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1
Jalenques, I. "SMP – Diagnostic et traitement personnalisés : un paradigme d’avenir dans les troubles schizophréniques." European Psychiatry 29, S3 (November 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’expression d’une même maladie. Outre l’intérêt théorique, identifier une étiologie revêt un intérêt pratique pour définir la stratégie thérapeutique la plus adaptée chez un patient donné car certaines caractéristiques cognitives ou évolutives ont une incidence sur les options thérapeutiques. Reste à déterminer un algorithme réaliste permettant de hiérarchiser outils et examens pour affiner le bilan diagnostique de l’ensemble des patients.L’évolution des troubles schizophréniques a été amplement modifiée suite à l’avènement des neuroleptiques en 1952. Les antipsychotiques de seconde génération sont venus compléter l’offre de soins. Les données récentes insistent sur la nécessité de traiter sans retard car la souffrance engendrée par la maladie est réelle. Avec les nouvelles molécules la prise en charge devrait être individualisée, prenant en compte les attentes et appréhensions des patients notamment face au traitement pharmacologique.Les troubles cognitifs très fréquents, hétérogènes, contribuent fortement au pronostic fonctionnel. Le profil des compétences dégradées et préservées est propre à chaque patient : une remédiation cognitive pertinente nécessite donc des prises en charge individualisées. Le bilan neuropsychologique, dans le cadre d’une évaluation intégrative multidisciplinaire, permet d’établir des liens entre les profils cognitif et fonctionnel. Les éventuelles indications de remédiation cognitive qui en découlent ne doivent pas viser l’amélioration des performances cognitives pour elles-mêmes, mais la réussite de projets concrets dans les domaines social ou professionnel à laquelle cette amélioration peut contribuer [1,2].
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Diederichs, Elmar. "Reinforcement Learning - A Technical Introduction." Journal of Autonomous Intelligence 2, no. 2 (August 19, 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 problem of goal-directed learning from interaction. The paper offers an opintionated introduction in the algorithmic advantages and drawbacks of several algorithmic approaches such that one can understand recent developments and open problems in reinforcement learning.
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Anderson, John R. "Methodologies for studying human knowledge." Behavioral and Brain Sciences 10, no. 3 (September 1987): 467–77. http://dx.doi.org/10.1017/s0140525x00023554.
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AbstractThe appropriate methodology for psychological research depends on whether one is studying mental algorithms or their implementation. Mental algorithms are abstract specifications of the steps taken by procedures that run in the mind. Implementational issues concern the speed and reliability of these procedures. The algorithmic level can be explored only by studying across-task variation. This contrasts with psychology's dominant methodology of looking for within-task generalities, which is appropriate only for studying implementational issues.The implementation-algorithm distinction is related to a number of other “levels” considered in cognitive science. Its realization in Anderson's ACT theory of cognition is discussed. Research at the algorithmic level is more promising because it is hard to make further fundamental scientific progress at the implementational level with the methodologies available. Protocol data, which are appropriate only for algorithm-level theories, provide a richer source than data at the implementational level. Research at the algorithmic level will also yield more insight into fundamental properties of human knowledge because it is the level at which significant learning transitions are defined.The best way to study the algorithmic level is to look for differential learning outcomes in pedagogical experiments that manipulate instructional experience. This provides control and prediction in realistically complex learning situations. The intelligent tutoring paradigm provides a particularly fruitful way to implement such experiments.The implications of this analysis for the issue of modularity of mind, the status of language, research on human/computer interaction, and connectionist models are also examined.
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Foley, J. M., M. J. Wright, A. L. Gooding, M. Ettenhofer, M. Kim, M. Choi, S. A. Castellon, et al. "Operationalization of the updated diagnostic algorithm for classifying HIV-related cognitive impairment and dementia." International Psychogeriatrics 23, no. 5 (November 19, 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 subgroups of interest emerged: (1) functional decline without cognitive impairment; (2) severe cognitive impairment and minimal functional compromise.Conclusions: The algorithm discriminates between HIV-infected cognitively impaired individuals. Diagnosis yields two unique profiles requiring further investigation. Findings largely support the algorithm's utility for diagnosing HIV-cognitive-impairment, but suggest distinct subsets of individuals with discrepant cognitive/functional performances that may not be readily apparent by conventional application of HAND diagnosis.
