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Статті в журналах з теми "Brain model: artificial intelligence"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Viorel, Gaftea. "BRAIN Journal - Computational Intelligence in a Human Brain Model." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 7, no. 2 (2016): 17–24. https://doi.org/10.5281/zenodo.1044298.

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ABSTRACT This paper focuses on the current trends in the domain of brain research and on the current stage of development of the research for software and hardware solutions, communication capabilities between human beings and machines, new technologies, nanoscience and Internet of Things (IoT) devices. The proposed model for the Human Brain assumes the main similarities between human intelligence and the chess game thinking process. Tactical and strategic reasoning and the need to follow the rules of the chess game are all very similar to the activities of the human brain. The main objective

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2

Kostas, Zotos. "Computer Algebra Systems & Artificial Intelligence." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 2 (2024): 427–36. https://doi.org/10.18662/brain/15.2/584.

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<em>From four-function calculators to calculators (or computers) with Computer Algebra System (CAS) software, Mathematics computing technology has advanced. With just a few button pushes, CASs can solve a wide range of mathematical problems, which is a true quantum leap in technology. The implications of having software in the classroom that can, for example, expand and factorize algebraic expressions, solve equations, differentiate functions, and find anti-derivatives are causing the mathematical community to engage in a heated debate about whether this is one of the most exciting or frighten

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3

Weigand, Edda. "Dialogue and Artificial Intelligence." Language and Dialogue 9, no. 2 (2019): 294–315. http://dx.doi.org/10.1075/ld.00042.wei.

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Abstract The article focuses on a few central issues of dialogic competence-in-performance which are still beyond the reach of models of Artificial Intelligence (AI). Learning machines have made an amazing step forward but still face barriers which cannot be crossed yet. Linguistics is still described at the level of Chomsky’s view of language competence. Modelling competence-in-performance requires a holistic model, such as the Mixed Game Model (Weigand 2010), which is capable of addressing the challenge of the ‘architecture of complexity’ (Simon 1962). The complex cannot be ‘the ontology of

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4

Zotos, Kostas. "Computer Algebra Systems & Artificial Intelligence." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 2 (2024): 427–36. http://dx.doi.org/10.18662/brain/15.2/584.

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From four-function calculators to calculators (or computers) with Computer Algebra System (CAS) software, Mathematics computing technology has advanced. With just a few button pushes, CASs can solve a wide range of mathematical problems, which is a true quantum leap in technology. The implications of having software in the classroom that can, for example, expand and factorize algebraic expressions, solve equations, differentiate functions, and find anti-derivatives are causing the mathematical community to engage in a heated debate about whether this is one of the most exciting or frightening

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5

Liu, Weijie. "Enhancing Brain-Computer Interface Performance and Security through Advanced Artificial Intelligence Techniques." Applied and Computational Engineering 154, no. 1 (2025): 1–6. https://doi.org/10.54254/2755-2721/2025.tj23002.

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The brain-computer interface has become a rapidly developing field, but it has also brought many problems with its development. The main issues are the sparse amount of brain-computer interface data, the inaccurate decoding and classification of data, and the data security of the brain-computer interface. With the development of artificial intelligence, artificial intelligence also provides solutions to many problems. This study mainly uses artificial intelligence algorithms to solve these problems. This paper reviews the integration of artificial intelligence techniquesspecifically transfer l

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6

Volobuev, Andrei N., Vasiliy F. Pyatin, Natalya P. Romanchuk, Petr I. Romanchuk, and Svetlana V. Bulgakova. "Modeling of stochastic brain function in artificial intelligence." Science and Innovations in Medicine 4, no. 3 (2019): 8–14. http://dx.doi.org/10.35693/2500-1388-2019-4-3-8-14.

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Objectives -research of stochastic brain function in respect to creation of artificial intelligence. Material and methods. Mathematical modeling principles were used for simulation of brain functioning in a stochastic mode. Results. Two types of brain activity were considered: determinated type, usually modeled using the perceptron, and stochastic type. It is shown, that stochastic brain function modeling is the necessary condition for AI to become capable of creativity, generation of new knowledge. Mathematical modeling of a neural network of the cerebral cortex, consisting of the set of the

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7

Yashchenko, V. O. "Artificial brain. Biological and artificial neural networks, advantages, disadvantages, and prospects for development." Mathematical machines and systems 2 (2023): 3–17. http://dx.doi.org/10.34121/1028-9763-2023-2-3-17.

