28,582 results
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2. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT
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Slart, Riemer H. J. A., Williams, Michelle C., Juarez-Orozco, Luis Eduardo, Rischpler, Christoph, Dweck, Marc R., Glaudemans, Andor W. J. M., Gimelli, Alessia, Georgoulias, Panagiotis, Gheysens, Olivier, Gaemperli, Oliver, Habib, Gilbert, Hustinx, Roland, Cosyns, Bernard, Verberne, Hein J., Hyafil, Fabien, Erba, Paola A., Lubberink, Mark, Slomka, Piotr, Išgum, Ivana, Visvikis, Dimitris, Kolossváry, Márton, and Saraste, Antti
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- 2021
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3. An Overview of Machine Learning in Orthopedic Surgery: An Educational Paper.
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Padash, Sirwa, Mickley, John P., Vera Garcia, Diana V., Nugen, Fred, Khosravi, Bardia, Erickson, Bradley J., Wyles, Cody C., and Taunton, Michael J.
- Abstract
The growth of artificial intelligence combined with the collection and storage of large amounts of data in the electronic medical record collection has created an opportunity for orthopedic research and translation into the clinical environment. Machine learning (ML) is a type of artificial intelligence tool well suited for processing the large amount of available data. Specific areas of ML frequently used by orthopedic surgeons performing total joint arthroplasty include tabular data analysis (spreadsheets), medical imaging processing, and natural language processing (extracting concepts from text). Previous studies have discussed models able to identify fractures in radiographs, identify implant type in radiographs, and determine the stage of osteoarthritis based on walking analysis. Despite the growing popularity of ML, there are limitations including its reliance on "good" data, potential for overfitting, long life cycle for creation, and ability to only perform one narrow task. This educational article will further discuss a general overview of ML, discussing these challenges and including examples of successfully published models. [Display omitted] [ABSTRACT FROM AUTHOR]
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- 2023
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4. ElmNet: a benchmark dataset for generating headlines from Persian papers
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Shenassa, Mohammad E. and Minaei-Bidgoli, Behrouz
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- 2022
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5. In silico prediction of chemical-induced hematotoxicity with machine learning and deep learning methods.
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Hua Y, Shi Y, Cui X, and Li X
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- Algorithms, Blood Cells drug effects, Databases, Chemical, Humans, Models, Molecular, Quantitative Structure-Activity Relationship, ROC Curve, Reproducibility of Results, Cheminformatics methods, Deep Learning, Drug Discovery methods, Drug-Related Side Effects and Adverse Reactions, Machine Learning
- Abstract
Chemical-induced hematotoxicity is an important concern in the drug discovery, since it can often be fatal when it happens. It is quite useful for us to give special attention to chemicals which can cause hematotoxicity. In the present study, we focused on in silico prediction of chemical-induced hematotoxicity with machine learning (ML) and deep learning (DL) methods. We collected a large data set contained 632 hematotoxic chemicals and 1525 approved drugs without hematotoxicity. Computational models were built using several different machine learning and deep learning algorithms integrated on the Online Chemical Modeling Environment (OCHEM). Based on the three best individual models, a consensus model was developed. It yielded the prediction accuracy of 0.83 and balanced accuracy of 0.77 on external validation. The consensus model and the best individual model developed with random forest regression and classification algorithm (RFR) and QNPR descriptors were made available at https://ochem.eu/article/135149 , respectively. The relevance of 8 commonly used molecular properties and chemical-induced hematotoxicity was also investigated. Several molecular properties have an obvious differentiating effect on chemical-induced hematotoxicity. Besides, 12 structural alerts responsible for chemical hematotoxicity were identified using frequency analysis of substructures from Klekota-Roth fingerprint. These results should provide meaningful knowledge and useful tools for hematotoxicity evaluation in drug discovery and environmental risk assessment., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
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- 2021
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6. Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN
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Muhammad Nur Ichsan, Nur Armita, Agus Eko Minarno, Fauzi Dwi Setiawan Sumadi, and Hariyady
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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%.
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- 2022
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7. COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model
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Irmak, Emrah
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- 2022
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8. How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts
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Kocak, Burak, Kus, Ece Ates, and Kilickesmez, Ozgur
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- 2021
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9. 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
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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]
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- 2023
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10. Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper.
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Vinodkumar, Prasoon Kumar, Karabulut, Dogus, Avots, Egils, Ozcinar, Cagri, and Anbarjafari, Gholamreza
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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]
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- 2024
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11. Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models.
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Sadr H, Salari A, Ashoobi MT, and Nazari M
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- Humans, Neural Networks, Computer, Cardiovascular Diseases diagnosis, Deep Learning, Machine Learning
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Background: The incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in decision-making and enhance predictive abilities., Objective: The goal of this paper is to introduce a model for precise cardiovascular disease prediction by combining machine learning and deep learning., Method: Two public heart disease classification datasets with 70,000 and 1190 records besides a locally collected dataset with 600 records were used in our experiments. Then, a model which makes use of both machine learning and deep learning was proposed in this paper. The proposed model employed CNN and LSTM, as the representatives of deep learning models, besides KNN and XGB, as the representatives of machine learning models. As each classifier defined the output classes, majority voting was then used as an ensemble learner to predict the final output class., Result: The proposed model obtained the highest classification performance based on all evaluation metrics on all datasets, demonstrating its suitability and reliability in forecasting the probability of cardiovascular disease., (© 2024. The Author(s).)
