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Journal articles on the topic 'Algorithme cognitif'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Jalenques, I. "SMP – Diagnostic et traitement personnalisés : un paradigme d’avenir dans les troubles schizophréniques." European Psychiatry 29, S3 (2014): 591–92. http://dx.doi.org/10.1016/j.eurpsy.2014.09.315.

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L’objectif d’un diagnostic et d’un programme thérapeutique personnalisés pour chaque patient souffrant de troubles schizophréniques n’est aujourd’hui qu’en partie atteint. Cette session fait le point sur les dernières avancées et celles à venir concernant les outils et stratégies diagnostiques ainsi que les thérapeutiques médicamenteuses et cognitives.Si l’hétérogénéité des tableaux cliniques répondant aux critères diagnostiques de schizophrénie est une constatation bien établie, on ne sait pas encore clairement ce que recouvre cette hétérogénéité : maladies distinctes ou variabilité d’express

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Abramova, I. "Methodology of the study block "algorithmic model" at the kindergarten and primary school." Bulletin of Science and Practice 4, no. 5 (2018): 689–99. https://doi.org/10.5281/zenodo.1246721.

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The formation of students’ skills of teaching computer literacy in the preschool educational institution (DOW) and in primary school (NSH) is a necessary component in the formation of professional competence of future teachers. The article deals with the methods of studying the content block “Algorithmic models”, the elements of which are aimed at fulfilling the following tasks: the formation of students’ ability to apply modern information techniques and technologies to ensure the quality of the educational process in DOW and NSH; the formation of a system of methodolo

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Foley, J. M., M. J. Wright, A. L. Gooding, et al. "Operationalization of the updated diagnostic algorithm for classifying HIV-related cognitive impairment and dementia." International Psychogeriatrics 23, no. 5 (2010): 835–43. http://dx.doi.org/10.1017/s1041610210002085.

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ABSTRACTBackground: This study applies the updated HIV-Associated Neurocognitive Disorders (HAND) diagnostic algorithm.Methods: Participants were 210 HIV-infected-adults, classified using proposed HAND criteria: HIV-Associated Dementia (HAD), Mild Neurocognitive Disorder (MND), Asymptomatic Neurocognitive Impairment (ANI).Results: The algorithm yielded: normal = 32.8%, ANI = 21.4%, MND = 34.3%, and HAD = 11.4%. Normal participants performed superior to HAND-defined participants on cognition, and HAD participants performed more poorly on global cognition and executive functioning. Two distinct

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Lee, Won Hee. "The Choice of Machine Learning Algorithms Impacts the Association between Brain-Predicted Age Difference and Cognitive Function." Mathematics 11, no. 5 (2023): 1229. http://dx.doi.org/10.3390/math11051229.

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Machine learning has been increasingly applied to neuroimaging data to compute personalized estimates of the biological age of an individual’s brain (brain age). The difference between an individual’s brain-predicted age and their chronological age (brainPAD) is used as a biomarker of brain aging and disease, but the potential contribution of different machine learning algorithms used for brain age prediction to the association between brainPAD and cognitive function has not been investigated yet. Here, we applied seven commonly used algorithms to the same multimodal brain imaging data (struct

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Dangi, Siddharth, Amy L. Orsborn, Helene G. Moorman, and Jose M. Carmena. "Design and Analysis of Closed-Loop Decoder Adaptation Algorithms for Brain-Machine Interfaces." Neural Computation 25, no. 7 (2013): 1693–731. http://dx.doi.org/10.1162/neco_a_00460.

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Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the corresponding update rules, and designing CLDA parameters. These design choices, combined with the specific settings of CLDA parameters, will directly affect the algorithm's ability to make decoder parameters converge to values that optimize performanc

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Grosse-Wentrup, Moritz, Akshey Kumar, Anja Meunier, and Manuel Zimmer. "Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition." PLOS Computational Biology 20, no. 12 (2024): e1012674. https://doi.org/10.1371/journal.pcbi.1012674.

