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Intelligent systems and methods to combat Covid-19
Joshi A., Dey N., Santosh K., Springer International Publishing, New York, NY, 2020. 239 pp.  Type: Book (978-9-811565-71-7)
Date Reviewed: Sep 6 2021

The book describes intelligent systems and methods to combat Covid-19. It is composed of ten chapters.

The first chapter describes multimodal algorithms to predict Covid-19. It provides background to distinguish between machine learning classification and clustering techniques. It also briefly elaborates on the efficacy of different machine learning algorithms, including random forests, gradient boosting, and k-means. In the end, the chapter predicts Covid-19 confirmed cases for the months of May and June 2020. This prediction is based on historical data using machine learning algorithms. However, the chapter does not identify the algorithms on which this prediction was made.

The second chapter, “Covid-19 Apps: Privacy and Security Concerns,” mentions many applications that provide various services to Covid-19 patients, such as tracing, registering, testing, and monitoring. The chapter highlights the privacy concerns of these applications, for example, location tracking, advertisements, and sacrificing patient freedom. The chapter also provides a few recommendations for strengthening privacy. However, the topics related to security are not covered.

The third chapter, “Coronavirus Outbreak: Multi-Objective Prediction and Optimization,” emphasizes the use of artificial intelligence (AI) for predicting Covid-19 spread rate. Mathematical models are needed for this prediction. The authors derive a multi-objective function for prediction. The model is based on different parameters such as number of positive cases, mortality count, transmission rate, economic disruption, and recovery cases. While the model is comprehensive, validation results are not included.

The fourth chapter proposes an AI-enabled framework to prevent the spread of Covid-19. The authors divide the region into three zones: red, orange, and green. This division is based on fuzzy weights. The weights are decided according to number of cases. The framework utilizes a face and body detection module to detect different symptoms such as cough, cold, and fever. In cases of positive symptoms, authorities may be notified. The chapter includes accuracy results for the proposed framework at different locations.

Chapter 5 looks at AI-based robotic drones to combat Covid-19. It describes the use of drones for various tasks, for example, identifying hot spots and distributing goods. The chapter also emphasizes the need for an effective 5G network in order to provide reliable and low-latency solutions in affected areas. The work presented in this chapter is based on a review of research papers.

The sixth chapter presents an overview of techniques for the “understanding and analysis” of Covid-19 chest X-ray images. The work aims to enhance the quality of chest X-ray/computed tomography (CT) images, such that a doctor can analyze an enhanced image and identify the infected area. The authors utilize a multiscale retinex image enhancement technique. They demonstrate the efficacy of their work by providing a comparison with other existing techniques.

The seventh chapter proposes a deep-learning-based solution to diagnose and identify Covid-19. Deep learning is used to analyze CT images. To address the lack of data, augmentation is incorporated. The authors also utilize the infectiousness of people in the latent as well as quarantine periods to predict Covid-19. However, there are no results to validate the proposed model.

In the eighth chapter, the author aims to address predicting Covid-19 and limiting its spread through a 3D approach: AI is used for predicting the disease, robotics is proposed to limit the spread of the disease, and the Internet of Things (IoT) is incorporated for patient monitoring. The author provides a high-level overview of the novel approaches being used for all three techniques. The focus of the chapter is on the breadth of topics.

The ninth chapter describes the impact of AI on social distancing. It initially presents background on AI, and then builds upon this material to describe how AI can be used to predict, identify, and limit the spread of the disease. The chapter also presents three case studies on the application of AI for Covid-19. The first case study is a mobile application being used in India to provide social distancing from Covid-19 patients. The second application is being implemented in Taiwan to identify the travel history of patients and highlight potential risks. In the third case study, from Korea, an AI system identifies high-risk groups and isolates positive cases to prevent disease outbreak.

The last chapter aims to utilize AI for post Covid-19 scenarios using business analytics. In that, models such as forecasting business, identifying profitable models, reducing cost, and forecasting cryptocurrency prices can be implemented. The content here is covered from a high-level perspective.

In conclusion, the book provides a broad look at AI-based applications. It is useful in acquiring knowledge about many AI-based applications and how they are being used to combat Covid-19.

Reviewer:  Jawwad Shamsi Review #: CR147348 (2202-0014)
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