Organizations have become highly reliant on a range of data sources that span structured, semi-structured, and unstructured data types. These repositories allow large-scale storage for faster ingestion and analytics but pose tremendous challenges of integration owing to schema and contextual differences. Traditional data integration methods, such as the ontology-based Resource Description Framework (RDF), are often inadequate when dealing with these challenges. They specifically struggle with the dynamic evolution of the schema of data sources, context-aware interpretation, and achieving interoperability across heterogeneous data sources. This paper presents an integrated system that augments resource description knowledge with token embeddings using the attention mechanism of the transformer model with relative positional encoding to overcome these weaknesses. Data from unstructured sources are used to create an embedding, whereas structured data are mapped into the RDF. The embeddings were then integrated into the RDF using hasEmbedding. Virtual transformations are employed to handle schema alignment and cosine similarity merges similar entities to provide a unified data view. Thus, the model explicitly integrates contextual knowledge within resource description knowledge triples, thereby improving the semantic representation. The proposed system uses a Simple Protocol and Resource Description Knowledge Query Language for the efficient querying of resource description knowledge, thus enhancing interoperability across domains. The proposed model produces a result that attains a good schema mapping accuracy of 97.82%, thus enabling more accurate and meaningful linking of heterogeneous datasets. Empirical trials involving use cases across human activity analysis and flood risk management prove the system’s robustness, scalability, and effectiveness for knowledge discovery while allowing cross-domain integration of heterogeneous types of data within intricate scenarios. The results show that incorporating embedding into RDF reduces dependence on strict, pre-defined ontologies, simplifies schema on-demand alignment, and allows unified querying without the need to curate the integrated data into a traditional data warehouse.

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.

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.

Artificial intelligence (AI) and wearable sensors are gradually transforming healthcare service delivery from the traditional hospital-centred model to the personal-portable-device-centred model. Studies have revealed that this transformation can provide an intelligent framework with automated solutions for clinicians to assess patients’ general health. Often, electronic systems are used to record numerous clinical records from patients. Vital sign data, which are critical clinical records are important traditional bioindicators for assessing a patient’s general physical health status and the degree of derangement happening from the baseline of the patient. The vital signs include blood pressure, body temperature, respiratory rate, and heart pulse rate. Knowing vital signs is the first critical step for any clinical evaluation, they also give clues to possible diseases and show progress towards illness recovery or deterioration. Techniques in machine learning (ML), a subfield of artificial intelligence (AI), have recently demonstrated an ability to improve analytical procedures when applied to clinical records and provide better evidence supporting clinical decisions. This literature review focuses on how researchers are exploring several benefits of embracing AI techniques and wearable sensors in tasks related to modernizing and optimizing healthcare data analyses. Likewise, challenges concerning issues associated with the use of ML and sensors in healthcare data analyses are also discussed. This review consequently highlights open research gaps and opportunities found in the literature for future studies.

A major cause for concern in hospitals is congestion, which brings about untoward hardship to patients due to long queues and delay in service delivery. This paper seeks to minimize the waiting time of patients by comparing the performance indicators of a single server and multi-server model at the Paediatrics Department of Muhammad Abdullahi Wase Specialist Hospital Kano (MAWSHK). In order to achieve this, primary data was obtained through direct observation which in turn is subjected to the test of goodness of fit to ascertain the distribution that best describes the data. The performance indicators comprising utilization factor, average number of patients in the queue, average number of patients in the system, average waiting time in queue and average waiting time in system for a single server and multi-server model were computed and analyzed respectively. Our findings indicate that the G/G/4 model performs better compared to the G/G/1 model as it minimizes the waiting time of patients

In this research, modification of separate ratio type exponential estimator introduced in an earlier study is proposed. Expressions for the bias and mean square error (MSE) of the proposed estimator up to first degree of approximation are derived. The optimum value of the constant which minimize the MSE of the suggested estimator is also obtained. In the same vein, efficiency comparisons between the proposed estimator and some related existing ones under the case of post-stratification is conducted. Empirical studies have been conducted to demonstrate the efficiencies of the suggested estimators over other considered estimators. The proposed MSE and Percentage Relative Efficiency (PRE) were used to evaluate the achievement of the modified estimator.

This study aims at comparing the performance of a Multi-Layer Feed-Forward Neural Network and exponential curve fitting Models for the estimation of airwaves associated with shallow water Controlled Source Electro-Magnetic (CSEM) data. The performance measure is based on Mean Square Error (MSE), Sum of Squares Error (SSE) and coefficient of determination (R2). The MLP-NN network produced better and superior results with low MSE of 1.13e-7, SSE of 0.00017 and higher R2 of 99.35%.

