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Huang, Huilin. "Strong Law of Large Numbers for Hidden Markov Chains Indexed by Cayley Trees". ISRN Probability and Statistics 2012 (23 de setembro de 2012): 1–11. http://dx.doi.org/10.5402/2012/768657.
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We extend the idea of hidden Markov chains on lines to the situation of hidden Markov chains indexed by Cayley trees. Then, we study the strong law of large numbers for hidden Markov chains indexed by Cayley trees. As a corollary, we get the strong limit law of the conditional sample entropy rate.
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Milone, Diego H., Leandro E. Di Persia e María E. Torres. "Denoising and recognition using hidden Markov models with observation distributions modeled by hidden Markov trees". Pattern Recognition 43, n.º 4 (abril de 2010): 1577–89. http://dx.doi.org/10.1016/j.patcog.2009.11.010.
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ANIGBOGU, J. C., e A. BELAÏD. "HIDDEN MARKOV MODELS IN TEXT RECOGNITION". International Journal of Pattern Recognition and Artificial Intelligence 09, n.º 06 (dezembro de 1995): 925–58. http://dx.doi.org/10.1142/s0218001495000389.
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A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for “advice” before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.
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Narayana, Pradyumna, J. Ross Beveridge e Bruce A. Draper. "Interacting Hidden Markov Models for Video Understanding". International Journal of Pattern Recognition and Artificial Intelligence 32, n.º 11 (24 de julho de 2018): 1855020. http://dx.doi.org/10.1142/s0218001418550200.
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People, cars and other moving objects in videos generate time series data that can be labeled in many ways. For example, classifiers can label motion tracks according to the object type, the action being performed, or the trajectory of the motion. These labels can be generated for every frame as long as the object stays in view, so object tracks can be modeled as Markov processes with multiple noisy observation streams. A challenge in video recognition is to recover the true state of the track (i.e. its class, action and trajectory) using Markov models without (a) counter-factually assuming that the streams are independent or (b) creating a fully coupled Hidden Markov Model (FCHMM) with an infeasibly large state space. This paper introduces a new method for labeling sequences of hidden states. The method exploits external consistency constraints among streams without modeling complex joint distributions between them. For example, common sense semantics suggest that trees cannot walk. This is an example of an external constraint between an object label (“tree”) and an action label (“walk”). The key to exploiting external constraints is a new variation of the Viterbi algorithm which we call the Viterbi–Segre (VS) algorithm. VS restricts the solution spaces of factorized HMMs to marginal distributions that are compatible with joint distributions satisfying sets of external constraints. Experiments on synthetic data show that VS does a better job of estimating true states with the given observations than the traditional Viterbi algorithm applied to (a) factorized HMMs, (b) FCHMMs, or (c) partially-coupled HMMs that model pairwise dependencies. We then show that VS outperforms factorized and pairwise HMMs on real video data sets for which FCHMMs cannot feasibly be trained.
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Fredes, Luis, e Jean-François Marckert. "Invariant measures of interacting particle systems: Algebraic aspects". ESAIM: Probability and Statistics 24 (2020): 526–80. http://dx.doi.org/10.1051/ps/2020008.
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Consider a continuous time particle system ηt = (ηt(k), k ∈ 𝕃), indexed by a lattice 𝕃 which will be either ℤ, ℤ∕nℤ, a segment {1, ⋯ , n}, or ℤd, and taking its values in the set Eκ𝕃 where Eκ = {0, ⋯ , κ − 1} for some fixed κ ∈{∞, 2, 3, ⋯ }. Assume that the Markovian evolution of the particle system (PS) is driven by some translation invariant local dynamics with bounded range, encoded by a jump rate matrix ⊤. These are standard settings, satisfied by the TASEP, the voter models, the contact processes. The aim of this paper is to provide some sufficient and/or necessary conditions on the matrix ⊤ so that this Markov process admits some simple invariant distribution, as a product measure (if 𝕃 is any of the spaces mentioned above), the law of a Markov process indexed by ℤ or [1, n] ∩ ℤ (if 𝕃 = ℤ or {1, …, n}), or a Gibbs measure if 𝕃 = ℤ/nℤ. Multiple applications follow: efficient ways to find invariant Markov laws for a given jump rate matrix or to prove that none exists. The voter models and the contact processes are shown not to possess any Markov laws as invariant distribution (for any memory m). (As usual, a random process X indexed by ℤ or ℕ is said to be a Markov chain with memory m ∈ {0, 1, 2, ⋯ } if ℙ(Xk ∈ A | Xk−i, i ≥ 1) = ℙ(Xk ∈ A | Xk−i, 1 ≤ i ≤ m), for any k.) We also prove that some models close to these models do. We exhibit PS admitting hidden Markov chains as invariant distribution and design many PS on ℤ2, with jump rates indexed by 2 × 2 squares, admitting product invariant measures.
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Durand, J. B., P. Goncalves e Y. Guedon. "Computational Methods for Hidden Markov Tree Models—An Application to Wavelet Trees". IEEE Transactions on Signal Processing 52, n.º 9 (setembro de 2004): 2551–60. http://dx.doi.org/10.1109/tsp.2004.832006.
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Tso, Brandt, e Joe L. Tseng. "Multi-resolution semantic-based imagery retrieval using hidden Markov models and decision trees". Expert Systems with Applications 37, n.º 6 (junho de 2010): 4425–34. http://dx.doi.org/10.1016/j.eswa.2009.11.086.
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Do, M. N. "Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models". IEEE Signal Processing Letters 10, n.º 4 (abril de 2003): 115–18. http://dx.doi.org/10.1109/lsp.2003.809034.
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Maua, D. D., C. P. De Campos, A. Benavoli e A. Antonucci. "Probabilistic Inference in Credal Networks: New Complexity Results". Journal of Artificial Intelligence Research 50 (28 de julho de 2014): 603–37. http://dx.doi.org/10.1613/jair.4355.
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Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The computational complexity of inferences on such models depends on the irrelevance/independence concept adopted. In this paper, we study inferential complexity under the concepts of epistemic irrelevance and strong independence. We show that inferences under strong independence are NP-hard even in trees with binary variables except for a single ternary one. We prove that under epistemic irrelevance the polynomial-time complexity of inferences in credal trees is not likely to extend to more general models (e.g., singly connected topologies). These results clearly distinguish networks that admit efficient inferences and those where inferences are most likely hard, and settle several open questions regarding their computational complexity. We show that these results remain valid even if we disallow the use of zero probabilities. We also show that the computation of bounds on the probability of the future state in a hidden Markov model is the same whether we assume epistemic irrelevance or strong independence, and we prove a similar result for inference in naive Bayes structures. These inferential equivalences are important for practitioners, as hidden Markov models and naive Bayes structures are used in real applications of imprecise probability.
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Segers, Johan. "One- versus multi-component regular variation and extremes of Markov trees". Advances in Applied Probability 52, n.º 3 (setembro de 2020): 855–78. http://dx.doi.org/10.1017/apr.2020.22.
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AbstractA Markov tree is a random vector indexed by the nodes of a tree whose distribution is determined by the distributions of pairs of neighbouring variables and a list of conditional independence relations. Upon an assumption on the tails of the Markov kernels associated to these pairs, the conditional distribution of the self-normalized random vector when the variable at the root of the tree tends to infinity converges weakly to a random vector of coupled random walks called a tail tree. If, in addition, the conditioning variable has a regularly varying tail, the Markov tree satisfies a form of one-component regular variation. Changing the location of the root, that is, changing the conditioning variable, yields a different tail tree. When the tails of the marginal distributions of the conditioning variables are balanced, these tail trees are connected by a formula that generalizes the time change formula for regularly varying stationary time series. The formula is most easily understood when the various one-component regular variation statements are tied up into a single multi-component statement. The theory of multi-component regular variation is worked out for general random vectors, not necessarily Markov trees, with an eye towards other models, graphical or otherwise.
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Azari, David P., Yu Hen Hu, Brady L. Miller, Brian V. Le e Robert G. Radwin. "Using Surgeon Hand Motions to Predict Surgical Maneuvers". Human Factors: The Journal of the Human Factors and Ergonomics Society 61, n.º 8 (23 de abril de 2019): 1326–39. http://dx.doi.org/10.1177/0018720819838901.
