Integrated Retinal Information System for Analyzing Kidney Condition

Iridology is a science and practice that can express body state based on the analysis of iris structure. The changes or disturbances of disease on body network will be informed by neuron nerve fiber to brain. This energy wave information spread to eye by brain, recorded and fixed by pupil.Then, these recorded fixation become data trails which can be detected by disturbance/disease that is filed by body organ. The research about iridology to analyzing kidney condition has been conducted before using Learning Vector Quantization (LVQ) method. The accuracy is not 100%. In this research, the researcher implements Support Vector Machine(SVM) in classifying the kidney condition to replace LVQ using Matlab R2007b. The accuracy in classifying the kidney condition for right eyes is 100% and for the left eyes is 100% in training set data. If we compared to the accuracy of classification using LVQ, implementing SVM is much better because by implementing LVQ, the accuracy is only 96% for right eyes and only 92% for left eyes

Crosslingual Document Embedding as Reduced-Rank Ridge Regression

There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding documents written in any language into a single, language-independent vector space. For training, our approach leverages a multilingual corpus where the same concept is covered in multiple languages (but not necessarily via exact translations), such as Wikipedia. Our method, Cr5 (Crosslingual reduced-rank ridge regression), starts by training a ridge-regression-based classifier that uses language-specific bag-of-word features in order to predict the concept that a given document is about. We show that, when constraining the learned weight matrix to be of low rank, it can be factored to obtain the desired mappings from language-specific bags-of-words to language-independent embeddings. As opposed to most prior methods, which use pretrained monolingual word vectors, postprocess them to make them crosslingual, and finally average word vectors to obtain document vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as document-level. Moreover, since our algorithm uses the singular value decomposition as its core operation, it is highly scalable. Experiments show that our method achieves state-of-the-art performance on a crosslingual document retrieval task. Finally, although not trained for embedding sentences and words, it also achieves competitive performance on crosslingual sentence and word retrieval tasks.Comment: In The Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19

Human Active Learning

Active machine learning (AML) is a popular research area in machine learning. It allows selection of the most informative instances in training data of the domain for manual labeling. AML aims to produce a highly accurate classifier using as few labeled instances as possible, thereby minimizing the cost of obtaining labeled data. As machines can learn from experience like humans do, using AML for human category learning may help human learning become more efficient and hence reduce the cost of teaching. This chapter is a review of recent research literature concerning the use of AML technique to enhance human learning and teaching. There are a few studies on the applications of AML to the human category learning domain. The most interesting study was by Castro et al., which showed that humans learn faster with better performance when they can actively select the informative instances from a pool of unlabeled data instead of random sampling. Although AML can facilitate object categorization for humans, there are still many challenges and questions that need to be addressed in the use of AML for modeling human categorization. In this chapter, we will discuss some of these challenges

Analyses and web interfaces for protein subcellular localization and gene expression data

Cataloged from PDF version of article.In order to benefit maximally from large scale molecular biology data generated by recent developments, it is important to proceed in an organized manner by developing databases, interfaces, data visualization and data interpretation tools. Protein subcellular localization and microarray gene expression are two of such fields that require immense computational effort before being used as a roadmap for the experimental biologist. Protein subcellular localization is important for elucidating protein function. We developed an automatically updated searchable and downloadable system called model organisms proteome subcellular localization database (MEP2SL) that hosts predicted localizations and known experimental localizations for nine eukaryotes. MEP2SL localizations highly correlated with high throughput localization experiments in yeast and were shown to have superior accuracies when compared with four other localization prediction tools based on two different datasets. Hence, MEP2SL system may serve as a reference source for protein subcellular localization information with its interface that provides various search and download options together with links and utilities for further annotations. Microarray gene expression technology enables monitoring of whole genome simultaneously. We developed an online installable searchable open source system called differentially expressed genes (DEG) that includes analysis and retrieval interfaces for Affymetrix HG-U133 Plus 2.0 arrays. DEG provides permanent data storage capabilities with its integration into a database and being an installable online tool and is valuable for groups who are not willing to submit their data on public servers.Bilen, BiterM.S

A learning-based approach to multi-agent decision-making

We propose a learning-based methodology to reconstruct private information held by a population of interacting agents in order to predict an exact outcome of the underlying multi-agent interaction process, here identified as a stationary action profile. We envision a scenario where an external observer, endowed with a learning procedure, is allowed to make queries and observe the agents' reactions through private action-reaction mappings, whose collective fixed point corresponds to a stationary profile. By adopting a smart query process to iteratively collect sensible data and update parametric estimates, we establish sufficient conditions to assess the asymptotic properties of the proposed learning-based methodology so that, if convergence happens, it can only be towards a stationary action profile. This fact yields two main consequences: i) learning locally-exact surrogates of the action-reaction mappings allows the external observer to succeed in its prediction task, and ii) working with assumptions so general that a stationary profile is not even guaranteed to exist, the established sufficient conditions hence act also as certificates for the existence of such a desirable profile. Extensive numerical simulations involving typical competitive multi-agent control and decision making problems illustrate the practical effectiveness of the proposed learning-based approach