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Explaining how neuronal activity gives rise to cognition arguably remains the most significant challenge in cognitive neuroscience. We introduce neuro-cognitive multilevel causal modeling (NC-MCM), a framework that bridges the explanatory gap between neuronal activity and cognition by construing cognitive states as (behaviorally and dynamically) causally consistent abstractions of neuronal states. Multilevel causal modeling allows us to interchangeably reason about the neuronal- and cognitive causes of behavior while maintaining a physicalist (in contrast to a strong dualist) position. We intr

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Zhao, Lu, and Mingyue Zhou. "A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO." Sensors 22, no. 18 (2022): 6796. http://dx.doi.org/10.3390/s22186796.

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The use of a cognitive radio power allocation algorithm is an effective method to improve spectral utilization. However, there are three problems with traditional cognitive radio power allocation algorithms: (1) based on the ideal channel model analysis, channel fluctuation is not considered; (2) they do not consider fairness among cognitive users; and (3) some algorithms are complex and locating the optimal power allocation scheme is not an easy task. For the above problems, this study establishes a robust model which adds the cognitive user transmission rate variance constraint to solve the

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Zhang, Chudi, Yunqi Song, Rundong Jiang, Jun Hu, and Shiyou Xu. "A Cognitive Electronic Jamming Decision-Making Method Based on Q-Learning and Ant Colony Fusion Algorithm." Remote Sensing 15, no. 12 (2023): 3108. http://dx.doi.org/10.3390/rs15123108.

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In order to improve the efficiency and adaptability of cognitive radar jamming decision-making, a fusion algorithm (Ant-QL) based on ant colony and Q-Learning is proposed in this paper. The algorithm does not rely on a priori information and enhances adaptability through real-time interactions between the jammer and the target radar. At the same time, it can be applied to single jammer and multiple jammer countermeasure scenarios with high jamming effects. First, traditional Q-Learning and DQN algorithms are discussed, and a radar jamming decision-making model is built for the simulation verif

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N. Sirhan, Najem, and Manel Martinez-Ramon. "Cognitive Radio Resource Scheduling using Multi-Agent Q-Learning for LTE." International journal of Computer Networks & Communications 14, no. 02 (2022): 77–95. http://dx.doi.org/10.5121/ijcnc.2022.14205.

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In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning (RL), more specifically, the Q-learning technique for scheduling two types of users. The first algorithm is called a Collaborative scheduling algorithm, and the second algorithm is called a Competitive scheduling algorithm. The first type of the scheduled users is the Primary Users (PUs), and they are the licensed subscribers that pay for their service. The second type of the sched

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Rostami, Soheil, Sajad Alabadi, Soheir Noori, Hayder Ahmed Shihab, Kamran Arshad, and Predrag Rapajic. "Spectrum Assignment Algorithm for Cognitive Machine-to-Machine Networks." Mobile Information Systems 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3282505.

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A novel aggregation-based spectrum assignment algorithm for Cognitive Machine-To-Machine (CM2M) networks is proposed. The introduced algorithm takes practical constraints including interference to the Licensed Users (LUs), co-channel interference (CCI) among CM2M devices, and Maximum Aggregation Span (MAS) into consideration. Simulation results show clearly that the proposed algorithm outperforms State-Of-The-Art (SOTA) algorithms in terms of spectrum utilisation and network capacity. Furthermore, the convergence analysis of the proposed algorithm verifies its high convergence rate.

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Huang, Jing, Ziheng Zhang, and Xiaogang Ruan. "An Improved Dyna-Q Algorithm Inspired by the Forward Prediction Mechanism in the Rat Brain for Mobile Robot Path Planning." Biomimetics 9, no. 6 (2024): 315. http://dx.doi.org/10.3390/biomimetics9060315.

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The traditional Model-Based Reinforcement Learning (MBRL) algorithm has high computational cost, poor convergence, and poor performance in robot spatial cognition and navigation tasks, and it cannot fully explain the ability of animals to quickly adapt to environmental changes and learn a variety of complex tasks. Studies have shown that vicarious trial and error (VTE) and the hippocampus forward prediction mechanism in rats and other mammals can be used as key components of action selection in MBRL to support “goal-oriented” behavior. Therefore, we propose an improved Dyna-Q algorithm inspire

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Sandeep Agrawal, Tanay. "Optimizing Chess Algorithms for Adaptive Gameplay and Cognitive Training." International Journal of Science and Research (IJSR) 13, no. 12 (2024): 18–19. https://doi.org/10.21275/sr241127153528.