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Objective: This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations. Background: Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries. Method: We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video. Results: Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants. Conclusion: Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants. Application: Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching.
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Bischof, Walter F. "Visual Learning: An Overview". Swiss Journal of Psychology 63, n.º 3 (setembro de 2004): 151–64. http://dx.doi.org/10.1024/1421-0185.63.3.151.
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A review is presented of modern approaches to the learning and recognition of complex patterns, including discriminant functions, neural networks, decision trees, and hidden Markov models. Next, several relational learning systems are introduced and discussed, in detail one specific technique, conditional rule generation. This technique is shown to be very flexible and useful for the learning of static patterns, such as objects, as well as dynamic patterns, such as movement patterns. The technique is illustrated with a number of very difficult visual learning problems.
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Tür, Gökhan, Dilek Hakkani-Tür, Andreas Stolcke e Elizabeth Shriberg. "Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation". Computational Linguistics 27, n.º 1 (março de 2001): 31–57. http://dx.doi.org/10.1162/089120101300346796.
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We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.
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Loomis, Samuel P., e James P. Crutchfield. "Exploring predictive states via Cantor embeddings and Wasserstein distance". Chaos: An Interdisciplinary Journal of Nonlinear Science 32, n.º 12 (dezembro de 2022): 123115. http://dx.doi.org/10.1063/5.0102603.
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Predictive states for stochastic processes are a nonparametric and interpretable construct with relevance across a multitude of modeling paradigms. Recent progress on the self-supervised reconstruction of predictive states from time-series data focused on the use of reproducing kernel Hilbert spaces. Here, we examine how Wasserstein distances may be used to detect predictive equivalences in symbolic data. We compute Wasserstein distances between distributions over sequences (“predictions”) using a finite-dimensional embedding of sequences based on the Cantor set for the underlying geometry. We show that exploratory data analysis using the resulting geometry via hierarchical clustering and dimension reduction provides insight into the temporal structure of processes ranging from the relatively simple (e.g., generated by finite-state hidden Markov models) to the very complex (e.g., generated by infinite-state indexed grammars).
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SNIR, SAGI, e TAMIR TULLER. "THE NET-HMM APPROACH: PHYLOGENETIC NETWORK INFERENCE BY COMBINING MAXIMUM LIKELIHOOD AND HIDDEN MARKOV MODELS". Journal of Bioinformatics and Computational Biology 07, n.º 04 (agosto de 2009): 625–44. http://dx.doi.org/10.1142/s021972000900428x.
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Horizontal gene transfer (HGT) is the event of transferring genetic material from one lineage in the evolutionary tree to a different lineage. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Although the prevailing assumption is of complete HGT, cases of partial HGT (which are also named chimeric HGT) where only part of a gene is horizontally transferred, have also been reported, albeit less frequently. In this work we suggest a new probabilistic model, the NET-HMM, for analyzing and modeling phylogenetic networks. This new model captures the biologically realistic assumption that neighboring sites of DNA or amino acid sequences are not independent, which increases the accuracy of the inference. The model describes the phylogenetic network as a Hidden Markov Model (HMM), where each hidden state is related to one of the network's trees. One of the advantages of the NET-HMM is its ability to infer partial HGT as well as complete HGT. We describe the properties of the NET-HMM, devise efficient algorithms for solving a set of problems related to it, and implement them in software. We also provide a novel complementary significance test for evaluating the fitness of a model (NET-HMM) to a given dataset. Using NET-HMM, we are able to answer interesting biological questions, such as inferring the length of partial HGT's and the affected nucleotides in the genomic sequences, as well as inferring the exact location of HGT events along the tree branches. These advantages are demonstrated through the analysis of synthetical inputs and three different biological inputs.
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Costes, Evelyne, Colin Smith, Michael Renton, Yann Guédon, Przemyslaw Prusinkiewicz e Christophe Godin. "MAppleT: simulation of apple tree development using mixed stochastic and biomechanical models". Functional Plant Biology 35, n.º 10 (2008): 936. http://dx.doi.org/10.1071/fp08081.
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Construction of tree architectural databases over years is time consuming and cannot easily capture event dynamics, especially when both tree topology and geometry are considered. The present project aimed to bring together models of topology and geometry in a single simulation such that the architecture of an apple tree may emerge from process interactions. This integration was performed using L-systems. A mixed approach was developed based on stochastic models to simulate plant topology and mechanistic model for the geometry. The succession of growth units (GUs) along axes and their branching structure were jointly modelled by a hierarchical hidden Markov model. A biomechanical model, derived from previous studies, was used to calculate stem form at the metamer scale, taking into account the intra-year dynamics of primary, secondary and fruit growth. Outputs consist of 3-D mock-ups – geometric models representing the progression of tree form over time. To asses these models, a sensitivity analysis was performed and descriptors were compared between simulated and digitised trees, including the total number of GUs in the entire tree, descriptors of shoot geometry (basal diameter, length), and descriptors of axis geometry (inclination, curvature). In conclusion, despite some limitations, MAppleT constitutes a useful tool for simulating development of apple trees in interaction with gravity.
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Prokofieva, A. V., e A. N. Shniperov. "A Markov Chain - Based Method for JPEG Image Steganalysis and Its Application in Combination with Various Machine Learning Algorithms". Vestnik NSU. Series: Information Technologies 20, n.º 4 (13 de junho de 2023): 61–75. http://dx.doi.org/10.25205/1818-7900-2022-20-4-61-75.
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The paper proposes a method of extracting the feature vector of images, which makes it possible to effectively detect the presence of hidden information in JPEG images embedded by various popular steganography tools. This method is based on the usage of the transition probability matrix. The essence of the method for extracting the feature vector of the image is to use the transition probability matrix and apply the image calibration method to improve the accuracy of steganalysis and reduce the number of false positives. For each image from the training and test sets a feature vector is found in this way, the number of elements is 324. Further, the models were trained on the training dataset by each of machine learning methods separately: decision trees with gradient boosting, linear models, k-nearest neighbors, support vector machines, neural networks, and artificial immune systems. To assess the capacity of the models the following metrics were used: accuracy, the rate of the false positive and false negative errors, and the confusion matrix. The results of classification by each of the above methods are given. For training and testing a dataset IStego100K was used, which consists of 208 thousand images of the same size 1024 x 1024 with different quality values in the range from 75 to 95. One of the J-UNIWARD, nsF5, and UERD steganography algorithms was used to embed a hidden message. As a result, we can observe that the proposed approach to extracting the feature vector makes it possible to detect the presence of hidden information embedded by non-adaptive steganography (Steghide, OutGuess and nsF5) in static JPEG images with high accuracy (more than 95%). However, for adaptive steganography methods (J-UNIWARD, UERD) the accuracy is less (about 50-60%).
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Ogundile, Olayinka, Oluwaseyi Babalola, Afolakemi Ogunbanwo, Olabisi Ogundile e Vipin Balyan. "Credit Card Fraud: Analysis of Feature Extraction Techniques for Ensemble Hidden Markov Model Prediction Approach". Applied Sciences 14, n.º 16 (21 de agosto de 2024): 7389. http://dx.doi.org/10.3390/app14167389.
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In the face of escalating credit card fraud due to the surge in e-commerce activities, effectively distinguishing between legitimate and fraudulent transactions has become increasingly challenging. To address this, various machine learning (ML) techniques have been employed to safeguard cardholders and financial institutions. This article explores the use of the Ensemble Hidden Markov Model (EHMM) combined with two distinct feature extraction methods: principal component analysis (PCA) and a proposed statistical feature set termed MRE, comprising Mean, Relative Amplitude, and Entropy. Both the PCA-EHMM and MRE-EHMM approaches were evaluated using a dataset of European cardholders and demonstrated comparable performance in terms of recall (sensitivity), specificity, precision, and F1-score. Notably, the MRE-EHMM method exhibited significantly reduced computational complexity, making it more suitable for real-time credit card fraud detection. Results also demonstrated that the PCA and MRE approaches perform significantly better when integrated with the EHMM in contrast to the conventional HMM approach. In addition, the proposed MRE-EHMM and PCA-EHMM techniques outperform other classic ML models, including random forest (RF), linear regression (LR), decision trees (DT) and K-nearest neighbour (KNN).
