scholarly journals Modelo de Predição de Conforto de Usuários do Transporte Coletivo

2021 ◽  
Author(s):  
Vanessa Barbosa Rolim ◽  
Fabiano Baldo
Keyword(s):  
Public Transportation ◽  
Promising Result ◽  
Machine Learning Techniques ◽  
Medium Size ◽  
Telemetry Data ◽  
Bus Drivers ◽  
The Public ◽  
Learning Techniques ◽  
Comfort Perception ◽  
Data Source

The small and medium-size cities are facing problems related to mobilitythat could be avoided by adopting the public transportationsystem, as buses and trains. However, in many Brazilian cities theuse of public transportation is neglected because it is considereduncomfortable, expensive and insecure. To attract passengers forsuch kind of transportation there are several possible approaches,the promotion of comfort perception is one of those. Several studieshave already approached this problem, however, few of themaddressed the perception of comfort felt by the passengers usingtelemetry data collected from the vehicle. Among the works thatuse such data, none of them applied data mining techniques toabstract a general model of comfort perception. Therefore, thiswork aims to apply mining techniques over telemetry data collectedfrom vehicles to build a comprehensible model to classifythe level of comfort of public transportation passengers. To achievethis objective machine learning techniques were used, centered ondecision trees. Due to the complexity of abstracting the model therewere constructed three models, one for each acceleration axis thatwere merged using a meta-classifier responsible to point out thepassenger general comfort. The results have reached an accuracyof 85,2%, which can be considered a promising result regardingthe difficulties of separating the data source in sets that can betteridentify the bus drivers behaviour.

scholarly journals ANALISIS BIAYA OPERASIONAL KENDARAAN (BOK) DAN WTP (WILLINGNESS TO PAY) PADA BUS AKAP JURUSAN BANJARMASIN - SAMARINDA

2018 ◽  
Vol 1 (2) ◽  
pp. 115
Author(s):  
Bagus Nugroho Putra ◽  
Utami Sylvia Lestari
Keyword(s):  
Willingness To Pay ◽  
Public Transportation ◽  
Operating Costs ◽  
Bus Drivers ◽  
The Public ◽  
Passenger Transportation ◽  
The Pacific ◽  
Wide Range ◽  
Transportation Facilities

Kota Banjarmasin adalah ibu kota provinsi dari Kalimantan Selatan dengan berbagai macam aktivitas. Salah satu angkutan umum Kota Banjarmasin adalah bus AKAP (Antar Kota Antar Provinsi) PO. Pulau Indah Jaya yang melayani transportasi penumpang dengan tujuan kota dan provinsi. Sarana transportasi memiliki tarif biaya yang ditentukan berdasarkan Biaya Operasional Kendaraan (BOK). Tujuan penelitian ini untuk mengetahui besar BOK bus AKAP dengan metode Pacific Consultant International (PCI) dan untuk mengetahui besar tarif yang diinginkan penumpang beserta tanggapan terhadap tarif yang berlaku saat ini Willingness To Pay (WTP).Penelitian ini dilakukan melalui survei wawancara untuk BOK kepada pihak PO. dan sopir bus dengan jumlah data 25 unit bus dan untuk WTP survei wawancara dengan sampel 200 penumpang bus di terminal KM 6 tempat pembelian tiket bus.Besar BOK yang dikeluarkan oleh PO. Pulau Indah Jaya Rp Rp 8.137.912.279 /tahun (bus Non AC) dengan tarif Rp 168.194 /penumpang dan Rp 8.712.239.580 /tahun (bus AC+Toilet) dengan tarif Rp 229.173 /penumpang. Besar nilai WTP bus Non AC Rp 185.000 /penumpang dan AC+Toilet Rp 245.000 /penumpang. Tanggapan terhadap tarif bus AKAP Non AC Rp 175.000 /penumpang dan AC+Toilet Rp 235.000 /penumpang adalah harga tarif bus  sesuai dengan kemampuan penumpang membayar dan mau membayar lebih dengan syarat adanya penambahan pelayanan dan fasilitas.Kata Kunci: Bus AKAP, Biaya Operasional Kendaraan (BOK), Willingness To Pay (WTP)banjarmasin city is the capital of the province of South Borneo with a wide range of activities. One of the public transportation of Banjarmasin city is the bus AKAP (Inter-City Inter Province) PO. Pulau Indah Jaya which serves passenger transportation with a city and provincial destinations. Transportation facilities have a fee that is determined based on Vehicle Operating Costs (VOC). The purpose of this study is to find out the size of the bus AKAP VOC with the Pacific Consultant International (PCI) method and to find out the number of tariffs desired by passengers along with responses to the current rates of Willingness To Pay (WTP).This research was conducted through interview surveys for VOC to PO. parties and bus drivers with data on 25 bus units and for WTP interview surveys with a sample of 200 bus passengers at the terminal KM 6 places to buy bus tickets.VOC amount issued by PO. Pulau Indah Jaya Rp. 8.137.912.279 /year (Non AC buses) with a tariff of Rp 168.194 /passenger and Rp. 8.712.239.580 /year (AC + Toilet bus) at a rate of Rp. 229.173 /passenger. The value of the Non AC WTP bus is Rp. 185.000 /passenger and the AC + Toilet Rp. 245.000 /passenger. The response to the AKAP Non AC bus fare of Rp. 175.000 /passenger and AC + Toilet Rp. 235.000 /passenger is the price of the bus fare according to the ability of the passengers to pay and pay more on the condition of additional services and facilities.Keywords: Bus AKAP, Vehicle Operating Costs (VOC), willingness to pay (WTP) 

