The Future of Computing: Ubiquitous Applications and Technologies E-Book
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- Herausgeber: Bentham Science Publishers
- Kategorie: Fachliteratur
- Sprache: Englisch
The Future of Computing: Ubiquitous Applications and Technologies explores the transformative power of ubiquitous computing across diverse fields, from healthcare and smart grids to home automation and digital forensics. Ubiquitous computing, which seamlessly integrates computing into everyday life, is reshaping industries and addressing significant challenges, such as data security, digital payments, and IoT optimization. This book provides expert insights and practical approaches, covering topics such as automated medical imaging, federated cloud assessments, smart grid security, and AI-driven control systems.
Key Features:
- Foundational and advanced concepts of ubiquitous computing across multiple industries.
- Security structures in IoT, AI applications, and data privacy.
- Real-world applications, including healthcare automation, smart homes, and digital forensics.
- Case studies on emerging trends in IoT, AIoT, and smart grid security.
Readership:
Ideal for students, researchers, and professionals in computing, IoT, artificial intelligence, and engineering fields.
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Seitenzahl: 212
Veröffentlichungsjahr: 2024
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PREFACE
The Future of Computing: Ubiquitous Applications and Technologies delves into the exciting world of ubiquitous computing and its diverse applications across various domains. Ubiquitous computing refers to the concept of seamlessly integrating computing technologies into our everyday lives, making them pervasive and invisible.
In this book, we explore the potential of ubiquitous computing in addressing critical challenges and revolutionizing different sectors. The chapters presented here offer a comprehensive overview of cutting-edge research and practical implementations, providing valuable insights into researchers, practitioners, and enthusiasts alike.
"Automated Analysis of Medical Images for Healthcare Domain," sheds light on the advancements in medical imaging analysis, leveraging the power of ubiquitous computing. The chapter explores how automated techniques can improve healthcare outcomes, facilitate diagnoses, and enhance patient care. The chapter titled, "Towards the Assessment of Federations of Clouds," examines the emerging trend of federated clouds and their implications. It discusses the challenges, benefits, and potential applications of federated cloud environments, showcasing how ubiquitous computing can optimize cloud-based services. The chapter, "Digital Payments & Financial Cyber Frauds in Rural India," explores the intersection of ubiquitous computing and financial inclusion in rural India. This chapter investigates digital payment systems and the challenges posed by financial cyber frauds, presenting potential solutions to enhance security and promote safe digital transactions. Another chapter, "Speed Control of DC Motor using PID Controller with Artificial Intelligence Techniques," delves into the realm of control systems and artificial intelligence. It showcases how ubiquitous computing, coupled with PID controllers and AI techniques, can optimize the performance of DC motors, enabling precise speed control. "Power System Harmonic Analysis and Elimination," focuses on the application of ubiquitous computing in power systems. It delves into the challenges posed by harmonics and explores advanced techniques for harmonic analysis and elimination, ultimately enhancing the reliability and efficiency of power grids. The next chapter, "AutoMate: Ubiquitous Smart Home System using Arduino and ESP8266 Module," presents an innovative approach to home automation. By leveraging ubiquitous computing technologies such as Arduino and ESP8266, the chapter demonstrates the development of a smart home system that seamlessly integrates devices and enhances user convenience. The next chapter, "Digital Forensics in Mobile Phones: An Overview of Data Acquisition Techniques and its Challenges," delves into the realm of digital forensics in the context of ubiquitous mobile devices. It provides an overview of data acquisition techniques, challenges, and emerging trends in mobile forensics, highlighting the importance of ubiquitous computing in investigations. The chapter titled, "IoT and AIoT: Applications, Challenges, and Optimization," explores the convergence of the Internet of Things (IoT) and Artificial Intelligence of Things (AIoT). It investigates the applications, challenges, and optimization strategies in this rapidly evolving field, showcasing the transformative potential of ubiquitous computing. The chapter "IoT Semantic of AI Security Structure for Smart Grid," focuses on the application of ubiquitous computing in securing smart grids. It presents an in-depth analysis of the semantic aspects of IoT and AI security structures, highlighting the importance of robust security measures for critical infrastructure.
Throughout this book, we strive to provide an insightful exploration of ubiquitous computing's applications and challenges across various domains. By bringing together expert perspectives and cutting-edge research, we aim to inspire further innovation and advancement
in this fascinating field. We hope that this book serves as a valuable resource, fostering a deeper understanding of ubiquitous computing and its limitless possibilities.
