6,407 results
Search Results
2. 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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3. 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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4. 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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5. 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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6. 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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7. 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
- Subjects
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
8. 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
- Subjects
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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]
- Published
- 2024
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9. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT.
- Author
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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, and Visvikis, Dimitris
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CARDIAC radionuclide imaging , *ARTIFICIAL intelligence , *SINGLE-photon emission computed tomography , *POSITRON emission tomography computed tomography , *COMPUTED tomography , *MACHINE learning - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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10. Artificial intelligence research in agriculture: a review
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Sood, Amit, Sharma, Rajendra Kumar, and Bhardwaj, Amit Kumar
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- 2022
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11. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association.
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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]
- Published
- 2019
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12. Studies in the Area of Artificial Intelligence Reported from Diponegoro University (Deep Learning on Medical Imaging in Identifying Kidney Stones: Review Paper).
- Abstract
A report from Diponegoro University discusses the use of artificial intelligence (AI) and deep learning in medical imaging to identify kidney stones. The research highlights that deep learning is more commonly used than traditional machine learning in this field. The aim of the study was to review existing literature on deep learning in medical imaging for detecting kidney stones. The research concludes that using the appropriate AI model with high accuracy can assist radiologists in accurately detecting kidney stones. This information may be useful for those interested in AI applications in medical diagnostics. [Extracted from the article]
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- 2024
13. When artificial intelligence meets the hospitality and tourism industry: an assessment framework to inform theory and management
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Huang, Arthur, Chao, Ying, de la Mora Velasco, Efrén, Bilgihan, Anil, and Wei, Wei
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- 2022
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14. An artificial intelligent manufacturing process for high-quality low-cost production
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Hassan, Noha M., Hamdan, Ameera, Shahin, Farah, Abdelmaksoud, Rowaida, and Bitar, Thurya
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- 2023
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15. Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review
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Moassefi, Mana, Rouzrokh, Pouria, Conte, Gian Marco, Vahdati, Sanaz, Fu, Tianyuan, Tahmasebi, Aylin, Younis, Mira, Farahani, Keyvan, Gentili, Amilcare, Kline, Timothy, Kitamura, Felipe C., Huo, Yuankai, Kuanar, Shiba, Younis, Khaled, Erickson, Bradley J., and Faghani, Shahriar
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- 2023
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16. Advanced Machine Learning and Deep Learning Approaches for Remote Sensing II.
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Jeon, Gwanggil
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REMOTE sensing ,MACHINE learning ,ARTIFICIAL neural networks ,DEEP learning ,ARTIFICIAL intelligence ,DISTANCE education - Abstract
This document is a summary of a special issue on advanced machine learning and deep learning techniques for remote sensing. The issue includes 16 research papers that cover a range of topics, including hyperspectral image classification, moving point target detection, radar echo extrapolation, and remote sensing object detection. Each paper introduces a novel approach or model and provides extensive testing and evaluation to demonstrate its effectiveness. The insights shared in this special issue are expected to contribute to future advancements in artificial intelligence-based remote sensing research. [Extracted from the article]
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- 2024
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17. Combining Machine Learning and Semantic Web: A Systematic Mapping Study.
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BREIT, ANNA, WALTERSDORFER, LAURA, EKAPUTRA, FAJAR J., SABOU, MARTA, EKELHART, ANDREAS, IANA, ANDREEA, PAULHEIM, HEIKO, PORTISCH, JAN, REVENKO, ARTEM, TEIJE, ANNETTE TEN, and VAN HARMELEN, FRANK
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ARTIFICIAL intelligence ,MACHINE learning ,SEMANTIC Web ,KNOWLEDGE graphs ,DEEP learning ,KNOWLEDGE representation (Information theory) - Abstract
In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining Machine Learning components with techniques developed by the SemanticWeb community--SemanticWebMachine Learning (SWeML). Due to its rapid growth and impact on several communities in thepast two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this article is a classification system for SWeML Systems that we publish as ontology. [ABSTRACT FROM AUTHOR]
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- 2023
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18. Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.