In this study, a Multi-Layer Perceptron Neural Network and Multiple Regression techniques are used to estimate airwaves associated with shallow water Controlled-Source Electro-Magnetic (CSEM) data. Both techniques are appropriate for the development of estimation models. However, multiple regression models make some assumptions about the underlying data. These assumptions include independence, normality and homogeneity of variance. Conversely, neural network based models are not constrained by such assumptions. The performance of the two techniques is calculated based on coefficient of determination (R 2 ) and mean square error (MSE). The results indicate that MLP produced better estimate for the airwaves with MSE of 0.0113 and R 2 of 0.9935.

This paper focuses on formulating a multiple regression model using matrix notation that can be used to predict the magnitude of airwaves in Shallow Water Sea Bed Logging (SBL) Data. The term airwaves refer to the propagated EM signals from the source antenna via atmosphere that is induced along air/sea surface and interferes with the subsurface signal. In shallow water, the airwaves have the ability to mask other subsurface responses possibly containing valuable information about subsurface resistive structure such as hydrocarbon reservoir. A fair representation of SBL environments was simulated to generate the airwaves data. Magnitude of airwaves at selected offset is used as the dependent variable. Whereas the predictor variables (independent variables) for the proposed multiple regression model are the frequency, seawater depth, seawater conductivity, sediment conductivity and offset. Akaike's Information Criterion (AIC) is used for selecting the multiple regression models. The formulated regression model is benchmarked with the theoretical well-known space-domain expression for the Airwaves estimation. The model reveals goodness of fit with R2 of 0.9561and the overall statistical significance of the estimated parameters F-value of 19.35. The result indicates that the magnitudes of airwaves predicted by the regression model are approximately consistent with theoretical model.

This research aims to apply the FASTICA and Infomax algorithm in the field of seabed logging, by utilizing the Principal Component Analysis (PCA) as preprocessor. All the three algorithms are statistical algorithms used for signal deconvolution and are respectively in the field of Independent Component Analysis (ICA). In seabed logging (SBL) implies the marine controlled source electromagnetic (CSEM) technique for the detection of hydrocarbons underneath the seabed floor. The results from SBL, indicate the presence of Hydrocarbon, but due to the presence of noise, in the form of airwaves, interfere with the signals from the subsurface and tend to dominate the receiver response. Hence, the Infomax and FASTICA de-convolution algorithms are used, considering PCA as a pre-processor to filter out the airwaves which disrupt the subsurface signals within the receiver response. The results obtained from simulations and their comparative analysis, indicate that the results from the infomax algorithm are better.

This paper focuses on the detection of hydrocarbon layers under the seabed using Electromagnetic methods and to prove the relationship between the thickness and resistivity constrast of the hydrocarbon. Simulations have been carried out by varying the depth of seawater from 1000m to 100m and the resistivity contrast and thickness for each level of depth is also varied. The electric field is also measured using various simulation models and graphs over different offsets. The results obtained prove that the resistivity property of Hydrocarbon is directly proportional to the thickness, and at particular points the presence of hydrocarbon layer is clearly significant.

One of the main challenges of using Marine Control Source Electro-Magnetic (MCSEM) sounding for Hydrocarbon de- tection has been the airwaves phenomena. In shallow water the response from the air half-space often masks the response from the subsurface. In this paper we present a curve fitting approach to identify a mathematical function or model that best describes the pattern of the airwaves data. The identified model can serve as a prediction model for the airwaves. Synthetic data are simulated in a geologic model that is fairly representative of the area where real MCSEM data were collected. Root Mean Square Error (RMSE), Sum of Square Error (SSE) and Coefficient of determination (R2) were used to evaluate the performance of the prediction model. The result indicates that exponential decay function can describe the airwaves data with RMSE of 3.1e-7, SSE of 1.3e-11 and R2 of 0.990.

In shallow water Sea Bed Logging (SBL) survey, air layer response from the Electro-Magnetic (EM) signals creates a disturbance known as the source-induced airwaves. The airwaves commonly denote the energy that propagates from the EM source via the atmosphere to the receiver on the seabed. As a result, the airwaves dominate the measured survey data, so that the sought-after signals from possible hydrocarbon layers in the subsurface can be totally masked. In this study, a 5x5 factorial design is used to analyze the effect of frequency, seawater conductivity, sediment conductivity, seawater depth and offset on the magnitude of airwaves. The result based on F-statistics, indicates that frequency has higher significant effect on the magnitude of the airwaves followed by the seawater depth, offset, seawater conductivity and sediment conductivity in that order.

