Genetic algorithm solution for double digest problem

The strongly NP-Hard Double Digest Problem, for reconstructing the physical map of DNA sequence, in now using for efficient genotyping. Most of the existing methods are inefficient in tackling large instances due to the large search space for the problem which grows as a factorial function (a!)(b!) of the numbers a and b of the DNA fragments generated by the two restriction enzymes. Also, none of the existing methods are able to handle the erroneous data. In this paper, we develop a novel method based on genetic algorithm for solving this problem and it is adapted to handle the erroneous data. Our genetic algorithm is implemented and compared with the other well-known existing algorithms. The obtained results show the efficiency (speedup) of our algorithm with respect to the other methods, specially for erroneous data

University of Tehran, ISSN 1016-1104 Parallel Generation of t-ary Trees

A parallel algorithm for generating t-ary tree sequences in reverse B-order is presented. The algorithm generates t-ary trees by 0-1 sequences, and each 0-1 sequences is generated in constant average time O(1). The algorithm is executed on a CREW SM SIMD model, and is adaptive and cost-optimal. Prior to the discussion of the parallel algorithm a new sequential generation with O(1) average time complexity, and ranking and unranking algorithms with O(t n) time complexity is also given

a Center of Excellence in Biomathematics,

Several biological features are presented by different types of trees. Two types of such trees are considered in this paper. the first type is trees with n external nodes that each internal node have at least two children, and are used in neuro-science and called neuronal dendritic trees. The second type is trees with n internal nodes and m external nodes. This type of trees represent the secondary structure of RNA sequences, and called RNA trees. In this paper, we present two new parallel algorithms for generation of these two biological trees. Both algorithms are adoptive and cost-optimal and generate the trees in B-order. Computations run in an SM SIMD model. Keywords: Neuronal Dendritic Trees, RNA Trees, Parallel Algorithm, B-order.

Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

The number of people diagnosed with dementia is expected to rise in the coming years. Given that there is currently no definite cure for dementia and the cost of care for this condition soars dramatically, slowing the decline and maintaining independent living are important goals for supporting people with dementia. This paper discusses a study that is called Technology Integrated Health Management (TIHM). TIHM is a technology assisted monitoring system that uses Internet of Things (IoT) enabled solutions for continuous monitoring of people with dementia in their own homes. We have developed machine learning algorithms to analyse the correlation between environmental data collected by IoT technologies in TIHM in order to monitor and facilitate the physical well-being of people with dementia. The algorithms are developed with different temporal granularity to process the data for long-term and short-term analysis. We extract higher-level activity patterns which are then used to detect any change in patients’ routines. We have also developed a hierarchical information fusion approach for detecting agitation, irritability and aggression. We have conducted evaluations using sensory data collected from homes of people with dementia. The proposed techniques are able to recognise agitation and unusual patterns with an accuracy of up to 80%