It gives us immense pleasure to express our gratitude to the individuals who have made significant contributions and provided valuable assistance throughout the creation of this book. We extend our heartfelt thanks to all the authors who submitted their chapters, as their contributions and insightful discussions have played a pivotal role in making this book a resounding success. We sincerely hope that readers will find great value and gain future insights from the diverse contributions made by these authors. Furthermore, this book serves as a catalyst, opening new avenues and opportunities for future research in the field of ubiquitous computing. We are deeply grateful to the dedicated team at Bentham Publication for their meticulous service and timely publication of this book, ensuring its availability to the wider audience. We would also like to extend our profound appreciation to our institutions/universities and colleagues for their unwavering support and encouragement throughout this endeavor. Their support has been instrumental in bringing this book to fruition.
Lastly, we would like to acknowledge and express our heartfelt gratitude to our families for their unwavering support, encouragement, and patience. Their understanding and belief in us have been a constant source of motivation. Once again, we extend our sincere thanks to all those who have contributed to the realization of this book. It is their collective efforts and support that have made this publication possible.
List of Contributors
Automated Analysis of Medical Images in the Healthcare Domain
Parul Chhabra1,*,Pradeep Kumar Bhatia1,Vipin Babbar1Abstract
During lab tests, thousands of medical images are generated to trace the disease's symptoms. Manual interpretation of this data may consume excessive time and thus may delay diagnosis. Timely detection of critical diseases is very important as their stage can be changed over an interval. Automated analysis of medical data can reduce the gap between disease detection and its diagnosis and it also reduces the overall computational cost. In this paper, this goal will be achieved using different methods (Classification/ Segmentation/ Image Encoding/ Decoding/ Registration/ Restoration/ Morphology).
INTRODUCTION
Traditional healthcare services follow different steps i.e. disease detection, diagnosis, and keeping track of a patient’s history for clinical decision-making, as shown in Fig. (1). Medical data produced by each step must be examined by expert practitioners to avoid the incorrect diagnosis.
The disease detection phase may produce a large set of medical images and precise analysis of these medical images plays an important role in the identification of disease. It can also be used to track the progress of diagnosis as well as different stages of disease w.r.t. patients.
Fig. (1)) Health care services.Machine learning can improve the efficiency of the analysis process and it can also be used to build a dataset/knowledgebase for healthcare services in such a way that patient/disease statistics can be shared worldwide. Medical images contain data in visual form and only expert practitioners can interpret that data [1-25].
To analyze this data automatically, machine learning offers the following ways as displayed in Fig. (2).
Fig. (2)) Medical image analysis. Classification: Medical images can be classified w.r.t. disease types/features etc. and they can be used to detect disease and diagnostic purposes [26].Segmentation: It is used to subdivide an image into multiple segments (i.e. objects/regions). It can be used for pathologies domain/object detection/ recognition, etc. [27].Image Encoding/Decoding: It is used to compress the image whereas decoding follows the reverse operation to obtain the original image [28].Registration: It can be used to align and stitch multiple images together for analysis purposes [29].Restoration: It is used to filter noise level in an image, in order to produce clear and refined output [30].Morphology: It deals with structural components, pixels, and shapes in a given image [31].Following are the challenges and limitations of automated medical image analysis:
It requires a large volume of medical datasets, in order to build a training model for prediction.Dataset validation is required to ensure the accuracy of the training model.Quite complex to update the existing dataset.Excessive computational resources are required to manage and process large-scale medical data.Expert medical practitioners are still required to ensure the validity of outcomes.The potential impact of automation of medical image analysis is given below:
It can reduce the processing time and computational cost for practitioners.It can increase the accuracy of clinical decision-making.It can optimize the errors in the diagnosis process.Training model can be updated using the patient’s history, and health recovery with respect to recommended treatment.Researchers have developed a few solutions for the analysis of medical imagery as discussed in the next section.
LITERATURE SURVEY
K. Rasheed et al. [6] investigated the various machine learning (ML) applications for the healthcare domain. Studies found that intelligent solutions can improve the diagnosis accuracy however, there are a few open issues i.e. lack of standards to generate the training models, dataset formats, incompatible interfaces for the data exchange, etc.
R. Buettner et al. [7] highlighted the various ML-based methods that can be utilized for medical image processing i.e. medical image encoding/decoding, segmentation, classification, image registration/restoration, morphological analysis, etc. Study shows that the accuracy of disease detection can be improved using these methods.
