Linear-PSO with binary search algorithm for DNA Motif Discovery / Hazaruddin Harun

Motif Discovery (MD) is the process of identifying meaningful patterns in DNA, RNA, or protein sequences. In the field of bioinformatics, a pattern is also known as a motif. Numerous algorithms had been developed for MD, but most of these were not designed to discover species specific motifs used in identifying a specifically selected species where the exact location of these motifs also needs to be identified. Evaluation of these algorithms showed that the results are unsatisfactory due to the lower validity and accuracy of these algorithms. At present, DNA sequencing analysis is the most utilised technique for species identification where patterns of DNA sequences are determined by comparing the sequence to comprehensive databases. However, several false and gap sequences had been identified to be present in these databases which lead to false identification. Therefore, this study addresses these problems by introducing a hybrid algorithm for MD. In this study, the MD is a process to discover all possible motifs that existed in DNA sequences whereas Motif Identification (MI) is a process to identify the correct motif that can represent a selected species. Particle Swarm Optimisation (PSO) was selected as the base algorithm that needs improvement and integration with other techniques. The Linear-PSO algorithm was the first version of improvement

Pemodelan Sistem Pesanan Berasaskan Internet Menggunakan UML

Electronic commerce is the latest development in business sector. It is becoming very important in Internet technology development and its usage is increasing rapidly. A standard for Internet based Ordering System is needed due to its high demand. It will speed up and reduce the cost of the system development process. The developed model will consist of necessary components for the ordering system and consumers will only need to choose any component required for their ordering system development process. It is hoped that this model will help in the future ordering system development

Species motif extraction using LPBS

This paper presents the use of the ̳Linear-PSO with Binary Search‘ (LPBS) algorithm for discovering motifs, especially species specific motifs.In this study, fragments of mitochondrial cytochrome C oxidase subunit I (COI/COX1) and genome of COI were collected from the Genbank online database.For the first experiment, the genome of COI was used as a reference set and other DNA sequences were used as a comparison set.All the collected DNA sequences are from the same species.The results show that the LPBS algorithm is able to discover motifs. For the second experiment, all the discovered motifs were used as a reference set and the genome of COI from other species were used as a comparison set.The results show that the LPBS algorithm is able to identify correct motifs for species identification

Ontology of zakat management system

Zakat Management System is a system that manages all the processes that are involved in zakat activities.At present, there exist no standard which can be utilized to develop Zakat Management System. In order to support the development of Zakat Management System, this paper provides the ontology of Zakat Management System aimed specifically to share the knowledge of zakat. Each person who are involved in the development of this system will hopefully share a common understanding of Zakat Management System. This in turn will make the process of development faster

Sistem penapisan untuk mengatasi ancaman internet dalam pendidikan

Internet merupakan satu media yang popular pada masa ini kerana banyak membantu pengguna untuk mencari dan menyebar maklumat.Walau bagaimanapun, terdapat maklumat dalam Internet yang tidak sesuai dan merbahaya terutama bagi golongan pelajar.Bagi menghalang pelajar daripada mencapai bahan-bahan negatif ini, industri perisian telah mengambil langkah membangunkan sistem perisian yang boleh menapis kandungan Internet.Bagaimanapun perisian penapisan yang sedia ada masih tidak dapat menjalankan fungsinya dengan baik di mana ia gagal menapis seratus peratus bahan yang tidak sepatutnya tetapi pada masa yang sama ia menahan beberapa peratus bahan yang sepatutnya boleh diterima. Perisian yang ada juga lebih menumpukan kepada penapisan bahan yang ditulis dalam Bahasa Inggeris sedangkan bahan yang tidak sesuai untuk pelajar terdapat juga dalam Bahasa Melayu. Oleh itu, kajian ini telah membangunkan perisian penapisan Internet yang menggunakan pendekatan berasaskan agen dan mekanisma gabungan beberapa katakunci Bahasa Melayu bagi membolehkan ia berfungsi dengan lebih berkesan. Katakunci yang digunakan dalam menentukan halaman mana yang perlu ditapis atau dilepaskan telah dipecahkan kepada dua kategori iaitu katakunci ‘halang’ dan katakunci ‘lepas’. Perisian dalam kajian ini mengaplikasikan ciri-ciri agen dan seterusnya dapat menapis atau melepaskan kandungan Internet yang mengandungi katakunci-katakunci tersebut

