Thematische Bibliographien / Intelligence – genetics

Auswahl der wissenschaftlichen Literatur zum Thema „Intelligence – genetics“

Autor: Grafiati

Veröffentlicht am 25. Juli 2024

Zuletzt aktualisiert am 26. Juli 2024

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Zeitschriftenartikel zum Thema "Intelligence – genetics"

Farzan, Raed. „Artificial intelligence in Immuno-genetics“. Bioinformation 20, Nr. 1 (31.01.2024): 29–35. http://dx.doi.org/10.6026/973206300200029.

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Rapid advancements in the field of artificial intelligence (AI) have opened up unprecedented opportunities to revolutionize various scientific domains, including immunology and genetics. Therefore, it is of interest to explore the emerging applications of AI in immunology and genetics, with the objective of enhancing our understanding of the dynamic intricacies of the immune system, disease etiology, and genetic variations. Hence, the use of AI methodologies in immunological and genetic datasets, thereby facilitating the development of innovative approaches in the realms of diagnosis, treatment, and personalized medicine is reviewed.

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BARNETT, S. A. „GENETICS OF INTELLIGENCE“. Developmental Medicine & Child Neurology 5, Nr. 5 (12.11.2008): 522. http://dx.doi.org/10.1111/j.1469-8749.1963.tb10712.x.

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Deary, Ian J., Frank M. Spinath und Timothy C. Bates. „Genetics of intelligence“. European Journal of Human Genetics 14, Nr. 6 (24.05.2006): 690–700. http://dx.doi.org/10.1038/sj.ejhg.5201588.

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Plomin, Robert, und Jenae Neiderhiser. „Quantitative Genetics, Molecular Genetics, and Intelligence“. Intelligence 15, Nr. 4 (Oktober 1991): 369–87. http://dx.doi.org/10.1016/0160-2896(91)90001-t.

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Sternberg, Robert J., Elena L. Grigorenko und Kenneth K. Kidd. „Intelligence, race, and genetics.“ American Psychologist 60, Nr. 1 (2005): 46–59. http://dx.doi.org/10.1037/0003-066x.60.1.46.

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VASYLKIVSKYI, Mikola, Ganna VARGATYUK und Olga BOLDYREVA. „INTELLIGENT RADIO INTERFACE WITH THE SUPPORT OF ARTIFICIAL INTELLIGENCE“. Herald of Khmelnytskyi National University. Technical sciences 217, Nr. 1 (23.02.2023): 26–32. http://dx.doi.org/10.31891/2307-5732-2023-317-1-26-32.

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The peculiarities of the implementation of the 6G intelligent radio interface infrastructure, which will use an individual configuration for each individual subscriber application and flexible services with lower overhead costs, have been studied. A personalized infrastructure consisting of an AI-enabled intelligent physical layer, an intelligent MAC controller, and an intelligent protocol is considered, followed by a potentially novel AI-based end-to-end (E2E) device. The intelligent controller is investigated, in particular the intelligent functions at the MAC level, which may become key components of the intelligent controller in the future. The joint optimization of these components, which will provide better system performance, is considered. It was determined that instead of using a complex mathematical method of optimization, it is possible to use machine learning, which has less complexity and can adapt to network conditions. A 6G radio interface design based on a combination of model-driven and data-driven artificial intelligence is investigated and is expected to provide customized radio interface optimization from pre-configuration to self-learning. The specifics of configuring the network scheme and transmission parameters at the level of subscriber equipment and services using a personalized radio interface to maximize the individual user experience without compromising the throughput of the system as a whole are determined. Artificial intelligence is considered, which will be a built-in function of the radio interface that creates an intelligent physical layer and is responsible for MAC access control, network management optimization (such as load balancing and power saving), replacing some non-linear or non-convex algorithms in receiver modules or compensation of shortcomings in non-linear models. Built-in intelligence has been studied, which will make the 6G physical layer more advanced and efficient, facilitate the optimization of structural elements of the physical layer and procedural design, including the possible change of the receiver architecture, will help implement new detection and positioning capabilities, which, in turn, will significantly affect the design of radio interface components. The requirements for the 6G network are defined, which provide for the creation of a single network with scanning and communication functions, which must be integrated into a single structure at the stage of radio interface design. The specifics of carefully designing a communication and scanning network that will offer full scanning capabilities and more fully meet all key performance indicators in the communications industry are explored.

