Shahreen Kasim

A compact hybrid feature vector for an accurate secondary structure prediction

Amino acid propensity score is one of the earliest successful methods used in protein secondary structure prediction. However, the score performs poorly on small-sized datasets and low-identity protein sequences. Based on current in silico method, secondary structure can be predicted from local folds or local protein structure. In biology, the evolution of secondary structure produces local protein structure with different lengths. To precisely predict secondary structures, we propose a derivative feature vector, DPS that utilizes the optimal length of the local protein structure. DPS is the unification of amino acid propensity score and dihedral angle score. This new feature vector is further normalized to level the edges. Prediction is performed by support vector machines (SVM) over the DPS feature vectors with class labels generated by secondary structure assignment method (SSAM) and secondary structure prediction method (SSPM). All experiments are carried out on RS126 sequences. The results from this proposed method also highlight the overall accuracy of our method compared to other state-of-the-art methods. The performance of our method was acceptable specifically in dealing with low number and low identity sequences.

Indoor Navigation Using A* Algorithm

This paper introduced an indoor navigation application that helps junior students in Faculty of Computer Science and Information Technology (FSKTM) to find their classroom location. This project implements the A* (pronounced A Star) path finding algorithm to calculate the shortest path for users. Users can choose to view the floor plan of the building or start navigation. Users can choose their starting point from the list and set their destination to start navigation. The application is then calculate the shortest path for users by implement the A* algorithm. The route path will show on the floor plan after calculation done. Thus, users will find this project is easy to use and time saving.

E-Code Checker Application

Nowadays, many areas in computer sciences use ontology such as knowledge engineering, software reuse, digital libraries, web on the heterogeneous information processing, semantic web, and information retrieval. The area of halal industry is the fastest growing global business across the world. The halal food industry is thus crucial for Muslims all over the world as it serves to ensure them that the food items they consume daily are syariah compliant. However, ontology has still not been used widely in the halal industry. Today, Muslim community still have problem to verify halal status for halal products in the market especially in foods consisting of E number. In this paper, ontology will apply at E numbers as a method to solve problems of various halal sources. There are various chemical ontology and databases found to help this ontology construction. The E numbers in this chemical ontology are codes for chemicals that can be used as food additives. With this E numbers ontology, Muslim community could identify and verify the halal status effectively for halal products in the market.

BBIS: Beacon Bus Information System

Lack of bus information for example bus timetable, status of the bus and messy advertisement on bulletin board at the bus stop will give negative impact to tourist. Therefore, a real-time update bus information bulletin board provides all information needed so that passengers can save their bus information searching time. Supported with Android or iOS, Beacon Bus Information System (BBIS) provides bus information between Batu Pahat and Kluang area. BBIS is a system that implements physical web technology and interaction on demand. It built on Backend-as-a-Service, a cloud solution and Firebase non relational database as data persistence backend and syncs between user client in the real-time. People walk through bus stop with smart device and do not require any application. Bluetooth Beacon is used to achieve smart devices best performance of data sharing. Intellij IDEA 15 is one of the tools that that used to develop the BBIS system. Multi-language included front end and backend supported Integration development environment (IDE) helped to speed up integration process.

CMIS: Crime Map Information System for Safety Environment

Crime Map is an online web based geographical information system that assists the public and users to visualize crime activities geographically. It acts as a platform for the public communities to share crime activities they encountered. Crime and violence plague the communities we are living in. As part of the community, crime prevention is everyones responsibility. The purpose of Crime Map is to provide insights of the crimes occurring around Malaysia and raise the publics awareness on crime activities in their neighbourhood. For that, Crime Map visualizes crime activities on a geographical heat maps, generated based on geospatial data. Crime Map analyse data obtained from crime reports to generate useful information on crime trends. At the end of the development, users should be able to make use of the system to access to details of crime reported, crime analysis and report crimes activities. The development of Crime Map also enable the public to obtain insights about crime activities in their area. Thus, enabling the public to work together with the law enforcer to prevent and fight crime.

