Use of Eggshell as a Partial Replacement for Sand in Concrete Used in Biomedical Applications

The recent researches show that cement mortar containing eggshell as a partial replacement of sand contained radiation absorption property. In these cement mortar samples, 5%, 10%, 15%, and 20% by mass of sand was replaced by crushed eggshells, and there was increase in radiation absorption. The result also showed that using eggshell as a partial replacement for sand leads to decrease in compressive strength of the cement mortar. Waste of any kind in the environment when its concentration is in excess can become a critical factor for humans, animals, and vegetation. The utilization of the waste is a priority today in order to achieve sustainable development. So, we have planned to increase that compressive strength by means of using seashell as a partial replacement for cement. The main objective of the project is to maintain radiation absorption by means of using eggshell along with seashell as a partial replacement for cement to increase the compressive strength.

Exploring Internet of Things and Artificial Intelligence for Smart Healthcare Solutions

Artificial intelligence integrated with the internet of things network could be used in the healthcare sector to improve patient care. The data obtained from the patient with the help of certain medical healthcare devices that include fitness trackers, mobile healthcare applications, and several wireless sensor networks integrated into the body of the patients promoted digital data that could be stored in the form of digital records. AI integrated with IoT could be able to predict diseases, monitor heartbeat rate, recommend preventive maintenance, measure temperature and body mass, and promote drug administration by having a review with the patient's medical history and detecting health defects. This chapter explores IoT and artificial intelligence for smart healthcare solutions.

Deep Learning Techniques for Biomedical Image Analysis in Healthcare

Biomedical image analysis is very relevant to public health and welfare. Deep learning is quickly growing and has shown enhanced performance in medical applications. It has also been widely extended in academia and industry. The utilization of various deep learning methods on medical imaging endeavours to create systems that can help in the identification of disease and the automation of interpreting biomedical images to help treatment planning. New advancements in machine learning are primarily about deep learning employed for identifying, classifying, and quantifying patterns in images in the medical field. Deep learning, a more precise convolutional neural network has given excellent performance over machine learning in solving visual problems. This chapter summarizes a review of different deep learning techniques used and how they are applied in medical image interpretation and future directions.

Deep Learning Models for Semantic Multi-Modal Medical Image Segmentation

In this chapter, the author paints a comprehensive picture of different deep learning models used in different multi-modal image segmentation tasks. This chapter is an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. Methods are classified according to the different types of multi-modal images and the corresponding types of convolution neural networks used in the segmentation task. The chapter starts with an introduction to CNN topology and describes various models like Hyper Dense Net, Organ Attention Net, UNet, VNet, Dilated Fully Convolutional Network, Transfer Learning, etc.

The Pivotal Role of Edge Computing With Machine Learning and Its Impact on Healthcare

The global market for IoT medical devices is expected to hit a peak of 500 billion by the year 2025, which could signal a significant paradigm shift in healthcare technology. This is possible due to the on-premises data centers or the cloud. Cloud computing and the internet of things (IoT) are the two technologies that have an explicit impact on our day-to-day living. These two technologies combined together are referred to as CloudIoT, which deals with several sectors including healthcare, agriculture, surveillance systems, etc. Therefore, the emergence of edge computing was required, which could reduce the network latency by pushing the computation to the “edge of the network.” Several concerns such as power consumption, real-time responses, and bandwidth consumption cost could also be addressed by edge computing. In the present situation, patient health data could be regularly monitored by certain wearable devices known as the smart telehealth systems that send an enormous amount of data through the wireless sensor network (WSN).

Demystification of Deep Learning-Driven Medical Image Processing and Its Impact on Future Biomedical Applications

The recent growth of big data has ushered in a new era of deep learning algorithms in every sphere of technological advance, including medicine, as well as in medical imaging, particularly radiology. However, the recent achievements of deep learning, in particular biomedical applications, have, to some extent, masked decades-long developments in computational technology for medical image analysis. The methods of multi-modality medical imaging have been implemented in clinical as well as research studies. Due to the reason that multi-modal image analysis and deep learning algorithms have seen fast development and provide certain benefits to biomedical applications, this chapter presents the importance of deep learning-driven medical imaging applications, future advancements, and techniques to enhance biomedical applications by employing deep learning.

A Comparative Study of Popular CNN Topologies Used for Imagenet Classification

Deep Learning is a relatively modern area that is a very important key in various fields such as computer vision with a trend of rapid exponential growth so that data are increasing. Since the introduction of AlexNet, the evolution of image analysis, recognition, and classification have become increasingly rapid and capable of replacing conventional algorithms used in vision tasks. This study focuses on the evolution (depth, width, multiple paths) presented in deep CNN architectures that are trained on the ImageNET database. In addition, an analysis of different characteristics of existing topologies is detailed in order to extract the various strategies used to obtain better performance.

Importance of Automation and Next-Generation IoT in Smart Healthcare

Generally, the sensors employed in healthcare are used for real-time monitoring of patients, such devices are termed IoT-driven sensors. These type of sensors are deployed for serious patients because of the non-invasive monitoring, for instance physiological status of patients will be monitored by the IoT-driven sensors, which gathers physiological information regarding the patient through gateways and later analysed by the doctors and then stored in cloud, which enhances quality of healthcare and lessens the cost burden of the patient. The working principle of IoT in remote health monitoring systems is that it tracks the vital signs of the patient in real-time, and if the vital signs are abnormal, then it acts based on the problem in patient and notifies the doctor for further analysis. The IoT-driven sensor is attached to the patient and transmits the data regarding the vital signs from the patient's location by employing a telecom network with a transmitter to a hospital that has a remote monitoring system that reads the incoming data about the patient's vital signs.

Heterogeneous Large-Scale Distributed Systems on Machine Learning

Tensor flow is an interface for communicating AI calculations and a use for performing calculations like this. A calculation communicated using tensor flow can be done with virtually zero changes in a wide range of heterogeneous frameworks, ranging from cell phones, for example, telephones and tablets to massive scale-appropriate structures of many computers and a large number of computational gadgets, for example, GPU cards. The framework is adaptable and can be used to communicate a wide range of calculations, including the preparation and derivation of calculations for deep neural network models, and has been used to guide the analysis and send AI frameworks to more than twelve software engineering zones and different fields, including discourse recognition, sight of PCs, electronic technology, data recovery, everyday language handling, retrieval of spatial data, and discovery of device medication. This chapter demonstrates the tensor flow interface and the interface we worked with at Google.

Artificial Intelligence and Reliability Metrics in Medical Image Analysis

Artificial intelligence (AI) in medical imaging is one of the most innovative healthcare applications. The work is mainly concentrated on certain regions of the human body that include neuroradiology, cardiovascular, abdomen, lung/thorax, breast, musculoskeletal injuries, etc. A perspective skill could be obtained from the increased amount of data and a range of possible options could be obtained from the AI though they are difficult to detect with the human eye. Experts, who occupy as a spearhead in the field of medicine in the digital era, could gather the information of the AI into healthcare. But the field of radiology includes many considerations such as diagnostic communication, medical judgment, policymaking, quality assurance, considering patient desire and values, etc. Through AI, doctors could easily gain the multidisciplinary clinical platform with more efficiency and execute the value-added task.