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Gonda, Dalibor, Viliam Ďuriš, Anna Tirpáková, and Gabriela Pavlovičová. "Teaching Algorithms to Develop the Algorithmic Thinking of Informatics Students." Mathematics 10, no. 20 (October 18, 2022): 3857. http://dx.doi.org/10.3390/math10203857.
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Modernization and the ever-increasing trend of introducing modern technologies into various areas of everyday life require school graduates with programming skills. The ability to program is closely related to computational thinking, which is based on algorithmic thinking. It is well known that algorithmic thinking is the ability of students to work with algorithms understood as a systematic description of problem-solving strategies. Algorithms can be considered as a fundamental phenomenon that forms a point of contact between mathematics and informatics. As part of an algorithmic graph theory seminar, we conducted an experiment where we solved the knight’s tour problem using the backtracking method to observe the change in students’ motivation to learn algorithms at a higher cognitive level. Seventy-four students participated in the experiment. Statistical analysis of the results of the experiment confirmed that the use of the algorithm with decision-making in teaching motivated students to learn algorithms with understanding.
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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 (March 2, 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 (structural and diffusion MRI) from 601 healthy participants aged 18–88 years in the Cambridge Centre for Ageing and Neuroscience to assess variations in brain-predicted age. The inter-algorithm similarity in brain-predicted age and brain regional regression weights was examined using the Pearson’s correlation analyses and hierarchical clustering. We then assessed to what extent machine learning algorithms impact the association between brainPAD and seven cognitive variables. The regression models achieved mean absolute errors of 5.46–7.72 years and Pearson’s correlation coefficients of 0.86–0.92 between predicted brain age and chronological age. Furthermore, we identified a substantial difference in linking brainPAD to cognitive measures, indicating that the choice of algorithm could be an important source of variability that confounds the relationship between brainPAD and cognition.
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Wahn, Basil, Laura Schmitz, Frauke Nora Gerster, and Matthias Weiss. "Offloading under cognitive load: Humans are willing to offload parts of an attentionally demanding task to an algorithm." PLOS ONE 18, no. 5 (May 19, 2023): e0286102. http://dx.doi.org/10.1371/journal.pone.0286102.
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In the near future, humans will increasingly be required to offload tasks to artificial systems to facilitate daily as well as professional activities. Yet, research has shown that humans are often averse to offloading tasks to algorithms (so-called “algorithmic aversion”). In the present study, we asked whether this aversion is also present when humans act under high cognitive load. Participants performed an attentionally demanding task (a multiple object tracking (MOT) task), which required them to track a subset of moving targets among distractors on a computer screen. Participants first performed the MOT task alone (Solo condition) and were then given the option to offload an unlimited number of targets to a computer partner (Joint condition). We found that participants significantly offloaded some (but not all) targets to the computer partner, thereby improving their individual tracking accuracy (Experiment 1). A similar tendency for offloading was observed when participants were informed beforehand that the computer partner’s tracking accuracy was flawless (Experiment 2). The present findings show that humans are willing to (partially) offload task demands to an algorithm to reduce their own cognitive load. We suggest that the cognitive load of a task is an important factor to consider when evaluating human tendencies for offloading cognition onto artificial systems.
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Amoore, Louise, and Rita Raley. "Securing with algorithms: Knowledge, decision, sovereignty." Security Dialogue 48, no. 1 (December 12, 2016): 3–10. http://dx.doi.org/10.1177/0967010616680753.
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Amid the deployment of algorithmic techniques for security – from the gathering of intelligence data to the proliferation of smart borders and predictive policing – what are the political and ethical stakes involved in securing with algorithms? Taking seriously the generative and world-making capacities of contemporary algorithms, this special issue draws attention to the embodied actions of algorithms as they extend cognition, agency and responsibility beyond the conventional sites of the human, the state and sovereignty. Though focusing on different modes of algorithmic security, each of the contributions to the special issue shares a concern with what it means to claim security on the terrain of incalculable and uncertain futures. To secure with algorithms is to reorient the embodied relation to uncertainty, so that human and non-human cognitive beings experimentally generate and learn what to bring to the surface of attention for a security action.