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The article analyzes the problem of developing artificial neural networks within the framework of creating an artificial brain. The structure and functions of the biological brain are considered. The brain performs many functions such as controlling the organism, coordinating movements, processing information, memory, thinking, attention, and regulating emotional states, and consists of billions of neurons interconnected by a multitude of connections in a biological neural network. The structure and functions of biological neural networks are discussed, and their advantages and disadvantages a

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8

Vinny, Madhulika, and Pawan Singh. "Review on the Artificial Brain Technology: BlueBrain." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 1, no. 1 (2020): 1–11. http://dx.doi.org/10.54060/jieee/001.01.003.

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Blue brain is a supercomputer programmed such that it can function as an artificial brain, which can also be called a virtual brain. IBM is developing this virtual brain which would be the world’s first such created machine. Its main aim is to create a machine in which the information of the actual brain can be uploaded. This would ensure that a person’s knowledge, personality, memories, and intelligence are preserved and safe. The Blue Brain project utilizes the technologies of reverse engineering and artificial intelligence at its core and is implemented through the use of supercomputers and

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9

Bayaral, Sedat, Evrim Gül, and Derya Avcı. "Classification of Brain Tumors Using Artificial Intelligence." International Journal of Innovative Engineering Applications 9, no. 1 (2025): 8–22. https://doi.org/10.46460/ijiea.1563426.

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Brain MRI is a medical image obtained by MRI, which stands for "Magnetic Resonance Imaging". Brain MRI uses magnetic fields and radio waves to create detailed images of the brain and surrounding tissues. Today, deep learning algorithms are used to detect brain tumors or classify different brain regions. In this study, feature extraction has been performed with current deep learning models using a dataset consisting of 7023 open access images obtained from patients from various parts of the world, and the results were evaluated by training Support Vector Machine (SVM) and XGBoost models with th

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10

K P, VISHNUPRIYA, JWALA JOSE, PRINCE JOY, SRITHA S, and GIBI K. S. "Brain-Inspired Artificial Intelligence: Revolutionizing Computing and Cognitive Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–8. https://doi.org/10.55041/ijsrem39825.

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Brain-inspired artificial intelligence (AI) is a rapidly evolving field that seeks to model computational systems after the structure, processes, and functioning of the human brain. By drawing from neuroscience and cognitive science, brain-inspired AI aims to improve the efficiency, scalability, and adaptability of machine learning algorithms. This paper explores the key technologies and advancements in the realm of brain-inspired AI, including neural networks, neuromorphic hardware, brain-computer interfaces, and algorithms inspired by biological learning mechanisms. Additionally, we will ana

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11

Sahitya, Pinnamaraju, Peddavontari Sriya, Shrikant Upadhyay, Nakshatram Tanmayee, and Udhay Kiran Kaitha. "Brain Tumor Detection and Classification using Artificial Intelligence." International Journal of Microsystems and IoT 2, no. 9 (2024): 1229–33. https://doi.org/10.5281/zenodo.14166992.

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The rapid development of artificial intelligence has brought new solutions in the field of medicine, especially in the analysis of medical images. The program introduces a new method called "Brain Tumor Detection, Classification Using Artificial Intelligence" to identify brain tumors accurately, efficiently. The system integrates the latest Technology that includes the YOLOV2 algorithm for tumor detection and the Mobile NetV2 architecture for tumor classification. This paper utilizes MATLAB environment, with graphics processing capabilities and artificial intelligence toolbox. The YOLOV2 algor

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12

Shah, Sumeet, Akshata Tattu, Ashna Sheregar, and Sneha Mane. "Disease Detection Using Artificial Intelligence." ITM Web of Conferences 44 (2022): 03050. http://dx.doi.org/10.1051/itmconf/20224403050.

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Currently, artificial intelligence is widely used to aid humans in a variety of ways. One area where artificial intelligence is particularly beneficial is medical image detection where diagnostic procedures require the collection and processing of large amounts of data for particular diseases. The topics covered in this paper include Pneumonia, Lung Cancer, and Brain Tumors. Early detection and treatment are crucial when treating these types of diseases. The paper describes the use of a convolutional neural network algorithm in order to process medical images so that it can aid in decision mak

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13

R, Dewi Agushinta, Fiena Rindani, Antonius Angga Kurniawan, Elevanita Anggari, and Rizky Akbar. "TOWARDS ADVANCED DEVELOPMENT OF CYBORG INTELLIGENCE." Jurnal Ilmiah Informatika Komputer 23, no. 3 (2018): 201–11. http://dx.doi.org/10.35760/ik.2018.v23i3.2375.

