The Hyperspectral Images (HSI) are now being widely popular due to the evolution of satellite imagery and camera technology. Remote sensing has also gained popularity and it is also closely related to HSI. HSI possesses a wide variety of spatial and spectral features. However, HSI also has a consider-able amount of useless or redundant data. This redundant data causes a lot of trouble during classifications as it possesses a huge range in contrast to RGB. Traditional classification techniques do not apply efficiently to HSI. Even if somehow the traditional techniques are applied to it, the results produced are inefficient and undesirable. The Convolutional Neural Network (CNN), which are widely famous for the classification of images, have their fair share of trouble when dealing with HSI. 2D CNNs is not very efficient and 3D CNNs increases the computational complexity. To overcome these issues a new hybrid CNN approach is used which uses sigmoid activation function at the output layer, using a 2D CNN with 3D CNN to generate the desired output. Here, we are using HSI classification using hybrid CNN i.e., 2D and 3D. The dataset used is the Indian pines dataset sigmoid classifier for classification. And we gain the Overall accuracy 99.34 %, average accuracy 99.27%, kappa 99.25%.

Cardiovascular disease is among the leading sources of the growing rate of morbidity and mortality worldwide, affecting roughly 50% of the adult age group in the healthcare sector. Heart disease claims the lives of about one person per minute in this modern era. Accurate detection methods for the timely identification of cardiovascular disorders are essential because there is rapid growth in the number of patients with this disease. The goal is to understand risk factors by analyzing the heart monitoring dataset using exploratory data analysis. This chapter proposes a heart disease prediction framework using soft voting-based ensemble learning techniques. Performance evaluation of the proposed framework and its comparison with the state-of-the-art models are done using a benchmark dataset in terms of accuracy, precision, sensitivity, specificity, and F1-score. Heart disease is a long-term problem with a greater risk of becoming worse over time. The proposed model has achieved an accuracy of 90.21%.

Background: Caused by Human Immuno-deficiency virus, leading the patient's immune system vulnerable to various infections and neurological disorder, AIDS have transformed into a socioeconomic and developmental concern. With the increasing population of India, the HIV epidemic will soon be a major cause of concern. The objective of the present study was to observe the demographic profile of HIV positive patients attending ART Centre at the Department of Medicine at Sanjay Gandhi Memorial Hospital & Shyam Shah Medical College, Rewa (M.P.) in year 2012-2014. Material & Methods: This was a data-based study. In this study total 1224 cases of all age group were taken, these patients were registered in ART center in between Jan. 2010 to Dec.2012. A pre-formed questionnaire (proforma) was made to enquire about socio-demographic-economic variables. The patient and their spouse were interviewed, examined and investigated according to pre-designed proforma with special reference to their occupation, spouse HIV status and high-risk behaviour. Results: In this study total number of ART attendees (patients) were 1224 and out of these total patients, 7.27% (n=89) were children (<15years) and 92.73% (n=1135) were adult (>15 years). Out of 89 children, 5.15% (n=63) were male and 2.12% (n=26) were female and out of 1135 adult patients, 53.26% (n=652) were male and 39.46% (n=483) were female. M/F ratio in children was 2.4/1 and M/F ratio in adult patients was 1.3/1. Conclusion: As the present study was done in rural area, where hygiene was poor and sanitation was bad and environment was dusty, that is why infectious complications were more common in the present study.

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Artificial Intelligence (AI) is changing the modern way of lifestyle by helping the person do their jobs in an efficient manner. The AI is currently in its starting phase and from now on it is of great use. IBM Watson is an AI which is used globally by different organizations, institutes and corporations. In this paper we have created a chatbot using IBM Watson Assistance which is helpful in querying about the disease and hospitals related query. This paper also discusses IBM Watson in detail, its applications, its working and case studies on the use of IBM Watson in the field of healthcare, visual recognition and a software company named BOX.