The existing algorithm employing the log-normal distribution lacks applicability in generating imprecise data. This paper addresses this limitation by first introducing the log-normal distribution as a means to handle imprecise data. Subsequently, we leverage the neutrosophic log-normal distribution to devise an algorithm specifically tailored for…

Unlike previous studies on fixed logistics nodes, this research explored how consumer distribution impacts store selection and inventory balance, integrating the "ship-from-store" strategy to increase fulfillment within multiperiod sales plans. Specifically, omnichannel retailers (O-tailer) must sequentially decide on inventory…

Sudoku is a puzzle that has a unique solution. No matter how many methods are used, the result will always be the same. The player thought that the number of givens or clues, the initial value on the Sudoku puzzles, would significantly determine the difficulty level, which is not necessarily correct. This research uses two search algorithms,…

With the emergence of Artificial Intelligence (AI), smart education has become an attractive topic. In a smart education system, automated classrooms and examination rooms could help reduce the economic cost of teaching, and thus improve teaching efficiency. However, existing AI algorithms suffer from low surveillance accuracies and high…

This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…

Often, eye-tracking researchers define areas of interest (AOIs) to analyze eye-tracking data. Although AOIs can be defined with systematic methods, researchers in organic chemistry education eye-tracking research often define them manually, as the semantic composition of the stimulus must be considered. Still, defining appropriate AOIs during data…

Team has been widely applied in various fields, in which the collaboration efficiency of a team is the main consideration under the constraints of skill requirements. In educational scenarios, an educational institution usually builds a team of students with different skills to attend a competition, in which team communication cost and team…

Generative artificial intelligence (AI) has the potential to revolutionize social science research. However, researchers face the difficult challenge of choosing a specific AI model, often without social science-specific guidance. To demonstrate the importance of this choice, we present an evaluation of the effect of alignment, or human-driven…

As science and technology advance rapidly, video semantic understanding (VSU) technology has made significant strides. This technology has garnered widespread recognition within the music industry and piqued the interest of film and television music creators. In the realm of film music creation, VSU technology serves as a powerful tool,…

Futures of education are increasingly defined through predictive technologies and methods. We conceptualize 'algorithmic futuring' as the use of data-driven digital methods and predictive infrastructures to anticipate educational futures and animate actions in the present towards their materialization. Specifically, we focus on algorithmic…

In organizational and academic settings, the strategic formation of teams is paramount, necessitating an approach that transcends conventional methodologies. This study introduces a novel application of multicriteria integer programming (MCIP), which simultaneously accommodates multiple criteria, thereby innovatively addressing the complex task of…

This exploratory study investigates the convergence of marketing communications and AI-powered technology in higher education, adopting a perspective on student interactions with generative AI tools. Through a comprehensive content analysis of learners' responses, we employed a blend of manual scrutiny, Python-generated Word Cloud, and Latent…

Although self-regulated learning (SRL) plays an important role in supporting online learning performance, the lack of student self-regulation skills poses a persistent problem to many educators. Recommender systems have the potential to promote SRL by delivering personalized feedback and tailoring learning strategies to meet individual learners'…

Popular item response theory (IRT) models are considered complex, mainly due to the inclusion of a random factor variable (latent variable). The random factor variable represents the incidental parameter problem since the number of parameters increases when including data of new persons. Therefore, IRT models require a specific estimation method…

The advent of generative Artificial Intelligence (AI) systems, such as large language model chatbots, is likely to have a significant impact in psychotherapy and counselling in the future. In this paper we consider the current state of AI in psychotherapy and counselling and the likely evolution of this field. We examine the ethical codes of…