs and improving the overall player experience.
A Novel Cyber Security Model Using Deep Transfer Learning
Authors: Ünal Çavuşoğlu, Devrim Akgun, Selman Hizal

Preventing attackers from interrupting or totally stopping critical services in cloud systems is a vital and challenging task. Today, machine learning-based algorithms and models are widely used, especially for the intelligent detection of zero-day attacks. Recently, deep learning methods that provide automatic feature extraction are designed to detect attacks automatically. In this study, we constructed a new deep learning model based on transfer learning for detecting and protecting cloud systems from malicious attacks. The developed deep transfer learning-based IDS converts network traffic into 2D preprocessed feature maps.Then the feature maps are processed with the transferred and fine-tuned convolutional layers of the deep learning model before the dense layer for detection and classification of traffic data. The results computed using the NSL-KDD test dataset reveal that the developed models achieve 89.74% multiclass and 92.58% binary classification accuracy. We performed another evaluation using only 20% of the training dataset as test data, and 80% for training. In this case, the model achieved 99.83% and 99.85% multiclass and binary classification accuracy, respectively.
Software fault prediction using deep learning techniques
Authors: Iqra Batool, Tamim Ahmed Khan

Software fault prediction (SFP) techniques identify faults at the early stages of the software development life cycle (SDLC). We find machine learning techniques commonly used for SFP compared to deep learning methods, which can produce more accurate results. Deep learning offers exceptional results in various domains, such as computer vision, natural language processing, and speech recognition. In this study, we use three deep learning methods, namely, long short-term memory (LSTM), bidirectional LSTM (BILSTM), and radial basis function network (RBFN) to predict software faults and compare our results with existing models to show how our results are more accurate. Our study uses Chidamber and Kemerer (CK) metrics-based datasets to conduct experiments and test our proposed algorithm. We conclude that LSTM and BILSTM perform better, whereas RBFN is faster in producing the required results. We use k-fold cross-validation to do the model evaluation. Our proposed models provide software developers with a more accurate and efficient SFP mechanism.
Deep Learning-Based Speed Breaker Detection
Authors: Mohamed Anas VT, Mohd Omar, Jameel Ahamad, Khaleel Ahmad, Mohd Anas Khan

Traffic operation factors include vehicle and cargo damage, environmental effects, access for emergency vehicles, transit routes, and traffic speeds and volumes. In India, traffic rule violations and high-speed vehicles contribute to a large number of accidents and fatalities on the road. To mitigate this issue, the government has implemented speed breakers as a safety measure. In most cases, speed humps are only advised for use on streets with a speed restriction of 30 mph (50 km/h) or less. However, many speed breakers lack proper signboards, and drivers often do not see them, leading to accidents. In light of this, a speed breaker detection system using deep learning is relevant and necessary in the current scenario. This research proposes a system that uses deep learning image detection algorithms to detect speed breakers in real-time, thereby increasing their visibility and reducing the risk of accidents. The system is trained using an Indian dataset and will be tested in various driving scenarios to evaluate its performance. The results are expected to show that the proposed system will outperform existing methods and have the potential to significantly improve road safety in India.
Manifold learning by a deep Gaussian process autoencoder
Authors: Francesco Camastra, Angelo Casolaro, Gennaro Iannuzzo

The paper presents a novel manifold learning algorithm, the deep Gaussian process autoencoder (DPGA), based on deep Gaussian processes. Deep Gaussian process autoencoder algorithm has the following two main characteristics. The former is a bottleneck structure, borrowed by variational autoencoders and the latter is based on the so-called doubly stochastic variational inference for deep Gaussian processes architecture (DSVI). The main novelties of the paper consist in DGPA algorithm and the experimental protocol for evaluating it. In fact, to the best of our knowledge, deep Gaussian processes algorithms have not been applied to manifold learning, yet. Besides, an experimental protocol is introduced, the so-called manifold learning performance protocol (MLPP), to compare quantitatively the geometric preserved properties of manifold learning projections of the proposed deep Gaussian process autoencoder with the ones of state-of-the-art manifold learning algorithms. Extensive experimental tests on eleven synthetic and five real datasets show that deep Gaussian process autoencoder compares favorably with the other manifold learning competitors.
A survey of deep learning-based 3D shape generation
Authors: Qun-Ce Xu, Tai-Jiang Mu, Yong-Liang Yang

