Artificial Intelligence and Natural Language 

Abstract: Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most important problems of mortality rate in the medical sector nowadays Based on this critical issue, a significant number of men and women are now suffering due to the lack of early screening systems and appropriate care each year However, patients’ lives can be saved with the fast detection of disease in the earliest stage In addition, the evaluation process of machine learning algorithm can detect the stage of this deadly disease much quicker with a reliable dataset In this paper, the overall study has been implemented based on four reliable approaches, such as Support Vector Machine (henceforth SVM), AdaBoost (henceforth AB), Linear Discriminant Analysis (henceforth LDA), and Gradient Boosting (henceforth GB) to get highly accurate results of prediction These algorithms are implemented on an online dataset of UCI machine learning repository The highest predictable accuracy is obtained from Gradient Boosting (GB) Classifiers which is about to 9980% accuracy Later, different performance evaluation metrics have also been displayed to show appropriate outcomes To end with, the most efficient and optimized algorithms for the proposed job can be selected depending on these benchmarks

Abstract: Studies concerning social patterns that appear as a result of propaganda and rumors generally tend to neglect considerations of the behavior of individuals that constitute these patterns. This places obvious limitations upon the scope of research. We propose a dynamical model for the mechanics of the processes of polarization and formation of echo chambers. This model is based on the Rashevsky neurological scheme of decision-making.

Abstract: Simultaneous Localization and Mapping (SLAM) is a challenging task in robotics. Researchers work hard on it, so several novel SLAM algorithms as well as enhancements for the known ones are published every year. We have selected recent (2013–mid. 2015) approaches that in theory can be run on mobile robot and evaluated it. This paper gives brief intuitive description of ORB-SLAM, LSD-SLAM, L-SLAM and OpenRatSLAM algorithms, then compares the algorithms theoretically (based on given description) and evaluates them with TUM RGB-D benchmark.

Abstract: Named Entity Recognition (NER) is one of the most common tasks of the natural language processing. The purpose of NER is to find and classify tokens in text documents into predefined categories called tags, such as person names, quantity expressions, percentage expressions, names of locations, organizations, as well as expression of time, currency and others. Although there is a number of approaches have been proposed for this task in Russian language, it still has a substantial potential for the better solutions. In this work, we studied several deep neural network models starting from vanilla Bi-directional Long Short Term Memory (Bi-LSTM) then supplementing it with Conditional Random Fields (CRF) as well as highway networks and finally adding external word embeddings. All models were evaluated across three datasets Gareev’s, Person-1000 and FactRuEval 2016. We found that extension of Bi-LSTM model with CRF significantly increased the quality of predictions. Encoding input tokens with external word embeddings reduced training time and allowed to achieve state of the art for the Russian NER task.