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Thenmozhi R, Shridevi S, Mohanty SN, García-Díaz V, Gupta D, Tiwari P, Shorfuzzaman M. Attribute-Based Adaptive Homomorphic Encryption for Big Data Security. BIG DATA 2024; 12:343-356. [PMID: 34898266 DOI: 10.1089/big.2021.0176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
There is a drastic increase in Internet usage across the globe, thanks to mobile phone penetration. This extreme Internet usage generates huge volumes of data, in other terms, big data. Security and privacy are the main issues to be considered in big data management. Hence, in this article, Attribute-based Adaptive Homomorphic Encryption (AAHE) is developed to enhance the security of big data. In the proposed methodology, Oppositional Based Black Widow Optimization (OBWO) is introduced to select the optimal key parameters by following the AAHE method. By considering oppositional function, Black Widow Optimization (BWO) convergence analysis was enhanced. The proposed methodology has different processes, namely, process setup, encryption, and decryption processes. The researcher evaluated the proposed methodology with non-abelian rings and the homomorphism process in ciphertext format. Further, it is also utilized in improving one-way security related to the conjugacy examination issue. Afterward, homomorphic encryption is developed to secure the big data. The study considered two types of big data such as adult datasets and anonymous Microsoft web datasets to validate the proposed methodology. With the help of performance metrics such as encryption time, decryption time, key size, processing time, downloading, and uploading time, the proposed method was evaluated and compared against conventional cryptography techniques such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). Further, the key generation process was also compared against conventional methods such as BWO, Particle Swarm Optimization (PSO), and Firefly Algorithm (FA). The results established that the proposed method is supreme than the compared methods and can be applied in real time in near future.
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Affiliation(s)
- R Thenmozhi
- Department of Computer Science and Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India
| | - S Shridevi
- Centre of Advanced Data Science, Vellore Institute of Technology, Vellore, Chennai, India
| | - Sachi Nandan Mohanty
- Department of Computer Science & Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, India
| | | | - Deepak Gupta
- Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Mohammad Shorfuzzaman
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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Kim MS, Lim BY, Lee K, Kwon HY. Effective Model Update for Adaptive Classification of Text Streams in a Distributed Learning Environment. SENSORS (BASEL, SWITZERLAND) 2022; 22:9298. [PMID: 36501999 PMCID: PMC9736177 DOI: 10.3390/s22239298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/18/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
In this study, we propose dynamic model update methods for the adaptive classification model of text streams in a distributed learning environment. In particular, we present two model update strategies: (1) the entire model update and (2) the partial model update. The former aims to maximize the model accuracy by periodically rebuilding the model based on the accumulated datasets including recent datasets. Its learning time incrementally increases as the datasets increase, but we alleviate the learning overhead by the distributed learning of the model. The latter fine-tunes the model only with a limited number of recent datasets, noting that the data streams are dependent on a recent event. Therefore, it accelerates the learning speed while maintaining a certain level of accuracy. To verify the proposed update strategies, we extensively apply them to not only fully trainable language models based on CNN, RNN, and Bi-LSTM, but also a pre-trained embedding model based on BERT. Through extensive experiments using two real tweet streaming datasets, we show that the entire model update improves the classification accuracy of the pre-trained offline model; the partial model update also improves it, which shows comparable accuracy with the entire model update, while significantly increasing the learning speed. We also validate the scalability of the proposed distributed learning architecture by showing that the model learning and inference time decrease as the number of worker nodes increases.
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Affiliation(s)
- Min-Seon Kim
- Department of Industrial Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
| | - Bo-Young Lim
- Department of Industrial Engineering, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
| | - Kisung Lee
- Division of Computer Science and Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Hyuk-Yoon Kwon
- Department of Industrial Engineering, The Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of Korea
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Tanwar A, Zhang J, Ive J, Gupta V, Guo Y. Phenotyping in clinical text with unsupervised numerical reasoning for patient stratification. Exp Biol Med (Maywood) 2022; 247:2038-2052. [PMID: 36217914 PMCID: PMC9791305 DOI: 10.1177/15353702221118092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Phenotypic information of patients, as expressed in clinical text, is important in many clinical applications such as identifying patients at risk of hard-to-diagnose conditions. Extracting and inferring some phenotypes from clinical text requires numerical reasoning, for example, a temperature of 102°F suggests the phenotype Fever. However, while current state-of-the-art phenotyping models using natural language processing (NLP) are in general very efficient in extracting phenotypes, they struggle to extract phenotypes that require numerical reasoning. In this article, we propose a novel unsupervised method that leverages external clinical knowledge and contextualized word embeddings by ClinicalBERT for numerical reasoning in different phenotypic contexts. Experiments show that the proposed method achieves significant improvement against unsupervised baseline methods with absolute increase in generalized Recall and F1 scores of up to 79% and 71%, respectively. Also, the proposed method outperforms supervised baseline methods with absolute increase in generalized Recall and F1 scores of up to 70% and 44%, respectively. In addition, we validate the methodology on clinical use cases where the detected phenotypes significantly contribute to patient stratification systems for a set of diseases, namely, HIV and myocardial infarction (heart attack). Moreover, we find that these phenotypes from clinical text can be used to impute the missing values in structured data, which enrich and improve data quality.