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- 2024
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12. A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease.
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Kaur A, Mittal M, Bhatti JS, Thareja S, and Singh S
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- Humans, Biomarkers, Neuroimaging methods, Positron-Emission Tomography methods, Magnetic Resonance Imaging methods, Algorithms, Alzheimer Disease diagnostic imaging, Alzheimer Disease diagnosis, Deep Learning, Machine Learning
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Background: Alzheimer's disease (AD) is the most prevalent cause of dementia, characterized by a steady decline in mental, behavioral, and social abilities and impairs a person's capacity for independent functioning. It is a fatal neurodegenerative disease primarily affecting older adults., Objectives: The purpose of this literature review is to investigate various AD detection techniques, datasets, input modalities, algorithms, libraries, and performance evaluation metrics used to determine which model or strategy may provide superior performance., Method: The initial search yielded 807 papers, but only 100 research articles were chosen after applying the inclusion-exclusion criteria. This SLR analyzed research items published between January 2019 and December 2022. The ACM, Elsevier, IEEE Xplore Digital Library, PubMed, Springer and Taylor & Francis were systematically searched. The current study considers articles that used Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), APOe4 genotype, Diffusion Tensor Imaging (DTI) and Cerebrospinal Fluid (CSF) biomarkers. The study was performed following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines., Conclusion: According to the literature survey, most studies (n = 76) used the DL strategy. The datasets used by studies were primarily derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The majority of studies (n = 73) used single-modality neuroimaging data, while the remaining used multi-modal input data. In a multi-modality approach, the combination of MRI and PET scans is commonly preferred. Also, Regarding the algorithm used, Convolution Neural Network (CNN) showed the highest accuracy, 100 %, in classifying AD vs. CN subjects whereas the SVM was the most common ML algorithm, with a maximum accuracy of 99.82 %., 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 © 2024 Elsevier B.V. All rights reserved.)
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- 2024
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13. Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.
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Fernandes JND, Cardoso VEM, Comesaña-Campos A, and Pinheira A
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- Humans, Brain pathology, Deep Learning, Stroke diagnosis, Machine Learning
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Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.
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- 2024
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14. Performance Evaluation of Deep, Shallow and Ensemble Machine Learning Methods for the Automated Classification of Alzheimer's Disease.
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Shaffi N, Subramanian K, Vimbi V, Hajamohideen F, Abdesselam A, and Mahmud M
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- Humans, Diagnosis, Computer-Assisted methods, Cognitive Dysfunction diagnostic imaging, Cognitive Dysfunction diagnosis, Cognitive Dysfunction classification, Neuroimaging methods, Neural Networks, Computer, Algorithms, Alzheimer Disease diagnostic imaging, Alzheimer Disease diagnosis, Alzheimer Disease classification, Deep Learning, Magnetic Resonance Imaging methods, Machine Learning
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Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3-5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability.
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- 2024
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15. SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features.
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Basith S, Pham NT, Manavalan B, and Lee G
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- Software, Computational Biology methods, Humans, Neural Networks, Computer, Allergens immunology, Allergens chemistry, Deep Learning, Machine Learning
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Allergy is a hypersensitive condition in which individuals develop objective symptoms when exposed to harmless substances at a dose that would cause no harm to a "normal" person. Most current computational methods for allergen identification rely on homology or conventional machine learning using limited set of feature descriptors or validation on specific datasets, making them inefficient and inaccurate. Here, we propose SEP-AlgPro for the accurate identification of allergen protein from sequence information. We analyzed 10 conventional protein-based features and 14 different features derived from protein language models to gauge their effectiveness in differentiating allergens from non-allergens using 15 different classifiers. However, the final optimized model employs top 10 feature descriptors with top seven machine learning classifiers. Results show that the features derived from protein language models exhibit superior discriminative capabilities compared to traditional feature sets. This enabled us to select the most discriminatory baseline models, whose predicted outputs were aggregated and used as input to a deep neural network for the final allergen prediction. Extensive case studies showed that SEP-AlgPro outperforms state-of-the-art predictors in accurately identifying allergens. A user-friendly web server was developed and made freely available at https://balalab-skku.org/SEP-AlgPro/, making it a powerful tool for identifying potential allergens., Competing Interests: Declaration of competing interest The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)
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- 2024
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16. In silico prediction of drug-induced ototoxicity using machine learning and deep learning methods.
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Huang X, Tang F, Hua Y, and Li X
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- Databases, Chemical, Drug Discovery, Glucosides toxicity, Humans, Models, Theoretical, User-Computer Interface, Deep Learning, Machine Learning, Ototoxicity etiology
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Drug-induced ototoxicity has become a serious global problem, because of leading to deafness in hundreds of thousands of people every year. It always results from exposure to drugs or environmental chemicals that cause the impairment and degeneration of the inner ear. Herein, we focused on the in silico modeling of drug-induced ototoxicity of chemicals. We collected 1,102 ototoxic medications and 1,705 non-ototoxic drugs. Based on the data set, a series of computational models were developed with different traditional machine learning and deep learning algorithms implemented on an online chemical database and modeling environment. Six ML models performed best on 5-fold cross-validation and test set. A consensus model was developed with the best individual models. These models were further validated with an external validation. The consensus model showed best predictive ability, with high accuracy of 0.95 on test set and 0.90 on validation set. The consensus model and the data sets used for model development are available at https://ochem.eu/model/46566321. Besides, 16 structural alerts responsible for drug-induced ototoxicity were identified. We hope the results could provide meaningful knowledge and useful tools for ototoxicity evaluation in drug discovery and environmental risk assessment., (© 2021 John Wiley & Sons A/S.)
