International Conference on Artificial Intelligence and Soft Computing 

About: International Conference on Artificial Intelligence and Soft Computing is an academic conference. The conference publishes majorly in the area(s): Artificial neural network & Fuzzy logic. Over the lifetime, 2100 publications have been published by the conference receiving 13562 citations.

A Similarity Measure for Intuitionistic Fuzzy Sets and Its Application in Supporting Medical Diagnostic Reasoning

Abstract: We propose a new similarity measure for intuitionistic fuzzy sets and show its usefulness in medical diagnostic reasoning. We point out advantages of this new concept over the most commonly used similarity measures being just the counterparts of distances. The measure we propose involves both similarity and dissimilarity.

Blockchain Based Smart Contracts : A Systematic Mapping Study

Abstract: An appealing feature of blockchain technology is smart contracts. A smart contract is executable code that runs on top of the blockchain to facilitate, execute and enforce an agreement between untrusted parties without the involvement of a trusted third party. In this paper, we conduct a systematic mapping study to collect all research that is relevant to smart contracts from a technical perspective. The aim of doing so is to identify current research topics and open challenges for future studies in smart contract research. We extract 24 papers from different scientific databases. The results show that about two thirds of the papers focus on identifying and tackling smart contract issues. Four key issues are identified, namely, codifying, security, privacy and performance issues. The rest of the papers focuses on smart contract applications or other smart contract related topics. Research gaps that need to be addressed in future studies are provided.

Active, a tool for building intelligent user interfaces

Abstract: Computers have become affordable, small, omnipresent and are often connected to the Internet. However, despite the availability of such rich environment, user interfaces have not been adapted to fully leverage its potential. To help with complex tasks, a new type of software is needed to provide more user-centric systems that act as "intelligent assistants", able to interact naturally with human users and with the information environment. Building an intelligent assistant is a difficult task that requires expertise in many fields ranging from artificial intelligence to core software and hardware engineering. We believe that providing a unified tool and methodology to create intelligent software will bring many benefits to this area of research. Our solution, the Active framework, combines an innovative production rule engine with communities of services to model and implement intelligent assistants. In the medical field, our approach is used to build an operating room assistant. Using natural modalities such as speech recognition and hand gestures, it enables surgeons to interact with computer based equipments of the operating room as if they were active members of the team. In a broader context, Active aims to ease the development of intelligent software by making required technologies more accessible.

Convolutional Neural Networks for Time Series Classification

Abstract: This article concerns identifying objects generating signals from various sensors Instead of using traditional hand-made time series features we feed the signals as input channels to a convolutional neural network The network learned low- and high-level features from data We describe the process of data preparation, filtering, and the structure of the convolutional network Experiment results showed that the network was able to learn to recognize objects with high accuracy

Comparison of Instances Seletion Algorithms I. Algorithms Survey

Abstract: Several methods were proposed to reduce the number of instances (vectors) in the learning set. Some of them extract only bad vectors while others try to remove as many instances as possible without significant degradation of the reduced dataset for learning. Several strategies to shrink training sets are compared here using different neural and machine learning classification algorithms. In part II (the accompanying paper) results on benchmarks databases have been presented.