92,054 results
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2. PRM-KGED: paper recommender model using knowledge graph embedding and deep neural network
- Author
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Thierry, Nimbeshaho, Bao, Bing-Kun, Ali, Zafar, Tan, Zhiyi, Christ Chatelain, Ingabire Batamira, and Kefalas, Pavlos
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- 2023
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3. A Review Paper about Deep Learning for Medical Image Analysis.
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Sistaninejhad B, Rasi H, and Nayeri P
- Subjects
- Child, Humans, Diagnostic Imaging methods, Neural Networks, Computer, Image Processing, Computer-Assisted methods, Deep Learning, COVID-19
- Abstract
Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Bagher Sistaninejhad et al.)
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- 2023
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4. 3D plasmonic coral nanoarchitecture paper for label-free human urine sensing and deep learning-assisted cancer screening.
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Linh VTN, Lee MY, Mun J, Kim Y, Kim H, Han IW, Park SG, Choi S, Kim DH, Rho J, and Jung HS
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- Humans, Early Detection of Cancer, Spectrum Analysis, Raman methods, Gold chemistry, Deep Learning, Biosensing Techniques methods, Neoplasms, Metal Nanoparticles chemistry
- Abstract
Practical human biofluid sensing requires a sensor device to differentiate patients from the normal group with high sensitivity and specificity. Label-free molecular identification from human biofluids allows direct classification of abnormal samples, providing insights for disease diagnosis and finding of new biomarkers. Here, we introduce a label-free surface-enhanced Raman scattering sensor based on a three-dimensional plasmonic coral nanoarchitecture (3D-PCN), which has strong electromagnetic field enhancement through multiple hot spots. The 3D-PCN was synthesized on a paper substrate via direct one-step gold reduction, forming a coral-like nanoarchitecture with high absorption property for biofluids. This was fabricated as a urine test strip and then integrated with a handheld Raman system to develop an on-site urine diagnostic platform. The developed platform successfully classified the human prostate and pancreatic cancer urines in a label-free method supported by two types of deep learning networks, with high clinical sensitivity and specificity. Our technology has the potential to be utilized not only for urinary cancer diagnosis but also for various human biofluid sensing systems as a future point-of-care testing platform., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)
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- 2023
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5. A fully-automated paper ECG digitisation algorithm using deep learning.
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Wu H, Patel KHK, Li X, Zhang B, Galazis C, Bajaj N, Sau A, Shi X, Sun L, Tao Y, Al-Qaysi H, Tarusan L, Yasmin N, Grewal N, Kapoor G, Waks JW, Kramer DB, Peters NS, and Ng FS
- Subjects
- Humans, Algorithms, Electrocardiography methods, Neural Networks, Computer, Deep Learning, Atrial Fibrillation diagnosis
- Abstract
There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60-70% and the average correlation of 3-by-1 ECGs achieved 80-90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects., (© 2022. The Author(s).)
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- 2022
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6. Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback
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Chenshu Liu, Songbin Ben, Chongwen Liu, Xianchao Li, Qingxia Meng, Yilin Hao, Qian Jiao, and Pinyi Yang
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Paper-based cultural relics ,Conservation ,Computer vision ,Deep learning ,Strain classification ,Web application ,Fine Arts ,Analytical chemistry ,QD71-142 - Abstract
Abstract Purpose Paper-based artifacts hold significant cultural and social values. However, paper is intrinsically fragile to microorganisms, such as mold, due to its cellulose composition, which can serve as a microorganisms’ nutrient source. Mold not only can damage papers’ structural integrity and pose significant challenges to conservation works but also may subject individuals attending the contaminated artifacts to health risks. Current approaches for strain identification usually require extensive training, prolonged time for analysis, expensive operation costs, and higher risks of secondary damage due to sampling. Thus, in current conservation practices with mold-contaminated artifacts, little pre-screening or strain identification was performed before mold removal, and the cleaning techniques are usually broad-spectrum rather than strain-specific. With deep learning showing promising applications across various domains, this study investigated the feasibility of using a convolutional neural network (CNN) for fast in-situ recognition and classification of mold on paper. Methods Molds were first non-invasively sampled from ancient Xuan Paper-based Chinese books from the Qing and Ming dynasties. Strains were identified using molecular biology methods and the four most prevalent strains were inoculated on Xuan paper to create mockups for image collection. Microscopic images of the molds as well as their stains situated on paper were collected using a compound microscope and commercial microscope lens for cell phone cameras, which were then used for training CNN models with a transfer learning scheme to perform the classification of mold. To enable involvement and contribution from the research community, a web interface that actuates the process while providing interactive features for users to learn about the information of the classified strain was constructed. Moreover, a feedback functionality in the web interface was embedded for catching potential classification errors, adding additional training images, or introducing new strains, all to refine the generalizability and robustness of the model. Results & Conclusion In the study, we have constructed a suite of high-confidence classification CNN models for the diagnostic process for mold contamination in conservation. At the same time, a web interface was constructed that allows recurrently refining the model with human feedback through engaging the research community. Overall, the proposed framework opens new avenues for effective and timely identification of mold, thus enabling proactive and targeted mold remediation strategies in conservation.
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- 2024
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7. Web-based diagnostic platform for microorganism-induced deterioration on paper-based cultural relics with iterative training from human feedback
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Liu, Chenshu, Ben, Songbin, Liu, Chongwen, Li, Xianchao, Meng, Qingxia, Hao, Yilin, Jiao, Qian, and Yang, Pinyi
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- 2024
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8. Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports
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Bassiouni, Mahmoud M., Hegazy, Islam, Rizk, Nouhad, El-Dahshan, El-Sayed A., and Salem, Abdelbadeeh M.
