Online korean skincare decision support system

Despite the explosive growth of electronic commerce and the rapidly increasing number of consumers who use interactive media for pre-purchase information search and online shopping, very little is known about how consumers make purchase decisions in such settings. One desirable form of interactivity from a consumer perspective is the implementation of sophisticated tools to assist shoppers in their purchase decisions by customizing the electronic shopping environment to their individual preferences

Fuzzy c-means clustering by incorporating biological knowledge and multi-stage filtering to improve gene function prediction

Gene expression is a process by which information from a gene is used in the synthesis of a functional gene product. Comprehensive studies of gene expression are useful for predicting gene functions, which includes predicting annotations for unknown gene functions. However, there are several issues that need to be addressed in gene function prediction, namely: solving multiple fuzzy clusters using biological knowledge and biological annotations in some existing databases. This includes, handling the high level expression and low level expression values. Therefore, this research was aimed at clustering gene expressions by incorporating biological knowledge in order to handle these issues. The basic Fuzzy c-Means (FCM) algorithm was introduced to address multiple fuzzy clusters in gene expression. Clustering Functional Annotation (CluFA) was developed to deal with insufficient knowledge via incorporating Gene Ontology (GO) datasets and multiple functional annotation databases. The GO datasets were used to determine number of clusters as well as clusters for genes. Meanwhile, the evidence codes in functional annotation databases were used to compute the strength of the association between data element and a particular cluster. The multi stage filtering-CluFA (msf-CluFA) was implemented by conducting filtering stages and applying an enhanced apriori algorithm in order to handle the high level expression and low level expression values. The performance of the proposed method was evaluated in terms of compactness and separation, consistency, and accuracy, using Eisen and Gasch datasets. Biological validation was also used to validate the gene function prediction, by cross checking them with the most recent annotation database. The results show that the proposed computational method achieved better results compared with other methods such as GOFuzzy, FuzzyK, and FuzzySOM in predicting unknown gene function

A hybrid of integer differential bees and flux balance analysis for improving succinate and lactate production

The production of succinate and lactate from E.coli become a demand in pharmaceutical industries. To increase the yield of the production, gene knockout technique was implemented in various hybrid optimization algorithms. In recent years, several hybrid optimizations have been introduced to optimize succinate and lactate production. However, the previous works were ineffective to produce the highest production due to the size and complexity of metabolic networks and the dynamic interaction between the components. Therefore, the main purpose of this study is to overcome the limitation of the existing algorithms which hybridizing Integer Differential Bees and Flux Balance Analysis (IDBFBA). The experimental results show a better performance in terms of growth rate and production yield of desired phenotypes compared to the method used in previous works

A Hybrid of Integer Differential Bees and Flux Balance Analysis for Improving Succinate and Lactate Production

The production of succinate and lactate from E.coli become a demand in pharmaceutical industries. To increase the yield of the production, gene knockout technique was implemented in various hybrid optimization algorithms. In recent years, several hybrid optimization have been introduced to optimize succinate and lactate production. However, the previous works were ineffective to produce the highest production due to the size and complexity of metabolic networks and the dynamic interaction between the components. Therefore, the main purpose of this study is to overcome the limitation of the existing algorithms which hybridizing Integer Differential Bees and Flux Balance Analysis (IDBFBA). The experimental results show a better performance in terms of growth rate and production yield of desired phenotypes compared to the method used in previous works

Time series predictive analysis based on hybridization of meta-heuristic algorithms

This paper presents a comparative study which involved five hybrid meta-heuristic methods to predict the weather five days in advance. The identified meta-heuristic methods namely Moth-flame Optimization (MFO), Cuckoo Search algorithm (CSA), Artificial Bee Colony (ABC), Firefly Algorithm (FA) and Differential Evolution (DE) are individually hybridized with a well-known machine learning technique namely Least Squares Support Vector Machines (LS-SVM). For experimental purposes, a total of 6 independent inputs are considered which were collected based on daily weather data. The efficiency of the MFO-LSSVM, CSLSSVM, ABC-LSSVM, FA-LSSVM, and DE-LSSVM was quantitatively analyzed based on Theil’s U and Root Mean Square Percentage Error. Overall, the experimental results demonstrate a good rival among the identified methods. However, the superiority goes to FA-LSSVM which was able to record lower error rates in prediction. The proposed prediction model could benefit many parties in continuity planning daily activities

