Author: Fatih ÖZYURT
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 16
Book Description
Brain tumor classification is a challenging task in the field of medical image processing. The present study proposes a hybrid method using Neutrosophy and Convolutional Neural Network (NS-CNN). It aims to classify tumor region areas that are segmented from brain images as benign and malignant. In the first stage, MRI images were segmented using the neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE) approach.
Brain Tumor Detection Based on Convolutional Neural Network with Neutrosophic Expert Maximum Fuzzy Sure Entropy
Author: Fatih ÖZYURT
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 16
Book Description
Brain tumor classification is a challenging task in the field of medical image processing. The present study proposes a hybrid method using Neutrosophy and Convolutional Neural Network (NS-CNN). It aims to classify tumor region areas that are segmented from brain images as benign and malignant. In the first stage, MRI images were segmented using the neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE) approach.
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 16
Book Description
Brain tumor classification is a challenging task in the field of medical image processing. The present study proposes a hybrid method using Neutrosophy and Convolutional Neural Network (NS-CNN). It aims to classify tumor region areas that are segmented from brain images as benign and malignant. In the first stage, MRI images were segmented using the neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE) approach.
Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics
Author: Florentin Smarandache
Publisher: Elsevier
ISBN: 0323994571
Category : Computers
Languages : en
Pages : 495
Book Description
Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics investigates and presents the many applications that have arisen in the last ten years using neutrosophic statistics in bioinformatics, medicine, agriculture and cognitive science. This book will be very useful to the scientific community, appealing to audiences interested in fuzzy, vague concepts from which uncertain data are collected, including academic researchers, practicing engineers and graduate students. Neutrosophic statistics is a generalization of classical statistics. In classical statistics, the data is known, formed by crisp numbers. In comparison, data in neutrosophic statistics has some indeterminacy. This data may be ambiguous, vague, imprecise, incomplete, and even unknown. Neutrosophic statistics refers to a set of data, such that the data or a part of it are indeterminate in some degree, and to methods used to analyze the data. Introduces the field of neutrosophic statistics and how it can solve problems working with indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data Presents various applications of neutrosophic statistics in the fields of bioinformatics, medicine, cognitive science and agriculture Provides practical examples and definitions of neutrosophic statistics in relation to the various types of indeterminacies
Publisher: Elsevier
ISBN: 0323994571
Category : Computers
Languages : en
Pages : 495
Book Description
Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics investigates and presents the many applications that have arisen in the last ten years using neutrosophic statistics in bioinformatics, medicine, agriculture and cognitive science. This book will be very useful to the scientific community, appealing to audiences interested in fuzzy, vague concepts from which uncertain data are collected, including academic researchers, practicing engineers and graduate students. Neutrosophic statistics is a generalization of classical statistics. In classical statistics, the data is known, formed by crisp numbers. In comparison, data in neutrosophic statistics has some indeterminacy. This data may be ambiguous, vague, imprecise, incomplete, and even unknown. Neutrosophic statistics refers to a set of data, such that the data or a part of it are indeterminate in some degree, and to methods used to analyze the data. Introduces the field of neutrosophic statistics and how it can solve problems working with indeterminate (imprecise, ambiguous, vague, incomplete, unknown) data Presents various applications of neutrosophic statistics in the fields of bioinformatics, medicine, cognitive science and agriculture Provides practical examples and definitions of neutrosophic statistics in relation to the various types of indeterminacies
A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction
Author: Shakhawan H. Wady
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 21
Book Description
This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor.
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 21
Book Description
This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor.
Proceedings of International Conference on Data Science and Applications
Author: Mukesh Saraswat
Publisher: Springer Nature
ISBN: 9811653488
Category : Technology & Engineering
Languages : en
Pages : 792
Book Description
This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2021), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from April 10 to 11, 2021. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
Publisher: Springer Nature
ISBN: 9811653488
Category : Technology & Engineering
Languages : en
Pages : 792
Book Description
This book gathers outstanding papers presented at the International Conference on Data Science and Applications (ICDSA 2021), organized by Soft Computing Research Society (SCRS) and Jadavpur University, Kolkata, India, from April 10 to 11, 2021. It covers theoretical and empirical developments in various areas of big data analytics, big data technologies, decision tree learning, wireless communication, wireless sensor networking, bioinformatics and systems, artificial neural networks, deep learning, genetic algorithms, data mining, fuzzy logic, optimization algorithms, image processing, computational intelligence in civil engineering, and creative computing.