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Tang, An, Tam, Roger, Cadrin-Chênevert, Alexandre, Guest, Will, Chong, Jaron, Barfett, Joseph, Chepelev, Leonid, Cairns, Robyn, Mitchell, J. Ross, Cicero, Mark D., Poudrette, Manuel Gaudreau, Jaremko, Jacob L., Reinhold, Caroline, Gallix, Benoit, Gray, Bruce, and Geis, Raym
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ARTIFICIAL intelligence , *HOSPITAL radiological services , *POLICY sciences , *PROFESSIONAL associations , *QUALITY assurance , *QUALITY control , *PATIENT-centered care - Abstract
Abstract Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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19. The use of artificial intelligence in musculoskeletal ultrasound: a systematic review of the literature
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Getzmann, Jonas M., Zantonelli, Giulia, Messina, Carmelo, Albano, Domenico, Serpi, Francesca, Gitto, Salvatore, and Sconfienza, Luca Maria
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- 2024
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20. Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review
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Șalgău, Cristiana Adina, Morar, Anca, Zgarta, Andrei Daniel, Ancuța, Diana-Larisa, Rădulescu, Alexandros, Mitrea, Ioan Liviu, and Tănase, Andrei Ovidiu
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- 2024
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21. AI-Empowered Methods for Smart Energy Consumption: A Review of Load Forecasting, Anomaly Detection and Demand Response
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Wang, Xinlin, Wang, Hao, Bhandari, Binayak, and Cheng, Leming
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- 2024
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22. Detection of Mathematical Expressions in Scientific Papers
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Carrión Ponz, Salvador
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Formulas ,Single-Shot Detectors ,Aprendizaje Profundo ,Reconeixement de Formes i Imatge Digital [Máster Universitario en Inteligencia Artificial, Reconocimiento de Formas e Imagen Digital-Màster Universitari en Intel·Ligència Artificial] ,Computer Vision ,Expresiones Matemáticas ,Pattern Recognition ,Real-Time ,Machine Learning ,Deep Learning ,Mathematical Expression ,Máster Universitario en Inteligencia Artificial, Reconocimiento de Formas e Imagen Digital-Màster Universitari en Intel·Ligència Artificial: Reconeixement de Formes i Imatge Digital ,Tiempo Real ,Artificial Intelligence ,Detección de Objetos ,Object Detection ,YOLO ,Reconocimiento de Patrones ,Aprendizaje Automático ,Visión Por Computador ,Inteligencia Artificial ,LENGUAJES Y SISTEMAS INFORMATICOS ,SSD - Abstract
[ES] En este trabajo, se aborda el problema de la detección de expresiones matemáticas en artículos científicos. Primero, se da una breve introducción al problema de la detección de objetos general, junto a las técnicas y métodos de evaluación más actuales. Luego, se presentan dos modelos del estado del arte (YOLO y SSD), y se realiza un estudio comparativo en el que se analizan los problemas encontrados cuando estos son aplicados a la detección de expresiones matemáticas. Finalmente, el trabajo termina con una discusión sobre cómo abordar estos problemas en modelos futuros y se ahonda en la posibilidad de derivar este trabajo hacia el problema del reconocimiento de expresiones matemáticas., [EN] In this work, we address the problem of detecting mathematical expressions in scientific papers. First, a brief introduction to the problem of general-object detection is given, along with state-of-the-art techniques and evaluation methods. Then, two state-of-the-art models (YOLO and SSD) are presented, and a comparative study is carried out where we analyze the problems and weaknesses found when they are applied to this specific task. Finally, the work ends with a discussion on how to tackle these problems in future models and the possibility of deriving this work towards the mathematical expression recognition problem.
