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Condizione: New. In English.

Condizione: New. In English.

Paperback or Softback. Condizione: New. Machine Learning-Based Prediction and Analysis of Student Examination Performance. Book.

Condizione: New.

Paperback. Condizione: new. Paperback. The analysis and prediction of student examination performance have become important applications of machine learning, educational data mining, and learning analytics. Machine Learning-Based Prediction and Analysis of Student Examination Performance provides a focused technical examination of computational approaches for analyzing academic data and predicting examination outcomes. The book connects machine learning, predictive analytics, educational data mining, statistical analysis, and academic performance assessment within an interdisciplinary framework.The book introduces the foundations of student performance analysis and examines the types of information that can contribute to computational models of academic outcomes. Examination scores, previous academic performance, attendance, study-related characteristics, assessment records, and other relevant educational variables can provide useful inputs for analytical models. The text considers how such information can be organized and transformed into meaningful features for prediction and classification.A central focus is placed on machine learning-based prediction of examination performance. The book discusses the general principles of supervised learning, predictive modeling, classification, regression, feature selection, model training, validation, and performance evaluation. These methods provide a framework for identifying patterns in educational datasets and estimating potential examination outcomes from available student-related information.The book further explores educational data mining as a means of discovering relationships and patterns within academic records. Data-driven analysis can help identify factors associated with differences in examination performance and provide a basis for understanding relationships among academic variables. The discussion considers data preprocessing, missing values, feature engineering, normalization, model selection, and dataset partitioning as important components of a reliable analytical workflow.Different machine learning approaches can produce different forms of predictive behavior depending on the structure of the educational data. The book examines general concepts associated with classification and regression models and considers how their predictions can be evaluated using appropriate statistical and machine learning metrics. Measures such as accuracy, precision, recall, F1 score, mean absolute error, and related evaluation criteria are discussed as tools for assessing predictive performance where appropriate.Attention is also given to the interpretation of machine learning results. Prediction alone is not sufficient for meaningful educational analysis; researchers and practitioners must also consider model limitations, data quality, generalization, and the context in which predictions are produced. The book therefore emphasizes responsible interpretation of computational results rather than presenting machine learning predictions as definitive judgments about individual students.The text also considers challenges associated with educational datasets. Student records may contain missing observations, class imbalance, heterogeneous variables, limited sample sizes, and differences in academic environments. These factors can influence model development and predictive reliability. Understanding such challenges is important when designing experiments, selecting features, validating models, and comparing alternative approaches.The broader role of learning analytics and data-driven educational decision support is also examined. Predictive models can help analyze academic trends and provide information that may support educational planning and targeted academic interventions. Machine Learning-Based Prediction and Analysis of Student Examination Performan Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. Lafz and Shayari'yan / ????? ?? ?????'?? (Paperback or Softback).

Condizione: New.

Soft cover. Condizione: New. ISBN:9789372022896.

Soft cover. Condizione: New. This book provides an in-depth exploration of arthroscopy, meticulously covering fundamental concepts, advanced techniques, and practical applications. Structured and systematic approach: Each chapter is systematically organized to facilitate understanding, starting from basic principles to advanced surgical techniques for various joints. Expert insights: Authored by a panel of seasoned arthroscopic surgeons, the book offers valuable insights drawn from years of clinical experience. Detailed illustrations and procedures: Includes step-by-step descriptions of key procedures, supported by diagrams and photographs for clarity and ease of comprehension. Fundamentals of arthroscopy, including indications, contraindications, and diagnostic techniques. Detailed protocols for knee and shoulder positioning, draping, and portal placement. Comprehensive sections on ACL, PCL, meniscus, and rotator cuff repair. Discussions on complex conditions such as anterior instability and frozen shoulder. Case-based learning: Features caselets summarizing real-life clinical scenarios, enhancing problem-solving skills and practical knowledge. Psychological perspective: Unique insights into the psychology of arthroscopic surgeons, addressing the mental and emotional aspects of the profession. Ideal for practitioners and learners: An invaluable resource for orthopedic surgeons, trainees, and medical professionals seeking to enhance their expertise in arthroscopy.…

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: Brand New. 220 pages. 6.00x0.46x9.00 inches. In Stock.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New. In English.