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Qi-Wen Zhang, Qi-Wen Zhang, and Qiao-Hong Bai Qi-Wen Zhang. "A Discrete Particle Swarm Optimization Algorithm Based on Neighbor Cognition to Solve the Problem of Social Influence Maximization." 電腦學刊 33, no. 4 (2022): 107–19. http://dx.doi.org/10.53106/199115992022083304009.

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<p>In view of the problem that the estimation method of node influence in social network is not comprehen-sive and the Particle Swarm Optimization (PSO) algorithm is easy to fall into the local optimal and the lo-cal search ability is insufficient. In this paper, we proposed a Neighbor Cognitive Discrete Particle Swarm Optimization (NCDPSO) algorithm. Aiming at the problem of influence in social networks, a new node influence measure method is proposed, the three-degree theory is introduced to comprehensively estimate the influence of nodes. In order to improve the global search ability

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Jastriebow, A., and K. Poczęta. "Analysis of multi-step algorithms for cognitive maps learning." Bulletin of the Polish Academy of Sciences Technical Sciences 62, no. 4 (2014): 735–41. http://dx.doi.org/10.2478/bpasts-2014-0079.

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Abstract This article is devoted to the analysis of multi-step algorithms for cognitive maps learning. Cognitive maps and multi-step supervised learning based on a gradient method and unsupervised one based on the non-linear Hebbian algorithm were described. Comparative analysis of these methods to one-step algorithms, from the point of view of the speed of convergence of a learning algorithm and the influence on the work of the decision systems was performed. Simulation results were done on prepared software tool ISEMK. Obtained results show that implementation of the multi-step technique giv

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Cervantes-Junco, Gabriel B., Enrique Rodriguez-Colina, Leonardo Palacios-Luengas, Michael Pascoe-Chalke, Pedro Lara-Velázquez, and Ricardo Marcelín-Jiménez. "Decision-Making Algorithm with Geographic Mobility for Cognitive Radio." Sensors 24, no. 5 (2024): 1540. http://dx.doi.org/10.3390/s24051540.

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The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and val

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Koval, Natalia. "Cognitive Algorithms for Learning Foreign Languages: Psycholinguistics Approach." Educational Challenges 26, no. 1 (2021): 64–73. http://dx.doi.org/10.34142/2709-7986.2021.26.1.06.

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The relevance of the undertaken research consists in considering psycholinguistics an interdisciplinary field, which studies the interrelation between mind and language. It is important to perceive learning foreign language as an act of cognition, experience, and creativity in the psycholinguistic aspect of studying. Psycholinguistics concerns with the study of the cognitive process that supports the acquisition and use of language.  The purpose of the paper is to reveal the importance of psycholinguistics approach and cognitive science for learning a foreign language in the context of p

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Yang, Xiao Long, Lin Ma, and Xue Zhi Tan. "Power-Weight Allocation Algorithm for OFDM-Based Cognitive Radio System." Advanced Materials Research 546-547 (July 2012): 932–36. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.932.

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Cognitive Radio (CR) has been proposed as one of the most promising technologies to provide efficient utilization of the limited wireless spectrum. In this paper, we investigate the power allocation for CR system based on orthogonal frequency division multiplexing (OFDM), and an improved allocation algorithm called power-weight algorithm is proposed. This algorithm performs initial allocation via Lagrange multiplier method, and then accomplishes second allocation based on power weight obtained in initial allocation. The analytical results reveal that the total transmission rate of all subcarri

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Yim, Daehyuk, Tae Young Yeo, and Moon Ho Park. "Mild cognitive impairment, dementia, and cognitive dysfunction screening using machine learning." Journal of International Medical Research 48, no. 7 (2020): 030006052093688. http://dx.doi.org/10.1177/0300060520936881.

