Indrajit Kar – författare

The Internet of Medical Things (IoMT) is a system that collects data from patients with the help of different sensory inputs, e.g., an accelerometer, electrocardiography, and electroencephalography. This text presents both theoretical and practical concepts related to the application of machine learning and Internet of Things (IoT) algorithms in analyzing data generated through healthcare systems.Illustrates the latest technologies in the healthcare domain and the Internet of Things infrastructure for storing smart electronic health recordsFocuses on the importance of machine learning algorithms and the significance of Internet of Things infrastructure for healthcare systemsShowcases the application of fog computing architecture and edge computing in novel aspects of modern healthcare servicesDiscusses unsupervised genetic algorithm-based automatic heart disease predictionCovers Internet of Things–based hardware mechanisms and machine learning algorithms to predict the stress level of patientsThe text is primarily written for graduate students and academic researchers in the fields of computer science and engineering, biomedical engineering, electrical engineering, and information technology.

The Internet of Medical Things (IoMT) is a system that collects data from patients with the help of different sensory inputs, e.g., an accelerometer, electrocardiography, and electroencephalography. This text presents both theoretical and practical concepts related to the application of machine learning and Internet of Things (IoT) algorithms in analyzing data generated through healthcare systems.Illustrates the latest technologies in the healthcare domain and the Internet of Things infrastructure for storing smart electronic health recordsFocuses on the importance of machine learning algorithms and the significance of Internet of Things infrastructure for healthcare systemsShowcases the application of fog computing architecture and edge computing in novel aspects of modern healthcare servicesDiscusses unsupervised genetic algorithm-based automatic heart disease predictionCovers Internet of Things–based hardware mechanisms and machine learning algorithms to predict the stress level of patientsThe text is primarily written for graduate students and academic researchers in the fields of computer science and engineering, biomedical engineering, electrical engineering, and information technology.

The Internet of Medical Things (IoMT) is a system that collects data from patients with the help of different sensory inputs, e.g., an accelerometer, electrocardiography, and electroencephalography. This text presents both theoretical and practical concepts related to the application of machine learning and Internet of Things (IoT) algorithms in analyzing data generated through healthcare systems.

Illustrates the latest technologies in the healthcare domain and the Internet of Things infrastructure for storing smart electronic health records Focuses on the importance of machine learning algorithms and the significance of Internet of Things infrastructure for healthcare systems Showcases the application of fog computing architecture and edge computing in novel aspects of modern healthcare services Discusses unsupervised genetic algorithm-based automatic heart disease prediction Covers Internet of Things–based hardware mechanisms and machine learning algorithms to predict the stress level of patients

The text is primarily written for graduate students and academic researchers in the fields of computer science and engineering, biomedical engineering, electrical engineering, and information technology.

The Internet of Medical Things (IoMT) is a system that collects data from patients with the help of different sensory inputs, e.g., an accelerometer, electrocardiography, and electroencephalography. This text presents both theoretical and practical concepts related to the application of machine learning and Internet of Things (IoT) algorithms in analyzing data generated through healthcare systems.

Illustrates the latest technologies in the healthcare domain and the Internet of Things infrastructure for storing smart electronic health records Focuses on the importance of machine learning algorithms and the significance of Internet of Things infrastructure for healthcare systems Showcases the application of fog computing architecture and edge computing in novel aspects of modern healthcare services Discusses unsupervised genetic algorithm-based automatic heart disease prediction Covers Internet of Things–based hardware mechanisms and machine learning algorithms to predict the stress level of patients

The text is primarily written for graduate students and academic researchers in the fields of computer science and engineering, biomedical engineering, electrical engineering, and information technology.

Build reliable Build reliable AI evaluation frameworks that measure quality, safety, grounding, and production readiness across modern LLM and SLM applicationsFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesDesign evaluation frameworks for LLMs, SLMs, multimodal, reasoning, and agentic AI systemsMeasure quality, safety, grounding, robustness, and production readiness with practical metricsApply unified evaluation methods to text, multimodal, and agentic AI systemsBook DescriptionModern AI systems are expected to do far more than generate fluent text. They should be able to retrieve information, reason through complex problems, understand images and documents, call external tools, execute workflows, and support critical business decisions. Evaluating these systems requires methods that go beyond traditional NLP benchmarks.Taking a product-first approach, this book presents evaluation as a continuous operational capability spanning training, inference, and end-to-end system operation. You'll learn how to connect evaluation metrics directly to deployment gates, rollback criteria, monitoring systems, and production reliability objectives.Using practical examples and real-world workflows, you'll explore evaluation strategies for text LLMs, vision-language models, multimodal conversational systems, mixture-of-experts architectures, reasoning models, agentic systems, retrieval pipelines, Text2SQL and Text2Cypher systems, embedding models, OCR workflows, and guardrail SLMs. You'll also learn how to manage non-determinism, design repeatable test suites, validate tool execution, and measure long-horizon agent behavior in production.By the end of the book, you'll be able to design robust evaluation systems that help teams deploy reliable, safe, and economically viable LLM-powered applications with confidence.*Email sign-up and proof of purchase requiredWhat you will learnDesign repeatable evaluation pipelines for LLM systemsAssess inference quality, latency, and operational costEvaluate multimodal, agentic, and reasoning AI systemsBuild regression gates and deployment evaluation workflowsDetect hallucinations and grounding failures in VLMsAssess routing stability in mixture-of-experts modelsEvaluate Text2SQL, OCR, and retrieval-based systemsTranslate evaluation signals into production decisionsWho this book is forML engineers, GenAI engineers, AI architects, data scientists, platform engineers, and engineering managers responsible for deploying LLM-powered systems in production will benefit from this book. Applied AI researchers and technical decision-makers looking to measure reliability, safety, and operational readiness across modern AI systems will also find it valuable. Readers should have a working understanding of machine learning, Python, and modern LLM concepts.