Proposed Machine Learning Model Research Articles

A Novel Hierarchical Extreme Machine-Learning-Based Approach for Linear Attenuation Coefficient Forecasting.

The development of reinforced polymer composite materials has had a significant influence on the challenging problem of shielding against high-energy photons, particularly X-rays and γ-rays in industrial and healthcare facilities. Heavy materials' shielding characteristics hold a lot of potential for bolstering concrete chunks. The mass attenuation coefficient is the main physical factor that is utilized to measure the narrow beam γ-ray attenuation of various combinations of magnetite and mineral powders with concrete. Data-driven machine learning approaches can be investigated to assess the gamma-ray shielding behavior of composites as an alternative to theoretical calculations, which are often time- and resource-intensive during workbench testing. We developed a dataset using magnetite and seventeen mineral powder combinations at different densities and water/cement ratios, exposed to photon energy ranging from 1 to 1006 kiloelectronvolt (KeV). The National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM) was used to compute the concrete's γ-ray shielding characteristics (LAC). The XCOM-calculated LACs and seventeen mineral powders were exploited using a range of machine learning (ML) regressors. The goal was to investigate whether the available dataset and XCOM-simulated LAC can be replicated using ML techniques in a data-driven approach. The minimum absolute error (MAE), root mean square error (RMSE), and R2score were employed to assess the performance of our proposed ML models, specifically a support vector machine (SVM), 1d-convolutional neural network (CNN), multi-Layer perceptrons (MLP), linear regressor, decision tree, hierarchical extreme machine learning (HELM), extreme learning machine (ELM), and random forest networks. Comparative results showed that our proposed HELM architecture outperformed state-of-the-art SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. Stepwise regression and correlation analysis were further used to evaluate the forecasting capability of ML techniques compared to the benchmark XCOM approach. According to the statistical analysis, the HELM model showed strong consistency between XCOM and predicted LAC values. Additionally, the HELM model performed better in terms of accuracy than the other models used in this study, yielding the highest R2score and the lowest MAE and RMSE.

Application of machine learning in the design and optimization of bimodal structural materials

Bimodal structures (BS) receive wide concern due to their enhanced combination between strength and ductility. However, it is generally time-consuming and labor-intensive to obtain adequate performance data through traditional experimental and simulation methods. In this paper, a small-sample machine learning (ML) model combined with crystal plasticity (CP) simulations is developed to predict mechanical properties for BS materials with different microstructures. The proposed ML model shows better prediction capacities with regard to yield strength and uniform elongation among traditional ML models, which are validated by both simulative and experimental data. Afterwards, the Pareto front for BS is obtained by multi-objective optimization algorithm (MOOA), showing the mechanical properties of BS with optimal microstructures are much better than those of homogenous structures with the uniform grain size. To realize personalized customization for different target performance, the Pareto front is divided into three parts, then the corresponding three-dimensional design diagrams are established to seek for the best strength and elongation. The proposed exploration framework integrates finite element simulation, ML and MOOA, which can identify the optimized design of heterogeneous structures (HS) using limited data.

Machine Learning Exciton Hamiltonians in Light-Harvesting Complexes.

We propose a machine learning (ML)-based strategy for an inexpensive calculation of excitonic properties of light-harvesting complexes (LHCs). The strategy uses classical molecular dynamics simulations of LHCs in their natural environment in combination with ML prediction of the excitonic Hamiltonian of the embedded aggregate of pigments. The proposed ML model can reproduce the effects of geometrical fluctuations together with those due to electrostatic and polarization interactions between the pigments and the protein. The training is performed on the chlorophylls of the major LHC of plants, but we demonstrate that the model is able to extrapolate well beyond the initial training set. Moreover, the accuracy in predicting the effects of the environment is tested on the simulation of the small changes observed in the absorption spectra of the wild-type and a mutant of a minor LHC.

