arXiv subjects

Saeed Jalili

Publications and source records attributed to Saeed Jalili.

3 recordsLinked to original sources

arXiv2021

Voxel selection framework based on meta-heuristic search and mutual information for brain decoding

Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. One of the crucial problems in the brain decoder is the selecting informative voxels. We propose a meta-heuristic voxel selection framework for brain decoding. It is composed of four phases: preprocessing of fMRI data; filtering insignificant voxels; postprocessing; and meta-heuristics selection. The main contribution is benefiting a meta-heuristics search algorithm to guide a wrapper voxel selection. The main criterion to nominate a voxel is based on its mutual information with the provided stimulus label. The results show impressive accuracy rates which are 90.66 +/- 3.66 and 91.61 +/- 8.24 for DS105 and DS107, respectively. This outperforms the most of existing brain decoders in similar validation conditions. The experimental results are very encouraging which can be successfully useId in the brain-computer interface.

q-bio.NC↗

arXiv2019

A Binary Particle Swarm Optimization Approach for Gene Expression Biclustering Problem

Microarray techniques are widely used in Gene expression analysis. These techniques are based on discovering submatrices of genes that share similar expression patterns across a set of experimental conditions with coherence constraint. Actually, these submatrices are called biclusters and the extraction process is called biclustering. In this paper we present a novel binary particle swarm optimization model for the gene expression biclustering problem. Hence, we apply the binary particle swarm optimization algorithm with a proposed measure, called Discretized Column-based Measure (DCM) as a novel cost function for evaluating biclusters where biological relevance, MSR and the size of the bicluster are considered as evaluation metrics for our results. Results are compared to the existing algorithms and they show the validity of our proposed approach.

q-bio.QM↗

arXiv2014

Beyond Batch Processing: Towards Real-Time and Streaming Big Data

Today, big data is generated from many sources and there is a huge demand for storing, managing, processing, and querying on big data. The MapReduce model and its counterpart open source implementation Hadoop, has proven itself as the de facto solution to big data processing. Hadoop is inherently designed for batch and high throughput processing jobs. Although Hadoop is very suitable for batch jobs but there is an increasing demand for non-batch processes on big data like: interactive jobs, real-time queries, and big data streams. Since Hadoop is not proper for these non-batch workloads, new solutions are proposed to these new challenges. In this article, we discuss two categories of these solutions: real-time processing, and stream processing for big data. For each category, we discuss paradigms, strengths and differences to Hadoop. We also introduce some practical systems and frameworks for each category. Finally, some simple experiments are done to show effectiveness of some solutions compared to available Hadoop-based solutions.

cs.DC↗