Big Data Preprocessing for Modern World: Opportunities and Challenges

Prakash, Andrea; Navya, Narem; Natarajan, Jayapandian

doi:10.1007/978-3-030-03146-6_37

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…Therefore, a preprocessing process is required to be performed to clean the collected data from noises and detected anomalies. In addition, to address the involved big data challenges, the feature selection techniques are used for the aim of dimensions reduction to store medical data in the cloud storage to simplify the classification phase for disease diagnosis and prediction process. Moreover, since the outcomes of data mining process and derived analytics are extremely related to the collected data, thus, besides the IoTD, the vital information including patients' habitual data and also medical history records are collected and applied to attain more accurate predictions and precise disease diagnosis.…”

Section: Resultsmentioning

confidence: 99%

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

Asghari

Rahmani

Javadi

2019

Trans Emerging Tel Tech

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Advanced technologies such as internet of things (IoT) and clouds have significantly influenced on modern medical monitoring systems. Analytical statistics derived from massive patients' medical data via different data analysis methods, contribute in remote medical monitoring, early diagnosis of diseases, predicting clinical events, and recommending vital health/medical instructions. According to existence of the same health/medical services in functional aspect, finding appropriate composite health/medical services by the patients has been remained as a major concern in modern medical systems. Regarding this challenge, in this paper, a medical monitoring scheme for cloud‐based IoT platform is proposed, in which the patients' medical conditions are derived through predicting diseases by mining her physiological data collected from IoT devices and other medical records. A disease diagnosis model is used to analyze the patients' medical data for the aim of offering a composite health/medical prescription. After confirming the outcomes by medical team, it is sent to the patient. Then, the patient indicates her nonfunctional requirements such as location, cost and time to find the most appropriate composite health/medical service based on her preferences. Experimental results reveal that the proposed scheme is successful in achieving effective diseases diagnosis for offering composite health/medical prescriptions.

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Section: Resultsmentioning

confidence: 99%

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

Asghari

Rahmani

Javadi

2019

Trans Emerging Tel Tech

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“…Dealing with missing data is a complex task and while there is no perfect solution, several strategies are available (Farhangfar et al, 2007): Although removing instances with missing values is the simplest method, it can lead to biased results or a loss of information (Alexandropoulos et al, 2019;Little and Rubin, 2002). Therefore, imputation is often used to establish a statistical relationship between the missing data and the other instances (tuples) in the dataset (Prakash et al, 2019).…”

Section: Handle the Missing Datamentioning

confidence: 99%

“…A complete collection of data preprocessing techniques was provided by García et al (2015), highlighting the gaps in real data caused by various factors, along with the most relevant proposed solutions. In addition, García et al (2016); Prakash et al (2019) introduced detailed data preprocessing methods for data mining in the context of big data. The selection methodology of techniques has been extensively discussed by Han et al (2012); Subasi (2020) to help researchers choose the appropriate techniques for data analysis.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Data Preprocessing Framework for Improved Learning: A Case Study of Tangier Container Terminal

Al Uahabi,

Attariuas,

Saleh

et al. 2024

Journal of Computer Science

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Container terminals are critical nodes within the maritime transportation system that have a vital function in global merchandise trade, handling a significant volume of cargo through the use of various equipment and personnel. Thus, the efficiency of container terminal operations relies heavily on the ability to collect, analyze, and utilize operational data. However, such data can be corrupted by noise, missing points, outliers, and incomplete or inconsistent information, making subsequent analysis or modeling challenging. This study proposes an adaptive data preprocessing framework tailored to the context of container terminal operations, using data from tangier container terminal as a case study, the leading container port in the Mediterranean and Africa, and also ranked 4 th in the CPPI 2022. This framework includes techniques for data integration, cleaning, transformation, and encoding to acquire high-quality data. In addition, the RFE feature selection method is employed to identify the most discriminative feature subset. Finally, the proposed approach, assessed using an extra tree regressor model, demonstrates strong prediction capabilities with an R-squared score of 95.4% based on the selected features for predicting the duration of vessels at port, highlighting that its integration into the terminal operating system can improve management efficiency.

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“…Consequently, for cleaning the gathered data from anomalies and noises, a preprocessing step should be performed. Also, to cope with the big data problems [60,61], the proper feature selection processes should be applied to reduce the dimensions for simplifying the process of classification. Therefore, addressing the related issues to collected data has a significant impact on effectiveness of classification methods.…”

Section: Data Acquiringmentioning

confidence: 99%

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Akhbarifar

Javadi

Rahmani

et al. 2020

Pers Ubiquit Comput

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Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients’ health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients’ health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients’ health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients’ sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

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Big Data Preprocessing for Modern World: Opportunities and Challenges

Cited by 11 publications

References 13 publications

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

A medical monitoring scheme and health‐medical service composition model in cloud‐based IoT platform

An Adaptive Data Preprocessing Framework for Improved Learning: A Case Study of Tangier Container Terminal

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

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