Parallel swarm intelligence strategies for large-scale clustering based on MapReduce with application to epigenetics of aging

Clustering is an important technique for data analysis and knowledge discovery. In the context of big data, it becomes a challenging issue due to the huge amount of data recently collected making conventional clustering algorithms inappropriate. The use of swarm intelligence algorithms has shown promising results when applied to data clustering of moderate size due to their decentralized and self-organized behavior. However, these algorithms exhibit limited capabilities when large data sets are involved. In this paper, we developed a decentralized distributed big data clustering solution using three swarm intelligence algorithms according to MapReduce framework. The developed framework allows cooperation between the three algorithms namely particle swarm optimization, ant colony optimization and artificial bees colony to achieve largely scalable data partitioning through a migration strategy. This latter reaps advantage of the combined exploration and exploitation capabilities of these algorithms to foster diversity. The framework is tested using amazon elastic map-reduce service (EMR) deploying up to 192 computer nodes and 30 gigabytes of data. Parallel metrics such as speed-up, size-up and scale-up are used to measure the elasticity and scalability of the framework. Our results are compared with their counterparts big data clustering results and show a significant improvement in terms of time and convergence to good quality solution. The developed model has been applied to epigenetics data clustering according to methylation features in CpG islands, gene body, and gene promoter in order to study the epigenetics impact on aging. Experimental results reveal that DNA-methylation changes slightly and not aberrantly with aging corroborating previous studies

A novel quantum behaved Particle Swarm optimization algorithm with chaotic search for image alignment.

In this paper we investigate the use of Artificial Immune Systems’ principles to cope with the satisfiability problem. We describe ClonSAT, a new iterative approach for solving the well known Maximum Satisfiability (Max-SAT) problem. This latter has been shown to be NP-hard if the number of variables per clause is greater than 3. The underlying idea is to harness the optimization capabilities of artificial clonal selection algorithm to achieve good quality solutions for MaxSAT problem. To foster the process, a local search has been used. The obtained results are very encouraging and show the feasibility and effectiveness of the proposed hybrid approach.King Saud Universit

Quantum Genetic Algorithm for Multiple RNA Structural Alignment

RNA structural alignment is one of key issues in bioinformatics. It aims to elucidate conserved structural regions among a set of sequences. Finding an accurate conserved structure is still difficult and a time consuming task that involves structural alignment as a prerequisite. In this work, structural alignment is viewed as an optimization process. A quantum based genetic algorithm is proposed to carry out this process. The main features of this algorithm consist in the quantum structure used to represent alignments and the quantum operators defining the overall evolutionary dynamic of the genetic algorithm. The quantum structure relies on the concept of qubit and allows efficient encoding of individuals. Experiments on a wide range of data sets have shown the effectiveness of the proposed framework and its ability to achieve good quality solutions

Using AI Chatbots in Education: Recent Advances Challenges and Use Case

Nowadays, younger generation is much more exposed to technology than previous generations used to. The recent advances in artificial intelligence (AI) and particularly natural language processing (NLP) and understanding (NLU) make it possible to reinforce and widespread the adoption of AI chatbots in education not only to help students in their administrative affairs or in aca-demic advising but also in assisting them and monitoring their performance dur-ing their learning experience. This paper presents a review of the different meth-ods and tools devoted to the design of chatbots with an emphasis on their use and challenges in the education field. Additionally, this paper focuses on language-related challenges and obstacles that hinder the implementation of English, Ara-bic, and other languages of chatbots. To show how AI chatbots benefit education, a use case is described where Hubert.ai chatbot has been used to assess students’ feedback regarding a machine learning course evaluation

A Quantum-Inspired Differential Evolution Algorithm for Solving the N-Queens Problem

In this paper, a quantum-inspired differential evolution algorithm for solving the N-queens problem is presented. The N-queens problem aims at placing N queens on an NxN chessboard, in such a way that no queen could capture any of the others. The proposed algorithm is a novel hybridization between differential evolution algorithms and quantum computing principles. Accordingly, differential evolution algorithms have been enhanced by the adoption of some quantum concepts such as quantum bits and states superposition. The use of the quantum interference has allowed this hybrid approach to have a remarkable efficiency and good results