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Krittanawong, Chayakrit, Kipp W. Johnson, Edward Choi, Scott Kaplin, Eric Venner, Mullai Murugan, Zhen Wang et al. „Artificial Intelligence and Cardiovascular Genetics“. Life 12, Nr. 2 (14.02.2022): 279. http://dx.doi.org/10.3390/life12020279.

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Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.

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Plomin, Robert, und Frank M. Spinath. „Intelligence: Genetics, Genes, and Genomics.“ Journal of Personality and Social Psychology 86, Nr. 1 (Januar 2004): 112–29. http://dx.doi.org/10.1037/0022-3514.86.1.112.

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Plomin, Robert, und Sophie von Stumm. „The new genetics of intelligence“. Nature Reviews Genetics 19, Nr. 3 (08.01.2018): 148–59. http://dx.doi.org/10.1038/nrg.2017.104.

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Plomin, Robert, und Stephen A. Petrill. „Genetics and intelligence: What's new?“ Intelligence 24, Nr. 1 (Januar 1997): 53–77. http://dx.doi.org/10.1016/s0160-2896(97)90013-1.

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Dissertationen zum Thema "Intelligence – genetics"

Avgan, Nesli. „The genetic basis of human cognition: Intelligence, learning and memory“. Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/122903/1/Nesli_Avgan_Thesis.pdf.

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The complex and highly polygenic traits of intelligence, learning and memory are fundamental functions of neurocognition. Despite improvements in neurogenetics, our knowledge on the genetic architecture of these functions remains poorly understood. This research investigated the contribution of genetic variation to cognitive performance variability in relation to intelligence, learning and memory using a well-defined, healthy and unrelated cohort via a gene-centric and a genome-wide approach. Results of this study validated several previously identified genes, provided new knowledge on various cognitive traits, and identified several novel regions for future studies that may have consequences for managing disorders involving cognitive impairments.

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Prabhakar, Nachiketh. „Deep Learning To Improve Hi-C Data“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1562582435975614.

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Ong, Vy Quoc. „Subgroup Analysis of Patients with Hepatocellular Carcinoma| A Quest for Statistical Algorithms for Tissue Classification Problem“. Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10840510.

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Hepatocellular carcinoma (HCC) is the most common type of liver cancer. This type of cancer has been observed with prevalence as the third leading cause of death from cancer worldwide and as the ninth leading cause of cancerous mortality in the United States. People with hepatitis B or C are considered to be at high risk for this kind of cancer. Remarkably poor prognostic HCC patients with low survival rates commonly possess intra-hepatic metastases that are either tumor thrombi in the portal vein or intra-hepatic spread. It is uncommon for them to die of extra-hepatic metastases. Therefore, identifying metastatic HCC has become vital and clinically challenging in efforts of timely therapeutic intervention to improve the survival rate of patients who suffer from this disease.

To date, studies that look for an accurate molecular profiling model have been developed to identify these patients in advance for a better treatment or intervention. An approach has been to focus on identifying individual candidate genes characterizing metastatic HCC. Another direction has been to find a global genome scale solution by using microarray technology to obtain a gene expression for this carcinoma. Among research following the latter was that developed by Qing-Hai Ye et al., Nature Medicine, Volume 9, Number 4, April 2003. They applied cDNA microarray-based gene expression profiling with compound co-variate predictors for primary HCC, metastatic HCC, and metastasis-free HCC binary classification tasks on a dataset of 87 observations and 9984 features taken from 40 hepatitis B-positive Chinese patients. Notably, a robust 153-gene model was generated to successfully classify tumor-thrombi-in-the-portal-vein samples with metastasis-free samples. However, they admitted distinguishing primary tumor samples from their matched-metastatic lesions were still a challenge. In this molecule signature, a gene named osteopontin, a secreted phosphoprotein, served as the lead gene in diagnosing HCC metastasis.