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The creation of machines with human intelligence is an primary and beneficial aim of artificial intelligence research. One interesting method in developing artificial intelligence is combining a biological method and machine intelligence. Cyborg Intelligence is a new scientific model for the integration of biological and machinery. Brain Machine Interface (BMI) provides an opportunity to integrate both intelligence at various levels. Based on BMI, neural signals can be read for the control of motor actuators and sensory information coding machine can be sent to a specific area of the brain. In

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14

D, Jeevitha, Muthu Geethalakshmi S, Nila I, and Santhoshi V. "Handwritten Letter Recognition using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2752–58. http://dx.doi.org/10.22214/ijraset.2022.42949.

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Abstract: Images are easily processed and analysed by the human brain. When the eye sees a particular image, the brain is able to instantly segment it and recognize its numerous aspects. This project proposes the Deep Learning conceptual models based on Convolutional Neural Network (CNN). A comparison between the algorithms reveals that the handwritten alphabets, classified based on CNNs outperforms other algorithms in terms of accuracy. In this project, different architectures of CNN algorithm are used: Manual Net, Alex Net, LeNet Architecture. These architectures contain a convolution layer,

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15

LIAO, Yunyan, Qing HUANG, Changjing WANG, Zhengkang ZUO, and Jiaxing LU. "Course Intelligent Brain Model Based on Crowd Intelligence." Wuhan University Journal of Natural Sciences 27, no. 4 (2022): 331–40. http://dx.doi.org/10.1051/wujns/2022274331.

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The development of artificial intelligence in education promotes the reform of teaching methods in the direction of intelligence and individuation. In this paper, the programming course is taken as an example to propose a curriculum intelligent brain model for open source swarm intelligence based on knowledge graph, and the bootstrapping framework is introduced to try to make the intelligent brain track the frontier like human beings and study several courses vertically. It studies the knowledge of subgraphs fusion of open-source software resources and domain semantics as well as the mining me

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16

Wang, Yingxu. "On Abstract Intelligence and Brain Informatics." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 4 (2012): 54–80. http://dx.doi.org/10.4018/jcini.2012100103.

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A key notion in abstract intelligence and cognitive informatics is that the brain and natural intelligence may only be explained by a hierarchical and reductive theory that maps the brain through the embodied neurological, physiological, cognitive, and logical levels from bottom-up induction and top-down deduction. This paper presents an abstract intelligence framework for modeling the structures and functions of the brain across these four levels. A set of abstract intelligent model, cognitive functional model, and neurophysiological model of the brain is systematically developed. On the basi

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17

Еськов, В. М., М. А. Филатов, Т. В. Воронюк, and И. С. Самойленко. "Models of Heuristic Brain Activity and Artificial Intelligence." Успехи кибернетики / Russian Journal of Cybernetics 4, no. 4(16) (2023): 32–40. http://dx.doi.org/10.51790/2712-9942-2023-4-4-03.

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познавательная деятельность человека связана с созданием (изучением) двух типов информации: объективно новой и субъективно новой. В проблеме создания искусственного интеллекта первый тип деятельности (создание объективно новой информации) занимает особую (главную) роль. В этом случае такие искусственные системы действительно могут заменить человека. В работе обсуждаются два новых режима работы искусственных нейросетей, которые имеют место в работе мозга человека. Оказалось, что введение этих двух режимов в работу уже существующих нейросетей позволяет моделировать эвристическую работу мозга. Та

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18

Li, Qijian. "Brain Tumor Detection Based on MRI Images and Artificial Intelligence." Applied and Computational Engineering 139, no. 1 (2025): 67–75. https://doi.org/10.54254/2755-2721/2025.23007.

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Brain tumors, as central nervous system diseases that endanger human health , require early and accurate detection to significantly improve patient survival rates. Magnetic Resonance Imaging (MRI), due to its superior soft-tissue contrast and non-invasiveness, has become a crucial tool in brain tumor diagnosis. However, traditional imaging diagnosis, which heavily relies on manual interpretation, suffers from limitations such as strong subjectivity and low efficiency. To address these issues, this paper proposes an automatic brain tumor detection method based on Convolutional Neural Networks (

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19

Mukkapati, Naveen, and M. S. Anbarasi. "Brain Tumor Classification Based on Enhanced CNN Model." Revue d'Intelligence Artificielle 36, no. 1 (2022): 125–30. http://dx.doi.org/10.18280/ria.360114.

