Search Results - "machines"

Correction: Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm by Michiko Ueda, Kohei Watanabe, Hajime Sueki

Connect to this object online.

Effect of discharge current and electrode size on material removal rate and wear ratio in electrical discharge machining / Ahsan Ali Khan, Mohammad Yeakub Ali and Md.... by Khan, Ahsan Ali, Ali, Mohammad Yeakub, Haque, Md. Mohafizul

Link Metadata

Towards development of robust machine learning model for Malaysian corporation: a systematic review of essential aspects for corporate credit risk assessment / Zulkifli Halim and Shuhaida Mohamed Shuhidan by Halim, Zulkifli, Mohamed Shuhidan, Shuhaida

Link Metadata

Web scraping and regression analysis based on machine learning for COVID-19 with rapid software platform / Aizal Yusrina Idris, Razan Bamoallem and Mohamad Harith Azfar Mohamad Hatta by Idris, Aizal Yusrina, Bamoallem, Razan, Mohamad Hatta, Mohamad Harith Azfar

Link Metadata

PELATIHAN DAN PRODUKTIFITAS KERJA: Studi Evaluasi Dampak Pelatihan Pegawai Seksi Produksi Machining Terhadap Produktifitas Kerja Pegawai di PT.... by Kamajaya, Bambang Haris

Link Metadata

ANALISIS SENTIMEN PADA REVIEW PENGGUNA E-COMMERCE BIDANG PANGAN MENGGUNAKAN METODESUPPORT VECTOR MACHINE : (Studi Kasus: Review Sayurbox Dan Tanihub Pada Google Play) by Geyessella Manik,

Link Metadata

On the path to AI Law's prophecies and the conceptual foundations of the machine learning age / by Grant, Thomas D., Wischik, Damon J.

Table of Contents: “…Prologue: Starting with logic -- CHAPTER 1: Two Revolutions -- CHAPTER 2: Getting past logic -- CHAPTER 3: Experience and data as input -- CHAPTER 4: Finding patterns as the path from input to output -- CHAPTER 5: Output as prophecy -- CHAPTER 6: Explanations of machine learning -- CHAPTER 7: Juries and other reliable predictors -- CHAPTER 8: Poisonous datasets, poisonous trees -- CHAPTER 9: From Holmes to AlphaGo -- CHAPTER 10:Conclusion -- EPILOGUE: Lessons in two directions.…”
Link to Metadata

On the path to AI Law's prophecies and the conceptual foundations of the machine learning age by Grant, Thomas D.

Subjects: “…Machine learning…”
DOAB: download the publication
DOAB: description of the publication

On the path to AI Law's prophecies and the conceptual foundations of the machine learning age by Grant, Thomas D.

Subjects: “…Machine learning…”
OAPEN Library: download the publication
OAPEN Library: description of the publication

Die mobile Arbeitsmaschine als komplexes System by Kautzmann, Timo

Subjects: “…Mobile Machines…”
DOAB: download the publication
DOAB: description of the publication

Clinical Text Mining: Secondary Use of Electronic Patient Records by Hercules Dalianis

Subjects: “…Support Vector Machines…”
DOAB: download the publication
DOAB: description of the publication

New Mechanism Explanation, Emergence and Reduction /

Subjects: “…Machine learning.…”
Link to Metadata

A Multi-Machine Learning Consensus Model Based on Clinical Features Reveals That Interleukin-10 Derived from Monocytes Leads to a Poor Prognosis in Patients with Coronavirus Disease-2019 by Yu J, Zhang Y, Ye Z, Tang K, Ma Y, Fu L, Cui T, Kang H, Yuan Y, Pan W

Connect to this object online.

Exploring predictors of welfare dependency 1, 3, and 5 years after mental health-related absence in danish municipalities between 2010 and 2012 using flexible machine learning modelling by Søren Skotte Bjerregaard

Connect to this object online.

Examining ethno-racial attitudes of the public in Twitter discourses related to the United States Supreme Court Dobbs vs. Jackson Women's Health Organization ruling: A machine learning approach by Otobo I. Ujah, Pelumi Olaore, Onome C. Nnorom, Chukwuemeka E. Ogbu, Russell S. Kirby

Connect to this object online.

EFFORTS TO IMPROVE THE ABILITY TO USE MEASURING INSTRUMENT STUDENT LEARNING THROUGH MEDIA MACROMEDIA FLASH IN CLASS X MECHANICAL MACHINING SMK 1 SEDAYU ACADEMIC YEAR 2012/2013 by Puput Hananto, Pairun Roniwijaya

Connect to this object online.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine by Lele Ye, Lele Ye, Xinya Tong, Kan Pan, Xinyu Shi, Binbing Xu, Xuyang Yao, Linpei Zhuo, Su Fang, Sangsang Tang, Zhuofeng Jiang, Xiangyang Xue, Weiguo Lu, Weiguo Lu, Weiguo Lu, Gangqiang Guo

Connect to this object online.

Can machine learning models predict maternal and newborn healthcare providers' perception of safety during the COVID-19 pandemic?... by Bassel Hammoud, Aline Semaan, Imad Elhajj, Lenka Benova

Connect to this object online.

Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2) by Robert A. Reed, Andrei S. Morgan, Andrei S. Morgan, Andrei S. Morgan, Jennifer Zeitlin, Pierre-Henri Jarreau, Pierre-Henri Jarreau, Héloïse Torchin, Héloïse Torchin, Véronique Pierrat, Véronique Pierrat, Pierre-Yves Ancel, Pierre-Yves Ancel, Babak Khoshnood

Connect to this object online.

Seasonal variations of arrhythmias and their impact on mortality in cancer patients with health disparities: A propensity score adjusted machine learning analysis of over 100 million hospitalizations across 3 years by Park Jong Kun, Monlezun Dominique, Kim Jin Wan, Going James, Khalaf Shaden, Honan Kevin, Ali Abdelrahman, Liu Victor, Barout Ahmad, Boone David, Safavi-Naeini Payam, Koutroumpakis Efstratios, Cilingiroglu Mehmet, Marmagkiolis Konstantinos, Iliescu Cezar, Karimzad Kaveh, Madjid Mohammad

Connect to this object online.