scholarly journals Optimization of operational costs of Call centers employing classification techniques

2020 ◽  
Vol 9 (11) ◽  
pp. e86691110491
Author(s):  
Amanda Ferreira de Moura ◽  
Cíntia Maria de Araújo Pinho ◽  
Domingos Márcio Rodrigues Napolitano ◽  
Fellipe Silva Martins ◽  
João Carlos Franco de Barros Fornari Junior
Keyword(s):  
Call Center ◽  
Supervised Machine Learning ◽  
Bank Credit ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Classification Techniques ◽  
Letter Of Credit ◽  
The Public ◽  
Operational Costs ◽  
Learning Techniques

The provision of credit to customers of banking chains through call center services has always been one of the resources that generate significant income for financial institutions, however, the service offers a cost, which is often above desirable to guarantee profitable contracting to Bank. Based on this, this work aims to evaluate the optimization of operational costs of call center, using classification techniques, through experimentation of supervised machine learning techniques to perform the classification task, in order to generate a predictive model, which offers a better performance in the operation of offering bank credit, to carry out an effective and productive action, conceiving greater savings for the company in identifying the public with greater adherence. For this, a database comprising 11,162 call records made from a bank offering its customers a letter of credit was employed. The results showed value correlations between variables, such as duration of the call, marital status, education level and even recurrence in adhering to subscribers' credit agreements. Through the application of the PCA to reduce dimensionality and classification models, such as AdaBoost, Gradient Boosting, SVM RBF, Naive Bayes, Random Forest, it was possible to perceive the consumer profile with good acquiescence for the investment proposal and a group of people with a high probability of not adhering to the letter of credit, so it was possible to outline an action directed to the public predisposed to the offer, minimizing expenses reaching greater profitability.

Early Intervention Systems: Predicting Adverse Interactions Between Police and the Public

2017 ◽  
Vol 29 (2) ◽  
pp. 190-209 ◽  
Author(s):  
Jennifer Helsby ◽  
Samuel Carton ◽  
Kenneth Joseph ◽  
Ayesha Mahmud ◽  
Youngsoo Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Intervention ◽  
Police Department ◽  
Data Sources ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Police Legitimacy ◽  
The Public ◽  
Learning Techniques ◽  
Improve Accuracy