The analysis is based on the metastatic status of HCC, which is clinically predetermined. However, the validation of the class definition is needed to investigate if the data are sufficient to translate the three classes predefined. We will use some statistical clustering algorithms to validate the class defined. After that, we will conduct variable selection to find markers that are differentially expressed genes among clinical groups validated from this research. Next, using the compound markers found by this research, we will develop a statistical model to predict a new patient’s HCC type for intervention. The generalized performance of the prediction model will be evaluated via a cross-validation test. This study aims to build a highly accurate model that renders a better classification of the fore-mentioned clinical groups of HCC and thus enhances the rate of predicting metastatic patients.

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Beiko, Robert G. „Evolutionary computing strategies for the detection of conserved patterns in genomic DNA“. Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/29009.

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The detection of regulatory sequences in DNA is a challenging problem, especially when considered in the context of whole genomes. The degree of sequence conservation of regulatory protein binding sites is often weak, and the sites are obscured by surrounding intergenic sequence. Since structural interactions are vital for protein-DNA interactions, structural representations of regulatory sites can yield a more accurate model and a better understanding of within-site variability. However, the use of multiple alternative representations of DNA introduces a requirement for novel algorithms that can create and test different combinations of DNA features. The Genetic Algorithm Neural Network (GANN) was designed to identify combinations of patterns that can be used to distinguish between different classes of training sequence. GANN trains a set of artificial neural networks to classify sets of sequence using either backpropagation or a genetic algorithm, and uses an 'outer genetic algorithm' to choose the best inputs from a pool of DNA features that can include sequence, structure, and weight matrix representations. When trained with a subset of upstream sequences from a whole genome, GANN was able to detect patterns such as the Shine-Dalgarno sequence in Escherichia coli K12, and sequences consistent with archaeal promoters in the archaeon Sulfolobus solfataricus P2. The Motif Genetic Algorithm (MGA) constructs motif representations by concatenating minimal units of DNA sequence and structure. This algorithm was used to model conserved patterns in DNA, including the binding sites for E. coli cyclic AMP activated protein (CAP), integration host factor (IHF), and two different promoter types recognized by alternative bacterial sigma factors. The CAP models were used to detect other putative binding sites in upstream regions of the E. coli K12 genome, while attempts to train an accurate model of IHF binding sites revealed an important role for structural representations in motif modeling.

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Takane, Marina. „Inference of gene regulatory networks from large scale gene expression data“. Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80883.

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With the advent of the age of genomics, an increasing number of genes have been identified and their functions documented. However, not as much is known of specific regulatory relations among genes (e.g. gene A up-regulates gene B). At the same time, there is an increasing number of large-scale gene expression datasets, in which the mRNA transcript levels of tens of thousands of genes are measured at a number of time points, or under a number of different conditions. A number of studies have proposed to find gene regulatory networks from such datasets. Our method is a modification of the continuous-time neural network method of Wahde & Hertz [25, 26]. The genetic algorithm used to update weights was replaced with Levenberg-Marquardt optimization. We tested our method on artificial data as well as Spellman's yeast cell cycle data [22]. Results indicated that this method was able to detect salient regulatory relations between genes.

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Смірнов, Олег Ювеналійович, Олег Ювенальевич Смирнов und Oleh Yuvenaliiovych Smirnov. „Генетична складова інтелекту“. Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/14286.

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Yeo, Ronald A., Sephira G. Ryman, den Heuvel Martijn P. van, Reus Marcel A. de, Rex E. Jung, Jessica Pommy, Andrew R. Mayer et al. „Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics“. Cambridge University Press, 2016. https://tud.qucosa.de/id/qucosa%3A70691.

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Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N = 116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity—connections among high degree “rich club” nodes, “feeder” connections to these rich club nodes, and “local” connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N = 101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA.

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Penke, Lars. „Neuroscientific approaches to general intelligence and cognitive ageing“. Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2011. http://dx.doi.org/10.18452/13979.

