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Investigating the use of Neural Networks in Enhancing Music Composition and Sound Quality

Investigating the use of Neural Networks in Enhancing Music ...

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2024 International Conference on Advances in Computing Research on Science Engineering and technology, ACROSET 2024

作者： Gupta, Sachin Inzimam Manivasagam Sanskriti University Department of Management Mathura India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350388800

An exciting new area of study in the world of music technology is the use of neural networks to the improvement of sound quality and the automation of musical creation. The goal of this research is to evaluate the potential of neural networks in boosting musical creation and reception. To determine whether algorithms based on neural networks can help us create groundbreaking music, we do extensive research into the field. First, the study presents a recurrent neural network (RNN)-based approach to composing melodic sequences of notes. This method draws on the remarkable pattern-recognition skills of neural networks to motivate richer musical production. Listening to a big library of pieces, the RNN model is able to learn how to construct new pieces that are enjoyable to human hearing. Synthesizing high-quality audio waveforms is crucial in modern music composition, and this study investigates the use of Generative Adversarial Networks (GANs) to this problem. The AudioWaveGAN approach utilizes GANs to produce high-quality sound, filtering out unwanted noise while also composing immersive pieces. Our study compares the proposed method to various established rivals. The criteria for evaluation are based on genuine conventional methods. The findings illustrate the superiority of the suggested technique in terms of originality, sound quality, and a combined score, emphasizing its potential to change music creation and production. This study represents a major advance toward using neural networks in music creation and audio processing. The research shows that these fresh methods open up exciting opportunities for artists, composers, and music producers who want to release hitherto unattainable wellsprings of inspiration and provide their listeners with fully enveloping aural journeys. © 2024 IEEE.

关键词： Generative adversarial networks

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Emotional Information-Based Hybrid Recommendation System 11th

Emotional Information-Based Hybrid Recommendation System

引用

11th International Conference on Soft Computing for Problem Solving, SocProS 2022

作者： Sharma, Manika Mittal, Raman Bharati, Ambuj Saxena, Deepika Singh, Ashutosh Kumar Department of Computer Application National Institute of Technology Kurukshetra Haryana Kurukshetra136119 India

ISBN: (纸本)9789811965241

In this technology-driven times, recommender systems play a crucial role in providing better user experience and attracting as many users as possible on the website. We propose a hybrid approach for selecting movies that incorporates emotional data in this study. The model will include the benefits of both content-based filtering and collaborative filtering to make up a hybrid model, and then, an additional parameter which is emotions further enhances the accuracy and efficiency of the model. The model is evaluated and compared with some other approaches, and it appears to perform better in a real-time environment. The model also tries to eliminate some of the existing limitations up to some extent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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Constructing Conditional Random Fields for Automated Land Cover Mapping from Hyper Spectral Images

Constructing Conditional Random Fields for Automated Land Co...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Prabha, S. Singh, Vaishali Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes an automatic land cover mapping technique using Conditional Random Fields (CRFs) from hyper-spectral snapshots. It introduces a hierarchical feature vector method to encode the labels' spatial relationships. The paper opinion associated paintings on land cover mappings and introduces the idea of CRFs inside the context of the land cowl category. A description of the facts, pre-processing techniques, and the parameters used for schooling and checking out the model is supplied. The paper develops and evaluates the CRF model, evaluating the results of other land cowl class strategies. Finally, the authors discuss the capacity packages of this technique in the context of other land cowl mapping obligations. This painting is innovative in its method to land cowl mapping and offers a successful implementation of the CRF model for this hassle. It explores using Conditional Random Fields (CRFs) to facilitate automatic land cover mapping from hyperspectral photos (HSI). The CRF method was implemented to estimate the land cover labels in an unsupervised gaining knowledge of style. To this stop, the pixel-level land cowl information for a given area changed into modeled as a graphical version, permitting place-degree marginal probability computations for a dense prediction. Experimental outcomes validated that the proposed technique ought to obtain a sizeable development in the accuracy of land cover mapping for the tested records set compared to its baseline. The proposed CRF-primarily based land cowl mapping method exhibited a higher convergence price and better nearby minima convergence. Moreover, the proposed approach was located to be sturdy in phrases of sensitivity to preliminary land cowl labels. © 2024 IEEE.

