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Journal articles on the topic 'Predictive Patterns'
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Tao, Lv, Yongtao Hao, Hao Yijie, and Shen Chunfeng. "K-Line Patterns’ Predictive Power Analysis Using the Methods of Similarity Match and Clustering." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3096917.
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Stock price prediction based on K-line patterns is the essence of candlestick technical analysis. However, there are some disputes on whether the K-line patterns have predictive power in academia. To help resolve the debate, this paper uses the data mining methods of pattern recognition, pattern clustering, and pattern knowledge mining to research the predictive power of K-line patterns. The similarity match model and nearest neighbor-clustering algorithm are proposed for solving the problem of similarity match and clustering of K-line series, respectively. The experiment includes testing the predictive power of the Three Inside Up pattern and Three Inside Down pattern with the testing dataset of the K-line series data of Shanghai 180 index component stocks over the latest 10 years. Experimental results show that (1) the predictive power of a pattern varies a great deal for different shapes and (2) each of the existing K-line patterns requires further classification based on the shape feature for improving the prediction performance.
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Kaufmann, Mareile, Simon Egbert, and Matthias Leese. "Predictive Policing and the Politics of Patterns." British Journal of Criminology 59, no. 3 (December 7, 2018): 674–92. http://dx.doi.org/10.1093/bjc/azy060.
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AbstractPatterns are the epistemological core of predictive policing. With the move towards digital prediction tools, the authority of the pattern is rearticulated and reinforced in police work. Based on empirical research about predictive policing software and practices, this article puts the authority of patterns into perspective. Introducing four ideal-typical styles of pattern identification, we illustrate that patterns are not based on a singular logic, but on varying rationalities that give form to and formalize different understandings about crime. Yet, patterns render such different modes of reasoning about crime, and the way in which they feed back into policing cultures, opaque. Ultimately, this invites a stronger reflection about the political nature of patterns.
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Rew, Jehyeok, Sungwoo Park, Yongjang Cho, Seungwon Jung, and Eenjun Hwang. "Animal Movement Prediction Based on Predictive Recurrent Neural Network." Sensors 19, no. 20 (October 11, 2019): 4411. http://dx.doi.org/10.3390/s19204411.
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Observing animal movements enables us to understand animal behavior changes, such as migration, interaction, foraging, and nesting. Based on spatiotemporal changes in weather and season, animals instinctively change their position for foraging, nesting, or breeding. It is known that moving patterns are closely related to their traits. Analyzing and predicting animals’ movement patterns according to spatiotemporal change offers an opportunity to understand their unique traits and acquire ecological insights into animals. Hence, in this paper, we propose an animal movement prediction scheme using a predictive recurrent neural network architecture. To do that, we first collect and investigate geo records of animals and conduct pattern refinement by using random forest interpolation. Then, we generate animal movement patterns using the kernel density estimation and build a predictive recurrent neural network model to consider the spatiotemporal changes. In the experiment, we perform various predictions using 14 K long-billed curlew locations that contain their five-year movements of the breeding, non-breeding, pre-breeding, and post-breeding seasons. The experimental results confirm that our predictive model based on recurrent neural networks can be effectively used to predict animal movement.
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Peppler-Lisbach, Cord. "Predictive modelling of historical and recent land-use patterns." Phytocoenologia 33, no. 4 (November 19, 2003): 565–90. http://dx.doi.org/10.1127/0340-269x/2003/0033-0565.
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Choi, Minkyu, and Jun Tani. "Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatiotemporal Scales RNN Model." Neural Computation 30, no. 1 (January 2018): 237–70. http://dx.doi.org/10.1162/neco_a_01026.
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This letter proposes a novel predictive coding type neural network model, the predictive multiple spatiotemporal scales recurrent neural network (P-MSTRNN). The P-MSTRNN learns to predict visually perceived human whole-body cyclic movement patterns by exploiting multiscale spatiotemporal constraints imposed on network dynamics by using differently sized receptive fields as well as different time constant values for each layer. After learning, the network can imitate target movement patterns by inferring or recognizing corresponding intentions by means of the regression of prediction error. Results show that the network can develop a functional hierarchy by developing a different type of dynamic structure at each layer. The letter examines how model performance during pattern generation, as well as predictive imitation, varies depending on the stage of learning. The number of limit cycle attractors corresponding to target movement patterns increases as learning proceeds. Transient dynamics developing early in the learning process successfully perform pattern generation and predictive imitation tasks. The letter concludes that exploitation of transient dynamics facilitates successful task performance during early learning periods.
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Collins, Tom, Robin Laney, Alistair Willis, and Paul H. Garthwaite. "Modeling Pattern Importance in Chopin's Mazurkas." Music Perception 28, no. 4 (April 1, 2011): 387–414. http://dx.doi.org/10.1525/mp.2011.28.4.387.
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This study relates various quantifiable characteristics of a musical pattern to subjective assessments of a pattern's salience. Via score analysis and listening, twelve music undergraduates examined excerpts taken from Chopin's mazurkas. They were instructed to rate already discovered patterns, giving high ratings to patterns that they thought were noticeable and/or important. Each undergraduate rated thirty specified patterns and ninety patterns were examined in total. Twenty-nine quantifiable attributes (some novel but most proposed previously) were determined for each pattern, such as the number of notes a pattern contained. A model useful for relating participants' ratings to the attributes was determined using variable selection and cross-validation. Individual participants were much poorer than the model at predicting the consensus ratings of other participants. While the favored model contains only three variables, many variables were identified as having some predictive value if considered in isolation. Implications for music psychology, analysis, and information retrieval are discussed.
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Daniel Breslin, Mg Ing Roberto. "ANALYSIS MODEL FOR NON-PREDICTIVE PATTERNS OF NON-IONIZING RADIATIONS." Engineering and Technology Journal 07, no. 12 (December 21, 2022): 1755–68. http://dx.doi.org/10.47191/etj/v7i12.01.
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Non-ionizing radiations have been, and still are, a problem whose approach arouses great social interest, due to its controversial implications for human health and the interest of telecommunications companies in the development and implementation of technologies wireless networks based on the propagation of electromagnetic waves. The recurring question generated from society is related to the possibility that said radiation affects health in some way. The present study is based on the hypothesis that, if there is any degree of affectation, it could be due to an effect found regarding non-predictive patterns of propagation of electromagnetic waves and punctual concentration of radiation in very small areas, generating differentials in the level of density of immission. A field work has been carried out looking for the elements of the urban and suburban environment that facilitate and/or enhance the concentration of radiation at a point and were analyzed with qualitative statistical models with binary logistic regression and association analysis, to develop a prediction pattern. possibility of existence of a point of high density of electromagnetic immersion depending on elements of the environment.
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Ju, Yeong Jo, Jeong Ran Lim, and Euy Sik Jeon. "Prediction of AI-Based Personal Thermal Comfort in a Car Using Machine-Learning Algorithm." Electronics 11, no. 3 (January 23, 2022): 340. http://dx.doi.org/10.3390/electronics11030340.