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Objective To develop a machine learning algorithm to identify cognitive dysfunction based on neuropsychological screening test results. Methods This retrospective study included 955 participants: 341 participants with dementia (dementia), 333 participants with mild cognitive impairment (MCI), and 341 participants who were cognitively healthy. All participants underwent evaluations including the Mini-Mental State Examination and the Montreal Cognitive Assessment. Each participant’s caregiver or informant was surveyed using the Korean Dementia Screening Questionnaire at the same visit. Different

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Zhang, Pei, Long Xiang Yang, and Xu Liu. "Subcarrier Allocation in Cognitive Radio Systems." Applied Mechanics and Materials 195-196 (August 2012): 154–58. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.154.

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In CR systems, resource allocation is very important, which can be operated by different rules, according to the different purposes. In this paper, we focus on subcarrier allocation in CR systems, where primary and CR users co-exist in adjacent bands, while keeping the total interference introduced to the PU band below a certain threshold and the total power allocated to the CR users under a constraint. First, we investigate Max-Rate subcarrier allocation algorithm (MaxR-SAA) which can achieve maximum transmit rate of the CR system. Then, focusing on the fact that the less interference introdu

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Ma, Yue Huai, Bin Zhang, Wei Zhang, and Meng Xiang Xu. "Joint Subcarrier and Power Allocation for Uplink Spectrum Sharing in Cognitive OFDM Networks: A Waterfilling Based Approach." Advanced Materials Research 756-759 (September 2013): 1979–83. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1979.

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In this paper, the subcarrier and power allocation problem for the orthogonal frequency division multiplexing (OFDM) cognitive radio network which coexists with the primary network is studied. A cognitive waterfilling (CWF) power allocation algorithm which based upon the classical waterfilling mechanism is proposed for the single SU scenario. As for multi-SU case, we present an efficient joint subcarrier and power allocation algorithm called MS-CWF algorithm. Simulation results show the effectiveness of the proposed CWF and MS-CWF algorithms.

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Liu, Hsiang Chuan. "Theoretical Approach to Reduced Q-Matrix for Cognition Diagnosis." Applied Mechanics and Materials 284-287 (January 2013): 3145–48. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3145.

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The main issue of the Q-matrix theory for cognition diagnosis is how to find the reduced Q-matrix containing the all efficient items. In this paper, based on the attribute structure matrix transformation, a novel recognition function for an efficient item vector is proposed. Two fast algorithms, transformation algorithm and expansion algorithm for finding the reduced Q-matrix are proposed as well. Some important properties are also discussed.

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Watson, David. "The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence." Minds and Machines 29, no. 3 (2019): 417–40. http://dx.doi.org/10.1007/s11023-019-09506-6.

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Abstract Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learn

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Wessler, Richard L., and Sheenah W. R. Hankin-Wessler. "Nonconscious Algorithms in Cognitive and Affective Processes." Journal of Cognitive Psychotherapy 3, no. 4 (1989): 243–54. http://dx.doi.org/10.1891/0889-8391.3.4.243.

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Nonconscious algorithms are stored routines for handling social information without the person’s awareness. In the author’s Cognitive Appraisal Therapy, Personal Rules of Living are nonconscious algorithms implicated in affect and action as (1) mediators of emotional experiences, (2) components in an interdependent system of cognition, affect, and action, and(3)preferenda. Motivational aspects of emotion are discussed in relation to the seeking of negative experiences for security of familiar affective states (Security-Seeking Maneuver). Clinical examples illustrate the interplay of these conc

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Gross, Alden, Yang An, Frank Lin, et al. "Derivation and Validation of an Algorithmic Classification of Early Cognitive Impairment." Innovation in Aging 5, Supplement_1 (2021): 436. http://dx.doi.org/10.1093/geroni/igab046.1696.

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Abstract The long prodromal period for dementia pathology demands valid and reliable approaches to detect cases before clinically recognizable symptoms emerge, by which time it may be too late to effectively intervene. We derived and compared several algorithms for early cognitive impairment (ECI) using longitudinal data on 1704 BLSA participants. Algorithms were based on cognitive impairment in various combinations of memory and non-memory tests, and the CDR. The best-performing algorithm was defined based on 1SD below age-and race-specific means in Card Rotations or California Verbal Learnin

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Wu, Di, Sheng Yao Yang, and J. C. Liu. "Cognitive Radio Decision Engine Based on Multi-Objective Genetic Algorithm." Applied Mechanics and Materials 48-49 (February 2011): 314–17. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.314.

