Metode Hidden Markov Model Untuk Pemantauan Masa Subur Wanita Berbasis Android

Hingga saat ini masa subur atau ovulasi pada wanita dapat di ketahui dengan metode servik, monitoring suhu basal, metode peak day dan metode standard day. Dalam penelitian ini dikembangkan metode saliva ferning, yaitu dengan mendeteksi melalui kristal air liur. Citra saliva ferning didapat melalui mikroskop digital dengan perbesaran 100 kali. Citra akan diklasifikasikan berdasarkan pola citra dengan pola acak, pola titik garis dan pola garis yang dominan. Berdasarkan perbedaan pola citra maka akan diklasifikasikan untuk menentukan masa tidak subur, masa transisi, dan masa subur yang sedang berlangsung. Nilai piksel dari citra akan diidentifikasi menggunakan metode hidden markov model dan ditampilkan pada smartphone android. Hasil penelitian menunjukkan nilai piksel, untuk masa tidak subur sebesar 8463 hingga 33302, masa transisi 39442 hingga 77315 dan masa subur diatas 77702, dengan akurasi identifikasi sebesar 95%. Sampel saliva yang baik yaitu saliva yang tidak terkontaminasi, umumnya di pagi hari setelah bangun tidur

Hidden Markov Model Identifiability via Tensors

The prevalence of hidden Markov models (HMMs) in various applications of statistical signal processing and communications is a testament to the power and flexibility of the model. In this paper, we link the identifiability problem with tensor decomposition, in particular, the Canonical Polyadic decomposition. Using recent results in deriving uniqueness conditions for tensor decomposition, we are able to provide a necessary and sufficient condition for the identification of the parameters of discrete time finite alphabet HMMs. This result resolves a long standing open problem regarding the derivation of a necessary and sufficient condition for uniquely identifying an HMM. We then further extend recent preliminary work on the identification of HMMs with multiple observers by deriving necessary and sufficient conditions for identifiability in this setting.Comment: Accepted to ISIT 2013. 5 pages, no figure

Hidden Markov Model

Hidden Markov Model (HMM) adalah peluasan dari rantai Markov di mana statenya tidak dapat diamati secara langsung (tersembunyi), tetapi hanya dapat diobservasi melalui suatu himpunan pengamatan lain. Pada HMM terdapat tiga permasalahan mendasar yang harus diselesaikan yakni evaluation problem, decoding problem, dan learning problem. Dalam paper ini, akan dijelaskan tentang Hidden Markov Model(HMM) dan solusi dari ketiga masalah mendasar dalam HMM tersebut, yakni evaluation problem dengan algoritma forward, decoding problem dengan algoritma viterbi, dan learning problem dengan algoritma Baum‐Welch. Kata kunci : Hidden Markov Model, evaluation problem, decoding problem, learning proble

Bayesian inference for Hidden Markov Model

� Hidden Markov Models can be considered an extension of mixture models, allowing for dependent observations. In a hierarchical Bayesian framework, we show how Reversible Jump Markov Chain Monte Carlo techniques can be used to estimate the parameters of a model, as well as the number of regimes. We consider a mixture of normal distributions characterized by different means and variances under each regime, extending the model proposed by Robert et al. (2000), based on a mixture of zero mean normal distributions.

HMM-guided frame querying for bandwidth-constrained video search

We design an agent to search for frames of interest in video stored on a remote server, under bandwidth constraints. Using a convolutional neural network to score individual frames and a hidden Markov model to propagate predictions across frames, our agent accurately identifies temporal regions of interest based on sparse, strategically sampled frames. On a subset of the ImageNet-VID dataset, we demonstrate that using a hidden Markov model to interpolate between frame scores allows requests of 98% of frames to be omitted, without compromising frame-of-interest classification accuracy