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- 2021
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17. Critical appraisal of a machine learning paper: A guide for the neurologist
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Pulikottil W Vinny, Rahul Garg, M V Padma Srivastava, Vivek Lal, and Venugoapalan Y Vishnu
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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.
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- 2021
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18. Real time attendance monitoring system.
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Lenin, Sahaya, Krishna, Rongala Siva, and Sriram, Elisetty
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MACHINE learning ,ELECTRONIC systems ,ELECTRONIC paper ,RASPBERRY Pi ,DATABASES ,DEEP learning - Abstract
Attendance monitoring is an essential task for any organization and traditional methods such as pen and paper or electronic card systems have their limitations with advancements. The proposed model is a real time face presence monitoring system using Raspberry Pi. The system captures image of individuals using camera and processes the images using deep learning algorithms to detect faces. These faces are then matched against a pre-existing database of known individuals. Door interlock controls add an extra layer of security, ensuring that only authorized person have access to restricted areas. The system provides a user-friendly interface to view everyone's attendance records and generate attendance management reports. The proposed system provides a reliable and accurate solutions for attendance tracking and can be used in various organizations. [ABSTRACT FROM AUTHOR]
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- 2024
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19. Special issue on intelligent systems: ISMIS 2022 selected papers.
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Ceci, Michelangelo, Flesca, Sergio, Manco, Giuseppe, and Masciari, Elio
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MACHINE learning ,ARTIFICIAL intelligence ,DECISION support systems ,KNOWLEDGE representation (Information theory) ,COMPUTER vision ,DEEP learning - Abstract
This document is a special issue of the Journal of Intelligent Information Systems, focusing on the selected papers from the International Symposium on Methodologies for Intelligent Systems (ISMIS 2022). The symposium, held in Cosenza, Italy, showcased research on various topics related to artificial intelligence, including decision support, knowledge representation, machine learning, computer vision, and more. The special issue includes eleven papers that have undergone rigorous peer-reviewing and cover a wide range of research topics, such as deep learning, anomaly detection, malware detection, sentiment classification, and healthcare professionals' burnout. The authors express their gratitude to the contributors and reviewers for their valuable contributions. [Extracted from the article]
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- 2024
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20. Automated echocardiographic diastolic function grading: A hybrid multi-task deep learning and machine learning approach.
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Cai Q, Lin M, Zhang M, Qin Y, Meng Y, Wang J, Leng C, Zhu W, Li J, You J, and Lu X
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- Humans, Female, Male, Middle Aged, Aged, Echocardiography methods, Echocardiography standards, Stroke Volume physiology, Algorithms, Deep Learning, Machine Learning, Diastole physiology, Ventricular Function, Left physiology
- Abstract
Background: Assessing left ventricular diastolic function (LVDF) with echocardiography as per ASE guidelines is tedious and time-consuming. The study aims to develop a fully automatic approach of this procedure by a lightweight hybrid algorithm combining deep learning (DL) and machine learning (ML)., Methods: The model features multi-modality input and multi-task output, measuring LV ejection fraction (LVEF), left atrial end-systolic volume (LAESV), and Doppler parameters: mitral E wave velocity (E), A wave velocity (A), mitral annulus e' velocity (e'), and tricuspid regurgitation velocity (TRmax). The algorithm was trained and tested on two internal datasets (862 and 239 echocardiograms) and validated using three external datasets, including EchoNet-Dynamic and CAMUS. The ASE diastolic function decision tree and total probability theory were used to provide diastolic grading probabilities., Results: The algorithm, named MMnet, demonstrated high accuracy in both test and validation datasets, with Dice coefficients for segmentation between 0.922 and 0.932 and classification accuracies between 0.9977 and 1.0. The mean absolute errors (MAEs) for LVEF and LAESV were 3.7 % and 5.8 ml, respectively, and for LVEF in external validation, MAEs ranged from 4.9 % to 5.6 %. The diastolic function grading accuracy was 0.88 with hard criteria and up to 0.98 with soft criteria which account for the top two probability in total probability theory., Conclusions: MMnet can automatically grade ASE diastolic function with high accuracy and efficiency by annotating 2D videos and Doppler images., Competing Interests: Declaration of competing interest Ms. Jiangtao Wang and Mr. Yuanlong Meng are employees of GE HealthCare, China. All the other co-authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024. Published by Elsevier B.V.)
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- 2024
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21. Hematologic cancer diagnosis and classification using machine and deep learning: State-of-the-art techniques and emerging research directives.