Paperback. Condizione: Brand New. 60 pages. Hindu language. 5.50x0.14x8.50 inches. In Stock.

Soft cover. Condizione: New. Orthopedic surgery stands at a transformative crossroads, where precision, personalization, and technological innovation are reshaping conventional practices. One of the most significant advancements in recent years is the integration of robotic technology into total knee arthroplasty (TKA). Once considered futuristic, robotic-assisted knee replacement has now become an essential and evolving aspect of modern surgical care, offering improved accuracy, consistency, and patient outcomes. As orthopedic surgeons who have navigated the transition from traditional to technology-driven arthroplasty, we have experienced firsthand the challenges, questions, excitement, and promise that come with adopting robotic surgery. With this book, "A Comprehensive Guide to Robotic Knee Arthroplasty," we aim to bridge the knowledge gap and provide a complete, practical, and unbiased guide for all those navigating this new landscape be it residents in training, early-career surgeons, or seasoned practitioners. This book was conceived in response to a genuine need for a single, authoritative resource that not only explains the mechanics of various robotic platforms but also contextualizes their use in real-world clinical scenarios. It is not merely a compilation of theoretical chapters; rather it represents the shared experience of orthopedic surgeons who have collectively performed thousands of robotic knee surgeries. It brings together the knowledge and insights of contributors who are innovators, educators, and front-line clinicians deeply engaged with this technology on a dayto- day basis. Throughout this book, we have made a conscious effort to keep the content evidence-based, yet clinically oriented and easy to navigate. The chapters begin with the evolution and rationale behind robotic TKA and gradually unfold into detailed discussions on different robotic systems CUVIS, MISSO, MAKO, VALYS, CORI, ROSA, and more. Each system is examined with a focus on its unique features, operational workflow, indications, benefits, limitations, and clinical outcomes. Comparisons between systems are drawn when relevant to aid in practical decisionmaking. In a field as dynamic as robotic knee arthroplasty, knowledge must evolve in tandem with technology. We hope this book will serve not only as a reference but also as a companion one that instills confidence, encourages critical thinking, and ultimately contributes to better outcomes for patients worldwide.…

Paperback. Condizione: New. Orthopaedic surgery stands at a transformative crossroads, where precision, personalisation, and technological innovation are reshaping conventional practices.One of the most significant advances in recent years is the integration of robotic technology into total knee arthroplasty (TKA). Once considered futuristic, robotic-assisted knee replacement has now become an essential and evolving aspect of modern surgical care, offering improved accuracy, consistency, and patient outcomes.Based on the authors' vast array of experience, this book is a comprehensive guide to robotic knee arthroplasty, not only explaining the mechanics of various robotic platforms, but also contextualising their use in real-world clinical scenarios.Throughout this book, a conscious effort is made to keep the content evidence-based, yet clinically oriented and easy to navigate.Chapters begin with the evolution and rationale behind robotic TKA and gradually unfold into detailed discussions on different robotic systems-CUVIS, MISSO, MAKO, VALYS, CORI, ROSA, and more.Each system is examined with a focus on its unique features, operational workflow, indications, benefits, limitations, and clinical outcomes. Comparisons between systems are drawn when relevant to aid in practical decision making.…

Taschenbuch. Condizione: Neu. Neuware.

