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Cultures of prediction : how engineering and science evolve with mathematical tools / / Ann Johnson, Johannes Lenhard
| Cultures of prediction : how engineering and science evolve with mathematical tools / / Ann Johnson, Johannes Lenhard |
| Autore | Johnson Ann <1965-2016, > |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Cambridge, Massachusetts : , : The MIT Press, , 2024 |
| Descrizione fisica | 1 online resource (272 pages) |
| Disciplina | 620.001/51 |
| Collana | Engineering studies |
| Soggetto topico |
Mathematical models - History
Engineering mathematics - History Predictive analytics Predictive control |
| ISBN |
0-262-37905-8
0-262-37904-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Hitting the target with mathematics -- Engineering knowledge, autonomy, and mathematics -- Overlapping modes in the behavior of molecules -- Systems thinking and the limits to growth -- The fluidity of computational models -- A transformation of Bayesian statistics -- Engineering thermodynamics. |
| Record Nr. | UNINA-9910901897503321 |
Johnson Ann <1965-2016, >
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| Cambridge, Massachusetts : , : The MIT Press, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Evolving Predictive Analytics in Healthcare : New AI Techniques for Real-Time Interventions
| Evolving Predictive Analytics in Healthcare : New AI Techniques for Real-Time Interventions |
| Autore | Kumar Abhishek |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Piraí : , : Institution of Engineering & Technology, , 2022 |
| Descrizione fisica | 1 online resource (362 pages) |
| Disciplina | 610.285 |
| Altri autori (Persone) |
DubeyAshutosh Kumar
BhatiaSurbhi KumarSwarn Avinash LeDac-Nhuong |
| Collana | Healthcare Technologies |
| Soggetto topico |
Artificial intelligence - Medical applications
Predictive analytics |
| ISBN |
1-83724-475-8
1-5231-5343-1 1-83953-512-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Intro -- Title -- Copyright -- Contents -- About the Editors -- 1 COVID-19 detection in X-ray images using customized CNN model -- 1.1 Introduction -- 1.2 Related work -- 1.2.1 Key contributions and proposed work -- 1.3 Materials and methods -- 1.3.1 Feature extraction and selection -- 1.4 Results and discussion -- 1.5 Conclusion and future scope -- References -- 2 Introducing deep learning in medical diagnosis -- 2.1 Introduction -- 2.2 Literature survey -- 2.3 Overview of DL algorithms -- 2.3.1 Convolutional neural network -- 2.3.2 Recurrent neural network -- 2.3.3 Long short-term memory 2.3.4 Restricted Boltzmann machine -- 2.3.5 Deep belief networks -- 2.4 Proposed DL framework for neuro disease diagnosis -- 2.4.1 FAST-RCNN -- 2.4.2 Ten fully connected layer -- 2.5 Preprocessing of dataset -- 2.6 Implementation and results -- 2.7 Conclusion -- References -- 3 Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML) -- 3.1 Introduction -- 3.1.1 DoS and DDoS attacks -- 3.1.2 Man-in-the-middle (MitM) attack -- 3.1.3 Phishing and spear-phishing attacks -- 3.1.4 Password attack -- 3.1.5 Eavesdropping attack -- 3.1.6 Malware attack 3.2 Related work -- 3.3 Cloud computing -- 3.3.1 Machine learning -- 3.3.2 Exploratory data analysis -- 3.4 Results -- References -- 4 Classification methodologies in healthcare -- 4.1 Introduction -- 4.2 Classification algorithms -- 4.2.1 Statistical data -- 4.2.2 Discriminant analysis -- 4.2.3 Decision tree -- 4.2.4 K-nearest neighbor (KNN) -- 4.2.5 Logistic regression (LR) -- 4.2.6 Bayesian classifier -- 4.2.7 Support vector machine (SVM) -- 4.3 Parameter identification -- 4.3.1 Feature selection for classi cation -- 4.4 Real-time applications 4.4.1 Classification of patients based on medical record -- 4.4.2 Predictive analytics and diagnostic analytics based on medical records -- 4.4.3 Classification of diseases based on medical imaging -- 4.4.4 Mixed reality-based automation to help aid aging society -- 4.4.5 Tiny ML-based classification systems for medical gadgets -- 4.4.6 Classification systems for insurance claim management -- 4.4.7 Case study: Inspectra from Perceptra -- 4.4.8 Deep learning for beginners -- References -- 5 Introducing deep learning in medical domain -- 5.1 Introduction -- 5.1.1 DL in a nutshell 5.1.2 History of DL in the medical field -- 5.1.3 Benefits of DL in the medical domain -- 5.1.4 Challenges and obstacles of DL in the medical domain -- 5.1.5 Opportunities of DL in the medical field -- 5.2 DL applications in the medical domain -- 5.2.1 Drug discovery and medicine precision -- 5.2.2 Detection of diseases -- 5.2.3 Diagnosing patients -- 5.2.4 Healthcare administration -- 5.3 DL for medical image analysis -- 5.3.1 Medical image detection -- 5.3.2 Medical image recognition -- 5.3.3 Medical image segmentation -- 5.3.4 Medical image registration. |