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Patel H, Shah H, Patel G, and Patel A
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- Humans, Diagnosis, Computer-Assisted methods, Deep Learning, Hematologic Neoplasms diagnosis, Hematologic Neoplasms classification, Machine Learning
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Hematology is the study of diagnosis and treatment options for blood diseases, including cancer. Cancer is considered one of the deadliest diseases across all age categories. Diagnosing such a deadly disease at the initial stage is essential to cure the disease. Hematologists and pathologists rely on microscopic evaluation of blood or bone marrow smear images to diagnose blood-related ailments. The abundance of overlapping cells, cells of varying densities among platelets, non-illumination levels, and the amount of red and white blood cells make it more difficult to diagnose illness using blood cell images. Pathologists are required to put more effort into the traditional, time-consuming system. Nowadays, it becomes possible with machine learning and deep learning techniques, to automate the diagnostic processes, categorize microscopic blood cells, and improve the accuracy of the procedure and its speed as the models developed using these methods may guide an assisting tool. In this article, we have acquired, analyzed, scrutinized, and finally selected around 57 research papers from various machine learning and deep learning methodologies that have been employed in the diagnosis of leukemia and its classification over the past 20 years, which have been published between the years 2003 and 2023 by PubMed, IEEE, Science Direct, Google Scholar and other pertinent sources. Our primary emphasis is on evaluating the advantages and limitations of analogous research endeavors to provide a concise and valuable research directive that can be of significant utility to fellow researchers in the field., 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 © 2024 Elsevier B.V. All rights reserved.)
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- 2024
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22. A hybrid framework for glaucoma detection through federated machine learning and deep learning models.
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Aljohani A and Aburasain RY
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- Humans, Neural Networks, Computer, Glaucoma diagnostic imaging, Glaucoma diagnosis, Deep Learning, Machine Learning
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Background: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts., Method: Developing a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment., Result: The hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis., Conclusion: In summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology's potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare., (© 2024. The Author(s).)
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- 2024
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23. An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).
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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.
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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]
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- 2023
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24. Prediction of the minimum fluidization velocity of different biomass types by artificial neural networks and empirical correlations
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Matos, Thenysson, Perazzini, Maisa Tonon Bitti, and Perazzini, Hugo
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- 2024
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25. Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.
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Jia T, Meng L, Li S, Liu J, and Wu D
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- Humans, Computer Security, Privacy, Brain-Computer Interfaces, Electroencephalography, Imagination physiology, Algorithms, Deep Learning, Machine Learning
- Abstract
Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.
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- 2024
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26. Content‐based and knowledge graph‐based paper recommendation: Exploring user preferences with the knowledge graphs for scientific paper recommendation.
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Tang, Hao, Liu, Baisong, and Qian, Jiangbo
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KNOWLEDGE graphs ,SCIENTIFIC knowledge ,DEEP learning ,CONVOLUTIONAL neural networks ,MACHINE learning ,RECOMMENDER systems ,USER-generated content - Abstract
Researchers usually face difficulties in finding scientific papers relevant to their research interests due to increasing growth. Recommender systems emerge as a leading solution to filter valuable items intelligently. Recently, deep learning algorithms, such as convolutional neural network, improved traditional recommendation technologies, for example, the graph‐based or content‐based methods. However, existing graph‐based methods ignore high‐order association between users and items on graphs, and content‐based methods ignore global features of texts for explicit user preferences. Therefore, this paper proposes a Content‐based and knowledge Graph‐based Paper Recommendation method (CGPRec), which uses a two‐layer self‐attention block to obtain global features of texts for more complete explicit user preferences, and proposes an improved graph convolutional network for modeling high‐order associations on the knowledge graph to mine implicit user preferences. And the knowledge graph in this paper is constructed with concept nodes, user nodes, paper nodes, and other meta‐data nodes. Experimental results on a public dataset, CiteULike‐a, and a real application log dataset, AHData, show that our model outperforms compared with baseline methods. [ABSTRACT FROM AUTHOR]
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- 2021
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27. Research Trends in Artificial Intelligence and Security—Bibliometric Analysis.