Data-Driven Shear Strength Prediction of FRP-Reinforced Concrete Beams without Stirrups Based on Machine Learning Methods

Due to the intrinsic complexity, there has been no widely accepted mechanics-based estimation model of the shear performance of Fiber-Reinforced Polymer (FRP)-reinforced concrete beams. Capitalizing on a large amount of previous experimental data, data-driven machine learning (ML) models could be potentially suitable for addressing this problem. In this paper, four existing shear design provisions are reviewed and four typical ML models are analyzed. The accuracy of codified methods and ML models are compared and analyzed based on our established extensive database of FRP-reinforced concrete beams with rectangular cross sections. A series of artificially selected features considering the shear-carrying mechanisms of FRP-reinforced beams are incorporated into the proposed ML models to show their influence on the model validity. Bayesian optimization is utilized to automatically tune the hyperparameters of different ML models. Compared to the most satisfying codified predictions from CSA S806, the best ML model, XGBoost, can provide more accurate and consistent predictions for the database, with R2 enhanced by 15% and the MAE and RMSE reduced by 59% and 52%, respectively. With the selected features based on domain knowledge, the performance of ML models is further enhanced, shown by the most important features being the added ones. With outstanding performance on a large database and singular test, the ML approaches have great potential in guiding the shear design of FRP-reinforced concrete.

Radiation-Free Microwave Technology for Breast Lesion Detection Using Supervised Machine Learning Model.

Mammography is the gold standard technology for breast screening, which has been demonstrated through different randomized controlled trials to reduce breast cancer mortality. However, mammography has limitations and potential harms, such as the use of ionizing radiation. To overcome the ionizing radiation exposure issues, a novel device (i.e. MammoWave) based on low-power radio-frequency signals has been developed for breast lesion detection. The MammoWave is a microwave device and is under clinical validation phase in several hospitals across Europe. The device transmits non-invasive microwave signals through the breast and accumulates the backscattered (returned) signatures, commonly denoted as the S21 signals in engineering terminology. Backscattered (complex) S21 signals exploit the contrast in dielectric properties of breasts with and without lesions. The proposed research is aimed to automatically segregate these two types of signal responses by applying appropriate supervised machine learning (ML) algorithm for the data emerging from this research. The support vector machine with radial basis function has been employed here. The proposed algorithm has been trained and tested using microwave breast response data collected at one of the clinical validation centres. Statistical evaluation indicates that the proposed ML model can recognise the MammoWave breasts signal with no radiological finding (NF) and with radiological findings (WF), i.e., may be the presence of benign or malignant lesions. A sensitivity of 84.40% and a specificity of 95.50% have been achieved in NF/WF recognition using the proposed ML model.

A tree-based modeling approach for matched case-control studies.

Conditional logistic regression (CLR) is the indisputable standard method for the analysis of matched case-control studies. However, CLR is strongly restricted with respect to the inclusion of non-linear effects and interactions of confounding variables. A novel tree-based modeling method is proposed which accounts for this issue and provides a flexible framework allowing for a more complex confounding structure. The proposed machine learning model is fitted within the framework of CLR and, therefore, allows to account for the matched strata in the data. A simulation study demonstrates the efficacy of the method. Furthermore, for illustration the method is applied to a matched case-control study on cervical cancer.

Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy.

Sleep is a fundamental and essential physiological process for recovering physiological function. Sleep disturbance or deprivation has been known to be a causative factor of various physiological and psychological disorders. Therefore, sleep evaluation is vital for diagnosing or monitoring those disorders. Although PSG (polysomnography) has been the gold standard for assessing sleep quality and classifying sleep stages, PSG has various limitations for common uses. In substitution for PSG, there has been vigorous research using actigraphy. For classifying sleep stages automatically, we propose machine learning models with HRV (heart rate variability)-related features and acceleration features, which were processed from the actigraphy (Maxim band) data. Those classification results were transformed into a binary classification for estimating sleep efficiency. With 30 subjects, we conducted PSG, and they slept overnight with wrist-type actigraphy. We assessed the performance of four proposed machine learning models. With HRV-related and raw features of actigraphy, Cohen's kappa was 0.974 (p < 0.001) for classifying sleep stages into five stages: wake (W), REM (Rapid Eye Movement) (R), Sleep N1 (Non-Rapid Eye Movement Stage 1, S1), Sleep N2 (Non-Rapid Eye Movement Stage 2, S2), Sleep N3 (Non-Rapid Eye Movement Stage 3, S3). In addition, our machine learning model for the estimation of sleep efficiency showed an accuracy of 0.86. Our model demonstrated that automated sleep classification results could perfectly match the PSG results. Since models with acceleration features showed modest performance in differentiating some sleep stages, further research on acceleration features must be done. In addition, the sleep efficiency model demonstrated modest results. However, an investigation into the effects of HRV-derived and acceleration features is required.