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Defining a passenger’s thermal comfort in a car cabin is difficult because of the narrow environment and various parameters. Although passenger comfort is predicted using a thermal-comfort scale in the overall cabin or a local area, the scale’s range of passenger comfort may differ owing to psychological factors and individual preferences. Among the many factors affecting such comfort levels, the temperature of the seat is one of the direct and significant environmental factors. Therefore, it is necessary to predict the cabin environment and seat-related personal thermal comfort. Accordingly, machine learning is used in this research to predict whether a passenger’s seat-heating-operation pattern can be predicted in a winter environment. The experiment measures the ambient factor and collects data on passenger heating-operation patterns using a device in an actual winter environment. The temperature is set as the input parameter in the measured data and the operation pattern is used as the output parameter. Based on the parameters, the predictive accuracy of the heating-operation pattern is investigated using machine learning. The algorithms used in the machine-learning train are Tree, SVM, and kNN. In addition, the predictive accuracy is tested using SVM and kNN, which shows a high validation accuracy based on the prediction results of the algorithm. In this research, the parameters predicting the personal thermal comfort of three passengers are investigated as a combination of input parameters, according to the passengers. As a result, the predictive accuracy of the operation pattern according to the tested input parameter is 0.96, showing the highest accuracy. Considering each passenger, the predictive accuracy has a maximum deviation of 30%. However, we verify that it indicates the level of accuracy in predicting a passenger’s heating-operation pattern. Accordingly, the possibility of operating a heating seat without a switch operation is confirmed through machine learning. The primary-stage research result reveals whether it is possible to predict objective personal thermal comfort using the passenger seat’s heating-operation pattern. Based on the results of this research, it is expected to be utilized for system construction based on the AI prediction of operation patterns according to the passenger through machine learning.
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Chien, Wen T., and W. C. Hung. "Investigation on the Predictive Model for Burr in Laser Cutting Titanium Alloy." Materials Science Forum 526 (October 2006): 133–38. http://dx.doi.org/10.4028/www.scientific.net/msf.526.133.
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The purpose of this study is to develop two predictive models for burr height in cutting titanium alloy plates by using Nd:YAG laser. Firstly, Taguchi method has been used to arrange the experimental scheme and analyze the results via analysis of mean . The important laser cutting parameters affecting burr height can be found. It shows that the pressure of assistant gas, the focusing position and the pulsed frequency are the most important cutting parameters in order. Then they have been chosen as the input variables for response surface methodology and used to construct a mathematical equation for predicting burr height. Secondly, the laser cutting parameters and experimental results obtained from conducting the schematic arrangement using Taguchi method and response surface methodology have been treated as training patterns and recalling patterns for the back-propagation neural network. As a result, a predictive model for burr height prediction in laser cutting titanium alloy has been established. To verify the accuracy of above two prediction models, there are 9 sets of experiment have been performed. It shows that the average error for predicting burr height by the mathematical equation derived from response surface methodology is 5.52% and by the predictive model established by back-propagation neural network is 4.51%, respectively. Obviously, both predictive models are good enough for the relational research and practical applications. It can be concluded that the procedure used in this research and the obtaining predictive models can be used practically in correlate industry.
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Ding, Jun, Anthony S. Wexler, and Stuart A. Binder-Macleod. "A predictive model of fatigue in human skeletal muscles." Journal of Applied Physiology 89, no. 4 (October 1, 2000): 1322–32. http://dx.doi.org/10.1152/jappl.2000.89.4.1322.
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Fatigue is a major limitation to the clinical application of functional electrical stimulation. The activation pattern used during electrical stimulation affects force and fatigue. Identifying the activation pattern that produces the greatest force and least fatigue for each patient is, therefore, of great importance. Mathematical models that predict muscle forces and fatigue produced by a wide range of stimulation patterns would facilitate the search for optimal patterns. Previously, we developed a mathematical isometric force model that successfully identified the stimulation patterns that produced the greatest forces from healthy subjects under nonfatigue and fatigue conditions. The present study introduces a four-parameter fatigue model, coupled with the force model that predicts the fatigue induced by different stimulation patterns on different days during isometric contractions. This fatigue model accounted for 90% of the variability in forces produced by different fatigue tests. The predicted forces at the end of fatigue testing differed from those observed by only 9%. This model demonstrates the potential for predicting muscle fatigue in response to a wide range of stimulation patterns.
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Hazarati Ehsanifard, Ghazaleh Sadat, Mansoureh Sadat Sadeghi, and Leili Panaghi. "The Role of Parental Bonding Perception in Predicting Communication Patterns of Couples." Journal of Research & Health 11, no. 1 (February 1, 2021): 21–28. http://dx.doi.org/10.32598/jrh.11.1.1618.1.
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Background: Parental bonding perception leads to different domains in future life. The goal of this study was to investigate the role of parental bonding perception in predicting the communication patterns of couples in Tehran. Methods: This correlational and descriptive study was done on 338 individuals in Tehran, Iran who voluntarily participated in the research in 2016. Communication Pattern Questionnaire (CPQ) and Parental Bonding Instrument (PBI) were used to collect data. Pearson correlation method and multiple regression were used for data analysis using the SPSS v. 22 software. Results: Data revealed that in the husband group, father care was the only predictor of higher scores of the mutual constructive communication pattern. In the group of wives, father indifference was the predictor of lower scores of constructive communication pattern and mother encouragement of dependency was the predictor of the higher scores of the constructive communication pattern. Mother encouragement of autonomy was the only predictor of lower scores of husband demand/ wife withdraws but no variable predicted wife demand/ husband withdraw pattern. In addition, fathers’ encouragement of autonomy in husbands was predictive of spouses’ constructive communication patterns. Also, fathers’ encouragement of autonomy in husbands was predictive of spouses’ constructive communication patterns. Conclusion: The association between parental bonding perception and couple’s communication patterns highlight the importance of early years of childhood and parent-child relationship in future life.
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Caginalp, G., and H. Laurent. "The predictive power of price patterns." Applied Mathematical Finance 5, no. 3-4 (September 1998): 181–205. http://dx.doi.org/10.1080/135048698334637.
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Dvoinin, A. M., and E. S. Trotskaya. "Cognitive Predictors of Academic Success: How Do the General Patterns Work in the Early Stages of Education?" Психологическая наука и образование 27, no. 2 (2022): 42–52. http://dx.doi.org/10.17759/pse.2022270204.
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The article provides an overview of modern works devoted to the study of cognitive predictors of academic success. The general patterns of forecasting are revealed: the most powerful and universal predictor of academic success at different stages of school education is psychometric intelligence; creativity is less significant and rather unstable. It is argued that these patterns are poorly traced at the level of preschool education. Particular cognitive functions are significant for predicting the future educational achievements of preschoolers: information processing speed, visual perception (in combination with motor functions), short-term memory, and attention. Spatial abilities have a certain prognostic potential, though reasoning in preschoolers is not a strong predictor of academic success; executive functions have the greatest predictive power. It is noted that the general patterns in predicting the academic success of students can be traced in elementary school: the predictive potentials of psychometric intelligence are revealed, the power of individual cognitive abilities (in particular, spatial abilities) increases, the contribution of executive functions to the prediction decreases. The general tendency for non-cognitive factors (educational motivation, some personality traits) to increase with age also begins to appear in elementary school.