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Ilić, Luka, Šijan, Aleksandar, Predić, Bratislav, Viduka, Dejan, and Karabašević, Darjan
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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]
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- 2024
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28. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT
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Oliver Gaemperli, Paola Anna Erba, Antti Saraste, Michelle C. Williams, Alessia Gimelli, Piotr J. Slomka, Christoph Rischpler, Roland Hustinx, Marc R. Dweck, Hein J. Verberne, Andor W. J. M. Glaudemans, Bernard Cosyns, Márton Kolossváry, Panagiotis Georgoulias, Luis Eduardo Juarez-Orozco, Ivana Išgum, Gilbert Habib, Mark Lubberink, Riemer H. J. A. Slart, Olivier Gheysens, Dimitris Visvikis, Fabien Hyafil, Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Translational Immunology Groningen (TRIGR), Cardiovascular Centre (CVC), IvI Research (FNWI), UCL - SSS/IREC/SLUC - Pôle St.-Luc, UCL - (SLuc) Centre du cancer, UCL - (SLuc) Service de médecine nucléaire, Clinical sciences, Cardio-vascular diseases, Cardiology, Slart, R, Williams, M, Juarez-Orozco, L, Rischpler, C, Dweck, M, Glaudemans, A, Gimelli, A, Georgoulias, P, Gheysens, O, Gaemperli, O, Habib, G, Hustinx, R, Cosyns, B, Verberne, H, Hyafil, F, Erba, P, Lubberink, M, Slomka, P, Isgum, I, Visvikis, D, Kolossvary, M, Saraste, A, University Medical Center Groningen [Groningen] (UMCG), University of Twente, University of Edinburgh, Utrecht University [Utrecht], University of Groningen [Groningen], Universität Duisburg-Essen = University of Duisburg-Essen [Essen], Fondazione Toscana Gabriele Monasterio, University Hospital of Larissa, Cliniques Universitaires Saint-Luc [Bruxelles], Université Catholique de Louvain = Catholic University of Louvain (UCL), Hirslanden Medical Center, Microbes évolution phylogénie et infections (MEPHI), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Hôpital de la Timone [CHU - APHM] (TIMONE), Institut Hospitalier Universitaire Méditerranée Infection (IHU Marseille), GIGA [Université Liège], Université de Liège, Universitair Ziekenhuis [Brussels, Belgium], University of Amsterdam [Amsterdam] (UvA), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), University of Pisa - Università di Pisa, Uppsala University, Uppsala University Hospital, Cedars-Sinai Medical Center, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Semmelweis University [Budapest], University of Turku, Turku University Hospital (TYKS), Radiology and Nuclear Medicine, ACS - Amsterdam Cardiovascular Sciences, Biomedical Engineering and Physics, ACS - Atherosclerosis & ischemic syndromes, ANS - Brain Imaging, and ACS - Heart failure & arrhythmias
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medicine.medical_specialty ,Medizin ,030204 cardiovascular system & hematology ,Guidelines ,Cardiovascular ,Multimodality imaging ,030218 nuclear medicine & medical imaging ,Multimodality ,03 medical and health sciences ,0302 clinical medicine ,[SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system ,[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,Artificial Intelligence ,Positron Emission Tomography Computed Tomography ,Machine learning ,medicine ,Humans ,[SDV.MP.PAR]Life Sciences [q-bio]/Microbiology and Parasitology/Parasitology ,Radiology, Nuclear Medicine and imaging ,Medical physics ,Position paper ,Deep learning ,Positron-Emission Tomography ,Tomography, Emission-Computed, Single-Photon ,Tomography, X-Ray Computed ,Nuclear Medicine ,Tomography ,[SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases ,PET-CT ,medicine.diagnostic_test ,business.industry ,Coronary computed tomography angiography ,General Medicine ,[SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology ,X-Ray Computed ,Functional imaging ,Positron emission tomography ,[SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology ,Radiologi och bildbehandling ,Applications of artificial intelligence ,Emission-Computed ,Cardiology and Cardiovascular Medicine ,business ,Emission computed tomography ,Radiology, Nuclear Medicine and Medical Imaging ,Single-Photon - Abstract
In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
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- 2021
29. A physics-driven and machine learning-based digital twinning approach to transient thermal systems
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Di Meglio, Armando, Massarotti, Nicola, and Nithiarasu, Perumal
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- 2024
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30. MLCAD: A Survey of Research in Machine Learning for CAD Keynote Paper.
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Rapp, Martin, Amrouch, Hussam, Lin, Yibo, Yu, Bei, Pan, David Z., Wolf, Marilyn, and Henkel, Jorg
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MACHINE learning , *CIRCUIT complexity , *COMPUTER-aided design , *ARTIFICIAL neural networks , *INTEGRATED circuits , *CONFIGURATION space , *MULTICASTING (Computer networks) - Abstract
Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in CAD that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions. [ABSTRACT FROM AUTHOR]
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- 2022
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31. Artificial intelligence and deep learning: considerations for financial institutions for compliance with the regulatory burden in the United Kingdom
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Singh, Charanjit
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- 2024
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32. Increased Accuracy on Image Classification of Game Rock Paper Scissors using CNN
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null Muhammad Nur Ichsan, null Nur Armita, null Agus Eko Minarno, null Fauzi Dwi Setiawan Sumadi, and null Hariyady
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Machine Learning ,Deep Learning ,Image Classification ,Neural Network ,CNN ,Klasifikasi Citra - 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%.
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- 2022
33. Comment on papers using machine learning for significant wave height time series prediction: Complex models do not outperform auto-regression.
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Jiang, Haoyu, Zhang, Yuan, Qian, Chengcheng, and Wang, Xuan
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ARTIFICIAL neural networks , *TIME series analysis , *PREDICTION models , *ARTIFICIAL intelligence , *MACHINE learning , *DECOMPOSITION method - Abstract
• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]
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- 2024
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34. Anomaly network intrusion detection system based on NetFlow using machine/deep learning.
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Adli, Touati B., Amokrane, Salem-Bilal B., Pavlović, Boban Z., Laidouni, Mohammad Zouaoui M., and Benyahia, Taki-eddine Ahmed A.
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DEEP learning ,MACHINE learning ,CONFERENCE papers ,MACHINING ,BIG data ,MACHINERY - Abstract
Copyright of Military Technical Courier / Vojnotehnicki Glasnik is the property of Military Technical Courier / Vojnotehnicki Glasnik and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2023
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35. A systematic literature review on recent trends of machine learning applications in additive manufacturing.