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Holmes, Ian. "Using evolutionary Expectation Maximization to estimate indel rates". Bioinformatics 21, n.º 10 (24 de fevereiro de 2005): 2294–300. http://dx.doi.org/10.1093/bioinformatics/bti177.
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Abstract Motivation The Expectation Maximization (EM) algorithm, in the form of the Baum–Welch algorithm (for hidden Markov models) or the Inside-Outside algorithm (for stochastic context-free grammars), is a powerful way to estimate the parameters of stochastic grammars for biological sequence analysis. To use this algorithm for multiple-sequence evolutionary modelling, it would be useful to apply the EM algorithm to estimate not only the probability parameters of the stochastic grammar, but also the instantaneous mutation rates of the underlying evolutionary model (to facilitate the development of stochastic grammars based on phylogenetic trees, also known as Statistical Alignment). Recently, we showed how to do this for the point substitution component of the evolutionary process; here, we extend these results to the indel process. Results We present an algorithm for maximum-likelihood estimation of insertion and deletion rates from multiple sequence alignments, using EM, under the single-residue indel model owing to Thorne, Kishino and Felsenstein (the ‘TKF91’ model). The algorithm converges extremely rapidly, gives accurate results on simulated data that are an improvement over parsimonious estimates (which are shown to underestimate the true indel rate), and gives plausible results on experimental data (coronavirus envelope domains). Owing to the algorithm's close similarity to the Baum–Welch algorithm for training hidden Markov models, it can be used in an ‘unsupervised’ fashion to estimate rates for unaligned sequences, or estimate several sets of rates for sequences with heterogenous rates. Availability Software implementing the algorithm and the benchmark is available under GPL from http://www.biowiki.org/ Contact ihh@berkeley.edu
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Griffin, Kevin, Thomas Harris, Sarah Bruner, Patrick McKenzie e Jeremy Hise. "Is the Radial Growth of Irrigated Urban Trees More Strongly Correlated to Light and Temperature than Water?" Arboriculture & Urban Forestry 47, n.º 5 (1 de setembro de 2021): 214–31. http://dx.doi.org/10.48044/jauf.2021.019.
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Background: Real-time monitoring of tree growth can provide novel information about trees in urban/suburban areas and the myriad ecosystem services they provide. By monitoring irrigated specimen trees, we tested the hypothesis that in trees with sufficient water, growth is governed by environmental factors regulating energy gain rather than by factors related to water use. Methods: Internet-enabled, high-resolution dendrometers were installed on 3 trees in Southampton, NY, USA. The instruments, along with a weather station, streamed data to a project web page that was updated once an hour. Growing periods were determined using a Hidden Markov Model based on a zero-growth model. Linear models and conditional inference trees correlated environmental variables to growth magnitude and rate of growth. Results: Growth was governed by the interacting environmental variables of air temperature, soil moisture, vapor pressure deficit (VPD), and took place primarily at night. Radial growth of spruce began April 14 after the accumulation of 69.7 °C growing degree days and ended September 7. Cedar growth began later (April 26) after the accumulation of 160.6 °C and ended later (November 3). During the observation period, these 3 modest suburban trees sequestered 115.1 kg of CO2. Conclusions: Though irrigated, residential tree growth in our experiment was affected by environmental factors relating to both water use and energy gain through photosynthesis. Linking tree growth to fluctuations in environmental conditions facilitates the development of a predictive understanding useful for ecosystem management and growth forecasting across future altering climates.
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Ghahramani, Zoubin. "Bayesian non-parametrics and the probabilistic approach to modelling". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, n.º 1984 (13 de fevereiro de 2013): 20110553. http://dx.doi.org/10.1098/rsta.2011.0553.
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Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman’s coalescent, Dirichlet diffusion trees and Wishart processes.
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Negrón, Claudia, Loreto Contador, Bruce D. Lampinen, Samuel G. Metcalf, Yann Guédon, Evelyne Costes e Theodore M. DeJong. "How different pruning severities alter shoot structure: a modelling approach in young ‘Nonpareil’ almond trees". Functional Plant Biology 42, n.º 3 (2015): 325. http://dx.doi.org/10.1071/fp14025.
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Axillary meristem fate patterns along shoots, also referred to as shoot structure, appear to be fairly consistent among trees within a genotype growing under similar conditions. Less is known about shoot structural plasticity following external manipulations, such as pruning. The aim of this study on almond (Prunus dulcis (Mill.)) shoots was to investigate how pruning severity affects the structure of 1-year-old shoots that grew after pruning (regrowth shoots), the 2-year-old portion of shoots that remained from the previous year’s growth after pruning (pruned shoots), and whether regrowth shoots reiterate the structure of the original 1-year-old shoots before pruning. Three pruning severities were imposed and the structures along the different shoots were assessed by building hidden semi-Markov models of axillary meristem fates. The structures of regrowth and pruned shoots depended on pruning severity, but maintained some of the original shoot characteristics. Regrowth shoots developed more complex structures with severe pruning, but had simpler structures than original shoots indicating progressive simplification with tree age. Pruned shoot structures were affected by the severity of pruning, by the structure when the shoots were 1 year old, and probably by local competition among buds. Changes in structure due to pruning can be modelled and be predictable.
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Mouaz, Bezoui, Cherif Walid, Beni-Hssane Abderrahim e Elmoutaouakkil Abdelmajid. "A new framework based on KNN and DT for speech identification through emphatic letters in Moroccan dialect". Indonesian Journal of Electrical Engineering and Computer Science 21, n.º 3 (10 de março de 2021): 1417. http://dx.doi.org/10.11591/ijeecs.v21.i3.pp1417-1423.
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<p class="keywords"><span id="docs-internal-guid-6347807a-7fff-e7da-a2d6-74cb8393677f"><span>Arabic dialects differ substantially from modern standard arabic and each other in terms of phonology, morphology, lexical choice and syntax. This makes the identification of dialects from speeches a very difficult task. In this paper, we introduce a speech recognition system that automatically identifies the gender of speaker, the emphatic letter pronounced and also the diacritic of these emphatic letters given a sample of author’s speeches. Firstly we examined the performance of the single case classifier hidden markov models (HMM) applied to the samples of our data corpus. Then we evaluated our proposed approach KNN-DT which is a hybridization of two classifiers namely decision trees (DT) and k-nearest neighbors (KNN). Both models are singularly applied directly to the data corpus to recognize the emphatic letter of the sound and to the diacritic and the gender of the speaker. This hybridization proved quite interesting; it improved the speech recognition accuracy by more than 10% compared to state-of-the-art approaches.</span></span></p>
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Schirmer, Michael, Michael Lehning e Jürg Schweizer. "Statistical forecasting of regional avalanche danger using simulated snow-cover data". Journal of Glaciology 55, n.º 193 (2009): 761–68. http://dx.doi.org/10.3189/002214309790152429.
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AbstractIn the past, numerical prediction of regional avalanche danger using statistical methods with meteorological input variables has shown insufficiently accurate results, possibly due to the lack of snowstratigraphy data. Detailed snow-cover data were rarely used because they were not readily available (manual observations). With the development and increasing use of snow-cover models this deficiency can now be rectified and model output can be used as input for forecasting models. We used the output of the physically based snow-cover model SNOWPACK combined with meteorological variables to investigate and establish a link to regional avalanche danger. Snow stratigraphy was simulated for the location of an automatic weather station near Davos, Switzerland, over nine winters. Only dry-snow situations were considered. A variety of selection algorithms was used to identify the most important simulated snow variables. Data mining and statistical methods, including classification trees, artificial neural networks, support vector machines, hidden Markov models and nearest-neighbour methods were trained on the forecasted regional avalanche danger (European avalanche danger scale). The best results were achieved with a nearest-neighbour method which used the avalanche danger level of the previous day as additional input. A cross-validated accuracy (hit rate) of 73% was obtained. This study suggests that modelled snow-stratigraphy variables, as provided by SNOWPACK, are able to improve numerical avalanche forecasting.