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Franklin, Janet. "Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients." Progress in Physical Geography: Earth and Environment 19, no. 4 (December 1995): 474–99. http://dx.doi.org/10.1177/030913339501900403.
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Predictive vegetation mapping can be defined as predicting the geographic distribution of the vegetation composition across a landscape from mapped environmental variables. Comput erized predictive vegetation mapping is made possible by the availability of digital maps of topography and other environmental variables such as soils, geology and climate variables, and geographic information system software for manipulating these data. Especially important to predictive vegetation mapping are interpolated climatic variables related to physiological tolerances, and topographic variables, derived from digital elevation grids, related to site energy and moisture balance. Predictive vegetation mapping is founded in ecological niche theory and gradient analysis, and driven by the need to map vegetation patterns over large areas for resource conservation planning, and to predict the effects of environmental change on vegetation distributions. Predictive vegetation mapping has advanced over the past two decades especially in conjunction with the development of remote sensing-based vegetation mapping and digital geographic information analysis. A number of statistical and, more recently, machine-learning methods have been used to develop and implement predictive vegetation models.
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Yan, Xiao-Yong, Chen Zhao, Ying Fan, Zengru Di, and Wen-Xu Wang. "Universal predictability of mobility patterns in cities." Journal of The Royal Society Interface 11, no. 100 (November 6, 2014): 20140834. http://dx.doi.org/10.1098/rsif.2014.0834.
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Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. By contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.
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Waheed, Shaikh Abdul, and P. Sheik Abdul Khader. "Healthcare Solutions for Children Who Stutter Through the Structural Equation Modeling and Predictive Modeling by Utilizing Historical Data of Stuttering." SAGE Open 11, no. 4 (October 2021): 215824402110581. http://dx.doi.org/10.1177/21582440211058195.
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Earlier studies established the role of demographic and temperamental features (DTFs) in the adaptation of childhood stuttering. However, these studies have been short on examining the latent interrelationships among DTFs and not utilizing them in predicting this disorder. This research article endeavors to examine latent interrelationships among DTFs in relation to childhood-stuttering. The purpose of the present is also to analyze whether DTFs can be utilized in predicting the likely risk of this speech disorder. Historical data on childhood stuttering was utilized for performing the invloved experiments of this research. “Structural-Equation-Modeling” (SEM) was applied to examine latent interrelationships among DTFs in relation to stuttering. The predictive analytics approach was employed to ensure whether DTFs of children can be utilized for predicting the likely risk of childhood-stuttering. SEM-based path analysis explored potential latent interrelationships among DTFs by separating them into categories of background and intermediate. By utilizing the same set of the DTFs, predictive models were able to classify children into stuttering and non-stuttering groups with optimal prediction accuracy. The outcomes of this study showed how the stuttering related historical data can be utilized in offering healthcare solutions for individuals with stuttering disorder. The outcomes of the present study also suggest that historical data on stuttering is a very rich source of hidden trends and patterns concerning this disorder. These hidden trends and patterns can be captured by applying a different type of structural and predictive modeling to understand the cause-and-effect relationship among variables in relation to stuttering. The SEM utilizes the cause-and-effect relationship among variables to explore latent-interrelationships between them. While predictive modeling utilizes the cause-and-effect relationship among variables to predict the possible risk of stuttering with optimal prediction accuracy.
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Li, L., Q. Tang, H. J. E. Kwon, Z. Wu, E. J. Kim, and H. S. Jung. "An Explanation for How FGFs Predict Species-Specific Tooth Cusp Patterns." Journal of Dental Research 97, no. 7 (February 28, 2018): 828–34. http://dx.doi.org/10.1177/0022034518759625.
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Species-specific cusp patterns result from the iterative formation of enamel knots, the epithelial signaling centers, at the future cusp positions. The expressions of fibroblast growth factors (FGFs), especially Fgf4, in the secondary enamel knots in the areas of the future cusp tips are generally used to manifest the appearance of species-specific tooth shapes. However, the mechanism underlying the predictive role of FGFs in species-specific cusp patterns remains obscure. Here, we demonstrated that gerbils, which have a lophodont pattern, exhibit a striped expression pattern of Fgf4, whereas mice, which have a bunodont pattern, have a spotted expression pattern, and these observations verify the predictive role of Fgf4 in species-specific cusp patterns. By manipulating FGFs’ signaling in the inner dental epithelium of gerbils, we provide evidence for the intracellular participation of FGF signaling, specifically FGF4 and FGF20, in Rac1- and RhoA-regulated cellular geometry remolding during the determination of different cusp patterns. Our study presents a novel explanation of how different FGF expression patterns produce different cusp patterns and implies that a conserved intracellular FGF-GTPase signaling module might represent an underlying developmental basis for evolutionary changes in cusp patterns.
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Radhakrishnan, Nita, Mehul Awasthi, and P. Mahalakshmi. "A survey on Predictive Analysis in Employment Trends." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 358. http://dx.doi.org/10.14419/ijet.v7i2.24.12082.
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This paper addresses the theories of using predictive analysis and Data Mining in arriving at suitable patterns and predicting paths and trends in the current Employment Scenario more specifically to the Engineering sector. India produces 1.5 million engineers every year, and yet there is a significant gap between their skills and the jobs and corresponding salaries they are offered. Recognizing the factors that influence this gap can help us bridge it. The survey shows that the ideal route to doing so, is by employing various Predictive analysis and Data Mining techniques on appropriate data sets, which help in addressing these issues. As per the survey, appropriate visualization techniques have also been used to extract the meaning from the prediction and analysis.
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Solomon, Selina S., Huizhen Tang, Elyse Sussman, and Adam Kohn. "Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans." Cerebral Cortex 31, no. 6 (March 4, 2021): 3136–52. http://dx.doi.org/10.1093/cercor/bhab014.
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Abstract A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.
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Radulian, M., M. Popa, O. Cărbunar, and M. Rogozea. "Seismicity patterns in Vrancea and predictive features." Acta Geodaetica et Geophysica Hungarica 43, no. 2-3 (June 2008): 163–73. http://dx.doi.org/10.1556/ageod.43.2008.2-3.6.
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Chung, Shu-Yun, and Han-Pang Huang. "Predictive Navigation by Understanding Human Motion Patterns." International Journal of Advanced Robotic Systems 8, no. 1 (January 2011): 3. http://dx.doi.org/10.5772/10529.
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Kiseleva, O. I., E. V. Poverennaya, M. A. Pyatnitskiy, E. V. Ilgisonis, V. G. Zgoda, O. A. Plotnikova, K. K. Sharafetdinov, et al. "DOES PROTEOMIC MIRROR REFLECT CLINICAL CHARACTERISTICS OF OBESITY?" http://eng.biomos.ru/conference/articles.htm 1, no. 19 (2021): 129–30. http://dx.doi.org/10.37747/2312-640x-2021-19-129-130.
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Protein patterns were collected, the presence or absence of which allows a fairly good prediction of the patient's weight. Such proteomic patterns with high predictive power should facilitate the transformation of potential biomarker candidates for clinical use for the early stratification of obesity therapy.
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Klaiman, Eldad, Jacob Gildenblat, Ido Ben-Shaul, Astrid Heller, Konstanty Korski, Astrid Christina Kiermaier, and Fabien Gaire. "Prediction of biomarker status, diagnosis and outcome from histology slides using deep learning-based hypothesis free feature extraction." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 3140. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.3140.