- Published
- 2019
23. Artificial intelligence algorithms to predict Italian real estate market prices
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Rampini, Luca and Re Cecconi, Fulvio
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- 2022
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24. Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023
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Jafari, Mahboobeh, Sadeghi, Delaram, Shoeibi, Afshin, Alinejad-Rokny, Hamid, Beheshti, Amin, García, David López, Chen, Zhaolin, Acharya, U. Rajendra, and Gorriz, Juan M.
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- 2024
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25. Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics
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Jia, Qiong, Zhu, Ying, Xu, Rui, Zhang, Yubin, and Zhao, Yihua
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- 2022
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26. Advances in Cybersecurity and Reliability.
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Alazab, Moutaz and Alazab, Ammar
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DEEP learning ,INTERNET security ,NATURAL language processing ,MACHINE learning ,ARTIFICIAL intelligence ,ADVANCED Encryption Standard - Abstract
This document is a collection of research papers on various topics related to cybersecurity. The papers cover a range of subjects, including mapping vulnerabilities to defense strategies, countermeasures for cybersecurity challenges in higher education, identifying malware packers, the role of blockchain technology in manufacturing, text-to-image synthesis, predicting cybersecurity attacks on IoT, enhancing data security in BYOD environments, encryption schemes for IoT systems, usable security, and an analysis of the ChatGPT language model. Each paper presents its findings and proposes solutions to address specific cybersecurity issues. The document aims to raise awareness and improve mitigation efforts against cyber threats, emphasizing the importance of collaboration between businesses and law enforcement agencies. [Extracted from the article]
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- 2024
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27. CLASP Papers in Computational Linguistics
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Simon, Dobnik and Shalom, Lappin
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computational linguistics ,logic ,language ,machine learning ,deep learning ,language technology ,neural networks ,artificial intelligence - Abstract
The past two decades have seen impressive progress in a variety of areas of AI, particularly NLP, through the application of machine learning methods to a wide range of tasks. With the intensive use of deep learning methods in recent years this work has produced significant improvements in the coverage and accuracy of NLP systems in such domains as speech recognition, topic identification, semantic interpretation, and image description generation. While deep learning is opening up exciting new approaches to long standing, difficult problems in computational linguistics, it also raises important foundational questions. Specifically, we do not have a clear formal understanding of why multi-level recursive deep neural networks achieve the success in learning and classification that they are delivering. It is also not obvious whether they should displace more traditional, logically driven methods, or be combined with them. Finally, we need to explore the extent, if any, to which both logical models and machine learning methods offer insights into the cognitive foundations of natural language. The aim of the Conference on Logic and Machine Learning in Natural Language (LAML) was to initiate a dialogue between these two approaches, where they have traditionally remained separate and in competition. The conference and this publication was supported by a grant from the Swedish Research Council (VR project 2014-39) for the establishment of the Centre for Linguistic Theory and Studies in Probability (CLASP) at Department of Philosophy, Linguistics and Theory of Science (FLoV), University of Gothenburg.
- Published
- 2017
28. Multimedia medical data-driven decision making.
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Chakraborty, Chinmay, Diván, Mario José, and Mahmoudi, Saïd
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MEDICAL decision making ,DEEP learning ,MACHINE learning ,ARTIFICIAL intelligence ,COMPUTATIONAL intelligence ,SIGNAL processing - Abstract
The data-driven decision-making solutions have become more demandable in healthcare for development, testing, and trials; it has intended to be a part of both hospitals and homes. The sixth paper by Ahmed et al. proposes institutional data collaboration alongside an adversarial evasion method to keep the data secure. In line with these efforts, the central theme of this Special Issue is to report novel methodologies, theories, technologies, techniques, and solutions for medical data analytics techniques for multimedia applications. [Extracted from the article]
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- 2022
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29. Early detection of dementia using artificial intelligence and multimodal features with a focus on neuroimaging: A systematic literature review
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Grigas, Ovidijus, Maskeliunas, Rytis, and Damaševičius, Robertas
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- 2024
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30. Deep learning—a route to WDM high-speed optical networks
- Author
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Rai, Saloni and Garg, Amit Kumar
- Published
- 2024
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31. Guest Editorial: Special issue on computational methods and artificial intelligence applications in low‐carbon energy systems.