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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 (July 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 performance. In this article, we present a general framework for the design and analysis of CLDA algorithms and support our results with experimental data of two monkeys performing a BMI task. First, we analyze and compare existing CLDA algorithms to highlight the importance of four critical design elements: the adaptation timescale, selective parameter adaptation, smooth decoder updates, and intuitive CLDA parameters. Second, we introduce mathematical convergence analysis using measures such as mean-squared error and KL divergence as a useful paradigm for evaluating the convergence properties of a prototype CLDA algorithm before experimental testing. By applying these measures to an existing CLDA algorithm, we demonstrate that our convergence analysis is an effective analytical tool that can ultimately inform and improve the design of CLDA algorithms.
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Ionescu, Claudiu Gabriel, and Monica Licu. "Are TikTok Algorithms Influencing Users’ Self-Perceived Identities and Personal Values? A Mini Review." Social Sciences 12, no. 8 (August 21, 2023): 465. http://dx.doi.org/10.3390/socsci12080465.
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The use of TikTok is more widespread now than ever, and it has a big impact on users’ daily lives, with self-perceived identity and personal values being topics of interest in light of the algorithmically curated content. This mini-review summarizes current findings related to the TikTok algorithm, and the impact it has on self-perceived identity, personal values, or related concepts of the Self. We pass through the contents of algorithmic literacy and emphasize its importance along with users’ attitudes toward algorithms. In the first part of our results, we show conceptual models of algorithms like the crystal framework, platform spirit, and collective imaginaries. In the second part, we talk about the degree of impact a social media algorithm may exert over an individual’s sense of self, understanding how the algorithmized self and domesticated algorithm are trying to sum up the dual development of this relationship. In the end, with the concept of Personal Engagement and the role of cognitive biases, we summarize the current findings and discuss the questions that still need to be addressed. Performing research on the topic of social media, especially TikTok, poses ethical, cultural, and regulatory challenges for researchers. That is why we will discuss the main theoretical frameworks that were published with their attached current studies and their impact on the current theoretical models as well as the limitations within these studies. Finally, we discuss further topics of interest related to the subject and possible perspectives, as well as recommendations regarding future research in areas like impact on personal values and identity, cognitive biases, and algorithmic literacy.
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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 (September 8, 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 maximum channel capacity time power allocation scheme by considering the worst-case channel transmission model, and finally solves this complex non-convex optimization problem by using the hybrid particle swarm algorithm. Simulation results show that the algorithm has good robustness, improves the fairness among the cognitive users, makes full use of the channel resources under the constraints, and has a simple algorithm, fast convergence, and good optimization results.
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Kazim, Emre, Adriano Soares Koshiyama, Airlie Hilliard, and Roseline Polle. "Systematizing Audit in Algorithmic Recruitment." Journal of Intelligence 9, no. 3 (September 17, 2021): 46. http://dx.doi.org/10.3390/jintelligence9030046.
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Business psychologists study and assess relevant individual differences, such as intelligence and personality, in the context of work. Such studies have informed the development of artificial intelligence systems (AI) designed to measure individual differences. This has been capitalized on by companies who have developed AI-driven recruitment solutions that include aggregation of appropriate candidates (Hiretual), interviewing through a chatbot (Paradox), video interview assessment (MyInterview), and CV-analysis (Textio), as well as estimation of psychometric characteristics through image-(Traitify) and game-based assessments (HireVue) and video interviews (Cammio). However, driven by concern that such high-impact technology must be used responsibly due to the potential for unfair hiring to result from the algorithms used by these tools, there is an active effort towards proving mechanisms of governance for such automation. In this article, we apply a systematic algorithm audit framework in the context of the ethically critical industry of algorithmic recruitment systems, exploring how audit assessments on AI-driven systems can be used to assure that such systems are being responsibly deployed in a fair and well-governed manner. We outline sources of risk for the use of algorithmic hiring tools, suggest the most appropriate opportunities for audits to take place, recommend ways to measure bias in algorithms, and discuss the transparency of algorithms.
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Opara, Ralf, and Florentin Wörgötter. "A Fast and Robust Cluster Update Algorithm for Image Segmentation in Spin-Lattice Models Without Annealing—Visual Latencies Revisited." Neural Computation 10, no. 6 (August 1, 1998): 1547–66. http://dx.doi.org/10.1162/089976698300017304.
