A novel Cp-Tree-based co-located classifier for big data analysis

Venkatesan, M.; Arunkumar, T.; Prabhavathy, P.

doi:10.1504/ijcnds.2015.070973

Cited by 5 publications

(3 citation statements)

References 56 publications

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“…For example, the use of virtual assistants increases the feeling of loyalty to the University [2] and helps the acquisition of new students [12]. It also helps to optimize the time of the teachers, allowing a distribution of the time on activities of greater academic value [15,35].…”

Section: Chatbots In Higher Educationmentioning

confidence: 99%

Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

Gonzalez

Neyem

Contreras‐McKay

et al. 2022

Comp Applic In Engineering

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Students of Software Engineering Capstone Courses face situations and challenges that grant them valuable experiences. However, as this knowledge is acquired through real-life exposure, it is difficult to transmit it across different generations. To deal with this problem, it has been proposed that students record their learnings through a lesson learned model. But the question of how future students can best benefit from these records remains unanswered. This study addresses this problem through the use of Artificial Intelligence (AI) Virtual Assistant combined with a recommender system. Artificial Intelligence Virtual Assistants, as conversational chatbots, are a manifestation of AI via the simulation of conversation with human users. The technology has the potential to provide personalized service to a range of stakeholders. Thus, we seek to move this trend forward by now pushing these capabilities into the field of higher education. Consequently, we aim to assist software engineering students by leveraging collective knowledge to enhance learning experiences.

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Section: Chatbots In Higher Educationmentioning

confidence: 99%

Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

Gonzalez

Neyem

Contreras‐McKay

et al. 2022

Comp Applic In Engineering

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show abstract

“…Section -5 suggests on the outcome of work based on experimental analysis and dataset preparation and Section-6 summarizes on outcome of work. Though detailed survey discusses [18] about various swarm models for prediction the need for an effective prediction model should be suggested.…”

Section: Fig 1 Functional Model Of Nelco For Customer Churn Analysimentioning

confidence: 99%

An Effective Architectural Model for Early Churn Prediction NELCO

Manivannan¹,

Saminathan²,

Saravanan³

2019

IJEAT

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Customer is an asset of any business organization, whose probable chances of churn is loss. Several challenges are to be considered towards controlling customer churn. Machine learning approach is needed to predict an early churn. Even though various soft computational approaches had been proposed, an optimized computational approach which identifies early churn prediction is necessary. The proposed approach NELCO predicts early customer churn using Negative Correlation Learning (NCL) which uses k-means neighbourhood discriminant similarity indices over network of ensemble values. NELCO proves to have an optimal accuracy towards early prediction of churn, as well as suggests that customer retention rate is higher over PSO, ACO approaches

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“…Though government organizations and emergency responders work together through their respective national disaster response frameworks, the sentiment of the affected people during and after the disaster determines the success of the disaster response and recovery process (Das & Dutta, 2020;Das, 2020). Venkatesan, Arunkumar, and Prabhavathy (2015) proposed a novel co-located classifier utilising the CP-Tree algorithm to handle complex spatial landslide big data. Pal et al (2015) proposed a method that employs an extra layer of compression while storing location data in the form of latitude-longitude (lat-long) pairs to the HBase database.…”

Section: Introductionmentioning

confidence: 99%

Promoting Data Science in Disaster Risk Reduction: Glimpses from the Global South

Das

Dutta

2023

J Data Sci. Info. Citation Studies

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There are coordinated attempts to use Data Science in disaster risk reduction (DRR) across the world. Different special interest groups are formed within the data science community to formulate different action plans and strategies to use data science in the DRR. Some examples of community-driven initiatives are the CODATA Task Group FAIR Data for Disaster Risk Research (FAIR-DRR), CODATA Task Group Linked Open Data for Global Disaster Risk Research (LODGD), and ISC Integrated Research on Disaster Risk (IRDR). This chapter highlights the data science approaches, models, frameworks, and stakeholders mapping while dealing with the data collection, analysis, and reporting for strengthening data-driven decision-making at different levels. The chapter also explores the FAIR Data Principles that aim at achieving the findable, accessible, interoperable, and reusable data resources at the institutional and national levels. FAIR Data ensures various data users increasingly engage with data related to disasters. We also discuss some case studies and success stories from the Global South, which may inspire other countries in data-driven decision-making while dealing with natural disasters. The much-discussed "Data, Information, Knowledge and Action" (DIKA) model is highlighted in this chapter. For example, the recently launched "Data-Knowledge-Action for Urban Systems" podcast series of CODATA and CEPT Research and Development Foundation (CRDF) in Ahmedabad, talks about how the DIKA model helps in creating intelligent systems for DRR using scientific and quantitative data. On a podcast in related Series, the DRR practitioners and data science researchers discussed the use of big data to predict natural disasters. This chapter highlights notable examples of how big data helped predict natural disasters across the world, particularly in the Global South.

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A novel Cp-Tree-based co-located classifier for big data analysis

Cited by 5 publications

References 56 publications

Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

Improving learning experiences in software engineering capstone courses using artificial intelligence virtual assistants

An Effective Architectural Model for Early Churn Prediction NELCO

Promoting Data Science in Disaster Risk Reduction: Glimpses from the Global South

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