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Brain tumor classification is important process for doctors to plan the treatment for patients based on the stages. Various CNN based architecture is applied for the brain tumor classification to improve the classification performance. Existing methods in brain tumor segmentation have the limitations of overfitting and lower efficiency in handling large dataset. In this research, for brain tumor segmentation purpose the enhanced CNN architecture based on U-Net, for pattern analysis purpose RefineNet and for classifying brain tumor purpose SegNet architecture is proposed. The brain tumor benchm

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20

Raju, Venkateshwarla Rama. "A study of brains complex organs-organisms with artificial intelligence system to evolve cardinal feature-manifestations of brain`s (self-organizing)." IP Indian Journal of Neurosciences 9, no. 4 (2023): 221–26. http://dx.doi.org/10.18231/j.ijn.2023.043.

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: Embedding carnal (somatic or physical) restraints over the artificial intelligent system (i.e., artificially-intelligent system) in ample the similar way that the ‘human-brain’ must grow, progress plus function in the physically real, tangible and biological constrictions that lets system to advance feature-manifestations of the brains of multifaceted organs and organisms so as to solve brain issues. : Placing carnal restraints on AI-based model-system, i.e., artificially intelligent system. : spatially embedded recurrent neural nets (RNNs), 3D Euclidean space, where message of fundamental n

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21

Esmaeili, Morteza, Riyas Vettukattil, Hasan Banitalebi, Nina R. Krogh, and Jonn Terje Geitung. "Explainable Artificial Intelligence for Human-Machine Interaction in Brain Tumor Localization." Journal of Personalized Medicine 11, no. 11 (2021): 1213. http://dx.doi.org/10.3390/jpm11111213.

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Primary malignancies in adult brains are globally fatal. Computer vision, especially recent developments in artificial intelligence (AI), have created opportunities to automatically characterize and diagnose tumor lesions in the brain. AI approaches have provided scores of unprecedented accuracy in different image analysis tasks, including differentiating tumor-containing brains from healthy brains. AI models, however, perform as a black box, concealing the rational interpretations that are an essential step towards translating AI imaging tools into clinical routine. An explainable AI approach

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22

Rajasekar, R., D. Menaga, A. Joshi, T. N. Sudhahar, S. Muthukumar, and T. Sakthi Sree. "Early brain tumor identification and segmentation using artificial intelligence." Multidisciplinary Science Journal 7, no. 7 (2024): 2025304. https://doi.org/10.31893/multiscience.2025304.

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In order to improve diagnostic precision and treatment planning, the present research outlines the use of state-of-the-art artificial intelligence (AI) techniques for brain tumor segmentation and early detection. Utilizing convolutional neural networks (CNNs) and U-Net topologies, two popular deep learning algorithms, we develop a system capable of automatically identifying and segmenting tumor regions from medical imaging data. Our implementation involves preprocessing steps to normalize and augment the dataset, followed by training the model on publicly available brain tumor datasets. The pe

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23

Apprich, Clemens. "Secret Agents." Digital Culture & Society 4, no. 1 (2018): 29–44. http://dx.doi.org/10.14361/dcs-2018-0104.

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Abstract “Good Old-Fashioned Artificial Intelligence” (GOFAI), which was based on a symbolic information-processing model of the mind, has been superseded by neural-network models to describe and create intelligence. Rather than a symbolic representation of the world, the idea is to mimic the structure of the brain in electronic form, whereby artificial neurons draw their own connections during a self-learning process. Critiquing such a brain physiological model, the following article takes up the idea of a “psychoanalysis of things” and applies it to artificial intelligence and machine learni

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24

Wafda, Andi. "Transfer Learning Advancements: A Comprehensive Literature Review on Text Analysis, Image Processing, and Health Data Analytics." Journal Artificial: Informatika dan Sistem Informasi 3, no. 1 (2025): 1–10. https://doi.org/10.54065/artificial.544.

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This literature review delves into the recent advancements in transfer learning, examining its applications and enhancements across diverse domains. Focused on the conclusions drawn from various studies, the review highlights the evolution of transfer learning in three key areas: text analysis, image processing, and health data analytics. In text analysis, innovations such as BERT, CNN-BiLSTM, AdapterFusion, and T-BERT Framework showcase the ongoing efforts to improve efficiency and adaptability in understanding complex natural language tasks. Similarly, in image processing, the review emphasi

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25

E., Okorie, Anyaragbu Hope, and Okoh C. C. "Computerized Diagnostic System for Brain Tumor Detection Using Artificial Intelligence." International Journal of Research 10, no. 8 (2023): 64–79. https://doi.org/10.5281/zenodo.8223452.