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Wang, Yishen, Zhou, Fei, Guerrero, Josep M., Baker, Kyri, Chen, Yize, Wang, Hao, Xu, Bolun, Xu, Qianwen, Zhu, Hong, and Agwan, Utkarsha
- Subjects
ARTIFICIAL intelligence ,ARTIFICIAL neural networks ,MACHINE learning ,REINFORCEMENT learning ,DEEP reinforcement learning ,DEEP learning - Abstract
This document is a guest editorial for a special issue on computational methods and artificial intelligence applications in low-carbon energy systems. The editorial highlights the urgent need for advanced computing and artificial intelligence in the clean energy transition to improve system reliability, economics, and sustainability. The special issue includes 19 original research articles covering topics such as energy forecasting, situational awareness, multi-energy system dispatch, and power system operation. The articles present state-of-the-art methods and techniques in these areas, including wind power forecasting, demand-side flexibility, fault diagnosis of photovoltaic strings, and energy management strategies. The authors express their gratitude to the participating authors and anonymous reviewers for their contributions to the special section. [Extracted from the article]
- Published
- 2024
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32. Topical collection on machine learning for big data analytics in smart healthcare systems.
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Jan, Mian Ahmad, Song, Houbing, Khan, Fazlullah, Rehman, Ateeq Ur, and Yang, Lie-Liang
- Subjects
MACHINE learning ,DEEP learning ,ARTIFICIAL intelligence ,BIG data ,CONVOLUTIONAL neural networks ,MEDICAL care - Published
- 2023
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33. Deep learning, deep change? Mapping the evolution and geography of a general purpose technology
- Author
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Klinger, Joel, Mateos-Garcia, Juan, and Stathoulopoulos, Konstantinos
- Published
- 2021
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34. Special Issue on Data Analysis and Artificial Intelligence for IoT.
- Author
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Shrestha, Bhanu, Cho, Seongsoo, and Seo, Changho
- Subjects
ARTIFICIAL intelligence ,DEEP learning ,INTRUSION detection systems (Computer security) ,MACHINE learning ,INTERNET of things ,CONVOLUTIONAL neural networks ,DATA analysis - Abstract
This paper [[7]] addresses the challenges Intrusion Detection Systems (IDS) face in the Internet of Things (IoT) context due to IoT data's high dimensionality and diversity. The Internet of Things (IoT) has become an increasingly popular technology in recent years, enabling interconnectivity and communication between devices and systems. As a result, there is a growing need for advanced data analysis techniques and artificial intelligence (AI) methods to process, analyze, and extract valuable insights from IoT data. The proposed technique for tracking a moving object of interest in a noisy video using a modified simplest color balance algorithm and a binarization algorithm proved to be effective in building training data for machine learning. [Extracted from the article]
- Published
- 2023
- Full Text
- View/download PDF
35. Methods and Applications of Data Mining in Business Domains.
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Amrit, Chintan and Abdi, Asad
- Subjects
DATA mining ,DEEP learning ,ARTIFICIAL neural networks ,MACHINE learning ,ARTIFICIAL intelligence ,DECISION support systems - Abstract
These papers collectively showcase the adaptability and effectiveness of data mining techniques, making substantial contributions to the broader realm of " I Methods and Applications of Data Mining in Business Domains i ". In a business context, the challenge is that one would like to see (i) how the algorithms can be repeatable in the real world, (ii) how the patterns mined can be utilized by the business, and (iii) how the resulting model can be understood and utilized in the business environment [[1]]. Additionally, they provide insights into factors influencing the adoption of business intelligence systems (BISs) in small and medium-sized enterprises (SMEs) [[26]], and conduct a systematic literature review on AI-based methods for automating business processes and decision support [[27]]. This Special Issue invited researchers to contribute original research in the field of data mining, particularly in its application to diverse domains, like healthcare, software development, logistics, and human resources. [Extracted from the article]
- Published
- 2023
- Full Text
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36. Exploring the State of Machine Learning and Deep Learning in Medicine: A Survey of the Italian Research Community.