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- 2022
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9. NSTU-BDTAKA: An open dataset for Bangladeshi paper currency detection and recognition
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Md. Jubayar Alam Rafi, Mohammad Rony, and Nazia Majadi
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Computer vision ,Deep learning ,Image analysis ,Taka detection ,Taka recognition ,YOLOv5 model ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Science (General) ,Q1-390 - Abstract
One of the most popular and well-established forms of payment in use today is paper money. Handling paper money might be challenging for those with vision impairments. Assistive technology has been reinventing itself throughout time to better serve the elderly and disabled people. To detect paper currency and extract other useful information from them, image processing techniques and other advanced technologies, such as Artificial Intelligence, Deep Learning, etc., can be used. In this paper, we present a meticulously curated and comprehensive dataset named ‘NSTU-BDTAKA’ tailored for the simultaneous detection and recognition of a specific object of cultural significance - the Bangladeshi paper currency (in Bengali it is called ‘Taka’). This research aims to facilitate the development and evaluation of models for both taka detection and recognition tasks, offering a rich resource for researchers and practitioners alike. The dataset is divided into two distinct components: (i) taka detection, and (ii) taka recognition. The taka detection subset comprises 3,111 high-resolution images, each meticulously annotated with rectangular bounding boxes that encompass instances of the taka. These annotations serve as ground truth for training and validating object detection models, and we adopt the state-of-the-art YOLOv5 architecture for this purpose. In the taka recognition subset, the dataset has been extended to include a vast collection of 28,875 images, each showcasing various instances of the taka captured in diverse contexts and environments. The recognition dataset is designed to address the nuanced task of taka recognition providing researchers with a comprehensive set of images to train, validate, and test recognition models. This subset encompasses challenges such as variations in lighting, scale, orientation, and occlusion, further enhancing the robustness of developed recognition algorithms. The dataset NSTU-BDTAKA not only serves as a benchmark for taka detection and recognition but also fosters advancements in object detection and recognition methods that can be extrapolated to other cultural artifacts and objects. We envision that the dataset will catalyze research efforts in the field of computer vision, enabling the development of more accurate, robust, and efficient models for both detection and recognition tasks.
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- 2024
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10. Research hotspots and trends of artificial intelligence in diabetic retinopathy based on bibliometrics and high-impact papers
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Ruo-Yu Wang, Wang-Ting Li, Shao-Chong Zhang, and Wei-Hua Yang
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artificial intelligence ,diabetic retinopathy ,bibliometrics ,citespace ,deep learning ,hotspots ,trends ,Ophthalmology ,RE1-994 - Abstract
AIM: To analyze research hotspots and trends of artificial intelligence in diabetic retinopathy(DR)based on bibliometrics and high-impact papers.METHODS: Papers on artificial intelligence in DR research published in the Web of Science Core Collection(WoSCC)from January 1, 2012, to December 31, 2022 were retrieved. The data was analyzed by CiteSpace software to examine annual publication number, countries, institutions, source journal, research categories, keywords, and to perform an in-depth analysis of high-impact papers.RESULTS: A total of 1 009 papers on artificial intelligence in DR from 79 countries were included in the study, with 272 papers published in 2022. Notably, China and India contributed 287 and 234 papers, respectively. The United Kingdom exhibited a centrality score of 0.31, while the United States boasted an impressive H-index of 48. Three prominent institutions in the United Kingdom(University of London, Moorfields Eye Hospital, and University College London)and one institution in Egypt(Egyptian Knowledge Bank)all achieved a notable H-index of 14. The primary academic disciplines associated with this research field encompassed ophthalmology, computer science, and artificial intelligence. Burst keywords in the years 2021~2022 included transfer learning, vessel segmentation, and convolutional neural networks.CONCLUSION: China emerged as the leading contributor in terms of publication number in this field, while the United States stood out as a key player. Notably, Egyptian Knowledge Bank and University of London assumed leading roles among research institutions. Additionally, IEEE Access was identified as the most active journal within this domain. The research focus in the field of artificial intelligence in DR has transitioned from AI applications in disease detection and grading to a more concentrated exploration of AI-assisted diagnostic systems. Transfer learning, vessel segmentation, and convolutional neural networks hold substantial promise for widespread applications in this field.
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- 2023
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11. Speech recognition for Kazakh language: a research paper.
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Kapyshev, Galym, Nurtas, Marat, and Altaibek, Aizhan
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SPEECH perception ,AUTOMATIC speech recognition ,NATURAL language processing ,LANGUAGE research ,DEEP learning ,MARKOV processes - Abstract
In recent years, the research pertaining to speech recognition technology in the Kazakh language has gained significant importance. This is due to the increasing demand for natural language processing applications in the region where Kazakh is predominantly spoken. Thus, there exists an urgent requirement for precise and dependable speech recognition systems. The research study examines the application of sophisticated deep learning methodologies, such as Natural Language Processing (NLP) and Hidden Markov Model (HMM), in facilitating speech recognition for the Kazakh language. Additionally, the investigation delves into how various techniques, including data preprocessing, acoustic modeling, and language modeling, can aid in devising effective speech recognition systems. The article deliberates on the feasible uses of speech recognition technology in the geographic area where Kazakh language is spoken and outlines its future research prospects. The investigation underscores the significance of persistent inquiry in this realm to confront distinctive obstacles encountered in creating speech recognition systems for languages with restricted resources. [ABSTRACT FROM AUTHOR]
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- 2024
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12. Improved GRU prediction of paper pulp press variables using different pre-processing methods
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Balduíno César Mateus, Mateus Mendes, José Torres Farinha, António Marques Cardoso, Rui Assis, and Hamzeh Soltanali
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Deep learning ,LOWESS ,forecasting failures ,industrial press ,recurrent neural network ,predictive maintenance ,Technology ,Manufactures ,TS1-2301 ,Business ,HF5001-6182 - Abstract
ABSTRACTPredictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector.Hence, this study contributes to examine the most efficient pre-processing approaches for predicting sensory data trends based on Gated Recurrent Unit (GRU) neural networks. To validate the model, the data from two paper pulp presses with several pre-processing methods are utilized for predicting the units’ conditions. The results of validation criteria show that pre-processing data using a LOWESS in combination with the Elimination of discrepant data filter achieves more stable results, the prediction error decreases, and the predicted values are easier to interpret. The model can anticipate future values with MAPE, RMSE and MAE of 1.2, 0.27 and 0.30 respectively. The errors are below the significance level. Moreover, it is identified that the best hyperparameters found for each paper pulp press must be different.