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<strong>Brain tumors are a significant health concern worldwide, and early detection plays a crucial role in improving patient outcomes. In this paper, we propose a computerized diagnostic system for brain tumor detection using artificial intelligence (AI) techniques. The aim is to develop an automated and accurate system that can assist medical professionals in the early diagnosis of brain tumors. The proposed system utilizes advanced AI algorithms, including machine learning and image processing, to analyze medical imaging data such as MRI scans. The system extracts relevant features from th

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26

Ji, Yuqing. "Deep learning-based artificial intelligence imaging probes for Alzheimers disease." Applied and Computational Engineering 17, no. 1 (2023): 1–9. http://dx.doi.org/10.54254/2755-2721/17/20230901.

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Brain medical imaging is a main diagnosis method for Alzheimers disease (AD). But the method relies on the physicians manual analysis which is subjective and time consuming. In recent years, artificial intelligence (AI) technology has been widely applied in clinical diagnosis. This thesis is about the deep learning model to be designed to realize the computer-aided diagnosis of medical images. A model of densely connected network (DenseNet) as an AI technology, automatically learns the semantic features related to AD diagnosis on the brain MRI images from ADNI data. At the same time, for solvi

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27

Bi, Xiaowang, Wei Liu, Huaiqin Liu, and Qun Shang. "Artificial Intelligence-based MRI Images for Brain in Prediction of Alzheimer's Disease." Journal of Healthcare Engineering 2021 (October 19, 2021): 1–7. http://dx.doi.org/10.1155/2021/8198552.

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The study aimed to explore the accuracy and stability of Deep metric learning (DML) algorithm in Magnetic Resonance Imaging (MRI) examination of Alzheimer's Disease (AD) patients. In this study, MRI data of patients obtained were from Alzheimer's Disease Neuroimaging Initiative (ADNI) database (A total of 180 AD cases, 88 women, 92 men; 188 samples in healthy conditions (HC), including 90 females and 98 males. 210 samples of mild cognitive impairment (MCI), 104 females and 106 males). On the basis of deep learning, an early AD diagnosis system was constructed using CNN (Convolutional Neural Ne

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V., Pattabiraman, and Harshit Singh. "Deep Learning based Brain Tumour Segmentation." WSEAS TRANSACTIONS ON COMPUTERS 19 (January 4, 2021): 234–41. http://dx.doi.org/10.37394/23205.2020.19.29.

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Artificial Intelligence has changed our outlook towards the whole world and it is regularly used to better understand all the data and information that surrounds us in our everyday lives. One such application of Artificial Intelligence in real world scenarios is extraction of data from various images and interpreting it in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application image segmentation has a variety of applications extending from complex medical field to regular pattern identi

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Noviandy, Teuku Rizky, Ghifari Maulana Idroes, Adi Purnawarman, et al. "Enhancing Early Detection of Alzheimer's Disease through MRI using Explainable Artificial Intelligence." Indonesian Journal of Case Reports 2, no. 2 (2024): 43–51. https://doi.org/10.60084/ijcr.v2i2.255.

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Alzheimer’s disease is a progressive brain disorder that causes memory loss and cognitive decline, affecting millions of people worldwide. Early detection is critical for slowing the disease's progression and improving patient outcomes. Magnetic Resonance Imaging (MRI) is widely used to identify brain changes associated with AD, but subtle abnormalities in the early stages are often difficult to detect using traditional methods. In this study, we used a deep learning approach with a model called ResNet-50 to analyze MRI scans and classify patients into four categories: Non-Demented, Very Mild

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30

Taşcı, Burak. "Attention Deep Feature Extraction from Brain MRIs in Explainable Mode: DGXAINet." Diagnostics 13, no. 5 (2023): 859. http://dx.doi.org/10.3390/diagnostics13050859.

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Artificial intelligence models do not provide information about exactly how the predictions are reached. This lack of transparency is a major drawback. Particularly in medical applications, interest in explainable artificial intelligence (XAI), which helps to develop methods of visualizing, explaining, and analyzing deep learning models, has increased recently. With explainable artificial intelligence, it is possible to understand whether the solutions offered by deep learning techniques are safe. This paper aims to diagnose a fatal disease such as a brain tumor faster and more accurately usin

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Hagiwara, Naruki, Shoma Sekizaki, Yuji Kuwahara, Tetsuya Asai, and Megumi Akai-Kasaya. "Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses." Polymers 13, no. 2 (2021): 312. http://dx.doi.org/10.3390/polym13020312.