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Image segmentation in spin-lattice models relies on the fast and reliable assignment of correct labels to those groups of spins that represent the same object. Commonly used local spin-update algorithms are slow because in each iteration only a single spin is flipped and a careful annealing schedule has to be designed in order to avoid local minima and correctly label larger areas. Updating of complete spin clusters is more efficient, but often clusters that should represent different objects will be conjoined. In this study, we propose a cluster update algorithm that, similar to most local update algorithms, calculates an energy function and determines the probability for flipping a whole cluster of spins by the energy gain calculated for a neighborhood of the regarded cluster. The novel algorithm, called energy-based cluster update (ECU algorithm) is compared to its predecessors. A convergence proof is derived, and it is shown that the algorithm outperforms local update algorithms by far in speed and reliability. At the same time it is more robust and noise tolerant than other versions of cluster update algorithms, making annealing completely unnecessary. The reduction in computational effort achieved this way allows us to segment real images in about 1–5 sec on a regular workstation. The ECU-algorithm can recover fine details of the images, and it is to a large degree robust with respect to luminance-gradients across objects. In a final step, we introduce luminance dependent visual latencies (Opara & Wörgötter, 1996; Wörgötter, Opara, Funke, & Eysel, 1996) into the spin-lattice model. This step guarantees that only spins representing pixels with similar luminance become activated at the same time. The energy function is then computed only for the interaction of the regarded cluster with the currently active spins. This latency mechanism improves the quality of the image segmentation by another 40%. The results shown are based on the evaluation of gray-level differences. It is important to realize that all algorithmic components can be transferred easily to arbitrary image features, like disparity, texture, and motion.
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Marques, Ana. "Writing with Automated Machines: Between Translation and Sabotage." Matlit Revista do Programa de Doutoramento em Materialidades da Literatura 6, no. 3 (August 10, 2018): 73–81. http://dx.doi.org/10.14195/2182-8830_6-3_6.
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A generative text is a system constituted by non-conscious and conscious cognizers, digital and analogue processes, and mathematical and linguistic modes of representation. But how do algorithms cognize? And how is meaning constructed in a system where authorial intentions and readers’ experiences and interpretations are mediated by algorithmic agents? Through the analysis of How It Is In Common Tongues (Cayley and Howe, 2012), I intend to discuss the tensions that arise from the encounter between algorithmic and human cognition, and between the regimes of information and expression. Drawing on Katherine Hayles’ view on the cognitive non-conscious and Claude Shannon’s information theory I will start by establishing a distinction between information and meaning, between communication and expression, and between the regimes of information and of the literary. To reflect on the political ecology of digital mediation (situated in the informational regime of cybernetics), I will consider Matteo Pasquinelli’s perspective on the co-evolution of technology and economics, and discuss how algorithmic cognitive processes embody and reinforce the structures of contemporary cognitive capitalism. Finally, I will discuss the strategies of resistance enabled by aesthetic approaches to computation, such as the ones explored in this case study.
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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 (June 14, 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 verification of each algorithm. Then, an improved Q-Learning algorithm is proposed to address the shortcomings of both algorithms. By introducing the pheromone mechanism of ant colony algorithms in Q-Learning and using the ε-greedy algorithm to balance the contradictory relationship between exploration and exploitation, the algorithm greatly avoids falling into a local optimum, thus accelerating the convergence speed of the algorithm with good stability and robustness in the convergence process. In order to better adapt to the cluster countermeasure environment in future battlefields, the algorithm and model are extended to cluster cooperative jamming decision-making. We map each jammer in the cluster to an intelligent ant searching for the optimal path, and multiple jammers interact with each other to obtain information. During the process of confrontation, the method greatly improves the convergence speed and stability and reduces the need for hardware and power resources of the jammer. Assuming that the number of jammers is three, the experimental simulation results of the convergence speed of the Ant-QL algorithm improve by 85.4%, 80.56% and 72% compared with the Q-Learning, DQN and improved Q-Learning algorithms, respectively. During the convergence process, the Ant-QL algorithm is very stable and efficient, and the algorithm complexity is low. After the algorithms converge, the average response times of the four algorithms are 6.99 × 10−4 s, 2.234 × 10−3 s, 2.21 × 10−4 s and 1.7 × 10−4 s, respectively. The results show that the improved Q-Learning algorithm and Ant-QL algorithm also have more advantages in terms of average response time after convergence.
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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 (March 31, 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 scheduled users is the Secondary Users (SUs), and they could be un-licensed subscribers that don't pay for their service, device-to-device communications, or sensors. Each user whether it’s a primary or secondary is considered as an agent. In the Collaborative scheduling algorithm, the primary user agents will collaborate in order to make a joint scheduling decision about allocating the resource blocks to each one of them, then the secondary user agents will compete among themselves to use the remaining resource blocks. In the Competitive scheduling algorithm, the primary user agents will compete among themselves over the available resources, then the secondary user agents will compete among themselves over the remaining resources. Experimental results show that both scheduling algorithms converged to almost 90% utilization of the spectrum, and provided fair shares of the spectrum among users.
