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Thematische Bibliographien / Hot spot prediction

Auswahl der wissenschaftlichen Literatur zum Thema „Hot spot prediction“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 1. Februar 2022

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Zeitschriftenartikel
Dissertationen
Bücher
Buchteile
Konferenzberichte

Zeitschriftenartikel zum Thema "Hot spot prediction":

1

Zhang, Sijia, Le Zhao, Chun-Hou Zheng und Junfeng Xia. „A feature-based approach to predict hot spots in protein–DNA binding interfaces“. Briefings in Bioinformatics 21, Nr. 3 (08.04.2019): 1038–46. http://dx.doi.org/10.1093/bib/bbz037.

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Abstract DNA-binding hot spot residues of proteins are dominant and fundamental interface residues that contribute most of the binding free energy of protein–DNA interfaces. As experimental methods for identifying hot spots are expensive and time consuming, computational approaches are urgently required in predicting hot spots on a large scale. In this work, we systematically assessed a wide variety of 114 features from a combination of the protein sequence, structure, network and solvent accessible information and their combinations along with various feature selection strategies for hot spot prediction. We then trained and compared four commonly used machine learning models, namely, support vector machine (SVM), random forest, Naïve Bayes and k-nearest neighbor, for the identification of hot spots using 10-fold cross-validation and the independent test set. Our results show that (1) features based on the solvent accessible surface area have significant effect on hot spot prediction; (2) different but complementary features generally enhance the prediction performance; and (3) SVM outperforms other machine learning methods on both training and independent test sets. In an effort to improve predictive performance, we developed a feature-based method, namely, PrPDH (Prediction of Protein–DNA binding Hot spots), for the prediction of hot spots in protein–DNA binding interfaces using SVM based on the selected 10 optimal features. Comparative results on benchmark data sets indicate that our predictor is able to achieve generally better performance in predicting hot spots compared to the state-of-the-art predictors. A user-friendly web server for PrPDH is well established and is freely available at http://bioinfo.ahu.edu.cn:8080/PrPDH.

2

Kenneth Morrow, John, und Shuxing Zhang. „Computational Prediction of Protein Hot Spot Residues“. Current Drug Metabolism 18, Nr. 9 (01.03.2012): 1255–65. http://dx.doi.org/10.2174/138920012799362909.

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3

Kenneth Morrow, John, und Shuxing Zhang. „Computational Prediction of Protein Hot Spot Residues“. Current Pharmaceutical Design 18, Nr. 9 (01.03.2012): 1255–65. http://dx.doi.org/10.2174/138161212799436412.

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4

Nair B.J, Bipin, und Lijo Joy. „A hybrid approach for hot spot prediction and deep representation of hematological protein – drug interactions“. International Journal of Engineering & Technology 7, Nr. 1.9 (01.03.2018): 145. http://dx.doi.org/10.14419/ijet.v7i1.9.9752.

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In our research work we will collect the data of drugs as well as protein regarding hematic diseases, then applying feature extraction as well as classification, predict hot spot and non-hot spot then we are predicting the hot region using prediction algorithm. Parallelly from the hematological drug we are extracting the feature using molecular finger print then classifying using a classifier and applying deep learning concept to reduce the dimensionality then finally using machine learning algorithm predicting which drug will interact with the help of a hybrid approach.

5

Tuncbag, N., O. Keskin und A. Gursoy. „HotPoint: hot spot prediction server for protein interfaces“. Nucleic Acids Research 38, Web Server (05.05.2010): W402—W406. http://dx.doi.org/10.1093/nar/gkq323.

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6

Liu, Siyu, Chuyao Liu und Lei Deng. „Machine Learning Approaches for Protein–Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment“. Molecules 23, Nr. 10 (04.10.2018): 2535. http://dx.doi.org/10.3390/molecules23102535.

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Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein–protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein–protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

7

Roll, Uri, Lewi Stone und Shai Meiri. „Hot-Spot Facts and Artifacts-Questioning Israel's Great Biodiversity“. Israel Journal of Ecology and Evolution 55, Nr. 3 (06.05.2009): 263–79. http://dx.doi.org/10.1560/ijee.55.3.263.

