Computational Intelligence in Oncology

Dr. Khalid Raza is Assistant Professor at the Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi. Dr. Raza has also served as “ICCR Chair Professor” at Faculty of Computer & Information Sciences, Ain Shams University, Cairo, Egypt. He has over 10 years of teaching & research experience in the field of computational intelligence and its various applications. He has contributed over 80 research articles in reputed journals and edited books, one single-authored book. Dr. Raza has reviewed over 100 research articles for reputed journals/conferences in the last 5 years. He has also edited 3 books with different publishers including Springer, Elsevier, and CRC Press. He is Academic Editor of PeerJ Computer Science. Dr. Raza has delivered several keynote addresses, invited talks, public lectures, and seminars in national and international conferences and workshops and chaired technical sessions in various national and international conferences. He has alsoexecuted two Indian governments funded research projects. Dr. Raza is Member of MIR Lab (USA), CSI (India), and SCRS (India). His research interest lies in computational intelligence and its applications in bioinformatics, viro-informatics, and health informatics.

• Part 1: Preliminaries.- Chapter 1. Computational Intelligence in Oncology: Past, Present, and Future.- Chapter 2. Machine Learning-based Models and their Applications in Diagnosis, Prognosis and Effective Cancer Therapeutics: Current State-of-the-art.- Chapter 3. Computational Intelligent Systems in Oncology: A Way towards Translational Healthcare.- Chapter 4. Computational Resources for Oncology Research: A Comprehensive Analysis.- Part 2: Cancer Detection, Diagnosis, Survival and Recurrence Prediction.- Chapter 5. Application of Convolutional Neural Networks in Cancer Diagnosis.- Chapter 6. Automatic Cancer Detection Using Probabilistic Convergence Theory.- Chapter 7. Computational Intelligence Methods for Cancer Survival Prediction.- Chapter 8. Breast Cancer Survival Prediction Using Machine Learning.- Chapter 9. Deep Learning Models for Classification of Brain Tumor with Magnetic Resonance Imaging Images Dataset.- Chapter 10. Predicting the Cancer Recurrence using Artificial NeuralNetworks.- Chapter 11.  Computer Intelligence in Detection of Malignant or Premalignant Oral Lesions: The Story so far.- Chapter 12. Fuzzy Logic-based Hybrid Models for Clinical Decision Support Systems in Cancer.- Part 3: Predicting Cancer Biomarkers, Therapeutic Targets, Drug Response, and Drug Design, Discovery, and Development.- Chapter 13. Predicting Biomarkers and Therapeutic Targets in Cancer.- Chapter 14. Computational Intelligence: A Step Forward in Cancer Biomarker Discovery and Therapeutic Target Prediction.- Chapter 15. Computational Intelligence Based Cheminformatics Model as Cancer Therapeutics.