In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.

Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors to integrate and analyze the data. One possible remedy to overcome these limitations is the interconnection of medical devices through the internet using an intelligent and distributed platform such as the Internet of Things (IoT) or the Internet of Health Things (IoHT) and Artificial Intelligence/Machine Learning (AI/ML). These concepts permit the integration of data from different sources to enhance the diagnosis/prognosis of the patient’s health state. Over the last several decades, the growth of information technology (IT), such as the IoT/IoHT and AI, has grown quickly as a new study topic in many academic and business disciplines, notably in healthcare. Recent advancements in healthcare delivery have allowed more people to have access to high-quality care and improve their overall health. This research reports recent advances in AI and IoT in monitoring vital health signs. It investigates current research on AI and the IoT, as well as key enabling technologies, notably AI and sensors-enabled applications and successful deployments. This study also examines the essential issues that are frequently faced in AI and IoT-assisted vital health signs monitoring, as well as the special concerns that must be addressed to enhance these systems in healthcare, and it proposes potential future research directions.

New generation databases also called NoSQL (Not only SQL) databases are highly scalable, flexible, and low-latent. These types of databases emerge as a result of the rigidity shown by traditional databases to handle today’s data which is voluminous, highly diversified and generated at a very high rate. With NoSQL, problems such as database expansion difficulties, low query performance and low storage capacity are addressed. However, the inherent complexity of contemporary datasets coupled with programmers’ low NoSQL modeling competence are increasingly making database modeling and design vastly challenging, especially when parameters like consistency, availability and scalability are to be balanced in accordance with system requirements. As such, a schema suggestion model for NoSQL databases is posed to address this balancing issue. The proposed model aims to abstractly suggest schemas at the initial stage of system development based on user defined system requirements and CRUD (Create, Read, Update and Delete) operations among others. This is achieved through the adaptation of exploratory and experimental approaches of research. Also, few mathematical formulas are introduced to calculate clusters availability during entity mappings. A comparison was conducted between the schema produced using the proposed model and the one without. Results obtained shows substantial improvement in the areas of security and read–write query performance.

The high dimensionality of software metric features has long been noted as a data quality problem that affects the performance of software defect prediction (SDP) models. This drawback makes it necessary to apply feature selection (FS) algorithm(s) in SDP processes. FS approaches can be categorized into three types, namely, filter FS (FFS), wrapper FS (WFS), and hybrid FS (HFS). HFS has been established as superior because it combines the strength of both FFS and WFS methods. However, selecting the most appropriate FFS (filter rank selection problem) for HFS is a challenge because the performance of FFS methods depends on the choice of datasets and classifiers. In addition, the local optima stagnation and high computational costs of WFS due to large search spaces are inherited by the HFS method. Therefore, as a solution, this study proposes a novel rank aggregation‐based hybrid multifilter wrapper feature selection (RAHMFWFS) method for the selection of relevant and irredundant features from software defect datasets. The proposed RAHMFWFS is divided into two stepwise stages. The first stage involves a rank aggregation‐based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non‐disjoint rank list. In the second stage, the aggregated ranked features are further preprocessed by an enhanced wrapper feature selection (EWFS) method based on a dynamic reranking strategy that is used to guide the feature subset selection process of the HFS method. This, in turn, reduces the number of evaluation cycles while amplifying or maintaining its prediction performance. The feasibility of the proposed RAHMFWFS was demonstrated on benchmarked software defect datasets with Naïve Bayes and Decision Tree classifiers, based on accuracy, the area under the curve (AUC), and F‐measure values. The experimental results showed the effectiveness of RAHMFWFS in addressing filter rank selection and local optima stagnation problems in HFS, as well as the ability to select optimal features from SDP datasets while maintaining or enhancing the performance of SDP models. To conclude, the proposed RAHMFWFS achieved good performance by improving the prediction performances of SDP models across the selected datasets, compared to existing state‐of‐the‐arts HFS methods.

Nowadays, data with several characteristics such as volume, variety etc. are generated daily, i.e. big data; its complexity cannot be overemphasized. On the other hand, schema free NoSQL databases keep emerging at almost the same phase to accommodate such data which cannot be efficiently managed by relational databases. However, this advancement brings about the challenge to model such flexible databases and capably manage big data despite its complexity. In doing that, developers tend to apply their relational modeling skills; nonetheless, such skills may not be directly compatible with NoSQL databases due to their schema flexibility, linear scalability among others. To alleviate this difficulty, we propose a standard for modeling NoSQL databases, document-stores in particular. The standard can be classified as i) cardinality notations, and ii) relationship modeling styles. With such standard, NoSQL document-store databases can be properly designed, automated database testing can be applied, and database performance and stability can be considerably improved. To achieve this, experimental method is applied. Also, exploratory approach was used to explore the available literature as well as experts consultations. All possible entity relationships were extracted, aggregated and compiled from a heuristic evaluation of existing 4 different document-store databases. An experiment was conducted to assess the effect of the proposed standards, results indicate a profound improvements in various aspect of document modeling when the proposed standards are adopted, especially in a large scaled databases.