- Author
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Bottrighi, Alessio and Pennisi, Marzio
- Subjects
DEEP learning ,MACHINE learning ,EXPERT systems ,SCIENTIFIC community ,ARTIFICIAL intelligence ,INFORMATION storage & retrieval systems - Abstract
Artificial intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade is remarkable. Specifically, machine learning (ML) and deep learning (DL) techniques in medicine have been increasingly adopted due to the growing abundance of health-related data, the improved suitability of such techniques for managing large datasets, and more computational power. ML and DL methodologies are fostering the development of new "intelligent" tools and expert systems to process data, to automatize human–machine interactions, and to deliver advanced predictive systems that are changing every aspect of the scientific research, industry, and society. The Italian scientific community was instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years. To this end, we selected all the papers published in the last five years with at least one of the authors affiliated to an Italian institution that in the title, in the abstract, or in the keywords present the terms "machine learning" or "deep learning" and reference a medical area. We focused our research on journal papers under the hypothesis that Italian researchers prefer to present novel but well-established research in scientific journals. We then analyzed the selected papers considering different dimensions, including the medical topic, the type of data, the pre-processing methods, the learning methods, and the evaluation methods. As a final outcome, a comprehensive overview of the Italian research landscape is given, highlighting how the community has increasingly worked on a very heterogeneous range of medical problems. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
37. Special issue on deep learning and big data analytics for medical e-diagnosis/AI-based e-diagnosis.
- Author
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Fong, Simon, Fortino, Giancarlo, Ghista, Dhanjoo, and Piccialli, Francesco
- Subjects
DEEP learning ,ARTIFICIAL neural networks ,MACHINE learning ,ARTIFICIAL intelligence ,BIG data ,CONVOLUTIONAL neural networks - Abstract
The model integrates artificial intelligence (AI) and big data analytics, utilizing IoMT devices for data acquisition and Hadoop ecosystem for managing big data. The field of medical diagnosis is currently undergoing a remarkable transformation with the emergence of artificial intelligence (AI) techniques, particularly deep learning and big data analytics. By harnessing the power of deep learning and big data analytics, AI-based e-diagnosis has the potential to revolutionize healthcare delivery. [Extracted from the article]
- Published
- 2023
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38. The efficiency of artificial intelligence methods for finding radiographic features in different endodontic treatments - a systematic review.
- Author
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Ramezanzade, Shaqayeq, Laurentiu, Tudor, Bakhshandah, Azam, Ibragimov, Bulat, Kvist, Thomas, EndoReCo, and Bjørndal, Lars
- Subjects
PERIAPICAL diseases ,ARTIFICIAL intelligence ,TOOTH roots ,ENDODONTICS ,MACHINE learning - Abstract
To assess the efficiency of AI methods in finding radiographic features in Endodontic treatment considerations. This review was based on the PRISMA guidelines and QUADAS 2 tool. A systematic search was performed of the literature on cases with endodontic treatments, comparing AI algorithms (test) versus conventional image assessments (control) for finding radiographic features. The search was conducted in PubMed, Scopus, Google Scholar and the Cochrane library. Inclusion criteria were studies on the use of AI and machine learning in endodontic treatments using dental X-rays. The initial search retrieved 1131 papers, from which 24 were included. High heterogeneity of the materials left out a meta-analysis. The reported subcategories were periapical lesion, vertical root fractures, predicting root/canal morphology, locating minor apical foramen, tooth segmentation and endodontic retreatment prediction. Radiographic features assessed were mostly periapical lesions. The studies mostly considered the decision of 1–3 experts as the reference for training their models. Almost half of the included materials campared their trained neural network model with other methods. More than 58% of studies had some level of bias. AI-based models have shown effectiveness in finding radiographic features in different endodontic treatments. While the reported accuracy measurements seem promising, the papers mostly were biased methodologically. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
39. Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review.