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Nach einem ausführlichem Überblick über den Kenntnisstand der Genetik und Neurowissenschaft von allgemeiner Intelligenz und einer methodischen Anmerkung zur Notwendigkeit der Berücksichtigung latenter Variablen in den kognitiven Neurowissenschaften am Beispiel einer Reanalyse publizierter Ergebnisse wir das am besten etablierte Gehirnkorrelat der Intelligenz, die Gehirngröße, aus evolutionsgenetischer Perspektive neu betrachtet. Schätzungen des Koeffizienten additiv-genetischer deuten an, dass es keine rezente direktionale Selektion auf Gehirngröße gegeben hat, was ihre Validität als Proxy für Intelligenz in evolutionären Studien in Frage stellt. Stattdessen deuten Korrelationen der Gesichtssymmetrie älterer Männer mit Intelligenz und Informationsverarbeitungsgeschwindigkeit an, dass organismusweite Entwicklungsstabilität eine wichtige Grundlage von unterschieden in kognitiven Fähigkeiten sein könnte. Im zweiten Teil dieser Arbeit geht es vornehmlich um die Alterung kognitiver Fähigkeiten, beginnend mit einem allgemeinen Überblick. Daten einer Stichprobe von über 130 Individuen zeigen dann, dass die Integrität verschiedener Nervenbahnen im Gehirn hoch korreliert, was die Extraktion eines Generalfaktors der Traktintegrität erlaubt, der mit Informationsverarbeitungsgeschwindigkeit korreliert. Der einzige Trakt mit schwacher Ladung auf diesem Generalfaktor ist das Splenium des Corpus Callosum, welches mit Veränderungen der Intelligenz über 6 Jahrzehnte korreliert und den Effekt des Bet2 adrenergischem Rezeptorgens (ADRB2) auf diese Veränderung mediiert, möglicherweise durch Effekte auf neuronale Komopensationsprozesse. Schließlich wird auf Basis neuer Analyseverfahren für Magnetresonanzdaten gezeigt, dass vermehrte Eiseneinlagerungen im Gehirn, vermutlich Marker für zerebrale Mikroblutungen, sowohl mit lebenslang stabilen Intelligenzunterschieden als auch mit der altersbedingten Veränderung kognitiver Fähigkeiten assoziiert sind.
After an extensive review of what is known about the genetics and neuroscience of general intelligence and a methodological note emphasising the necessity to consider latent variables in cognitive neuroscience studies, exemplified by a re-analysis of published results, the most well-established brain correlate of intelligence, brain size, is revisited from an evolutionary genetic perspective. Estimates of the coefficient of additive genetic variation in brain size suggest that there was no recent directional selection on brain size, questioning its validity as a proxy for intelligence in evolutionary analyses. Instead, correlations of facial fluctuating asymmetry with intelligence and information processing speed in old men suggest that organism-wide developmental stability might be an important cause of individual differences in cognitive ability. The second half of the thesis focuses on cognitive ageing, beginning with a general review. In a sample of over 130 subjects it has then been found that the integrity of different white matter tracts in the brain is highly correlated, allowing for the extraction of a general factor of white matter tract integrity, which is correlated with information processing speed. The only tract not loading highly on this general factor is the splenium of the corpus callosum, which is correlated with changes in intelligence over 6 decades and mediates the effect of the beta2 adrenergic receptor gene (ADRB2) on cognitive ageing, possibly due to its involvement in neuronal compensation processes. Finally, using a novel analytic method for magnetic resonance data, it is shown that more iron depositions in the brain, presumably markers of a history of cerebral microbleeds, are associated with both lifelong-stable intelligence differences and age-related decline in cognitive functioning.

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Horstman, Benjamin Philip. „Detecting Epistasis Effect in Genome-Wide Association Studies Based on Permutation Tests and Ensemble Approaches“. Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1270577390.

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Thesis (Master of Sciences (Engineering))--Case Western Reserve University, 2010
Department of EECS - Computer and Information Sciences Title from PDF (viewed on 2010-05-25) Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

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Kravchenko, Evgenija. „Association between cognitive measures, global brain surface area, genetics, and screen-time in young adolescents : Estimation of causal inference with machine learning“. Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290033.

