## 2 Data-Cleaning Applications
Cleanix, KATARA, and BigDansing are not able to predict the correct values through a machine learning approach and cannot determine how to eliminate the mixed random number [12]; however, the SCARE technique, though constructed via machine learning, can only replace the missing values with the most precise value. When dealing with noisy data, the common approach in applications with machine learning and artificial neural network (ANN) is to address noise reduction and suppression [11; 18; 19]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Key Features** & **Approach** \\ \hline Cleanix & Scalability, unification, and Usability & Rule Selection \\ SCARE & Scalability & Machine Learning \\  & & Technique \\ KATARA & Easy Specification, & Knowledge-base and \\  & Pattern Validation & Crowdsourcing \\  & Data Annotation & \\ BigDansing & Efficiency, Scalability & Rule Specification \\  & and ease of use & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data-cleansing methods for big data. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Volume** & **Veracity** & **Velocity** \\ \hline \multirow{3}{*}{**Cleaning Technique Data**} & \multirow{3}{*}{**Scalability**} & **No need for** & \\  & & **Extensive Data** & **No need for Human** \\  & & & **Quality Rule** & **Domain Expert** \\  & & & **Optimisation** & \\ \hline Cleanix & x & & \\ SCARE & x & & x \\ KATARA & & x & x \\ BigDansing & x & & x \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Currently used data-cleaning technique. Given all of the above data-cleaning techniques, noise and random sample reduction is a significant part of their scope of discussion, which is vital in biomedical data signal analyses acquired in wearable biomedical sensors due to its subjectiveness in a different kind of environment--an environment often filled with varying sorts of noises, such as thermal and acoustic noises and interference. Moreover, since most wearable biomedical sensors are of low power, by suppressing these kinds of noises through signal processing by applying the filtering threshold method, the unsupervised classification is not effective under a low SNR. When the spectral characteristics of the noise are so similar or near that of the sensor-received signal, the detection performance may be degraded [20]. When it applies to the classification problem of an artificial neural network (ANN), obtaining the correct values through comprehensive and extensive quantisation in data-signal processing is essential. Still, it is not sufficient to identify which of the gathered data have a predictive power, as some authors mention that \"Data analytics is not about having the information known, but about discovering the predictive power behind the data collected\" [12]. Correct values of the data do not guarantee that it holds a valid value in predicting and summarising the entirety of the multidimensional datasets. Mining those correct values is an integral part of data mining in every machine learning and big data analysis. Complicated and straightforward mistakes are unavoidably present in data input and acquisition. Although much effort could be expended into this front-end procedure to reduce entry mistakes, the truth remains that mistakes in massive datasets are prevalent. Field error rates usually are about 5% or higher unless an organisation takes extraordinary precautions to prevent data inaccuracies [21]. Moreover, this rate is still quite high, at such a rate that it might lead to erroneous interpretation and decision making. In the case of cleaning the noisy data via biomedical sensors, most researchers use principal component analysis, such as a reduction in dimensionality or feature space [22; 23; 24; 25; 26], feature extraction in further data visualisation [27; 28; 29], and feature selection tools in machine and deep learning applications [30; 31; 32; 33; 34] for machine learning applications.