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"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."

"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."

"This state-of-the-art survey offers a collection of papers from the workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning."

"The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging."

"Indonesia, sebagai salah satu negara pengekspor ikan terbesar di dunia, menghadapi tantangan serius dalam sektor perikanan akibat illegal, unreported, unregulated (IUU) fishing. Meskipun telah ada pengawas yang ditugaskan, namun praktik ini masih ditemukan, sehingga perlu teknologi pengawasan di atas kapal. Telah dikembangkan model yang dapat mengklasifikasikan jenis ikan di kapal melalui video CCTV namun masih perlu dilengkapi dengan kemampuan memprediksi berat ikan. Dengan metode ensemble learning yang dipilih karena memiliki kinerja yang lebih baik dibanding model individual, penelitian ini bertujuan untuk membangun model prediksi berat melalui citra dari sistem CCTV. Kemampuan untuk memprediksi berat ikan akan memberikan metode bagi pemerintah untuk melakukan pengecekan apakah hasil tangkapan yang dilaporkan sesuai dengan tangkapan yang terjadi di lapangan. Dari pengujian yang dilakukan, algoritma Catboost Regression menunjukkan kinerja terbaik di antara semua model yang diuji. Pada dataset gabungan, dengan rasio data split 90:10, CatBoost mencapai  score 0.986, MAE 9.794, MSE 293.493, dan RMSE 17.132. Untuk dataset cumi dengan rasio 90:10, nilai metrik yang diperoleh adalah  0.025, MAE 18.451, MSE 660.629, dan RMSE 25.702. Sementara pada dataset ikan dengan rasio 90:10, CatBoost menunjukkan kinerja sangat baik dengan  0.980, MAE 5.825, MSE 146.713, dan RMSE 10.129. Model yang dipilih dengan kinerja yang paling baik adalah model dengan dataset ikan dengan MAE 5.825, yang berarti nilai error dari rata-rata berat ikan yang ditimbang adalah 1.29%. Hasil ini menunjukkan bahwa Catboost Regression mampu memprediksi berat ikan dengan akurasi yang tinggi dibandingkan model regresi lainnya pada dataset yang digunakan, dengan pemilihan rasio data split yang optimal.

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Indonesia, as one of the largest fish-exporting countries in the world, faces serious challenges in its fisheries sector due to illegal, unreported, and unregulated (IUU) fishing. Despite having monitoring officers assigned, these practices are still found, necessitating the use of surveillance technology on vessels. A model has been developed that can classify fish species on ships using CCTV footage, but it still needs to be enhanced with the ability to predict the weight of the fish. Ensemble learning methods, chosen for their superior performance compared to individual models, are being used in this research to build a weight prediction model using images from the CCTV system. The ability to predict fish weight will provide the government with a method to verify whether the reported catches match what is caught at sea. From the tests conducted, the Catboost Regression algorithm demonstrated the best performance among all tested models. On the combined dataset with a 90:10 train-test split ratio, CatBoost achieved an  score of 0.986, MAE of 9.794, MSE of 293.493, and RMSE of 17.132. For the squid dataset with a 90:10 ratio, the metrics obtained were an  of 0.025, MAE of 18.451, MSE of 660.629, and RMSE of 25.702. Meanwhile, for the fish dataset with the same ratio, CatBoost showed excellent performance with an  of 0.980, MAE of 5.825, MSE of 146.713, and RMSE of 10.129. The best-performing model is the one with the fish dataset, achieving an MAE of 5.825, which translates to an error rate of 1.29% in the average weight of the fish weighed. These results indicate that Catboost Regression can predict fish weight with high accuracy compared to other regression models used on the dataset, with optimal data split ratio."

"Stroke merupakan penyebab utama kematian dan kecacatan di Indonesia. Mengingat sempitnya jendela waktu pengobatan stroke iskemik hiperakut dan potensi komplikasi yang terkait dengan intervensi trombolisis, prognostikasi yang akurat esensial dalam memastikan terapi yang cepat dan tepat. Penelitian ini memanfaatkan pembelajaran mesin, khususnya Random Forest (RF), bertujuan untuk mengembangkan model yang mampu memprediksi hasil klinis (Δ NIHSS) pasien stroke iskemik hiperakut setelah trombolisis, berdasarkan CT scan otak, data klinis, dan nilai laboratorium. Klasifikasi Δ NIHSS menggunakan tiga skenario berbeda —CT, CT + Data klinis, dan CT + Data klinis + Data lab— dan dikategorikan menjadi 2 dan 3 kelas yang akan digunakan dalam pemantauan model prediksi mana yang memberikan performa paling optimal. Pengumpulan data studi kohort ini diperoleh saat kedatangan awal pasien, terdiri dari data klinis, laboratorium, dan data CT otak non-kontras dari rekam medis dan Picture Archiving Communication System (PACS) Rumah Sakit Cipto Mangunkusumo Jakarta dengan periode 10 tahun sejak November 2014 hingga Februari 2023 dan total 145 pasien. Arsitektur dari Bacchi et al.1 yakni convolutional neural network (CNN) dan model pembelajaran mesin konvensional lainnya juga dianalisis sebagai pendekatan alternatif. Hasil penelitian menunjukkan bahwa algoritma RF (2 kelas) menggunakan data validasi dan skenario CT + Data klinis + Data lab menampilkan akurasi tertinggi (75%) dan unggul dalam sensitivitas dan spesifisitas (0,61 dan 0,59). Performa metrik juga menunjukkan tren peningkatan dari setiap skenario. Model ini diharapkan dapat meningkatkan efisiensi penatalaksanaan stroke iskemik hiperakut dengan memberikan informasi tambahan kepada klinisi dalam pengambilan keputusan terkait intervensi trombolisis.

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Stroke is the leading cause of both mortality and disability in Indonesia. Given the narrow time frame for treating acute ischemic stroke and the potential complications associated with thrombolysis intervention, accurate prognostication is essential to ensure a prompt and appropriate treatment. The National Institutes of Health Stroke Scale (NIHSS) can be utilized to identify individuals who may benefit from reperfusion therapy. The data for this cohort study acquired during the initial presentation, comprising clinical, laboratory, and non-contrast brain CT data from the medical records and Picture Archiving Communication System (PACS) of Cipto Mangunkusumo Hospital Jakarta. The study included 145 patients who experienced acute ischemic stroke and received thrombolysis treatment from November 2014 to February 2023. Currently, there is no clinical outcome prediction model for hyperacute ischemic stroke using data from Indonesia. By utilizing machine learning, specifically Random Forest, the author aims to develop a model capable of predicting the clinical outcome (Δ NIHSS) of hyperacute ischemic stroke patients following thrombolysis, based on brain CT scans, clinical data, and laboratory values. The classification of Δ NIHSS used three distinctive scenarios —CT, CT + Clinic, and CT + Clinic + Lab— and is categorized by 2 and 3 classes will be used in monitoring which prediction model gives optimal performance. Architecture derived from the research conducted by Bacchi et al.1 employed a convolutional neural network (CNN) and other conventional machine learning models were also analyzed as alternative approach. Result revealed that RF algorithm (2 classes) using data validation and CT + Clinic + Lab scenario displays the highest accuracy (75%) and excels in sensitivity and specificity (0,61 and 0,59). The performance metrics show continuous improvement, indicating that this model can enhance hyperacute ischemic stroke management by providing clinicians with additional decision-making support for thrombolysis intervention."