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Screen media activity such as watching TV and videos, playing video games, and using social media has become a popular leisure activity for children and adolescents. The effect of screen time has been a highly debated topic; however, there is still very little known about it. Using a dataset from the Adolescent Brain Cognitive Development longitudinal study 4 217 young adolescents, that met the requirements, could be retrieved for this thesis project after processing of the data. This thesis project investigated causal order between genetic effect (cognitive performance Polygenic scores (PGSs)), screen time activity, brain morphology (structural Magnetic Resonance Imaging (sMRI) for surface area and cortical thickness), lack of perseverance, and cognitive performance (crystallized IQ) with a machine learning algorithm DirectLiNGAM. A clear correlation between screen media activity and PGS was found for all types of screen time activities but only video games and social media correlated to the global surface area. Furthermore, TV and video seem to affect lack of perseverance, and lack of perseverance, in turn, affects time spent on video games. These findings imply that different types of social media are not as alike as we thought and can affect adolescents differently. Taken together, these findings support previous research on screen media activity's effect on lack of perseverance, brain morphology, and cognitive performance, and propose new causal inference between genetics and screen time. Lastly, the algorithm used in this thesis project inferred reasonable causal orders and can be seen as a very good complement to today's causal modeling.
Skärmaktivitet som att titta på TV och video, spela videospel och använda sociala medier har blivit en populär fritidsaktivitet för barn och ungdomar. Effekten av skärmtid har varit ett mycket debatterat ämne; det finns dock fortfarande mycket lite kunskap om det. Med hjälp av datasetet från Adolescent Brain Cognitive Development långtidsstudien kunde 4 217 ungdomar, som uppfyllde specifika krav, väljas ut för detta avhandlingsprojekt efter bearbetning av datan. Detta avhandlingsprojekt undersökte kausal ordning mellan genetisk effekt (Polygenic scores (PGS) för kognitiv prestation), skärmtidsaktivitet, hjärnmorfologi (strukturell Magnet Resonans Imaging (sMRI) för hjärnans ytarea och hjärnbarks tjocklek), brist på ihärdighet och kognitiv förmåga (kristalliserad IQ) med en maskininlärningsalgoritm DirectLiNGAM. Tydlig korrelation mellan skärmaktivitet och PGS hittades för alla typer av skärmaktiviteter men endast videospel och sociala medier korrelerade till den globala ytarean. Dessutom verkar TV och video påverka brist på ihärdighet och brist på ihärdighet i sin tur påverkar hur mycket tid som spenderas på videospel. Dessa resultat antyder att olika typer av sociala medier inte är så lika som vi trodde och kan påverka ungdomar olika. Sammanlagt stöder dessa upptäckter tidigare forskning om skärmtidseffekt på brist på ihärdighet, hjärnmorfologi och kognitiv förmåga och föreslår en ny kausal inferens mellan genetik och skärmtid. Slutligen ledde algoritmen som användes i detta avhandlingsprojekt fram till rimliga kausala ordningar och kan ses som ett mycket bra komplement till dagens kausala modellering.

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Bücher zum Thema "Intelligence – genetics"

Weiss, Volkmar. Psychogenetik der Intelligenz = Psychogenetics of intelligence. Dortmund: Verlag Modernes Lernen, 1986.

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1959-, Roleff Tamara L., Hrsg. Genetics and intelligence. San Diego, CA: Greenhaven Press, 1996.

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Kate, Webb, Goode Jamie, Bock Gregory und Novartis Foundation, Hrsg. The nature of intelligence. Chichester, West Sussex, England: John Wiley & Sons, 2000.

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Jensen, Arthur Robert. Intelligence, race, and genetics: Conversations with Arthur R. Jensen. Boulder, Colo: Westview Press, 2002.

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Alan, McGregor, Hrsg. Evolution, creative intelligence, and intergroup competition. Washington, D.C: Cliveden Press, 1986.

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Pearson, Roger. Race, intelligence, and bias in academe. Washington, D.C: Scott-Townsend Publishers, 1991.

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R, Koza John, Hrsg. Genetic programming IV: Routine human-competitive machine intelligence. Norwell, Mass: Kluwer Academic Publishers, 2003.

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Workshop on Foundations of Genetic Algorithms (9th 2007 Mexico City, Mexico). Foundations of genetic algorithms: 9th international workshop, FOGA 2007, Mexico City, Mexico, January 8-11, 2007 : revised selected papers. Berlin: Springer, 2007.

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S, Gazzaniga Michael. Nature's mind: The biological roots of thinking, emotions, sexuality, language, and intelligence. New York: BasicBooks, 1992.

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Storfer, Miles D. Intelligence and giftedness: The contributions of heredity and early environment. San Francisco: Jossey-Bass Publishers, 1990.

