Predicting Daily Confirmed COVID-19 Cases in India

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

Criticality of E-Privacy and Data Leakage Amid the Pandemic

The global pandemic has led to an undeniable surge in using digital technologies due to the social distancing norms and nationwide lockdowns. Firms and organizations are conforming to the new culture of work and life. The use of internet services, digital devices, and cloud systems has seen surges in usage from 40% to 100%, compared to pre-lockdown levels. With the rapid growth of this technological use, people are exposing their digital assets, presence, and behavior out to the binary world where AI-driven data analysis algorithms, data-gathering systems, and spyware are continuously monitoring their behavior. These subconsciously exposed data are then carried forward for delivering customized ads and recommend features.

Plant Disease Detection Using Machine Learning Approaches

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

Prediction of Neurological Disorders Using Visual Saliency

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.

Image Pre-Processing and Paddy Pests Detection Using Tensorflow

Agriculture is one of the important sources of earning worldwide. With the rapid expansion of the human population and food security for all, the agriculture sector needs to be boosted to increase the yield. Agriculture is the prime source of livelihood in India for more than 50% of the total population. As per Indian agriculture and allied industries industry report, agriculture is one of the major contributors in gross value. Agricultural crops suffer heavy losses due to insect damage and plant diseases. Worldwide, out of the crop losses, major losses are caused by plant pests. In this chapter, various image pre-processing methods and the need of pre-preprocessing are discussed in detail. For image classification, TensorFlow deep neural network is presented. Deep learning model is used for automatic and early detection of paddy pests. Early detection of the pests will aid farmers in adopting necessary preventive measures. Multiple ways to reduce overfitting during model training are also suggested.

Image Classification Using Deep Neural Networks

Facial expression recognition is an activity that is performed by every human in their day-to-day lives. Each one of us analyses the expressions of the individuals we interact with to understand how people interact and respond with us. The malicious intentions of a thief or a person to be interviewed can be recognized with the help of his facial features and gestures. Face recognition from picture or video is a well-known point in biometrics inquiry. Numerous open places, for the most part, have reconnaissance cameras, and these cameras have their noteworthy security incentives. It is generally recognized that face recognition has assumed a significant job in reconnaissance framework. The genuine favorable circumstances of face-based distinguishing proof over different biometrics are uniqueness. Since the human face is a unique item having a high level of inconstancy in its appearance, face location is a troublesome issue in computer vision. This chapter explores emotion detection using facial images.

Deep Learning Models for Detection and Diagnosis of Alzheimer's Disease

The chapter explores the implications of deep learning in medical sciences, focusing on deep learning concerning natural language processing, computer vision, reinforcement learning, big data, and blockchain influence on some areas of medicine and construction of end-to-end systems with the help of these computational techniques. The deliberation of computer vision in the study is mainly concerned with medical imaging and further usage of natural language processing to spheres such as electronic wellbeing record data. Application of deep learning in genetic mapping and DNA sequencing termed as genomics and implications of reinforcement learning about surgeries assisted by robots are also overviewed.

Data Analytics to Predict, Detect, and Monitor Chronic Autoimmune Diseases Using Machine Learning Algorithms

Developments in machine learning techniques for classification and regression exposed the access of detecting sophisticated patterns from various domain-penetrating data. In biomedical applications, enormous amounts of medical data are produced and collected to predict disease type and stage of the disease. Detection and prediction of diseases, such as diabetes, lung cancer, brain cancer, heart disease, and liver diseases, requires huge tests and that increases the size of patient medical data. Robust prediction of a patient's disease from the huge data set is an important agenda in in this chapter. The challenge of applying a machine learning method is to select the best algorithm within the disease prediction framework. This chapter opts for robust machine learning algorithms for various diseases by using case studies. This usually analyzes each dimension of disease, independently checking the identified value between the limits to monitor the condition of the disease.

Pandemic Management Using Artificial Intelligence-Based Safety Measures

The pandemic corona virus disease (COVID-19) caused by the virus ‘SARS-CoV-2' continues affecting the health and affluence of the worldwide population. The role of artificial intelligence in improving safety and health conditions has been studied in the chapter. The various fields of artificial intelligence such as machine learning, computer vision, deep learning, and natural language processing are contributing to almost every field ranging from healthcare, agriculture, automotive, astronomy, and many others. For overcoming a global outbreak such as COVID-19, conventional approaches are not feasible enough, and therefore the requirement for the more robust and automated techniques for making predictions in advance is essential. The vision of this chapter is to assess and survey the impact of artificial intelligence-based approaches in the management of pandemics and recommend procedures for the enhancement of the currently used techniques along with the imminent research areas in artificial intelligence for controlling pandemics.

Landmark Recognition Using Ensemble-Based Machine Learning Models

Recognizing landmarks in images with machine learning is an excellent topic for research today. Landmark recognition is an important field in computer vision. In this field, we train the machine learning models to identify and recognize the closed distinctly distinguishable objects in a digital image. In general, if we consider a digital image to be a set of coordinates of different pixels, a landmark is said to be enclosed in that closed polygon formed by the pixels that may be considered as a distinct and distinguishable thing in one or the other sense. Landmark recognition is an important subject area of image classification since it is considered as one of the first steps towards reaching complete computer vision. The extremely broad definition of a landmark makes it eligible to be considered as one of the leading problems in image classification tasks. Since the task is considered to be a very broad one, the solutions to the task hold no easy procedures. This chapter explores landmark recognition using ensemble-based machine learning models.