Department of Electronics & Communication Engineering, Cambridge Institute of Technology, Ranchi 834001, India.
Department of Computer Science & Engineering, Cambridge Institute of Technology, Ranchi 834001, India.
Comput Math Methods Med. 2022 Jul 25;2022:8717263. doi: 10.1155/2022/8717263. eCollection 2022.
Speech is one form of biometric that combines both physiological and behavioral features. It is beneficial for remote-access transactions over telecommunication networks. Presently, this task is the most challenging one for researchers. People's mental status in the form of emotions is quite complex, and its complexity depends upon internal behavior. Emotion and facial behavior are essential characteristics through which human internal thought can be predicted. Speech is one of the mechanisms through which human's various internal reflections can be expected and extracted by focusing on the vocal track, the flow of voice, voice frequency, etc. Human voice specimens of different ages can be emotions that can be predicted through a deep learning approach using feature removal behavior prediction that will help build a step intelligent healthcare system strong and provide data to various doctors of medical institutes and hospitals to understand the physiological behavior of humans. Healthcare is a clinical area with data concentrated where many details are accessed, generated, and circulated periodically. Healthcare systems with many existing approaches like tracing and tracking continuously disclose the system's constraints in controlling patient data privacy and security. In the healthcare system, majority of the work involves swapping or using decisively confidential and personal data. A key issue is the modeling of approaches that guarantee the value of health-related data while protecting privacy and observing high behavioral standards. This will encourage large-scale perception, especially as healthcare information collection is expected to continue far off this current ongoing pandemic. So, the research section is looking for a privacy-preserving, secure, and sustainable system by using a technology called Blockchain. Data related to healthcare and distribution among institutions is a very challenging task. Storage of facts in the centralized form is a targeted choice for cyber hackers and initiates an accordant sight of patients' facts which will cause a problem in sharing information over a network. So, this research paper's approach based on Blockchain for sharing sufferer data in a secured manner is presented. Finally, the proposed model for extracting optimum value in error rate and accuracy was analyzed using different feature removal approaches to determine which feature removal performs better with different voice specimen variations. The proposed method increases the rate of correct evidence collection and minimizes the loss and authentication issues and using feature extraction based on text validation increases the sustainability of the healthcare system.
语音是一种结合了生理和行为特征的生物识别形式。它有利于通过电信网络进行远程访问交易。目前，这是研究人员面临的最具挑战性的任务。人们以情绪形式表现出的精神状态非常复杂，其复杂性取决于内在行为。情绪和面部行为是通过关注语音轨迹、语音流、语音频率等，预测人类内部思维的重要特征。语音是人类各种内部反射的机制之一，可以通过关注语音轨迹、语音流、语音频率等特征来预测和提取。不同年龄的人类语音样本可以通过使用特征去除行为预测的深度学习方法来预测情绪，这将有助于建立一个强大的智能医疗保健系统，并为各医疗机构和医院的医生提供数据，以了解人类的生理行为。医疗保健是一个数据集中的临床领域，其中有许多细节被定期访问、生成和传播。许多现有方法的医疗保健系统，如跟踪和追踪，不断揭示系统在控制患者数据隐私和安全方面的限制。在医疗保健系统中，大部分工作涉及交换或使用果断的机密和个人数据。一个关键问题是建模方法，这些方法保证了与健康相关的数据的价值，同时保护隐私并遵守高标准的行为准则。这将鼓励大规模的感知，特别是因为预计医疗保健信息的收集将继续远远超出当前正在进行的大流行。因此，研究部分正在通过使用一种称为区块链的技术寻找一个隐私保护、安全和可持续的系统。在医疗机构之间分配与医疗保健相关的数据是一项非常具有挑战性的任务。以集中的形式存储事实是网络黑客的目标选择，并引发了对患者事实的一致看法，这将导致在网络上共享信息的问题。因此，提出了一种基于区块链的安全共享患者数据的方法。最后，使用不同的特征去除方法分析了用于提取错误率和准确性最佳值的提出模型，以确定哪种特征去除方法在不同的语音样本变化下表现更好。该方法提高了正确证据收集的比率，最小化了损失和认证问题，并且使用基于文本验证的特征提取提高了医疗保健系统的可持续性。
