Presents a precis of 37 years work by the author, spanning from 1968 when he lived in Uganda to 2005 in the UK. This book represents approximately one thousand pages of the original work, with the story covering Reincarnation; the Human Soul; Karma; God and Proof of His Existence; the Ever-expanding Universe; World War III; Love, and more.

Despite its being one of Roger Penrose’s greatest contributions to spacetime physics, there is a dearth of philosophical literature on twistor theory. The one exception to this is Bain (2006)—but although excellent, there remains much to be said on the foundations and philosophy of twistor theory. In this article, we (a) present for philosophers an introduction to twistor theory, (b) consider how the spacetime–twistor correspondence interacts with the philosophical literature on theoretical equivalence, and (c) explore the bearing which twistor theory (...) might have on philosophical issues such as the status of dynamics, the geometrisation of physics, spacetime ontology, the emergence of spacetime, and symmetry-to-reality inferences. We close with an elaboration of a variety of further opportunities for philosophical investigation into twistor theory. (shrink)

Brain tumor classification plays a niche role in medical prognosis and effective treatment process. We have proposed a combined feature and image-based classifier for brain tumor image classification in this study. Carious deep neural network and deep convolutional neural networks -based architectures are proposed for image classification, namely, actual image feature-based classifier, segmented image feature-based classifier, actual and segmented image feature-based classifier, actual image-based classifier, segmented image-based classifier, actual and segmented image-based classifier, and finally, CFIC. The Kaggle Brain Tumor Detection (...) 2020 dataset has been used to train and test the proposed classifiers. Among the various classifiers proposed, the CFIC performs better than all other proposed methods. The proposed CFIC method gives significantly better results in terms of sensitivity, specificity, and accuracy with 98.86, 97.14, and 98.97%, respectively, compared with the existing classification methods. (shrink)