RFID data reliability optimiser based on two dimensions bloom filter

Radio frequency identification (RFID) is a flexible deployment technology that has been adopted in many applications especially in supply chain management. RFID system used radio waves to perform wireless interaction to detect and read data from the tagged object. However, RFID data streams contain a lot of false positive and duplicate readings. Both types of readings need to be removes to ensure reliability of information produced from the data streams. In this paper, a single approach, which based on Bloom filter was proposed to remove both dirty data from the RFID data streams. The noise and duplicate data filtering algorithm was constructed based on bloom filter. There are two bloom filters in one algorithm where each filter holds function either to remove noise data and to recognize data as correct reading from duplicate data reading. Experimental results show that our proposed approach outperformed other existing approaches in terms of data reliability

Comparative analysis of spatio/spectro-temporal data modelling techniques

A fundamental challenge in spatio/spectro-temporal data (SSTD) is to learn the pattern and extract meaningful information that lies within the data. The close interrelationship between the space and temporal components of SSTD directly increases the complexity and challenges in modelling the data [1]. Other challenges include the dynamic pattern of spatial components features and inconsistency in the number of samples and feature-length used in the training and sampling datasets [2]. Data pre-processing method such as removal of irregular-feature data structure, however, may cause data loss which will lead to the final result become error prone. Despite the difficulties to process information from SSTD, several works on predictive modelling have been published, including applications on brain data processing [3], stroke data [4-5], forecasting of weather-driven damage in electrical distribution system [6], and ecological or environmental event prediction [7]. According to [8], environmental events often occur in a predictable temporal structure. Hence, the ability to exploit spiking neural network (SNN) by incorporating SSTD modelling techniques may be able to aid the process of discovering the hidden pattern and relationship between the two components of STTD; time and space. Recent work in [5], stated that most events occurring in nature form SSTD which requires measuring spatial or/and spectral components over time. Therefore, this paper presents the comparative analysis between various techniques used to process information from SSTD. Section 2 overviews two different inference-based techniques for SSTD modelling which includes global modelling, local modelling, and personalized modelling; and data modelling for SSTD classifier including, support vector machines (SVM), Evolving Classification Function (ECF), k-Nearest Neighbor (kNN), weighted k-Nearest Neighbor (wkNN), and weighted-weighted k-Nearest Neighbor (wwkNN). Section 3 presents the results of the assessment both SSTD inference-based modelling techniques and data training algorithms, while Section 4 concludes the analysis and ideas for future works

Path apps for box of ramadhan

Map helps to track targeted location but still unable to find actual destination of rural area due to unclear addresses. This problem also faced by owner and members of the Box of Ramadhan when they need to give delivery services to underprivileged people cause low efficiency of service provided. Thus, this project is conducted to design and develop an application called Path Apps for Box of Ramadhan for Android device user to solve problems of reach destination and get related information to reduce overall time spending. Unified Modelling Language diagram used to show the relationship and interaction among all classes. The proposed system is categories into two different interfaces as admin interface and user interface. The application consists of few modules such as login and registration, user list, profile, current location, route, multiple markers and address list and chat modules. Time management, route planning and inventory will be under control by user according to program schedule. This contribute to high efficiency of work

Protein structure prediction using robust principal component analysis and support vector machine

Existence of bioinformatics is to increase the further understanding of biological process. Proteins structure is one of the major challenges in structural bioinformatics. With former knowledge of the structure, the quality of secondary structure, prediction of tertiary structure, and prediction function of amino acid from its sequence increase significantly. Recently, the gap between sequence known and structure known proteins had increase dramatically. So it is compulsory to understand on proteins structure to overcome this problem so further functional analysis could be easier. The research applying RPCA algorithm to extract the essential features from the original highdimensional input vectors. Then the process followed by experimenting SVM with RBF kernel. The proposed method obtains accuracy by 84.41% for training dataset and 89.09% for testing dataset. The result then compared with the same method but PCA was applied as the feature extraction. The prediction assessment is conducted by analyzing the accuracy and number of principal component selected. It shows that combination of RPCA and SVM produce a high quality classification of protein structur

Evaluate the performance of SVM kernel functions for multiclass cancer classification

Multiclass cancer classification is basically one of the challenging fields in machine learning which a fast growing technology that use human behaviour as examples. Supervised classification such Support Vector Machine (SVM) has been used to classify the dataset on classification by its own function and merely known as kernel function. Kernel function has stated to have a problem especially in selecting their best kernels based on a specific datasets and tasks. Besides, there is an issue stated that the kernels function have a high impossibility to distribute the data in straight line. Here, three basic kernel functions was used and tested with selected dataset and they are linear kernel, polynomial kernel and Radial Basis Function (RBF) kernel function. The three kernels were tested by different dataset to gain the accuracy. For a comparison, this study conducting a test by with and without feature selection in SVM classification kernel function since both tests will give different result and thus give a big meaning to the study