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Israel's biological diversity has been praised as being particularly rich in relation to its size; however this assumption was never tested when taking into account the empirical form of the species-area relationship. Here we compared the species richness of different countries to see if the Israeli diversity is exceptionally rich when area is accurately accounted for. We compared richness of amphibians, birds, mammals, reptiles, flowering plants, conifers and cycads, and ferns in all the world's countries. We further tested the effects of mean latitude, altitude span, and insularity on species richness both for all world countries and just for Mediterranean countries. For all taxa and in all tests, Israel lies within the prediction intervals of the models. Out of 42 tests, Israel's residuals lie in the upper decile of positive residuals once: for reptiles, when compared to all world countries, taking all predicting factors into account. Using only countries larger than 1000 km2, Israel was placed as top residual when compared to other Mediterranean countries for mammals and reptiles. We therefore conclude that Israel's species richness does not significantly exceed the expected values for a country its size. This is true when comparing it to either world or just Mediterranean countries. Adding more predicting factors does not change this fact.

8

Wang, Ao, und Yimin Xuan. „Multiscale prediction of localized hot-spot phenomena in solar cells“. Renewable Energy 146 (Februar 2020): 1292–300. http://dx.doi.org/10.1016/j.renene.2019.07.073.

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9

Grosdidier, Solene, und Juan Fernandez-Recio. „Protein-protein Docking and Hot-spot Prediction for Drug Discovery“. Current Pharmaceutical Design 18, Nr. 30 (23.08.2012): 4607–18. http://dx.doi.org/10.2174/138161212802651599.

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10

Zhang, Ming, und Wei Chen. „Hot Spot Data Prediction Model Based on Wavelet Neural Network“. Mathematical Problems in Engineering 2018 (30.10.2018): 1–10. http://dx.doi.org/10.1155/2018/3719564.

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The novel hybrid multilevel storage system will be popular with SSD being integrated into traditional storage systems. To improve the performance of data migration between solid-state hard disk and hard disk according to the characteristics of each storage device, identifying the hot data block is significant issue. The hot data block prediction model based on wavelet neural network is built and trained by using historical data. This prediction model can overcome the cumulative effect of traditional statistical methods and has strong sensitivity to I/O loads with random variations. The experimental results show that the proposed model has better accuracy and faster learning speed than BP neural network model. In addition, it has less dependence on sample data and has better generalization ability and robustness. This model can be applied to the data migration of distributed hybrid storage systems to improve performance.

Mehr Quellen

Dissertationen zum Thema "Hot spot prediction":

1

Karaca, Haldun. „Prediction Of Hot-spot And Top-oil Temperatures Of Power Transformers According To Ieee Standards C57.110-1998 And C57.91-1995“. Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609140/index.pdf.

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In this thesis, the effects of Harmonics on the Top Oil and Hot Spot Temperatures of Power Transformers used in Turkish Electricity Transmission System have been investigated. Due to the solid state equipment, the harmonic levels increase. This effect raises the losses and temperatures in the transformer windings. None of the power transformers currently used in Turkey has measuring equipment suitable for measuring the Hot-Spot temperatures. In this study, a computer program is written in LABVIEW which measures the harmonics and calculates the temperatures in accordance with the methods recommended in IEEE Standards C57.110-1998 and C57.91-1995. Also for sample transformers the work has been verified by measuring the Top-Oil temperatures of the transformers and then comparing with the calculated results.

2

Pradhan, Manoj Kumar. „Conformal Thermal Models for Optimal Loading and Elapsed Life Estimation of Power Transformers“. Thesis, Indian Institute of Science, 2004. http://hdl.handle.net/2005/97.