Marine Control Source Electro-Magnetic (MCSEM) survey is a technique for remote identification of subsea floor structures of the earth's interior using Electro-Magnetic (EM) signals.Air wave signal is major problem associated with the data recorded by this technique in shallow water environment.The air wave signals are parts of the EM signals that propagate from EM source via the atmosphere and induced along air/sea surface.These air wave signals has the ability to limit and mask the electromagnetic response of a subsurface resistive body so that signals from subsurface, possibly containing valuable information about a resistive hydrocarbon reservoir is hardly distinguishable.This paper presents the application of a feed forward multi-layer perceptron neural networks model for estimation of air waves in MCSEM survey data based on offset and sea water depth values.The proposed model has 3 hidden layers with sigmoid activation function, an output layer with purelin transfer function and Levenberg-Marquardt (trainlm) as the training function.Simulated airwave data for ten sea water depths from 1000m to 100m at interval of 100m were used as the training data.Coefficient of multiple determination and Mean Square Error (MSE) obtained from the multi-layer perceptron model and the estimation with multiple linear regression model are compared.Preliminary results demonstrate that multi-layer perceptron neural networks are a viable technique for the estimation of air waves in MCSEM data.

Sea Bed Logging (SBL) is an offshore geophysical technique that can give information about resistivity variation beneath the seafloor. This information is crucial in offshore oil and gas exploration. However, data collected through this technique in shallow water at low frequencies is associated with a problem termed "air wave effect". The air wave effect is a phenomena resulting from Electro-Magnetic (EM) waves produced by the antenna (source) which interact with air-sea interface to generate air waves that diffuse from sea surface to the receivers. These air wave signals dominate the receivers at far offsets to the source and consequently, the refracted signal due the target is hardly distinguishable. The refracted signals from the target being masked by the airwaves can make it difficult to identify the hydrocarbon reservoir. The aim of this study is to investigate the sea water depth for the presence of air waves. Synthetic data are generated by simulating SBL environment without Hydro-Carbon (HC) target and varying the sea water depth from 1000m to 100m with the interval of 100m. The simulated distances for the source-receiver separation (offset) are divided into five ranges. The magnitude versus offset plot together with the Friedman and Wilcoxon statistical test are used to analyze the data. Results show that the air waves are present at 400m of sea water depth and below.

The problem of function fitting for certain geophysical problem such as Control Source Electro-Magnetic (CSEM) can be solved using a partially recurrent network called Elman Neural Networks (ENN). ENN is one of the subclasses of partial recurrent neural networks. A Recurrent Neural Network (RNN) is an important class of neural networks where connections between units form a directed cycle. The Elman network differs from conventional neural network structure, in that it has addition layer (context layer) with feedback connection from the output of the hidden layer to its input. This feedback path allows Elman networks to recognize and generate temporal patterns, as well as spatial patterns. ENN has an advantage of having a low probability of being affected by external noise. Also, it can be trained to act as an independent system simulator. This study presents an application of ENN in function fitting for CSEM data. The synthetic training data has been generated using Computer Simulation Technology (CST) software. As a preliminary study, the data set was selected carefully representing a no hydrocarbon reservoir CSEM simulation. The trained Elman network shows an encouraging good fitting with MSE as low as 0.000275.

Detection of hydrocarbons (HC) by a Controlled Source Electromagnetic (CSEM), based on resistivity contrast, makes electromagnetic (EM) waves convincing method for HC detection in deep water exploration. However, HC survey done in shallow water is difficult due to a phenomenon called “air wave effect”. The waves that are produced by EM transmitter interact with air-sea interface to generate air waves that diffuse from the sea surface to the receivers. These air waves dominate the measured EM data such that the presence of the HC may not be detected. This work is a verification of the effect of air waves in shallow water environment. Data with hydrocarbon at 500m depth and data without hydrocarbon were simulated using CST EM Studio for this study for sea water depths from 1000m to 100m. Results have shown that the presence of hydrocarbon in shallow water is shielded by air waves.

Marine Controlled Source Electro-Magnetic (CSEM) for hydrocarbon exploration survey data can be classified into two groups. The ability to classify the raw survey data is crucial since this classification may indicate the presence of hydrocarbon. This paper presents the preliminary results of applying discriminant analysis in classifying CSEM data into dichotomous groups based on their electric field (E-field) and magnetic field (B-field) values. Two types of data, with and without hydrocarbon, were simulated and used to develop the discriminant model. Statistical analysis is carried out to test the significance of the discriminant function as a whole, the discrimination between groups and the extent to which that variable makes a unique contribution to the prediction of group membership. The results obtain indicates the potential of the discriminant analysis in classifying the data.