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The performance optimization of cognitive radio is a multi-objective optimization problem. Existing genetic algorithms are difficult to assign the weight of each objective when the linear weighting method is used to simplify the multi-objective optimization problem into a single objective optimization problem. In this paper, we propose a new cognitive decision engine algorithm using multi-objective genetic algorithm with population adaptation. A multicarrier system is used for simulation analysis, and experimental results show that the proposed algorithm is effective and meets the real-time re

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Lv, Chun Ying, Ji Yang Wang, and Fei Yu. "Dynamic Spectrum Allocation Using Q-Learning in Cognitive Radio Systems." Applied Mechanics and Materials 427-429 (September 2013): 1579–84. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1579.

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In this paper we present an improved dynamic spectrum allocation algorithm based on the intelligence of Q-learning. The state space, action space and reward function of the algorithm are built, and, the agents are guided to perform actions through designing the reward function. Numerical simulation results show that the proposed algorithm can improve system throughput efficiently compared to other algorithms. Facing the status of spectrum resources is tension and spectrum utilization is low, it can also boost the spectrum using condition in the future.

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Venkat Ram V, Raja, M. Raghavender Sharma, and D. Gopinath. "Forecasting of Cognitive Neurological Aspects Using Machine Learning Ensemble Algorithms." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 588–95. https://doi.org/10.21275/sr25312153537.

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Chaudhary, Sunita. "An Efficient Medical Image Processing Approach Based on a Cognitive Marine Predators Algorithm." International Journal on Future Revolution in Computer Science & Communication Engineering 8, no. 1 (2022): 08–14. http://dx.doi.org/10.17762/ijfrcsce.v8i1.2084.

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Image processing aims to enhance the image's quality such that it is simple for both people and robots to understand. Medical image processing and Biomedical signal processing have many conceptual similarities. Medical image processing involves evaluation, enhancement, and presentation. The focus of medical imaging is on obtaining photographs for both therapeutic and diagnostic reasons. In the existing Marine Predator Algorithm, different disadvantages are experienced when various automated optimization algorithms are used to the problem of ECG categorization. The proposed method follows the f

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Zhang, Yuan, Weihua Wu, Wei He, and Nan Zhao. "Algorithm Design and Convergence Analysis for Coexistence of Cognitive Radio Networks in Unlicensed Spectrum." Sensors 23, no. 24 (2023): 9705. http://dx.doi.org/10.3390/s23249705.

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This paper focuses on achieving the low-cost coexistence of the networks in an unlicensed spectrum by making them operate on non-overlapping channels. For achieving this goal, we first give a universal convergence analysis framework for the unlicensed spectrum allocation algorithm. Then, a one-timescale iteration-adjustable unlicensed spectrum allocation algorithm is developed, where the step size and timescale parameter can be jointly adjusted based on the system performance requirement and signal overhead concern. After that, we derive the sufficient condition for the one-timescale algorithm

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Et.al, C. Jayasri. "A Novel Swarm Intelligence Optimized Spectrum Sensing Approach For Cognitive Radio Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 136–43. http://dx.doi.org/10.17762/turcomat.v12i6.1278.

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Spectrum sensing technique have been employed for the detection of various spectrum holes in the transmission of data for the secondary users that do not interfere with the transmission of data of the primary user. The technique known as Cognitive Radio (CR) is the one that efficiently uses the entire spectrum. The primary component of the CR is Spectrum sensing. There are certainly other factors that are considered to be important such as capabilities of cognition and awareness of sensing as well. Identified are different heuristic algorithms that are developed for solving numeric problems in

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K., Haritha, Judy M. V., Konstantinos Papageorgiou, and Elpiniki Papageorgiou. "Distributed Genetic Algorithm for Community Detection in Large Graphs with a Parallel Fuzzy Cognitive Map for Focal Node Identification." Applied Sciences 13, no. 15 (2023): 8735. http://dx.doi.org/10.3390/app13158735.