Deep learning has been successfully used for tasks in the 2D image domain. Research on 3D computer vision and deep geometry learning has also attracted attention. Considerable achievements have been made regarding feature extraction and discrimination of 3D shapes. Following recent advances in deep generative models such as generative adversarial networks, effective generation of 3D shapes has become an active research topic. Unlike 2D images with a regular grid structure, 3D shapes have various representations, such as voxels, point clouds, meshes, and implicit functions. For deep learning of 3D shapes, shape representation has to be taken into account as there is no unified representation that can cover all tasks well. Factors such as the representativeness of geometry and topology often largely affect the quality of the generated 3D shapes. In this survey, we comprehensively review works on deep-learning-based 3D shape generation by classifying and discussing them in terms of the underlying shape representation and the architecture of the shape generator. The advantages and disadvantages of each class are further analyzed. We also consider the 3D shape datasets commonly used for shape generation. Finally, we present several potential research directions that hopefully can inspire future works on this topic.
Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children
Authors: Melina Koechli, Fraser M. Callaghan, Barbara E. U. Burkhardt, Maélène Lohézic, Xucheng Zhu, Beate Rücker, Emanuela R. Valsangiacomo Buechel, Christian J. Kellenberger, Julia Geiger

BackgroundVentricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds.ObjectiveTo assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences.Material and methodsTwenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1–17 years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (semi-)automatically in both datasets retrospectively by two experienced readers who visually assessed image quality employing a 4-point grading scale. Scan times and image quality were compared using the Wilcoxon rank-sum test. Volumetrics were assessed with linear regression and Bland–Altman analyses, and measurement agreement with intraclass correlation coefficient (ICC).ResultsMean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8 s vs. 218.3 ± 44.8 s; P < 0.001). No significant differences in biventricular volumetrics were found. Left ventricular (LV) mass was increased in the deep learning cine compared with the standard cine sequence (71.4 ± 33.1 g vs. 69.9 ± 32.5 g; P < 0.05). All volumetric measurements had an excellent agreement with ICC > 0.9 except for ejection fraction (EF) (LVEF 0.81, RVEF 0.73). The image quality of deep learning cine images was decreased for end-diastolic and end-systolic contours, papillary muscles, and valve depiction (2.9 ± 0.5 vs. 3.5 ± 0.4; P < 0.05).ConclusionDeep learning cine volumetrics did not differ significantly from standard cine results except for LV mass, which was slightly overestimated with deep learning cine. Deep learning cine sequences result in a significant reduction in scan time with only slightly lower image quality.Graphical Abstract
Comparative Analysis of Diabetic Retinopathy Classification Approaches Using Machine Learning and Deep Learning Techniques
Authors: Ruchika Bala, Arun Sharma, Nidhi Goel

Diabetic retinopathy (DR) is an eye disease caused due to excess of sugar in retinal blood vessels and obstructs vision. Regular and timely diagnosis can prevent the severity of diabetic retinopathy at an initial stage. Manual diagnosis of diabetic retinopathy is time-consuming and thus a plethora of work has been done by researchers to automate the classification of diabetic retinopathy using machine learning and deep learning techniques. The present review pivots around the research papers covering recent and effective automated DR classification techniques from 2011 to 2022. A comparative analysis of these papers highlights the summary of DR classification datasets, pre-processing techniques, various advanced classification algorithms, and their performance. Along with the summary and analysis of the classification techniques, the present paper demonstrates the experimentation of eight pre-trained convolution neural network models on DR benchmark classification datasets. This is to help the researchers to choose the best pre-trained classification model for the corresponding DR dataset. The use of deep learning and machine learning algorithms demonstrated excellent performance; however, researchers still need to explore the design constraints of the classification models to have effective results. Attention mechanisms and vision transformers are recent breakthroughs that can be used to solve classification challenges. The objective of this article is to provide a single platform for researchers to access state-of-the-art work in the classification of diabetic retinopathy, the results of various pre-trained models on DR benchmark classification datasets, and future research prospects for the researchers working in this challenging area.
Heart disease prediction using machine learning, deep Learning and optimization techniques-A semantic review
Authors: Girish Shrikrushnarao Bhavekar, Agam Das Goswami, Chafle Pratiksha Vasantrao, Amit K. Gaikwad, Amol V. Zade, Harsha Vyawahare

