<abstract xmlns="http://www.w3.org/1999/xhtml">In this paper we propose a novel algorithm CLAR to build an agent based routing protocol using ACO technique in cluster based mobile ad-hoc networks. Clustering makes possible hierarchical routing in which paths are recorded between cluster-heads instead of nodes. This reduces routing overheads. Presence of an unstable and non-reliable cluster-head degrades the performance of the network since cluster-heads of the network take active role in routing messages between a source destination pair. The proposed algorithm chooses the most reliable and stable node as cluster-head depending on four criteria i.e.: stability, battery power, degree, and trust value of the node. It also uses a multiagent based routing algorithm to generate load balanced routes between source and destination in cluster-based network. Performance of CLAR is compared with other existing efficient algorithms such as AODV, CBRP, Wu &amp; Li’s Algorithm, dominating set based clustering algorithms, agent-based algorithms named as SACO and MALBACO. Simulation results prove that the proposed protocol combines merits of both clustering approach and agent based approach and provides efficient packet delivery even in the presence of high load in the network</abstract>

Assistant Professor ,Department of Information Technology, CIEM, Kolkata 700040,India ,  Phone:+919836732484

Professor, Department of Computer Science & Technology, Bengal Engineering & Science University, Shibpur, Howrah 711103, India, Phone:+919830899022

Associate Professor, Department of Computer Science & Engineering,West Bengal University of Technology, Kolkata 700064, India ,Phone: +919830507213

Clar: A Novel Cluster Oriented Agent Based Routing Protocol for Manet

<abstract xmlns="http://www.w3.org/1999/xhtml"> The problem of DNA sequence assembly is well known for its high complexity. Experimental errors of di erent kinds present in data and huge sizes of the problem instances make this problem very hard to solve. In order to deal with such data, advanced efficient heuristics must be constructed. Here, we propose a new approach to the sequence assembly problem, modeled as the problem of searching for paths in an acyclic digraph. Since the graph representing an assembly instance is not acyclic in general, it is heuristically transformed into the acyclic form. This approach reduces the time of computations significantly and allows to maintain high quality of produced solutions. </abstract>

Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland

Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland

Luxembourg Centre for Systems Biomedicine, University of Luxembourg

Dna Sequence Assembly Involving an Acyclic Graph Model

<abstract xmlns="http://www.w3.org/1999/xhtml"> Learning from non-stationary environments is a very popular research topic. There already exist algorithms that deal with the concept drift problem. Among them there are online or incremental learners, which process data instance by instance. Their knowledge representation can take different forms such as decision rules, which have not received enough attention in learning with concept drift. This paper reviews incremental rule-based learners designed for changing environments. It describes four of the proposed algorithms: FLORA, AQ11-PM+WAH, FACIL and VFDR. Those four solutions can be compared on several criteria, like: type of processed data, adjustment to changes, type of the maintained memory, knowledge representation, and others.</abstract>

Incremental Rule-Based Learners for Handling Concept Drift: An Overview

<abstract xmlns="http://www.w3.org/1999/xhtml"> Widely-referenced approaches to collaborative filtering (CF) are based on the use of an input matrix that represents each user profile as a vector in a space of items and each item as a vector in a space of users. When the behavioral input data have the form of (userX, likes, itemY) and (userX, dislikes, itemY) triples one has to propose a representation of the user feedback data that is more suitable for the use of propositional data than the ordinary user-item ratings matrix. We propose to use an element-fact matrix, in which columns represent RDF-like behavioral data triples and rows represent users, items, and relations. By following such a triple-based approach to the bi-relational behavioral data representation we are able to improve the quality of collaborative filtering. One of the key findings of the research presented in this paper is that the proposed bi-relational behavioral data representation, while combined with reflective matrix processing, significantly outperforms state-of-the-art collaborative filtering methods based on the use of a ‘standard’ user-item matrix.</abstract>

Collaborative Filtering Based on Bi-Relational Data Representation

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Foundations of Computing and Decision Sciences

ISSN: 0867-6356 (print version)  Foundations of Computing and Decision Sciences (until 1990 Foundations of Control Engineering) is a quarterly peer-reviewed international journal published since 1975. One of its main aims is its focus on the links between Computing (understood in the sense defined in the report of the ACM Task Force on the Core of Computer Science chaired by Peter J. Denning: Computing as a Discipline, CACM, Vol. 32, No. 1, 1989) and broadly understood Decision Sciences.  Scope of topics  The journal provides a forum for exchange of ideas for researchers in the fields of computing and decision sciences. The scope of the journal covers various areas of computing and decision sciences, particularly:    approximate reasoning,  artificial intelligence,  combinatorial optimization,  computational complexity theory,  databases and data warehouses,  intelligent decision support,  knowledge engineering,  machine learning and data mining,  metaheuristics,  multiple criteria decision analysis,  networking and distributed systems,  parallel computing and concurrency,  production and project scheduling,  scheduling theory,  soft and granular computing,  software engineering.     Why submit   International editorial board composed of distinguished scholars highly representative of the field of computing and decision sciences,  Quick turnaround,  Comprehensive peer review with at least two highly competent referees assessing each manuscript,  Promotion of published papers in the vast scientific community of researchers working in the field.   Rejection Rate   ca. 60%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process (iThenticate, https://www.ithenticate.com/ ). 