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This study addresses the importance of focal nodes in understanding the structural composition of networks. To identify these crucial nodes, a novel technique based on parallel Fuzzy Cognitive Maps (FCMs) is proposed. By utilising the focal nodes produced by the parallel FCMs, the algorithm efficiently creates initial clusters within the population. The community discovery process is accelerated through a distributed genetic algorithm that leverages the focal nodes obtained from the parallel FCM. This approach mitigates the randomness of the algorithm, addressing the limitations of the random

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Thabtah, Fadi, and David Peebles. "Assessment for Alzheimer’s Disease Advancement Using Classification Models with Rules." Applied Sciences 13, no. 22 (2023): 12152. http://dx.doi.org/10.3390/app132212152.

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Pre-diagnosis of common dementia conditions such as Alzheimer’s disease (AD) in the initial stages is crucial to help in early intervention, treatment plan design, disease management, and for providing quicker healthcare access. Current assessments are often stressful, invasive, and unavailable in most countries worldwide. In addition, many cognitive assessments are time-consuming and rarely cover all cognitive domains involved in dementia diagnosis. Therefore, the design and implementation of an intelligent method for dementia signs of progression from a few cognitive items in a manner that i

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Park, Jin-Hyuck. "Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment." American Journal of Alzheimer's Disease & Other Dementiasr 35 (January 1, 2020): 153331752092716. http://dx.doi.org/10.1177/1533317520927163.

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Background: The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically. Objective: This study was to evaluate the efficacy machine learning algorithms based on the mSTS-MCI and Korean version of MoCA. Method: In total, 103 healthy individuals and 74 patients with MCI were randomly divided into training and test data sets, respectively. The algorithm using TensorFlow was trained based on the training data set, and then its accuracy was calc

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Wang, Yingxu, Mehrdad Valipour, and Omar A. Zatarain. "Quantitative Semantic Analysis and Comprehension by Cognitive Machine Learning." International Journal of Cognitive Informatics and Natural Intelligence 10, no. 3 (2016): 13–28. http://dx.doi.org/10.4018/ijcini.2016070102.

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Knowledge learning is the sixth and the most fundamental category of machine learning mimicking the brain. It is recognized that the semantic space of machine knowledge is a hierarchical concept network (HCN), which can be rigorously represented by formal concepts in concept algebra and semantic algebra. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic analysis based on machine learning. Semantic equivalence between formal concepts is rigorously measured by an Algorithm of Concept Equivalence Analysis (ACEA). The semantic hierarchy amo

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Liu, Jing, Huibin Lu, Xiuru Zhang, et al. "Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?" Entropy 25, no. 3 (2023): 396. http://dx.doi.org/10.3390/e25030396.

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So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroen

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Wu, Jui-Yu. "Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches." Mathematical Problems in Engineering 2012 (2012): 1–36. http://dx.doi.org/10.1155/2012/841410.

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This work presents a hybrid real-coded genetic algorithm with a particle swarm optimization (RGA-PSO) algorithm and a hybrid artificial immune algorithm with a PSO (AIA-PSO) algorithm for solving 13 constrained global optimization (CGO) problems, including six nonlinear programming and seven generalized polynomial programming optimization problems. External RGA and AIA approaches are used to optimize the constriction coefficient, cognitive parameter, social parameter, penalty parameter, and mutation probability of an internal PSO algorithm. CGO problems are then solved using the internal PSO a

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Kertanah, Kertanah, Wiwit Pura Nurmayanti, Sri Rahmatun Aini, Lalu Muh Amrullah, and Muhammad Sya'roni. "Comparison of Algorithms K-Means and DBSCAN for Clustering Student Cognitive Learning Outcomes in Physics Subject." Kappa Journal 7, no. 2 (2023): 251–55. http://dx.doi.org/10.29408/kpj.v7i2.18428.

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Clustering is an activity of grouping data into the same group based on similarity. The purpose of the study is to cluster and determine student cognitive learning outcomes characteristics. Cluster analysis was conducted on student cognitive learning outcomes using algorithms K-Means and DBSCAN. Both algorithms are appropriate to have been applied to the overlapping data such as student learning outcomes data. Also, their advantages are scaling large datasets and outliers. The data used in this study is student cognitive learning outcomes - final and mid-term exams grade X in physics subject.

