Purpose Halal tourism is a subset of tourism activities geared towards Muslim which are aligned with the Islamic principles. As a response to this, many food operators have realised the importance of having a halal certification to establish a better market position. In the context of Indonesia, it is yet to be known what attitudes the food operators have towards halal certification and what attributes characterised those who have obtained the certification. Therefore, this study aims to examine the attributes of food operators and their attitudes towards halal certification in Indonesia. Design/methodology/approach A survey and structured interview were conducted on 298 food operators in Bandung, a city in Indonesia, between August and December 2018. Seven hypotheses were proposed and tested to evaluate the association between halal certification and food operators’ attributes and their attitudes towards it. Findings The results of the study suggested that food operators who had halal certification can be characterised by the number of branches the businesses have, the knowledge of halal tourism and knowledge on the market segment. However, the age of their business was found not related to halal certification. In terms of attitudes, the study found that performance beliefs, intention to apply and target market segment had associated with halal certification. Practical implications The outcomes of the study could provide information to entities and agencies involved in the tourism industry that consider targeting Muslim travellers as their market segment. Halal certification could be an approach to facilitate tourism marketing and consequently increase the performance of food-related business sectors. Originality/value This study provides evidence that could lead to a better understanding of the attributes of food operators and their attitudes towards halal certification in the context of Indonesia’s tourism industry.

Blockchain technology and cryptocurrency are attracting increasing attention from consumers, investors, investment industry and regulators. Cryptocurrency has great potential to be used for transaction or investment in the future. However, level of awareness of the blockchain technology and cryptocurrency is still at infant stage, specifically in developing countries. Thus, this study aims to investigate the level of awareness, trust and adoption of blockchain technology among blockchain community in Malaysia. Quantitative approach was adopted in this study where a new questionnaire was developed in the first phase to measure the level of awareness, adoption, and trust of blockchain technology applications among Malaysian blockchain communities. The resulting questionnaire consists of items on respondents’ demographic, their awareness, trust, and adoption of FinTech particularly on blockchain technology and cryptocurrency. In the second phase, a pilot study was conducted to validated the new questionnaire from 304 respondents. Reliability test using Cronbach’s alpha with a value of 0.908. A real survey was also conducted in this phase using the validated queationnaire and data were obtained online from 304 respondents. Descriptive statistics were used in the analysis during the third phase of the study, and results demonstrate that the awareness level of blockchain technology and cryptocurrency are at the intermediate level. Nevertheless, the majority of respondents are confident and trust that the blockchain technology can offer a stable and secure platform, which gives positive impact on the application of the technology. Empirical results provide significant insights into the development of the blockchain technology industry in the country.

Interest in Generator Maintenance Scheduling (GMS) has increased due to the advent of demand-related expansion in size for modern power systems. Timely maintenance plays a significant role in minimizing failures and helps in averting cost incurred as a result of production shutdowns. The GMS problem is a complex and nonlinear optimization problem that specifies the schedule for carrying out planned preventive maintenance on power generation units. There is no clear concept to GMS model types and choosing the appropriate maintenance scheduling type. Thus, this paper presented a comprehensive review on GMS models in electrical power systems that covers the maintenance strategies, main elements of GMS models, and optimization methods used in solving GMS models. The list of references comprised related works from the years 2000 until 2020, which were classified into three based on the objectives. A new type of objective function for the GMS models was among the suggestions provided. A numerical example which focuses on a multi-objective GMS model and a proposed multi-objective Pareto ant colony system algorithm are also presented. The results of this review will not only enable researchers to gain a good overview of the existing GMS models for electrical power systems but also provide a source of references in choosing an appropriate maintenance scheduling strategy that is suitable with the type of generating unit and existing operating conditions.

The performance of different mechanisms utilised to perform anomaly detection depends heavily on the group of features used. Therefore, dealing with a multi-dimensional dataset that typically contains a large number of attributes has caused problems to classification accuracy. Not all attributes in the dataset can be used in the classification process since some features may lead to low performance of classifiers. Feature selection (FS) is a good mechanism that minimises the dimension of high-dimensional datasets. Modified binary grey wolf optimization (MBGWO) is a metaheuristic algorithm that has been successfully used for FS. However, the MBGWO algorithm has a drawback in selecting sub-optimal feature sets from an original set of features. This drawback is related to the linearly decreasing value of a parameter where there is no control between the exploration and exploitation processes. This study proposed an enhanced binary grey wolf optimization (EBGWO) algorithm for FS in anomaly detection by controlling the balancing parameter. The new method focused on obtaining a value for a parameter that controlled the trade-off between exploration and exploitation. Evaluation of the proposed method was on the NLS-KDD dataset with different attack classes and compared with other benchmark algorithms, such as binary bat algorithm, binary particle swarm optimization, and four variants of grey wolf optimiser for FS. The experimental results indicated that EBGWO was superior than other algorithms, where it obtained 19 features only out of a total of 41 features with 87.46% classification accuracy. The proposed algorithm can be applied to detect anomaly in network intrusion and outliers in data that are significant but difficult to find.

The function of operators in an evolutionary algorithm (EA) is very crucial as the operators have a strong effect on the performance of the EA. In this paper, a new selection operator is introduced for a real valued encoding problem, which specifically exists in a shrimp diet formulation problem. This newly developed selection operator is a hybrid between two well-known established selection operators: roulette wheel and binary tournament selection. A comparison of the performance of the proposed operator and the other existing operator was made for evaluation purposes. The result shows that the proposed roulette-tournament selection is better in terms of its ability to provide many good feasible solutions when a population size of 30 is used. Thus, the proposed roulette-tournament is suitable and comparable to established selection for solving a real valued shrimp diet formulation problem. The selection operator can also be generalized to any problems related to EA.