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Power and Generator Transformers are important and expensive elements of a power system. Inadvertent failure of Power Transformers would cause long interruption in power supply with consequent loss of reliability and revenue to the supply utilities. The mineral oil impregnated paper, OIP, is an insulation of choice in large power transformers in view of its excellent dielectric and other properties, besides being relatively inexpensive. During the normal working regime of the transformer, the insulation thereof is subjected to various stresses, the more important among them are, electrical, thermal, mechanical and chemical. Each of these stresses, appearing singly, or in combination, would lead to a time variant deterioration in the properties of insulation, called Ageing. This normal and inevitable process of degradation in the several essential properties of the insulation is irreversible, is a non-Markov physico-chemical reaction kinetic process. The speed or the rapidity of insulation deterioration is a very strong function of the magnitude of the stresses and the duration over which they acted. This is further compounded, if the stresses are in synergy. During the processes of ageing, some, or all the vital properties undergo subtle changes, more often, not in step with the duration of time over which the damage has been accumulated. Often, these changes are non monotonic, thus presenting a random or a chaotic picture and understanding the processes leading to eventual failure becomes difficult. But, there is some order in this chaos, in that, the time average of the changes over short intervals of time, seems to indicate some degree of predictability. The status of insulation at any given point in time is assessed by measuring such of those properties as are sensitive to the amount of ageing and comparing it with earlier measurements. This procedure, called the Diagnostic or nondestructive Testing, has been in vogue for some time now. Of the many parameters used as sensitive indices of the dynamics of insulation degradation, temporal changes in temperatures at different locations in the body of the transformer, more precisely, the winding hot spots (HST) and top oil temperature (TOT) are believed to give a fairly accurate indication of the rate of degradation. Further, an accurate estimation of the temperatures would enable to determine the loading limit (loadability) of power transformer. To estimate the temperature rise reasonably accurately, one has to resort to classical mathematical techniques involving formulation and solution of boundary value problem of heat conduction under carefully prescribed boundary conditions. Several complications are encountered in the development of the governing equations for the emergent heat transfer problems. The more important among them are, the inhomogeneous composition of the insulation structure and of the conductor, divergent flow patterns of the oil phase and inordinately varying thermal properties of conductor and insulation. Validation and reconfirmation of the findings of the thermal models can be made using state of the art methods, such as, Artificial Intelligence (AI) techniques, Artificial Neural Network (ANN) and Genetic Algorithm (GA). Over the years, different criteria have been prescribed for the prediction of terminal or end of life (EOL) of equipment from the standpoint of its insulation. But, thus far, no straightforward and unequivocal criterion is forth coming. Calculation of elapsed life in line with the existing methodology, given by IEEE, IEC, introduces unacceptable degrees of uncertainty. It is needless to say that, any conformal procedure proposed in the accurate prediction of EOL, has to be based on a technically feasible and economically viable consideration. A systematic study for understanding the dynamical nature of ageing in transformers in actual service is precluded for reasons very well known. Laboratory experiments on prototypes or pro-rated units fabricated based on similarity studies, are performed under controlled conditions and at accelerated stress levels to reduce experimental time. The results thereof can then be judiciously extrapolated to normal operating conditions and for full size equipment. The terms of reference of the present work are as follows; 1. Computation of TOT and HST Theoretical model based on Boundary Value Problem of Heat Conduction Application of AI Techniques 2. Experimental Investigation for estimating the Elapsed Life of transformers Based on the experimental investigation a semi-empirical expression has been developed to estimate the loss of life of power and station transformer by analyzing gas content and furfural dissolved in oil without performing off-line and destructive tests.

3

Kašpárek, Jan. „Predikce aktivních míst v proteinech“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220054.

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Knowledge of protein hot spots and the ability to successfully predict them while using only primary protein structure has been a worldwide scientific goal for several decades. This thesis describes the importance of hot spots and sums up advances achieved in this field of study so far. Besides that we introduce hot spot prediction algorithm using only a primary protein structure, based primarily on signal processing techniques. To convert protein sequence to numerical signal we use the EIIP attribute, while further processing is carried out via means of S-transform. The algorithm achieves sensitivity of more than 60 %, positive predictive value exceeds 50 % and the main advantage over competitive algorithms is its simplicity and low computational requirements.

4

Vestin, Albin, und Gustav Strandberg. „Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms“. Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.

5

Chung, Hsin-Line, und 鍾欣霖. „Building Integrated and Hybrid Prediction Systems for Computational Identification of Protein-Protein Interaction Hot Spot Residues by Using Motif Recognition, Sequential and Spatial Properties“. Thesis, 2015. http://ndltd.ncl.edu.tw/handle/833svr.