关键词： category Conditional evaluating hyper spectral schooling

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Improving Efficiency and Accuracy in Clinical Diagnostic Decision Making with Deep Learning

Improving Efficiency and Accuracy in Clinical Diagnostic Dec...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kharbas, Vikas Kumar Gobi, N. Kumar, Dinesh Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Deep learning is a modern form of synthetic intelligence (AI) based on large quantities of statistics and algorithms that allow computer systems to approximate features and solutions, ensuing in more excellent, efficient, and accurate selections. In the scientific field, deep gaining knowledge is increasingly used to help clinicians with diagnostic decision-making, decreasing the value and attempt associated with diagnostic approaches. This paper discusses the potential of deep learning to enhance the performance and accuracy of medical diagnostic selection-making. The paper provides an overview of the cutting-edge nation of the field, with a particular awareness of picture processing and its application to improve accuracy and decrease the price associated with scientific diagnoses. It also provides a few capacity challenges and pitfalls and outlines the issues essential to ensure a hit implementation and successful results. Eventually, it offers a few recommendations for similar studies to increase deep getting to know algorithms further. Deep gaining knowledge has been broadly explored in recent years as a powerful tool for enhancing the accuracy and efficiency of clinical diagnostic decision-making. Deep studying models can investigate patterns in medical datasets, deliberating the traits of affected person information and ailment signs to generate extra specific and reliable diagnoses. By incorporating extra facts such as transcripts from affected person-clinician interactions and medical imaging, those fashions may be trained to assist clinicians in identifying illnesses early and provide well-timed and correct analysis. Moreover, deep learning allows for incorporating case-primarily based reasoning and presents the capacity to combine scientific knowledge in real-time. By having extra sturdy and accurate models, clinicians can reduce the time required to diagnose a condition and make treatment choices. Deep gaining knowledge also lets in for the integration o

关键词： disorder enhance healthcare value

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Utilizing Deep Learning in Pre-Clinical and Clinical Diagnosis of Peripheral Neuropathies

Utilizing Deep Learning in Pre-Clinical and Clinical Diagnos...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Kharbas, Vikas Kumar Mahajan, Prerna Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Peripheral neuropathies are a group of problems that affect the peripheral frightened machine, often leading to a spread of symptoms and impairments. Diagnosis of these situations is frequently imprecise and time-consuming, leading to delays in remedy. Deep studying techniques provide the capacity to automate and enhance the analysis of peripheral neuropathies. Through deep gaining knowledge, clinicians can obtain more excellent correct diagnoses based on enter from MRI pics, photographs of nerve biopsies, or different imaging facts. Moreover, deep studying may be used to create characteristic vectors from different medical features and EEGs that may facilitate the popularity of signs and symptoms and diagnoses extra speedy and more appropriate than traditional methods. This paper explores the capacity of deep learning to accurately diagnose and stratify peripheral neuropathies in pre-scientific and medical settings. It gives a top-level view of contemporary studies on deep getting-to-know strategies for spotting signs and symptoms and diagnosing peripheral neuropathies. Similarly, the paper outlines capacity applications and blessings of deep studying for diagnosing peripheral neuropathies and discusses areas of destiny studies. The software of deep mastering in the pre-medical and clinical prognosis of peripheral neuropathies has been increasingly studied in latest years. Deep mastering fashions, consisting of convolutional neural networks, have shown promising effects in analyzing electromyography (EMG) alerts for the early detection, quantification, and class of peripheral neuropathies, complementing and, in a few cases surpassing conventional diagnostic methods. Moreover, those methods have been advanced as they should be detected and differentiated between myopathic and neuropathic abnormalities and muscle activity. Those algorithms can also distinguish between the diverse kinds of peripheral neuropathies and are potentially useful for early detection and reme

关键词： illnesses peripheral photographs prognosis styarchitectures ling

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A Robust Ensemble Learning Approach for Medical Image Segmentation with Transfer Learning

A Robust Ensemble Learning Approach for Medical Image Segmen...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Kavitha, R. Singh, Shweta Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes a robust ensemble mastering approach for clinical picture segmentation. The proposed technique combines a convolution neural community (CNN) with a switch studying-based totally ensemble model. The CNN is pre-educated with a dataset containing clinical photo modalities, particularly Magnetic Resonance Imaging (MRI) and computed tomography (CT)., a weight-averaged ensemble model is acquired between the two scientific photograph modalities. This version is an initial way to the clinical image segmentation problem. Eventually, the ensemble model is great-tuned with additional imaging statistics from an unmarried modality to improve the segmentation accuracy. The proposed technique is evaluated on a range of datasets, and the effects display that it achieves competitive performance compared to the country of the artwork. Moreover, the experiments reveal that combining the transfer gaining knowledge of-based totally ensemble mastering approach with extra imaging information enhances the accuracy of medical picture segmentation. © 2024 IEEE.

关键词： additional convolution learning state subsequently

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Prediction of Reliability of Data in Underwater Communication Network Using Fuzzy Logic

Prediction of Reliability of Data in Underwater Communicatio...