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Xames, Md Doulotuzzaman, Torsha, Fariha Kabir, and Sarwar, Ferdous
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MACHINE learning ,INDUSTRY 4.0 ,MANUFACTURING processes ,CONFERENCE papers ,PERIODICAL articles - Abstract
Additive manufacturing (AM) offers the advantage of producing complex parts more efficiently and in a lesser production cycle time as compared to conventional subtractive manufacturing processes. It also provides higher flexibility for diverse applications by facilitating the use of a variety of materials and different processing technologies. With the exceptional growth of computing capability, researchers are extensively using machine learning (ML) techniques to control the performance of every phase of AM processes, such as design, process parameters modeling, process monitoring and control, quality inspection, and validation. Also, ML methods have made it possible to develop cybermanufacturing for AM systems and thus revolutionized Industry 4.0. This paper presents the state-of-the-art applications of ML in solving numerous problems related to AM processes. We give an overview of the research trends in this domain through a systematic literature review of relevant journal articles and conference papers. We summarize recent development and existing challenges to point out the direction of future research scope. This paper can provide AM researchers and practitioners with the latest information consequential for further development. [ABSTRACT FROM AUTHOR]
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- 2023
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36. Special Issue of Natural Logic Meets Machine Learning (NALOMA): Selected Papers from the First Three Workshops of NALOMA.
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Kalouli, Aikaterini-Lida, Abzianidze, Lasha, and Chatzikyriakidis, Stergios
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DEEP learning ,MACHINE learning ,QUESTION answering systems ,LANGUAGE models ,NATURAL language processing ,ARTIFICIAL neural networks ,MACHINE translating - Abstract
The text discusses the intersection of natural language understanding (NLU) and reasoning in the context of large language models (LLMs) and traditional logic-based approaches. It highlights the strengths and weaknesses of both approaches and explores the potential for hybrid models that combine symbolic and distributional representations. The text also mentions specific applications of hybrid approaches in natural language inference, question-answering, sentiment analysis, and dialog. The document concludes by introducing a special issue that features selected contributions from the NALOMA workshop series, which focuses on hybrid methods in NLU. [Extracted from the article]
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- 2024
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37. Introduction to the virtual collection of papers on Artificial neural networks: applications in X‐ray photon science and crystallography.
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Ekeberg, Tomas
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ARTIFICIAL neural networks , *DEEP learning , *CRYSTALLOGRAPHY , *ARTIFICIAL intelligence , *MACHINE learning , *PHOTONS - Abstract
Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]
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- 2024
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38. Machine learning methods for prediction of cancer driver genes: a survey paper.
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Andrades, Renan and Recamonde-Mendoza, Mariana
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CANCER genes , *SOMATIC mutation , *DEEP learning , *GENETIC mutation , *MACHINE learning , *THERAPEUTICS , *DIAGNOSIS , *SCIENTIFIC community - Abstract
Identifying the genes and mutations that drive the emergence of tumors is a critical step to improving our understanding of cancer and identifying new directions for disease diagnosis and treatment. Despite the large volume of genomics data, the precise detection of driver mutations and their carrying genes, known as cancer driver genes, from the millions of possible somatic mutations remains a challenge. Computational methods play an increasingly important role in discovering genomic patterns associated with cancer drivers and developing predictive models to identify these elements. Machine learning (ML), including deep learning, has been the engine behind many of these efforts and provides excellent opportunities for tackling remaining gaps in the field. Thus, this survey aims to perform a comprehensive analysis of ML-based computational approaches to identify cancer driver mutations and genes, providing an integrated, panoramic view of the broad data and algorithmic landscape within this scientific problem. We discuss how the interactions among data types and ML algorithms have been explored in previous solutions and outline current analytical limitations that deserve further attention from the scientific community. We hope that by helping readers become more familiar with significant developments in the field brought by ML, we may inspire new researchers to address open problems and advance our knowledge towards cancer driver discovery. [ABSTRACT FROM AUTHOR]
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- 2022
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39. Process Mining Workshops. ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23-28, 2022, Revised Selected Papers.
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Montali, Marco, Montali, Marco, Senderovich, Arik, and Weidlich, Matthias
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Business mathematics & systems ,Data mining ,Health & safety aspects of IT ,Information technology: general issues ,Machine learning ,business process management ,conformance checking ,data science ,deep learning ,event data ,health informatics ,knowledge graphs ,machine learning ,predictive process monitoring ,process analytics ,process discovery ,process mining ,process querying ,streaming analytics - Abstract
Summary: This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23-28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: - 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) - 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) - 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) - 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) - 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) - 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) - 1st International Workshop on Education meets Process Mining (EduPM) - 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)
40. A Meta-Survey on Intelligent Energy-Efficient Buildings.
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Islam, Md Babul, Guerrieri, Antonio, Gravina, Raffaele, and Fortino, Giancarlo
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MACHINE learning ,REINFORCEMENT learning ,SMART cities ,DEEP learning ,INDUSTRIAL ecology ,INTELLIGENT buildings - Abstract
The rise of the Internet of Things (IoT) has enabled the development of smart cities, intelligent buildings, and advanced industrial ecosystems. When the IoT is matched with machine learning (ML), the advantages of the resulting enhanced environments can span, for example, from energy optimization to security improvement and comfort enhancement. Together, IoT and ML technologies are widely used in smart buildings, in particular, to reduce energy consumption and create Intelligent Energy-Efficient Buildings (IEEBs). In IEEBs, ML models are typically used to analyze and predict various factors such as temperature, humidity, light, occupancy, and human behavior with the aim of optimizing building systems. In the literature, many review papers have been presented so far in the field of IEEBs. Such papers mostly focus on specific subfields of ML or on a limited number of papers. This paper presents a systematic meta-survey, i.e., a review of review articles, that compares the state of the art in the field of IEEBs using the Prisma approach. In more detail, our meta-survey aims to give a broader view, with respect to the already published surveys, of the state-of-the-art in the IEEB field, investigating the use of supervised, unsupervised, semi-supervised, and self-supervised models in a variety of IEEB-based scenarios. Moreover, our paper aims to compare the already published surveys by answering five important research questions about IEEB definitions, architectures, methods/models used, datasets and real implementations utilized, and main challenges/research directions defined. This meta-survey provides insights that are useful both for newcomers to the field and for researchers who want to learn more about the methodologies and technologies used for IEEBs' design and implementation. [ABSTRACT FROM AUTHOR]
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- 2024
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41. Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms.