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Moraru, Cristina. "VirClust—A Tool for Hierarchical Clustering, Core Protein Detection and Annotation of (Prokaryotic) Viruses". Viruses 15, n.º 4 (19 de abril de 2023): 1007. http://dx.doi.org/10.3390/v15041007.
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Recent years have seen major changes in the classification criteria and taxonomy of viruses. The current classification scheme, also called “megataxonomy of viruses”, recognizes six different viral realms, defined based on the presence of viral hallmark genes (VHGs). Within the realms, viruses are classified into hierarchical taxons, ideally defined by the phylogeny of their shared genes. To enable the detection of shared genes, viruses have first to be clustered, and there is currently a need for tools to assist with virus clustering and classification. Here, VirClust is presented. It is a novel, reference-free tool capable of performing: (i) protein clustering, based on BLASTp and Hidden Markov Models (HMMs) similarities; (ii) hierarchical clustering of viruses based on intergenomic distances calculated from their shared protein content; (iii) identification of core proteins and (iv) annotation of viral proteins. VirClust has flexible parameters both for protein clustering and for splitting the viral genome tree into smaller genome clusters, corresponding to different taxonomic levels. Benchmarking on a phage dataset showed that the genome trees produced by VirClust match the current ICTV classification at family, sub-family and genus levels. VirClust is freely available, as a web-service and stand-alone tool.
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Aman, Pawar. "Crime Prevention and Addressing Violence Against Women". International Journal for Research in Applied Science and Engineering Technology 12, n.º 11 (30 de novembro de 2024): 609–13. http://dx.doi.org/10.22214/ijraset.2024.65134.
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The safety of women remains a pressing societal concern, with many facing threats like harassment, rape, molestation, and domestic abuse due to various sociocultural factors. The Internet of Things (IoT) has emerged as a promising tool to address these issues. This study systematically reviews research papers on IoT-based devices for women's safety, analyzing key features, wearable components, sensor types, and machine learning algorithms used. The review covers articles published between 2016 and 2022. It finds that pulse-rate and pressure sensors are commonly used to monitor women in distress, while technologies like GPS, GSM, and Raspberry Pi enable alert transmission. Machine learning algorithms such as logistic regression, hidden Markov models, and decision trees help identify women at risk and prevent dangerous situations. The review also highlights the need for improved systems that focus on automatic alert generation with minimal human interaction and greater accuracy. In addition, the study proposes a taxonomy categorizing various techniques, features, and sensors, along with an architectural model for developing IoT-based safety devices. Finally, it underscores the importance of integrating multiple sensors to enhance threat detection accuracy, while identifying gaps and challenges in practical applications.
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Stolcke, Andreas, Klaus Ries, Noah Coccaro, Elizabeth Shriberg, Rebecca Bates, Daniel Jurafsky, Paul Taylor, Rachel Martin, Carol Van Ess-Dykema e Marie Meteer. "Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech". Computational Linguistics 26, n.º 3 (setembro de 2000): 339–73. http://dx.doi.org/10.1162/089120100561737.
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We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as STATEMENT, Question, BACKCHANNEL, Agreement, Disagreement, and Apology. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65% based on errorful, automatically recognized words and prosody, and 71% based on word transcripts, compared to a chance baseline accuracy of 35% and human accuracy of 84%) and a small reduction in word recognition error.
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Lee, Michael D. "GToTree: a user-friendly workflow for phylogenomics". Bioinformatics 35, n.º 20 (13 de março de 2019): 4162–64. http://dx.doi.org/10.1093/bioinformatics/btz188.
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Abstract Summary Genome-level evolutionary inference (i.e. phylogenomics) is becoming an increasingly essential step in many biologists’ work. Accordingly, there are several tools available for the major steps in a phylogenomics workflow. But for the biologist whose main focus is not bioinformatics, much of the computational work required—such as accessing genomic data on large scales, integrating genomes from different file formats, performing required filtering, stitching different tools together etc.—can be prohibitive. Here I introduce GToTree, a command-line tool that can take any combination of fasta files, GenBank files and/or NCBI assembly accessions as input and outputs an alignment file, estimates of genome completeness and redundancy, and a phylogenomic tree based on a specified single-copy gene (SCG) set. Although GToTree can work with any custom hidden Markov Models (HMMs), also included are 13 newly generated SCG-set HMMs for different lineages and levels of resolution, built based on searches of ∼12 000 bacterial and archaeal high-quality genomes. GToTree aims to give more researchers the capability to make phylogenomic trees. Availability and implementation GToTree is open-source and freely available for download from: github.com/AstrobioMike/GToTree. It is implemented primarily in bash with helper scripts written in python. Supplementary information Supplementary data are available at Bioinformatics online.
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Prado, Lenio, Marcelo Fonseca, José V. Bernardes, Mateus G. Santos, Edson C. Bortoni e Guilherme S. Bastos. "Forecast of Operational Downtime of the Generating Units for Sediment Cleaning in the Water Intakes: A Case of the Jirau Hydropower Plant". Energies 16, n.º 17 (1 de setembro de 2023): 6354. http://dx.doi.org/10.3390/en16176354.
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Hydropower plants (HPP) in the Amazon basin suffer from issues caused by trees and sediments carried by the river. The Jirau HPP, located in the occidental Amazon basin, is directly affected by high sediment transportation. These materials accumulate in the water intakes and obstruct the trash racks installed in the intake system to prevent the entry of materials. As a result, head losses negatively impact the efficiency of the generating units and the power production capacity. The HPP operation team must monitor these losses and take action timely to clear the intakes. One of the possible actions is to stop the GU to let the sediment settle down. Therefore, intelligent methods are required to predict the downtime for sediment settling and restoring operational functionality. Thus, this work proposes a technique that utilizes hidden Markov models and Bayesian networks to predict the fifty Jirau generation units’ downtime, thereby reducing their inactive time and providing methodologies for establishing operating rules. The model is based on accurate operational data extracted from the hydropower plant, which ensures greater fidelity to the daily operational reality of the plant. The results demonstrate the model’s effectiveness and indicate the extent of the impact on downtime under varying sediment levels and when neighboring units are generating or inactive.
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Patil, Prof Pradnya, Prof Minal Sonkar, Prof Pallavi Patil, Prof Priyanka Deshmukh e Prof Trupti Patil. "A Comprehensive Strategy for Detecting Credit Card Fraud in E-Commerce Utilizing DNS Authentication". International Journal of Soft Computing and Engineering 14, n.º 5 (30 de novembro de 2024): 30–35. http://dx.doi.org/10.35940/ijsce.f3656.14051124.
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E commerce has transformed global trade, enabling businesses to reach audiences worldwide since the World Wide Webs inception in 1990. Companies like Amazon demonstrate this growth, evolving from a small online bookstore to a retail giant. E commerces appeal lies in its global reach, cost efficiency, and 24 slash 7 availability. However, security challenges, especially credit card fraud, remain significant, causing substantial losses to businesses, particularly small and medium sized enterprises. Addressing fraud in e-commerce through machine learning techniques is crucial. Techniques such as Logistic Regression, Decision Trees, and Hidden Markov Models each offer unique advantages and limitations for detecting fraud, with some able to operate in realtime. These methods help reduce false positives and improve fraud detection, making them integral to secure e commerce environments. This paper introduces a system that uses disposable domain names and custom DNS servers to detect transaction inconsistencies, thus addressing proxy based fraud attempts. By generating unique hostnames for each transaction, the system enables real time monitoring and validation of client transactions. This DNS profiling approach strengthens e commerce security, reduces financial risks, and enhances trust. The findings underscore the need for advanced fraud detection, contributing to safer online transactions and offering valuable insights for future secure e commerce systems.
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Tibbetts, Jake, Bethany L. Goldblum, Christopher Stewart e Arman Hashemizadeh. "Classification of Nuclear Reactor Operations Using Spatial Importance and Multisensor Networks". Journal of Nuclear Engineering 3, n.º 4 (22 de setembro de 2022): 243–62. http://dx.doi.org/10.3390/jne3040014.