Cardiovascular disease holds the position of being the foremost cause of death worldwide. Heart Disease Prediction (HDP) is a difficult task as it needs advanced knowledge with better experience. Moreover, it encounters numerous significant challenges in clinical data analysis. While many researchers have focused on predicting heart disease, the performance metric, namely prediction accuracy, remains suboptimal. The accurate HDP can help the person to prevent himself from life threats and at the same time, inaccurate prediction can prove to be fatal. To solve these issues, in this review work several Deep Learning (DL), Machine Learning (ML) and optimization based HDP techniques are discussed. In recent times, many researchers have been utilizing different DL and ML algorithms to help the professionals and health care industry for the prediction of heart disease. Further, it discussed about various optimization-based algorithms and its performance analysis. Therefore, this review paper suggests that the optimization-based HDP algorithm could assist doctors in predicting the occurrence of heart disease in advance and offering suitable treatment.
A domain knowledge-based interpretable deep learning system for improving clinical breast ultrasound diagnosis
Authors: Lin Yan, Zhiying Liang, Hao Zhang, Gaosong Zhang, Weiwei Zheng, Chunguang Han, Dongsheng Yu, Hanqi Zhang, Xinxin Xie, Chang Liu, Wenxin Zhang, Hui Zheng, Jing Pei, Dinggang Shen, Xuejun Qian

BackgroundThough deep learning has consistently demonstrated advantages in the automatic interpretation of breast ultrasound images, its black-box nature hinders potential interactions with radiologists, posing obstacles for clinical deployment.MethodsWe proposed a domain knowledge-based interpretable deep learning system for improving breast cancer risk prediction via paired multimodal ultrasound images. The deep learning system was developed on 4320 multimodal breast ultrasound images of 1440 biopsy-confirmed lesions from 1348 prospectively enrolled patients across two hospitals between August 2019 and December 2022. The lesions were allocated to 70% training cohort, 10% validation cohort, and 20% test cohort based on case recruitment date.ResultsHere, we show that the interpretable deep learning system can predict breast cancer risk as accurately as experienced radiologists, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval = 0.882 – 0.921), sensitivity of 75.2%, and specificity of 91.8% on the test cohort. With the aid of the deep learning system, particularly its inherent explainable features, junior radiologists tend to achieve better clinical outcomes, while senior radiologists experience increased confidence levels. Multimodal ultrasound images augmented with domain knowledge-based reasoning cues enable an effective human-machine collaboration at a high level of prediction performance.ConclusionsSuch a clinically applicable deep learning system may be incorporated into future breast cancer screening and support assisted or second-read workflows.
Deep learning for osteoporosis screening using an anteroposterior hip radiograph image
Authors: Artit Boonrod, Prarinthorn Piyaprapaphan, Nut Kittipongphat, Daris Theerakulpisut, Arunnit Boonrod

PurposeOsteoporosis is a common bone disorder characterized by decreased bone mineral density (BMD) and increased bone fragility, which can lead to fractures and eventually cause morbidity and mortality. It is of great concern that the one-year mortality rate for osteoporotic hip fractures could be as high as 22%, regardless of the treatment. Currently, BMD measurement is the standard method for osteoporosis diagnosis, but it is costly and requires special equipment. While a plain radiograph can be obtained more simply and inexpensively, it is not used for diagnosis. Deep learning technologies had been applied to various medical contexts, yet few to osteoporosis unless they were trained on the advanced investigative images, such as computed tomography. The purpose of this study was to develop a deep learning model using the anteroposterior hip radiograph images and measure its diagnostic accuracy for osteoporosis.MethodsWe retrospectively collected all anteroposterior hip radiograph images of patients from 2013 to 2021 at a tertiary care hospital. The BMD measurements of the included patients were reviewed, and the radiograph images that had a time interval of more than two years from the measurements were excluded. All images were randomized using a computer-generated unequal allocation into two datasets, i.e., 80% of images were used for the training dataset and the remaining 20% for the test dataset. The T score of BMD obtained from the ipsilateral femoral neck of the same patient closest to the date of the performed radiograph was chosen. The T score cutoff value of − 2.5 was used to diagnose osteoporosis. Five deep learning models were trained on the training dataset, and their diagnostic performances were evaluated using the test dataset. Finally, the best model was determined by the area under the curves (AUC).ResultsA total of 363 anteroposterior hip radiograph images were identified. The average time interval between the performed radiograph and the BMD measurement was 6.6 months. Two-hundred-thirteen images were labeled as non-osteoporosis (T score >  − 2.5), and the other 150 images as osteoporosis (T score ≤  − 2.5). The best-selected deep learning model achieved an AUC of 0.91 and accuracy of 0.82.ConclusionsThis study demonstrates the potential of deep learning for osteoporosis screening using anteroposterior hip radiographs. The results suggest that the deep learning model might potentially be used as a screening tool to find patients at risk for osteoporosis to perform further BMD measurement.