An Overview of Particle Swarm Optimization Variants

AbstractParticle swarm optimization (PSO) is a stochastic algorithm used for the optimization problems proposed by Kennedy [1] in 1995. It is a very good technique for the optimization problems. But still there is a drawback in the PSO is that it stuck in the local minima. To improve the performance of PSO, the researchers proposed the different variants of PSO. Some researchers try to improve it by improving initialization of the swarm. Some of them introduce the new parameters like constriction coefficient and inertia weight. Some researchers define the different method of inertia weight to improve the performance of PSO. Some researchers work on the global and local best particles by introducing the mutation operators in the PSO. In this paper, we will see the different variants of PSO with respect to initialization, inertia weight and mutation operators

Contour matching using ant colony optimization and curve evolution

Shape retrieval is a very important topic in computer vision. Image retrieval consists of selecting images that fulfil specific criteria from a collection of images. This thesis concentrates on contour-based image retrieval, in which we only explore the information located on the shape contour. There are many different kinds of shape retrieval methods. Most of the research in this field has till now concentrated on matching methods and how to achieve a meaningful correspondence. The matching process consist of finding correspondence between the points located on the designed contours. However, the huge number of incorporated points in the correspondence makes the matching process more complex. Furthermore, this scheme does not support computation of the correspondence intuitively without considering noise effect and distortions. Hence, heuristics methods are convoked to find acceptable solution. Moreover, some researches focus on improving polygonal modelling methods of a contour in such a way that the resulted contour is a good approximation of the original contour, which can be used to reduce the number of incorporated points in the matching. In this thesis, a novel approach for Ant Colony Optimization (ACO) contour matching that can be used to find an acceptable matching between contour shapes is developed. A polygonal evolution method proposed previously is selected to simplify the extracted contour. The main reason behind selecting this method is due to the use of a stopping criterion which must be predetermined. The match process is formulated as a Quadratic Assignment Problem (QAP) and resolved by using ACO. An approximated similarity is computed using original shape context descriptor and the Euclidean metric. The experimental results justify that the proposed approach is invariant to noise and distortions, and it is more robust to noise and distortion compared to the previously introduced Dominant Point (DP) Approach. This work serves as the fundamental study for assessing the Bender Test to diagnose dyslexic and non-dyslexic symptom in children

Finite Element Analysis (FEA) in Electronics Devices and Photonics through Process Oriented Guided Inquiry Learning (POGIL)

Advance and complex mathematical skill such as finite element analysis are traditionally required to the understanding of electronics devices and photonics applications. Unless the students want to further their studies in theoretical field using complex calculations, students are offered an alternate learning skill such as POGIL by using software simulation packages with embedded FEA to help them visualize the abstract of photonics theories. Students were allowed to modify the original template given in the package, but in the learning process, they have to answer several guided questions in the activity specified. We presented two studies: Thermo Photovoltaic (TPV) cell and Acoustic Levitator (AL), prepared by two groups of third year physics students using COMSOL software and POGIL. As a result, students were able to complete their activities with a new skill of a standard researche

Knowledge modelling for an Intelligent Tutoring System (ITS) domain : Precalculus

This paper presents a knowledge model (KM) of an ITS with Precalculus as the domain knowledge. The Knowledge Model is represented by Semantic Net, organized as interconnected knowledge entities in the form of nodes. The comprehensiveness of the knowledge domain depends largely on the richness of the interconnectivity of nodes. A working prototype, built using an object oriented paradigm, exists that shows the correctness and feasibility of this model. Any standard Java compatible World Wide Web (WWW) browser with be able to access the knowledge model witb a simple Graphical User Interface (GUI) to view the content of its structure

Biological-based semi-supervised clustering algorithm to improve gene function prediction

Analysis of simultaneous clustering of gene expression with biological knowledge has now become an importanttechnique and standard practice to present a proper interpretation of the data and its underlying biology. However, commonclustering algorithms do not provide a comprehensive approach that look into the three categories of annotations; biologicalprocess, molecular function, and cellular component, and were not tested with different functional annotation database formats.Furthermore, the traditional clustering algorithms use random initialization which causes inconsistent cluster generation and areunable to determine the number of clusters involved. In this paper, we present a novel computational framework called CluFA(Clustering Functional Annotation) for semi-supervised clustering of gene expression data. The framework consists of threestages: (i) preparation of Gene Ontology (GO) datasets, functional annotation databases, and testing datasets, (ii) a fuzzy c -means clustering to find the optimal clusters; and (iii) analysis of computational evaluation and biological validation from theresults obtained. With combination of the three GO term categories (biological process, molecular function, and cellularcomponent) and functional annotation databases (Saccharomyces Genome Database (SGD), the Yeast Database at MunichInformation Centre for Protein Sequences (MIPS), and Entrez), the CluFA is able to determine the number of clusters andreduce random initialization. In addition, CluFA is more comprehensive in its capability to predict the functions of unknowngenes. We tested our new computational framework for semi-supervised clustering of yeast gene expression data based onmultiple functional annotation databases. Experimental results show that 76 clusters have been identified via GO slim dataset.By applying SGD, Entrez, and MIPS functional annotation database to reduce random initialization, performance on bothcomputational evaluation and biological validation were improved. By the usage of comprehensive GO term categories, thelowest compactness and separation values were achieved. Therefore, from this experiment, we can conclude that CluFA hadimproved the gene function prediction through the utilization of GO and gene expression values using the fuzzy c -meansclustering algorithm by cross referencing it with the latest SGD annotation