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Distributed multisensor networks record multiple data streams that can be used as inputs to machine learning models designed to classify operations relevant to proliferation at nuclear reactors. The goal of this work is to demonstrate methods to assess the importance of each node (a single multisensor) and region (a group of proximate multisensors) to machine learning model performance in a reactor monitoring scenario. This, in turn, provides insight into model behavior, a critical requirement of data-driven applications in nuclear security. Using data collected at the High Flux Isotope Reactor at Oak Ridge National Laboratory via a network of Merlyn multisensors, two different models were trained to classify the reactor’s operational state: a hidden Markov model (HMM), which is simpler and more transparent, and a feed-forward neural network, which is less inherently interpretable. Traditional wrapper methods for feature importance were extended to identify nodes and regions in the multisensor network with strong positive and negative impacts on the classification problem. These spatial-importance algorithms were evaluated on the two different classifiers. The classification accuracy was then improved relative to baseline models via feature selection from 0.583 to 0.839 and from 0.811 ± 0.005 to 0.884 ± 0.004 for the HMM and feed-forward neural network, respectively. While some differences in node and region importance were observed when using different classifiers and wrapper methods, the nodes near the facility’s cooling tower were consistently identified as important—a conclusion further supported by studies on feature importance in decision trees. Node and region importance methods are model-agnostic, inform feature selection for improved model performance, and can provide insight into opaque classification models in the nuclear security domain.
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Yordanova, Kristina, Stefan Lüdtke, Samuel Whitehouse, Frank Krüger, Adeline Paiement, Majid Mirmehdi, Ian Craddock e Thomas Kirste. "Analysing Cooking Behaviour in Home Settings: Towards Health Monitoring". Sensors 19, n.º 3 (4 de fevereiro de 2019): 646. http://dx.doi.org/10.3390/s19030646.
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Wellbeing is often affected by health-related conditions. Among them are nutrition-related health conditions, which can significantly decrease the quality of life. We envision a system that monitors the kitchen activities of patients and that based on the detected eating behaviour could provide clinicians with indicators for improving a patient’s health. To be successful, such system has to reason about the person’s actions and goals. To address this problem, we introduce a symbolic behaviour recognition approach, called Computational Causal Behaviour Models (CCBM). CCBM combines symbolic representation of person’s behaviour with probabilistic inference to reason about one’s actions, the type of meal being prepared, and its potential health impact. To evaluate the approach, we use a cooking dataset of unscripted kitchen activities, which contains data from various sensors in a real kitchen. The results show that the approach is able to reason about the person’s cooking actions. It is also able to recognise the goal in terms of type of prepared meal and whether it is healthy. Furthermore, we compare CCBM to state-of-the-art approaches such as Hidden Markov Models (HMM) and decision trees (DT). The results show that our approach performs comparable to the HMM and DT when used for activity recognition. It outperformed the HMM for goal recognition of the type of meal with median accuracy of 1 compared to median accuracy of 0.12 when applying the HMM. Our approach also outperformed the HMM for recognising whether a meal is healthy with a median accuracy of 1 compared to median accuracy of 0.5 with the HMM.
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Shchetinin, E. Yu. "AUTOMATIC ARRHYTHMIA DETECTION BASED ON THE ANALYSIS OF ELECTROCARDIOGRAMS WITH DEEP LEARNING". Vestnik komp'iuternykh i informatsionnykh tekhnologii, n.º 203 (maio de 2021): 18–27. http://dx.doi.org/10.14489/vkit.2021.05.pp.018-027.
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According to the World Health Organization, cardiovascular diseases (CVD) are one of the most common causes of death in the world. The most effective clinical method for visualizing the cardiac electrical activity is electrocardiography (ECG). Automated ECG analysis has been of great interest in the medical researches. The problem of automated detection of cardiac arrhythmias may be reduced to the ECG signals classification. To solve this task such methods were used as Hidden Markov Models (HMM), discrete wavelet transforms (DWT), support vector machine (SVM) etc. Now days, the deep learning models began to play the major role in solving this problem. In this paper, for the classification of ECG signals, a number of models of deep neural networks, including deep convolutional, recurrent based on short-term long memory have been developed and implemented. To improve the classification accuracy of individual classes of the studied data, the CNN-LSTM deep model was built, which combines convolutional and recurrent networks. In addition the following machine learning algorithms were used for ECG signals classification: support vector machine (SVM), decision trees (DT), random forest (RF) and extreme gradient boosting (XGB). To test the performance of the proposed models, MIT-BIH database was used, a freely available dataset that is widely used to evaluate the effectiveness of ECG signal classification algorithms. The results of a comparative analysis of various algorithms for the quality of classification for individual classes showed that machine learning algorithms classify classes with a large volume of samples well. For example, SVM and DT classify samples from class N and Q with an accuracy of 92 and 97%, respectively, while samples from classes S and F are classified with much worse accuracy of 63%. At the same time, analyzing and comparing the performance of various neural network models based on the obtained estimates of the classification accuracy, it can be argued that CNN LSTM model allows not only a high classification accuracy of 99.37%, but also high values of other indicators of classification quality, such as F1- metric, precision, and recall.The proposed algorithms for the automated detection of cardiac arrhythmias can be applied in biomedical applications that analyze the electrocardiogram and help physicians diagnose cardiac arrhythmias more accurately.
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Ding, Luyu, Yang Lv, Ruixiang Jiang, Wenjie Zhao, Qifeng Li, Baozhu Yang, Ligen Yu, Weihong Ma, Ronghua Gao e Qinyang Yu. "Predicting the Feed Intake of Cattle Based on Jaw Movement Using a Triaxial Accelerometer". Agriculture 12, n.º 7 (21 de junho de 2022): 899. http://dx.doi.org/10.3390/agriculture12070899.
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The use of an accelerometer is considered as a promising method for the automatic measurement of the feeding behavior or feed intake of cattle, with great significance in facilitating daily management. To address further need for commercial use, an efficient classification algorithm at a low sample frequency is needed to reduce the amount of recorded data to increase the battery life of the monitoring device, and a high-precision model needs to be developed to predict feed intake on the basis of feeding behavior. Accelerograms for the jaw movement and feed intake of 13 mid-lactating cows were collected during feeding with a sampling frequency of 1 Hz at three different positions: the nasolabial levator muscle (P1), the right masseter muscle (P2), and the left lower lip muscle (P3). A behavior identification framework was developed to recognize jaw movements including ingesting, chewing and ingesting–chewing through extreme gradient boosting (XGB) integrated with the hidden Markov model solved by the Viterbi algorithm (HMM–Viterbi). Fourteen machine learning models were established and compared in order to predict feed intake rate through the accelerometer signals of recognized jaw movement activities. The developed behavior identification framework could effectively recognize different jaw movement activities with a precision of 99% at a window size of 10 s. The measured feed intake rate was 190 ± 89 g/min and could be predicted efficiently using the extra trees regressor (ETR), whose R2, RMSE, and NME were 0.97, 0.36 and 0.05, respectively. The three investigated monitoring sites may have affected the accuracy of feed intake prediction, but not behavior identification. P1 was recommended as the proper monitoring site, and the results of this study provide a reference for the further development of a wearable device equipped with accelerometers to measure feeding behavior and to predict feed intake.
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Shapiro, Jason W., e Catherine Putonti. "Rephine.r: a pipeline for correcting gene calls and clusters to improve phage pangenomes and phylogenies". PeerJ 9 (6 de agosto de 2021): e11950. http://dx.doi.org/10.7717/peerj.11950.