- Author
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Raki, Hind, Aalaila, Yahya, Taktour, Ayoub, and Peluffo-Ordóñez, Diego H.
- Subjects
FOOD science ,ARTIFICIAL intelligence ,MACHINE learning ,FOODBORNE diseases ,FOOD safety ,DEEP learning - Abstract
On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our TriScope Keywords-based Synthesis methodology. All available literature related to our topic was investigated according to our criteria of inclusion and exclusion. The final count of data papers was subject to deep reading and analysis to extract the necessary information to answer our research questions. Although many studies have been conducted, limited attention has been paid to outlining the applications of AI tools combined with analytical strategies for crop-based food safety specifically. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
40. Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier.
- Author
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Soliman, Khaled, Sobh, Mohamed, and Bahaa-Eldin, Ayman M.
- Subjects
ARTIFICIAL intelligence ,DIGITAL transformation - Abstract
The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach. The most effective PE headers that can highly differentiate between benign and malware files were selected to train the model on 15 PE features to speed up the classification process and achieve real-time detection for malicious executables. The evaluation results showed that RMED succeeded in shrinking the classification time to 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. In conclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework that leverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. DETECTION OF DEEP FAKES USING DEEP LEARNING.
- Author
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D., ANJANI SUPUTRI DEVI, T., SAI KISHORE, V., VENKATA SRI SAI TEJASWI, G., VENKATA SUBRAHMANYA SIVARAM, K., SUBHAN SAHEB S., and K., VIKAS KUMAR
- Subjects
DEEP learning ,MACHINE learning ,CONVOLUTIONAL neural networks ,ARTIFICIAL intelligence ,RECURRENT neural networks ,FEATURE extraction - Abstract
Deep learning algorithms have simplified the process of creating indistinguishable synthetic videos, or deep fakes, because of the unparalleled increase in processing power. It is concerning because these face-swapped manipulations are often used in a variety of contexts, such as blackmail and political manipulation. This paper presents a revolutionary deep learning-based approach to accurately discriminating between real and Artificial Intelligence (AI)-generated false films. Using a ResNext Convolutional Neural Network (CNN) for frame-level feature extraction, this method makes use of an automated mechanism intended to identify replacement and re-enactment deep fakes. A Recurrent Neural Network (RNN) equipped with Long Short-Term Memory (LSTM) training is utilized to classify videos and distinguish between real and modified ones. The system demonstrates the effectiveness of a straightforward and reliable methodology, in addition to utilizing complex neural network topologies. Through testing, this paper showcases how well the system can accurately identify videos playing a crucial role in ongoing initiatives to combat the increasing dangers posed by the proliferation of deep fake content in society. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction.
- Author
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Conte, Luana, Rizzo, Emanuele, Grassi, Tiziana, Bagordo, Francesco, De Matteis, Elisabetta, and De Nunzio, Giorgio
- Subjects
ARTIFICIAL intelligence ,DEEP learning ,SYSTEMS software ,GENETIC counseling ,COMPUTER-aided diagnosis ,COUNSELING - Abstract
Pedigree charts remain essential in oncological genetic counseling for identifying individuals with an increased risk of developing hereditary tumors. However, this valuable data source often remains confined to paper files, going unused. We propose a computer-aided detection/diagnosis system, based on machine learning and deep learning techniques, capable of the following: (1) assisting genetic oncologists in digitizing paper-based pedigree charts, and in generating new digital ones, and (2) automatically predicting the genetic predisposition risk directly from these digital pedigree charts. To the best of our knowledge, there are no similar studies in the current literature, and consequently, no utilization of software based on artificial intelligence on pedigree charts has been made public yet. By incorporating medical images and other data from omics sciences, there is also a fertile ground for training additional artificial intelligence systems, broadening the software predictive capabilities. We plan to bridge the gap between scientific advancements and practical implementation by modernizing and enhancing existing oncological genetic counseling services. This would mark the pioneering development of an AI-based application designed to enhance various aspects of genetic counseling, leading to improved patient care and advancements in the field of oncogenetics. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Bioinspired Artificial Intelligence Applications 2023.