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Buchteile zum Thema "Intelligence – genetics"

Brody, Nathan. „Intelligence and the behavioral genetics of personality.“ In Nature, nurture & psychology., 161–78. Washington: American Psychological Association, 1993. http://dx.doi.org/10.1037/10131-007.

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McGuffin, Peter. „The Quantitative and Molecular Genetics of Human Intelligence“. In The Nature of Intelligence, 243–59. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/0470870850.ch15.

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Kreinovich, Vladik, und Max Shpak. „Decomposable Aggregability in Population Genetics and Evolutionary Computations: Algorithms and Computational Complexity“. In Computational Intelligence in Medical Informatics, 69–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75767-2_4.

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Gialluisi, Alessandro, Benedetta Izzi, Giovanni de Gaetano und Licia Iacoviello. „Epidemiology, Genetics and Epigenetics of Biological Aging: One or More Aging Systems?“ In Artificial Intelligence for Healthy Longevity, 115–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-35176-1_6.

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Yang, Qifan, Sophia I. Thomopoulos, Linda Ding, Wesley Surento, Paul M. Thompson und Neda Jahanshad. „Support Vector Based Autoregressive Mixed Models of Longitudinal Brain Changes and Corresponding Genetics in Alzheimer’s Disease“. In Predictive Intelligence in Medicine, 160–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32281-6_17.

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Crow, T. J. „A Single Locus for Psychosis and Intelligence in the Exchange Region of the Sex Chromosomes?“ In Ethical Issues of Molecular Genetics in Psychiatry, 12–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76429-5_2.

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Laazaar, Kaoutar, und Noureddine Boutammachte. „Modelling and Optimization of Stirling Engine for Waste Heat Recovery from Cement Plant Based on Adiabatic Model and Genetics Algorithms“. In Artificial Intelligence and Industrial Applications, 287–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53970-2_27.

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Chiang, Ming-Chang, Marina Barysheva, Agatha D. Lee, Sarah Madsen, Andrea D. Klunder, Arthur W. Toga, Katie L. McMahon et al. „Brain Fiber Architecture, Genetics, and Intelligence: A High Angular Resolution Diffusion Imaging (HARDI) Study“. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 1060–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85988-8_126.

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Hazotte, Cyril, Hélène Mayer, Younes Djaghloul, Thibaud Latour, Philipp Sonnleitner, Martin Brunner, Ulrich Keller, Eric Francois und Romain Martin. „The Genetics Lab: An Innovative Tool for Assessment of Intelligence by Mean of Complex Problem Solving“. In Informatics Engineering and Information Science, 296–310. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25483-3_24.

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Hedjazi, Seyyed Mahdi, und Samane Sadat Marjani. „Pruned Genetic Algorithm“. In Artificial Intelligence and Computational Intelligence, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16527-6_25.

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Konferenzberichte zum Thema "Intelligence – genetics"

Matthews, D., T. Pabiou, R. D. Evans, C. Beder und A. Daly. „129. Predicting carcass cut yields in cattle from digital images using artificial intelligence“. In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_129.

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Budiarto, Arif, und Bens Pardamean. „Explainable Supervised Method for Genetics Ancestry Estimation“. In 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI). IEEE, 2021. http://dx.doi.org/10.1109/iccsai53272.2021.9609748.

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Anguera, Jaume, Aurora Andujar, Jeevani Jayasinghe, Disala Uduwawala, Muhammad K. Khattak und Sungtek Kahng. „Nature-Inspired High-Directivity Microstrip Antennas: Fractals and Genetics“. In 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2016. http://dx.doi.org/10.1109/cicn.2016.46.

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Chandra, Mukesh, Pallavi Somvanshi, B. N. Mishra und Amod Tiwari. „Genetics of Yellow Mosaic Virus Resistance in Mung bean“. In 2010 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2010. http://dx.doi.org/10.1109/iccic.2010.5705760.

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Li, Chang, Mingyong Hu und Xing Han. „Reliability Optimum Design for Bevel Gear Driven Systems Based on Genetics Algorithm“. In 2016 International Conference on Artificial Intelligence and Engineering Applications. Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/aiea-16.2016.66.

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Bie, Lin, Rui Zhang und Zhiteng Wang. „Quantum genetics clustering algorithm based on high-Dimensional and multi-chain coding scheme“. In 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI ). IEEE, 2018. http://dx.doi.org/10.1109/icaci.2018.8377478.