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Background A pangenome is the collection of all genes found in a set of related genomes. For microbes, these genomes are often different strains of the same species, and the pangenome offers a means to compare gene content variation with differences in phenotypes, ecology, and phylogenetic relatedness. Though most frequently applied to bacteria, there is growing interest in adapting pangenome analysis to bacteriophages. However, working with phage genomes presents new challenges. First, most phage families are under-sampled, and homologous genes in related viruses can be difficult to identify. Second, homing endonucleases and intron-like sequences may be present, resulting in fragmented gene calls. Each of these issues can reduce the accuracy of standard pangenome analysis tools. Methods We developed an R pipeline called Rephine.r that takes as input the gene clusters produced by an initial pangenomics workflow. Rephine.r then proceeds in two primary steps. First, it identifies three common causes of fragmented gene calls: (1) indels creating early stop codons and new start codons; (2) interruption by a selfish genetic element; and (3) splitting at the ends of the reported genome. Fragmented genes are then fused to create new sequence alignments. In tandem, Rephine.r searches for distant homologs separated into different gene families using Hidden Markov Models. Significant hits are used to merge families into larger clusters. A final round of fragment identification is then run, and results may be used to infer single-copy core genomes and phylogenetic trees. Results We applied Rephine.r to three well-studied phage groups: the Tevenvirinae (e.g., T4), the Studiervirinae (e.g., T7), and the Pbunaviruses (e.g., PB1). In each case, Rephine.r recovered additional members of the single-copy core genome and increased the overall bootstrap support of the phylogeny. The Rephine.r pipeline is provided through GitHub (https://www.github.com/coevoeco/Rephine.r) as a single script for automated analysis and with utility functions to assist in building single-copy core genomes and predicting the sources of fragmented genes.
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Milone, D. H., e L. E. Di Persia. "Learning Hidden Markov Models with Hidden Markov Trees as Observation Distributions". INTELIGENCIA ARTIFICIAL 12, n.º 37 (12 de março de 2008). http://dx.doi.org/10.4114/ia.v12i37.953.
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Adam, Timo, Marius Ötting e Rouven Michels. "Markov-switching decision trees". AStA Advances in Statistical Analysis, 29 de maio de 2024. http://dx.doi.org/10.1007/s10182-024-00501-6.
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AbstractDecision trees constitute a simple yet powerful and interpretable machine learning tool. While tree-based methods are designed only for cross-sectional data, we propose an approach that combines decision trees with time series modeling and thereby bridges the gap between machine learning and statistics. In particular, we combine decision trees with hidden Markov models where, for any time point, an underlying (hidden) Markov chain selects the tree that generates the corresponding observation. We propose an estimation approach that is based on the expectation-maximisation algorithm and assess its feasibility in simulation experiments. In our real-data application, we use eight seasons of National Football League (NFL) data to predict play calls conditional on covariates, such as the current quarter and the score, where the model’s states can be linked to the teams’ strategies. R code that implements the proposed method is available on GitHub.
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Rautiainen, Heidi, Moudud Alam, Paul G. Blackwell e Anna Skarin. "Identification of reindeer fine-scale foraging behaviour using tri-axial accelerometer data". Movement Ecology 10, n.º 1 (20 de setembro de 2022). http://dx.doi.org/10.1186/s40462-022-00339-0.
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AbstractAnimal behavioural responses to the environment ultimately affect their survival. Monitoring animal fine-scale behaviour may improve understanding of animal functional response to the environment and provide an important indicator of the welfare of both wild and domesticated species. In this study, we illustrate the application of collar-attached acceleration sensors for investigating reindeer fine-scale behaviour. Using data from 19 reindeer, we tested the supervised machine learning algorithms Random forests, Support vector machines, and hidden Markov models to classify reindeer behaviour into seven classes: grazing, browsing low from shrubs or browsing high from trees, inactivity, walking, trotting, and other behaviours. We implemented leave-one-subject-out cross-validation to assess generalizable results on new individuals. Our main results illustrated that hidden Markov models were able to classify collar-attached accelerometer data into all our pre-defined behaviours of reindeer with reasonable accuracy while Random forests and Support vector machines were biased towards dominant classes. Random forests using 5-s windows had the highest overall accuracy (85%), while hidden Markov models were able to best predict individual behaviours and handle rare behaviours such as trotting and browsing high. We conclude that hidden Markov models provide a useful tool to remotely monitor reindeer and potentially other large herbivore species behaviour. These methods will allow us to quantify fine-scale behavioural processes in relation to environmental events.
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Abbondandolo, Alberto, Florian Henning, Christof Külske e Pietro Majer. "Infinite-Volume States with Irreducible Localization Sets for Gradient Models on Trees". Journal of Statistical Physics 191, n.º 6 (27 de maio de 2024). http://dx.doi.org/10.1007/s10955-024-03278-9.
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AbstractWe consider general classes of gradient models on regular trees with spin values in a countable Abelian group S such as $${\mathbb {Z}}$$ Z or $${\mathbb {Z}}_q$$ Z q . This includes unbounded spin models like the p-SOS model and finite-alphabet clock models. Under a strong coupling (low temperature) condition on the interaction, we prove the existence of families of distinct homogeneous tree-indexed Markov chain Gibbs states $$\mu _A$$ μ A whose single-site marginals concentrate on a given finite subset $$A\subset S$$ A ⊂ S of spin values. The existence of such states is a new and robust phenomenon which is of particular relevance for infinite spin models. These states are extremal in the set of homogeneous Gibbs states, and in particular cannot be decomposed into homogeneous Markov-chain Gibbs states with a single-valued concentration center. Whether they are also extremal in the set of all Gibbs states remains an open, challenging question. As a further application of the method we obtain the existence of new types of gradient Gibbs states with $${\mathbb {Z}}$$ Z -valued spins, whose single-site marginals do not localize, but whose correlation structure depends on the finite set A, where we provide explicit expressions for the correlation between the height-increments along disjoint edges.
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"Comments on "Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models"". IEEE Signal Processing Letters 10, n.º 8 (agosto de 2003): 250. http://dx.doi.org/10.1109/lsp.2003.816070.
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Hsiao, Janet H., Jeehye An, Veronica Kit Sum Hui, Yueyuan Zheng e Antoni B. Chan. "Understanding the role of eye movement consistency in face recognition and autism through integrating deep neural networks and hidden Markov models". npj Science of Learning 7, n.º 1 (25 de outubro de 2022). http://dx.doi.org/10.1038/s41539-022-00139-6.
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AbstractGreater eyes-focused eye movement pattern during face recognition is associated with better performance in adults but not in children. We test the hypothesis that higher eye movement consistency across trials, instead of a greater eyes-focused pattern, predicts better performance in children since it reflects capacity in developing visual routines. We first simulated visual routine development through combining deep neural network and hidden Markov model that jointly learn perceptual representations and eye movement strategies for face recognition. The model accounted for the advantage of eyes-focused pattern in adults, and predicted that in children (partially trained models) consistency but not pattern of eye movements predicted recognition performance. This result was then verified with data from typically developing children. In addition, lower eye movement consistency in children was associated with autism diagnosis, particularly autistic traits in social skills. Thus, children’s face recognition involves visual routine development through social exposure, indexed by eye movement consistency.
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Iloga, Sylvain. "Accurate comparison of tree sets using HMM-based descriptor vectors". Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées Volume 36 - Special issue CRI... (23 de agosto de 2022). http://dx.doi.org/10.46298/arima.9107.
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Trees are among the most studied data structures and several techniques have consequently been developed for comparing two trees belonging to the same category. Until the end of year 2020, there was a serious lack of suitable metrics for comparing two weighted trees or two trees from different categories. The problem of comparing two tree sets was not also specifically addressed. These limitations have been overcome in a paper published in 2021 where a customizable metric based on hidden Markov models has been proposed for comparing two tree sets, each containing a mixture of trees belonging to various categories. Unfortunately, that metric does not allow the use of non metric-dependent classifiers which take descriptor vectors as inputs. This paper addresses this drawback by deriving a descriptor vector for each tree set using meta-information related to its corresponding models. The comparison between two tree sets is then realized by comparing their associated descriptor vectors. Classification experiments carried out on the databases FirstLast-L (FL), FirstLast-LW (FLW) and Stanford Sentiment Treebank (SSTB) respectively showed best accuracies of 99.75%, 99.75% and 87.22%. These performances are respectively 40.75% and 20.52% better than the tree Edit distance respectively for FLW and SSTB. Additional clustering experiments exhibited 54.25%, 98.75% and 75.53% of correctly clustered instances for FL, FLW and SSTB. No clustering was performed in existing work.
