A Survey on the Revamped Future of Transport

Transport, a movement activity from home to work, studyor market place is one of the basic needs like food, shelter clothes. Nowadays, sharing and adaptive transport services are in demand due to increased traffic and rush to public transport; contributing to saving fuel, fuel cost, and above all time. Transport sharing consists of car sharing, auto-sharing and also bike sharing. . Where in, two or more individuals targeting the same location opts to share transport system. The transport sharing model, especially taxi services also adds various adaptive facilities a one-way and two-way station-based sharing system. These services help individuals to reserve their onlineseats with bus and car-sharing. This paper reviews various existing shared and effective ways of transportation. The observations include different route matching and applied machine learning techniques used by the models which are placed in working using Google maps, APIs, sensors, and Smartphone applications to get and track thedestination.In addition, the manuscript also presents the assessment taken on the communal sphere to acquire various transport facilities adopted by people and the different challenges faced by them.

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Applied Machine Learning Techniques to Find Patterns and Trends in the Use of Bicycle Sharing Systems Influenced by Traffic Accidents and Violent Events in Guadalajara, Mexico

Metaheuristics in Machine Learning: Theory and Applications - Studies in Computational Intelligence ◽

10.1007/978-3-030-70542-8_19 ◽

2021 ◽

pp. 475-490

Author(s):

Adrian Barradas ◽

Andrea Gomez-Alfaro ◽

Rosa-María Cantón-Croda

Keyword(s):

Machine Learning ◽

Traffic Accidents ◽

Machine Learning Techniques ◽

Applied Machine Learning ◽

Learning Techniques ◽

Violent Events

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Bike Sharing Prediction using Deep Neural Networks

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.1.3.30 ◽

2017 ◽

Vol 1 (3) ◽

pp. 83 ◽

Cited By ~ 4

Author(s):

Chandrasegar Thirumalai ◽

Ravisankar Koppuravuri

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Neural Nets ◽

Machine Learning Techniques ◽

Business Decisions ◽

Learning Techniques ◽

Bike Sharing ◽

The Way

In this paper, we will use deep neural networks for predicting the bike sharing usage based on previous years usage data. We will use because deep neural nets for getting higher accuracy. Deep neural nets are quite different from other machine learning techniques; here we can add many numbers of hidden layers to improve the accuracy of our prediction and the model can be trained in the way we want such that we can achieve the results we want. Nowadays many AI experts will say that deep learning is the best AI technique available now and we can achieve some unbelievable results using this technique. Now we will use that technique to predict bike sharing usage of a rental company to make sure they can take good business decisions based on previous years data.

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Prediction of Bike Sharing Demand

Oriental journal of computer science and technology ◽

10.13005/ojcst/10.01.30 ◽

2017 ◽

Vol 10 (1) ◽

pp. 219-226 ◽

Cited By ~ 1

Author(s):

Purnima Sachdeva ◽

K N Sarvanan

Keyword(s):

New York ◽

Linear Combination ◽

Random Forests ◽

Traffic Congestion ◽

Urban Areas ◽

Machine Learning Techniques ◽

Health Consciousness ◽

Learning Techniques ◽

The World ◽

Bike Sharing

Bike sharing systems have been gaining prominence all over the world with more than 500 successful systems being deployed in major cities like New York, Washington, London. With an increasing awareness of the harms of fossil based mean of transportation, problems of traffic congestion in cities and increasing health consciousness in urban areas, citizens are adopting bike sharing systems with zest. Even developing countries like India are adopting the trend with a bike sharing system in the pipeline for Karnataka. This paper tackles the problem of predicting the number of bikes which will be rented at any given hour in a given city, henceforth referred to as the problem of ‘Bike Sharing Demand’. In this vein, this paper investigates the efficacy of standard machine learning techniques namely SVM, Regression, Random Forests, Boosting by implementing and analyzing their performance with respect to each other.This paper also presents two novel methods, Linear Combination and Discriminating Linear Combination, for the ‘Bike Sharing Demand’ problem which supersede the aforementioned techniques as good estimates in the real world.

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Meta-analysis of voice disorders databases and applied machine learning techniques

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2020404 ◽

2020 ◽

Vol 17 (6) ◽

pp. 7958-7979

Author(s):

Sidra Abid Syed ◽

◽

Munaf Rashid ◽

Samreen Hussain ◽

◽

...

Keyword(s):

Machine Learning ◽

Meta Analysis ◽

Voice Disorders ◽

Machine Learning Techniques ◽

Applied Machine Learning ◽

Learning Techniques

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Catalogue of new Herbig Ae/Be and classical Be stars

Astronomy and Astrophysics ◽

10.1051/0004-6361/202037731 ◽

2020 ◽

Vol 638 ◽

pp. A21 ◽

Cited By ~ 3

Author(s):

M. Vioque ◽

R. D. Oudmaijer ◽

M. Schreiner ◽

I. Mendigutía ◽

D. Baines ◽

...