| Altri titoli varianti | Evolving Predictive Analytics in Healthcare |
| Record Nr. | UNINA-9911007017203321 |
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Kumar Abhishek
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| Piraí : , : Institution of Engineering & Technology, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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The Fifth Phase : An Insight-Driven Approach to Business Transformation
| The Fifth Phase : An Insight-Driven Approach to Business Transformation |
| Autore | Powell Mark |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | London : , : LID Publishing, , 2024 |
| Descrizione fisica | 1 online resource (121 pages) |
| Soggetto topico |
Artificial intelligence
Predictive analytics |
| ISBN | 9781915951007 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Back Cover -- Title -- Contents -- Foreword -- Introduction -- 1. The Situation Centre -- 2. The First Phase: A Scientific Revolution -- 3. The Second Phase: Taylorism with Computers -- 4. The Third Phase: Process Management -- 5. The Fourth Phase: Drowning in Data Lakes -- 6. The Intelligence of Machines -- 7. Not All Data Are Created Equal -- 8. Leaving the Tool-o-Sphere -- 9. Moving Beyond the Familiar -- 10. New Drugs from Known Ones: The Biovista Story -- 11. Leading Transformational Change -- Notes -- Acknowledgements -- Copyright |
| Record Nr. | UNINA-9911008986403321 |
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Powell Mark
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| London : , : LID Publishing, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Prediction in Medicine
| Prediction in Medicine |
| Autore | Verma Neeta |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Sharjah : , : Bentham Science Publishers, , 2024 |
| Descrizione fisica | 1 online resource (339 pages) |
| Altri autori (Persone) |
SinghalAnjali
SinghVijai |
| Soggetto topico |
Machine learning
Predictive analytics |
| ISBN |
9789815305128
9815305123 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Title -- Copyright -- End User License Agreement -- Contents -- Foreword -- Preface -- List of Contributors -- Predictive Analysis: Forecasting Patient's Outcomes and Medical Trends -- Alka Singhal1,* and Dhanalekshmi Gopinathan1 -- INTRODUCTION -- Impact of Technology on Healthcare -- Improved Patient Care -- Enhanced Diagnostics and Treatment -- Medication Management -- Preventive Healthcare -- Big Data and Analytics -- Improved Communication -- Enhanced Research and Development -- Patient Empowerment -- Efficiency and Cost Reduction -- Predictive Analysis and Healthcare -- Disease Prevention and Early Intervention -- Optimizing Treatment Plans -- Reducing Hospital Readmissions -- Resource Allocation and Operational Efficiency -- Chronic Disease Management -- Fraud Detection and Revenue Management -- Personalized Medicine -- Population Health Management -- Enhancing Patient Engagement -- Preparing for Public Health Challenges -- PRINCIPLES OF HEALTH PREDICTIVE ANALYSIS -- Uncertainty and Error Measurement -- Focus of Health Forecasting -- Data Aggregation and Accuracy -- Horizons of Health Forecasting -- PATTERNS IN HEALTH PREDICTIVE ANALYSIS -- Temporal Patterns -- Applications -- Example -- Spatial Patterns -- Applications -- Example -- Epidemiological Patterns -- Applications -- Example -- Genetic Patterns -- Applications -- Example -- Social and Behavioral Patterns -- Applications -- Example -- Clinical Patterns -- Applications -- Example -- Environmental Patterns -- Applications -- Example -- Pharmacological Patterns -- Applications -- Example -- Technological Patterns -- Applications -- Example -- Economic Patterns -- Applications -- Example -- STEPS IN PREDICTIVE ANALYSIS MODELING -- Planning -- Problem Definition -- Data Collection -- Data Preparation -- Data Cleaning -- Feature Selection -- Model Building.