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3140 Background: Recently, histological pattern signatures obtained from diagnostic H&E images have been found to predict mutation, biomarker status or outcome. We report here on a novel deep learning based framework designed to identify and extract predictive histological signatures. We have applied this framework in 3 experiments, predicting specifically the microsatellite status (MSS) of colorectal cancer (CRC), breast cancer (BC) micrometastasis in Lymph nodes (LN) and Pathologic Complete Response (pCR) in BC diagnostic biopsies. Methods: Our deep learning based algorithm was trained on histology images at 20X magnification. Algorithms were trained for binary classification for each of the three cohorts. We used 75% of the images for training and test our algorithm on the remaining 25% of the images. Cohort details are as follows: MSS for CRC: 94 patients’ H&E stained tissue images from the Roche internal CRC80 dataset (MSS n =24; MSI n = 70) were used. BC LN: 270 patients’ H&E stained tissue images from the CAMELYON16 dataset ( LN(+) n = 110 ; LN(-), n =160) were used. pCR for BC: 225 patients’ H&E stained tissue images from the Tryphaena Study BO22280, neoadjuvant, Trastuzumab/Pertuzumab chemotherapy combination trial. (pCR=111, non-pCR n=114). Results: We report and assess algorithm performance on each of the cohorts by Area Under the Curve (AUC). Prediction of MSS in the CRC80 status yielded AUC 0.9. Prediction of LN invasion on CAMELYON16 dataset yielded AUC 0.85. Prediction of pCR on the Tryphaena cohort yielded an AUC of 0.8. Conclusions: We present a new approach to generate predictive signatures based on conventional diagnostic H&E images and a novel machine learning framework. The CRC80 and CAMELYON16 cohorts served as a confidence building experiments with predictive features well known by clinicians and visually confirmed. The predictive algorithm for pCR in the Tryphaena cohort yielded both response prediction and the high predictive value FOVs. These included tissue patterns which have not until now been considered to influence on the prediction of pCR.
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Zhu, Yitan, Thomas S. Brettin, Fangfang Xia, Maulik Shukla, Alexander Partin, Hyunseung Yoo, and Rick L. Stevens. "Enhanced co-expression extrapolation (COXEN) gene selection method for building anticancer drug response prediction models." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14073-e14073. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14073.
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e14073 Background: Accurate prediction of tumor response to a drug treatment is of paramount importance for precision oncology. The co-expression extrapolation (COXEN) gene selection approach has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug. Here, we enhance the original COXEN approach to select genes that are predictive of the efficacies of multiple drugs simultaneously for building general drug response prediction model. Methods: We implemented two methods to select predictive genes. The first method ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs. The second method uses a linear regression model to evaluate the prediction power of a gene for all drugs while the drugs are one-hot encoded in the regression model. Among the predictive genes, we further select genes by evaluating the preservation of co-expression patterns between cancer cases with drug response data available and cancer cases for which drug response needs to be predicted, because the preservation of co-expression patterns indicates the similarity of genomic regulations between cancer cases. Results: To test the enhanced COXEN method, we used a lightGBM regression model to predict drug response based on the selected genes on two benchmark in vitro drug screening datasets. The table below compares the performance of prediction models built based on 200 genes selected by the enhanced COXEN method to that of models built on 200 genes randomly picked from the LINCS gene set, which includes 976 “landmark” genes well-representing cellular transcriptomic changes identified in the Library of Integrated Network-Based Cellular Signatures (LINCS) project. The enhanced COXEN approach selects genes better than random LINCS genes as demonstrated by the increased average coefficient of determination (R2) for predicting the area under the dose response curve through cross-validation. Pair-wise t-test indicates the improvement is statistically significant (p-value ≤ 0.05) on both datasets. Conclusions: Our result demonstrates the benefit of using an enhanced COXEN approach to select genes for building general drug response prediction model. [Table: see text]
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Grimm, Volker, and Steven F. Railsback. "Pattern-oriented modelling: a ‘multi-scope’ for predictive systems ecology." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1586 (January 19, 2012): 298–310. http://dx.doi.org/10.1098/rstb.2011.0180.
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Modern ecology recognizes that modelling systems across scales and at multiple levels—especially to link population and ecosystem dynamics to individual adaptive behaviour—is essential for making the science predictive. ‘Pattern-oriented modelling’ (POM) is a strategy for doing just this. POM is the multi-criteria design, selection and calibration of models of complex systems. POM starts with identifying a set of patterns observed at multiple scales and levels that characterize a system with respect to the particular problem being modelled; a model from which the patterns emerge should contain the right mechanisms to address the problem. These patterns are then used to (i) determine what scales, entities, variables and processes the model needs, (ii) test and select submodels to represent key low-level processes such as adaptive behaviour, and (iii) find useful parameter values during calibration. Patterns are already often used in these ways, but a mini-review of applications of POM confirms that making the selection and use of patterns more explicit and rigorous can facilitate the development of models with the right level of complexity to understand ecological systems and predict their response to novel conditions.
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Mamaril, Julius Cesar O., and Melvin A. Ballera. "Multiple educational data mining approaches to discover patterns in university admissions for program prediction." International Journal of Informatics and Communication Technology (IJ-ICT) 11, no. 1 (April 1, 2022): 45. http://dx.doi.org/10.11591/ijict.v11i1.pp45-56.
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<span>This paper presented the utilization of pattern discovery techniques by using multiple relationships and clustering educational data mining approaches to establish a knowledge base that will aid in the prediction of ideal college program selection and enrollment forecasting for incoming freshmen. Results show a significant level of accuracy in predicting college programs for students by mining two years of student college admission and graduation final grade scholastic records. The results of educational predictive data mining methods can be applied in improving the services of the admission department of an educational institution, particularly in its course alignment, student mentoring, admission forecast, marketing, and enrollment preparedness.</span>
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Hossein, Najafi, and Darryl W. Miller. "Predicting motion picture box office performance using temporal tweet patterns." International Journal of Intelligent Computing and Cybernetics 11, no. 1 (March 12, 2018): 64–80. http://dx.doi.org/10.1108/ijicc-04-2017-0033.
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Purpose The purpose of this paper is to investigate temporal tweet patterns and their effectiveness in predicting the financial performance of a movie. Specifically, how tweet patterns are formed prior to and after a movie’s release and their usefulness in predicting a movie’s success is explored. Design/methodology/approach Volume was measured and sentiment analysis was performed on a sample of Tweets posted four days before and after the release of 86 movies. The temporal pattern of tweeting for financially successful movies was compared with those that were financial disappointments. Using temporal tweet patterns, a number of machine learning models were developed and their predictive performance was compared. Findings Results show that the temporal patterns of tweet volume, length and sentiment differ between “hits” and “busts” in the days surrounding their releases. Compared with “busts” the tweet pattern for “hits” reveal higher volume, shorter length, and more favourable sentiment. Discriminant patterns in social media features occur days in advance of a movie’s release and can be used to develop models for predicting a movie’s success. Originality/value Analysis of temporal tweet patterns and their usefulness in predicting box office returns is the main contribution of this research. Results of this research could lead to development of analytical tools allowing motion picture studios to accurately predict and possibly influence the opening night box-office receipts prior to the release of the movie. Also, the specific temporal tweet patterns presented by this work may be applied to problems in other areas of research.
