001447062 000__ 06363cam\a2200541\i\4500
001447062 001__ 1447062
001447062 003__ OCoLC
001447062 005__ 20230310004100.0
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001447062 019__ $$a1319344665$$a1319428275$$a1346260296
001447062 020__ $$a9781484282175$$q(electronic book)
001447062 020__ $$a1484282175$$q(electronic book)
001447062 020__ $$z9781484282168
001447062 020__ $$z1484282167
001447062 0247_ $$a10.1007/978-1-4842-8217-5$$2doi
001447062 035__ $$aSP(OCoLC)1321789939
001447062 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dORMDA$$dGW5XE$$dYDX$$dEBLCP$$dYDX$$dOCLCQ$$dOCLCF$$dN$T$$dUKAHL$$dOCLCQ
001447062 049__ $$aISEA
001447062 050_4 $$aTA347.A78$$bN65 2022
001447062 08204 $$a610.28563$$223/eng/20220602
001447062 1001_ $$aNokeri, Tshepo Chris,$$eauthor.
001447062 24510 $$aArtificial intelligence in medical sciences and psychology :$$bwith application of machine language, computer vision, and NLP techniques /$$cTshepo Chris Nokeri.
001447062 264_1 $$aBerkeley, CA :$$bApress L.P.,$$c2022.
001447062 300__ $$a1 online resource (178 pages) :$$billustrations (chiefly color)
001447062 336__ $$atext$$btxt$$2rdacontent
001447062 337__ $$acomputer$$bc$$2rdamedia
001447062 338__ $$aonline resource$$bcr$$2rdacarrier
001447062 500__ $$aIncludes index.
001447062 5050_ $$aIntro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Chapter 1: An Introduction to Artificial Intelligence in Medical Sciences and Psychology -- Context of the Book -- The Book's Central Point -- Artificial Intelligence Subsets Covered in this Book -- Structure of the Book -- Tools Used in This Book -- Python Distribution Package -- Anaconda Distribution Package -- Jupyter Notebook -- Python Libraries -- Encapsulating Artificial Intelligence -- Implementing Algorithms -- Supervised Algorithms -- Unsupervised Algorithms -- Artificial Neural Networks -- Conclusion
001447062 5058_ $$aChapter 2: Realizing Patterns in Diseases with Neural Networks -- Classifying Cardiovascular Disease Diagnosis Outcome Data by Executing a Deep Belief Network -- Preprocessing the Cardiovascular Disease Diagnosis Outcome Data -- Debunking Deep Belief Networks -- Designing the Deep Belief Network -- Relu Activation Function -- Sigmoid Activation Function -- Training the Deep Belief Network -- Outlining the Deep Belief Network's Predictions -- Considering the Deep Neural Network's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation
001447062 5058_ $$aBinary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Classifying Diabetes Diagnosis Outcome Data by Executing a Deep Belief Network -- Executing a Deep Belief Network to Classify Diabetes Diagnosis Outcome Data -- Outlining the Deep Belief Network's Predictions -- Considering the Deep Neural Network's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation -- Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Conclusion
001447062 5058_ $$aChapter 3: A Case for COVID-19: Considering the Hidden States and Simulation Results -- Executing the Hidden Markov Model -- Descriptive Analysis -- Carrying Out the Gaussian Hidden Markov Model -- Considering the Hidden States in US Confirmed COVID-19 Cases with the Gaussian Hidden Markov Model -- Simulating US Confirmed COVID-19 Cases with the Monte Carlo Simulation Method -- US Confirmed COVID-19 Cases Simulation Results -- Conclusion -- Chapter 4: Cancer Segmentation with Neural Networks -- Exploring Cancer -- Exploring Skin Cancer
001447062 5058_ $$aClassifying Patient Skin Cancer Outcomes by Executing a CNN -- A CNN Pipeline -- A CNN's Architectural Structure -- Classifying Skin Cancer Diagnosis Image Data by Executing a CNN -- Preprocessing the Training Skin Cancer Image Data -- Preprocessing the Validation Skin Cancer Image Data -- Generating the Training Skin Cancer Diagnosis Image Data -- Tuning the Training Skin Cancer Image Data -- Executing the CNN to Classify Skin Cancer Diagnosis Image Data -- Considering the CNN's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation
001447062 506__ $$aAccess limited to authorized users.
001447062 520__ $$aGet started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine learning engineers, medical students, and researchers. What You Will Learn Apply artificial neural networks when modelling medical data Know the standard method for Markov decision making and medical data simulation Understand survival analysis methods for investigating data from a clinical trial Understand medical record categorization Measure personality differences using psychological models Who This Book Is For Machine learning engineers and software engineers working on healthcare-related projects involving AI, including healthcare professionals interested in knowing how AI can improve their work setting.
001447062 588__ $$aDescription based on print version record.
001447062 650_0 $$aArtificial intelligence.
001447062 650_0 $$aAutomatic data collection systems.
001447062 655_0 $$aElectronic books.
001447062 77608 $$iPrint version:$$aNokeri, Tshepo Chris.$$tArtificial Intelligence in Medical Sciences and Psychology.$$dBerkeley, CA : Apress L.P., ©2022$$z9781484282168
001447062 852__ $$bebk
001447062 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8217-5$$zOnline Access$$91397441.1
001447062 909CO $$ooai:library.usi.edu:1447062$$pGLOBAL_SET
001447062 980__ $$aBIB
001447062 980__ $$aEBOOK
001447062 982__ $$aEbook
001447062 983__ $$aOnline
001447062 994__ $$a92$$bISE