Neutrosophic Sets and Systems, vol. 50/2022
Author: Florentin Smarandache
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 674
Book Description
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc. Neutrosophy is a new branch of philosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. This theory considers every notion or idea together with its opposite or negation and with their spectrum of neutralities in between them (i.e. notions or ideas supporting neither nor ). The and ideas together are referred to as . Neutrosophy is a generalization of Hegel's dialectics (the last one is based on and only). According to this theory every idea tends to be neutralized and balanced by and ideas - as a state of equilibrium. In a classical way , , are disjoint two by two. But, since in many cases the borders between notions are vague, imprecise, Sorites, it is possible that , , (and of course) have common parts two by two, or even all three of them as well. Neutrosophic Set and Neutrosophic Logic are generalizations of the fuzzy set and respectively fuzzy logic (especially of intuitionistic fuzzy set and respectively intuitionistic fuzzy logic).
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 674
Book Description
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc. Neutrosophy is a new branch of philosophy that studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. This theory considers every notion or idea together with its opposite or negation
A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: An Experimental Case on a Limited COVID-19 Chest X-Ray Dataset
Author: Nour Eldeen M. Khalifa
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 17
Book Description
Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models over a limited COVID-19 chest x-ray dataset will be presented. The study relies on neutrosophic set theory, as it shows a huge potential for solving many computers problems related to the detection, and the classification domains.
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 17
Book Description
Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models over a limited COVID-19 chest x-ray dataset will be presented. The study relies on neutrosophic set theory, as it shows a huge potential for solving many computers problems related to the detection, and the classification domains.
Neutrosophic Sets and Systems, Vol. 41, 2021
Author: Florentin Smarandache
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 314
Book Description
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc.
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 314
Book Description
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc.
Proceedings of International Conference on Communication and Computational Technologies
Author: Sandeep Kumar
Publisher: Springer Nature
ISBN: 9819934850
Category : Technology & Engineering
Languages : en
Pages : 992
Book Description
This book gathers selected papers presented at 5th International Conference on Communication and Computational Technologies (ICCCT 2023), jointly organized by Soft Computing Research Society (SCRS) and Rajasthan Institute of Engineering & Technology (RIET), Jaipur, during January 28–29, 2023. The book is a collection of state-of-the art research work in the cutting-edge technologies related to the communication and intelligent systems. The topics covered are algorithms and applications of intelligent systems, informatics and applications, and communication and control systems.
Publisher: Springer Nature
ISBN: 9819934850
Category : Technology & Engineering
Languages : en
Pages : 992
Book Description
This book gathers selected papers presented at 5th International Conference on Communication and Computational Technologies (ICCCT 2023), jointly organized by Soft Computing Research Society (SCRS) and Rajasthan Institute of Engineering & Technology (RIET), Jaipur, during January 28–29, 2023. The book is a collection of state-of-the art research work in the cutting-edge technologies related to the communication and intelligent systems. The topics covered are algorithms and applications of intelligent systems, informatics and applications, and communication and control systems.
Human Cancer Diagnosis and Detection Using Exascale Computing
Author: Kapil Joshi
Publisher: John Wiley & Sons
ISBN: 1394197683
Category : Medical
Languages : en
Pages : 340
Book Description
Human Cancer Diagnosis and Detection Using Exascale Computing The book provides an in-depth exploration of how high-performance computing, particularly exascale computing, can be used to revolutionize cancer diagnosis and detection; it also serves as a bridge between the worlds of computational science and clinical oncology. Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer. The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection. The chapters cover a wide range of topics, from the fundamentals of exascale computing and its application to cancer genomics to the development of advanced imaging techniques and machine learning algorithms. Explored is the integration of data analytics, artificial intelligence, and high-performance computing to move cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer. Audience This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.