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Lin, Yuandong, Ma, Ji, Cheng, Jun-Hu, and Sun, Da-Wen
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MACHINE learning , *DEEP learning , *SENSOR arrays , *PATTERN recognition systems , *MULTIVARIATE analysis , *FEATURE extraction - Abstract
• Qualitative and quantitative detection of amine gases could be achieved by CSA. • A visible detection of beef freshness using the amine-responsive CSA was proposed. • ResNet34 had the best performance for beef freshness detection based on CSA. • T-SNE could further visualize and understand the classification process of DL. This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t -distributed stochastic neighbour embedding (t -SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness. [ABSTRACT FROM AUTHOR]
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- 2024
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42. Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion.
- Author
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Deng, Weichu, Wei, Huanchun, Huang, Teng, Cao, Cong, Peng, Yun, and Hu, Xuan
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DEEP learning ,BLOCKCHAINS ,ELECTRONIC paper ,SMART structures ,MACHINE learning ,SOURCE code - Abstract
With the rapid development and widespread application of blockchain technology in recent years, smart contracts running on blockchains often face security vulnerability problems, resulting in significant economic losses. Unlike traditional programs, smart contracts cannot be modified once deployed, and vulnerabilities cannot be remedied. Therefore, the vulnerability detection of smart contracts has become a research focus. Most existing vulnerability detection methods are based on rules defined by experts, which are inefficient and have poor scalability. Although there have been studies using machine learning methods to extract contract features for vulnerability detection, the features considered are singular, and it is impossible to fully utilize smart contract information. In order to overcome the limitations of existing methods, this paper proposes a smart contract vulnerability detection method based on deep learning and multimodal decision fusion. This method also considers the code semantics and control structure information of smart contracts. It integrates the source code, operation code, and control-flow modes through the multimodal decision fusion method. The deep learning method extracts five features used to represent contracts and achieves high accuracy and recall rates. The experimental results show that the detection accuracy of our method for arithmetic vulnerability, re-entrant vulnerability, transaction order dependence, and Ethernet locking vulnerability can reach 91.6%, 90.9%, 94.8%, and 89.5%, respectively, and the detected AUC values can reach 0.834, 0.852, 0.886, and 0.825, respectively. This shows that our method has a good vulnerability detection effect. Furthermore, ablation experiments show that the multimodal decision fusion method contributes significantly to the fusion of different modalities. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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43. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.
- Author
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Abels, Esther, Pantanowitz, Liron, Aeffner, Famke, Zarella, Mark D, Laak, Jeroen, Bui, Marilyn M, Vemuri, Venkata NP, Parwani, Anil V, Gibbs, Jeff, Agosto‐Arroyo, Emmanuel, Beck, Andrew H, and Kozlowski, Cleopatra
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ELECTRONIC paper ,BEST practices ,ARTIFICIAL neural networks ,PATHOLOGY - Abstract
In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]
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- 2019
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44. A bibliometric and social network analysis of data-driven heuristic methods for logistics problems.
- Author
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Deniz, Nurcan and Ozceylan, Eren
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SOCIAL network analysis ,HEURISTIC ,BIBLIOMETRICS ,MATERIALS handling ,DEEP learning ,MACHINE learning ,FREIGHT forwarders - Abstract
Transport and logistics systems include a range of activities that deal with all sorts of decisions and operations from material handling to vehicle routing. One of the main challenges for transport and logistics processes is to deal with large-scale and complex problems. However, with increasingly diverse sets of operational real-world data becoming available, data-driven heuristic approaches are promising to pave the path for solving the problems in the field of transport and logistics. Thus, a comprehensive review is needed to observe the reflections of this path in literature. To bridge this gap, a total of 40 papers on the topic of 'data-driven heuristic approaches to logistics and transportation problems' are determined. Before the categorization and content analysis; descriptive, bibliometric and social network analysis are carried out to identify the current state of the literature. All the papers are systemically reviewed based on different perspectives, namely data-driven methodology, heuristics, sub-problems and etc. Based on the review, suggestions for future research are likewise provided. Subsequently, machine learning and deep learning methods are considered to be among the most promising data-driven methodologies. The review may be useful for academicians, researchers, and practitioners for a better understanding of data-driven heuristic approaches to transportation and logistics problems. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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45. Voice separation and recognition using machine learning and deep learning a review paper.
- Author
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ibrahemm, Zaineb h. and Shihab, Ammar I.