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- 2023
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13. Ash Detection of Coal Slime Flotation Tailings Based on Chromatographic Filter Paper Sampling and Multi-Scale Residual Network.
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Wenbo Zhu, Neng Liu, Zhengjun Zhu, Haibing Li, Weijie Fu, Zhongbo Zhang, and Xinghao Zhang
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COAL ash ,FILTER paper ,FLOTATION ,FEATURE extraction ,DEEP learning ,FOAM - Abstract
The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam, impurities, and changing lighting conditions that disrupt the collection of tailings images. To address this challenge, we present a method for ash content detection in coal slime flotation tailings. This method utilizes chromatographic filter paper sampling and a multi-scale residual network, which we refer to as MRCN. Initially, tailings are sampled using chromatographic filter paper to obtain static tailings images, effectively isolating interference factors at the flotation site. Subsequently, the MRCN, consisting of a multi-scale residual network, is employed to extract image features and compute ash content. Within the MRCN structure, tailings images undergo convolution operations through two parallel branches that utilize convolution kernels of different sizes, enabling the extraction of image features at various scales and capturing a more comprehensive representation of the ash content information. Furthermore, a channel attention mechanism is integrated to enhance the performance of the model. The combination of the multi-scale residual structure and the channel attention mechanism within MRCN results in robust capabilities for image feature extraction and ash content detection. Comparative experiments demonstrate that this proposed approach, based on chromatographic filter paper sampling and the multi-scale residual network, exhibits significantly superior performance in the detection of ash content in coal slime flotation tailings. [ABSTRACT FROM AUTHOR]
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- 2023
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14. A personalized paper recommendation method based on knowledge graph and transformer encoder with a self-attention mechanism.
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Gao, Li, Lan, Yu, Yu, Zhen, and Zhu, Jian-min
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KNOWLEDGE graphs ,DEEP learning ,RECOMMENDER systems ,TRANSFORMER models ,DIGITAL Object Identifiers ,ELECTRONIC publications - Abstract
Paper recommendation with personalized methods helps researchers to track the latest academic trends and master cutting-edge academic trends efficiently. Meanwhile, the methods of previous paper recommendation suffer from three problems: data sparsity of content-based and collaborative filtering methods; Graph-based recommendations do not fully consider the personalized information of authors and their articles; Cold start based on deep learning. To overcome those difficulties, we propose a personalized paper recommendation method based on a knowledge graph and Transformer encoder (KGTE) with a self-attention mechanism. Firstly, we add auxiliary information (article title, publication year, citation times, and abstract) as attributes to the nodes of knowledge graph(KG), which contain author, digital object unique identifier(DOI) and keywords. Secondly, BERT is used to represent the semantic information features of the article and Transformer is introduced to fully integrate the feature context. After that, by using RippleNet, we traverse the knowledge graph, filter the user preference distribution and form a set of pre recommended nodes with multi_hop nodes. Finally, the prediction layer sorts the set and gets a Top_n paper recommendation. In the experiments on the DBLP and Aminer datasets, the precision value of KGTE improved by an average of 2.59% over the existing baseline methods DER and 4.23% improvement in NDCG. [ABSTRACT FROM AUTHOR]
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- 2023
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15. Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper
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Prasoon Kumar Vinodkumar, Dogus Karabulut, Egils Avots, Cagri Ozcinar, and Gholamreza Anbarjafari
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deep learning ,3D reconstruction ,3D augmentation ,3D registration ,point cloud ,voxel ,Science ,Astrophysics ,QB460-466 ,Physics ,QC1-999 - Abstract
The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future.
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- 2024
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16. Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries.
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Folks, Ryan D., Naik, Bhiken I., Brown, Donald E., and Durieux, Marcel E.
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- *
MEDICAL records , *ARTIFICIAL neural networks , *COMPUTER vision , *DIASTOLIC blood pressure , *MEDICAL personnel , *DEEP learning , *SYSTOLIC blood pressure - Abstract
Background: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. Methods: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. Results: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. Conclusions: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries. [ABSTRACT FROM AUTHOR]
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- 2024
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17. A novel application of XAI in squinting models: A position paper
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Kenneth Wenger, Katayoun Hossein Abadi, Damian Fozard, Kayvan Tirdad, Alex Dela Cruz, and Alireza Sadeghian
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Artificial Intelligence ,Deep learning ,Pathology ,Explainable AI ,XAI ,Safety critical AI ,Cybernetics ,Q300-390 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Artificial Intelligence, and Machine Learning especially, are becoming increasingly foundational to our collective future. Recent developments around generative models such as ChatGPT, and DALL-E represent just the tip of the iceberg in new gadgets that will change the way we live our lives. Convolutional Neural Networks (CNNs) and Transformer models are at the heart of advancements in the autonomous vehicles and health care industries as well. Yet these models, as impressive as they are, still make plenty of mistakes without justifying or explaining what aspects of the input or internal state, was responsible for the error. Often, the goal of automation is to increase throughput, processing as many tasks as possible in a short a period of time. For some use cases the cost of mistakes might be acceptable as long as production is increased above some set margin. However, in health care, autonomous vehicles, and financial applications, the cost of a mistake might have catastrophic consequences. For this reason, industries where single mistakes can be costly are less enthusiastic about early AI adoption. The field of eXplainable AI (XAI) has attracted significant attention in recent years with the goal of producing algorithms that shed light into the decision-making process of neural networks. In this paper we show how robust vision pipelines can be built using XAI algorithms with the goal of producing automated watchdogs that actively monitor the decision-making process of neural networks for signs of mistakes or ambiguous data. We call these robust vision pipelines, squinting pipelines.