Algorithm Selection -- Training the Model -- Model Evaluation -- Validation Dataset -- Metrics -- Model Selection and Fine-Tuning -- Hyperparameter Tuning -- Comparing Models -- Implementation -- Deployment -- Monitoring and Maintenance -- Continuous Monitoring -- Model Maintenance -- Predictive Analytics Modeling -- STEPS IN PREDICTIVE ANALYSIS MODELING IN HEALTHCARE -- Step 1 -- Step 2 -- Step 3 -- Step 4 -- Step 5 -- Step 6 -- Step 7 -- Predictive Analysis in Healthcare Using Machine Learning -- Predictions on Cardiovascular Diseases -- Diabetes Predictions -- Hepatitis Disease Prediction -- Cancer Predictions Using Machine Learning -- Predictive Analysis in Healthcare Using Artificial Intelligence (AI) -- Disease Diagnosis and Risk Prediction -- Patient Outcomes and Treatment Optimization -- Chronic Disease Management -- Fraud Detection and Revenue Cycle Management -- Resource Allocation and Operational Efficiency -- Drug Discovery and Development -- Natural Language Processing (NLP) for Unstructured Data -- CHALLENGES IN PREDICTIVE ANALYSIS IN HEALTHCARE -- CONCLUSION -- REFERENCES -- Prediction and Analysis of Digital Health Records, Geonomics, and Radiology Using Machine Learning -- Sundeep Raj1,*, Arun Prakash Agarwal1, Sandesh Tripathi2 and Nidhi Gupta1 -- INTRODUCTION -- OVERVIEW OF ARTIFICIAL INTELLIGENCE -- Different Learning Methodologies -- Healthcare Applications of Artificial Intelligence -- Digital Health Records -- Radiology -- Genetic Engineering and Genomics -- CHALLENGES AND RISKS -- CONCLUSION -- REFERENCES -- Medical Imaging Using Machine Learning and Deep Learning: A Survey -- Uma Sharma1,*, Deeksha Sharma1, Pooja Pathak2, Sanjay Kumar Singh2 and Pushpanjali Singh3 -- INTRODUCTION -- MEDICAL IMAGE ANALYSIS -- Medical Imaging -- X-Ray Imaging -- Ultrasound Imaging -- Magnetic Resonance Imaging -- Computerized Tomography. Mammography -- MACHINE LEARNING -- Machine Learning Techniques -- Supervised Learning -- Unsupervised Learning -- DEEP LEARNING -- CNN (Convolution Neural Network) -- Basic Building Blocks of CNN -- Convolutional Layer -- Rectified Linear Unit (RELU) or Activation Layer -- Pooling Layer -- Fully Connected Layer -- RNN (Recurrent Neural Network) -- MEDICAL IMAGING ANALYSIS WITH MACHINE LEARNING AND DEEP LEARNING -- Image Preprocessing -- Segmentation -- Feature Extraction -- Pattern Recognition or Classification -- OPEN-SOURCE TOOLS -- CONCLUSION -- REFERENCES -- Applications of Machine Learning Practices in Human Healthcare Management Systems -- Ajay Satija1,*, Priti Pahuja2, Dipti Singh3 and Athar Hussain4 -- INTRODUCTION -- RESEARCH OBJECTIVES -- NEED FOR MACHINE LEARNING IN THE HEALTHCARE INDUSTRY -- CHALLENGES OF MACHINE LEARNING IN THE MEDICAL INDUSTRY -- Data Availability and Quality -- Data Security and Privacy -- Interpretability and Transparency -- Limited Sample Sizes -- Regulatory Compliance -- Integration into Healthcare Systems -- Bias and Fairness -- Clinical Adoption and Validation -- APPLICATIONS OF MACHINE LEARNING IN HEALTHCARE -- Machine Learning in Medical Diagnosis -- Machine Learning in Clinical Trail -- Patient Enrolment and Eligibility Requirements -- Trial Protocol Design and Optimization -- Endpoint Prediction and Biomarker Identification -- Data Monitoring and Quality Assurance -- Drug Development and Discovery -- Predicting and Tracking Adverse Events -- Real-world Evidence (RWE) Generation -- Machine Learning in Drug Development -- Target Identification -- Predicting Drug-Drug Interactions -- Machine Learning Models Help with Drug Formulation Optimization -- Clinical Trial Optimization -- Drug Efficacy Prediction -- Drug Repurposing -- Toxicity Prediction -- Genomic Medicine -- Patient Stratification. Utilization of Real-World Information -- Data Integration -- Market Access and Commercialization -- Robotic-based Surgery -- Machine Learning in Organ Image Processing -- RISK MANAGEMENT IN HEALTHCARE THROUGH MACHINE LEARNING -- Finding and Preventing Fraud -- Medical Decision Assistance Frameworks -- Risk Management for Security and Privacy -- Monitoring Adverse Drug Events -- FUTURE SCOPE OF MACHINE LEARNING IN THE HEALTHCARE INDUSTRY -- Personalized Medicine -- Better Diagnostics -- Drug Discovery and Development -- Robotics and Surgery -- Mental Health -- Public Health -- Administrative Efficient -- Research and Development -- Worldwide Health -- CONCLUSIONS -- REFERENCES -- Multimodal Deep Learning in Medical Diagnostics: A Comprehensive