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ARTIFICIAL neural networks ,AUTOMATIC speech recognition ,DEEP learning ,MACHINE learning ,SPEECH perception ,SPEECH - Abstract
Voice isolation, a prominent research area in the field of speech processing, has garnered a great deal of attention due to its prospective implications in numerous domains. Deep neural networks (DNNs) have emerged as a potent instrument for addressing the challenges associated with vocal isolation. This paper presents a comprehensive study on the use of DNNs for voice isolation, focusing on speech recognition and speaker identification tasks. The proposed method uses frequency domain and time domain techniques to improve the separation of target utterances from background noise. The experimental results demonstrate the efficacy of the proposed method, revealing substantial improvements in voice isolation precision and robustness. This study's findings contribute to the increasing corpus of research on voice isolation techniques and provide valuable insights into the application of DNNs to improve speech processing tasks . [ABSTRACT FROM AUTHOR]
- Published
- 2023
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46. Critical Appraisal of a Machine Learning Paper: A Guide for the Neurologist.
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Vinny, Pulikottil W., Garg, Rahul, Srivastava, M. V. Padma, Lal, Vivek, and Vishnu, Venugoapalan Y.
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DEEP learning , *NEUROLOGISTS , *EVIDENCE-based medicine , *MACHINE learning , *BENCHMARKING (Management) , *TERMS & phrases , *ARTIFICIAL neural networks , *PREDICTION models , *ALGORITHMS - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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- View/download PDF
47. Feature Mining and Sensitivity Analysis with Adaptive Sparse Attention for Bearing Fault Diagnosis.
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Jiang, Qinglei, Bao, Binbin, Hou, Xiuqun, Huang, Anzheng, Jiang, Jiajie, and Mao, Zhiwei
- Subjects
FAULT diagnosis ,SENSITIVITY analysis ,RECOMMENDER systems ,FEATURE selection ,FILTER paper ,MACHINE learning - Abstract
Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis output results; thus, the attention mechanism is particularly important for the selection of features. However, global attention focuses on all sequences, which is computationally expensive and not ideal for fault diagnosis tasks. The local attention mechanism ignores the relationship between non-adjacent sequences. To address the respective shortcomings of global attention and local attention, an adaptive sparse attention network is proposed in this paper to filter fault-sensitive information by soft threshold filtering. In addition, the effects of different signal representation domains on fault diagnosis results are investigated to filter out signal representation forms with better performance. Finally, the proposed adaptive sparse attention network is applied to cross-working conditions diagnosis of bearings. The adaptive sparse attention mechanism focuses on the signal characteristics of different frequency bands for different fault types. The proposed network model achieves better overall performance when comparing the cross-conditions diagnosis accuracy and model convergence speed. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
48. Machine Learning and Graph Signal Processing Applied to Healthcare: A Review.
- Author
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Calazans, Maria Alice Andrade, Ferreira, Felipe A. B. S., Santos, Fernando A. N., Madeiro, Francisco, and Lima, Juliano B.
- Subjects
PATTERN recognition systems ,SIGNAL processing ,DEEP learning ,GRAPH theory ,SIGNALS & signaling - Abstract
Signal processing is a very useful field of study in the interpretation of signals in many everyday applications. In the case of applications with time-varying signals, one possibility is to consider them as graphs, so graph theory arises, which extends classical methods to the non-Euclidean domain. In addition, machine learning techniques have been widely used in pattern recognition activities in a wide variety of tasks, including health sciences. The objective of this work is to identify and analyze the papers in the literature that address the use of machine learning applied to graph signal processing in health sciences. A search was performed in four databases (Science Direct, IEEE Xplore, ACM, and MDPI), using search strings to identify papers that are in the scope of this review. Finally, 45 papers were included in the analysis, the first being published in 2015, which indicates an emerging area. Among the gaps found, we can mention the need for better clinical interpretability of the results obtained in the papers, that is not to restrict the results or conclusions simply to performance metrics. In addition, a possible research direction is the use of new transforms. It is also important to make new public datasets available that can be used to train the models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Comment on Martínez-Delgado et al. Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions. Sensors 2021, 21 , 5273.
- Author
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Misplon, Josiah Z. R., Saini, Varun, Sloves, Brianna P., Meerts, Sarah H., and Musicant, David R.
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INSULIN ,CARBOHYDRATES ,TYPE 1 diabetes ,GLUCOSE ,MACHINE learning ,ABSORPTION - Abstract
The paper "Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions" (Sensors 2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model's root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors' code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. The quiet revolution in machine vision - A state-of-the-art survey paper, including historical review, perspectives, and future directions
- Author
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Mark F. Hansen, Melvyn L. Smith, and Lyndon N. Smith
- Subjects
0209 industrial biotechnology ,General Computer Science ,Computer science ,Machine vision ,media_common.quotation_subject ,Control (management) ,Big data ,Centre for Machine Vision ,02 engineering and technology ,state-of-the-art ,Field (computer science) ,020901 industrial engineering & automation ,Engineering ,0202 electrical engineering, electronic engineering, information engineering ,Quality (business) ,Industrial Revolution ,media_common ,business.industry ,General Engineering ,deep learning ,machine vision ,Data science ,machine learning ,Key (cryptography) ,020201 artificial intelligence & image processing ,State (computer science) ,business - Abstract
Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.
- Published
- 2021
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