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- 2023
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18. Generation of Highlights From Research Papers Using Pointer-Generator Networks and SciBERT Embeddings
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Tohida Rehman, Debarshi Kumar Sanyal, Samiran Chattopadhyay, Plaban Kumar Bhowmick, and Partha Pratim Das
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Deep learning ,natural language generation ,pointer-generator network ,SciBERT ,scientific data ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Nowadays many research articles are prefaced with research highlights to summarize the main findings of the paper. Highlights not only help researchers precisely and quickly identify the contributions of a paper, they also enhance the discoverability of the article via search engines. We aim to automatically construct research highlights given certain segments of a research paper. We use a pointer-generator network with coverage mechanism and a contextual embedding layer at the input that encodes the input tokens into SciBERT embeddings. We test our model on a benchmark dataset, CSPubSum, and also present MixSub, a new multi-disciplinary corpus of papers for automatic research highlight generation. For both CSPubSum and MixSub, we have observed that the proposed model achieves the best performance compared to related variants and other models proposed in the literature. On the CSPubSum dataset, our model achieves the best performance when the input is only the abstract of a paper as opposed to other segments of the paper. It produces ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 38.26, 14.26 and 35.51, respectively, METEOR score of 32.62, and BERTScore F1 of 86.65 which outperform all other baselines. On the new MixSub dataset, where only the abstract is the input, our proposed model (when trained on the whole training corpus without distinguishing between the subject categories) achieves ROUGE-1, ROUGE-2 and ROUGE-L F1-scores of 31.78, 9.76 and 29.3, respectively, METEOR score of 24.00, and BERTScore F1 of 85.25.
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- 2023
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19. A study on deep learning model based on global-local structure for crowd flow prediction.
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Go H and Park S
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- Humans, SARS-CoV-2, Deep Learning, COVID-19 epidemiology, Crowding
- Abstract
Crowd flow prediction has been studied for a variety of purposes, ranging from the private sector such as location selection of stores according to the characteristics of commercial districts and customer-tailored marketing to the public sector for social infrastructure design such as transportation networks. Its importance is even greater in light of the spread of contagious diseases such as COVID-19. In many cases, crowd flow can be divided into subgroups by common characteristics such as gender, age, location type, etc. If we use such hierarchical structure of the data effectively, we can improve prediction accuracy of crowd flow for subgroups. But the existing prediction models do not consider such hierarchical structure of the data. In this study, we propose a deep learning model based on global-local structure of the crowd flow data, which utilizes the overall(global) and subdivided by the types of sites(local) crowd flow data simultaneously to predict the crowd flow of each subgroup. The experiment result shows that the proposed model improves the prediction accuracy of each sub-divided subgroup by 5.2% (Table 5 Cat #9)-45.95% (Table 11 Cat #5), depending on the data set. This result comes from the comparison with the related works under the same condition that use target category data to predict each subgroup. In addition, when we refine the global data composition by considering the correlation between subgroups and excluding low correlated subgroups, the prediction accuracy is further improved by 5.6-48.65%., (© 2024. The Author(s).)
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- 2024
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20. Improved GRU prediction of paper pulp press variables using different pre-processing methods.
- Author
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Mateus, Balduíno César, Mendes, Mateus, Torres Farinha, José, Marques Cardoso, António, Assis, Rui, and Soltanali, Hamzeh
- Subjects
PAPER pulp ,RECURRENT neural networks - Abstract
Predictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector. Hence, this study contributes to examine the most efficient pre-processing approaches for predicting sensory data trends based on Gated Recurrent Unit (GRU) neural networks. To validate the model, the data from two paper pulp presses with several pre-processing methods are utilized for predicting the units' conditions. The results of validation criteria show that pre-processing data using a LOWESS in combination with the Elimination of discrepant data filter achieves more stable results, the prediction error decreases, and the predicted values are easier to interpret. The model can anticipate future values with MAPE, RMSE and MAE of 1.2, 0.27 and 0.30 respectively. The errors are below the significance level. Moreover, it is identified that the best hyperparameters found for each paper pulp press must be different. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
21. Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists
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Longjiang Zhang, Zhao Shi, Min Chen, Yingmin Chen, Jingliang Cheng, Li Fan, Nan Hong, Wenxiao Jia, Guihua Jiang, Shenghong Ju, Xiaogang Li, Xiuli Li, Changhong Liang, Weihua Liao, Shiyuan Liu, Zaiming Lu, Lin Ma, Ke Ren, Pengfei Rong, Bin Song, Gang Sun, Rongpin Wang, Zhibo Wen, Haibo Xu, Kai Xu, Fuhua Yan, Yizhou Yu, Yunfei Zha, Fandong Zhang, Minwen Zheng, Zhen Zhou, Wenzhen Zhu, Guangming Lu, and Zhengyu Jin
- Subjects
Cerebrovascular diseases ,Deep learning ,Study design ,Medical imaging ,Medical technology ,R855-855.5 - Abstract
In recent years, with the development of artificial intelligence, especially deep learning technology, researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice. However, because of the complexity and flexibility of the deep learning algorithms, these researches have great variability on model building, validation process, performance description and results interpretation. The lack of a reliable, consistent, standardized design protocol has, to a certain extent, affected the progress of clinical translation and technology development of computer aided detection systems. After reviewing a large number of literatures and extensive discussion with domestic experts, this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases. With further research and application expansion, this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools.
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- 2022
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22. Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers
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Nils Hütten, Miguel Alves Gomes, Florian Hölken, Karlo Andricevic, Richard Meyes, and Tobias Meisen
- Subjects
automated visual inspection ,industrial applications ,deep learning ,computer vision ,convolutional neural network ,vision transformer ,Technology ,Applied mathematics. Quantitative methods ,T57-57.97 - Abstract
Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.