Keyword(s):

Main Sequence ◽

Current Knowledge ◽

Machine Learning Techniques ◽

High Mass ◽

Formation Mechanisms ◽

Be Stars ◽

Homogeneous Sample ◽

Applied Machine Learning ◽

Learning Techniques ◽

Order Of Magnitude

Context. The intermediate-mass pre-main sequence Herbig Ae/Be stars are key to understanding the differences in formation mechanisms between low- and high-mass stars. The study of the general properties of these objects is hampered by the lack of a well-defined, homogeneous sample, and because few and mostly serendipitously discovered sources are known. Aims. Our goal is to identify new Herbig Ae/Be candidates to create a homogeneous and well defined catalogue of these objects. Methods. We have applied machine learning techniques to 4 150 983 sources with data from Gaia DR2, 2MASS, WISE, and IPHAS or VPHAS+. Several observables were chosen to identify new Herbig Ae/Be candidates based on our current knowledge of this class, which is characterised by infrared excesses, photometric variabilities, and Hα emission lines. Classical techniques are not efficient for identifying new Herbig Ae/Be stars mainly because of their similarity with classical Be stars, with which they share many characteristics. By focusing on disentangling these two types of objects, our algorithm has also identified new classical Be stars. Results. We have obtained a large catalogue of 8470 new pre-main sequence candidates and another catalogue of 693 new classical Be candidates with a completeness of 78.8 ± 1.4% and 85.5 ± 1.2%, respectively. Of the catalogue of pre-main sequence candidates, at least 1361 sources are potentially new Herbig Ae/Be candidates according to their position in the Hertzsprung-Russell diagram. In this study we present the methodology used, evaluate the quality of the catalogues, and perform an analysis of their flaws and biases. For this assessment, we make use of observables that have not been accounted for by the algorithm and hence are selection-independent, such as coordinates and parallax based distances. The catalogue of new Herbig Ae/Be stars that we present here increases the number of known objects of the class by an order of magnitude.

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Modeling Physico-Chemical ADMET Endpoints With Multitask Graph Convolutional Networks

10.26434/chemrxiv.9205853.v1 ◽

2019 ◽

Author(s):

Floriane Montanari ◽

Lara Kuhnke ◽

Antonius ter Laak ◽

Djork-Arné Clevert

Keyword(s):

Chemical Properties ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Convolutional Network ◽

Convolutional Networks ◽

Modeling Methods ◽

Applied Machine Learning ◽

Learning Techniques ◽

Physico Chemical ◽

New Compounds

Simple physico-chemical properties like logD, solubility or serum albumin binding have a direct impact on the likelihood of success of compounds in clinical trials. Here, we collected all the Bayer in house data related to these properties and applied machine learning techniques to predict them for new compounds. We report that, for the endpoints studied here, a multitask graph convolutional network appears a highly competitive choice. The new model shows increased predictive performance on all endpoints compared to previous modeling methods.<br>

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Modeling Physico-Chemical ADMET Endpoints With Multitask Graph Convolutional Networks

10.26434/chemrxiv.9205853 ◽

2019 ◽

Author(s):

Floriane Montanari ◽

Lara Kuhnke ◽

Antonius ter Laak ◽

Djork-Arné Clevert

Keyword(s):

Chemical Properties ◽

Predictive Performance ◽

Machine Learning Techniques ◽

Convolutional Network ◽

Convolutional Networks ◽

Modeling Methods ◽

Applied Machine Learning ◽

Learning Techniques ◽

Physico Chemical ◽

New Compounds

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A pilot study of a machine-learning tool to assist in the diagnosis of hand arthritis (Preprint)

10.2196/preprints.20354 ◽

2020 ◽

Author(s):

Mark Daly Reed ◽

Timothy James Le Souef ◽

Elliot Rampono

Keyword(s):

Machine Learning ◽

Inflammatory Arthritis ◽

Machine Learning Techniques ◽

Patient Questionnaire ◽

Machine Learning Methods ◽

Applied Machine Learning ◽

Learning Techniques ◽

Examination Result ◽

Delays In Diagnosis ◽

Rheumatology Practice

BACKGROUND Arthritis is a common condition, which frequently involves the hands. Patients with inflammatory arthritis have been shown to experience significant delays in diagnosis. OBJECTIVE We sought to develop and test a screening tool combining an image of a patient’s hands, a short series of questions, and a single examination technique, to determine the most likely diagnosis in a patient presenting with hand arthritis. Machine learning techniques were used to develop separate algorithms for each component, which were combined to produce a diagnosis. METHODS 280 consecutive new patients presenting to a Rheumatology practice with hand arthritis were enrolled. Each patient completed a 9-part questionnaire, had photographs taken of each hand, and had a single examination result recorded. The Rheumatologist diagnosis was recorded following a 45-minute consultation. The photograph algorithm was developed from a library of 1000 images, and machine learning techniques were applied to the questionnaire results, training several models against the diagnosis from the Rheumatologist. RESULTS The combined algorithms in this study were able to predict inﬂammatory arthritis with an accuracy, precision, recall and specificity of 96·8%, 97·2%, 98·6% and 90·5% respectively. Similar results were found when inflammatory arthritis was subclassified into rheumatoid arthritis and psoriatic arthritis. The corresponding figures for osteoarthritis were 79·6%, 85·9%, 61·9% and 92·6%. CONCLUSIONS This study demonstrates a novel application of a combined image-processing and a patient questionnaire with applied machine-learning methods, to facilitate the diagnosis of patients presenting with hand arthritis. Preliminary results are encouraging for the application of such techniques in clinical practice. CLINICALTRIAL Not applicable.

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