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碩士
國立中央大學
資訊工程學系
103
In a protein–protein interface, a small subset of residues contribute to the majority of the binding free energy, called the “hot spot”. Identifying and understanding hot spots and their mechanisms would have significant implications for bioinformatics and practical applications. Recently, many differences approaches have been used for predicted hot spot residues. We present an effective hot spot residues prediction system, HotSpotFinder, which contains motif recognition, sequential and spatial features and integrates feature set by two-step feature selection method. Through the two predictor of the system, called HotSpotFinder-Integrated and HotSpotFinder-Hybrid, to predict PPI hot spot residues. A total 38 optimal integrated feature and a novel system designed concept are provided and compared with other computational hot spot prediction models, HotSpotFinder offers significant performance improvement in terms of precision, MCC, F1 score and sensitivity, even in the independent dataset.

6

João, Paulo Abel de Almeida. „Modelo preditivo da criminalidade – georeferenciação ao concelho de Lisboa“. Master's thesis, 2010. http://hdl.handle.net/10362/3424.

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Dissertação apresentada como requisito parcial para a obtenção do grau de Mestre em Estatística e Gestão de Informação
Pretende-se elaborar um modelo preditivo ou processo analítico e sistemático de descoberta do conhecimento, orientado segundo os princípios da pertinência e da oportunidade, que detecte os hot spots da criminalidade, que faça uma previsão e propensão de ocorrência e ainda, que faça uma previsão da sua evolução, estagnação ou redução, sendo realizado a partir do estabelecimento de correlações entre conjuntos de dados criminais ocorridos no primeiro semestre do ano de 2007 no concelho de Lisboa. Este modelo poderá posteriormente ser aplicado a outras regiões do país.

Bücher zum Thema "Hot spot prediction":

1

Railsback, Steven F., und Bret C. Harvey. Modeling Populations of Adaptive Individuals. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691195285.001.0001.

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Ecologists now recognize that the dynamics of populations, communities, and ecosystems are strongly affected by adaptive individual behaviors. Yet until now, we have lacked effective and flexible methods for modeling such dynamics. Traditional ecological models become impractical with the inclusion of behavior, and the optimization approaches of behavioral ecology cannot be used when future conditions are unpredictable due to feedbacks from the behavior of other individuals. This book provides a comprehensive introduction to state- and prediction-based theory, or SPT, a powerful new approach to modeling trade-off behaviors in contexts such as individual-based population models where feedbacks and variability make optimization impossible. This book features a wealth of examples that range from highly simplified behavior models to complex population models in which individuals make adaptive trade-off decisions about habitat and activity selection in highly heterogeneous environments. The book explains how SPT builds on key concepts from the state-based dynamic modeling theory of behavioral ecology, and how it combines explicit predictions of future conditions with approximations of a fitness measure to represent how individuals make good—not optimal—decisions that they revise as conditions change. The resulting models are realistic, testable, adaptable, and invaluable for answering fundamental questions in ecology and forecasting ecological outcomes of real-world scenarios.

2

Reichmann, Werner. The Interactional Foundations of Economic Forecasting. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198820802.003.0005.

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How do economic forecasters produce legitimate and credible predictions of the economic future, despite most of the economy being transmutable and indeterminate? Using data from a case study of economic forecasting institutes in Germany, this chapter argues that the production of credible economic futures depends on an epistemic process embedded in various forms of interaction. This interactional foundation—through ‘foretalk’ and ‘epistemic participation’ in networks of internal and external interlocutors—sharpens economic forecasts in three ways. First, it brings to light new imaginaries of the economic future, allowing forecasters to spot emerging developments they would otherwise have missed. Second, it ensures the forecasts’ social legitimacy. And finally, it increases the forecasts’ epistemic quality by providing decentralized information about the intentions and assumptions of key economic and political actors.

3

Llewellyn, Sue. What Do Dreams Do? Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198818953.001.0001.