- Author
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Wei, Haoran, Tao, Fei, Huang, Zhenghua, and Long, Yanhua
- Subjects
ARTIFICIAL intelligence ,DEEP learning ,REINFORCEMENT learning ,MACHINE learning ,DEEP reinforcement learning ,NATURAL language processing - Abstract
This document discusses the rapid development of Artificial Intelligence (AI) and its bioinspired applications. It highlights the benefits of bioinspired AI, such as increased accuracy in image and speech processing, reduced cost and energy usage through edge devices, and enhanced bio-signal quality. However, it also acknowledges the challenges posed by improper AI utilization, such as the generation of fake news and security issues. The document calls for research papers on bioinspired AI applications to explore its potential and address these challenges. It includes examples of research papers that utilize deep reinforcement learning for robot task sequencing, propose a real-time multi-surveillance pedestrian target detection model, develop an intelligent breast mass classification approach, and introduce a bio-inspired object detection algorithm for remote sensing images. The document concludes by emphasizing the importance of biomimetic artificial intelligence in various fields and promoting further research in this area. [Extracted from the article]
- Published
- 2024
- Full Text
- View/download PDF
44. Call for Papers.
- Subjects
- *
ARTIFICIAL intelligence , *REINFORCEMENT learning , *MACHINE learning , *DEEP learning , *INTELLIGENT networks - Published
- 2022
- Full Text
- View/download PDF
45. Machine Learning in X-ray Diagnosis for Oral Health: A Review of Recent Progress.
- Author
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Martins, Mónica Vieira, Baptista, Luís, Luís, Henrique, Assunção, Victor, Araújo, Mário-Rui, and Realinho, Valentim
- Subjects
RADIOSCOPIC diagnosis ,MACHINE learning ,CONE beam computed tomography ,X-ray imaging ,ARTIFICIAL intelligence - Abstract
The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, powered by the availability of clinical dental images. The present work aims to investigate recent progress concerning the application of ML in the diagnosis of oral diseases using oral X-ray imaging, namely the quality and outcome of such methods. The specific research question was developed using the PICOT methodology. The review was conducted in the Web of Science, Science Direct, and IEEE Xplore databases, for articles reporting the use of ML and AI for diagnostic purposes in X-ray-based oral imaging. Imaging types included panoramic, periapical, bitewing X-ray images, and oral cone beam computed tomography (CBCT). The search was limited to papers published in the English language from 2018 to 2022. The initial search included 104 papers that were assessed for eligibility. Of these, 22 were included for a final appraisal. The full text of the articles was carefully analyzed and the relevant data such as the clinical application, the ML models, the metrics used to assess their performance, and the characteristics of the datasets, were registered for further analysis. The paper discusses the opportunities, challenges, and limitations found. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
46. Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review.
- Author
-
Abo-Zahhad, Mohammed, El-Malek, Ahmed H. Abd, Sayed, Mohammed S., and Gitau, Susan Njeri
- Subjects
RETAINED surgical items ,MACHINE learning ,DEEP learning ,MEDICAL personnel ,ARTIFICIAL intelligence ,IMAGE analysis - Abstract
Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their incidence. RSIs are objects inadvertently left within patients' bodies after surgery, which can lead to severe consequences such as infections and death. The repercussions highlight the critical need to address this issue. Machine learning (ML) and deep learning (DL) have displayed considerable potential for enhancing the prevention of RSIs through heightened precision and decreased reliance on human involvement. ML techniques are finding an expanding number of applications in medicine, ranging from automated imaging analysis to diagnosis. DL has enabled substantial advances in the prediction capabilities of computers by combining the availability of massive volumes of data with extremely effective learning algorithms. This paper reviews and evaluates recently published articles on the application of ML and DL in RSIs prevention and diagnosis, stressing the need for a multi-layered approach that leverages each method's strengths to mitigate RSI risks. It highlights the key findings, advantages, and limitations of the different techniques used. Extensive datasets for training ML and DL models could enhance RSI detection systems. This paper also discusses the various datasets used by researchers for training the models. In addition, future directions for improving these technologies for RSI diagnosis and prevention are considered. By merging ML and DL with current procedures, it is conceivable to substantially minimize RSIs, enhance patient safety, and elevate surgical care standards. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
47. A review of asset management using artificial intelligence‐based machine learning models: Applications for the electric power and energy system.