引用

2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Surendra Gobi, N. Yadav, Rakesh Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper uses fuzzy logic to propose a reliability prediction set of rules for the underwater communication community. The set of rules uses fixed fuzzy rules to expect communication reliability among nodes in an underwater environment. Those fuzzy rules are derived from a reliability model based on the characteristics of an ordinary underwater conversation system, such as physical and environmental elements. The algorithm is then examined using accurate statistics from an underwater communication test. The effects imply that this approach is promising in predicting the reliability of the community. It has also been discovered that reliability prediction accuracy can be advanced if the bushy guidelines include extra factors, the sign channel's records rate, and the machine's performance parameters. This study offers a practical approach to improving the performance and reliability of underwater communique networks. © 2024 IEEE.

关键词： algorithm communication environmental fuzzy logic

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Investigating the Integrity of Irrigation Systems Through Hyper Spectral Image Analysis

Investigating the Integrity of Irrigation Systems Through Hy...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Ahmed, Zahid Preethi, D. Yadav, Dhananjay Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This research aims to use hyperspectral picture analysis to research the integrity of irrigation systems, hoping to reduce power and water utilization. Traditional methods for measuring irrigation integrity are timeingesting, luxurious, and unreliable. With the software of the advanced imaging era, this project seeks to create a detailed model of an irrigation system so one can discover and diagnose troubles quickly and appropriately. A huge-statistics technique utilizing spectral statistics mixed with professional gadget wisdom gives the potential irrigation structures control. analysis might be conducted using a combination of airborne imaging and computational strategies along with satellite tv for pc and in-area evaluation. Those techniques will assist in offering a value-effective monitoring machine for identifying and diagnosing irrigation troubles and detecting modifications that can suggest water misuse or inefficient layout. Using this era, we intend to enhance irrigation structures' overall performance and integrity. © 2024 IEEE.

关键词： combination diagnosing hyperspectral measuring research aims

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Presentation Analysis of Reasoning Radio-Based Routing Protocols in Wireless Mesh Networks

Presentation Analysis of Reasoning Radio-Based Routing Proto...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Ritesh Kharbas, Vikas Kumar Mehta, Deepak Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Reasoning radio-based total routing protocols present a promising technique for permitting powerful conversation in wi-fi mesh networks. By using the spectrum sensing capabilities of Reasoning radio, routers can intelligently pick out and pick out spectrum assets to ahead records, thereby allowing for improved network throughput and reliability. In this paper, we carry out a comprehensive Presentation evaluation of routing protocols primarily based on Reasoning radio in wi-fi mesh networks. Mainly, we Presentation metrics including packet shipping fee, latency, jitter, and energy Presentation. We examine various routing protocols from an empirical and theoretical angle and speak about the strengths and weaknesses of every technique. Subsequently, our consequences provide insights into the capability advantages and tradeoffs of Reasoning radio-based routing protocols in wireless mesh networks, offering practical steerage to designers and engineers within the subject. © 2024 IEEE.

关键词： Consequences Engineers Networks Practical Reasoning Routing Spectrum

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Maximizing the Accuracy of Time Series Analysis for the Early Detection of Rhabdomyosarcomas

Maximizing the Accuracy of Time Series Analysis for the Earl...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Prabhu, A. Gautam, Anupam Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Rhabdomyosarcomas are a rare malignant tender tissue tumor that generally gives in younger children and teenagers. Early prognosis and remedy are essential for successful outcomes. Time collection evaluation is a valuable tool for recognizing styles and trends in medical facts, mainly for rare situations, which include Rhabdomyosarcomas. It has consequently been increasingly employed to detect early signs and symptoms of the ailment. On this look, we are conscious of investigating and optimizing techniques for time collection analysis. It is an excellent way to enhance its application and accuracy in identifying early symptoms and signs of rhabdomyosarcoma. We examine present strategies and suggest improvement techniques, along with function extraction and system mastering techniques. We further inspect the effectiveness of our strategies by conducting experiments on a dataset installed from scientific facts and literature of rhabdomyosarcoma instances. Those experiments show promising effects, indicating that our proposed strategies can considerably increase the accuracy and sensitivity of time series evaluation for the early detection of rhabdomyosarcoma and cause higher prognoses for affected sufferers. The focal point of this study is to maximize the accuracy of time series analysis for the early detection of rhabdomyosarcoma. Time collection analysis includes: • The gathering of temporal information from multiple sources. • The assessment of these records. • The interpretation of correlations between the facts points. This study aims to utilize these techniques to discover diffused adjustments in affected person information so that you can perceive the onset of the disorder in advance than would be possible with traditional techniques. The examination will expand algorithms to systematically and accurately procedure the temporal statistics and discover adjustments indicative of Rhabdomyosarcomas. In addition, the look will rent gadget learning to boost the dete

关键词： malignant recognizing Rhabdomyosarcomas strategies Time

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