Adverse interactions between police and the public hurt police legitimacy, cause harm to both officers and the public, and result in costly litigation. Early intervention systems (EISs) that flag officers considered most likely to be involved in one of these adverse events are an important tool for police supervision and for targeting interventions such as counseling or training. However, the EISs that exist are not data-driven and based on supervisor intuition. We have developed a data-driven EIS that uses a diverse set of data sources from the Charlotte-Mecklenburg Police Department and machine learning techniques to more accurately predict the officers who will have an adverse event. Our approach is able to significantly improve accuracy compared with their existing EIS: Preliminary results indicate a 20% reduction in false positives and a 75% increase in true positives.

scholarly journals Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques

10.2196/23957 ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. e23957
Author(s):  
Chengda Zheng ◽  
Jia Xue ◽  
Yumin Sun ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Public Discourse ◽  
Latent Dirichlet Allocation ◽  
Prime Minister ◽  
Machine Learning Techniques ◽  
Future Health ◽  
The Public ◽  
Learning Techniques ◽  
The Government

Background During the COVID-19 pandemic in Canada, Prime Minister Justin Trudeau provided updates on the novel coronavirus and the government’s responses to the pandemic in his daily briefings from March 13 to May 22, 2020, delivered on the official Canadian Broadcasting Corporation (CBC) YouTube channel. Objective The aim of this study was to examine comments on Canadian Prime Minister Trudeau’s COVID-19 daily briefings by YouTube users and track these comments to extract the changing dynamics of the opinions and concerns of the public over time. Methods We used machine learning techniques to longitudinally analyze a total of 46,732 English YouTube comments that were retrieved from 57 videos of Prime Minister Trudeau’s COVID-19 daily briefings from March 13 to May 22, 2020. A natural language processing model, latent Dirichlet allocation, was used to choose salient topics among the sampled comments for each of the 57 videos. Thematic analysis was used to classify and summarize these salient topics into different prominent themes. Results We found 11 prominent themes, including strict border measures, public responses to Prime Minister Trudeau’s policies, essential work and frontline workers, individuals’ financial challenges, rental and mortgage subsidies, quarantine, government financial aid for enterprises and individuals, personal protective equipment, Canada and China’s relationship, vaccines, and reopening. Conclusions This study is the first to longitudinally investigate public discourse and concerns related to Prime Minister Trudeau’s daily COVID-19 briefings in Canada. This study contributes to establishing a real-time feedback loop between the public and public health officials on social media. Hearing and reacting to real concerns from the public can enhance trust between the government and the public to prepare for future health emergencies.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

scholarly journals Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Jorge D. Mello-Román ◽  
Julio C. Mello-Román ◽  
Santiago Gómez-Guerrero ◽  
Miguel García-Torres
Keyword(s):  
Public Health ◽  
Medical Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
The Public ◽  
Learning Techniques ◽  
Previous Diagnosis ◽  
Vector Machines ◽  
High Incidence

Early diagnosis of dengue continues to be a concern for public health in countries with a high incidence of this disease. In this work, we compared two machine learning techniques: artificial neural networks (ANN) and support vector machines (SVM) as assistance tools for medical diagnosis. The performance of classification models was evaluated in a real dataset of patients with a previous diagnosis of dengue extracted from the public health system of Paraguay during the period 2012–2016. The ANN multilayer perceptron achieved better results with an average of 96% accuracy, 96% sensitivity, and 97% specificity, with low variation in thirty different partitions of the dataset. In comparison, SVM polynomial obtained results above 90% for accuracy, sensitivity, and specificity.

scholarly journals Building a New Sentiment Analysis Dataset for Uzbek Language and Creating Baseline Models

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 37
Author(s):  
Elmurod Kuriyozov ◽  
Sanatbek Matlatipov
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Medium Size ◽  
Learning Models ◽  
Important Goal ◽  
Processing Technologies ◽  
Learning Techniques ◽  
Polarity Classification ◽  
Analysis Models

Making natural language processing technologies available for low-resource languages is an important goal to improve the access to technology in their communities of speakers. In this paper, we provide the first annotated corpora for polarity classification for Uzbek language. Our methodology considers collecting a medium-size manually annotated dataset and a larger-size dataset automatically translated from existing resources. Then, we use these datasets to train sentiment analysis models on the Uzbek language, using both traditional machine learning techniques and recent deep learning models.