- Author
-
Rajora, Gopal Lal, Sanz‐Bobi, Miguel A., Tjernberg, Lina Bertling, and Urrea Cabus, José Eduardo
- Subjects
ARTIFICIAL intelligence ,ASSET management ,ASSET protection ,MACHINE learning ,DEEP learning ,SUSTAINABILITY - Abstract
Power system protection and asset management present persistent technical challenges, particularly in the context of the smart grid and renewable energy sectors. This paper aims to address these challenges by providing a comprehensive assessment of machine learning applications for effective asset management in power systems. The study focuses on the increasing demand for energy production while maintaining environmental sustainability and efficiency. By harnessing the power of modern technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL), this research explores how ML techniques can be leveraged as powerful tools for the power industry. By showcasing practical applications and success stories, this paper demonstrates the growing acceptance of machine learning as a significant technology for current and future business needs in the power sector. Additionally, the study examines the barriers and difficulties of large‐scale ML deployment in practical settings while exploring potential opportunities for these tactics. Through this overview, insights into the transformative potential of ML in shaping the future of power system asset management are provided. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. A COMPARATIVE EXPLORATION OF ACTIVATION FUNCTIONS FOR IMAGE CLASSIFICATION IN CONVOLUTIONAL NEURAL NETWORKS.
- Author
-
MAKHDOOM, FAIZA and RAHMAN, JAMSHAID UL
- Subjects
ARTIFICIAL neural networks ,DEEP learning ,ARTIFICIAL intelligence ,MACHINE learning ,DIGITAL image processing ,COMPUTER vision - Abstract
Activation functions play a crucial role in enabling neural networks to carry out tasks with increased flexibility by introducing non-linearity. The selection of appropriate activation functions becomes even more crucial, especially in the context of deeper networks where the objective is to learn more intricate patterns. Among various deep learning tools, Convolutional Neural Networks (CNNs) stand out for their exceptional ability to learn complex visual patterns. In practice, ReLu is commonly employed in convolutional layers of CNNs, yet other activation functions like Swish can demonstrate superior training performance while maintaining good testing accuracy on different datasets. This paper presents an optimally refined strategy for deep learning-based image classification tasks by incorporating CNNs with advanced activation functions and an adjustable setting of layers. A thorough analysis has been conducted to support the effectiveness of various activation functions when coupled with the favorable softmax loss, rendering them suitable for ensuring a stable training process. The results obtained on the CIFAR-10 dataset demonstrate the favorability and stability of the adopted strategy throughout the training process. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Call for Papers.
- Subjects
- *
ARTIFICIAL intelligence , *REINFORCEMENT learning , *MACHINE learning , *DEEP learning , *INTELLIGENT networks - Abstract
The article reports that With the continued growth of IoT devices and their deployment, manually managing and connecting them is impractical and presents multiple challenges. To that end, Zero Touch Networks that rely on software-based modules instead of dedicated propriety hardware become a viable potential solution. The overall aim of zero-touch networks is for machines to learn how to become more autonomous so that we can delegate complex, mundane tasks to them.
- Published
- 2022
- Full Text
- View/download PDF
50. Call for Papers.
- Subjects
- *
ARTIFICIAL intelligence , *REINFORCEMENT learning , *MACHINE learning , *DEEP learning , *INTELLIGENT networks - Published
- 2022
- Full Text
- View/download PDF
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