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Ahmed, Zahid, Subbulakshmi Ganesan und Rashmi Tirkey. „Understanding the Potential of Grouping Algorithms for Genetics Clustering“. In 2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA). IEEE, 2024. http://dx.doi.org/10.1109/aimla59606.2024.10531608.

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Chen, Minmin, Yixin Chen, Michael R. Brent und Aaron E. Tenney. „Gradient-Based Feature Selection for Conditional Random Fields and its Applications in Computational Genetics“. In 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.82.

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Aswinseshadri, K., und V. Thulasi Bai. „Feature Selection in Brain Computer Interface Using Genetics Method“. In 2015 IEEE International Conferences on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; and Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM). IEEE, 2015. http://dx.doi.org/10.1109/cit/iucc/dasc/picom.2015.39.

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Nojima, Yusuke, Kazuhiro Watanabe und Hisao Ishibuchi. „Variants of heuristic rule generation from multiple patterns in Michigan-style fuzzy genetics-based machine learning“. In 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE, 2015. http://dx.doi.org/10.1109/taai.2015.7407091.

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Berichte der Organisationen zum Thema "Intelligence – genetics"

Willson. L51756 State of the Art Intelligent Control for Large Engines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), September 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other tasks, however, computers perform very poorly at tasks that humans find to be trivial. A discussion of the differences in the way humans and computer process information is crucial to an understanding of the field of artificial intelligence. In this project, several artificial intelligence/ intelligent control systems were examined: heuristic search techniques, adaptive control, expert systems, fuzzy logic, neural networks, and genetic algorithms. Of these, neural networks showed the most potential for use on large bore engines because of their ability to recognize patterns in incomplete, noisy data. Two sets of experimental tests were conducted to test the predictive capabilities of neural networks. The first involved predicting the ignition timing from combustion pressure histories; the best networks responded within a specified tolerance level 90% to 98.8% of the time. In the second experiment, neural networks were used to predict NOx, A/F ratio, and fuel consumption. NOx prediction accuracy was 91.4%, A/F ratio accuracy was 82.9%, and fuel consumption accuracy was 52.9%. This report documents the assessment of the state of the art of artificial intelligence for application to the monitoring and control of large-bore natural gas engines.

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Tarozzi, Martina Elena. Next Generation Sequencing Technologies, Bioinformatics and Artificial Intelligence: A Shared Time-line. MZB Standard Enterprise, Juli 2024. http://dx.doi.org/10.57098/scirevs.biology.3.2.2.

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This review provides a comprehensive overview of the fast-paced and intertwined evolution of three pivotal fields: next-generation sequencing (NGS) technologies, bioinformatics, and artificial intelligence (AI). The paper begins by tracing the development of sequencing technologies and highlights how advancements in genetic sequencing have led to an explosion of biological data, necessitating the rise of bioinformatics for data management and analysis. The review next covers the primary steps and methods used in bioinformatic analysis and concludes by reporting some of the technical and biological challenges in which AI methods have been applied.

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PERFORMANCE OPTIMIZATION OF A STEEL-UHPC COMPOSITE ORTHOTROPIC BRIDGE WITH INTELLIGENT ALGORITHM. The Hong Kong Institute of Steel Construction, August 2022. http://dx.doi.org/10.18057/icass2020.p.160.

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To address the problems of pavement damage and fatigue cracking of orthotropic steel deck (OSD) in bridges, an innovative composite bridge deck composed of OSD with open ribs and ultra-high performance concrete (UHPC) layer was proposed. Firstly, the stress responses of fatigue-prone details in the composite bridge deck were investigated by refined two-scale finite element analysis. The results show that the rib-to-deck joint can achieve an infinite fatigue life, while the floorbeam detail of rib-tofloorbeam joint indicates finite fatigue life. Then, response surface models of stress ranges of fatigue details and structure weight were derived via both the central composite design and response surface method. Finally, to improve the fatigue performance for achieving an infinite fatigue life under relatively low structure weight, the multi-objective optimization was executed by an Improved Non-dominated Sorting Genetic Algorithm (NSGA-II). The obtained Pareto front shows that there is a strong competition between the stress range of fatigue-prone detail and structure weight.

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