Blockchain technology is a private, secure, trustworthy, and transparent information exchange performed in a decentralised manner. In this case, the coordination and validation efforts are simplified as the records are designed to update regularly and there is no difference in the two databases. This review focuses on how the blockchain addresses scalability challenges and provides solutions in the healthcare field through the implementation of blockchain technology. Accordingly, 16 solutions fell under two main areas, namely storage optimization and redesign of blockchain. However, limitations persist, including block size, high volume of data, transactions, number of nodes, and protocol challenges. This review consists of six stages, namely identification of research question, procedures of research, screening of relevant articles, keywording based on the abstract, data extraction, and mapping process. Through Atlas.ti software, the selected keywords were used to analyse through the relevant articles. As a result, 48 codes and 403 quotations were compiled. Manual coding was performed to categorise the quotations. The codes were then mapped onto the network as a mapping process. Notably, 16 solutions fell under two main areas, namely storage optimization and redesign of blockchain. Basically, there are 3 solutions compiled for storage optimization and 13 solutions for the redesign of the blockchain, namely blockchain modelling, read mechanism, write mechanism, and bi-directional network.

Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cutting-edge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images.

The productivity and profitability of poultry farming are crucial to support its affordability issues in food security. Criteria in productivity measurement, including Feed Conversion Ratio (FCR) calculation, whereas economic management is essential for profitability. Hence, best management practices need to be implemented throughout the growth period for optimizing the poultry performance. This review provides a comprehensive overview of computer vision technology for poultry industry research. This review relies on the use of several online databases to identify key works in the area of computer vision in a poultry farm. We recommend our search by focusing on four keywords, ‘computer vision’ and ‘poultry’ or ‘chicken’ or ‘broiler’ that had been published between 2010 and early 2020 with open access provided by University Teknologi Malaysia only. All the selected papers were manually examined and sorted to determine their relevance to computer vision in a poultry farm. We focus on the latest developments by focusing on the hardware and software parts used to analyze the poultry data with some examples of various representative studies on poultry farming. Notably, hardware parts can be classified into camera types, lighting units and camera position, whereas software parts can be categorized into data acquisition and analysis software types as well as data processing and analysis methods that can be implemented into the software types. This paper concludes by highlighting the future works and key challenges that needed to be addressed to assure the quality of this technology prior to the successful implementation of the poultry industry.

Disruptive technology, blockchain is propelling a technological intervention in healthcare due to its unique features and advantages. The healthcare industry is migrating to Health 4.0. Therefore, peer-to-peer (P2P) transactions in a decentralized and distributed manner make blockchain more lucrative to serve the needs of the healthcare industry of today. The revolutionary system blockchain has been discussed in the field of healthcare over the past five years. Hence, a systematic investigation of the existing body of knowledge concerning blockchain research in the healthcare domain is essential. The motivation of this study is to support further study based on the current research trend analysis through graphical visualization and analysis of the bibliographic material. Therefore, this study maps the expansion of scientific and academic research conducted concerning blockchain relevant to healthcare by utilizing a bibliometric analytic method to understand the state of the art. Bibliometric statistics have been utilized to analyze current scientific articles published in the Scopus database from 2016 to 2019. In addition, an overview of the publication trends over the first three months of 2020 has been undertaken to understand the research trend for the current year so far. The study serves the purpose of mapping research development trends in this area. The outcome discovered some beneficial insights such as the yearly trend of publications, top listed authors, institutes, countries and publishers from around the world. Moreover, this paper assists scholars to develop a theoretical framework to provide a primary source of reference in this field for further study of blockchain technology in the healthcare domain.

We proposed a novel approach to select a wavelet for artifact removal in electroencephalogram (EEG) by comparing their relative wavelet energies before and after thresholding. Relative wavelet energy (RWE) gives information about the relative energy associated with different frequency bands and can be considered as a time-scale density. RWE can be used as a tool to detect and characterize a specific phenomenon in time and frequency planes. We used Lifting Wavelet Transform to remove the common artifacts exist in EEG i.e. blink and eye movements. Three basic steps involved are to transform the EEG, hard thresholding the wavelet coefficients and the corrected EEG is obtained by inverse transform these threshold coefficients. It is of paramount important to select a suitable wavelet and threshold to accomplish this task. From this study, we concluded that cdf4.4 outperformed db4 and haar wavelets by removing the artifacts at the correct times and frequency bands.

In the field of information security, hash functions are considered important as they are used to ensure message integrity and authentication. Despite various available methods to design hash functions, the methods have been proven to time inefficient and have security flaws (such as a lack of collision resistance or susceptibility to birthday attacks). In the current study, we propose a novel hash function scheme based on a new Mersenne number transform. The suggested hash function called Hash Mersenne Number Transform (HMNT) takes an arbitrary length as input to generate a hash value with variable lengths (128, 256 and 512-bits or longer). The proposed scheme is evaluated in terms of the sensitivity of the hash value to the message, secret key and image, distribution of hashes, confusion and diffusion, robustness against collision and birthday attacks, alongside flexibility. Based on the simulation outcomes, the suggested scheme possess high sensitivity to the original message, the secret key and images, along with strong collision resistance. In conclusion, the proposed hash scheme is simple and efficient compared with the existing hash functions, making it viable for practical implementation.