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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 (May 23, 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 inspired by the forward prediction mechanism of the hippocampus to solve the above problems and tackle the exploration–exploitation dilemma of Reinforcement Learning (RL). This algorithm alternately presents the potential path in the future for mobile robots and dynamically adjusts the sweep length according to the decision certainty, so as to determine action selection. We test the performance of the algorithm in a two-dimensional maze environment with static and dynamic obstacles, respectively. Compared with classic RL algorithms like State-Action-Reward-State-Action (SARSA) and Dyna-Q, the algorithm can speed up spatial cognition and improve the global search ability of path planning. In addition, our method reflects key features of how the brain organizes MBRL to effectively solve difficult tasks such as navigation, and it provides a new idea for spatial cognitive tasks from a biological perspective.
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Shin, Donghee. "A Cross-National Study on the Perception of Algorithm News in the East and the West." Journal of Global Information Management 29, no. 2 (March 2021): 77–101. http://dx.doi.org/10.4018/jgim.2021030105.
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Although algorithms have been widely used to deliver useful services, how users actually experience algorithm-driven news remains unclear. This study examines user attitude and perception of algorithmic journalism and identifies the similarities and differences in experience and satisfaction formation. A comparative study between the United States (U.S.) and South Korea was conducted to examine how the two countries' users experience the quality of algorithm-driven news services and how individuals perceive the topics of fairness, accountability, and transparency. The notable similarities and differences are found by performing a comparison of cognitive processes. The major attitudes toward algorithm news are similar between the two countries, although the weights placed on the qualities differ. South Korean users put more weight on performance qualities, and U.S. users place relatively greater emphasis on procedural features. Different patterns of algorithm news experience imply the contextual nature of algorithm: how users perceive and feel about topics in algorithm news and how they use and engage with algorithm news depend on the context where the experience is taking place. The analysis suggests the importance of user-perceived issues and the contextual nature of such issues.
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Andreasen, Torsten, Knut Ove Eliassen, and Frederik Tygstrup. "Fremtidshandel: Fiktionen i den finansielle spekulations tidsalder." K&K - Kultur og Klasse 45, no. 124 (December 31, 2017): 253–70. http://dx.doi.org/10.7146/kok.v45i124.103922.
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Robert Harris’ novel The Fear Index (2012) takes place over 24 hours on May 6 2010 where the life of eccentric former physicist Alex Hoffmann – now hedge fund owner and developer of trading algorithms – is threatened while the market collapses. It is unclear whether the trader suffers from a schizophrenic psychosis or whether his algorithm VIXAL-4 has actually overpowered him. The fund’s portfolio is “all out of shape” and Hoffmann’s personality “had grown lopsided”, as the algorithmic monster prevails via the complete breakdown of both the market and its maker. Via an analysis of The Fear Index and its references to romantic literature, the article examines the consequences of trade via algorithmic operations beyond human perception and cognition for the relation between cultural imagination and the future.
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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 (August 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 of the PSO, the “neighbor cognition” factor is proposed to enhance the breadth of learning; and the following bee strategy is introduced to pro-pose particle density and survivability to control the number of elite clones, so as to solve the problem of insufficient local search ability of the algorithm. Finally, the validity of the proposed algorithm is verified by testing on real data sets and comparing with other algorithms.</p> <p> </p>
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Koval, Natalia. "Cognitive Algorithms for Learning Foreign Languages: Psycholinguistics Approach." Educational Challenges 26, no. 1 (March 31, 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 psycholinguistic approach and cognitive methods for learning second language, based on achievements of the “Scientific School of A.V. Khutorsky”.
Methodology is of an overview-analytical nature with an attempt to apply cognitive techniques to learning. Our observations on the psycholinguistic approach and the cognitive methods are based on the “Myth of Niels Bohr and the barometer question” by Alexander Calandra.
Results. The analysis made it possible to determine how the logic of reflections has been explored from the lens of psycholinguistics and how the range of cognitive methods can be enlisted to learn a foreign language. It turns next to an overview of cognitive techniques used in psycholinguistics as applied to study. The verbal presentation of the idea is not only a form of compressed thought or interactive, creative cognition, but it also has a literary quality and makes use of a range of devices in a way. In the article, the solution formation reflects the features of transforming mental representations about the multidimensional space of life.