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- 2024
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23. Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN
- Author
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Muhammad Nur Ichsan, Nur Armita, Agus Eko Minarno, Fauzi Dwi Setiawan Sumadi, and Hariyady
- Subjects
cnn ,deep learning ,image classification ,machine learning ,neural network ,Systems engineering ,TA168 ,Information technology ,T58.5-58.64 - Abstract
Rock Paper Scissors is one of the most popular games in the world, because of their easy and simple way to play among young and elderly people. The point of this game is to do the draw or just to find out who loses or wins. The pandemic conditions made people unable to meet face-to-face and could only play this game virtually. To carry out this activity in a virtual way, this research facilitates a model in the form of image classification to distinguish the hand gestures s in the form of rock, paper, and scissors. This classification process utilizes the Convolutional Neural Network (CNN) method. This method is one type of artificial neural network in terms of image classification. CNN uses three stages, namely convolutional layer, pooling layer, and fully connected layer. The implementation of this method for hand gesture classification in the form of rock, scissors, and paper images in this study shows an increased average accuracy towards the previous study from 97.66% to 99%.
- Published
- 2022
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24. Special Issue "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023".
- Author
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Kim, Byung-Seo, Afzal, Muhammad Khalil, and Ullah, Rehmat
- Subjects
- *
MULTICASTING (Computer networks) , *INFORMATION technology , *SENSOR networks , *ARTIFICIAL neural networks , *DEEP learning , *BEAM steering , *INTEGRATED circuit design , *COMPUTER network security - Abstract
This document is a summary of a special issue of the journal Sensors, titled "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023." The special issue features selected papers from the 10th and 11th International Conferences on Green and Human Information Technology (ICGHITs), which were held in Korea and Thailand. The conferences focused on the theme of "Emerging Artificial Intelligent (AI)+X technology" and "Hyper Automation + Human AI" respectively. The selected papers cover various topics such as network security, routing protocols, signal detection, and clustering mechanisms, all incorporating AI-based methods. The issue also includes papers on topics like secure authentication, distance estimation in RFID systems, energy optimization in smart homes, blockchain technology, and radar signal detection. The authors emphasize the importance of both technology and humanity in advancing green and information technologies. [Extracted from the article]
- Published
- 2024
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25. Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper.
- Author
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Vinodkumar, Prasoon Kumar, Karabulut, Dogus, Avots, Egils, Ozcinar, Cagri, and Anbarjafari, Gholamreza
- Subjects
- *
DEEP learning , *COMPUTER vision , *GRAPH neural networks , *ARTIFICIAL intelligence , *MACHINE learning , *GENERATIVE adversarial networks - Abstract
The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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26. Reconfigurable intelligent surface based hybrid precoding for THz communications (invited paper)
- Author
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Yu Lu, Mo Hao, and Richard Mackenzie
- Subjects
reconfigurable intelligent surface (ris) ,thz communication ,massive multiple-input multiple-output (mimo) ,hybrid precoding ,deep learning ,Telecommunication ,TK5101-6720 - Abstract
Benefiting from the growth of the bandwidth, Terahertz (THz) communication can support the new application with explosive requirements of the ultra-high-speed rates for future 6G wireless systems. In order to compensate for the path loss of high frequency, massive Multiple-Input Multiple-Output (MIMO) can be utilized for high array gains by beamforming. However, the existing THz communication with massive MIMO has remarkably high energy consumption because a large number of analog phase shifters should be used to realize the analog beamforming. To solve this problem, a Reconfigurable Intelligent Surface (RIS) based hybrid precoding architecture for THz communication is developed in this paper, where the energy-hungry phased array is replaced by the energy-efficient RIS to realize the analog beamforming of the hybrid precoding. Then, based on the proposed RIS-based architecture, a sum-rate maximization problem for hybrid precoding is investigated. Since the phase shifts implemented by RIS in practice are often discrete, this sum-rate maximization problem with a non-convex constraint is challenging. Next, the sum-rate maximization problem is reformulated as a parallel Deep Neural Network (DNN) based classification problem, which can be solved by the proposed low-complexity Deep Learning based Multiple Discrete Classification (DL-MDC) hybrid precoding scheme. Finally, we provide numerous simulation results to show that the proposed DL-MDC scheme works well both in the theoretical Saleh-Valenzuela channel model and practical 3GPP channel model. Compared with existing iterative search algorithms, the proposed DL-MDC scheme significantly reduces the runtime with a negligible performance loss.
- Published
- 2022
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27. A Graph-Based Topic Modeling Approach to Detection of Irrelevant Citations.
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Pham, Phu, Le, Hieu, Tam, Nguyen Thanh, and Tran, Quang-Dieu
- Subjects
NATURAL language processing ,DEEP learning ,MACHINE learning ,INFORMATION retrieval - Abstract
In the recent years, the academic paper influence analysis has been widely studied due to its potential applications in the multiple areas of science information metric and retrieval. By identifying the academic influence of papers, authors, etc., we can directly support researchers to easily reach academic papers. These recommended candidate papers are not only highly relevant with their desired research topics but also highly-attended by the research community within these topics. For very recent years, the rapid developments of academic networks, like Google Scholar, Research Gate, CiteSeerX, etc., have significantly boosted the number of new published papers annually. It also helps to strengthen the borderless cooperation between researchers who are interested on the same research topics. However, these current academic networks still lack the capabilities of provisioning researchers deeper into most-influenced papers. They also largely ignore quite/irrelevant papers, which are not fully related with their current interest topics. Moreover, the distributions of topics within these academic papers are considered as varying and it is difficult to extract the main concentrated topics in these papers. Thus, it leads to challenges for researchers to find their appropriated/high-qualified reference resources while doing researches. To overcome this limitation, in this paper, we proposed a novel approach of paper influence analysis through their content-based and citation relationship-based analyses within the biographical network. In order to effectively extract the topic-based relevance from papers, we apply the integrated graph-based citation relationship analysis with topic modeling approach to automatically learn the distributions of keyword-based labeled topics in forms of unsupervised learning approach, named as TopCite. Then, we base on the constructed graph-based paper–topic structure to identify their relevancy levels. Upon the identified relevancy levels between papers, we can support for improving the accuracy performance of other bibliographic network mining tasks, such as paper similarity measurement, recommendation, etc. Extensive experiments in real-world AMiner bibliographic dataset demonstrate the effectiveness of our proposed ideas in this paper. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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28. Toward Interactive Music Generation: A Position Paper
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Shayan Dadman, Bernt Arild Bremdal, Borre Bang, and Rune Dalmo
- Subjects
Deep learning ,multi-agent systems ,music composition ,music creativity ,music generation ,music information retrieval ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations.