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Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary ele

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32

Nuobu, Gengpan. "Transformer model: Explainability and prospectiveness." Applied and Computational Engineering 20, no. 1 (2023): 88–99. http://dx.doi.org/10.54254/2755-2721/20/20231079.

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The purpose of Artificial Intelligence(AI) is to simulate learning process of human brain by strong computing power and appropriate algorithm, so that the machine can develop judging ability at work as human. Current AI mainly relies on Deep Learning model which is based on artificial neural network, like Convolutional Neural Network(CNN) in computer visualization, but that also takes with some defects. This paper introduces defects of CNN and discusses Transformer model in solving unexplainability of traditional CNN algorithm. To discuss why the Transformer model and attention mechanism are c

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Sokač, Mateo, Leo Mršić, Mislav Balković, and Maja Brkljačić. "Bridging Artificial Intelligence and Neurological Signals (BRAINS): A Novel Framework for Electroencephalogram-Based Image Generation." Information 15, no. 7 (2024): 405. http://dx.doi.org/10.3390/info15070405.

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Recent advancements in cognitive neuroscience, particularly in electroencephalogram (EEG) signal processing, image generation, and brain–computer interfaces (BCIs), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of artificial intelligence (AI) to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and

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Laurenţiu-Mihai, Treapăt, and Gheorghiu Anda. "BRAIN Journal - Artificial Systems and Models for Risk Covering Operations." BRAIN - Broad Research in Artificial Intelligence and Neuroscience 8, no. 1 (2017): 59–72. https://doi.org/10.5281/zenodo.1045022.

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ABSTRACT Mainly, this paper focuses on the roles of artificial intelligence based systems and especially on risk-covering operations. In this context, the paper comes with theoretical explanations on real-life based examples and applications. From a general perspective, the paper enriches its value with a wide discussion on the related subject. The paper aims to revise the volatilities’ estimation models and the correlations between the various time series and also by presenting the Risk Metrics methodology, as explained is a case study. The advantages that the VaR estimation offers, consist o

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Thotipalayam Andavan Mohanprakash, Madhumitha Kulandaivel, Samuel Rosaline, et al. "Detection of Brain Cancer through Enhanced Particle Swarm Optimization in Artificial Intelligence Approach." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 2 (2023): 174–86. http://dx.doi.org/10.37934/araset.33.2.174186.

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Brain cancer is deadly and requires prompt detection and treatment. We propose a complete brain cancer detection method using binary encoding, adaptive thresholding, edge-based segmentation, particle swarm optimization (PSO), wavelet transform, and neural networks. First, binary encoding converts categorical patient data and medical history information into binary vectors for fast analysis. Adaptive thresholding then handles image lighting and contrast to optimize brain image segmentation. Brain tumor boundaries are identified via edge-based segmentation. This method isolates tumor areas for i

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Ushuluddin, Achmad, Abd Madjid, Siswanto Masruri, and Mohammad Affan. "Shifting paradigm: from Intellectual Quotient, Emotional Quotient, and Spiritual Quotient toward Ruhani Quotient in ruhiology perspectives." Indonesian Journal of Islam and Muslim Societies 11, no. 1 (2021): 139–62. http://dx.doi.org/10.18326/ijims.v11i1.139-162.

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There are three theories of human intelligence, namely Intellectual Quotient (IQ), Emotional Quotient (EQ), and Spiritual Quotient (SQ). In its subsequent development, following the SQ era that considered the God Spot in the human brain as a source of intelligence, the concept of the Heart’s Code (HC) indicates that the source of intelligence lies in the heart, not the brain. The SQ model proposed by Zohar-Marshall and the HC model suggested by Pearsall only touched the biological and psychological realms, namely the material brain and the material heart. Both have yet to touch upon the transc

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Bell, Anthony J. "Levels and loops: the future of artificial intelligence and neuroscience." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 354, no. 1392 (1999): 2013–20. http://dx.doi.org/10.1098/rstb.1999.0540.

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In discussing artificial intelligence and neuroscience, I will focus on two themes. The first is the universality of cycles (or loops): sets of variables that affect each other in such a way that any feed–forward account of causality and control, while informative, is misleading. The second theme is based around the observation that a computer is an intrinsically dualistic entity, with its physical set–up designed so as not to interfere with its logical set–up, which executes the computation. The brain is different. When analysed empirically at several different levels (cellular, molecular), i

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Abdusalomov, Akmalbek, Mekhriddin Rakhimov, Jakhongir Karimberdiyev, Guzal Belalova, and Young Im Cho. "Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments." Bioengineering 11, no. 6 (2024): 627. http://dx.doi.org/10.3390/bioengineering11060627.

