Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models

Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much more strenuous than in 2D images. For 4D time-series tomograms, this is usually handled by segmenting the constituent tomograms independently through time with 3D convolutional neural networks. Inter-volume information is therefore not utilized, potentially leading to temporal incoherence. In this paper, we attempt to resolve this by proposing two hidden Markov model variants that refine 4D segmentation labels made by 3D convolutional neural networks working on each time point. Our models utilize not only inter-volume information, but also the prediction confidence generated by the 3D segmentation convolutional neural networks themselves. To the best of our knowledge, this is the first attempt to refine 4D segmentations made by 3D convolutional neural networks using hidden Markov models. During experiments we test our models, qualitatively, quantitatively and behaviourally, using prespecified segmentations. We demonstrate in the domain of time series tomograms which are typically undersampled to allow more frequent capture; a particularly challenging problem. Finally, our dataset and code is publicly available

Implementasi Analisis Sentimen dalam Klasifikasi Artikel Berita Daring Menggunakan Algoritma Hybrid Convolutional Neural Network (Hybrid CNN-HMM)

Era Big Data dan Internet of Things telah merambah hampir pada semua bidang, di antaranya informasi komunikasi, pendidikan, industri bahkan dalam bidang keuangan. Seperti yang telah diketahui, bidang informasi dan komunikasi merupakan bidang yang menjadi sumber paling berpengaruh pada bidang keuangan. Bidang ini akan sangat dipengaruhi oleh informasi yang muncul dan berkembang di masyarakat. Informasi negatif maupun positif yang muncul melalui berita akan mempengaruhi saham. Untuk mengetahui hal tersebut, diperlukan sebuah metode yang mampu melakukan hal tersebut. Salah satu metode yang dapat digunakan adalah analisis sentimen. Pada penelitian ini akan dikembangkan algoritma untuk mencari sentimen/opini yang berkembang melalui pemberitaan di media berita online / daring (dalam jaringan). Untuk mendapatkan berita dari potal berita tersebut, terlebih dahulu akan dilakukan pengambilan data menggunakan metode web crawling. Kemudian untuk melakukan pengenalana sentimen/opini akan digunakan algoritma Hybrid Convolutional Neural Network-Hidden Markov Models (Hybrid CNN-HMM). Hasil dari metode tersebut menunjukkan performansi pembelajaran 99.92% dan performansi pengujian sekitar 70,45 %. ================================================================================================================================= The Big Data era and the Internet of Things have penetrated almost all fields, including information on communication, education, industry and even in the financial sector. As is well known, the field of information and communication is a field that has become the most influential source of finance. This field will be greatly influenced by information that arises and develops in the community. Negative and positive information that appears through the news will affect shares. To find out this, a method is needed to do this. One method that can be used is sentiment analysis. In this study, algorithms will be developed to find sentiments/opinions that develop through reporting in online news media. To get news from the total news, first, it will take data retrieval using the web crawling method. Then to identify sentiments/opinions will be used Hybrid Convolutional Neural Network algorithm - Hidden Markov Models (Hybrid CNN-HMM). The results of the method show a learning performance of around 99.92% and a testing performance of around 70.45%