- Published
- 2022
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29. Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress.
- Author
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Schilling M, Unterberg-Buchwald C, Lotz J, and Uecker M
- Subjects
- Humans, Stroke Volume, Magnetic Resonance Imaging, Cine methods, Retrospective Studies, Magnetic Resonance Imaging methods, Heart Ventricles, Reproducibility of Results, Ventricular Function, Left, Deep Learning
- Abstract
In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice's coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice's coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice's coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice's coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future., (© 2024. The Author(s).)
- Published
- 2024
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30. A deep learning system for predicting time to progression of diabetic retinopathy.
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- Humans, Machine Learning, Algorithms, Diabetic Retinopathy, Deep Learning, Diabetes Mellitus
- Published
- 2024
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- View/download PDF
31. fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task
- Author
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Tengfei Ma, Wentian Chen, Xin Li, Yuting Xia, Xinhua Zhu, and Sailing He
- Subjects
BCI ,fNIRS ,CNN ,TSC ,rock–paper–scissors ,deep learning ,Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Biology (General) ,QH301-705.5 ,Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).
- Published
- 2021
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32. Object Detection Model, Image Data and Results from the 'When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and Around State Game Lands in Pennsylvania' Paper
- Author
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Jeff Blackadar, Benjamin Carter, and Weston Conner
- Subjects
relict charcoal hearth ,rch ,mask r-cnn ,pennsylvania ,digital elevation model ,iron production ,deep learning ,Archaeology ,CC1-960 - Abstract
These data were used to build an object detection model to locate Relict Charcoal Hearths (RCH) as described in the paper “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” [1]. This is the second grouping of data for the paper above. The first grouping is also available in this journal, see “Geospatial and image data from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” paper” [2]. These files consist of: JPEGs representing tiles of larger Slope TIFF files derived from LiDAR for the State Game Lands (SGL) of Pennsylvania, United States [3456]. A subset of these tiles was used to train the model. A Shapefile of points of known relict charcoal hearths (RCH). XML files representing the pixel points of known RCHs on JPEG files used for training. Jupyter notebooks of programs used to prepare data and train a Mask R-CNN model. The Mask R-CNN model H5 file. Shapefile and GeoJSON of object detection results from the model showing locations of possible RCH in all SGLs. XML files representing the pixel points of predicted RCH on JPEG files used for predictions. GeoJSON of results using cluster analysis. These data are stored onzenodo.org. The programs are stored on Github.com.
- Published
- 2021
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33. A Hybrid Model Based on LFM and BiGRU Toward Research Paper Recommendation
- Author
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Xu Zhao, Hui Kang, Tie Feng, Chenkun Meng, and Ziqing Nie
- Subjects
Recommender systems ,deep learning ,LFM ,BiGRU ,user attention ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.
- Published
- 2020
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34. Critical appraisal of a machine learning paper: A guide for the neurologist
- Author
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Pulikottil W Vinny, Rahul Garg, M V Padma Srivastava, Vivek Lal, and Venugoapalan Y Vishnu
- Subjects
critical appraisal ,deep learning ,machine learning ,neural networks ,Neurology. Diseases of the nervous system ,RC346-429 - Abstract
Machine learning (ML), a form of artificial intelligence (AI), is being increasingly employed in neurology. Reported performance metrics often match or exceed the efficiency of average clinicians. The neurologist is easily baffled by the underlying concepts and terminologies associated with ML studies. The superlative performance metrics of ML algorithms often hide the opaque nature of its inner workings. Questions regarding ML model's interpretability and reproducibility of its results in real-world scenarios, need emphasis. Given an abundance of time and information, the expert clinician should be able to deliver comparable predictions to ML models, a useful benchmark while evaluating its performance. Predictive performance metrics of ML models should not be confused with causal inference between its input and output. ML and clinical gestalt should compete in a randomized controlled trial before they can complement each other for screening, triaging, providing second opinions and modifying treatment.
- Published
- 2021
- Full Text
- View/download PDF
35. Geospatial and Image Data from the 'When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and Around State Game Lands in Pennsylvania' Paper
- Author
-
Weston Conner, Benjamin Carter, and Jeff Blackadar
- Subjects
relict charcoal hearth ,rch ,mask r-cnn ,pennsylvania ,digital elevation model ,iron production ,deep learning ,Archaeology ,CC1-960 - Abstract
These data were used to build an object detection model to locate Relict Charcoal Hearths (RCH) as described in the paper “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” [1]. This is the first grouping of data for the paper above. The second grouping is also available in this journal, see “Object detection model, image data and results from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” paper”. These files consist of georeferenced Digital Elevation Model, Hillshade and Slope files derived from LiDAR for the State Game Lands (SGL). Included is a Shapefile and GeoJSON of State Game Land borders as well as the program used for downloading the LiDAR files. These data are stored on 'Zenodo.org'.
- Published
- 2021
- Full Text
- View/download PDF
36. Deep Learning on Medical Imaging in Identifying Kidney Stones: Review Paper
- Author
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Sulaksono Nanang, Adi Kusworo, and Isnanto dan Rizal
- Subjects
artificial intelligence ,deep learning ,medical imaging ,kidney stones ,Environmental sciences ,GE1-350 - Abstract
Medical imaging is currently using artificial intelligence-based technologies to aid evaluate diagnostic information images, particularly in enforcing kidney stones. Artificial intelligence technology continues to develop, many studies show that deep learning is more widely used compared to traditional machine learning, so an Artificial intelligence system is needed to assist the accuracy of health diagnoses, thus helping in the field of radiology health. The aim of the research is to use artificial intelligence with deep learning models to help detect abnormalities in the kidneys. This research method is a literature review of Scopus data related to deep learning in medical imaging in detecting kidney stones. The results of using Artificial Intelligence in medical imaging can be used in diagnosing diseases including detecting Covid-19, musculoskeletal, calcium scores on Cardiac CT, liver tumors, urinary tract lesions, examination of the abdomen and kidney stones. Utilization of Artificial Intelligence in detecting kidney stones can be done with various classification models including XResNet-50, ExDark19, CystoNet, CNN, ANN. Using the right model and having a high accuracy value can help radiologists to accurately detect kidney stones.
