Data-driven model with predictive ability are important to be used in medical and healthcare. However, the most challenging task in predictive modeling is to construct a prediction model, which can be addressed using machine learning (ML) methods. The methods are used to learn and trained the model using a gene expression dataset without being programmed explicitly. Due to the vast amount of gene expression data, this task becomes complex and time consuming. This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology. The development of cancer classification methods based on ML and DL is mostly focused on this review. Although many methods have been applied to the cancer classification problem, recent progress shows that most of the successful techniques are those based on supervised and DL methods. In addition, the sources of the healthcare dataset are also described. The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. Currently, it seems that there is highly demanded further development of efficient classification methods to address the expansion of healthcare applications.

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Data transportation over resources constraint and noisy channel of wireless sensor network (WSN) is very challenging in term of guaranteeing the data survival along the transmission. However, with the convergence of different research areas such as routing, source and channel coding techniques, the WSN technology has successfully been tremendously developed. This paper proposes an on-the-fly data recovery (ODR) scheme using network coding in order to enhance the robustness of the network against packet loss. Along with the ODR scheme, the packet loss formulation is presented while a network model for a network coding designed is also introduced namely for erasure channel. The data generated by the sources are transferred to the destination through relay nodes via three transmission paths. In ODR process, the lost packet is recovered by a relay node by listening to the transmission of two adjacent nodes and performing the XOR operation on the listened packets. We provide the analytical study on network coding performance and conducting the simulation experiment to verify it. In the simulation studies, we have also compared the performance of the network using network coding with and without packet recovery. The result shows that the number of packet loss has been reduced significantly using the proposed scheme compared to the network with normal network coding.