Machine Learning Applied to Anomaly Detection on 5G O-RAN Architecture

This article presents a study with feasibility and performance analysis of machine learning (ML) techniques using supervised techniques for anomaly detection problems in a 5G communication network. The proposed ML models (Multilayer Perceptron, Decision Tree, and Support Vector Machine) were used to classify data into anomaly or non-anomaly based on two 5G Open Radio Access Network (O-RAN) datasets with various key performance indicators (KPIs). Furthermore, we propose a strategy that devotes to labeling anomalous situations, leveraging the t-Distributed Stochastic Neighbor Embedding (tSNE) technique atop datasets enclosing multiple KPIs. The results were significant, with an accuracy above 90% for all use cases considered.

A Prediction Model Created by Comparing Tree-Based Methods To Assess Coronary Artery Disease

Aim: As people's quality of life and habits have changed, Coronary Artery Disease (CAD) has become the leading cause of death globally. It is a complicated cardiac disease with various risk factors and a wide range of symptoms. An early and accurate diagnosis of CAD allows for the quick administration of appropriate treatment, which contributes to a decreased mortality rate. Machine learning (ML) algorithms for CAD prediction and treatment decisions are quickly being developed and implemented in clinical practice. Predictive models based on machine learning algorithms may aid health personnel in the early diagnosis of CAD, lowering mortality. Thus, this study goal is to forecast the elements that may be connected with CAD using tree-based approaches, which are one of the machine learning methods, and to discover which factor is more effective on CAD. Material and Methods: The open-access heart disease dataset was used within the scope of the study to investigate the risk factors related with CAD. The data set used contains the values of 333 patients, as well as 20 input and 1 target variables. The 10-fold cross validation approach was employed in the modeling, and the data set was divided as 80%: 20% as training and test datasets. For model assessment, the measures of accuracy (ACC), balanced accuracy (b-ACC), sensitivity (SE), specificity (SP), positive predictive value (ppv), negative predictive value (npv), and F1-score were utilized. Results: The values of ACC, b-ACC, SE, SP, ppv, npv, and F1-score performance metrics were 9 98.5%, 98.8%, 97.7%, 100%, 100%, 95.8% and 98.8%, respectively, as a consequence of the estimate model results created with the XGBoost approach, which has the best performance among tree-based models. When the groups with or without CAD were compared, a statistically significant difference was found in terms of the age variable. There is also a significant relationship between the active, lifestyle, ihd, dm, ecgpatt, qwave variables and the presence/absence of the CAD variable. When the variable significance values obtained as a result of modeling with the highest performing XGBoost are examined, it is seen that the variables that most associated with CAD are ekgpatt: normal, ekgpatt: ST-depression, ekgpatt: T-inversion, qwave: yes, age, bpdias, height, LDL, HR, IVSD: with LVH, bpsyDM. Conclusion: According to the performance criteria of the forecasting models used, CAD gave distinctively successful results in forecasting. By identifying risk factors associated with CAD, the proposed machine learning models can provide clinicians with practical, cost-effective and beneficial assistance in making accurate predictive decisions.

Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index.

Determining the DNA fragmentation index (DFI) by the sperm chromatin dispersion (SCD) test involves manual counting of stained sperms with halo and no halo. The aim of this study is to build a robust artificial intelligence-based solution to predict the DFI. This is a retrospective experimental study conducted in a secondary in vitro fertilisation setup. We obtained 24,415 images from 30 patients after the SCD test using a phase-contrast microscope. We classified the dataset into two, binary (halo/no halo) and multiclass (big/medium/small halo/degraded (DEG)/dust). Our approach consists of a training and prediction phase. The 30 patients' images were divided into training (24) and prediction (6) sets. A pre-processing method M was developed to automatically segment the images to detect sperm-like regions and was annotated by three embryologists. To interpret the findings, the precision-recall curve and F1 score were utilised. Binary and multiclass datasets containing 8887 and 15,528 cropped sperm image regions showed an accuracy of 80.15% versus 75.25%. A precision-recall curve was determined and the binary and multiclass datasets obtained an F1 score of 0.81 versus 0.72. A confusion matrix was applied for predicted and actuals for the multiclass approach where small halo and medium halo confusion were found to be highest. Our proposed machine learning model can standardise and aid in arriving at accurate results without using expensive software. It provides accurate information about healthy and DEG sperms in a given sample, thereby attaining better clinical outcomes. The binary approach performed better with our model than the multiclass approach. However, the multiclass approach can highlight the distribution of fragmented and non-fragmented sperms.