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Runthala, Ashish, K. Sowmya, Shamantha Nasika, Bhimavarapu Sai, Atanu Talukdar, Vijayakumar Rajendran, S. Karthikeyan, T. Silambarasan e Manmohan Sharma. "Refined Evolutionary Trees Through an Exceptionally Compatible Alignment-Substitution Model". Journal of Applied Biology & Biotechnology, 2024. http://dx.doi.org/10.7324/jabb.2024.163103.
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A phylogenetic tree commonly represents evolutionary relationships within a set of protein sequences. Various methods and strategies have been used to improve the accuracy of phylogenetic trees, but their capacity to derive a biologically credible relationship appears to be overestimated. Although the quality of the protein sequence alignment and the choice of substitution matrix are preliminary constraints to define the biological accuracy of the overlapped residues, the alignment is not iteratively optimized through the statistical testing of residue-substitution models. The exact alignment protocol and substitution model information are by default used for every sequence set by a server to construct an often-irrelevant phylogenetic tree, and no sequence-based tailoring of phylogenetic strategy is implemented by any server. Rigorously constructing 270 evolutionary trees, constructed using IQ-TREE based on 13 different alignments (Clustal-Omega, Kalign, MAFFT, MUSCLE, TCoffee, and Promals3D, as well as their HHPred-based hidden Markov model [HMM] alignments using HHPred) and nine substitution models (Dayhoff, JJT, block substitution matrix62, WAG, probability matrix from blocks [PMB], direct computation with mutability [DCMUT], JTTDCmut, LG, and variable time), the present study highlights the failure of the current methods and emphasizes the need for a more accurate scrutiny of the entire phylogenetic methodology. MUSCLE alignment and the LG and Dayhoff matrices yield more accurate phylogenetic results for sequences shorter than 500 residues for the log-likelihood measure. Moreover, Kalign 1 HMM alignment yields the top-ranked tree with the lowest tree length score with only the PMB matrix, making this substitution model more accurate in terms of total tree length score. The suggested strategy would be beneficial for understanding the potential pitfalls of phylogenetic inference and would aid us in deriving a more accurate evolutionary relationship for a sequence dataset.
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Zhang, Dao-Feng, Wei He, Zongze Shao, Iftikhar Ahmed, Yuqin Zhang, Wen-Jun Li e Zhe Zhao. "EasyCGTree: a pipeline for prokaryotic phylogenomic analysis based on core gene sets". BMC Bioinformatics 24, n.º 1 (14 de outubro de 2023). http://dx.doi.org/10.1186/s12859-023-05527-2.
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Abstract Background Genome-scale phylogenetic analysis based on core gene sets is routinely used in microbiological research. However, the techniques are still not approachable for individuals with little bioinformatics experience. Here, we present EasyCGTree, a user-friendly and cross-platform pipeline to reconstruct genome-scale maximum-likehood (ML) phylogenetic tree using supermatrix (SM) and supertree (ST) approaches. Results EasyCGTree was implemented in Perl programming languages and was built using a collection of published reputable programs. All the programs were precompiled as standalone executable files and contained in the EasyCGTree package. It can run after installing Perl language environment. Several profile hidden Markov models (HMMs) of core gene sets were prepared in advance to construct a profile HMM database (PHD) that was enclosed in the package and available for homolog searching. Customized gene sets can also be used to build profile HMM and added to the PHD via EasyCGTree. Taking 43 genomes of the genus Paracoccus as the testing data set, consensus (a variant of the typical SM), SM, and ST trees were inferred via EasyCGTree successfully, and the SM trees were compared with those inferred via the pipelines UBCG and bcgTree, using the metrics of cophenetic correlation coefficients (CCC) and Robinson–Foulds distance (topological distance). The results suggested that EasyCGTree can infer SM trees with nearly identical topology (distance < 0.1) and accuracy (CCC > 0.99) to those of trees inferred with the two pipelines. Conclusions EasyCGTree is an all-in-one automatic pipeline from input data to phylogenomic tree with guaranteed accuracy, and is much easier to install and use than the reference pipelines. In addition, ST is implemented in EasyCGTree conveniently and can be used to explore prokaryotic evolutionary signals from a different perspective. The EasyCGTree version 4 is freely available for Linux and Windows users at Github (https://github.com/zdf1987/EasyCGTree4).
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Welty Peachey, Allysen M., Ethan R. Moses, Adesola J. Johnson, Meredith G. M. Lehman, James M. Yoder, Stefano G. De Faveri, Jodie Cheesman, Nicholas C. Manoukis e Matthew S. Siderhurst. "Wind effects on individual male and female Bactrocera jarvisi (Diptera: Tephritidae) tracked using harmonic radar". Environmental Entomology, 28 de outubro de 2024. http://dx.doi.org/10.1093/ee/nvae108.
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Abstract Wind affects the movement of most volant insects. While the effects of wind on dispersal are relatively well understood at the population level, how wind influences the movement parameters of individual insects in the wild is less clear. Tephritid fruit flies, such as Bactrocera jarvisi, are major horticultural pests worldwide and while most tephritids are nondispersive when host plants are plentiful, records exist for potentially wind-assisted movements up to 200 km. In this study, harmonic radar (HR) was used to track the movements of both male and female lab-reared B. jarvisi in a papaya field. Overall flight directions were found to be correlated with wind direction, as were the subset of between-tree movements, while within-tree movements were not. Furthermore, the effect of wind direction on fly trajectories varied by step-distance but not strongly with wind speed. Mean path distance, step distance, flight direction, turning angle, and flight propensity did not vary by sex. Both male and female movements are well fit by 2-state hidden Markov models further supporting the observation that B. jarvisi move differently within (short steps with random direction) and between (longer more directional steps) trees. Data on flight directionality and step-distances determined in this study provide parameters for models that may help enhance current surveillance, control, and eradication methods, such as optimizing trap placements and pesticide applications, determining release sites for parasitoids, and setting quarantine boundaries after incursions.
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Weiss, David A., Adriano M. F. Borsa, Aurélie Pala, Audrey J. Sederberg e Garrett B. Stanley. "A machine learning approach for real-time cortical state estimation". Journal of Neural Engineering, 17 de janeiro de 2024. http://dx.doi.org/10.1088/1741-2552/ad1f7b.
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Abstract Objective. Cortical function is under constant modulation by internally-driven, latent variables that regulate excitability, collectively known as “cortical state”. Despite a vast literature in this area, the estimation of cortical state remains relatively ad hoc, and not amenable to real-time implementation. Here, we implement robust, data-driven, and fast algorithms that address several technical challenges for online cortical state estimation. Approach. We use unsupervised Gaussian Mixture Models (GMMs) to identify discrete, emergent clusters in spontaneous local field potential (LFP) signals in cortex. We then extend our approach to a temporally-informed Hidden semi-Markov Model (HSMM) with Gaussian observations to better model and infer cortical state transitions. Finally, we implement our HSMM cortical state inference algorithms in a real-time system, evaluating their performance in emulation experiments. Main results. Unsupervised clustering approaches reveal emergent state-like structure in spontaneous electrophysiological data that recapitulate arousal-related cortical states as indexed by behavioral indicators. HSMMs enable cortical state inferences in a real-time context by modeling the temporal dynamics of cortical state switching. Using HSMMs provides robustness to state estimates arising from noisy, sequential electrophysiological data. Significance. To our knowledge, this work represents the first implementation of a real-time software tool for continuously decoding cortical states with high temporal resolution (40 ms). The software tools that we provide can facilitate our understanding of how cortical states dynamically modulate cortical function on a moment-by-moment basis and provide a basis for state-aware brain machine interfaces across health and disease.
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Gholamzadeh, Marsa, Hamidreza Abtahi e Reza Safdari. "Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review". BMC Medical Research Methodology 22, n.º 1 (23 de dezembro de 2022). http://dx.doi.org/10.1186/s12874-022-01823-2.