D. Tellez et al. [8] developed an image compression method that encodes the histopathology dataset and uses neural networks to compress noise level input. Outcomes show that it can produce refined images with optimal reconstruction error and these images can be easily interpreted by practitioners.
P. Seeböck [9] introduced an ML-based method that uses supervised learning for the analysis of retina images. It enforces binary classification, noise filtering, and clustering over input data to detect anomalies. Experimental results show that it has an average accuracy/ROC curve.
K. Gong et al. [10] introduced an image reconstruction method that builds a learning model using neural networks. It estimates the energy levels (low/high) in a given input and uses multipliers to reconstruct the images. Experiments show that it is more efficient as compared to traditional denoising methods.
Y. Qi et al. [11] developed a neural network-based method to improve the quality of images. It estimates different parameters (contrast/coherence/signal-to-noise- ratio) to reproduce the high-resolution images. Analysis indicates that it is more efficient as compared to existing solutions.
Q. Abbas et al. [12] developed an ML model to analyze medical images. It builds metrics using various processes (segmentation/regression/regeneration/ augmentation/loss function/data loading). Experiments indicate that these metrics can be used to enhance the diagnosis accuracy as well as reduce operational costs.
X. Zhou et al. [13] developed a neural network to classify histopathological data. It uses segmentation to produce outcomes and tests have shown that it is more accurate and efficient as compared to traditional deep-learning neural networks.
H. Guan et al. [14] conducted a survey to analyze the impact of different factors associated with medical images i.e. data type/volume/quality etc. Analysis shows that the accuracy of disease detection and decision-making is affected by learning methods (supervised/unsupervised/semi-supervised) and computational costs may vary due to heterogeneous data types.
X. Wang et al. [15] explored the relationship between image analysis and diagnostic accuracy and developed a classification model using supervised learning. It builds labeled data for each input to perform classification. Outcomes show that it has an optimal computational cost, higher accuracy, and efficiency in contrast with traditional methods.
H. Pinckaers et al. [16] used neural networks to improve the quality of image data. It extracts the Metadata of images and forms a correlation metric to produce high-resolution data. The analysis has shown that it can manage variations in the input size, pixel size, etc. and it has an optimal ROC value.
B. M. Rashed et al. [17] investigated different ML algorithms that can be used for data mining of medical images and these are Support Vector Machine (SVM)/ k-Nearest Neighbors (KNN)/Decision Trees/Random Forest/Logistic Regression. The common processes found in the study are prediction, data mining, classification, regression, clustering, dimension reduction, etc. that can be used for image analysis.
K. Naveen et al. [18] studied the role of machine learning/deep learning algorithms for the medical image analysis. It has been found that learning methods can affect the accuracy of analysis and the computational cost may also vary. This study states that the optimal selection of a learning method is necessary to ensure good outcomes in the classification process.
S. P. Shayesteh et al. [19] developed a method to extract features from ultrasound samples. It uses logistic regression classifier to perform selective feature selection. The analysis has shown that it offers optimal sensitivity with higher accuracy of disease detection in contrast to existing solutions.
T. Zhang et al. [20] identified that noise in images can degrade the outcomes of a classifier and introduced a noise adaption solution that enforces noise patterns over ultrasound samples. Outcomes show that it has higher accuracy under the constraints of noise level variations.
Geetha et al. [21] compared the performance of supervised/unsupervised learning approaches with respect to healthcare data. The analysis has shown that the accuracy of a prediction model may vary with respect to learning methods. Also, the complexity of medical data types may reduce its efficiency, which may also affect the decision making process and increase the operational cost.
N. Nahar et al. [22] explored the association between disease detection and the analysis of X-ray images. The study found that deep learning algorithms can efficiently predict the presence of diseases in given input samples and improve the diagnostic accuracy. However, the study also indicates that there is no single solution to analyze different types of medical imagery.
U. Khan et al. [23] found that ML algorithms can be used to extract the clinical data from medical imagery efficiently by using classification and segmentation
techniques. The outcomes of the analysis can be further used to improve the accuracy of diagnosis and clinical decision-making.
A. Sivasangari et al. [24] developed a solution to identify brain tumor using neural networks. First of all, it subdivided the brain cells into two different categories (healthy/non-healthy) and performed classification to detect the tumor in the given samples. Experimental results were more accurate and efficient as compared to the existing tumor detection methods.
A. Markfort et al. [25
