Artificial intelligence (AI) has emerged as a powerful technology that has the potential to transform education. This study aims to comprehensively understand students' perspectives on using AI within educational settings to gain insights about the role of AI in education and investigate their perceptions regarding the advantages, challenges, and expectations associated with integrating AI into the learning process. We analyzed the student responses from a survey that targeted students from diverse academic backgrounds and educational levels. The results show that, in general, students have a positive perception of AI and believe AI is beneficial for education. However, they are still concerned about some of the drawbacks of using AI. Therefore, it is necessary to take steps to minimize the negative impact while continuing to take advantage of the advantages of AI in education.

In this manuscript, we extend the traditional multi-attributive border approximation area comparison (MABAC) method for the multiple-criteria group decision-making (MCGDM) with triangular fuzzy neutrosophic numbers (TFNNs) to propose the TFNNs-MABAC method. In the proposed method, we utilize the TFNNs to express the values of criteria for each alternative in MCGDM problems. First, we briefly acquaint the basic concept of TFNNs and describe its corresponding some operation laws, the functions of score and accuracy, and the normalized hamming distance. We then review two aggregation operators of TFNNs. Afterward, we combine the traditional MABAC method with the triangular fuzzy neutrosophic evaluation and provide a sequence of calculation procedures of the TFNNs-MABAC method. After comparing it with some TFNNs aggregation operators and another method, the results showed that our extended MABAC method can not only effectively handle the conflicting attributes, but also practically deal with incomplete and indeterminate information in the MCGDM problem. Therefore, the extended MABAC method is more effective, conformable, and reasonable. Finally, an investment selection problem is demonstrated as a practice to verify the reasonability of our MABAC method.

Multi-objective optimization based on ratio analysis plus full multiplicative form (MULTIMOORA) is an efficient decision-making method for solving multi-criteria group decision-making (MCGDM) processes. It uses three strategies to examine different alternatives and to determine their evaluation values. These strategies include ratio system approach (RSA), reference point approach (RPA), and full multiplicative form (FMF). However, this method presents some challenges in the examination model, such as the ability to aggregate and determine the final result according to these strategies without considering value differences, the complexity of calculating the aggregation and multi-time comparisons, and the probability of distinguishing circular reasoning rules in dominance theory. In addition, determining an appropriate instrument for handling uncertainty, inconsistency, and incompleteness of information and a suitable weight for each criterion and decision-maker for reducing human interventions are also considered and will be a complex MCGDM process. To overcome these weaknesses, we propose an extended MULTIMOORA based on trapezoidal fuzzy neutrosophic numbers (TraFNNs) for MCGDM. We integrated it with ordinal priority approach (OPA) method to decide the initial weights for decision-makers and criteria without human subjective assessment. In addition, we used correlation coefficient and standard deviation (CCSD) technique to statistically compute the relationship between these strategies in resolving unique weights to obtain realistic results and eliminate the above issues. Finally, sensitivity and comparative analyses demonstrate the capability and effectivity of the extended method.

Decades ago, a bank thoroughly utilized experts to determine financial decisions based on their assessments by evaluating risks qualitatively against the company’s financial statements, business scenarios, and individual or conventional client interview results. However, this provoked a relatively subjective evaluation process and faced more complex criteria conditions with specific requirements. In addition, policymakers continued to use uncertain information in the evaluation process despite the increasing concerns over inconsistency, indeterminacy, and uncertainty in data that have emerged and become significant challenges in decision-making. Therefore, this paper introduces an integrated decision support framework based on spherical fuzzy numbers (SFNs) for overcoming a banking financial aid recipient evaluation. The proposed framework combines two developed algorithms, which are a spherical fuzzy number method based on the removal effects of criteria (SFN-MEREC) and a spherical fuzzy number ranking of alternatives through functional mapping of criterion sub-intervals into a single interval (SFN-RAFSI) algorithm. The SFN-MEREC is used to determine objective criteria weight coefficients, whereas the SFN-RAFSI is used to select the appropriate banking financial aid recipient. The proposed framework exhibits the results in line and a positive correlation with other spherical fuzzy decision-making methods authenticated through comparative analysis and weighted Spearman’s rank correlation coefficient evaluations. Finally, a rank reversal simulation analysis demonstrates the framework’s ability and stability in managing inconsistent, indeterminate, and uncertain data, ensuring its robustness throughout the evaluation process.

In this research the Kovats retention index of 51 fragrance and flavor substances was successfully predicted using Artificial Neural Network (ANN). ANN has been run three times with each number of iterations of 1000, 5000, and 10000. These three iterations are chosen to see the best iteration in generating the R2 and RMSE. This research indicates that the number of iterations of 5000 is the best iteration after testing. The study obtained R 2 = 0.986 and RMSE = 28.99, with an average difference between predicted and observed is 2.5%. From these results, it can be understood that the ANN model can predict the Kovats retention indices of fragrance and flavor substance quite well.

Many scholarships have been routinely offered by the educational institutions including Syiah Kuala University. This university has extensively distributed academic achievement and Financial Aid scholarships. Generally, students who have submitted all requirement documents and applied to these scholarships will be selected by a scholarship committee. By using the scholarship committee preferences, the results obtained an amount of vagueness and subjective. The selection process is also too difficult and take so long time. In order to solve this problem, a decision support system (DSS) using Multiple Attribute Decision Making with Simple Additive Weighting Method was demonstrated by using a numerical example to determine the scholarship recipients. The method is able to display a list of the scholarship recipients by sorting the best alternative values from a number of alternative values in descending order. In this paper, we used ten scholarship recipient candidates as the alternatives and four scholarship requirements as the criteria or the attributes. The results obtained with finding the weight values of each attribute, then ranking the optimal score value of each alternative. Finally, this method can be used in this case study.