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Abstract Background Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation. Method A systematic search was conducted in five electronic databases from January 2000 to June 2022. Then, the title, abstracts, and full text of extracted articles were screened based on the PRISMA checklist. Then, eligible articles were selected according to inclusion criteria. The information regarding developed models was extracted from reviewed articles using a data extraction sheet. Results Searches yielded 414 citations. Of them, 136 studies were excluded after the title and abstract screening. Finally, 16 articles were determined as eligible studies that met our inclusion criteria. The objectives of eligible articles are classified into eight main categories. The applied machine learning methods include the Support vector machine (SVM) (n = 5, 31.25%) technique, logistic regression (n = 4, 25%), Random Forests (RF) (n = 4, 25%), Bayesian network (BN) (n = 3, 18.75%), linear regression (LR) (n = 3, 18.75%), Decision Tree (DT) (n = 3, 18.75%), neural networks (n = 3, 18.75%), Markov Model (n = 1, 6.25%), KNN (n = 1, 6.25%), K-means (n = 1, 6.25%), Gradient Boosting trees (XGBoost) (n = 1, 6.25%), and Convolutional Neural Network (CNN) (n = 1, 6.25%). Most studies (n = 11) employed more than one machine learning technique or combination of different techniques to make their models. The data obtained from pulmonary function tests were the most used as input variables in predictive model development. Most studies (n = 10) used only post-transplant patient information to develop their models. Also, UNOS was recognized as the most desirable data source in the reviewed articles. In most cases, clinicians succeeded to predict acute diseases incidence after lung transplantation (n = 4) or estimate survival rate (n = 4) by developing machine learning models. Conclusion The outcomes of these developed prediction models could aid clinicians to make better and more reliable decisions by extracting new knowledge from the huge volume of lung transplantation data.
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Adhikari, Saugat, Da Yan, Zhe Jiang, Jiao Han, Zelin Xu, Yupu Zhang, Arpan Sainju e Yang Zhou. "Scaling Terrain-Aware Spatial Machine Learning for Flood Mapping on Large Scale Earth Imagery Data". ACM Transactions on Spatial Algorithms and Systems, 5 de novembro de 2024. http://dx.doi.org/10.1145/3703157.
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The accurate and prompt mapping of flood-affected regions is important for effective disaster management, including damage assessment and relief efforts. While high-resolution optical imagery from satellites during disasters presents an opportunity for automated flood inundation mapping, existing segmentation models face challenges due to noises like cloud cover and tree canopies. Thanks to the digital elevation model (DEM) data readily available from sources such as United States Geological Survey (USGS), terrain guidance was utilized by recent graphical models such as hidden Markov trees (HMTs) to improve segmentation quality. Unfortunately, these methods either can only handle a small area where water levels at different locations are assumed to be consistent, or require restricted assumptions such as there is only one river channel. This paper presents an algorithm for flood extent mapping on large-scale Earth imagery, applicable to a large geographic area with multiple river channels. Since water level can vary a lot from upstream to downstream, we propose to detect river pixels in order to partition the remaining pixels into localized zones, each with a unique water level. In each zone, water at all locations flow to the same river entry point. Pixels in each zone are organized by an HMT to capture water flow directions guided by elevations. Moreover, a novel regularization scheme is designed to enforce inter-zone consistency by penalizing pixel-pairs of adjacent zones that violate terrain guidance. Efficient parallelization is made possible by coloring the zone adjacency graph to identify zones and zone-pairs that have no dependency and hence can be processed in parallel, and incremental one-pass terrain-guided scanning is conducted wherever applicable to reuse computations. Experiments demonstrate that our solution is more accurate than existing solutions, and can efficiently and accurately map out flooding pixels in a giant area of size 24,805 × 40,129. Despite the imbalanced workloads caused by a few large zonal HMTs dominating the serial computing time, our parallelization approach is effective and manages to achieve up to 14.3 × speedup on a machine with Intel Xeon Gold 6126 CPU @ 2.60GHz (24 cores, 48 threads) using 32 threads.
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Colcombet-Cazenave, Baptiste, Karen Druart, Crystel Bonnet, Christine Petit, Olivier Spérandio, Julien Guglielmini e Nicolas Wolff. "Phylogenetic analysis of Harmonin homology domains". BMC Bioinformatics 22, n.º 1 (14 de abril de 2021). http://dx.doi.org/10.1186/s12859-021-04116-5.
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Abstract Background Harmonin Homogy Domains (HHD) are recently identified orphan domains of about 70 residues folded in a compact five alpha-helix bundle that proved to be versatile in terms of function, allowing for direct binding to a partner as well as regulating the affinity and specificity of adjacent domains for their own targets. Adding their small size and rather simple fold, HHDs appear as convenient modules to regulate protein–protein interactions in various biological contexts. Surprisingly, only nine HHDs have been detected in six proteins, mainly expressed in sensory neurons. Results Here, we built a profile Hidden Markov Model to screen the entire UniProtKB for new HHD-containing proteins. Every hit was manually annotated, using a clustering approach, confirming that only a few proteins contain HHDs. We report the phylogenetic coverage of each protein and build a phylogenetic tree to trace the evolution of HHDs. We suggest that a HHD ancestor is shared with Paired Amphipathic Helices (PAH) domains, a four-helix bundle partially sharing fold and functional properties. We characterized amino-acid sequences of the various HHDs using pairwise BLASTP scoring coupled with community clustering and manually assessed sequence features among each individual family. These sequence features were analyzed using reported structures as well as homology models to highlight structural motifs underlying HHDs fold. We show that functional divergence is carried out by subtle differences in sequences that automatized approaches failed to detect. Conclusions We provide the first HHD databases, including sequences and conservation, phylogenic trees and a list of HHD variants found in the auditory system, which are available for the community. This case study highlights surprising phylogenetic properties found in orphan domains and will assist further studies of HHDs. We unveil the implication of HHDs in their various binding interfaces using conservation across families and a new protein–protein surface predictor. Finally, we discussed the functional consequences of three identified pathogenic HHD variants involved in Hoyeraal-Hreidarsson syndrome and of three newly reported pathogenic variants identified in patients suffering from Usher Syndrome.
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Ventura, Francesco, José Pedro Granadeiro, Paulo Catry, Carina Gjerdrum, Federico De Pascalis, Filipe Viveiros, Isamberto Silva, Dilia Menezes, Vítor H. Paiva e Mónica C. Silva. "Allochrony is shaped by foraging niche segregation rather than adaptation to the windscape in long-ranging seabirds". Movement Ecology 12, n.º 1 (2 de abril de 2024). http://dx.doi.org/10.1186/s40462-024-00463-z.
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Abstract Background Ecological segregation allows populations to reduce competition and coexist in sympatry. Using as model organisms two closely related gadfly petrels endemic to the Madeira archipelago and breeding with a two month allochrony, we investigated how movement and foraging preferences shape ecological segregation in sympatric species. We tested the hypothesis that the breeding allochrony is underpinned by foraging niche segregation. Additionally, we investigated whether our data supported the hypothesis that allochrony is driven by species-specific adaptations to different windscapes. Methods We present contemporaneous tracking and stable isotopes datasets for Zino’s (Pterodroma madeira) and Desertas (Pterodroma deserta) petrels. We quantified the year-round distribution of the petrels, characterised their isotopic niches and quantified their habitat preferences using machine learning (boosted regression trees). Hidden-Markov-models were used to investigate the effect of wind on the central-place movement speed, and a simulation framework was developed to investigate whether each species breeds at times when the windscape is most favourable to sustain their trips. Results Despite substantial spatial overlap throughout the year, the petrels exhibited diverging isotopic niches and habitat preferences during breeding. Both species used a vast pelagic region in the North Atlantic, but targeted two different mesopelagic ecoregions and showed a preference for habitats mostly differing in sea surface temperature values. Based on our simulation framework, we found that both species would perform trips of similar speed during the other species’ breeding season. Conclusions The different breeding schedules between the species are underpinned by differences in foraging habitat preferences and adaptation to the local environment, rather than to the windscape. Nevertheless, the larger Desertas petrels exploited significantly windier conditions, potentially unsustainable for the smaller Zino’s petrels. Furthermore, due to larger mass and likely higher fasting endurance, Desertas petrels engaged in central-place-foraging movements that covered more ground and lasted longer than those of Zino’s petrels. Ultimately, patterns of ecological segregation in sympatric seabirds are shaped by a complex interplay between foraging and movement ecology, where morphology, foraging trip regulation and fasting endurance have an important– yet poorly understood– role.