Classify a Protein Domain Using Sigmoid Support Vector Machine

Protein domains are portion block of protein sequence that evolved independent function. Therefore, the classification of protein domain is becoming very importance in order to produce new sequence with new function. However the main issue in protein domain classification is to classify the domain correctly into their category since the sequence coincidently classify to both category. Therefore, to overcome this issue, this paper proposed a computational method to classify protein domain from protein subsequences and protein structure information using sigmoid kernel function. The proposed method consists of three phases: pre-processing, protein structure information generating and post-processing. The pre-processing phase selects potential protein. The protein structure information generating phase used several calculations to generate protein structure information in order to optimize the domain signal information. The classification phase involves Sigmoid SVM and performance evaluation. The performance of the proposed method is evaluated in terms of sensitivity and specificity on single- domain and multiple-domain using dataset SCOP 1.75. This method showed an improvement of prediction in term of sensitivity, specificity and accuracy.

An improved gSVM-SCADL2 with firefly algorithm for identification of informative genes and pathways

Incorporation of pathway knowledge into microarray analysis has been favoured by researchers owing to the improved biological interpretation of the analysis outcome. However, most of the pathway data are manually curated without specific biological context. Inclusion of non-informative genes in the analysis of context specific microarray data could lead to classifier with poor discriminative power. Thus, one of the main challenges is how to effectively identify informative genes from the pathway data. This paper proposes a firefly optimised penalised support vector machine with SCADL2 penalty function SVM-SCADL2-FFA in optimising tuning parameters for each pathway for efficient identification of informative genes and pathways. Experiments are done on lung cancer and gender data sets. Tenfold CV is used to evaluate the performance in terms of accuracy, specificity, sensitivity and F-score. The identified informative genes are validated through online databases. Our proposed method shows consistent improvements compared to previous works.

Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme

Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.

M-learning application for Basic Computer Architecture

M-learning is a mobile technology in learning and teaching which also another extension to the conventional learning practice specifically e-learning. Mostly, accessing of resources in e-learning is done through fixed nodes such as notebook and desktop PC which generally are restricted either by location, time or both. Through m-learning, accessing to learning resources is independent of time and location. Therefore, this study emphasizes on developing m-learning application for Basic Computer Architecture for supporting teaching and learning process. The application involves modules for notes, flash card and quiz which enables user to learn fundamental topics of the course by using mobile devices while offline. Prototype model is used as the development methodology. This m-learning application has been developed using JAVA programming language specifically J2ME MIDP 2.0 and CLDC 1.1. Netbeans IDE was used to design the interface. As a result, this prototype is expected to be as an alternative learning material which enables user to learn fundamental topics in Basic Computer Architecture course included Basic Structure of Computer, Basic Processing Unit, Arithmetic Unit, and Memory System.

Pairwise protein substring alignment with latent semantic analysis and support vector machines to detect remote protein homology

Remote protein homology detection has been widely used as a part of the analysis of protein structure and function. In this study, the good quality of protein feature vectors is the main aspect to detect remote protein homology; as it will assist discriminative classifier model to discriminate all the proteins into homologue or non-homologue members precisely. In order for the protein feature vectors to be characterized as having good quality, the feature vectors must contain high protein structural similarity information and are represented in low dimension which is free from any contaminated data. In this study, the contaminated data which originates from protein dataset was investigated. This contaminated data may prevent remote protein homology detection framework to produce the best representation of high protein structural similarity information in order to detect the homology of proteins. To reduce the contaminated data and extract high protein structural similarity information, some research has been done on the extraction of protein feature vectors and protein similarity. The extraction of protein feature vectors of good quality is believed could assist in getting better result for remote protein homology detection. Where, the good quality of protein feature vectors containing the useful protein similarity information and represent in low dimension will be used to identify protein family precisely by discriminative classifier model. Referring to this factor, a method which combines Protein Substring Scoring (PSS) and Pairwise Protein Substring Alignment (PPSA) from sequence comparison model, chi-square and Singular Value Decomposition (SVD) from generative model, and Support Vector Machine (SVM) as discriminative classifier model is introduced.