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Wang, Zhujun, Jianping Wang, Yingmei Xing, Yalan Yang, and Kaixuan Liu. "Estimating Human Body Dimensions Using RBF Artificial Neural Networks Technology and Its Application in Activewear Pattern Making." Applied Sciences 9, no. 6 (March 18, 2019): 1140. http://dx.doi.org/10.3390/app9061140.
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Nowadays, the popularity of the internet has continuously increased. Predicting human body dimensions intelligently would be beneficial to improve the precision and efficiency of pattern making for enterprises in the apparel industry. In this study, a new predictive model for estimating body dimensions related to garment pattern making is put forward based on radial basis function (RBF) artificial neural networks (ANNs). The model presented in this study was trained and tested using the anthropometric data of 200 adult males between the ages 20 and 48. The detailed body dimensions related to pattern making could be obtained by inputting four easy-to-measure key dimensions into the RBF ANN model. From the simulation results, when spreading parameter σ and momentum factor α were set to 0.012 and 1, the three-layer model with 4, 72, and 8 neurons in the input, hidden, and output layers, respectively, showed maximum accuracy, after being trained by a dataset with 180 samples. Moreover, compared with a classic linear regression model and the back propagation (BP) ANN model according to mean squared error, the predictive performance of the RBF ANN model put forward in this study was better than the other two models. Therefore, it is feasible for the presented predictive model to design garment patterns, especially for tight-fitting garment patterns like activewear. The estimating accuracy of the proposed model would be further improved if trained by more appropriate datasets in the future.
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Macreadie, Peter I., Rod M. Connolly, Gregory P. Jenkins, Jeremy S. Hindell, and Michael J. Keough. "Edge patterns in aquatic invertebrates explained by predictive models." Marine and Freshwater Research 61, no. 2 (2010): 214. http://dx.doi.org/10.1071/mf09072.
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Predictive frameworks for understanding and describing how animals respond to habitat fragmentation, particularly across edges, have been largely restricted to terrestrial systems. Abundances of zooplankton and meiofauna were measured across seagrass–sand edges and the patterns compared with predictive models of edge effects. Artificial seagrass patches were placed on bare sand, and zooplankton and meiofauna were sampled with tube traps at five positions (from patch edges: 12, 60 and 130 cm into seagrass; and 12 and 60 cm onto sand). Position effects consisted of the following three general patterns: (1) increases in abundance around the seagrass–sand edge (total abundance and cumaceans); (2) declining abundance from seagrass onto sand (calanoid copepods, harpacticoid copepods and amphipods); and (3) increasing abundance from seagrass onto sand (crustacean nauplii and bivalve larvae). The first two patterns are consistent with resource-distribution models, either as higher resources at the confluence of adjacent habitats or supplementation of resources from high-quality to low-quality habitat. The third pattern is consistent with reductions in zooplankton abundance as a consequence of predation or attenuation of currents by seagrass. The results show that predictive models of edge effects can apply to aquatic animals and that edges are important in structuring zooplankton and meiofauna assemblages in seagrass.
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Gao, Guang Xia, Zhi Wang Zhang, and Shi Yong Kang. "Chinese Semantic Word-Formation Analysis Using FKP-MCO Classifier Based on Layered and Weighted GED." Applied Mechanics and Materials 284-287 (January 2013): 3044–50. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3044.
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For Chinese information processing, automatic classification based on a large-scale database for different patterns of semantic word-formation can remarkably improve the identification for the unregistered word, automatic lexicography, semantic analysis, and other applications. However, owing to noise, anomalies, nonlinear characteristics, class-imbalance, and other uncertainties in word-formation data, the predictive performance of multi-criteria optimization classifier (MCOC) and other traditional data mining approaches will rapidly degenerate. In this paper we put forward an novel MCOC with fuzzification, kernel, and penalty factors (FKP-MCOC) based on layered and weighted graph edit distance (GED): firstly the layered and weighted GEDs between each semantic word-formation graph and prototype graphs are calculated and used for the dissimilarity measure, then the normalized GEDs are embedded into a new feature vector space, and FKP-MCO classifier based on the feature vector space is built for predicting the patterns of semantic word-formation. Our experimental results of Chinese word-formation analysis and comparison with support vector machine (SVM) show that our proposed approach can increase the separation of different patterns, the predictive performance of semantic pattern of a new compound word.
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Zhu, Yitan, Thomas Brettin, Yvonne A. Evrard, Fangfang Xia, Alexander Partin, Maulik Shukla, Hyunseung Yoo, James H. Doroshow, and Rick L. Stevens. "Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models." Genes 11, no. 9 (September 11, 2020): 1070. http://dx.doi.org/10.3390/genes11091070.
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The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.
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Guidotti, Roberto, Cosimo Del Gratta, Mauro Gianni Perrucci, Gian Luca Romani, and Antonino Raffone. "Neuroplasticity within and between Functional Brain Networks in Mental Training Based on Long-Term Meditation." Brain Sciences 11, no. 8 (August 18, 2021): 1086. http://dx.doi.org/10.3390/brainsci11081086.
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(1) The effects of intensive mental training based on meditation on the functional and structural organization of the human brain have been addressed by several neuroscientific studies. However, how large-scale connectivity patterns are affected by long-term practice of the main forms of meditation, Focused Attention (FA) and Open Monitoring (OM), as well as by aging, has not yet been elucidated. (2) Using functional Magnetic Resonance Imaging (fMRI) and multivariate pattern analysis, we investigated the impact of meditation expertise and age on functional connectivity patterns in large-scale brain networks during different meditation styles in long-term meditators. (3) The results show that fMRI connectivity patterns in multiple key brain networks can differentially predict the meditation expertise and age of long-term meditators. Expertise-predictive patterns are differently affected by FA and OM, while age-predictive patterns are not influenced by the meditation form. The FA meditation connectivity pattern modulated by expertise included nodes and connections implicated in focusing, sustaining and monitoring attention, while OM patterns included nodes associated with cognitive control and emotion regulation. (4) The study highlights a long-term effect of meditation practice on multivariate patterns of functional brain connectivity and suggests that meditation expertise is associated with specific neuroplastic changes in connectivity patterns within and between multiple brain networks.
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Rudilosso, Salvatore, Alejandro Rodríguez, Sergio Amaro, Víctor Obach, Arturo Renú, Laura Llull, Carlos Laredo, et al. "Value of Vascular and Non-Vascular Pattern on Computed Tomography Perfusion in Patients With Acute Isolated Aphasia." Stroke 51, no. 8 (August 2020): 2480–87. http://dx.doi.org/10.1161/strokeaha.120.028821.
