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Sonia Khetarpaul

Bio: Sonia Khetarpaul is an academic researcher from Shiv Nadar University. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 4, co-authored 15 publications receiving 72 citations. Previous affiliations of Sonia Khetarpaul include Indian Institute of Technology Delhi & Indian Institutes of Technology.

Papers
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Journal ArticleDOI
TL;DR: This work proposes a solution to determine optimal meeting location for two moving users in the Euclidean space and generalizes the problem by considering variable number of moving users and evaluating optimal meeting point on the road network.
Abstract: Scheduling a meeting is a difficult task for people who have overbooked calendars and often have many constraints. This activity becomes further complex when the meeting is to be scheduled between parties who are situated in geographically distant locations of a city and have varying traveling patterns. To achieve this, we first propose a solution to determine optimal meeting location for two moving users in the Euclidean space. Then, we generalize the problem by considering variable number of moving users and evaluate optimal meeting point on the road network. We extend the work of Yan et al. (Proc VLDB Endow 4(11):1–11, 2011) in this domain by incorporating some real life constraints like variable number of users, varying travel patterns, flexible meeting point and considering road network distance. Experiments are performed on a real-world dataset and show that our method is effective in stated conditions.

1 citations

Book ChapterDOI
29 Jan 2021
TL;DR: In this article, the authors study people's opinion on climate change and analyze the data to identify the common topics which garner discussion and derive the possible explanation in terms of different factors.
Abstract: Climate change is a topic that is frequently debated on social media A vast majority in the debate cite scientific evidence to recognize the existence of a man-made climate change and its impacts on environment as well as society The opinion of the masses is critical to dealing with various issues arising due to climate change, such as global warming In this work, we study people’s opinion on climate change and analyze the data to identify the common topics which garner discussion Our aim is to analyze the dataset, explore the popular belief of a region and then derive the possible explanation in terms of different factors This analysis could help us in determining the extent to which different factors affect people’s opinion By building sentiment analysis models, performing topic modelling and using other appropriate technologies, we can visualise the sentiment pattern to understand the factors affecting them
Book ChapterDOI
29 Jan 2021
TL;DR: In this paper, a real-time detection of the degree of offensiveness and alerting the user typing the message is presented. But the proposed solution is integrated into an app, that displays the relevant statistics as well as visualization of the user's typing pattern.
Abstract: Cyberbullying is one of the leading causes of mental health issues in the younger population, often leading to depression, stress, and suicidal tendencies. Often it is observed that timely intervention can bring awareness towards unintentional cyberbullying. This paper presents an approach to the prevention of cyberbullying via social networking. The objective of this work is real-time detection of the degree of offensiveness and alerting the user typing the message. We also propose corrective measures, by displaying an alternative word for any offensive word that is typed in the suggestion box in real-time. This enables the user to prevent unintentional cyberbullying. The proposed solution is integrated into an app, that displays the relevant statistics as well as visualization of the user typing pattern. The model to detect the offensiveness percentage can achieve 97.77% accuracy and is the backbone of the entire approach.

Cited by
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BookDOI
01 Jan 2013
TL;DR: This paper focuses on the conceptual benefits and risks such an integration of sensor data into social media in the case of a patient room and introduces a way to deal with these problems.
Abstract: In a hospital, information exchange is essential to save lives and to prevent life-endangering mistakes. Information exchange is supported by a hospital information system (HIS). From a theoretical perspective, the deployment of an HIS is promising because it reduces errors and duplication of information. In practice, however, there are some major problems concerning the usage of such a system. One way to deal with these problems is introduced in this paper: the integration of sensor data into social media. The paper concentrates on the conceptual benefits and risks such an integration may generate. It focuses on the case of a patient room.

47 citations

Journal ArticleDOI
TL;DR: A thorough description of a method that can be used to generate a number of different variables related to the constructs of mobility and participation from GPS data, with the help of ST-DBSCAN, a spatiotemporal data mining algorithm is provided.

45 citations

01 Jan 2003
TL;DR: In this article, a method of learning a Bayesian model of a traveler moving through an urban environment is presented, which simultaneously learns a unified model of the traveler's current mode of transportation as well as his most likely route, in an unsupervised manner.
Abstract: We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.

30 citations

Journal ArticleDOI
TL;DR: The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy and halve the time cost of predicted arrival time calculation.
Abstract: The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) – F1, F2 and F3 – are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation. First published online 02 May 2017

25 citations

Proceedings ArticleDOI
26 May 2016
TL;DR: A new Physical-Social-aware Interesting Place Discovery (PSIPD) scheme which jointly exploits the location's physical dependency and the visitor's social dependency to solve the interesting place discovery problem using an unsupervised approach.
Abstract: This paper presents an unsupervised approach toaccurately discover interesting places in a city from location-basedsocial sensing applications, a new sensing applicationparadigm that collects observations of physical world fromLocation-based Social Networks (LBSN). While there are alarge amount of prior works on personalized Point of Interests(POI) recommendation systems, they used supervised learningapproaches that did not work for users who have little orno historic (training) data. In this paper, we focused onan interesting place discovery problem where the goal isto accurately discover the interesting places in a city thataverage people may have strong interests to visit (e.g., parks, museums, historic sites, etc.) using unsupervised approaches. Inparticular, we develop a new Physical-Social-aware InterestingPlace Discovery (PSIPD) scheme which jointly exploits thelocation's physical dependency and the visitor's social dependencyto solve the interesting place discovery problem using anunsupervised approach. We compare our solution with state-of-the-art baselines using two real world data traces from LBSN. The results showed that our approach achieved significantperformance improvements compared to all baselines in termsof both estimation accuracy and ranking performance.

25 citations