- Published
- 2023
- Full Text
- View/download PDF
37. Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning.
- Author
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Hoang DT, Shulman ED, Turakulov R, Abdullaev Z, Singh O, Campagnolo EM, Lalchungnunga H, Stone EA, Nasrallah MP, Ruppin E, and Aldape K
- Subjects
- Humans, CpG Islands genetics, Female, Male, Deep Learning, DNA Methylation, Central Nervous System Neoplasms genetics, Central Nervous System Neoplasms pathology
- Abstract
Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
- Published
- 2024
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- View/download PDF
38. Deep learning-based skin care product recommendation: A focus on cosmetic ingredient analysis and facial skin conditions.
- Author
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Lee J, Yoon H, Kim S, Lee C, Lee J, and Yoo S
- Subjects
- Humans, Skin drug effects, Skin Diseases, Cosmetics administration & dosage, Cosmetics chemistry, Deep Learning, Skin Care methods, Face
- Abstract
Background: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics., Aims: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis., Methods: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual., Results: Our cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems., Conclusion: The result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations., (© 2024 The Authors. Journal of Cosmetic Dermatology published by Wiley Periodicals LLC.)
- Published
- 2024
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39. Editorial: Special Issue on Selected Papers from the 33rd Annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2021).
- Author
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Tsihrintzis, George A., Virvou, Maria, and Hatzilygeroudis, Ioannis
- Subjects
ARTIFICIAL intelligence ,DEEP learning ,CONFERENCES & conventions ,GENETIC algorithms ,PROGRAMMING languages ,GENE regulatory networks - Published
- 2023
- Full Text
- View/download PDF
40. Research Trends in Artificial Intelligence and Security—Bibliometric Analysis.
- Author
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Ilić, Luka, Šijan, Aleksandar, Predić, Bratislav, Viduka, Dejan, and Karabašević, Darjan
- Subjects
DEEP learning ,BIBLIOMETRICS ,ARTIFICIAL intelligence ,WEB analytics ,MACHINE learning ,PUBLIC health infrastructure - Abstract
This paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020 to 2024, using primary data from the Clarivate Analytics Web of Science Core Collection. The analysis includes the distribution of studies by year, the number of studies and citation rankings in journals, and the identification of leading countries, institutions, and authors in the field of AI research. Additionally, we investigate the distribution of studies by Web of Science categories, authors, affiliations, publication years, countries/regions, publishers, research areas, and citations per year. Key findings indicate a continued growth of interest in topics such as deep learning, machine learning, and security in AI over the past few years. We also identify leading countries and institutions active in researching this area. Awareness of data security is essential for the responsible application of AI technologies. Robust security frameworks are important to mitigate risks associated with AI integration into critical infrastructure such as healthcare and finance. Ensuring the integrity and confidentiality of data managed by AI systems is not only a technical challenge but also a societal necessity, demanding interdisciplinary collaboration and policy development. This analysis provides a deeper understanding of the current state of research in the field of AI and identifies key areas for further research and innovation. Furthermore, these findings may be valuable to practitioners and decision-makers seeking to understand current trends and innovations in AI to enhance their business processes and practices. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. A Comparison between Digital-Game-Based and Paper-Based Learning for EFL Undergraduate Students' Vocabulary Learning †.
- Author
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Sianturi, Alex Dharmawan and Hung, Ruei-Tang
- Subjects
ENGLISH as a foreign language ,ARTIFICIAL intelligence ,DEEP learning ,DIGITAL technology ,VOCABULARY - Abstract
This research aimed to compare two strategies for vocabulary learning (digital-game-based learning and paper-based learning). The research was conducted during the first semester of the academic year 2022/2023. A total of 40 EFL undergraduate students within the Applied English Program of a private university located in the middle part of Taiwan were selected and divided into two groups: digital-based (n = 20) and paper-based (n = 20). The instrument developed by the researcher was pre- and post-vocabulary tests for both groups. The pre-vocabulary test was implemented to identify the level of students' prior knowledge of vocabulary mastery. For the intervention, Kahoot! quiz exercises were conducted weekly for the digital-game-based group, while the paper-based group received the same quiz on paper every week. The post-vocabulary tests showed no significant difference between the students using digital-game-based quizzes and paper-based quizzes during the six-week intervention. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
42. An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).
- Author
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Parwani, Anil V., Patel, Ankush, Ming Zhou, Cheville, John C., Tizhoosh, Hamid, Humphrey, Peter, Reuter, Victor E., and True, Lawrence D.
- Subjects
- *
DEEP learning , *ITERATIVE learning control , *PATHOLOGY , *IMAGE analysis , *MACHINE learning - Abstract
Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
43. Fault detection system for paper cup machine based on real-time image processing.
- Author
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Aydın, Alaaddin and Güney, Selda
- Subjects
- *
PROGRAMMABLE controllers , *OBJECT recognition (Computer vision) , *SERVOMECHANISMS , *ARTIFICIAL intelligence , *DEEP learning , *DIGITAL image processing , *PRODUCT image , *IMAGE processing - Abstract
In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
44. The application value of deep learning-based nomograms in benign-malignant discrimination of TI-RADS category 4 thyroid nodules.