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Background and Purpose: Acute onset aphasia may be due to stroke but also to other causes, which are commonly referred to as stroke mimics. We hypothesized that, in patients with acute isolated aphasia, distinct brain perfusion patterns are related to the cause and the clinical outcome. Herein, we analyzed the prognostic yield and the diagnostic usefulness of computed tomography perfusion (CTP) in patients with acute isolated aphasia. Methods: From a single-center registry, we selected a cohort of 154 patients presenting with acute isolated aphasia who had a whole-brain CTP study available. We collected the main clinical and radiological data. We categorized brain perfusion studies on CTP into vascular and nonvascular perfusion patterns and the cause of aphasia as ischemic stroke, transient ischemic attack, stroke mimic, and undetermined cause. The primary clinical outcome was the persistence of aphasia at discharge. We analyzed the sensitivity, specificity, positive and negative predictive values of perfusion patterns to predict complete clinical recovery and ischemic stroke on follow-up imaging. Results: The cause of aphasia was an ischemic stroke in 58 patients (38%), transient ischemic attack in 3 (2%), stroke mimic in 68 (44%), and undetermined in 25 (16%). CTP showed vascular and nonvascular perfusion pattern in 62 (40%) and 92 (60%) patients, respectively. Overall, complete recovery occurred in 116 patients (75%). A nonvascular perfusion pattern predicted complete recovery (sensitivity 75.9%, specificity 89.5%, positive predictive value 95.7%, and negative predictive value 54.8%), and a vascular perfusion pattern was highly predictive of ischemic stroke (sensitivity 94.8%, specificity 92.7%, positive predictive value 88.7%, and negative predictive value 96.7%). The 3 patients with ischemic stroke without a vascular perfusion pattern fully recovered at discharge. Conclusions: CTP has prognostic value in the workup of patients with acute isolated aphasia. A nonvascular pattern is associated with higher odds of full recovery and may prompt the search for alternative causes of the symptoms.
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Ramadhan, Aditya, Irma Palupi, and Bambang Ari Wahyudi. "Candlestick Patterns Recognition using CNN-LSTM Model to Predict Financial Trading Position in Stock Market." Journal of Computer System and Informatics (JoSYC) 3, no. 4 (September 3, 2022): 339–47. http://dx.doi.org/10.47065/josyc.v3i4.2133.
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Investors need analytical tools to predict the price and to determine trading positions. Candlestick pattern is one of the analytical tools that predict price trends. However, the patterns are difficult to recognize, and some studies show doubts regarding the robustness of the recognizing system. In this study, we tested the predictive ability of candlestick patterns to determine trading positions. We use Gramian Angular Field (GAF) to encode candlestick patterns as images to recognize 3-hour and 5-hour of 6 candlestick patterns with Convolutional Neural Network (CNN), coupled with the Long short-term memory (LSTM) model to predict the close price. The trading position consists of buying and selling position with a hold period of several hours. Our results show CNN successfully detected 3-hour and 5-hour GAF candlestick patterns with an accuracy of 90% and 93%. LSTM can predict the close price trend with 155.458 RMSE scores and 0.9754% MAPE with 10-hour look back. With a hold duration of three hours and CNN-LSTM as an additional model, the test data's 85 candlestick patterns are recognized with 82.7% accuracy, compared to 60% accuracy of profitable trading positions when CNN candlestick pattern recognition is used alone. Compared to employing CNN candlestick pattern identification alone, the CNN-LSTM model combination can improve the prediction power of candlestick patterns and offer more lucrative trading positions.
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Valero-Cuevas, Francisco J. "Predictive Modulation of Muscle Coordination Pattern Magnitude Scales Fingertip Force Magnitude Over the Voluntary Range." Journal of Neurophysiology 83, no. 3 (March 1, 2000): 1469–79. http://dx.doi.org/10.1152/jn.2000.83.3.1469.
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Human fingers have sufficiently more muscles than joints such that every fingertip force of submaximal magnitude can be produced by an infinite number of muscle coordination patterns. Nevertheless, the nervous system seems to effortlessly select muscle coordination patterns when sequentially producing fingertip forces of low, moderate, and maximal magnitude. The hypothesis of this study is that the selection of coordination patterns to produce submaximal forces is simplified by the appropriate modulation of the magnitude of a muscle coordination pattern capable of producing the largest expected fingertip force. In each of three directions, eight subjects were asked to sequentially produce fingertip forces of low, moderate, and maximal magnitude with their dominant forefinger. Muscle activity was described by fine-wire electromyograms (EMGs) simultaneously collected from all muscles of the forefinger. A muscle coordination pattern was defined as the vector list of the EMG activity of each muscle. For all force directions, statistically significant muscle coordination patterns similar to those previously reported for 100% of maximal fingertip forces were found for 50% of maximal voluntary force. Furthermore the coordination pattern and fingertip force vector magnitudes were highly correlated ( r > 0.88). Average coordination pattern vectors at 50 and 100% of maximal force were highly correlated with each other, as well as with individual coordination pattern vectors in the ramp transitions preceding them. In contrast to this consistency of EMG coordination patterns, predictions using a musculoskeletal computer model of the forefinger show that force magnitudes ≤50% of maximal fingertip force can be produced by coordination patterns drastically different from those needed for maximal force. Thus when modulating fingertip force magnitude across the voluntary range, the number of contributing muscles and the relative activity among them was not changed. Rather, the production of low and moderate forces seems to be simplified by appropriately scaling the magnitude of a coordination pattern capable of producing the highest force expected.
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Van Hoof, V. O., A. T. Van Oosterom, L. G. Lepoutre, and M. E. De Broe. "Alkaline Phosphatase Isoenzyme Patterns in Malignant Disease." Clinical Chemistry 38, no. 12 (December 1, 1992): 2546–51. http://dx.doi.org/10.1093/clinchem/38.12.2546.
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Abstract Early treatment of patients with malignant disease and liver or bone metastasis may increase their survival time. We have used the activity patterns of liver and bone isoenzymes of alkaline phosphatase (ALP), separated by agarose gel electrophoresis, to detect early metastasis. We studied ALP isoenzyme patterns in a background population of 101 patients with no evidence of any disease that might influence this pattern; a healthy reference population (n = 330); and the following three groups of patients: 143 with malignant disease, 47 with nonmalignant liver disease, and 22 with nonmalignant bone disease. Cutoff and predictive values of liver ALP, high-molecular-mass (high-M(r)) ALP, and bone ALP were established for detecting liver and bone metastasis. The positive predictive value of liver and high-M(r) ALP was higher than that of total ALP in detecting liver metastasis, but liver and high-M(r) ALP did not enable us to differentiate between malignant and nonmalignant liver disease. Total ALP activity was of slightly more value than liver and high-M(r) ALP in enabling us to rule out liver metastasis. From bone ALP activity we could not distinguish between nonmalignant bone disease and bone metastasis. The negative predictive value of bone ALP in the diagnosis of bone metastasis was low, but its positive predictive value was high and superior to that of total ALP.
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Wu, Jianxiao, Simon B. Eickhoff, Felix Hoffstaedter, Kaustubh R. Patil, Holger Schwender, B. T. Thomas Yeo, and Sarah Genon. "A Connectivity-Based Psychometric Prediction Framework for Brain–Behavior Relationship Studies." Cerebral Cortex 31, no. 8 (April 22, 2021): 3732–51. http://dx.doi.org/10.1093/cercor/bhab044.