Exploration of Cardiovascular Risk Prediction -- Sonia Raj1,* and Neelima Bayappu1 -- INTRODUCTION -- DATA PREPARATION AND PREPROCESSING -- Image Dataset Characteristics -- Clinical Data Characteristics -- Demographics -- Medical History -- Medication and Treatment Records -- Laboratory Tests -- Vital Signs -- Imaging Data -- Clinical Assessments -- Symptoms and Subjective Data -- Electronic Health Records (EHRs) -- Environmental Factors -- Socioeconomic Variables -- Genetic and Genomic Data -- METHODOLOGY -- Multimodal Data Fusion -- Multimodal Deep Learning Algorithms -- MULTIMODAL DEEP LEARNING FOR CARDIOVASCULAR DISEASES -- CHALLENGES -- CONCLUSION -- REFERENCES -- Hypertension Detection System Using Machine Learning -- Amrita Bhatnagar1,* and Kamna Singh1 -- INTRODUCTION -- CHARACTERISTICS OF HYPERTENSION DETECTION SYSTEM -- Accurate Predictions -- Early Detection -- Personalized Risk Assessment -- Interpretability -- User-Friendly Interface -- Integration with Healthcare Workflow -- Security and Privacy -- Continuous Improvement -- Validation and Compliance -- PROCESS OF HYPERTENSION DETECTION MODEL. Data Collection -- Wearable Devices -- Clinical Trials -- Public Health Databases -- Data Variables -- Various Data Collection Methods -- Data Quality Control -- Record Keeping -- Participant Recruitment -- Data Annotation -- Data Validation -- Example of Datasets -- Framingham Heart Study -- PTB Diagnostic ECG Database -- PhysioNet -- Data Preprocessing -- Data Gathering -- Data Cleaning -- Data Transformation with Feature Scaling -- Feature Engineering -- Temporal Aggregation -- Balancing the Dataset -- Normalization -- Feature Selection on Data Sets -- Correlation Analysis -- Information Gain -- SelectKBest -- Data Splitting -- Random Sampling -- Stratified Random Sampling -- Nonrandom Sampling -- Machine Learning Models for Hyper Tension Detection -- Logistic Regression -- Support Vector Machines (SVM) -- Random Forest -- Gradient Boosting Algorithms (e.g., XGBoost, LightGBM) -- Artificial Neural Networks (ANN) -- K-Nearest Neighbors (KNN) -- Decision Trees -- Naive Bayes -- Ensemble Methods -- Gaussian Processes -- Long Short-Term Memory (LSTM) Networks -- Testing and Interoperability -- Preprocess Test Data -- Load Trained Model -- Predict on Test Data -- Interpret Results -- Adjust and Refine -- Deploy the Model (Optional) -- Continuous Monitoring and Updating -- Ethical Considerations -- Applications of Hypertension Detection System -- Early Diagnosis and Prevention -- Personalized Health Monitoring -- Clinical Decision Support -- Population Health Management -- Employee Wellness Programs -- Integration with Electronic Health Records (EHR) -- Pharmacovigilance and Medication Adherence -- Health Coaching Platforms -- Clinical Trials and Research -- Public Health Campaigns -- Existing Models -- DeepHype -- Hypertension Detection Using Wearable Devices -- Mobile Health (mHealth) Apps -- Integration of Genetic Information -- Telehealth Platforms. Explainable AI (XAI). |
| Record Nr. | UNINA-9911046643303321 |
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Verma Neeta
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| Sharjah : , : Bentham Science Publishers, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Predictive Analytics with SAS and R : Core Concepts, Tools, and Implementation / / by Ramchandra S Mangrulkar, Pallavi Vijay Chavan
| Predictive Analytics with SAS and R : Core Concepts, Tools, and Implementation / / by Ramchandra S Mangrulkar, Pallavi Vijay Chavan |
| Autore | Mangrulkar Ramchandra S |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2025 |
| Descrizione fisica | 1 online resource (175 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) | Vijay ChavanPallavi |
| Soggetto topico |
Predictive analytics
SAS (Computer program language) R (Computer program language) Artificial intelligence |
| ISBN | 9798868809057 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1 Introduction to Analytics -- Chapter 2 Simple Linear Regression -- Chapter 3 Multiple Linear Regression -- Chapter 4 Multivariate Analysis and Prediction -- Chapter 5 Time Series Analysis. |
| Record Nr. | UNINA-9910983384903321 |
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Mangrulkar Ramchandra S
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| Berkeley, CA : , : Apress : , : Imprint : Apress, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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