Predicting Travel Mode of Individuals by Machine Learning
نویسندگان
چکیده
منابع مشابه
Predicting Phospholipidosis Using Machine Learning
Phospholipidosis is an adverse effect caused by numerous cationic amphiphilic drugs and can affect many cell types. It is characterized by the excess accumulation of phospholipids and is most reliably identified by electron microscopy of cells revealing the presence of lamellar inclusion bodies. The development of phospholipidosis can cause a delay in the drug development process, and the impor...
متن کاملDiagnosing Breast Cancer by Machine Learning
Background and Aim: Cancer and in particular Breast cancer are among the diseases that have the highest mortality rate in Iran after heart disease. The accurate prognosis for Breast cancer is important, and the presence of various symptoms and features of this disease makes it difficult for doctors to diagnose. This study aimed to identify the factors affecting Breast cancer, modeling and ultim...
متن کاملPredicting mutagenicity of aromatic amines by various machine learning approaches.
Aromatic amines are prevalently used in a wide variety of industries and are ubiquitous in foods and environment. Many of this class of compounds are potentially mutagenic or even carcinogenic, and the assessment and prediction of their mutagenicity are of practical importance because mutagenicity and carcinogenicity are toxicological end points that play major roles in the genesis of cancer an...
متن کاملAnalyzing the performance of different machine learning methods in determining the transportation mode using trajectory data
With the widespread advent of the smart phones equipping with Global Positioning System (GPS), a huge volume of users’ trajectory data was generated. To facilitate urban management and present appropriate services to users, studying these data was raised as a widespread research filed and has been developing since then. In this research, the transportation mode of users’ trajectories was identi...
متن کاملAn Intelligent Machine Learning-Based Protection of AC Microgrids Using Dynamic Mode Decomposition
An intelligent strategy for the protection of AC microgrids is presented in this paper. This method was halving to an initial signal processing step and a machine learning-based forecasting step. The initial stage investigates currents and voltages with a window-based approach based on the dynamic decomposition method (DDM) and then involves the norms of the signals to the resultant DDM data. T...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transportation Research Procedia
سال: 2015
ISSN: 2352-1465
DOI: 10.1016/j.trpro.2015.09.037