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What is a dream? It’s a complex, non-obvious pattern derived from your experience. But you haven’t actually experienced it. Strange. Revealing complex, hidden patterns makes dreams odd. Dreams associate elements of different experiences to make something new: a pattern you didn’t know was there until you dreamt it. Patterns are discernible forms in the way something happens or is done. Some patterns are easy to spot, being certain and obvious: night follows day. Patterns in human/animal experiences are less obvious because, first, the patterned elements appear at different times or places and, second, the pattern exhibits tendencies not certainties. Spotting such patterns depends on non-obvious associations. If prompted with ‘sea’, while awake, your logical brain makes obvious associations, ‘beach’ or ‘boat’, with a seaside pattern i.e. beach-boat-seaside. But after awakening from dreaming, when your brain is still tuned to non-obvious associations, ‘sick’ may come to mind. A less obvious element of sea experiences. You tend to seasickness when it’s rough. But you also get sick if you eat shellfish, have a migraine, or travel in cars—but only if you read. Sea–rough–car–read–shellfish–migraine. Visualizing these non-obvious associations between elements of different experiences becomes dream-like. Dreaming brains evolved to identify non-obvious associations. Across evolutionary time, you didn’t want to get sick. Survival depended on being well enough to anticipate the non-obvious patterns of predators and human competitors, while securing access to food and water. Making associations drives many, if not all, brain functions. Dream associations support memory, emotional stability, creativity, unconscious decision-making, and prediction, while also contributing to mental illness. This book explains how.

4

McCrea, Michael A., und Lindsay D. Nelson. Effects of Multiple Concussions. Herausgegeben von Ruben Echemendia und Grant L. Iverson. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199896585.013.10.

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There is growing concern that suffering multiple sport-related concussions may increase an athlete’s risk of cumulative neurocognitive and neurobehavioral impairment. Many concerns have not been well-validated, however, owing to limited samples of repeatedly concussed players. In this article, we review the theoretical risks and current evidence regarding the extent to which repeat concussions impact players’ experience of and recovery following successive injuries. Concussion effects are considered at multiple levels (e.g., self-reported physical and psychiatric symptoms, neuropsychological performance, and neurophysiological measures) across both the acute and chronic phases of recovery. Recommendations for applying findings to injury management decisions are provided. Although repeat concussions appear to have the potential for cumulative neurophysiological burden, a number of factors (e.g., individual risk for experiencing or responding poorly to injury, recovery time between injuries) appear important to explain discrepant findings among studies and to translate general scientific principles into clinical decisions for individual players. Future work that accumulates larger, prospective samples will allow for clearer delineation of the factors that appear important for predicting how recurrent concussions impact individual athletes.

Buchteile zum Thema "Hot spot prediction":

1

Park, Jong Ho, Sung Chil Jung, Changlei Zhang und Kil To Chong. „Neural Network Hot Spot Prediction Algorithm for Shared Web Caching System“. In Web Technologies Research and Development - APWeb 2005, 795–806. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31849-1_76.

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2

Liu, Qian, und Jinyan Li. „Protein Binding Interfaces and Their Binding Hot Spot Prediction: A Survey“. In Translational Bioinformatics, 79–106. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7975-4_5.

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3

Corcoran, Jonathan, Ian D. Wilson, Owen M. Lewis und J. Andrew Ware. „Data Clustering and Rule Abduction to Facilitate Crime Hot Spot Prediction“. In Computational Intelligence. Theory and Applications, 807–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_80.

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4

Pulisheru, Kumara Swamy, und Anil Kumar Birru. „Prediction of Hot Spot and Hot Tear of the Al–Cu Cast Alloy by Casting Simulation Software“. In Lecture Notes on Multidisciplinary Industrial Engineering, 459–67. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9072-3_40.

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5

Deshmukh, Shilpa S., und Basava Annappa. „Prediction of Crime Hot Spots Using Spatiotemporal Ordinary Kriging“. In Integrated Intelligent Computing, Communication and Security, 683–91. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8797-4_70.

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6

Hart, Timothy C. „Hot Spots of Crime: Methods and Predictive Analytics“. In Geographies of Behavioural Health, Crime, and Disorder, 87–103. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33467-3_5.

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7

Preto, António J., Pedro Matos-Filipe, José G. de Almeida, Joana Mourão und Irina S. Moreira. „Predicting Hot Spots Using a Deep Neural Network Approach“. In Methods in Molecular Biology, 267–88. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0826-5_13.

