MapReduce-iterative support vector machine classifier: novel fraud detection systems in healthcare insurance industry

Arockiam, Jenita Mary; Pushpanathan, Angelin Claret Seraphim

doi:10.11591/ijece.v13i1.pp756-769

Cited by 8 publications

(7 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As for d values, they vary from 0 to 1. Based on the result obtained by testing various parameter combination, we are confident that ARIMA model (5,1,9) is the best for forecasting daily average temperature for in Beni Mellal taking the dataset as reference, as shown in 4. As demonstrated in Table 4, the MAE of GRU is the smallest when compared with the rest of the deep learning algorithms and the ARIMA model.…”

Section: The Proposed Arima Modelmentioning

confidence: 86%

“…Traditional theory-driven numerical weather prediction [4] systems face various hurdles [5], such as an insufficient understanding of physical principles and difficulties extracting valuable knowledge from a flood of recorded observations. The successful integration of deep learning techniques, driven by data, has been observed across several industries including natural language processing [6], autonomous vehicles [7] [8], and fraud detection [9], [10]. The proficiency of deep learning in discovering intricate patterns in data and making accurate predictions results in its widespread adoption in these sectors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of time series temperature prediction with auto-regressive integrated moving average and recurrent neural network

Jdi,

Falih

2024

IJECE

View full text Add to dashboard Cite

The region of Beni Mellal, Morocco is heavily dependent on the agricultural sector as its primary source of income. Accurate temperature prediction in agriculture has many benefits including improved crop planning, reduced crop damage, optimized irrigation systems and more sustainable agricultural practices. By having a better understanding of the expected temperature patterns, farmers can make informed decisions on planting schedules, protect crops from extreme temperature events, and use resources more efficiently. The lack of data-driven studies in agriculture impedes the digitalization of farming and the advancement of accurate long-term temperature prediction models. This underscores the significance of research to identify the optimal machine learning models for that purpose. A 22-year time series dataset (2000-2022) is used in the study. The machine-learning model auto-regressive integrated moving average (ARIMA) and deep learning models simple recurrent neural network (SimpleRNN), gated recurrent unit (GRU), and long short-term memory (LSTM) were applied to the time series. The results are evaluated based on the mean absolute error (MAE). The findings indicate that the deep learning models outperformed the machine-learning model, with the GRU model achieving the lowest MAE.

show abstract

Section: The Proposed Arima Modelmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Comparison of time series temperature prediction with auto-regressive integrated moving average and recurrent neural network

Jdi,

Falih

2024

IJECE

View full text Add to dashboard Cite

show abstract

“…Researchers who are interested in exploring other types of illegal behaviors could turn their attention toward insurance fraud. This area of research has been the focus of studies in areas such as engineering (Arockiam & Pushpanathan, 2023; Sathya & Balakumar, 2022) and finance (Aslam et al, 2022; Yankol‐Schalck, 2022). Meanwhile, it could also be explored in the context of consumers who decide to take advantage of their insurance benefits in a fraudulent manner.…”

Section: Future Research Agendamentioning

confidence: 99%

Theory of planned behavior in consumer behavior research: A systematic literature review

Rozenkowska

2023

Int J Consumer Studies

View full text Add to dashboard Cite

This study aimed to systematically review and categorize studies on consumer behavior based on theory of planned behavior (TPB), its core constructs, or extensions, and to provide directions for future research agenda. Scopus and the Web of Science were consulted for studies based on TPB, its parts, or extensions. The inclusion criteria were studies published in peer‐reviewed journals, in English, and within the past decade (i.e., between 2012 and 2021). Graphical methods were used to visually display research findings. For the purpose of literature clustering, MAXQDA 2020 software was employed. In total, 118 scientific, peer‐reviewed sources were included in the review. Two categories, five clusters, and seven subclusters emerged from the literature set. The results revealed a significant research tendency toward exploring consumer green behavior and consumer purchase intention of food products. The least‐explored research themes were focused on consumer intention toward and purchase behavior of clothing, green vehicles, and green personal care products. The review confirmed the growing prevalence of TPB in consumer behavior research aimed at exploring factors preceding behavior.

show abstract

“…Certain times those complex queries generate some general sub-expressions in one or multi queries processing as a batch. It is also critical to decrease the amount of RDF queries and execution time for huge number of relatable data in distributed environment [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Structured query language query join optimization by using rademacher averages and mapreduce algorithms

Chandrashekariah,

H. A.

2024

Bulletin EEI

View full text Add to dashboard Cite

Query optimization involves identifying and implementing the most effective and efficient methods and strategies to enhance the performance of queries. This is achieved by intelligently utilizing system resources and considering various performance metrics. Table joining optimization involves optimizing the process of combining two or more tables within a database. Structured query language (SQL) optimization is the progress of utilizing SQL queries in the possible way to achieve fast and accurate database results. SQL optimization is critical to decreasing the no of queries in research description framework (RDF) and the time for processing a huge number of relatable data. In this paper, four new algorithms are proposed such as hash-join, sort-merge, rademacher averages and mapreduce for the progress of SQL query join optimization. The proposed model is evaluated and tested using waterloo sparql diversity test suite (WatDiv) and lehigh university benchmark (LUBM) benchmark datasets in terms of time execution. The results represented that the proposed method achieved an enhanced performance of less execution time for various queries such as Q3 of 5362, Q8 of 5921, Q9 of 5854 and Q10 of 5691 milliseconds. The proposed gives better performance than other existing methods like hybrid database-map reduction system (AQUA+) and join query processing (JQPro).

show abstract

MapReduce-iterative support vector machine classifier: novel fraud detection systems in healthcare insurance industry

Cited by 8 publications

References 25 publications

Comparison of time series temperature prediction with auto-regressive integrated moving average and recurrent neural network

Comparison of time series temperature prediction with auto-regressive integrated moving average and recurrent neural network

Theory of planned behavior in consumer behavior research: A systematic literature review

Structured query language query join optimization by using rademacher averages and mapreduce algorithms

Contact Info

Product

Resources

About