Demand Prediction Based on Machine Learning Algorithms for Optimal Distribution of Insulin

The pharmaceutical supply chain is a complex network involving a wide range of stakeholders and requiring many steps to ensure the availability and right distribution of drugs. So, good demand prediction is important to improve the satisfaction of patients' needs. Thus, this study proposes a demand prediction based on machine learning algorithms in order to forecast the required quantity of insulin in the USA. Our study concentrated on the application of some machine learning models: random forest, multiple regression, and artificial neural network. We compare the performance of these three models based on the regression metrics: the root mean squared error, the coefficient of determination and the R-value, in order to choose the best model that is appropriate for our research. After that, the value of the uncertain demand predicted by the best proposed machine learning model will be used in a mathematical model, which focuses on the downstream part of pharmaceutical supply chain between the distributor and the demand zones, and it aims to maximize customer satisfaction.

A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

The use of Amazon Web Services is growing rapidly as more users are adopting the technology. It has various functionalities that can be used by large corporates and individuals as well. Sentiment analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related emotions. In this research work, sentiment analysis is performed on the AWS Elastic Compute Cloud (EC2) through Twitter data. The data is managed to the EC2 by using elastic load balancing. The collected data is subjected to preprocessing approaches to clean the data, and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative sentiments. High accuracy of 94.17% is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

Design and Development of a Machine Learning-Based Task Orchestrator for Intelligent Systems on Edge Networks

This paper proposes an edge-centric workload orchestration approach that uses machine learning in a three-tier vehicular architecture (edge, cloud via roadside unit, or cloud via cellular base station). The orchestrator uses a wireless network at the edge to receive and send over-the-air requests from vehicles, considering a metropolitan network connects the entire edge structure to support the high mobility of devices, allowing vehicles to share information and resources. Additionally, suppose the edge is congested, or its resources are unavailable. In that case, cloud resources will be used via roadside or cellular networks using a wide-area network to meet the tasks’ time constraints. Moreover, the proposed machine learning model uses variance-based sensitivity analysis to determine which inputs influence the model’s final decision. The experiments performed on the EdgeCloudSim simulator are based on modelling computational and network resources besides the representation of vehicles. The results indicate that our approach best fits task offloading over the air, outperforming the comparative experiments between the one-stage(our approach) model against two-stage and random models. Furthermore, by using our one-stage model that outputs the average of the prediction interval and the variance of this interval, we can measure how confident our model is in its prediction.

From Data to Diagnosis: Applied Machine Learning for Stroke Prediction in Computational Healthcare

Stroke is a leading cause of disability and mortality worldwide, emphasizing the need for accurate and timely prediction methods. In recent years, advancements in machine learning and computational healthcare have shown promising results in various medical domains. This paper presents a comprehensive study on the application of machine learning techniques for stroke prediction in computational healthcare. The objective of this research is to develop a robust and accurate stroke prediction model that can assist healthcare professionals in identifying individuals at high risk of stroke. Leveraging a diverse dataset consisting of demographic information, medical history, and clinical measurements, a range of machine learning algorithms is employed to extract meaningful patterns and relationships. Feature selection techniques are utilized to identify the most relevant predictors, ensuring optimal model performance. Through rigorous experimentation and evaluation, the proposed machine learning model demonstrates superior performance in stroke prediction compared to traditional risk assessment methods. The implications of this research extend beyond stroke prediction, with the proposed methods serving as a foundation for the development of similar predictive models in other healthcare domains.