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H. Abood, May, and Hikmat N. Abdullah. "Efficient Cyclostationary Spectrum Sensing Using Low Complexity FFT Algorithms." Iraqi Journal of Information and Communication Technology 7, no. 1 (2024): 35–46. http://dx.doi.org/10.31987/ijict.7.1.245.

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One of the major problems of cyclostationary spectrum sensing (CSS) system in cognitive radios (CR) are the implementation complexity. One possible way to reduce CSS complexity is to use efficient algorithms for performing Fast Fourier Transformation (FFT). Over the years, a lot of different FFT algorithms have been created. This includes the Split-Radix algorithm., the Fast Hartley Transform (FHT), and slide DFT. This paper investigates the suitable FFT algorithm among the aforementioned techniques, cyclostationary feature detection (CFD)-based spectrum sensing stands out. The methods have be

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Li, Ye, and Xiaohu Shi. "Mine Pressure Prediction Study Based on Fuzzy Cognitive Maps." International Journal of Computational Intelligence and Applications 19, no. 03 (2020): 2050023. http://dx.doi.org/10.1142/s1469026820500236.

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The study on the prediction of mine pressure, while exploiting in coal mine, is a critical and technical guarantee for coal mine safety and production. In this paper, primarily due to the actual demand for the prediction of mine pressure, a practical prediction model Mine Pressure Prediction (MPP) was proposed based on fuzzy cognitive maps (FCMs). The Real Coded Genetic Algorithm (RCGA) was proposed to solve the problem by introducing the weight regularization and dropout regularization. A numerical example involving in-situ monitoring data is studied. Mean Square Error (MSE) and fitness funct

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K.S, Yuvaraj, Priya P., and Thiagarasu V. "Target Channel Selection Algorithm for Cognitive Radio Network." International Journal of Engineering & Technology 7, no. 4.6 (2018): 198. http://dx.doi.org/10.14419/ijet.v7i4.6.20462.

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Cognitive radio network has attained more popularity because of giving prominent solution for spectrum inefficiency due to static spectrum allocation. This research proposed target channel selection algorithm which have been employed to improving the throughput and reducing number of handoffs. The proposed target channel selection algorithm is select the channel based on the idle probability and efficiency reward of channel and these are calculated from the surveillance of earlier usage statistics. When the channel selection is based on the above factor, the Secondary User gets chance to utili

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Diederichs, Elmar. "Reinforcement Learning - A Technical Introduction." Journal of Autonomous Intelligence 2, no. 2 (2019): 25. http://dx.doi.org/10.32629/jai.v2i2.45.

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Reinforcement learning provides a cognitive science perspective to behavior and sequential decision making provided that RL-algorithms introduce a computational concept of agency to the learning problem. Hence it addresses an abstract class of problems that can be characterized as follows: An algorithm confronted with information from an unknown environment is supposed to find stepwise an optimal way to behave based only on some sparse, delayed or noisy feedback from some environment, that changes according to the algorithm's behavior. Hence reinforcement learning offers an abstraction to the

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Amari, Shun-ichi. "The EM Algorithm and Information Geometry in Neural Network Learning." Neural Computation 7, no. 1 (1995): 13–18. http://dx.doi.org/10.1162/neco.1995.7.1.13.

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Hidden units play an important role in neural networks, although their activation values are unknown in many learning situations. The EM algorithm (statistical algorithm) and the em algorithm (information-geometric one) have been proposed so far in this connection, and the effectiveness of such algorithms is recognized in many areas of research. The present note points out that these two algorithms are equivalent under a certain condition, although they are different in general.

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Xi, Zichen. "Analysis of Adaptive Equalization Algorithms." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 295–305. http://dx.doi.org/10.54097/hset.v70i.12477.

