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The purpose of this study is to explore the perceptions of Muslim consumers on the Islamic Chamber Research & Information Center (ICRIC) Halal logo. In particular, the paper evaluates the main messages of the logo and describes the organization’s characteristics behind this logo.

This exploratory research conducts a semi-structured interview with visual aids method to identify the Malay Muslim’s perceptions toward the ICRIC Halal logo.

Results of the study indicate that the word “Halal” in Arabic characters is a very strong visual and emotional element of the logo because it is eye-catching and projects a strong image of credibility and trustworthiness. Using Islamic graphical design in a Halal logo can assist businesses to succeed in the marketplace. Simplicity, appropriate font type and size, suitable colors and total harmony of all elements make a Halal logo attractive and meaningful; the logo signals trust and mirrors the values of the organization.

This research used a qualitative research approach to analyze the perceptions of 25 Malay Muslim students.

Practical implications of this study provide a new window for all Halal certification bodies to realize the importance of the different elements of the Halal logo.

This research attempts to introduce a unique Halal logo that is approved by 57 Muslim countries. This unity assists religious consumers with various mathahib to purchase Halal products with confidence.

This pioneer study explores the Muslim consumers’ perceptions of a specific Halal logo in the marketplace.

This paper aims to investigate the impact of halal awareness on the relationship between halal supply chain knowledge and purchase intention for halal meat products in the millennial generation.

The quantitative approach with the respondents of 177 millennial generations in Indonesia is selected to understand the relationships between variables. Structural equation model-partial least square is used to analyze the relationship between variables.

The findings of this study found that the purchase intention of halal products in the millennial generation is influenced by several factors such as halal supply chain knowledge, halal certification and logo and religious beliefs. However, the results of this study also show that concern for halal products failed to moderate the relationship between these three main variables on the purchase intention of halal products.

This study provides insights into the concern that strengthens the relationship between the main variables on the intention to purchase halal meat products for the millennial generation.

This study aims to examine the halal brand image, halal logo and halal awareness of non-Muslim customers on attitude and behavioral intention.

This study uses a quantitative survey approach to 400 respondents consisting of 400 non-Muslim millennials in Indonesia, Malaysia and Singapore who have to buy halal cosmetics two years later. The sampling technique is purposive sampling. The analysis technique used in this study is structural equation modeling.

This study showed that halal logo, halal awareness and halal brand image have an effect on customer attitude. Then, the halal logo, halal awareness and halal brand image have an influence on behavioral intention. The existence of this positive signal has provided a great opportunity for businesses to make profits by meeting the demand for the halal market. Consumption of halal cosmetics produced by manufacturers attracts many non-Muslim consumers because of the safety, comfort and cleanliness of product ingredients, which of them must be given by the halal product, especially for cosmetics.

This study broadens the understanding of the attitudes and behavioral intentions of non-Muslim consumers toward halal cosmetic products. Furthermore, this research has provided a deeper interpretation of non-Muslim understanding of halal logos, halal brand images and halal awareness which have been minimal in research studies.

This study contributes to the literature related to the behavioral intentions of millennial non-Muslim consumers for halal cosmetics. Therefore, respondents in the study were specific, that is, non-Muslims who are millennial generation in the Indonesia, Malaysia and Singapore context.

The purpose of this paper is to determine the factors that affect foreign consumers’ purchase intention towards purchasing halal food products in South Africa. These factors are halal awareness, halal logo and attitude, which are important factors in affecting the intention of consumers.

Data were collected via a self-questionnaire with a sample of 230 foreign consumers. For analysing these data, a structural equation modelling technique was used in this study.

Based on the study’s results, all factors significantly influence foreign consumers’ intention towards purchasing halal food products and, subsequently, their buying behaviour. Interestingly, the study found that attitudes and halal awareness of non-Muslim consumers are very high compared with those of the Muslim consumers. Although the study addressed halal food consumers, most of the respondents participated in the study were non-Muslims and the majority of them were Christians.

The respondents were only limited to the Cape Town city in South Africa, and the focus was only on five variables related to halal food consumers, namely, halal awareness halal logo, attitude, purchase intention and buying behaviour.

This study can be used to develop halal food products to attract both Muslim and non-Muslim consumers, who are foreigners to a particular country.

This study is one of the first studies seeking to determine the factors that affect foreign consumers with regard to the purchase of halal food products in South Africa. It is regarded one of the first attempts to determine halal awareness, halal logo and attitude and how purchase intention and buying behaviour can be influenced.