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Medical imaging and deep learning models are essential to the early identification and diagnosis of brain cancers, facilitating timely intervention and improving patient outcomes. This research paper investigates the integration of YOLOv5, a state-of-the-art object detection framework, with non-local neural networks (NLNNs) to improve brain tumor detection’s robustness and accuracy. This study begins by curating a comprehensive dataset comprising brain MRI scans from various sources. To facilitate effective fusion, the YOLOv5 and NLNNs, K-means+, and spatial pyramid pooling fast+ (SPPF+) modul

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Peng, Rongzhao. "The study and application of brain tumors classification based on artificial intelligence." Applied and Computational Engineering 16, no. 1 (2023): 200–204. http://dx.doi.org/10.54254/2755-2721/16/20230891.

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The application of AI in the medical field has been increasingly popular due to its ability to enhance the accuracy and efficiency of diagnoses. A plurality of individuals employs diverse techniques and refine existing methods in order to achieve greater precision. This paper provides an overview of the methodology utilized in early research papers for brain tumors classification. This includes the input of datasets, preprocessing, model building, training and testing, evaluation, and application. In addition, this paper presents four models used in early research, including the Artificial neu

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Shagrir, Oron. "The Brain as an Input–Output Model of the World." Minds and Machines 28, no. 1 (2017): 53–75. http://dx.doi.org/10.1007/s11023-017-9443-4.

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Cristina, Dumitru, Zamfirache Florin, Trimbitas Adina, and Mihaela Radu Beatrice. "BRAIN. Broad Research in Artificial Intelligence and Neuroscience - Total Sleep Deprivation Modulates Working Memory and Depression in Aged Rats." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 4 (2024): 55–72. https://doi.org/10.70594/brain/15.4/5.

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Sleep deprivation has shown promise in rapidly alleviating depression symptoms without adverse effects, its impact on memory remains unclear, especially in depressed individuals. The study investigates the effects of total sleep deprivation on depression symptomatology and memory in aged rats with induced depression. The experiments were carried out on 8 adult male Sprague Dawley rats. A depression model was induced using the chronic retention stress paradigm. Total Sleep Deprivation was achieved using the “gentle handling” procedure. Behavioral tests, including the open field test

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Alzahrani, A. Khuzaim, Ahmed A. Alsheikhy, Tawfeeq Shawly, Mohammad Barr, and Hossam E. Ahmed. "A New Artificial Intelligence-Based Model for Amyotrophic Lateral Sclerosis Prediction." International Journal of Intelligent Systems 2023 (December 31, 2023): 1–10. http://dx.doi.org/10.1155/2023/1172288.

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Currently, amyotrophic lateral sclerosis (ALS) disease is considered fatal since it affects the central nervous system with no cure or clear treatments. This disease affects the spinal cord, more specifically, the lower motor neurons (LMNs) and the upper motor neurons (UMNs) inside the brain along with their networks. Various solutions have been developed to predict ALS. Some of these solutions were implemented using different deep-learning methods (DLMs). Nevertheless, this disease is considered a tough task and a huge challenge. This article proposes a reliable model to predict ALS disease b

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I. Zanoon, Nabeel, Abdullah A. Alhaj, and Khalid Alkharabsheh. "An artificial intelligence approach to smart exam supervision using YOLOv5 and siamese network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 3920. http://dx.doi.org/10.11591/ijai.v13.i4.pp3920-3929.

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<p>Artificial intelligence has introduced revolutionary and innovative solutions to many complex problems by automating processes or tasks that used to require human power. The limited capabilities of human efforts in real-time monitoring have led to artificial intelligence becoming increasingly popular. Artificial intelligence helps develop the monitoring process by analysing data and extracting accurate results. Artificial intelligence is also capable of providing surveillance cameras with a digital brain that analyses images and live video clips without human intervention. Deep learni

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Nabeel, I. Zanoon, A. Alhaj Abdullah, and Alkharabsheh Khalid. "An artificial intelligence approach to smart exam supervision using YOLO v5 and siamese network." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 3920–29. https://doi.org/10.11591/ijai.v13.i4.pp3920-3929.