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8

Zhang, Wenjuan, Lin Wang, Zhiwei Sun, Bianqiang Zhang, Qiaoqiao Tang und Qiang Gao. „Prediction of Hot Spots in Dimer Interface of Green Fluorescent Protein“. In Lecture Notes in Electrical Engineering, 349–55. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4801-2_35.

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9

Gan, Haomin, Jing Hu, Xiaolong Zhang, Qianqian Huang und Jiafu Zhao. „Accurate Prediction of Hot Spots with Greedy Gradient Boosting Decision Tree“. In Intelligent Computing Theories and Application, 353–64. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_43.

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10

Sun, Zhen, Jun Zhang, Chun-Hou Zheng, Bing Wang und Peng Chen. „Accurate Prediction of Protein Hot Spots Residues Based on Gentle AdaBoost Algorithm“. In Intelligent Computing Theories and Application, 742–49. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_74.

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Konferenzberichte zum Thema "Hot spot prediction":

1

Bender, Christopher J., und Robert G. Dean. „Erosional Hot Spot Prediction through Wave Analysis“. In Fourth International Symposium on Ocean Wave Measurement and Analysis. Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40604(273)132.

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2

Moreira, Irina, José Almeida, António Preto, Rita Melo, Zeynep Gümüş, Joaquim Costa und Alexandre Bonvin. „Co-evolution importance on binding Hot-Spot prediction methods“. In MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition. Basel, Switzerland: MDPI, 2017. http://dx.doi.org/10.3390/mol2net-02-03889.

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3

Repantis, Thomas, und Vana Kalogeraki. „Hot-spot prediction and alleviation in distributed stream processing applications“. In 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN). IEEE, 2008. http://dx.doi.org/10.1109/dsn.2008.4630103.

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4

Vidja, Akash, Harshad Nayakpara, Raghavendra Bhalera und Kshitij Bhargava. „Methods for Calculating the Transformer Hot-Spot Temperature and Lifetime Prediction“. In 2018 3rd International Conference for Convergence in Technology (I2CT). IEEE, 2018. http://dx.doi.org/10.1109/i2ct.2018.8529398.

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Saripalli, P., G. V. R. Kiran, R. R. Shankar, H. Narware und N. Bindal. „Load Prediction and Hot Spot Detection Models for Autonomic Cloud Computing“. In 2011 IEEE 4th International Conference on Utility and Cloud Computing (UCC 2011). IEEE, 2011. http://dx.doi.org/10.1109/ucc.2011.66.

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Yoo, Sung Goo, und Kil To Chong. „Hot Spot Prediction Algorithm for Shared Web Caching System Using NN“. In 2007 International Symposium on Information Technology Convergence (ISITC 2007). IEEE, 2007. http://dx.doi.org/10.1109/isitc.2007.33.

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Ali, Hasnain, Raphael Delair, Duc-Thinh Pham, Sameer Alam und Michael Schultz. „Dynamic Hot Spot Prediction by Learning Spatial- Temporal Utilization of Taxiway Intersections“. In 2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT). IEEE, 2020. http://dx.doi.org/10.1109/aida-at48540.2020.9049186.

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Wang, Shoufeng, Fan Li, Hao Ni, Lexi Xu, Meifang Jing, Junyi Yu und Xidong Wang. „Rush Hour Capacity Enhancement in 5G Network Based on Hot Spot Floating Prediction“. In 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS). IEEE, 2019. http://dx.doi.org/10.1109/iucc/dsci/smartcns.2019.00137.

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Tetzlaff, Dirk, und Sabine Glesner. „Static prediction of recursion frequency using machine learning to enable hot spot optimizations“. In 2012 IEEE 10th Symposium on Embedded Systems for Real-time Multimedia (ESTIMedia). IEEE, 2012. http://dx.doi.org/10.1109/estimedia.2012.6507027.

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Janicki, Marcin, Zbigniew Kulesza und Andrzej Napieralski. „Distributed network of remote sensors for real time prediction of hot spot temperature values“. In 2010 Ninth IEEE Sensors Conference (SENSORS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icsens.2010.5690097.

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