DEVELOPMENT OF A NEURAL NETWORK STRUCTURE FOR INTRUSION DETECTION ANALYSIS

The right security solutions in the information and communication world are critical to network security by providing real-time network protection against network vulnerabilities and data usage. An effective intrusion detection strategy is able to use a holistic approach to protect critical systems from unauthorized access or attacks. The paper examines the latest scientific achievements and research related to the analysis of detection of network intrusions using machine learning (ML) methods. The article describes a complex security solution based on machine learning (ML) for network intrusion detection using a complex controlled ML structure and ensemble feature selection methods. In addition, a comparative analysis of several MH models and function selection methods is provided. The article develops a general mechanism for detecting and achieving higher accuracy with a minimum frequency of false positive results (FPR). The paper uses datasets and the results show that the detection model can successfully identify 99.3% of intrusions with the lowest error rate of 0.5%, which shows better performance compared to existing solutions. The article combines the selection of ensemble functions and ensemble machine learning approaches as a detection mechanism in SBB to detect network anomalies. An experimental study was conducted with feature sets obtained from nine feature selection methods, and then these feature sets were combined to obtain the minimum number of features using majority voting. A comparative analysis of sets of functions was carried out. Controlled methods are used, which are more efficient with a balanced data set. To make the training dataset balanced, the data type (benign or attacking) with the minimum number of data instances in that training dataset was first selected. An ensemble feature selection and ensemble classification algorithm is implemented to improve the overall performance of the proposed machine learning model. Prospects for the development of further research are proposed.

SIEMS: A Secure Intelligent Energy Management System for Industrial IoT Applications

Microgrids are industrial technologies that can provide energy resources for the Internet of Things (IoT) demands in smart grids. Hybrid microgrids supply quality power to the IoT devices and ensure high resiliency in supply and demand for PV-based grid-tied microgrids. In this system, the usage of predictive energy management systems (EMS) is essential to dispatch power from different resources, while the battery energy storage system (BESS) is feeding the loads. In this article, we deploy a one-day-ahead prediction algorithm using a deep neural network for a fast-response BESS in an intelligent energy management system (I-EMS) that is called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIEMS</i> . The main role of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIEMS</i> is to maintain the SOC at high rates based on the one-day-ahead information about solar power, which depends on meteorological conditions. The remaining power is supplied by the main grid for sustained power streaming between BESS and end-users. Considering the usage of information and communication technology components in the microgrids, the main objective of this article is focused on the hybrid microgrid performance under cyber-physical security adversarial attacks. Fast gradient sign, basic iterative, and DeepFool methods, which are investigated for the first time in power systems e.g., smart grid and microgrids, in order to produce perturbation for training data. To secure the microgrid’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIEMS</i> , we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two</i> Defence algorithms based on defensive distillation and adversarial training strategies for the first time in EMSs. We apply and evaluate these benchmark adversarial attack and Defence methods against the proposed machine learning models to increase the robustness of the models in the system against adversarial attacks.

Machine learning-based prognostic modeling of patients with acute heart failure receiving furosemide in intensive care units.

This study developed machine learning models to predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation in patients with acute heart failure receiving furosemide in intensive care units. An extensive database comprising static and dynamic features obtained from a Japanese hospital chain was used to construct and train the machine learning models. The results revealed that the proposed machine learning models predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation with good accuracy. However, the optimal models vary depending on the predicted outcomes. The linear support vector machine classification models exhibited the highest in-hospital mortality and mechanical ventilation prediction accuracy, with the area under the receiver operating characteristic curve of 0.73 and 0.73, respectively, whereas the multi-layer neural network exhibited the highest accuracy for acute renal replacement therapy initiation prediction with an area under the receiver operating characteristic curve of 0.70. In conclusion, this study demonstrated that machine learning models could help predict the clinical outcomes of patients with acute heart failure receiving furosemide. However, the optimal models may differ depending on the outcome of interest.