Conclusions. According to the research, the paper concludes that cognitive methods are the ability to create judgments that are paradoxical in form and deep in content, perceived as deviating from the norm, and humor also presupposes the presence of the inverse ability to perceive such judgments in their entirety and depth and emotional brightness.
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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 (December 1, 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 gives certain possibilities to get quicker values of target relations values and improve the operation of the learned system.
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Végh, Ladislav. "Models of Data Structures in Educational Visualizations for Supporting Teaching and Learning Algorithms and Computer Programming." International Journal of Advanced Natural Sciences and Engineering Researches 7, no. 5 (June 21, 2023): 147–54. http://dx.doi.org/10.59287/ijanser.916.
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Teaching and learning computer programming is challenging for many undergraduate first-year computer science students. During introductory programming courses, novice programmers need to learn some basic algorithms, gain algorithmic thinking, improve their logical and problem-solving thinking skills, and learn data types, data structures, and the syntax of the chosen programming language. In literature, we can find various methods of teaching programming that can motivate students and reduce students’ cognitive load during the learning process of computer programming, e.g., using robotic kits, microcontrollers, microworld environments, virtual worlds, serious games, interactive animations, and visualizations. In this paper, we focus mainly on algorithm visualizations, especially on the different models of data structures that can be effectively used in educational visualizations. First, we show how a vector (one-dimensional array), a matrix (two-dimensional array), a singly linked list, and a graph can be represented by various models. Next, we also demonstrate some examples of interactive educational algorithm animations for teaching and learning elementary algorithms and some sorting algorithms, e.g., swapping two variables, summing elements of the array, mirroring the array, searching the minimum or maximum of the array, searching the index of minimum or maximum of the array, sorting elements of thearray using simple exchange sort, bubblesort, insertion sort, minsort, maxsort, quicksort, or mergesort. Finally, in the last part of the paper, we summarize our experiences in teaching algorithmization and computer programming using algorithm animations and visualizations and draw some conclusions.
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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 (February 28, 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 validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability.
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Lyons, Jack C. "Algorithm and Parameters: Solving the Generality Problem for Reliabilism." Philosophical Review 128, no. 4 (October 1, 2019): 463–509. http://dx.doi.org/10.1215/00318108-7697876.
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The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings of folk psychology, and from much of the epistemology literature. But it is principled and empirically grounded, and shows good prospects for yielding the desired epistemological verdicts. The paper articulates and elaborates the theory, drawing out some of its consequences. Toward the end, the fleshed-out theory is applied to two important case studies: hallucination and cognitive penetration of perception.
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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 (July 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 machine learning algorithms were applied, and their overall accuracies, Cohen’s kappa, receiver operating characteristic curves, and areas under the curve (AUCs) were calculated. Results The overall screening accuracies for MCI, dementia, and cognitive dysfunction (MCI or dementia) using a machine learning algorithm were approximately 67.8% to 93.5%, 96.8% to 99.9%, and 75.8% to 99.9%, respectively. Their kappa statistics ranged from 0.351 to 1.000. The AUCs of the machine learning models were statistically superior to those of the competing screening model. Conclusion This study suggests that a machine learning algorithm can be used as a supportive tool in the screening of MCI, dementia, and cognitive dysfunction.
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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 subcarriers is much closer to the optimal algorithm (i.e. the greedy algorithm) than the water-level algorithm, the Krongold algorithm and adaptive water-filling algorithm, subject to transmit power constraint and bit error rate (BER) constraint. These algorithms are all based on water-filling thought. Finally, simulation results verify our analysis.
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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 (January 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 concepts with self-image and the task of cognitive psychotherapy.
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Watson, David. "The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence." Minds and Machines 29, no. 3 (September 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 learning methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.
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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 introduced to the PU band, the better the PU can work; we proposed a Min-Interference subcarrier allocation algorithm (MinI-SAA). Further, aiming at achieving fairness among all the SUs, we propose a subcarrier allocation algorithm, which is termed as Fair-Rate subcarrier allocation algorithm (FairR-SAA). Numerical results are obtained for the behaviors and performance of our proposed algorithms.
