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"Access to big data, the “new commodity” for the 21st century economies, and its uses and potential abuses, has both conceptual and methodological impacts for the field of comparative and international education. This book examines, from a comparative perspective, the impact of the movement from the so-called knowledge-based economy towards the Intelligent Economy, which is premised upon the application of knowledge. Knowledge, the central component of the knowledge-based economy, is becoming less important in an era that is projected to be dominated and defined by the integration of complex technologies under the banner of the fourth industrial revolution. In this new era that blends the physical with the cyber-physical, the rise of education intelligence means that clients including countries, organizations, and other stakeholders are equipped with cutting-edge data in the form of predicative analytics, and knowledge about global educational predictions of future outcomes and trends. In this sense, this timely volume links the advent of this new technological revolution to the world of governance and policy formulation in education in order to open a broader discussion about the systemic and human implications for education of the emerging intelligent economy. By providing a unique comparative perspective on the Educational Intelligent economy, this book will prove invaluable for researchers and scholars in the areas of comparative education, artificial intelligence and educational policy."

"The proceedings covers advanced and multi-disciplinary research on design of smart computing and informatics. The theme of the book broadly focuses on various innovation paradigms in system knowledge, intelligence and sustainability that may be applied to provide realistic solution to varied problems in society, environment and industries. The volume publishes quality work pertaining to the scope of the conference which is extended towards deployment of emerging computational and knowledge transfer approaches, optimizing solutions in varied disciplines of science, technology and healthcare."

"Memprediksi niat kunjungan kembali memainkan peran penting dalam kebangkitan kembali waktu pandemi yang akan menguntungkan keunggulan kompetitif jangka pendek dan jangka panjang. Penelitian ini mengkaji faktor-faktor penentu niat berkunjung kembali dari analisis sentimen berbasis aspek dan pembelajaran mesin. Pendekatan big data diterapkan pada empat set data atraksi, hotel bintang 4&5, hotel bintang 3, dan motel dengan 49.399 ulasan dari TripAdvisor. Kami menerapkan metode pemodelan topik untuk mengekstrak aspek dan atribut, menghasilkan 10 aspek untuk kategorisasi hotel 4&5 dan kumpulan data atraksi, 6 aspek pada kumpulan data hotel bintang 3 dan Motel. Hasil analisis sentimen menunjukkan bahwa sentimen wisatawan secara positif dan negatif juga mempengaruhi kemungkinan niat berkunjung kembali. Peneliti menerapkan metode Logistic Regression, Random Forest Classifier, Decision Tree, k-NN, dan XGBoost untuk memprediksi niat berkunjung kembali yang menghasilkan tiga topik utama yang mendominasi probabilitas niat berkunjung kembali untuk masing-masing dataset. Aspek Properti pada hotel bintang 4&5 dan hotel bintang 3 mengindikasikan memiliki kemungkinan tinggi untuk niat berkunjung kembali. Sedangkan aspek Motels pada Atmosfir, Aktivitas Wisata, dan Durasi cenderung memiliki probabilitas niat berkunjung kembali. Aspek atraksi pada Harga, Layanan, Suasana meningkatkan kemungkinan niat berkunjung kembali. Studi ini berkontribusi pada pemanfaatan data besar dan pembelajaran mesin di industri pariwisata dan perhotelan dengan berfokus pada strategi inovatif sebagai pengurangan biaya untuk mempertahankan niat kunjungan kembali di kebangkitan kembali dari pandemi.

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Predicting revisit intention plays a crucial role in the reawakening time of pandemic that will benefit short-term and long-term competitive advantage. This study examines the determiner factors of revisit intention from aspect-based sentiment analysis and machine learning. A big data approach was applied on four datasets of attractions, hotel 4&5 stars, hotel 3 stars, and motels with 49,399 reviews from TripAdvisor. We applied a topic modeling method to extract aspects and attributes, resulting in 10 aspects for hotel 4&5 categorization and attractions dataset, 6 aspects on hotel 3 stars and Motels dataset. Results on sentiment analysis show that tourists’ sentiment in positives and negatives also affect probability of revisit intention. Researchers applied methods of Logistic Regression, Random Forest Classifier, Decision Tree, k-NN, andXGBoost to predict revisit intention resulting in three main topics that have dominated probability on revisit intention for each dataset respectively. Aspect Properties on hotels 4&5 stars and hotel 3 stars indicate to have a high probability of revisit intention. Meanwhile, Motels' aspects on Atmosphere, Tourist Activities, and Duration tend to have a probability of revisit intention. Attraction’s aspects on Price, Services, Ambience increase probability of revisit intention. This study contributes to the utilization of big data and machine learning in tourism and hospitality industry by focusing on an innovative strategy as cost reduction to maintain revisit intention in the reawakening from pandemic."

"They present new and innovative developments and applications, divided into technical stream sections on data mining, data mining and machine learning, planning and optimisation, and knowledge management and prediction, followed by application stream sections on language and classification, recommendation, practical applications and systems, and data mining and machine learning. The volume also includes the text of short papers presented as posters at the conference."

"The current book is the first publication of a complete overview of machine learning methodologies for the medical and health sector. It was written as a training companion, and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health and health care. In eighty chapters eighty different machine learning methodologies are reviewed, in combination with data examples for self-assessment. Each chapter can be studied without the need to consult other chapters.The amount of data stored in the world's databases doubles every 20 months, and clinicians, familiar with traditional statistical methods, are at a loss to analyze them. Traditional methods have, indeed, difficulty to identify outliers in large datasets, and to find patterns in big data and data with multiple exposure / outcome variables. In addition, analysis-rules for surveys and questionnaires, which are currently common methods of data collection, are, essentially, missing. Fortunately, the new discipline, machine learning, is able to cover all of these limitations.So far medical professionals have been rather reluctant to use machine learning. Also, in the field of diagnosis making, few doctors may want a computer checking them, are interested in collaboration with a computer or with computer engineers. Adequate health and health care will, however, soon be impossible without proper data supervision from modern machine learning methodologies like cluster models, neural networks, and other data mining methodologies.Each chapter starts with purposes and scientific questions. Then, step-by-step analyses, using data examples, are given. Finally, a paragraph with conclusion, and references to the corresponding sites of three introductory textbooks, previously written by the same authors, is given."