The purpose of this paper is to devise a tool based on computational fluid dynamics (CFD) and machine learning (ML), for the assessment of potential airborne microbial transmission in enclosed spaces. A gated recurrent units neural network (GRU-NN) is presented to learn and predict the behaviour of droplets expelled through breaths via particle tracking data sets.

A computational methodology is used for investigating how infectious particles that originated in one location are transported by air and spread throughout a room. High-fidelity prediction of indoor airflow is obtained by means of an in-house parallel CFD solver, which uses a one equation Spalart–Allmaras turbulence model. Several flow scenarios are considered by varying different ventilation conditions and source locations. The CFD model is used for computing the trajectories of the particles emitted by human breath. The numerical results are used for the ML training.

In this work, it is shown that the developed ML model, based on the GRU-NN, can accurately predict the airborne particle movement across an indoor environment for different vent operation conditions and source locations. The numerical results in this paper prove that the presented methodology is able to provide accurate predictions of the time evolution of particle distribution at different locations of the enclosed space.

This study paves the way for the development of efficient and reliable tools for predicting virus airborne movement under different ventilation conditions and different human positions within an indoor environment, potentially leading to the new design. A parametric study is carried out to evaluate the impact of system settings on time variation particles emitted by human breath within the space considered.

This paper aims to ascertain whether halal certification mark, halal brand quality and halal awareness influence Nigerian consumers when making buying decisions.

The researchers reflect on the newly collected data to shed light on the above issues from the perspective of Nigerian consumers. To this end, a questionnaire was developed and used to collect data from 282 respondents. The data collected were analyzed using both descriptive and inferential statistics.

This study found that halal certification mark and halal brand quality are the most influential factors that contributed to the consumers’ buying decisions in Nigeria.

Based on the findings of this study, the researchers have argued that more efforts are needed in the area of halal awareness in Nigeria. Similarly, the study argues that halal brand quality should always be held at the esteemed position. Based on the study’s findings, the authors have been able to fill the literature gap, particularly in the context of the Nigerian halal industry.

This paper aims at the problem of attaching the data of doctors, patients and the real-time sensor data of the exoskeleton to the cloud in intelligent rehabilitation applications. This study designed the exoskeleton cloud-brain platform and validated its safety assessment.

According to the dimension of data and the transmission speed, this paper implements a three-layer cloud-brain platform of exoskeleton based on Alibaba Cloud's Lambda-like architecture. At the same time, given the human–machine safety status detection problem of the exoskeleton, this paper built a personalized machine-learning safety detection module for users with the multi-dimensional sensor data cloned by the cloud-brain platform. This module includes an abnormality detection model, prediction model and state classification model of the human–machine state.

These functions of the exoskeleton cloud-brain and the algorithms based on it were validated by the experiments, they meet the needs of use.

This thesis innovatively proposes a cloud-brain platform for exoskeletons, beginning the digitalization and intelligence of the exoskeletal rehabilitation process and laying the foundation for future intelligent assistance systems.

This paper aims to describe the use of a meta-learning framework for recommending cost-sensitive classification methods with the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?”

This paper describes the use of a meta-learning framework for recommending cost-sensitive classification methods for the aim of answering an important question that arises in machine learning, namely, “Among all the available classification algorithms, and in considering a specific type of data and cost, which is the best algorithm for my problem?” The framework is based on the idea of applying machine learning techniques to discover knowledge about the performance of different machine learning algorithms. It includes components that repeatedly apply different classification methods on data sets and measures their performance. The characteristics of the data sets, combined with the algorithms and the performance provide the training examples. A decision tree algorithm is applied to the training examples to induce the knowledge, which can then be used to recommend algorithms for new data sets. The paper makes a contribution to both meta-learning and cost-sensitive machine learning approaches. Those both fields are not new, however, building a recommender that recommends the optimal case-sensitive approach for a given data problem is the contribution. The proposed solution is implemented in WEKA and evaluated by applying it on different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system. The developed solution takes the misclassification cost into consideration during the learning process, which is not available in the compared project.

The proposed solution is implemented in WEKA and evaluated by applying it to different data sets and comparing the results with existing studies available in the literature. The results show that a developed meta-learning solution produces better results than METAL, a well-known meta-learning system.

The paper presents a major piece of new information in writing for the first time. Meta-learning work has been done before but this paper presents a new meta-learning framework that is costs sensitive.