An optimal mesh algorithm for remote protein homology detection

Remote protein homology detection is a problem of detecting evolutionary relationship between proteins at low sequence similarity level. Among several problems in remote protein homology detection include the questions of determining which combination of multiple alignment and classification techniques is the best as well as the misalignment of protein sequences during the alignment process. Therefore, this paper deals with remote protein homology detection via assessing the impact of using structural information on protein multiple alignments over sequence information. This paper further presents the best combinations of multiple alignment and classification programs to be chosen. This paper also improves the quality of the multiple alignments via integration of a refinement algorithm. The framework of this paperbegan with datasets preparation on datasets from SCOP version 1.73, followed by multiple alignments of the protein sequences using CLUSTALW, MAFFT, ProbCons and T-Coffee for sequence-based multiple alignments and 3DCoffee, MAMMOTH-mult, MUSTANG and PROMALS3D for structural-based multiple alignments. Next, a refinement algorithm was applied on the protein sequences to reduce misalignments. Lastly, the aligned protein sequences were classified using the pHMMs generative classifier such as HMMER and SAM and also SVMs discriminative classifier such as SVM-Fold and SVM-Struct. The performances of assessed programs were evaluated using ROC, Precision and Recall tests. The result from this paper shows that the combination of refined SVM-Struct and PROMALS3D performs the best against other programs, which suggests that this combination is the best for RPHD. This paper also shows that the use of the refinement algorithm increases the performance of the multiple alignments programs by at least 4%

An optimal mesh algorithm for remote protein homology detection

Remote protein homology detection is a problem of detecting evolutionary relationship between proteins at low sequence similarity level. Among several problems in remote protein homology detection include the questions of determining which combination of multiple alignment and classification techniques is the best as well as the misalignment of protein sequences during the alignment process. Therefore, this paper deals with remote protein homology detection via assessing the impact of using structural information on protein multiple alignments over sequence information. This paper further presents the best combinations of multiple alignment and classification programs to be chosen. This paper also improves the quality of the multiple alignments via integration of a refinement algorithm. The framework of this paper began with datasets preparation on datasets from SCOP version 1.73, followed by multiple alignments of the protein sequences using CLUSTALW, MAFFT, ProbCons and T-Coffee for sequence-based multiple alignments and 3DCoffee, MAMMOTH-mult, MUSTANG and PROMALS3D for structural-based multiple alignments. Next, a refinement algorithm was applied on the protein sequences to reduce misalignments. Lastly, the aligned protein sequences were classified using the pHMMs generative classifier such as HMMER and SAM and also SVMs discriminative classifier such as SVM-Fold and SVM-Struct. The performances of assessed programs were evaluated using ROC, Precision and Recall tests. The result from this paper shows that the combination of refined SVM-Struct and PROMALS3D performs the best against other programs, which suggests that this combination is the best for RPHD. This paper also shows that the use of the refinement algorithm increases the performance of the multiple alignments programs by at least 4%

Class wise image retrieval through scalable color descriptor and edge histogram descriptor

Various domains such as medical science, forensics science and education etc. are generating lot of images on daily bases. As a result of these content generation large image databases are available. These databases are considered as very helpful such as suspects can be searched from forensics database, similarly medical image database can be utilized for the diagnosis purposes. However, proposer management of these databases like, storing and retrieving of images is the demand of the day. Relevant content searching from these databases is a difficult task, however content based image retrieval (CBIR) playing a very important role for searching the relevant contents from these large databases. But this approach is facing some issues. One of the famous issues of CBIR is to describe the image in terms of as feature. This research work aimed is to present a new scheme of image representation by combing the texture and color signature to increase the accuracy of CBIR. Color signatures are generated through Scalable Color Descriptor (SCD) while texture feature are extracted by Edge Histogram Descriptor (EHD). The proposed technique is assessed by testing on the coral image data set and validated by comparing the results with other CBIR approaches