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Adaptive equalization algorithms play a pivotal role in suppressing inter-symbol interference in wireless channels. Contemporarily, with the rapid development of science and technology, there is still a lack of unified cognition for adaptive equalization algorithms. Therefore, this study systematically discusses the research status and development process of adaptive equalization algorithms, focusing on the least mean square algorithm (LMS), constant modulus blind equalization algorithm (CMA) and neural network algorithm. Subsequently, based on Matlab simulation, their performance is analyzed

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Wang, Zhe. "Audio Signal Acquisition and Processing System Based on Model DSP Rapid Design." Security and Communication Networks 2022 (May 19, 2022): 1–9. http://dx.doi.org/10.1155/2022/4593339.

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When using DSP technology, technicians can easily and conveniently replace DSP audio processors, make second-party equipment, improve processor performance, reduce application costs, and receive and make music to meet the needs of different signals. Introduces knowledge of speech comprehension, which includes theoretical and cognitive processes, such as pre-speech characterization, final discovery, behavior, structure, and knowledge. By analyzing and comparing various common features, Mel frequency cepstrum coefficient is used to determine the physical features, and the results of traditional

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Baldi, Pierre, and Yves Chauvin. "Smooth On-Line Learning Algorithms for Hidden Markov Models." Neural Computation 6, no. 2 (1994): 307–18. http://dx.doi.org/10.1162/neco.1994.6.2.307.

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A simple learning algorithm for Hidden Markov Models (HMMs) is presented together with a number of variations. Unlike other classical algorithms such as the Baum-Welch algorithm, the algorithms described are smooth and can be used on-line (after each example presentation) or in batch mode, with or without the usual Viterbi most likely path approximation. The algorithms have simple expressions that result from using a normalized-exponential representation for the HMM parameters. All the algorithms presented are proved to be exact or approximate gradient optimization algorithms with respect to l

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Yao, Changhua, Lei Zhu, Yongxing Jia, and Lei Wang. "Demand-aware traffic cooperation for self-organizing cognitive small-cell networks." International Journal of Distributed Sensor Networks 15, no. 1 (2019): 155014771881728. http://dx.doi.org/10.1177/1550147718817289.

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This article investigates the problem of efficient spectrum access for traffic demands of self-organizing cognitive small-cell networks, using the coalitional game approach. In particular, we propose a novel spectrum and time two-dimensional Traffic Cooperation Coalitional Game model which aims to improve the network throughput. The main motivation is to complete the data traffics of users, and the main idea is to make use of spectrum resource efficiently by reducing mutual interference in the spectrum dimension and considering cooperative data transmission in the time dimension at the same ti

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Chen, Ruizhi, and Ling Li. "Analyzing and Accelerating the Bottlenecks of Training Deep SNNs With Backpropagation." Neural Computation 32, no. 12 (2020): 2557–600. http://dx.doi.org/10.1162/neco_a_01319.

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Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training deep SNNs is still challenging compared to convolutional neural networks (CNNs). The SNN training algorithms have not achieved the same performance as CNNs. In this letter, we aim to understand the intrinsic limitations of SNN training to design better algorithms. First, the pros and cons of typical SNN training algorithms are analyzed. Then it is found that the spatiotemporal backpropagation algorithm (STBP) has potential in training deep SNNs due

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Klee, Matthias, Kenneth Langa, and Anja Leist. "ADJUSTING FOR COUNTRY-LEVEL VARIATION IN DEMENTIA PREVALENCE WITH CLASSIFICATION ALGORITHMS IN SHARE." Innovation in Aging 6, Supplement_1 (2022): 486. http://dx.doi.org/10.1093/geroni/igac059.1873.

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Abstract Background. Population-level dementia prevalence depends on societal factors and individual-level risk and protective factors. To improve our understanding of how these factors interact, we can use cross-national surveys such as the Survey of Health, Ageing and Retirement in Europe (SHARE). However, in absence of validated cognitive assessments, adjusting for underdiagnosis of dementia is needed. The present study sought to explore the usefulness of the Langa-Weir and alternative algorithms to detect probable dementia while accounting for country-level variation in estimated prevalenc

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Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.

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Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which

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Jaakkola, Tommi, Michael I. Jordan, and Satinder P. Singh. "On the Convergence of Stochastic Iterative Dynamic Programming Algorithms." Neural Computation 6, no. 6 (1994): 1185–201. http://dx.doi.org/10.1162/neco.1994.6.6.1185.

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Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(λ) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of conve

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