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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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Gross, Alden, Yang An, Frank Lin, Luigi Ferrucci, Jennifer Schrack, Yuri Agrawal, and Susan Resnick. "Derivation and Validation of an Algorithmic Classification of Early Cognitive Impairment." Innovation in Aging 5, Supplement_1 (December 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 Learning Test immediate recall, two tests that in prior work show the earliest declines prior to dementia onset. While this ECI algorithm showed low concordance with concurrent adjudicated MCI/dementia (AUC: 0.63, sensitivity: 0.54, specificity: 0.73), it was among the best predictors of progression to MCI/dementia (HR: 3.65, 95% CI: 1.69,7.87). This algorithm may be useful in epidemiologic work to evaluate risk factors for early cognitive impairment.
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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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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 (April 10, 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 optimization. As the problem has been established as NP-hard, it is essential to bring a low computation complexity heuristic solution. A greedy algorithm is used for optimizing spectrum sharing. Particle Swarm Optimization (PSO) remains an efficient and popular algorithm due to its low need for a tuning parameter, high accuracy, low time for processing, fast convergence, and simplicity. In this work, the PSO has been proposed for spectrum sensing, and this has shown better performance than the Greedy Algorithm used for the CR network spectrum sensing.
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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 requirement.
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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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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 (March 31, 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 flow outlined here: data collection, image preprocessing using histogram equalization, segmentation using the Otsu threshold algorithm, feature extraction using the contour method, feature selection using the Neighborhood Component Analysis (NCA) algorithm, and Cognitive Marine Predator Algorithm (CMPA) as the proposed method. By using the Cognitive Marine Predators Algorithm (CMPA), base layers are fused to use the greatest feasible parameters, producing enhanced high-quality output images. Finally, the image processing performance is analyzed. The proposed approaches overcome the drawbacks of existing algorithms and increase the quality of medical images efficiently.
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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 (December 8, 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. Furthermore, the upper bound of convergence error of the one-timescale spectrum allocation algorithm is obtained. Due to the multi-timescale evolution of the network states in the wireless network, we further propose a two-timescale iteration-adjustable joint frequency selection and frequency allocation algorithm, where the frequency selection iteration timescale is set according to the slow-changing statistical channel state information (CSI), whereas the frequency allocation iteration timescale is set according to the fast-changing local CSI. Then, we derive the convergence condition of two-timescale algorithms and the upper bound of the corresponding convergence error. The experimentalresults show that the small timescale adjustment parameter and large step size can help decrease the convergence error. Moreover, compared with traditional algorithms, the two-timescale policy can achieve throughput similar to traditional algorithms with very low iteration overhead.
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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 (November 8, 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 is accessible, easy, affordable, quick to perform, and does not require special and expensive resources is desirable. This paper investigates the issue of dementia progression by proposing a new classification algorithm called Alzheimer’s Disease Class Rules (AD-CR). The AD-CR algorithm learns models from the distinctive feature subsets that contain rules with low overlapping among their cognitive items yet are easily interpreted by clinicians during clinical assessment. An empirical evaluation of the Disease Neuroimaging Initiative data repository (ADNI) datasets shows that the AD-CR algorithm offers good performance (accuracy, sensitivity, etc.) when compared with other machine learning algorithms. The AD-CR algorithm was superior in comparison to the other algorithms overall since it reached a performance above 92%, 92.38% accuracy, 91.30% sensitivity, and 93.50% specificity when processing data subsets with cognitive and demographic attributes.
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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 (July 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 among formal concepts is quantitatively determined by an Algorithm of Relational Semantic Classification (ARSC). Experiments applying Algorithms ACEA and ARSC on a set of formal concepts have been successfully conducted, which demonstrate a deep machine understanding of formal concepts and quantitative relations in the hierarchical semantic space by machine learning beyond human empirical perspectives.
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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 calculated based on the test data set. The cost was calculated via logistic regression in this case. Result: Predictive power of the algorithms was higher than those of the original tests. In particular, the algorithm based on the mSTS-MCI showed the highest positive-predictive value. Conclusion: The machine learning algorithms predicting MCI showed the comparable findings with the conventional screening tools.
