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PurposeThis paper proposes a fuzzy random multi-objective portfolio model with different entropy measures and designs a hybrid algorithm to solve the proposed model.Design/methodology/approachBecause random uncertainty and fuzzy uncertainty are often combined in a real-world setting, the security returns are considered as fuzzy random numbers. In the model, the authors also consider the effects of different entropy measures, including Yager's entropy, Shannon's entropy and min-max entropy. During the process of solving the model, the authors use a ranking method to convert the expected return into a crisp number. To find the optimal solution efficiently, a fuzzy programming technique based on artificial bee colony (ABC) algorithm is also proposed.Findings(1) The return of optimal portfolio increases while the level of investor risk aversion increases. (2) The difference of the investment weights of the optimal portfolio obtained with Yager's entropy are much smaller than that of the min–max entropy. (3) The performance of the ABC algorithm on solving the proposed model is superior than other intelligent algorithms such as the genetic algorithm, differential evolution and particle swarm optimization.Originality/valueTo the best of the authors' knowledge, no effect has been made to consider a fuzzy random portfolio model with different entropy measures. Thus, the novelty of the research is constructing a fuzzy random multi-objective portfolio model with different entropy measures and designing a hybrid fuzzy programming-ABC algorithm to solve the proposed model.

Since the beginning of the digital music era, the number of available digital music resources has skyrocketed. The genre of music is a significant classification to use when elaborating music; the role of music tags in locating and categorizing electronic music services is essential. To categorize such a large music archive manually would be prohibitively expensive and time-consuming, rendering it obsolete. This study’s main contributions to knowledge are the following: This article will break down the music into many MIDI (music played on a digital musical instrument) movements, playing way close by analysis movement, character extraction from passages, and character sequencing from movement so that you may get a clearer picture of what you are hearing. The procedure includes the following steps: extracting the note character matrix, extracting the subject and segmentation grouping based on the note character matrix, researching and extracting beneficial characteristics based on the theme of the segments, and composing the feature sequence. It is challenging for the sorter to acquire spatial and contextual knowledge about music using traditional classification techniques due to its shallow structure. This study uses the unique pattern of input MIDI segments, which are used to probe the relationship between recurrent neural networks and attention. The approach for music classification is verified when paired with the testing precision of the same-length segment categorization; thus, gathering MIDI tracks 1920 along with genre tags from the network to construct statistics sets and perform music classification analysis.

Vocal music teaching through E-learning platforms and in-person classrooms requires intense attention and practice. The vocals and strings with/without instrument support are required for improving the voice, pitch presentation, and learning improvement. With digitalization, the assessments are performed using a computer and process-aided technologies for musical performance evaluation. This article introduces a Leveled-Fuzzy Logic Approach (LFLA) for evaluating the musical performance of the vocal teaching method. The evaluation improves the teaching mode by matching the actual learning guidelines. The vocal music teaching guidelines vary for different musical sessions and types. Such different aspects are analyzed using leveled fuzzy; the session flow is analyzed for maximum inference with the actual learning process. The number of learning levels forms the crisp input and the input is analyzed until the fuzzification extracts precise high matching for the guided and real-time teaching. The crisp inputs are induced for combinational inference during the fuzzification process using lagging features. The lagging features such as time, sessions, and evaluation per session are considered for fuzzification. The proposed approach is verified using the metrics assessment rate, lag detection, assessment time, and errors.