Artificial intelligence in Internet of things

Functioning of the Internet is persistently transforming from the Internet of computers(IoC)to the‘Internet of things(IoT)’.Furthermore,massively interconnected systems,also known as cyber-physical systems(CPSs),are emerging from the assimilation of many facets like infrastructure,embedded devices,smart objects,humans,and physical environments.What the authors are heading to is a huge‘Internet of Everything in a Smart Cyber Physical Earth’.IoT and CPS conjugated with‘data science’may emerge as the next‘smart revolution’.The concern that arises then is to handle the huge data generated with the much weaker existing computation power.The research in data science and artificial intelligence(AI)has been striving to give an answer to this problem.Thus,IoT with AI can become a huge breakthrough.This is not just about saving money,smart things,reducing human effort,or any trending hype.This is much more than that– easing human life.There are,however,some serious issues like the security concerns and ethical issues which will go on plaguing IoT.The big picture is not how fascinating IoT with AI seems,but how the common people perceive it– a boon,a burden,or a threat.

Soft computing application in bioinformatics

This article provides an outline on a recent application of soft computing for the mining of microarray gene expressions.We describe investigations with an evolutionary-rough feature selection algorithm for feature selection and classification on cancer data.Rough set theory is employed to generate reducts,which represent the minimal sets of non-redundant features capable of discerning between all objects,in a multi-objective framework.The experimental results demonstrate the effectiveness of the methodology on three cancer datasets.

miRT:A Database of Validated Transcription Start Sites of Human MicroRNAs

MicroRNAs （miRNAs） are small endogenous non-coding RNAs of about 22 nt in length that take crucial roles in many biological pro cesses. These short RNAs regulate the expression of mRNAs by binding to their 3＇-UTRs or by translational repression. Many of the current studies focus on how mature miRNAs regulate mRNAs, however, very limited knowledge is available regarding their transcrip- tional loci. It is known that primary miRNAs （pri-miRs） are first transcribed from the DNA, followed by the formation of precursor miRNAs （pre-miRs） by endonuclease activity, which finally produces the mature miRNAs. Till date, many of the pre-miRs and mature miRNAs have been experimentally verified. But unfortunately, identification of the loci of pri-miRs, promoters and associated transcrip- tion start sites （TSSs） are still in progress. TSSs of only about 40% of the known mature miRNAs in human have been reported. This information, albeit limited, may be useful for further study of the regulation of miRNAs. In this paper, we provide a novel database of validated miRNA TSSs, miRT, by collecting data from several experimental studies that validate miRNA TSSs and are available for full download. We present miRT as a web server and it is also possible to convert the TSS loci between different genome built, miRT might be a valuable resource for advanced research on miRNA regulation, which is freely accessible at： http：//www.isical.ac.in/~bioinfo_miu/ miRT/miRT.php.

A New Line Symmetry Distance and Its Application to Data Clustering

In this paper, at first a new line-symmetry-based distance is proposed. The properties of the proposed distance are then elaborately described. Kd-tree-based nearest neighbor search is used to reduce the complexity of computing the proposed line-symmetry-based distance. Thereafter an evolutionary clustering technique is developed that uses the new linesymmetry-based distance measure for assigning points to different clusters. Adaptive mutation and crossover probabilities are used to accelerate the proposed clustering technique. The proposed GA with line-symmetry-distance-based （GALSD） clustering technique is able to detect any type of clusters, irrespective of their geometrical shape and overlapping nature, as long as they possess the characteristics of line symmetry. GALSD is compared with the existing well-known K-means clustering algorithm and a newly developed genetic point-symmetry-distance-based clustering technique （GAPS） for three artificial and two real-life data sets. The efficacy of the proposed line-symmetry-based distance is then shown in recognizing human face from a given image.