دانلود مقاله تاثیر ایجاد و تست مدل بهینه سازی هوش مصنوعی

خلاصه
معرفی
کار مرتبط
بحث
نتیجه
در دسترس بودن داده ها
تضاد علاقه
منابع

Abstract
Introduction
Related Work
Discussion
Conclusion
Data Availability
Conflicts of Interest
References

چکیده
شبکه‌های عصبی کانولوشن (CNN) اغلب در کارهایی که شامل پردازش بینایی هستند استفاده می‌شوند و تصاویر نامشخص می‌توانند عملکرد شبکه‌های عصبی کانولوشنال را مختل کرده و زمان محاسباتی آن را افزایش دهند. علاوه بر این، هوش مصنوعی (AI) و یادگیری ماشین (ML) فناوری‌های مرتبطی هستند که شاخه‌ای از علوم کامپیوتر در نظر گرفته می‌شوند که برای شبیه‌سازی و تقویت هوش انسانی استفاده می‌شوند. در مراقبت‌های بهداشتی الکترونیک، هوش مصنوعی و ML می‌توانند برای بهینه‌سازی کار، پردازش خودکار مقادیر زیادی از داده‌های پزشکی و ارائه پشتیبانی مؤثر تصمیم‌گیری پزشکی مورد استفاده قرار گیرند. در این مقاله، نویسندگان چندین مدل اصلی هوش مصنوعی را که در حال حاضر در بازار باز شده‌اند، برای مرجع انتخاب می‌کنند. در این مقاله، مدل بهینه‌سازی شده (AL-CNN) برای تشخیص تصویر نویز مورد آزمایش قرار می‌گیرد و مدل AL-CNN با استفاده از توابع فعال‌سازی، عملیات ماتریس و روش‌های تشخیص ویژگی ایجاد می‌شود و تصاویر نویزدار پس از پیکربندی سفارشی پردازش می‌شوند. این مدل نه تنها نیازی به آماده سازی قبلی در هنگام پردازش تصاویر ندارد، بلکه دقت برخورد با نویز در شبکه های عصبی کانولوشن را نیز بهبود می بخشد. در AL-CNN در این مقاله، معماری شبکه عصبی کانولوشن شامل یک لایه نویز و یک لایه است که می توان به طور خودکار اندازه آن را تغییر داد. پس از مقایسه آزمایش‌های تشخیص، میزان دقت AL-CNN 20٪ بیشتر از MatConvNet-moderate است و میزان دقت 40٪ بیشتر از MatConvNet-chronic است. در مجموعه دوم آزمایش ها، دقت به ترتیب 50 و 70 درصد از MXNet و TensorFlow بیشتر است. علاوه بر این، نویسندگان لایه کانولوشن، لایه ادغام و عملکرد تلفات AL-CNN را در پارامترهای مختلف بهینه کردند که به ترتیب ثبات پردازش نویز را بهبود بخشید. پس از سفارشی‌سازی دو بهینه‌سازی پیکربندی، نویسندگان دریافتند که دومین AL-CNN بهینه‌سازی‌شده دقت تشخیص بالاتری دارد و پس از تست بهینه‌سازی، با افزایش تعداد تشخیص در تعداد بسیار کوتاهی، نرخ خطا را می‌توان به طور مداوم کاهش داد.

Abstract

Convolutional neural networks (CNNs) are often used in tasks involving vision processing, and unclear images can hinder the performance of convolutional neural networks and increase its computational time. Furthermore, artificial intelligence (AI) and machine learning (ML) are related technologies, which are considered a branch of computer science, which are used to simulate and enhance human intelligence. In e-healthcare, AI and ML can be used to optimize the workflow, automatically process large amounts of medical data, and provide effective medical decision support. In this paper, the authors take several mainstream artificial intelligence models currently open on the market for reference. In this paper, the optimized model (AL-CNN) is tested for noise image recognition, and the AL-CNN model is established by using activation functions, matrix operations, and feature recognition methods, and the noisy images are processed after custom configuration. Not only does this model require no prior preparation when processing images, but it also improves the accuracy of dealing with noise in convolutional neural networks. In the AL-CNN in this paper, the architecture of the convolutional neural network includes a noise layer and a layer that can be automatically resized. After the comparison of the recognition experiments, the accuracy rate of AL-CNN is 20% higher than that of MatConvNet-moderate, and the accuracy rate is 40% higher than that of MatConvNet-chronic. In the second set of experiments, the accuracy exceeds MXNet and TensorFlow by 50% and 70%, respectively. In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. After customizing the two configuration optimizations, the authors found that the second optimized AL-CNN has higher recognition accuracy, and after the optimization test, the error rate can be continuously decreased as the number of recognition increases in a very short number of times.

Introduction

Convolutional neural network (CNN) is a kind of artificial neural network, unlike recurrent neural, Boltzmann machine, etc., because the visual system is stimulated by neural mechanisms, so the biological model of convolutional neural network can recognize two-dimensional shapes. Convolutional neural network systems can perform convolution operations. Because of its uniqueness, it has excellent performance in many aspects of life, such as image classification, retrieval, and computer-related visual tasks. With more and more research on convolutional neural networks today, this technology will be applied to road engineering, medical imaging, and artificial intelligence involving vision in the future. Also, it achieves better performance than traditional technology.

In the 21st century, artificial intelligence is constantly developing, but at present there is no intelligent model in the true sense that can possess the computing and recognition ability of the traditional neural network system of animals. The concept of deep learning is proposed in this context. Unlike traditional machine learning models, deep learning will complete learning tasks through feature learning and the participation of feature abstraction. The working principle behind the convolutional neural network is deep learning, and because of its excellent processing effect and accuracy, it has played an unprecedented role in many fields of production and life. It mainly includes printing and publishing, logistics and transportation, medical management, and other fields. In the case that there are so many precedents for convolutional neural networks to create excellent results in many fields, this article will demonstrate an intelligent model building and optimization process based on convolutional neural networks. This process has practical experience for practical operators or field beginners to construct their own program models. Moreover, in the field of e-healthcare, applications of AI and machine learning can be widely used in a variety of different scenarios. For example, they can be used to identify a patient’s condition and provide personalized treatment advice. In addition, AI and ML can be used to track a patient’s condition and to remind doctors to examine or adjust treatment options.

Conclusion

In this paper, the authors established an artificial intelligence model AL-CNN based on convolutional neural network and tested its computational efficiency when processing images with different noise points. When AL-CNN processes low-quality images, it does not need any external tools, does not occupy memory, and has fast computing speed. The authors designed a variety of different types of noise to simulate the processing effect of the experiment and compared the model with MatConvNet-moderate, MatConvNet-chronic, MXNet, and TensorFlow. The results show that the model is more efficient than other types in processing images. In order to make this model more stable, the authors made a custom optimization, adding a noise layer and an automatic adjustment tool to the convolutional neural network. At the same time, considering the operational efficiency of different components in AL-CNN, this paper also performs corresponding optimizations for different noise problems. In order to improve the processing performance of CNN, this paper also designs an automatic adjustment noise reduction algorithm based on convolutional neural network in response to different situations. Experimental results show that the optimized AL-CNN has better efficiency in noisy image processing compared with MatConvNet-moderate, MatConvNet-chronic, MXNet, and TensorFlow. Also, because the model does not require prior preparation or other tools, it is faster to process. Finally, AI and ML can be used to automatically identify medical images to improve diagnostic accuracy. They can also be used to analyze patient medical records and data, predict future disease trends, and provide more accurate treatment advice to physicians. Overall, AI and ML in e-healthcare contribute to improve efficiency, provide better care, and lower healthcare costs.