- Author
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Zhang X, Jia C, Sun M, and Ma Z
- Subjects
- Humans, Nomograms, Retrospective Studies, Ultrasonography methods, Deep Learning, Paraganglioma, Thyroid Neoplasms pathology, Thyroid Nodule diagnostic imaging, Thyroid Nodule pathology
- Abstract
Thyroid nodules are a common occurrence, and although most are non-cancerous, some can be malignant. The American College of Radiology has developed the Thyroid Imaging Reporting and Data System (TI-RADS) to standardize the interpretation and reporting of thyroid ultrasound results. Within TI-RADS, a category 4 designation signifies a thyroid nodule with an intermediate level of suspicion for malignancy. Accurate classification of these nodules is crucial for proper management, as it can potentially reduce unnecessary surgeries and improve patient outcomes. This study utilized deep learning techniques to effectively classify TI-RADS category 4 thyroid nodules as either benign or malignant. A total of 500 patients were included in the study and randomly divided into a training group (350 patients) and a test group (150 patients). The YOLOv3 model was constructed and evaluated using various metrics, achieving an 84% accuracy in the classification of TI-RADS category 4 thyroid nodules. Based on the predictions of the model, along with clinical and ultrasound data, a nomogram was developed. The performance of the nomogram was superior in both the training and testing groups. Furthermore, the calibration curve demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis further confirmed that the nomogram provided greater net benefits. Ultimately, the YOLOv3 model and nomogram successfully improved the accuracy of distinguishing between benign and malignant TI-RADS category 4 thyroid nodules, which is crucial for proper management and improved patient outcomes., (© 2024. The Author(s).)
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- 2024
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45. Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis.
- Author
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Shen Y, Yang X, Liu H, and Li Z
- Subjects
- Humans, Cluster Analysis, Databases, Factual, Electric Power Supplies, Europe epidemiology, Deep Learning
- Abstract
Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field., (© 2024. The Author(s).)
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- 2024
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46. A compact review of progress and prospects of deep learning in drug discovery.
- Author
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Li H, Zou L, Kowah JAH, He D, Liu Z, Ding X, Wen H, Wang L, Yuan M, and Liu X
- Subjects
- Molecular Docking Simulation, Neural Networks, Computer, Drug Discovery methods, Machine Learning, Drug Design, Deep Learning
- Abstract
Background: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development., Results: This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
- Published
- 2023
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47. SiLK-SLAM: accurate, robust and versatile visual SLAM with simple learned keypoints
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Yao, Jianjun and Li, Yingzhao
- Published
- 2024
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48. Image Processing Technique for Authentication of Indian Paper Currency.
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Colaco, Rencita Maria, V. G., Narendra, and B. V., Ravindra
- Subjects
DEEP learning ,IMAGE processing ,IMAGE segmentation ,REAL economy ,EDGE detection (Image processing) ,GRAYSCALE model ,HARD currencies ,BUSINESSMEN - Abstract
As we all know day by day the technology is getting better and better, the production of counterfeit currency has been rapidly increasing. The counterfeit currency problem is faced by almost all countries. Since the real economy is affected, it has affected the economy of the country. Even when the drastic step of demonetization was taken in 2016 to overcome counterfeit currency, this problem did not end. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for small scale businesses or for a common man these machines are not affordable. There are lot of researches taking place on this matter by using deep learning, image processing and machine learning techniques. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion, preprocessing etc. The proposed system will detect the counterfeit currency of new denominations by distinguishing each denomination based on its size and depending on the features of each currency the comparison takes place. Based on the features matched, it detects whether the currency is counterfeit or not. The system have advantages like simplicity, reliability and cost effective. Which is affordable by a common man since the common man is the one who will be effected most, when the counterfeit currency are circulated in the market because he has to pay the real value of that currency. [ABSTRACT FROM AUTHOR]
- Published
- 2023
49. fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task
- Author
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Yuting Xia, Tengfei Ma, Xin Li, Chen Wentian, Sailing He, and Xinhua Zhu
- Subjects
Time series classification ,Technology ,Computer science ,QH301-705.5 ,Speech recognition ,QC1-999 ,fNIRS ,02 engineering and technology ,Task (project management) ,03 medical and health sciences ,0302 clinical medicine ,Signal classification ,Motor imagery ,General Materials Science ,Biology (General) ,BCI ,Instrumentation ,QD1-999 ,Brain–computer interface ,TSC ,Fluid Flow and Transfer Processes ,business.industry ,Process Chemistry and Technology ,Deep learning ,Physics ,General Engineering ,deep learning ,rock–paper–scissors ,021001 nanoscience & nanotechnology ,Engineering (General). Civil engineering (General) ,Computer Science Applications ,Chemistry ,Duration (music) ,Artificial intelligence ,TA1-2040 ,0210 nano-technology ,business ,030217 neurology & neurosurgery ,CNN - Abstract
To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).
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- 2021
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50. The Arquive of Tatuoca Magnetic Observatory Brazil: from paper to intelligent bytes
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Cristian Berrio-Zapata, Ester Ferreira da Silva, Mayara Costa Pinheiro, Vinicius Augusto Carvalho de Abreu, Cristiano Mendel Martins, Mario Augusto Gongora, and Kelso Dunman
- Subjects
Big Data ,records management ,Observatories ,Deep learning ,Geomagnetism ,geophysics computing ,information retrieval systems ,Collaboration ,Data mining - Abstract
The Magnetic Observatory of Tatuoca (TTB) was installed by Observatório Nacional (ON) in 1957, near Belém city in the state of Pará, Brazilian Amazon. Its history goes back to 1933, when a Danish mission used this location to collect data, due to its privileged position near the terrestrial equator. Between 1957 and 2007, TTB produced 18,000 magnetograms on paper using photographic variometers, and other associated documents like absolute value forms and yearbooks. Data was obtained manually from these graphs with rulers and grids, taking 24 average readings per day, that is, one per hour. In 2017, the Federal University of Pará (UFPA in the Portuguese acronym) and ON collaborated to rescue this physical archive. In 2022 UFPA took a step forward and proposed not only digitizing the documents but also developing an intelligent agent capable of reading and extracting the information of the curves with a resolution better than an hour, being this the central goal of the project. If the project succeeds, it will rescue 50 years of data imprisoned in paper, increasing measurement sensitivity far beyond what these sources used to give. This will also open the possibility of applying the same AI to similar documents in other observatories or disciplines like seismography. This article recaps the project, and the complex challenges faced in articulating Archival Science principles with AI and Geoscience.
- Published
- 2022
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