Application of SAR time-series and deep learning for estimating landslide occurence time

The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure

Physics-informed deep learning for three dimensional black holes

According to AdS/DL (Anti de Sitter/ Deep Learning) correspondence given by \cite{Has}, in this paper with a data-driven approach and leveraging holography principle we have designed an artificial neural network architecture to produce metric field of planar BTZ and quintessence black holes. Data has been collected by choosing minimally coupled massive scalar field with quantum fluctuations and try to process two emergent and ground-truth metrics versus the holographic parameter which plays role of depth of the neural network. Loss or error function which shows rate of deviation of these two metrics in presence of penalty regularization term reaches to its minimum value when values of the learning rate approach to the observed steepest gradient point. Values of the regularization or penalty term of the quantum scalar field has critical role to matching this two mentioned metric. Also we design an algorithm which helps us to find optimum value for learning parameter and at last we understand that loss function convergence heavily depends on the number of epochs and learning rate.Comment: 28 pages, 1 table and 13 figures, extension with quintessenc

Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care

Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches improve detection of FH in electronic health records, though clinical impact is under-explored. We assessed performance of an array of machine-learning approaches for enhancing detection of FH, and their clinical utility, within a large primary care population. A retrospective cohort study was done using routine primary care clinical records of 4,027,775 individuals from the United Kingdom with total cholesterol measured from 1 January 1999 to 25 June 2019. Predictive accuracy of five common machine-learning algorithms (logistic regression, random forest, gradient boosting machines, neural networks and ensemble learning) were assessed for detecting FH. Predictive accuracy was assessed by area under the receiver operating curves (AUC) and expected vs observed calibration slope; with clinical utility assessed by expected case-review workload and likelihood ratios. There were 7928 incident diagnoses of FH. In addition to known clinical features of FH (raised total cholesterol or LDL-C and family history of premature coronary heart disease), machine-learning (ML) algorithms identified features such as raised triglycerides which reduced the likelihood of FH. Apart from logistic regression (AUC, 0.81), all four other ML approaches had similarly high predictive accuracy (AUC > 0.89). Calibration slope ranged from 0.997 for gradient boosting machines to 1.857 for logistic regression. Among those screened, high probability cases requiring clinical review varied from 0.73% using ensemble learning to 10.16% using deep learning, but with positive predictive values of 15.5% and 2.8% respectively. Ensemble learning exhibited a dominant positive likelihood ratio (45.5) compared to all other ML models (7.0–14.4). Machine-learning models show similar high accuracy in detecting FH, offering opportunities to increase diagnosis. However, the clinical case-finding workload required for yield of cases will differ substantially between models

AGE ESTIMATION UNTUK INTELLIGENT ADVERTISING PADA POSTER DIGITAL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Sebagai bagian dari intelligent advertising, age estimation digunakan untuk menyesuaikan iklan dari hasil estimasi usia audience. Age estimation (AE) dapat dibangun menggunakan deep learning menggunakan ConvNet dengan kendala seperti data training wajah usia tua yang sedikit, ketidak seimbangan dataset di dalamnya, serta membutuhkan jumlah data yang besar. Salah satu solusi dari permasalahan ini adalah melakukan data augmentasi menggunakan model generatif ACGAN untuk melakukan generate gambar sesuai dengan kelas. Intelligent advertising pada poster digital hanya disimulasikan pada komputer. Simulasi intelligent advertising berfungsi dengan baik terlepas dari terbatasnya iklan dan tidak konsistennya hasil estimasi usia. Hasil dari penggunaan model generatifACGAN untuk data augmentation berhasil meningkatkan performa hasil pada model AE terlepas dari rendahnya skor IS dan FID serta kualitas gambar yang dihasilkan. Hasil data augmentation lebih terlihat pada model B dengan peningkatan akurasi cumulative score sebesar 4,8% dan skor MAE sebesar 1,297

Deep Learning Techniques for Geospatial Data Analysis

Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.Comment: This is a pre-print of the following chapter: Arvind W. Kiwelekar, Geetanjali S. Mahamunkar, Laxman D. Netak, Valmik B Nikam, {\em Deep Learning Techniques for Geospatial Data Analysis}, published in {\bf Machine Learning Paradigms}, edited by George A. TsihrintzisLakhmi C. Jain, 2020, publisher Springer, Cham reproduced with permission of publisher Springer, Cha