Study on the predictions of gene function and protein structure using multi-SVM and hybrid EDA

制度:新 ; 報告番号:甲3199号 ; 学位の種類:博士(工学) ; 授与年月日:2011/3/15 ; 早大学位記番号:新549

An In-Depth Study on Open-Set Camera Model Identification

Camera model identification refers to the problem of linking a picture to the camera model used to shoot it. As this might be an enabling factor in different forensic applications to single out possible suspects (e.g., detecting the author of child abuse or terrorist propaganda material), many accurate camera model attribution methods have been developed in the literature. One of their main drawbacks, however, is the typical closed-set assumption of the problem. This means that an investigated photograph is always assigned to one camera model within a set of known ones present during investigation, i.e., training time, and the fact that the picture can come from a completely unrelated camera model during actual testing is usually ignored. Under realistic conditions, it is not possible to assume that every picture under analysis belongs to one of the available camera models. To deal with this issue, in this paper, we present the first in-depth study on the possibility of solving the camera model identification problem in open-set scenarios. Given a photograph, we aim at detecting whether it comes from one of the known camera models of interest or from an unknown one. We compare different feature extraction algorithms and classifiers specially targeting open-set recognition. We also evaluate possible open-set training protocols that can be applied along with any open-set classifier, observing that a simple of those alternatives obtains best results. Thorough testing on independent datasets shows that it is possible to leverage a recently proposed convolutional neural network as feature extractor paired with a properly trained open-set classifier aiming at solving the open-set camera model attribution problem even to small-scale image patches, improving over state-of-the-art available solutions.Comment: Published through IEEE Access journa

Extracting Maya Glyphs from Degraded Ancient Documents via Image Segmentation

We present a system for automatically extracting hieroglyph strokes from images of degraded ancient Maya codices. Our system adopts a region-based image segmentation framework. Multi-resolution super-pixels are first extracted to represent each image. A Support Vector Machine (SVM) classifier is used to label each super-pixel region with a probability to belong to foreground glyph strokes. Pixelwise probability maps from multiple super-pixel resolution scales are then aggregated to cope with various stroke widths and background noise. A fully connected Conditional Random Field model is then applied to improve the labeling consistency. Segmentation results show that our system preserves delicate local details of the historic Maya glyphs with various stroke widths and also reduces background noise. As an application, we conduct retrieval experiments using the extracted binary images. Experimental results show that our automatically extracted glyph strokes achieve comparable retrieval results to those obtained using glyphs manually segmented by epigraphers in our team

Framework for Detection and Localization of Coronary Non-Calcified Plaques in Cardiac CTA using Mean Radial Profiles

Background and Objective: The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac imaging and image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to their high intensity values. However, the detection of non-calcified plaques in CTA is still a challenging problem because of lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose the use of mean radial profiles for the detection of non-calcified plaques in CTA imagery. Methods: Accordingly, we computed radial profiles by averaging the image intensity in concentric rings around the vessel centreline in a first stage. In the subsequent stage, an SVM classifier is applied to identify the abnormal coronary segments. For occluded segments, we further propose a derivative-based method to localize the position and length of the plaque inside the segment. Results: A total of 32 CTA volumes were analysed and a detection accuracy of 88.4% with respect to the manual expert was achieved. The plaque localization accuracy was computed using the Dice similarity coefficient and a mean of 83.2% was achieved. Conclusion: The consistent performance for multi-vendor, multi-institution data demonstrates the reproducibility of our method across different CTA datasets with a good agreement with manual expert annotations

Framework for Detection and Localization of Coronary Non-Calcified Plaques in Cardiac CTA using Mean Radial Profiles

Background and Objective: The high mortality rate associated with coronary heart disease (CHD) has driven intensive research in cardiac imaging and image analysis. The advent of computed tomography angiography (CTA) has turned non-invasive diagnosis of cardiovascular anomalies into reality as calcified coronary plaques can be easily identified due to their high intensity values. However, the detection of non-calcified plaques in CTA is still a challenging problem because of lower intensity values, which are often similar to the nearby blood and muscle tissues. In this work, we propose the use of mean radial profiles for the detection of non-calcified plaques in CTA imagery. Methods: Accordingly, we computed radial profiles by averaging the image intensity in concentric rings around the vessel centreline in a first stage. In the subsequent stage, an SVM classifier is applied to identify the abnormal coronary segments. For occluded segments, we further propose a derivative-based method to localize the position and length of the plaque inside the segment. Results: A total of 32 CTA volumes were analysed and a detection accuracy of 88.4% with respect to the manual expert was achieved. The plaque localization accuracy was computed using the Dice similarity coefficient and a mean of 83.2% was achieved. Conclusion: The consistent performance for multi-vendor, multi-institution data demonstrates the reproducibility of our method across different CTA datasets with a good agreement with manual expert annotations

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications

Klasifikasi Multilabel pada Hadis Bukhari Terjemahan Bahasa Indonesia Menggunakan Mutual Information dan Support Vector Machine

Hadis merupakan sumber hukum kedua bagi umat Islam setelah Al-Quran. Banyak sekali hadis yang telah diriwayatkan, namun Hadis Bukhari memiliki tingkat kesahihan paling tinggi menurut para ulama. Seiring dengan perkembangan teknologi, hadis sangatlah mudah didapatkan melalui dunia digital. Akan tetapi untuk mempelajari hadis tidak semudah yang kita bayangkan. Banyaknya hadis yang ada dan juga belum dikategorikan membuat belajar hadis dengan kategori tertentu sangat sulit dilakukan. Oleh sebab itu penulis melakukan penelitian klasifikasi anjuran, larangan dan informasi pada Hadis Sahih Al-Bukhari terjemahan Bahasa Indonesia yang diharapkan dapat mempermudah masyarakat dalam mempelajari hadis. Proses klasifikasi menggunakan model unigram/bigram dengan Mutual Information (MI) sebagai seleksi fitur dan Support Vector Machine (SVM) sebagai metode klasifikasi. Pada penelitian ini dilakukan beberapa skenario pengujian dengan memodifikasi term model, preprocessing, feature selection dan menggunakan beberapa metode klasifikasi untuk membuktikan bahwa SVM merupakan salah satu metode klasifikasi teks yang cocok digunakan. Pengujian dengan menggunakan model unigram, tidak menggunakan stopword/stemming, menggunakan MI dan menggunakan SVM memberikan nilai hamming loss terbaik yaitu 0.0686. Hasil penelitian yang diperoleh juga menunjukkan bahwa metode SVM dengan menggunakan MI lebih baik daripada metode klasifikasi teks yang lain. Kata kunci: Hadis Bukhari, Hamming loss, Klasifikasi, Mutual Information, Preprocessing, Support Vector Machine

A Survey of Face Recognition

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023