DNA motif identification using LPBS

In recent years, several deoxyribonucleic acid (DNA)-based approaches have been developed for species identification including DNA sequencing. The search for motif or patterns in DNA sequences is important in many fields especially in biology. In this paper, a new particle swarm optimization (PSO) approach for discovering species-specific motifs was proposed. The new method named as Linear-PSO with Binary Search (LPBS) is developed to discover motifs of specific species through DNA sequences. This enhanced method integrates Linear-PSO and binary search technique to minimize the execution time and to increase the correctness in identifying the motif.In this study, two fragments samples of ‘mitochondrial cytochrome C oxidase subunit I’ (COI or COX1) were collected from the Genbank online database. DNA sequences for the first sample are fragments of COI for one species and the second samples are a complete COI from a different species. The genome of COI was used as a reference set and other DNA sequences were used as a comparison set. The results show that the LPBS algorithm is able to discover motifs of a species when using DNA sequences from the same fragment of COI

A modified algorithm for species specific motif discovery

Motif discovery can be used to categorize unknown DNA sequences into their corresponding families. For this study, PSO was modified for discovering motif.The modified Linear-PSO is chosen even though it is a slower because linear search is not a choice but a necessary criteria for identifying motif of pig (Sus Scrofa).Pig motif identification is a critical for halal authentication.The modified Linear-PSO algorithm used linear number for population initializing and next position updating.For each cycle, only a particle called ‘target motif’ was selected and compared with other DNA sequences for fitness calculation. Motif discovered can be used as a standard motif for species identification. Experimental results show that the modified algorithm is able to identify motifs as expected. This study showed that a slower algorithm is still needed and has value based on how critical the problem is

Color image enhancement of acute leukemia cells in blood microscopic image for leukemia detection sample

Leukemia is a type of cancer that affects the white blood cell. Early detection of leukemia is important to reduce the rate of mortality. In order to detect acute leukemia, conventional screening method based on microscopic image is used, where sample of blood cell will be taken from the suspected leukemia patient and manually white blood cell (WBC) condition is observed using microscope. The manual screening process is tedious, time consuming and usually prone to error due to low contrast between the nucleus and cytoplasm of WBCs. This report introduces a new enhancement method which is a combination of Particle swarm optimization (PSO) algorithm and contrast stretching, known as Hybrid PSO-Contrast stretching (HPSO-CS). The PSO has been used to optimize the fitness criterion in order to improve the contrast and detail in microscopic image by adapting the parameters as a contribution to enhancement technique. In this study, PSO algorithm is used to perform image segmentation to remove all the unwanted part such as red blood cell (RBC), platelet and also the background while retain the WBC part. The segmentation algorithm uses saturation S-component based on Hue, Saturation, Intensity (HSI) color model. After the segmentation is done, contrast stretching process is applied to the original image to stretch intensity of the pixel. Then the segmented image is combined with the resultant image that has been stretched to produce the enhanced image. The results of the proposed method are evaluated by using mean-square error (MSE), Peak-signal-to-noise-ratio (PSNR) and Absolute mean brightness error (AMBE). This proposed method is benchmarked by comparing against two image enhancement methods, global enhancement and Class Limited Adaptive Histogram Equalization (CLAHE). Based on the results, it can be concluded that quality of the enhanced image for the proposed method is much better with the lowest MSE (2067.651), AMBE (43.51827) and highest PSNR (14.98671) compared to th..

Intelligent segmentation of fruit images using an integrated thresholding and adaptive K-means method (TSNKM)

Recent years, vision-based fruit grading system is gaining importance in fruit classification process.In developing the fruit grading system, image segmentation is required for analyzing the fruit objects automatically.Image segmentation is a process that divides a digital image into separate regions with the aim to obtain only the interest objects and remove the background. Currently, there are several segmentation techniques which have been used in object identification such as thresholding and clustering techniques.However, the conventional techniques have difficulties in segmenting fruit images which captured under natural illumination due to the existence of non-uniform illumination on the object surface.The presence of different illuminations influences the appearance of the interest objects and thus misleads the object analysis.Therefore, this research has produced an innovative segmentation algorithm for fruit images which is able to increase the segmentation accuracy.The developed algorithm is an integration of modified thresholding and adaptive K-means method.The integration of both methods is required to increase the segmentation accuracy for fruits images with different surface colour.The results showed that the innovative method is able to segment the fruits images with high accuracy value