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Torregrosa J, Bello-Orgaz G, Martínez-Cámara E, Ser JD, Camacho D. A survey on extremism analysis using natural language processing: definitions, literature review, trends and challenges. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:1-37. [PMID: 35039755 PMCID: PMC8754364 DOI: 10.1007/s12652-021-03658-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 12/12/2021] [Indexed: 06/14/2023]
Abstract
Extremism has grown as a global problem for society in recent years, especially after the apparition of movements such as jihadism. This and other extremist groups have taken advantage of different approaches, such as the use of Social Media, to spread their ideology, promote their acts and recruit followers. The extremist discourse, therefore, is reflected on the language used by these groups. Natural language processing (NLP) provides a way of detecting this type of content, and several authors make use of it to describe and discriminate the discourse held by these groups, with the final objective of detecting and preventing its spread. Following this approach, this survey aims to review the contributions of NLP to the field of extremism research, providing the reader with a comprehensive picture of the state of the art of this research area. The content includes a first conceptualization of the term extremism, the elements that compose an extremist discourse and the differences with other terms. After that, a review description and comparison of the frequently used NLP techniques is presented, including how they were applied, the insights they provided, the most frequently used NLP software tools, descriptive and classification applications, and the availability of datasets and data sources for research. Finally, research questions are approached and answered with highlights from the review, while future trends, challenges and directions derived from these highlights are suggested towards stimulating further research in this exciting research area.
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Affiliation(s)
- Javier Torregrosa
- Computer Systems Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain
| | - Gema Bello-Orgaz
- Computer Systems Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain
| | - Eugenio Martínez-Cámara
- Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain
| | - Javier Del Ser
- TECNALIA, Basque Research and Technology Alliance (BRTA), Mendaro, Spain
| | - David Camacho
- Computer Systems Engineering Department, Universidad Politécnica de Madrid, Madrid, Spain
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Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. WATER 2021. [DOI: 10.3390/w13233470] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A lack of adequate consideration of underwater image enhancement gives room for more research into the field. The global background light has not been adequately addressed amid the presence of backscattering. This paper presents a technique based on pixel differences between global and local patches in scene depth estimation. The pixel variance is based on green and red, green and blue, and red and blue channels besides the absolute mean intensity functions. The global background light is extracted based on a moving average of the impact of suspended light and the brightest pixels within the image color channels. We introduce the block-greedy algorithm in a novel Convolutional Neural Network (CNN) proposed to normalize different color channels’ attenuation ratios and select regions with the lowest variance. We address the discontinuity associated with underwater images by transforming both local and global pixel values. We minimize energy in the proposed CNN via a novel Markov random field to smooth edges and improve the final underwater image features. A comparison of the performance of the proposed technique against existing state-of-the-art algorithms using entropy, Underwater Color Image Quality Evaluation (UCIQE), Underwater Image Quality Measure (UIQM), Underwater Image Colorfulness Measure (UICM), and Underwater Image Sharpness Measure (UISM) indicate better performance of the proposed approach in terms of average and consistency. As it concerns to averagely, UICM has higher values in the technique than the reference methods, which explainsits higher color balance. The μ values of UCIQE, UISM, and UICM of the proposed method supersede those of the existing techniques. The proposed noted a percent improvement of 0.4%, 4.8%, 9.7%, 5.1% and 7.2% in entropy, UCIQE, UIQM, UICM and UISM respectively compared to the best existing techniques. Consequently, dehazed images have sharp, colorful, and clear features in most images when compared to those resulting from the existing state-of-the-art methods. Stable σ values explain the consistency in visual analysis in terms of sharpness of color and clarity of features in most of the proposed image results when compared with reference methods. Our own assessment shows that only weakness of the proposed technique is that it only applies to underwater images. Future research could seek to establish edge strengthening without color saturation enhancement.
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Shankar K, Perumal E, Díaz VG, Tiwari P, Gupta D, Saudagar AKJ, Muhammad K. An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images. Appl Soft Comput 2021; 113:107878. [PMID: 34512217 PMCID: PMC8423750 DOI: 10.1016/j.asoc.2021.107878] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 08/20/2021] [Accepted: 09/02/2021] [Indexed: 12/18/2022]
Abstract
In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.