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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 (July 28, 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 population selection commonly found in genetic algorithms. The proposed algorithm improves the performance of the genetic algorithm by enabling informed decision making and forming a better initial population. This enhancement leads to improved convergence and overall algorithm performance. Furthermore, as graph sizes grow, traditional algorithms struggle to handle the increased complexity. To address this challenge, distributed algorithms are necessary for effectively managing larger data sizes and complexity. The proposed method is evaluated on diverse benchmark networks, encompassing both weighted and unweighted networks. The results demonstrate the superior scalability and performance of the proposed approach compared to the existing state-of-the-art methods.
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Liu, Jing, Huibin Lu, Xiuru Zhang, Xiaoli Li, Lei Wang, Shimin Yin, and Dong Cui. "Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?" Entropy 25, no. 3 (February 21, 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) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.
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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 (August 18, 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. Applying the two proposed algorithms K-Means and DBSCAN, the best cluster algorithm to have been used for clustering analysis is K-Means which is based on the highest silhouette score of 0.43, while the silhouette score of DBSCAN is 0.39 respectively. Using the best cluster, the K-Means algorithm, found two types of clusters – cluster 1 consists of 132 students who have a high average score, and cluster 2 shows 183 students who have a low average score in both final and mid-term exams respectively. From the analysis results, most students still have low cognitive learning outcomes 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 (May 3, 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 been thoroughly compared based on computational time, object size, code size, data dependence (real or complex), and the amount of mathematical operations involved in the computations. Simulation results show that slide FFT is the suitable frequency domain transformation algorithm to use in implementing cyclostationary spectrum sensing in cognitive radios as compared to the other considered algorithms where it provides a significant reduction in FFT stage computation complexity reach to 17% in SRFFT , 78% in FHT and 82% in SDFT while keeping the detection probability at satisfactory level.
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Tatarchenko, Ksenia. "Thinking Algorithmically." Historical Studies in the Natural Sciences 49, no. 2 (April 1, 2019): 194–225. http://dx.doi.org/10.1525/hsns.2019.49.2.194.
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Cold War competition shaped the process of computerization in both East and West during the second half of the twentieth century. This article combines insights from Science and Technology Studies, which brought the analysis of Cold War technopolitics beyond the context of the nation-state, with approaches from Critical Algorithm Studies, to question the algorithm’s role in the global “computer revolution.” It traces the algorithm’s trajectory across several geographical, political, and discursive spaces to argue that its mutable cultural valences made the algorithm a universalizing attribute for representing human-machine interactions across the ideological divide. It shows that discourses about the human capacity to devise algorithms, a practice central to computer programming, became a space for negotiating different versions of modern subjectivity. This article focuses on two related episodes to demonstrate how the notion of “algorithmic thinking” became explicitly associated with a range of politicized agendas, each claiming the algorithm’s power. On one hand, the coupling of “algorithm” and “thinking” was used to describe a naturalized cognitive capacity shared among the members of the international scientific community and projected backward to the medieval scholar Al-Khwarizmi. On the other hand, the universal spread of “algorithmic thinking” became the educational goal of a late Soviet computer literacy campaign under the slogan of “Programming, the Second Literacy,” a metaphor and a political vision conceived to bring about the Socialist “Information Age.”
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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 algorithm. The performances of the proposed RGA-PSO and AIA-PSO algorithms are evaluated using 13 CGO problems. Moreover, numerical results obtained using the proposed RGA-PSO and AIA-PSO algorithms are compared with those obtained using published individual GA and AIA approaches. Experimental results indicate that the proposed RGA-PSO and AIA-PSO algorithms converge to a global optimum solution to a CGO problem. Furthermore, the optimum parameter settings of the internal PSO algorithm can be obtained using the external RGA and AIA approaches. Also, the proposed RGA-PSO and AIA-PSO algorithms outperform some published individual GA and AIA approaches. Therefore, the proposed RGA-PSO and AIA-PSO algorithms are highly promising stochastic global optimization methods for solving CGO problems.
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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 (August 5, 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 function were used to evaluate the applicability of MPP model which is trained by RCGA, Regularization Genetic Algorithm (RGA) and Weight and Dropout RGA optimization algorithms. The numerical results demonstrate that the proposed Weight and Dropout RGA is better than the other two algorithms, and realizing the requirement for prediction of mine pressure in the coal mine production.
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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 (September 25, 2018): 198. http://dx.doi.org/10.14419/ijet.v7i4.6.20462.
Full textAbstract:
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 utilize the Primary user channel in maximum time to achieve optimal goal. The results show that proposed algorithm-based channel selection shows the better performance than existing algorithms in factors such as throughput, number of handoffs and collision rate.