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Abstract The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain–behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions’ connectivity profiles. We first illustrate two main applications: 1) single brain region’s predictive power for a range of psychometric variables and 2) single psychometric variable’s predictive power variation across brain region. We compare the patterns of brain–behavior provided by these approaches to the brain–behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain–behavior relationships, as well as the unique insight into brain–behavior relationships offered by this approach.
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Ding, Jun, Stuart A. Binder-Macleod, and Anthony S. Wexler. "Two-step, predictive, isometric force model tested on data from human and rat muscles." Journal of Applied Physiology 85, no. 6 (December 1, 1998): 2176–89. http://dx.doi.org/10.1152/jappl.1998.85.6.2176.
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Functional electrical stimulation can assist paralyzed individuals to perform functional movements, but muscle fatigue is a major limitation to its practical use. An accurate and predictive mathematical model can facilitate the design of stimulation patterns that optimize aspects of the force transient while minimizing fatigue. Solution nonuniqueness, a major shortcoming in previous work, was overcome with a simpler model. The model was tested on data collected during isometric contractions of rat gastrocnemius muscles and human quadriceps femoris muscles under various physiological conditions. For each condition tested, parameter values were identified using the force response to one or two stimulation trains. The parameterized model was then used to predict forces in response to other stimulation patterns. The predicted forces closely matched the measured forces. The model was not sensitive to initial parameter estimates, demonstrating solution uniqueness. By predicting the force that develops in response to an arbitrary pattern of stimulation, we envision the present model helping identify optimal stimulation patterns for activation of skeletal muscle during functional electrical stimulation.
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Chien, Wen T., and S. W. Chang. "Study on the Predictive Model for Shear Strength in Laser Welding Stainless Steel." Materials Science Forum 505-507 (January 2006): 205–10. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.205.
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A predictive model is presented for the prediction of shear strength in laser welding AISI304 stainless steel. Welding experiments conducted using a pulsed Nd:YAG laser machine while the laser welding parameters and their levels have been arranged according to design of experiments of Taguchi method. The tensile tests are performed after welding and the measurements of tensile strength are further calculated for shear strength. The data can be analyzed using the principles of Taguchi method for determining the optimal laser welding parameters and for investigating the most significant laser welding parameter on shear strength. Furthermore, the results are treated as the training and recalling patterns for constructing a predictive model using back-propagation neuron network to predict shear strength for the range of laser welding operation tested. It is indicated that welding speed is the most significant affecting parameters on shear strength. In addition, an increase in welding speed causes a decrease in shear strength is found. An average error 5.75％for shear strength can be found by comparing the experimental results obtained from conducting verification tests with the predicting values obtained from the established predictive model. It shows that the predictive model is capable of good predicting behavior of laser welding AISI304 stainless steel.
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Batoğlu, Figen, Sibel Demirel, Emin Özmert, Yesim Gedik Oguz, and Pelin Özyol. "Autofluorescence Patterns as a Predictive Factor for Neovascularization." Optometry and Vision Science 91, no. 8 (August 2014): 950–55. http://dx.doi.org/10.1097/opx.0000000000000321.
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Sanders, Mark N., and Ian Civil. "ADULT SPLENIC INJURIES: TREATMENT PATTERNS AND PREDICTIVE INDICATORS." Australian and New Zealand Journal of Surgery 69, no. 6 (June 1999): 430–32. http://dx.doi.org/10.1046/j.1440-1622.1999.01594.x.
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Schachat, Sandra R. "The wing pattern of Moerarchis Durrant, 1914 (Lepidoptera: Tineidae) clarifies transitions between predictive models." Royal Society Open Science 4, no. 3 (March 2017): 161002. http://dx.doi.org/10.1098/rsos.161002.
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The evolution of wing pattern in Lepidoptera is a popular area of inquiry but few studies have examined microlepidoptera, with fewer still focusing on intraspecific variation. The tineid genus Moerarchis Durrant, 1914 includes two species with high intraspecific variation of wing pattern. A subset of the specimens examined here provide, to my knowledge, the first examples of wing patterns that follow both the ‘alternating wing-margin’ and ‘uniform wing-margin’ models in different regions along the costa. These models can also be evaluated along the dorsum of Moerarchis , where a similar transition between the two models can be seen. Fusion of veins is shown not to effect wing pattern, in agreement with previous inferences that the plesiomorphic location of wing veins constrains the development of colour pattern. The significant correlation between wing length and number of wing pattern elements in Moerarchis australasiella shows that wing size can act as a major determinant of wing pattern complexity. Lastly, some M. australasiella specimens have wing patterns that conform entirely to the ‘uniform wing-margin’ model and contain more than six bands, providing new empirical insight into the century-old question of how wing venation constrains wing patterns with seven or more bands.
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Rustagi, Mitanshi, and Neha Goel. "Predictive Analytics: A study of its Advantages and Applications." IARS' International Research Journal 12, no. 01 (February 28, 2022): 60–63. http://dx.doi.org/10.51611/iars.irj.v12i01.2022.192.
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The science of predictive analytics gives a line of future insight developed in the area of data analytics. Through predictive analytics, organizations or industries can identify the patterns within the data and make future forecasts on the basis of existing data and analytics techniques such as artificial intelligence, machine learning, pattern recognition. Machine Learning works on the idea of identifying the best suitable model for the data.
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Fidan, Mehmet, and Ömer Nezih Gerek. "Mycielski Based 2d-Predictive Image Coding Algorithm." Applied Mechanics and Materials 850 (August 2016): 144–51. http://dx.doi.org/10.4028/www.scientific.net/amm.850.144.
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The Mycielski method is a prospering prediction algorithm which is based on searching and finding largest repeated binary patterns. It uses infinite-past data to devise a rule based prediction method on a time series. In this work, a novel two-dimensional (image processing) version of the Mycielski algorithm is proposed. Since the dimensionality definition of “past” data increases in two-dimensional signals, the proposed algorithm also needs to handle how the boundaries of the pixel cliques are iteratively extended in the neighborhood of a current pixel. The clique extension invokes novel similarity search strategies that depend on the chosen physical distance metric. The proposed prediction algorithm is used for predictive image compression and performance comparisons with other predictive coding methods are presented.
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Terzer, S., L. I. Wassenaar, L. J. Araguás-Araguás, and P. K. Aggarwal. "Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models." Hydrology and Earth System Sciences 17, no. 11 (November 29, 2013): 4713–28. http://dx.doi.org/10.5194/hess-17-4713-2013.
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Abstract. A regionalized cluster-based water isotope prediction (RCWIP) approach, based on the Global Network of Isotopes in Precipitation (GNIP), was demonstrated for the purposes of predicting point- and large-scale spatio-temporal patterns of the stable isotope composition (δ2H, δ18O) of precipitation around the world. Unlike earlier global domain and fixed regressor models, RCWIP predefined 36 climatic cluster domains and tested all model combinations from an array of climatic and spatial regressor variables to obtain the best predictive approach to each cluster domain, as indicated by root-mean-squared error (RMSE) and variogram analysis. Fuzzy membership fractions were thereafter used as the weights to seamlessly amalgamate results of the optimized climatic zone prediction models into a single predictive mapping product, such as global or regional amount-weighted mean annual, mean monthly, or growing-season δ18O/δ2H in precipitation. Comparative tests revealed the RCWIP approach outperformed classical global-fixed regression–interpolation-based models more than 67% of the time, and clearly improved upon predictive accuracy and precision. All RCWIP isotope mapping products are available as gridded GeoTIFF files from the IAEA website (www.iaea.org/water) and are for use in hydrology, climatology, food authenticity, ecology, and forensics.