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Affiliation(s)
- K Shankar
- Federal University of Piauí, Teresina 64049-550, Brazil
| | - Eswaran Perumal
- Department of Computer Applications, Alagappa University, Karaikudi, India
| | - Vicente García Díaz
- Department of Computer Science, School of Computer Science Engineering, University of Oviedo, Spain
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Finland
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, New Delhi, India
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
| | - Khan Muhammad
- Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul 143-747, Republic of Korea
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Madhavan MV, Khamparia A, Gupta D, Pande S, Tiwari P, Hossain MS. Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning. Neural Comput Appl 2021; 35:13907-13920. [PMID: 34127892 PMCID: PMC8188748 DOI: 10.1007/s00521-021-06171-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 05/25/2021] [Indexed: 12/31/2022]
Abstract
Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.
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Affiliation(s)
- Mangena Venu Madhavan
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab India
| | - Aditya Khamparia
- Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Amethi, India
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Rohini, India
| | - Sagar Pande
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab India
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - M. Shamim Hossain
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
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Shankar K, Perumal E, Tiwari P, Shorfuzzaman M, Gupta D. Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images. MULTIMEDIA SYSTEMS 2021; 28:1175-1187. [PMID: 34075280 PMCID: PMC8158467 DOI: 10.1007/s00530-021-00800-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 04/17/2021] [Indexed: 06/12/2023]
Abstract
In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill-posed problem. This paper presents a new metaheuristic-based fusion model for COVID-19 diagnosis using chest X-ray images. The proposed model comprises different preprocessing, feature extraction, and classification processes. Initially, the Weiner filtering (WF) technique is used for the preprocessing of images. Then, the fusion-based feature extraction process takes place by the incorporation of gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRM), and local binary patterns (LBP). Afterward, the salp swarm algorithm (SSA) selected the optimal feature subset. Finally, an artificial neural network (ANN) is applied as a classification process to classify infected and healthy patients. The proposed model's performance has been assessed using the Chest X-ray image dataset, and the results are examined under diverse aspects. The obtained results confirmed the presented model's superior performance over the state of art methods.
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Affiliation(s)
- K. Shankar
- Department of Computer Applications, Alagappa University, Karaikudi, India
| | - Eswaran Perumal
- Department of Computer Applications, Alagappa University, Karaikudi, India
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Mohammad Shorfuzzaman
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944 Saudi Arabia
| | - Deepak Gupta
- Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, India
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He L, Guo C, Tiwari P, Pandey HM, Dang W. Intelligent system for depression scale estimation with facial expressions and case study in industrial intelligence. INT J INTELL SYST 2021. [DOI: 10.1002/int.22426] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Lang He
- Computer Science, School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an Shaanxi China
| | - Chenguang Guo
- Department of Electronics and Information Engineering, School of Electronics and Information Northwestern Polytechnical University Xi'an China
| | - Prayag Tiwari
- Department of Computer Science Aalto University Espoo Finland
| | | | - Wei Dang
- Shaanxi Mental Health Center Xi'an Shaanxi China
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Khamparia A, Bharati S, Podder P, Gupta D, Khanna A, Phung TK, Thanh DNH. Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2021; 32:747-765. [PMID: 33456204 PMCID: PMC7798373 DOI: 10.1007/s11045-020-00756-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 12/09/2020] [Accepted: 12/19/2020] [Indexed: 02/06/2023]
Abstract
Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.
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Affiliation(s)
- Aditya Khamparia
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab India
| | - Subrato Bharati
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205 Bangladesh
| | - Prajoy Podder
- Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205 Bangladesh
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Ashish Khanna
- Maharaja Agrasen Institute of Technology, Delhi, India
| | - Thai Kim Phung
- School of Business Information Technology, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Dang N. H. Thanh
- School of Business Information Technology, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam
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Mangano A, Valle V, Dreifuss NH, Aguiluz G, Masrur MA. Role of Artificial Intelligence (AI) in Surgery: Introduction, General Principles, and Potential Applications. Surg Technol Int 2020; 38:17-21. [PMID: 33370842 DOI: 10.52198/21.sti.38.so1369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
AI (Artificial intelligence) is an interdisciplinary field aimed at the development of algorithms to endow machines with the capability of executing cognitive tasks. The number of publications regarding AI and surgery has increased dramatically over the last two decades. This phenomenon can partly be explained by the exponential growth in computing power available to the largest AI training runs. AI can be classified into different sub-domains with extensive potential clinical applications in the surgical setting. AI will increasingly become a major component of clinical practice in surgery. The aim of the present Narrative Review is to give a general introduction and summarized overview of AI, as well as to present additional remarks on potential surgical applications and future perspectives in surgery.
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Affiliation(s)
- Alberto Mangano
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Valentina Valle
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Nicolas H Dreifuss
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Gabriela Aguiluz
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
| | - Mario A Masrur
- Division of General, Minimally Invasive and Robotic Surgery, University of Illinois at Chicago, Chicago, IL, USA
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