Publisher: John Wiley & Sons
ISBN: 1394197683
Category : Medical
Languages : en
Pages : 340
Book Description
Human Cancer Diagnosis and Detection Using Exascale Computing The book provides an in-depth exploration of how high-performance computing, particularly exascale computing, can be used to revolutionize cancer diagnosis and detection; it also serves as a bridge between the worlds of computational science and clinical oncology. Exascale computing has the potential to increase our ability in terms of computation to develop efficient methods for a better healthcare system. This technology promises to revolutionize cancer diagnosis and detection, ushering in an era of unprecedented precision, speed, and efficiency. The fusion of exascale computing with the field of oncology has the potential to redefine the boundaries of what is possible in the fight against cancer. The book is a comprehensive exploration of this transformative unification of science, medicine, and technology. It delves deeply into the realm of exascale computing and its profound implications for cancer research and patient care. The 18 chapters are authored by experts from diverse fields who have dedicated their careers to pushing the boundaries of what is achievable in the realm of cancer diagnosis and detection. The chapters cover a wide range of topics, from the fundamentals of exascale computing and its application to cancer genomics to the development of advanced imaging techniques and machine learning algorithms. Explored is the integration of data analytics, artificial intelligence, and high-performance computing to move cancer research to the next phase and support the creation of novel medical tools and technology for the detection and diagnosis of cancer. Audience This book has a wide audience from both computer sciences (information technology, computer vision, artificial intelligence, software engineering, applied mathematics) and the medical field (biomedical engineering, bioinformatics, oncology). Researchers, practitioners and students will find this groundbreaking book novel and very useful.
Deep Learning Methods and Applications in Brain Imaging for the Diagnosis of Neurological and Psychiatric Disorders
Author: Hao Zhang
Publisher: Frontiers Media SA
ISBN: 2832555500
Category : Science
Languages : en
Pages : 151
Book Description
Brain imaging has been successfully used to generate image-based biomarkers for various neurological and psychiatric disorders, such as Alzheimer’s and related dementias, Parkinson’s disease, stroke, traumatic brain injury, brain tumors, depression, schizophrenia, etc. However, accurate brain image-based diagnosis at the individual level remains elusive, and this applies to the diagnosis of neuropathological diseases as well as clinical syndromes. In recent years, deep learning techniques, due to their ability to learn complex patterns from large amounts of data, have had remarkable success in various fields, such as computer vision and natural language processing. Applying deep learning methods to brain imaging-assisted diagnosis, while promising, is facing challenges such as insufficiently labeled data, difficulty in interpreting diagnosis results, variations in data acquisition in multi-site projects, integration of multimodal data, clinical heterogeneity, etc. The goal of this research topic is to gather cutting-edge research that showcases the application of deep learning methods in brain imaging for the diagnosis of neurological and psychiatric disorders. We encourage submissions that demonstrate novel approaches to overcome various abovementioned difficulties and achieve more accurate, reliable, generalizable, and interpretable diagnosis of neurological and psychiatric disorders in this field.
Publisher: Frontiers Media SA
ISBN: 2832555500
Category : Science
Languages : en
Pages : 151
Book Description
Brain imaging has been successfully used to generate image-based biomarkers for various neurological and psychiatric disorders, such as Alzheimer’s and related dementias, Parkinson’s disease, stroke, traumatic brain injury, brain tumors, depression, schizophrenia, etc. However, accurate brain image-based diagnosis at the individual level remains elusive, and this applies to the diagnosis of neuropathological diseases as well as clinical syndromes. In recent years, deep learning techniques, due to their ability to learn complex patterns from large amounts of data, have had remarkable success in various fields, such as computer vision and natural language processing. Applying deep learning methods to brain imaging-assisted diagnosis, while promising, is facing challenges such as insufficiently labeled data, difficulty in interpreting diagnosis results, variations in data acquisition in multi-site projects, integration of multimodal data, clinical heterogeneity, etc. The goal of this research topic is to gather cutting-edge research that showcases the application of deep learning methods in brain imaging for the diagnosis of neurological and psychiatric disorders. We encourage submissions that demonstrate novel approaches to overcome various abovementioned difficulties and achieve more accurate, reliable, generalizable, and interpretable diagnosis of neurological and psychiatric disorders in this field.