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Artificial intelligence has introduced revolutionary and innovative solutions to many complex problems by automating processes or tasks that used to require human power. The limited capabilities of human efforts in real-time monitoring have led to artificial intelligence becoming increasingly popular. Artificial intelligence helps develop the monitoring process by analyzing data and extracting accurate results. Artificial intelligence is also capable of providing surveillance cameras with a digital brain that analyzes images and live video clips without human intervention. Deep learning models

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Sadegh-Zadeh, Seyed-Ali, Elham Fakhri, Mahboobe Bahrami, et al. "An Approach toward Artificial Intelligence Alzheimer’s Disease Diagnosis Using Brain Signals." Diagnostics 13, no. 3 (2023): 477. http://dx.doi.org/10.3390/diagnostics13030477.

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Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel m

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Ibric, Svetlana, Zorica Djuric, Jelena Parojcic, and Jelena Petrovic. "Artificial intelligence in pharmaceutical product formulation: Neural computing." Chemical Industry and Chemical Engineering Quarterly 15, no. 4 (2009): 227–36. http://dx.doi.org/10.2298/ciceq0904227i.

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The properties of a formulation are determined not only by the ratios in which the ingredients are combined but also by the processing conditions. Although the relationships between the ingredient levels, processing conditions, and product performance may be known anecdotally, they can rarely be quantified. In the past, formulators tended to use statistical techniques to model their formulations, relying on response surfaces to provide a mechanism for optimization. However, the optimization by such a method can be misleading, especially if the formulation is complex. More recently, advances in

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MANIADAKIS, MICHAIL, and PANOS TRAHANIAS. "MODELLING ROBOTIC COGNITIVE MECHANISMS BY HIERARCHICAL COOPERATIVE COEVOLUTION." International Journal on Artificial Intelligence Tools 16, no. 06 (2007): 935–66. http://dx.doi.org/10.1142/s0218213007003643.

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Recently, many brain modelling efforts attempt to support cognitive abilities of artificial organisms. The present work introduces a computational framework to address brain modelling, emphasizing on the integrative performance of substructures. Specifically, we present an agent-based representation of brain areas, together with a hierarchical cooperative coevolutionary scheme, which is able to highlight both the speciality of brain areas and their cooperative performance. The inherent ability of coevolutionary methods to design cooperative partial structures supports the design of partial bra

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Prasannam, Reemitha P., Priyanka A. Arul Murugan, Rajesh L. Narayanan, Mahesh Jagadeson, Vishnu Prasad S., and Indrapriyadharshini K. "Artificial intelligence in dental practice: a review." International Journal Of Community Medicine And Public Health 10, no. 5 (2023): 1955–60. http://dx.doi.org/10.18203/2394-6040.ijcmph20231302.

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The human brain is a distinctly complex structure with several interlinked neurons that transmit signals all over the body. The search for an excellent model mimicking the human mind has led to a sophisticated breakthrough in what's referred to as artificial intelligence (AI). AI methodologies have determined programs in numerous disciplines ranging from telecommunication, aerospace, robotics, medical analysis, alternate marketplace, law, science, or entertainment to name some. Medical clinical decision support system (CDSS), a factor of AI is being carried out in dentistry which includes Arti

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Kırboğa, Kevser Kübra, Ecir Uğur Küçüksille, and Utku Köse. "Ignition of Small Molecule Inhibitors in Friedreich's Ataxia with Explainable Artificial Intelligence." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 14, no. 3 (2023): 287–313. http://dx.doi.org/10.18662/brain/14.3/475.

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Iron (Fe) chelating medicines and Histone deacetylase (HDAC) inhibitors are two therapy options for hereditary Friedreich's Ataxia that have been shown to improve clinical results (FA). Fe chelation molecules can minimize the quantity of stored Fe, and HDAC inhibitors can boost the expression of the Frataxin (FXN) gene in enhancing FA. A complete quantitative structure-activity relationship (QSAR) search of inhibitors from the ChEMBL database is reported in this paper, which includes 437 compounds for Fe chelation and 1,354 compounds for HDAC inhibitors. For further investigation, the IC50 was

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Kieran, Greer. "BRAIN. Broad Research in Artificial Intelligence and Neuroscience-New Ideas for Brain Modelling 4." https://www.edusoft.ro/brain/index.php/brain/article/view/815/921 9, no. 2 (2018): 155–67. https://doi.org/10.5281/zenodo.1245919.

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This paper continues the research that considers a new cognitive model based strongly on the human brain. In particular, it considers the neural binding structure of an earlier paper. It also describes some new methods in the areas of image processing and behaviour simulation. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as t

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