Cyberattacks Detection in IoMT using Machine Learning Techniques

Information and Communication Technology (ICT) has changed the computing paradigm. Various new channels for communication are created through these developments, and the Internet of Things (IoT) is one of them. Internet of Medical Things (IoMT) is a part of IoT in which medical devices are connected through a network. IoMT has resolved many traditional health-related problems and has some security concerns. This article uses three Machine Learning algorithms, Random Forest, Gradient Boosting, and Support Vector Machine (SVM), to detect cyberattacks. Machine Learning models are best for performing cyberattack detection. Proposed Machine Learning models are evaluated on the WUSTL EHMS 2020 dataset, which consists of main in-themiddle, data injection, and spoofing attacks. The evaluation of the result analysis shows that the proposed Machine Learning models outperformed existing techniques.

Predicting graft failure in pediatric liver transplantation based on early biomarkers using machine learning models

The early detection of graft failure in pediatric liver transplantation is crucial for appropriate intervention. Graft failure is associated with numerous perioperative risk factors. This study aimed to develop an individualized predictive model for 90-days graft failure in pediatric liver transplantation using machine learning methods. We conducted a single-center retrospective cohort study. A total of 87 liver transplantation cases performed in patients aged < 12 years at the Severance Hospital between January 2010 and September 2020 were included as data samples. Preoperative conditions of recipients and donors, intraoperative care, postoperative serial laboratory parameters, and events observed within seven days of surgery were collected as features. A least absolute shrinkage and selection operator (LASSO) -based method was used for feature selection to overcome the high dimensionality and collinearity of variables. Among 146 features, four variables were selected as the resultant features, namely, preoperative hepatic encephalopathy, sodium level at the end of surgery, hepatic artery thrombosis, and total bilirubin level on postoperative day 7. These features were selected from different times and represent distinct clinical aspects. The model with logistic regression demonstrated the best prediction performance among various machine learning methods tested (area under the receiver operating characteristic curve (AUROC) = 0.898 and area under the precision–recall curve (AUPR) = 0.882). The risk scoring system developed based on the logistic regression model showed an AUROC of 0.910 and an AUPR of 0.830. Together, the prediction of graft failure in pediatric liver transplantation using the proposed machine learning model exhibited superior discrimination power and, therefore, can provide valuable information to clinicians for their decision making during the postoperative management of the patients.

Spatiotemporal modeling to predict soil moisture for sustainable smart irrigation

In this paper, we have proposed spatiotemporal soil moisture modeling to improve understanding of soil moisture and variability for sustainable irrigation practices. Increasing population growth and climatic changes have intensified the need to implement effective measures to conserve water in irrigation practices. Effective irrigation in parkland influences the overall plant growth processes including the final appearance of the plants. Furthermore, over-irrigation and poor water management in parks and gardens lead to wastage of water which may result in seepage, runoff, and leaching of nutrients into nearby streams. Conversely, under-irrigation results in reduced plant growth and unappealing appearance. Thus, appropriate irrigation management is required to maintain a lush green landscape. Due to large variability in soil properties, environmental conditions and landscape features, the soil moisture level might not be uniformly distributed within a given landscape. Hence, it is essential to understand the soil moisture distribution in the field. There are few existing soil moisture modeling exist but these modelings have not considered multiple vertical depth and time domain. Using Internet of Things(IoT) enabled Cyber–physical system and machine learning techniques, this paper has presented a soil moisture modeling for multiple vertical soil depth for a robust understanding of soil’s and plants need for sustainable sprinkling of the water. The proposed model has used both statistical and deep learning techniques to understand moisture variability in both vertical depth and time. The proposed model has used Correlation Index (CI) to understand multi depth moisture variability on the influence of soil types and local weather parameters, such as precipitation and temperature. To predict moisture data in multi depth, the model has evaluated the use of both statistical machine learning models, such as Support vector regression(SVR) and linear regression (LR), and deep learning models, such as Long Short-Term Memory (LSTM) using seasonal and non-seasonal dataset. The experiment has revealed closely related variability pattern between wind speed and soil moisture in multi depth whereas soil type shows a loosely related variability pattern with moisture in higher depth. With these diverse datasets, the proposed machine learning model has achieved almost 90% success rate to predict moisture content in higher depth using data of lower depth to reduce cumbersome sensors array deployment in higher depth for live moisture visualization. This proposed multiple depth and time domain-based model can enable a smart water dispersing system to conserve water and make a positive contribution to a sustainable future.