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Impact of Covid-19 and the importance of seamless integration of information technology in tourism industrial business processes in Sri Lanka
No full textThe tourism industry is one of the largest sectors within the service industries on a global scale which facing many challenges and has been enhancing business innovation accordingly. Considering the current COVID-19 global pandemic challenges has directed several countries to impose movement controls, the closure of the boundaries between countries, and travel limitations across the globe. The tourism industry is the most directly impacted industry among the other industries. Regardless of the size of the organization, the businesses are facing a high percentage of cancellations and business closure due to the downwards of the income. The systematic literature review has identified the gap in the lack of research as taken place to find the solution to overcome the current challenges. So the research study aimed to investigate the impact of the COVID-19 and the importance of the IT and business processes integration as a solution to face the challenges in the tourism industry. The investigation has been executed through the interview questionnaires and important data has been gathered for the analysis. The critical data analysis indicated the key findings to achieve the research objectives. The IT and business processes integration has been applied through the alignment model. The research also indicated the standardization for the suggested model and the business recovery plan and business continuity strategy for the tourism industry
Penerbitan jurnal bidang Pengajian Islam di Malaysia: suatu sorotan terhadap pangkalan data Myjurnal
No full textSalah satu bentuk penulisan ilmiah ialah artikel yang diterbitkan oleh jurnal. Arena pendidikan di Malaysia turut giat menerbitkan jurnal-jurnal ilmiah yang berkualiti. Sehingga tahun 2020, terdapat lebih 500 jurnal diterbitkan di Malaysia dan disenaraikan dalam pangkalan data MyJurnal. Terdapat 107 buah jurnal bertemakan penulisan dalam bidang pengajian Islam. Kajian ini bertujuan untuk mengenal pasti tema dan gaya penulisan rujukan yang ditetapkan oleh para penerbit jurnal bidang pengajian Islam. Kajian ini mengaplikasikan metodologi kajian secara kualitatif berdasarkan pendekatan analisis kandungan. Hasil kajian mendapati terdapat lima tema utama bagi jurnal-jurnal berkenaan, iaitu bidang pengajian Islam secara umum, bidang syariah, bidang al-Quran dan hadis, bidang akidah dan pemikiran dan bidang sejarah Islam. Selain itu, 94 buah jurnal (87.85 %) menggunakan gaya rujukan American Psychological Association (APA), sementara selebihnya, iaitu 13 buah jurnal (12.14 %) mengaplikasikan gaya The Chicago Manual of Style (CMOS). Oleh itu, pendedahan ini mampu membantu para penulis dalam penyediaan dan penulisan manuskrip artikel. Pemilihan tema dan pematuhan format yang digariskan oleh pihak penerbit merupakan kriteria utama kepada penerimaan sesebuah artikel untuk diterbitkan dalam jurnal ilmiah
Ensemble meta classifier with sampling and feature selection for data with imbalance multiclass problem
No full textEnsemble learning by combining several single or another ensemble classifier is one of the procedures to solve the imbalance problem in multiclass data. However, this approach is still facing the question of how the ensemble methods obtain their higher performance. In this paper, the investigation is carried out on the design of the ensemble meta classifier with sampling and feature selection for imbalance multiclass data. The specific objectives are 1) to improve the ensemble classifier through data-level approach (sampling and feature selection)2) to perform experiments on sampling, feature selection, and ensemble classifier modeland 3) to evaluate the performance of the ensemble classifier. To fulfill the objectives, a preliminary data collection of Malaysian plants leaf images was prepared, experimented, and comparing the results. The ensemble design is also tested with another three high imbalance ratio benchmark data. It is found that the design using sampling, feature selection and ensemble classifier method using AdaboostM1 with Random Forest (also an ensemble classifier) provides the improved performance throughout the investigation. The result of this study is important to the ongoing problem of multiclass imbalance where specific structure and its performance can be improved in terms of processing time and accuracy
An improved grey wolf optimization-based learning of artificial neural network for medical data classification
No full textGrey wolf optimization (GWO) is a recent and popular swarm-based metaheuristic approach. It has been used in numerous fields such as numerical optimization, engineering problems, and machine learning. The different variants of GWO have been developed in the last 5 years for solving optimization problems in diverse fields. Like other metaheuristic algorithms, GWO also suffers from local optima and slow convergence problems, resulted in degraded performance. An adequate equilibrium among exploration and exploitation is a key factor to the success of meta-heuristic algorithms especially for optimization task. In this paper, a new variant of GWO, called inertia motivated GWO (IMGWO) is proposed. The aim of IMGWO is to establish better balance between exploration and exploitation. Traditionally, artificial neural network (ANN) with backpropagation (BP) depends on initial values and in turn, attains poor convergence. The metaheuristic approaches are better alternative instead of BP. The proposed IMGWO is used to train the ANN to prove its competency in terms of prediction. The proposed IMGWO-ANN is used for medical diagnosis task. Some benchmark medical datasets including heart disease, breast cancer, hepatitis, and parkinson\u27s diseases are used for assessing the performance of IMGWO-ANN. The performance measures are described in terms of mean squared errors (MSEs), classification accuracies, sensitivities, specificities, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. It is found that IMGWO outperforms than three popular metaheuristic approaches including GWO, genetic algorithm (GA), and particle swarm optimization (PSO). Results confirmed the potency of IMGWO as a viable learning technique for an AN
Adsorption of malachite green dye using spent coffee ground biochar: optimisation using response surface methodology
No full textUsed coffee grounds usually end up as landfill. However, the unique structural properties of its porous surface make coffee grounds can be transformed into biochar and performed as an alternative low cost adsorbent. Malachite green (MG) is a readily water soluble dye which is used extensively in textile and aquaculture industries. The mordant complex structures of MG generate destructive effects to animals and environment. In this study, adsorption of malachite green using spent coffee ground biochar as adsorbent was investigated. The experiments were designed in two methods: classical and optimisation by response surface methodology. Three parameters were studied, which are adsorbent dosage, contact time and pH while the responses in this study are malachite green removal (%) and adsorption capacity (mg/g). Optimisation studies were performed using response surface methodology. Quadratic model was chosen for both response and studied using central composite design. The correlation coefficient, R2 for the quadratic model of malachite green removal (%) and adsorption capacity (mg/g) were 0.95 and 0.99, respectively. The optimum malachite green removal (%) predicted was found at 99.27%, by using 0.12 g of adsorbent dosage, 43.05 minutes of contact time and pH of 9.45 at desirability of 1.0. The optimum adsorption capacity (mg/g) predicted was found at 118.01 mg/g, by using 0.02 g of adsorbent dosage, 60 minutes of contact time and pH of 10.24 at desirability of 0.98. So, it was concluded that the spent coffee ground biochar can be used as an effective adsorbent for malachite green removal from aqueous solution