Taschenbuch. Condizione: Neu. Neuware.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: new. Paperback. The analysis and prediction of student examination performance have become important applications of machine learning, educational data mining, and learning analytics. Machine Learning-Based Prediction and Analysis of Student Examination Performance provides a focused technical examination of computational approaches for analyzing academic data and predicting examination outcomes. The book connects machine learning, predictive analytics, educational data mining, statistical analysis, and academic performance assessment within an interdisciplinary framework.The book introduces the foundations of student performance analysis and examines the types of information that can contribute to computational models of academic outcomes. Examination scores, previous academic performance, attendance, study-related characteristics, assessment records, and other relevant educational variables can provide useful inputs for analytical models. The text considers how such information can be organized and transformed into meaningful features for prediction and classification.A central focus is placed on machine learning-based prediction of examination performance. The book discusses the general principles of supervised learning, predictive modeling, classification, regression, feature selection, model training, validation, and performance evaluation. These methods provide a framework for identifying patterns in educational datasets and estimating potential examination outcomes from available student-related information.The book further explores educational data mining as a means of discovering relationships and patterns within academic records. Data-driven analysis can help identify factors associated with differences in examination performance and provide a basis for understanding relationships among academic variables. The discussion considers data preprocessing, missing values, feature engineering, normalization, model selection, and dataset partitioning as important components of a reliable analytical workflow.Different machine learning approaches can produce different forms of predictive behavior depending on the structure of the educational data. The book examines general concepts associated with classification and regression models and considers how their predictions can be evaluated using appropriate statistical and machine learning metrics. Measures such as accuracy, precision, recall, F1 score, mean absolute error, and related evaluation criteria are discussed as tools for assessing predictive performance where appropriate.Attention is also given to the interpretation of machine learning results. Prediction alone is not sufficient for meaningful educational analysis; researchers and practitioners must also consider model limitations, data quality, generalization, and the context in which predictions are produced. The book therefore emphasizes responsible interpretation of computational results rather than presenting machine learning predictions as definitive judgments about individual students.The text also considers challenges associated with educational datasets. Student records may contain missing observations, class imbalance, heterogeneous variables, limited sample sizes, and differences in academic environments. These factors can influence model development and predictive reliability. Understanding such challenges is important when designing experiments, selecting features, validating models, and comparing alternative approaches.The broader role of learning analytics and data-driven educational decision support is also examined. Predictive models can help analyze academic trends and provide information that may support educational planning and targeted academic interventions. Machine Learning-Based Prediction and Analysis of Student Examinat Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. The analysis and prediction of student examination performance have become important applications of machine learning, educational data mining, and learning analytics. Machine Learning-Based Prediction and Analysis of Student Examination Performance provides a focused technical examination of computational approaches for analyzing academic data and predicting examination outcomes. The book connects machine learning, predictive analytics, educational data mining, statistical analysis, and academic performance assessment within an interdisciplinary framework.The book introduces the foundations of student performance analysis and examines the types of information that can contribute to computational models of academic outcomes. Examination scores, previous academic performance, attendance, study-related characteristics, assessment records, and other relevant educational variables can provide useful inputs for analytical models. The text considers how such information can be organized and transformed into meaningful features for prediction and classification.A central focus is placed on machine learning-based prediction of examination performance. The book discusses the general principles of supervised learning, predictive modeling, classification, regression, feature selection, model training, validation, and performance evaluation. These methods provide a framework for identifying patterns in educational datasets and estimating potential examination outcomes from available student-related information.The book further explores educational data mining as a means of discovering relationships and patterns within academic records. Data-driven analysis can help identify factors associated with differences in examination performance and provide a basis for understanding relationships among academic variables. The discussion considers data preprocessing, missing values, feature engineering, normalization, model selection, and dataset partitioning as important components of a reliable analytical workflow.Different machine learning approaches can produce different forms of predictive behavior depending on the structure of the educational data. The book examines general concepts associated with classification and regression models and considers how their predictions can be evaluated using appropriate statistical and machine learning metrics. Measures such as accuracy, precision, recall, F1 score, mean absolute error, and related evaluation criteria are discussed as tools for assessing predictive performance where appropriate.Attention is also given to the interpretation of machine learning results. Prediction alone is not sufficient for meaningful educational analysis; researchers and practitioners must also consider model limitations, data quality, generalization, and the context in which predictions are produced. The book therefore emphasizes responsible interpretation of computational results rather than presenting machine learning predictions as definitive judgments about individual students.The text also considers challenges associated with educational datasets. Student records may contain missing observations, class imbalance, heterogeneous variables, limited sample sizes, and differences in academic environments. These factors can influence model development and predictive reliability. Understanding such challenges is important when designing experiments, selecting features, validating models, and comparing alternative approaches.The broader role of learning analytics and data-driven educational decision support is also examined. Predictive models can help analyze academic trends and provide information that may support educational planning and targeted academic interventions. Machine Learning-Based Prediction and Analysis of Student Examinat Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…