The use of Transformer models in music generation has greatly contributed to the development of automatic music generation, to produce MIDI sequences with long-range dependencies. These models, which are particularly effective for processing sequential information, outperformed such models as RNNs in terms of better representation of long-term dependencies across compositions. Prominent sources, like Dewangan, Singh, and Verma’s work on developing Musical sequences, etc., stress the importance of new training methodologies that are free from distortions to structures, but also to creativity. Furthermore, more recent adaptations of such methods are the Music Transformer (Huang etal., 2019) and its circuitous extension, the Multitrack Music Transformer (Dong etal., 2022), which have embedded more complex structures of music into Transformers for better coverage of human-like concepts. Ha and colleagues suggest a technique they refer to as Compositional Steering in a work published in 2022 and another technique called Stylistic Clustering introduced by Zhang and colleagues in 2024. Thus, there are still issues to be addressed: the tendency of AI-generated music to be emotionally shallow, and the problems of using AI in music creation. This research considers these changes, seeking to examine the effectiveness of Transformer models in improving the efficacy of music therapy and psychological treatments with the use of individualized music creation. Investigates the interplay between AI and human creativity in composing and utilizing music for both artistic and therapeutic purposes.

The performance analysis in the present study examines the OTP in contrast to popular contemporary cryptographic techniques like AES, RSA and so on. Four criteria are to be examined: Their security, their protection against attacks with quantum computers, the required key management and their practical feasibility. OTP is presented as an ideal solution in terms of security theory while being critically described in terms of the application to a large-scale system including the problem of key generation, distribution, and storage. However other contemporary methods like the AES or RSA are relatively more suitable to be used in real life systems because the execution time and space complexities are more bearable. However, as the technology of quantum computers develops, these algorithms become most threatened by quantum attacks, especially through those such as Shor’s and Grover’s. The paper also explores the concept of post-quantum cryptography as a developing field for finding solutions to protect encryption from such threats backed by quantum computing. These alternatives remain still in the early stage of development but appear to have great possibilities to protect digital communications in a world in which quantum computers could crack the more conventional cryptographic protocols. The study reveals that there is a need to achieve proper proportionalities in designing cryptographic processes and the security measures offered to ensure that they are not only strong but also implementable. OTP, being a concept of perfect secrecy, however, is not feasible for use in large applications hence why there is a need to search for algorithms that offer high-security levels while at the same time operational. With the advancement of quantum computing, it is essential that the future work be directed toward the proposition of algorithms and communication paradigms that would be both effect and efficient, and, most importantly, secure from attacks utilizing capabilities of quantum computers. Hence, the findings underscore the need for designs of cryptographic protocols that support a high level of security during deployment while still being practical for future implementation as the technologies change.

The development of communication technologies has been rapid leading to a connector world where information can be produced and passed in record time. But these occurrences have served to increase the potential of unauthorized breach into secure information databases. In elaborating on the framework for the construction of DSTS, this paper presents a hybrid model – namely, the application of One-Time Pad (OTP) cryptography combined with Lempel-Ziv-Welch (LZW) compression. DSTS improves data hiding capability, provides a secure system, and resists being detected. Therefore, compared to the existing methods, this method utilizes the strength of OTP that cannot be cracked along with the method of LZW compression to advance the design of sophisticated secure communication systems.

Social enterprises are unique organizations that help in solving social problems including poverty, women and children oppression, and environmental conservation. Yet, these organizations are confronted with constraints such as funding, policy disadvantages and some other limitations that pose a threat to the sustainability of the project. This research uses Graph Neural Networks to verify how entities interact in the context of social enterprises, the performance indicators, and the issues that they experience. To build up the directed graph showing details about the four highlighted social enterprises–Grameen Bank, BRAC, SEWA and Eco-Enterprises along with their goals and challenges, such as poverty reduction, scalability, sustainability and funding constraints, and policy gaps, each of the four has been highlighted as below: The GNN analysis in this study provides a clear understanding of interaction between them, as important challenge like funding and policy gaps becomes clear as the factors hindering success. The paper also discusses the ability of GNNs to capture various types of relations and provides recommendations for social ventures, government, and financiers. The implications highlight the role of general, sustainable development objectives to address the issues of scalability and in relation to the policy dimensions. The study contributes to the existing literature on methods of computational social sciences and presents a novel approach leveraging GNNs for social enterprise analysis and presents the applicability of GNNs in enhancing decision making and strategic planning for social enterprises.

In this paper, we introduced the concept of the heptapartitioned neutrosophic soft set (HP-NSS), a novel extension and generalization of the neutrosophic soft set theory. To improve accuracy, the indeterminacy is divided into five additional possibilities, which are as follows: absolute true, relative true, contradiction, unknown, and ignorance, relative false and absolute false. We extend the concept of neutrosophic soft topological spaces by introducing heptapartitioned neutrosophic soft topological spaces (HPNSTS), a novel generalization that incorporates seven distinct partitions to model uncertainty, vagueness, and indeterminacy in topological structures. Three new definitions are introduced and these definitions are p-open, pre-open and semi-open sets. Special attention is focused on p-open sets, and a number of results related to p-open sets are addressed. Machine learning and graphical algorithms, such as K-Means clustering, Heat maps, Elbow method, Feature correlation, 2D-normalized t-SNE, and parallel coordinates of 3D T-SNE, were used and visualized for a real-world application involving the romantic feelings of young boys and girls across various dimensions.