Review of fMRI Data Analysis

2014 ◽  
Vol 5 (2) ◽  
pp. 1-26
Author(s):  
Shantipriya Parida ◽  
Satchidananda Dehuri
Keyword(s):  
Machine Learning ◽  
Promising Result ◽  
Mental States ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Fmri Data ◽  
Brain State ◽  
Hybrid Techniques ◽  
Learning Techniques ◽  
Training Samples

Classification of brain states obtained through functional magnetic resonance imaging (fMRI) poses a serious challenges for neuroimaging community to uncover discriminating patterns of brain state activity that define independent thought processes. This challenge came into existence because of the large number of voxels in a typical fMRI scan, the classifier is presented with a massive feature set coupled with a relatively small training samples. One of the most popular research topics in last few years is the application of machine learning algorithms for mental states classification, decoding brain activation, and finding the variable of interest from fMRI data. In classification scenario, different algorithms have different biases, in the sequel performances differs across datasets, and for a particular dataset the accuracy varies from classifier to classifier. To overcome the limitations of individual techniques, hybridization or fusion of these machine learning techniques emerged in recent years which have shown promising result and open up new direction of research. This paper reviews the machine learning techniques ranging from individual classifiers, ensemble, and hybrid techniques used in cognitive classification with a well balance treatment of their applications, performance, and limitations. It also discusses many open research challenges for further research.

scholarly journals Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

2021 ◽  
Vol 10 (6) ◽  
pp. 373
Author(s):  
Amir Karami ◽  
Rachana Redd Kadari ◽  
Lekha Panati ◽  
Siva Prasad Nooli ◽  
Harshini Bheemreddy ◽  
...  
Keyword(s):  
Machine Learning Techniques ◽  
Small Scale ◽  
P Value ◽  
Large Samples ◽  
Twitter Data ◽  
Learning Techniques ◽  
Significant Difference ◽  
Data Source ◽  
Twitter Users ◽  
Scale Data

Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and latitude. These geotagged Twitter data can help to study human activities and movements for different applications. Compared to the mostly small-scale data samples in different domains, such as social science, collecting geotagged data offers large samples. There is a fundamental question whether geotagged users can represent non-geotagged users. While some studies have investigated the question from different perspectives, they did not investigate profile information and the contents of tweets of geotagged and non-geotagged users. This empirical study addresses this limitation by applying text mining, statistical analysis, and machine learning techniques on Twitter data comprising more than 88,000 users and over 170 million tweets. Our findings show that there is a significant difference (p-value < 0.001) between geotagged and non-geotagged users based on 73% of the features obtained from the users’ profiles and tweets. The features can also help to distinguish between geotagged and non-geotagged users with around 80% accuracy. This research illustrates that geotagged users do not represent the Twitter population.

scholarly journals Automating Historical Source Transcription

2021 ◽  
Vol 10 ◽  
pp. 59-63
Author(s):  
Gunnar Thorvaldsen
Keyword(s):  
Machine Learning ◽  
20Th Century ◽  
Machine Learning Techniques ◽  
Population Register ◽  
Historical Source ◽  
Population Groups ◽  
The Public ◽  
Learning Techniques ◽  
Central Population ◽  
Text Version

Transcribing the 1950 Norwegian census with 3.3 million person records and linking it to the Central Population Register (CPR) provides longitudinal information about significant population groups during the understudied period of the mid-20th century. Since this source is closed to the public, we receive no help from genealogists and rather use machine learning techniques to semi-automate the transcription. First the scanned manuscripts are split into individual cells and multiple names are divided. After the birthdates were transcribed manually in India, a lookup routine searches for families with matching sets of birthdates in the 1960 census and the CPR. After manual checks with GUI routines, the names are copied to the text version of the 1950 census, also storing the links to the CPR. Other fields like occupations or gender contain numeric or letter codes and are transcribed wholesale with routines interpreting the layout of the graphical images. Work employing these methods has also started on the 1930 census, which is the last of the Norwegian censuses to be transcribed.

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