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- 저자 : Savita Yadav (검색결과 4 건)
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Predictive Analysis for Airbnb Listing Rating using scalable Big Data platform
Savita Yadav,Samyuktha Muralidharan,Jongwook Woo 한국경영정보학회 2021 한국경영정보학회 학술대회논문집 Vol.2021 No.11
This paper aims to build predictive models for Airbnb Rating using the Big Data systems, which is distributed parallel computing systems. We use Machine Learning algorithms to build models to predict a rating of the Airbnb listing. The Airbnb ratings can help hosts improve the listing and the hospitality to gain more potential customers. On the other hand, the guests can make a decision based on the ratings that previous guests provided. It is essential to understand customer experience and its role in forming customer rating behavior. The overall ratings provided by customers are reflections of their experiences with a product or service. We use Two-Class Classification models to predict if the listing has a high or low rating based on the features of the listing. We compare the results and the performance of rating prediction models. The comparison is illustrated in terms of the accuracy metrics and computing time.
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Yadav, Savita,Chakraborty, Pinaki,Kaul, Arshia,Pooja, Pooja,Gupta, Bhavya,Garg, Anchal The Korean Pediatric Society 2020 Clinical and Experimental Pediatrics (CEP) Vol.63 No.6
Background: Children today get access to smartphones at an early age. However, their ability to use mobile apps has not yet been studied in detail. Purpose: This study aimed to assess the ability of children aged 2-8 years to perform touchscreen gestures and follow prompting techniques, i.e., ways apps provide instructions on how to use them. Methods: We developed one mobile app to test the ability of children to perform various touchscreen gestures and another mobile app to test their ability to follow various prompting techniques. We used these apps in this study of 90 children in a kindergarten and a primary school in New Delhi in July 2019. We noted the touchscreen gestures that the children could perform and the most sophisticated prompting technique that they could follow. Results: Two- and 3-year-old children could not follow any prompting technique and only a minority (27%) could tap the touchscreen at an intended place. Four- to 6-year-old children could perform simple gestures like a tap and slide (57%) and follow instructions provided through animation (63%). Seven- and 8-year-old children could perform more sophisticated gestures like dragging and dropping (30%) and follow instructions provided in audio and video formats (34%). We observed a significant difference between the number of touchscreen gestures that the children could perform and the number of prompting techniques that they could follow (F=544.0407, P<0.05). No significant difference was observed in the performance of female versus male children (P>0.05). Conclusion: Children gradually learn to use mobile apps beginning at 2 years of age. They become comfortable performing single-finger gestures and following nontextual prompting techniques by 8 years of age. We recommend that these results be considered in the development of mobile apps for children.
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An Extended Work Architecture for Online Threat Prediction in Tweeter Dataset
Sheoran, Savita Kumari,Yadav, Partibha International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.1
Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.
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Scalable Prediction Models for Airbnb Listing in Spark Big Data Cluster using GPU-accelerated RAPIDS
Samyuktha Muralidharan,Savita Yadav,허정우,이상훈,우종욱 한국정보통신학회 2022 Journal of information and communication convergen Vol.20 No.2
We aim to build predictive models for Airbnb’s prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.
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