An efficient method to build music generative model by controlling both general and local note characteristics DOI Creative Commons

Thinh Do Quang,

Trang Hoang

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2023, Номер 35(9), С. 101761 - 101761

Опубликована: Сен. 20, 2023

It has been shown that since the rapid development of entertainment industry, music generation become a focused research topic. Numerous methods for creating music, or musical notes specifically have announced, each with distinct characteristics and advantages. These usually concentrated on these two aspects: overall harmony whole score link between adjacent notes, which this referred respectively as general local aspects. This study proposes model combined is capable deriving benefits from both aspects, hence good quality in terms quantitative qualitative evaluations. Various results based those discussed judged efficient enhancing well future opportunities. The value Average Pitch Interval (API) achieved remarkable 1.43, along note range 12.145; while subjective aspect, survey participants gave 6.81 generated yet only about 70% them can distinguish genuine pieces music.

Язык: Английский

Detection of Isotopes in Urban Source Search Low-Count Gamma Spectra Using Hopfield Neural Networks DOI

Luis Valdez,

Miltiadis Alamaniotis,

Eugene Moore

и другие.

Nuclear Technology, Год журнала: 2025, Номер unknown, С. 1 - 12

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

0

Unsupervised logic mining with a binary clonal selection algorithm in multi-unit discrete Hopfield neural networks via weighted systematic 2 satisfiability DOI Creative Commons
Nurul Atiqah Romli, Nur Fariha Syaqina Zulkepli, Mohd Shareduwan Mohd Kasihmuddin

и другие.

AIMS Mathematics, Год журнала: 2024, Номер 9(8), С. 22321 - 22365

Опубликована: Янв. 1, 2024

<p>Evaluating behavioral patterns through logic mining within a given dataset has become primary focus in current research. Unfortunately, there are several weaknesses the research regarding models, including an uncertainty of attribute selected model, random distribution negative literals logical structure, non-optimal computation best logic, and generation overfitting solutions. Motivated by these limitations, novel model incorporating mechanism to control literal systematic Satisfiability, namely Weighted Systematic 2 Satisfiability Discrete Hopfield Neural Network, is proposed as structure represent behavior dataset. For we used ratio <italic>r</italic> structures prevent solutions optimize synaptic weight values. A new computational approach considering both true false classification values learning system was applied this work preserve significant Additionally, unsupervised techniques such Topological Data Analysis were ensure reliability attributes model. The comparative experiments models utilizing 20 repository real-life datasets conducted from repositories assess their efficiency. Following results, dominated all metrics for average rank. ranks each metric Accuracy (7.95), Sensitivity (7.55), Specificity (7.93), Negative Predictive Value (7.50), Mathews Correlation Coefficient (7.85). Numerical results in-depth analysis demonstrated that consistently produced optimal induced represented performance study.</p>

Язык: Английский

Процитировано

2

Conditional random <i>k</i> satisfiability modeling for <i>k</i> = 1, 2 (CRAN2SAT) with non-monotonic Smish activation function in discrete Hopfield neural network DOI Creative Commons
Nurshazneem Roslan, Saratha Sathasivam, Farah Liyana Azizan

и другие.

AIMS Mathematics, Год журнала: 2024, Номер 9(2), С. 3711 - 3956

Опубликована: Янв. 1, 2024

<abstract> <p>The current development of logic satisfiability in discrete Hopfield neural networks (DHNN)has been segregated into systematic and non-systematic logic. Most the research tends to improve logical rules various extents, such as introducing ratio a negative literal flexible hybrid structure that combines structures. However, existing rule exhibited drawback concerning impact within structure. Therefore, this paper presented novel class called conditional random <italic>k</italic> for = 1, 2 while intentionally disregarding both positive literals second-order clauses. The proposed was embedded network with ultimate goal minimizing cost function. Moreover, non-monotonic Smish activation function has introduced aim enhancing quality final neuronal state. performance new compared other state art conjunction five different types functions. Based on findings, obtained lower learning error, highest total neuron variation <italic>TV</italic> 857 lowest average Jaccard index, <italic>JSI</italic> 0.5802. On top that, highlights its capability DHNN based result improvement <italic>Zm</italic> <italic>TV</italic>. is consistently throughout all function, showing outperforms functions terms <italic>TV.</italic> This presents an alternative strategy mining technique. finding will be particular interest especially areas artificial network, function.</p> </abstract>

Язык: Английский

Процитировано

1

Special major 1, 3 satisfiability logic in discrete Hopfield neural networks DOI Creative Commons
Gaeithry Manoharam, Azleena Mohd Kassim, Suad Abdeen

и другие.

AIMS Mathematics, Год журнала: 2024, Номер 9(5), С. 12090 - 12127

Опубликована: Янв. 1, 2024

<abstract> <p>Currently, the discrete Hopfield neural network deals with challenges related to searching space and limited memory capacity. To address this issue, we propose integrating logical rules into regulate neuron connections. This approach requires adopting a specific logic framework that ensures consistently reaches lowest global energy state. In context, novel called major 1,3 satisfiability was introduced. places higher emphasis on third-order clauses compared first-order clauses. The proposed is trained by exhaustive search algorithm, aiming minimize cost function toward zero. evaluate model effectiveness, compare model's learning retrieval errors those of existing non-systematic structure, which primarily relies similarity index measures benchmark state through extensive simulation studies. Certainly, random exhibited more solution when ratio exceeds 0.7% As experimental results other state-of-the-art models, it became evident achieved significant in capturing overall These findings emphasize notable enhancements performance capabilities network.</p> </abstract>

Язык: Английский

Процитировано

1

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network DOI
Luis Valdez, Miltiadis Alamaniotis, Alexander Heifetz

и другие.

Опубликована: Янв. 31, 2024

Язык: Английский

Процитировано

0

A modified fuzzy K-nearest neighbor using sine cosine algorithm for two-classes and multi-classes datasets DOI Open Access
Chengfeng Zheng, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor

и другие.

AIP conference proceedings, Год журнала: 2024, Номер 3080, С. 040001 - 040001

Опубликована: Янв. 1, 2024

The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity effectiveness. Based on the advantages, study this paper delves deeper into key parameters that influence performance of algorithm, implemented modifications such as integrating reverse learning adding elite opposition solution create modified Sine Cosine Algorithm (the SCA). Furthermore, by combining fuzzy k-nearest neighbor with SCA, simulates numeric datasets two or multiple classes, analyzes results. accuracy rate (ACC) achieved SCA FKNN is compared other models, data comparison results tables presented for each. proposed obvious advantages rate(ACC).

Язык: Английский

Процитировано

0

Flexibility of S-type random K satisfiability in hopfield neural network DOI
Suad Abdeen, Gaeithry Manoharam

AIP conference proceedings, Год журнала: 2024, Номер 3158, С. 030001 - 030001

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Exploring the efficacy of a supervised learning approach in 3 satisfiability reverse analysis method DOI
Nur ‘Afifah Rusdi, Nurul Atiqah Romli, Gaeithry Manoharam

и другие.

AIP conference proceedings, Год журнала: 2024, Номер 3158, С. 030009 - 030009

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

The effects of logical permutation in the 3-satisfiability reverse analysis method DOI

Amierah Abdul Malik,

Gaeithry Manoharam

AIP conference proceedings, Год журнала: 2024, Номер 3158, С. 030002 - 030002

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Logic mining model in 3-satisfiability reverse analysis into discrete hopfield neural network DOI
Gaeithry Manoharam, Nurul Atiqah Romli, Suad Abdeen

и другие.

AIP conference proceedings, Год журнала: 2024, Номер 3158, С. 050003 - 050003

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0