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With so many placement opportunities around the students in their final or prefinal year, they start to feel the strain of the season. The students feel the need to be aware of their position and how to increase their chances of being hired. Hence, a system to guide their career is one of the needs of the day.

The job role prediction system utilizes machine learning techniques such as Naïve Bayes, K-Nearest Neighbor, Support Vector machines (SVM) and Artificial Neural Networks (ANN) to suggest a student’s job role based on their academic performance and course outcomes (CO), out of which ANN performs better. The system uses the Mepco Schlenk Engineering College curriculum, placement and students’ Assessment data sets, in which the CO and syllabus are used to determine the skills that the student has gained from their courses. The necessary skills for a job position are then extracted from the job advertisements. The system compares the student’s skills with the required skills for the job role based on the placement prediction result.

The system predicts placement possibilities with an accuracy of 93.33 and 98% precision. Also, the skill analysis for students gives the students information about their skill-set strengths and weaknesses.

For skill-set analysis, only the direct assessment of the students is considered. Indirect assessment shall also be considered for future scope.

The model is adaptable and flexible (customizable) to any type of academic institute or universities.

The research will be very much useful for the students community to bridge the gap between the academic and industrial needs.

Several works are done for career guidance for the students. However, these career guidance methodologies are designed only using the curriculum and students’ basic personal information. The proposed system will consider the students’ academic performance through direct assessment, along with their curriculum and basic personal information.

This study aims to provide an insight into research related to Muslim travellers in the past 42 years.

Using 342 articles collected from the Scopus database from 1981 to 2023, this study adopted the Bibliometrix in RStudio package and Biblioshiny Web application to analyse the research on Muslim travellers in two main categories: overview and intellectual structures.

The first publication related to Muslim travellers occurred in 1981 and number of publications remained few in the first three decades. Starting 2015, publications on Muslim travellers experienced a growing development of discussions and publications. Four prominent research clusters were identified: “halal tourism”, “hajj”, “Islamic tourism” and “tourist post-purchase”. Themes within the research on Muslim travellers have evolved from the “pilgrimage” to “Islamic tourism” theme. Then, the “Islamic tourism” theme has been expanded to a variety of topics that were primarily relevant to Muslim tourist behaviour. Themes related to “climate change” and “Syria” have been identified as the niche themes that need further study.

Scopus database is regularly updated as the number of papers and journals may increase or decrease from time to time. This may impact on the fluctuation of the theme analysis from the article search at that time.

This study reviews publications related to Muslim travellers over the past four decades. Accordingly, it can aid interested researchers and stakeholders in gaining a more thorough understanding of Muslim traveller research.

School effectiveness has attracted some currency in educational research globally since the 1960s though such studies mostly point to the efforts of principal leadership as the basis for promoting effective schools. However, in the case of Ghana, there is a lack of research conducted in the area, and due to that, this study sought to explore internal public perspectives of what constitutes school effectiveness in the Colleges of Education in the Upper East Region of Ghana.

This study employed the convergent parallel mixed-method design otherwise called concurrent mixed-method design. The population for the study comprised second and third-year students, tutors and leadership of the colleges. In total, 308 respondents constituted the sample size. The breakdown is 257 students in all, 41 tutors and 10 leaders of the colleges. Two instruments, namely, an in-depth interview guide and a questionnaire were used to elicit responses to address the object of this study.

The study revealed that the characteristics of effective schools include the high academic performance of students and a good show of disciplined behavior by both students and staff in the colleges among others.

To the best of our knowledge, during the search for studies conducted on school effectiveness, there is no scientific study done in Ghana highlighting the attributes of effective educational institutions. Most of the studies conducted in the area of educational studies only focused on principal leadership, educational access, participation and equity at the level of pre-tertiary institutions.

For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

The transition to college presents significant challenges for many students as they navigate new academic and social experiences. In the USA, 30% of first-year students drop out before their second year. Research indicates that mentoring programs help students achieve social integration and likely have a positive effect on their transition to college. This research study was conducted with education students to better understand the potential impacts of peer mentorship.

Student mentors and mentees were matched by attributes such as their concentration within the education major, gender, sports they played and whether they were first-generation matriculants. Data collection utilized two surveys one before the peer mentoring process and one after the process.

The findings suggest that peer mentoring improved first-generation students' sense of belonging to both their major and the college. Peer mentors also experienced increased belongingness. The transfer rate among participants of 2% was a significant drop from previous years.

The success of the peer mentoring experience was possibly due to the intentional matching process based on certain attributes. Additionally, taking a leadership role increased a sense of belonging in the peer mentors.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

This research investigates the effects of consumers' online information searching on their dining satisfaction in upscale restaurants during the pandemic. Customers frequently rely on online sources to gather information about upscale restaurants prior to their visits.

Data from 307 diners across the top ten popular upscale restaurants in Hong Kong were analysed by using SEM to explore the links between customers' needs, information search, restaurant attributes and customer satisfaction.

This study uncovers customers' online search behaviours and identifies restaurant attributes that are associated with customer satisfaction, which were not typically emphasised before the COVID-19 pandemic. Driven by their social and psychological needs, customers devoted more time to reading written comments by other consumers compared to visual images or self-descriptions from restaurants. Only service attribute significantly influenced customer satisfaction, while food and price attributes were not significant.

The findings of this study provide valuable insights for researchers and practitioners, shedding light on the altered needs and preferences of consumers following the unprecedented health crisis.

This study contributes to the development of expectancy disconfirmation theory and needs theory through the investigation of consumers' online information searching behaviours and dining satisfaction in upscale restaurants during the pandemic. By identifying the most important attributes influencing customer satisfaction, this research can aid upscale restaurants in developing effective marketing strategies and enhancing customer experiences.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.