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Terzer, S., L. I. Wassenaar, L. J. Araguás-Araguás, and P. K. Aggarwal. "Global isoscapes for δ<sup>18</sup>O and δ<sup>2</sup>H in precipitation: improved prediction using regionalized climatic regression models." Hydrology and Earth System Sciences Discussions 10, no. 6 (June 11, 2013): 7351–93. http://dx.doi.org/10.5194/hessd-10-7351-2013.
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Abstract. A Regionalized Climatic Water Isotope Prediction (RCWIP) approach, based on the Global Network for Isotopes in Precipitation (GNIP), was demonstrated for the purposes of predicting point- and large-scale spatiotemporal patterns of the stable isotope compositions of water (δ2H, δ18O) in precipitation around the world. Unlike earlier global domain and fixed regressor models, RCWIP pre-defined thirty-six climatic cluster domains, and tested all model combinations from an array of climatic and spatial regressor variables to obtain the best predictive approach to each cluster domain, as indicated by RMSE and variogram analysis. Fuzzy membership fractions were thereafter used as the weights to seamlessly amalgamate results of the optimized climatic zone prediction models into a single predictive mapping product, such as global or regional amount-weighted mean annual, mean monthly or growing-season δ18O/δ2H in precipitation. Comparative tests revealed the RCWIP approach outperformed classical global-fixed regression-interpolation based models more than 67% of the time, and significantly improved upon predictive accuracy and precision. All RCWIP isotope mapping products are available as gridded GeoTIFF files from the IAEA website (www.iaea.org/water) and are for use in hydrology, climatology, food authenticity, ecology, and forensics.
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Ziesler, Pamela S., Douglas B. Rideout, and Robin Reich. "Modelling conditional burn probability patterns for large wildland fires." International Journal of Wildland Fire 22, no. 5 (2013): 579. http://dx.doi.org/10.1071/wf11185.
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We present a technique for modelling conditional burn probability patterns in two dimensions for large wildland fires. The intended use for the model is strategic program planning when information about future fire weather and event durations is unavailable and estimates of the average probabilistic shape and extent of large fires on a landscape are needed. To model average conditional burn probability patterns, we organised historical fire data from Yellowstone National Park, USA, into a set of grids; one grid per fire. We captured various spatial relationships inherent in the gridded data through use of geometric variables in the main model and by incorporating an autoregressive covariance structure. The final model had ‘good’ predictive ability with an AUC of 0.81 (1.0 is perfect prediction) and the estimated coefficients are consistent with theory and reflect how fires usually behave on the study site landscape. This technique produces a predictive model with finer detail than most landscape-wide models of burn probability and it has advantages over simulation methods for strategic planning because it does not require multiple runs of spread simulation models or information on fire duration.
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Urban, Kamila, and Marek Urban. "Anchoring effect of performance feedback on accuracy of metacognitive monitoring in preschool children." Europe’s Journal of Psychology 17, no. 1 (February 26, 2021): 104–18. http://dx.doi.org/10.5964/ejop.2397.
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Preschool children are generally inaccurate at evaluating past and predicting future performance. The present study examines the effect of performance feedback on the accuracy of preschoolers’ predictive judgments and tests whether performance feedback acts as an anchor for postdictive judgments. In Experiment 1, preschool children (n = 40) solved number patterns, and in Experiment 2 they solved object patterns (n = 59). The results in both experiments revealed, firstly, that children receiving performance feedback made more accurate predictive judgments and lowered their certainty after their incorrect answer. Secondly, the children relied on performance feedback more than on actual task experience when making postdictive judgments, indicating that performance feedback was used as an anchor for subsequent postdictive judgments.
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Gospodarowicz, M. K., S. B. Sutcliffe, T. C. Brown, T. Chua, and R. S. Bush. "Patterns of disease in localized extranodal lymphomas." Journal of Clinical Oncology 5, no. 6 (June 1987): 875–80. http://dx.doi.org/10.1200/jco.1987.5.6.875.
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The site of origin of lymphoid tissue is an important determinant of lymphocyte migration patterns. The association of gastrointestinal (GI) and Waldeyer's ring lymphoma and the unique lymphocyte migration pattern of gut-associated lymphoid tissue (GALT) have been previously described. To establish whether predictive clinical patterns of disease occur in localized Non-Hodgkin's lymphoma, survival and relapse patterns for 496 patients with stage I and II non-Hodgkin's lymphoma (NHL) treated with loco-regional irradiation (XRT) alone were examined. We identified 139 patients with GALT lymphoma (defined as arising from primitive gut and including Waldeyers' ring, thyroid, and GI lymphomas) and 87 patients with extranodal non-gut-associated lymphoma (ENL). Survival and relapse data were assessed in multifactorial analysis to correct for previously identified other prognostic variables. GALT lymphomas (GALT-L) have a survival advantage compared with other ENL (P = .017) independent of stage and histology. A difference in distant relapse (DR) rate between GALT-L and other ENL (P = .0002) was also identified. The presentation site of localized extranodal NHL is predictive of clinical behavior and is an independent determinant of outcome. This may be an expression of lymphocytic origin and determinants of migration patterns.
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Nandel Syofneri, Sarjon Defit, and Sumijan. "Implementasi Metode Backpropagation untuk Memprediksi Tingkat Kelulusan Uji Kopetensi Siswa." Jurnal Informasi & Teknologi 1, no. 4 (September 26, 2019): 12–17. http://dx.doi.org/10.37034/jidt.v1i4.13.
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Vocational High School (SMK) 2 Pekanbaru is a Vocational School in Industrial Technology. At present there are 2400 students with 14 majors. In students the level of will in students is still low. Resulting in a low graduation rate for students. This happened because of the difficulty in predicting the level of competency examination passing at SMK Negeri 2 Pekanbaru. The purpose of this study is to assist in predicting the passing level of competency exams so as to produce predictions of student graduation. The method used is the Backpropagation method. With this method data processing can be done using input values and targets that you want to produce. So that it can predict the graduation of students in the expertise competency test. Furthermore, the data to be managed is a recapitulation of the average vocational values majoring in computer network engineering from semester 1 to semester 5 with aspects of knowledge on the target students of 2017 Academic Year and 2018 Academic Year obtained from the sum of all subjects in each semester. The results of calculations using the Backpropagation method with the Matlab application will be predictive in producing grades for students' graduation rates in the future. So that the accuracy value will be obtained in the prediction. With the results of testing the accuracy of prediction student competency tests with patterns 5-4-1 reaching 85%, with patterns 5-6-1 reaching 95%, patterns 5-8-1 reaching 70%, patterns 5-10-1 reaching 85% % and with 5-12-1 patterns it reaches 85%. Of the five patterns, the best accuracy rate of 5-6-1 is 95%. The prediction results using the Bacpropagation method can become knowledge in the next year. So that the system parameters used in testing can be recognized properly.