PurposeThis research is set to assess the achievement of digital economy through digitalization adoption (DA) among Small Medium-sized Enterprises (SMEs). For more insightful findings, a comparison between the service-based and non-service-based industries was performed. Besides, it endeavours to identify the important and performing dimension of the Technology-Organization-Environment (TOE) framework. The purpose of this paper is to address these issues.Design/methodology/approachQuantitative approach through purposive sampling technique was used to collect data from the SMEs. Variance-based structural equation modeling was adopted to assess the model and multi-group analysis (MGA) was executed to examine the difference between the two classified industries. For the identification of the dimension, Importance-Performance Map Aanalysis (IPMA) was carried out.FindingsTechnology and organization recorded significant positive influence on digitalization adoption but not environment. Digitalization adoption between the two classified industries shows divergent results. IPMA concur the importance and performance of the technology and organization dimension, in which SMEs shall focus on for digitalization adoption.Research limitations/implicationsThis is cross sectional research and data were collected at a single time frame. Hence, the result is a state-or-art finding. Assuming that if there are changes in government policies, the results may differ. Besides, there are other possible groupings that could affect the results in which is not covered in the present research.Practical implicationsThe findings imply that the DA amongst SMEs has yet to achieve its full spectrum, which indicates Malaysia has yet to fully embrace digital economy. Nevertheless, DA is the fundamental for a successful digital economy.Social implicationsThis research provides the general public an overview that SMEs are adopting digitalization with various degree. This specifies that the society is paving towards digitalization with the SMEs actively adopting more digital technologies.Originality/valueThe novelty of this research arises from the utilization of the TOE framework to link to the achievement of the national digital economy. Additionally, current research adopted a rigorous approach to investigate the issue by using MGA, the hierarchical component model (HCM) and IPMA for holistic findings.

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Accuracy of cancerous gene classification is a central challenge in clinical cancer research. Microarray-based gene biomarkers have proved the performance and its ability over traditional clinical parameters. However, gene biomarkers of an individual are less robustness due to litter reproducibility between different cohorts of patients. Several methods incorporating pathway information such as directed random walk have been proposed to infer the pathway activity. This paper discusses the implementation of group specific tuning parameter in directed random walk algorithm. In this experiment, gene expression data and pathway data are used as input data. Throughout this experiment, more significant pathway activities can be identified which increases the accuracy of cancer classification. The lung cancer gene is used as the experimental dataset, with which, the sDRW is used in determining significant pathways. More risk-active pathways are identified throughout this experiment.

Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

nowadays, heart disease is one of the major diseases that cause death. It is a matter for us to concern in today's highly chaotic life style that leads to various diseases. Early prediction of identification to heart-related diseases has been investigated by many researchers. The death rate can be further brought down if we can predict or identify the heart disease earlier. There are many studies that explore the different classification algorithms for classification and prediction of heart disease. This research studied the prediction of heart disease by using five different techniques in WEKA tools by using the input attributes of the dataset. This research used 13 attributes, such as sex, blood pressure, cholesterol and other medical terms to detect the likelihood of a patient getting heart disease. The classification techniques, namely J48, Decision Stump, Random Forest, Sequential Minimal Optimization (SMO), and Multilayer Perceptron used to analyze the heart disease. Performance measurement for this study are the accuracy of correct classification, mean absolute error and kappa statistics of the classifier. The result shows that Multilayer Perceptron Neural Networks is the most suited for early prediction of heart diseases.