Development Of Model Research Articles

Optimized kernel extreme learning machine using Sine Cosine Algorithm for prediction of unconfined compression strength of MICP cemented soil.

Microbially induced calcite precipitation (MICP) is an eco-friendly bio-remediation technology. The unconfined compressive strength (UCS) of MICP cemented soil is an important indicator of repair effectiveness. This study proposes a machine learning technique utilizing the Sine Cosine Algorithm (SCA) to optimize the regularization coefficient C and kernel width γ of the kernel extreme learning machine (KELM) to predict the UCS of MICP cemented soil. To evaluate the performance of the proposed models, a dataset containing 180 groups of the UCS of MICP cemented soil was obtained. The results obtained by SCA-KELM were compared with those obtained by the Random Forest algorithm (RF), Support Vector Machine (SVM), and KELM. The performance of these models was evaluated by the scores of MAE, RMSE, and R2. The results indicate that the SCA-KELM algorithm exhibits optimal prediction performance (Total score: 21). After optimizing KELM with SCA, the total score improved by 110%, suggesting that SCA significantly enhances the KELM performance. After model development, the optimal population size for SCA-KELM was determined to be 50. Based on the mutual information test, an innovative method was developed for categorizing factor sensitivity by employing importance scores as the partitioning criterion. This method categorizes the influencing factors into three tiers: high (importance score: 8.03-11.14%), medium (importance score: 5.93-7.25%), and low (importance score: 3.23-5.18%). These results suggest that the proposed SCA-KELM algorithm can be regarded as a powerful tool for predicting the UCS of MICP cemented soil.

LEVERAGING AI AND DATA ANALYTICS FOR ENHANCING FINANCIAL INCLUSION IN DEVELOPING ECONOMIES

Financial inclusion, defined as the access and usage of financial services by all individuals and businesses, is critical for fostering economic development and reducing poverty, particularly in developing economies. However, significant portions of the population in these regions remain underserved or excluded from formal financial systems. Leveraging artificial intelligence (AI) and data analytics presents a promising avenue for addressing the challenges of financial exclusion and advancing financial inclusion in developing economies. This Review explores the role of AI and data analytics in enhancing financial inclusion. Firstly, AI technologies such as machine learning and natural language processing enable the analysis of vast datasets to identify patterns, behaviors, and creditworthiness of individuals and small businesses, thereby facilitating more accurate risk assessment and decision-making by financial institutions. Additionally, AI-powered chatbots and virtual assistants offer personalized financial guidance and support, improving accessibility to financial services for marginalized populations who may have limited literacy or access to traditional banking channels. Moreover, data analytics plays a crucial role in expanding financial inclusion by providing insights into customer preferences, spending habits, and transaction histories. By harnessing these insights, financial service providers can tailor their products and services to better meet the diverse needs of underserved communities, thereby increasing uptake and usage of formal financial services. Furthermore, data analytics enables the development of alternative credit scoring models that leverage non-traditional data sources such as mobile phone usage and utility payments, allowing individuals with limited credit histories to access credit on favorable terms. However, several challenges must be addressed to fully realize the potential of AI and data analytics in enhancing financial inclusion. These include concerns related to data privacy and security, ensuring the fairness and transparency of AI algorithms, and bridging the digital divide to ensure equitable access to technology-enabled financial services. In conclusion, leveraging AI and data analytics holds significant promise for enhancing financial inclusion in developing economies. By harnessing the power of these technologies, policymakers, financial institutions, and other stakeholders can work towards building more inclusive and resilient financial systems that empower individuals and businesses to participate more fully in the formal economy.
 Keywords: AI, Data Analytics, Financial Inclusion, Developing Economies, Leveraging.

Towards Mental Health Analysis in Social Media for Low-resourced Languages

The surge in internet use for expression of personal thoughts and beliefs has made it increasingly feasible for the social Natural Language Processing (NLP) research community to find and validate associations between social media posts and mental health status . Cross-sectional and longitudinal studies of low-resourced social media data bring to fore the importance of real-time responsible Artificial Intelligence (AI) models for mental health analysis in native languages. Aiming at classifying research for social computing and tracking advances in the development of learning-based models, we propose a comprehensive survey on mental health analysis for social media and posit the need of analyzing low-resourced social media data for mental health . We first classify three components for computing on social media as: SM - data mining/natural language processing on social media , IA - integrated applications with social media data and user-network modeling, and NM - user and network modeling on social networks. To this end, we posit the need of mental health analysis in different languages of East Asia (e.g., Chinese, Japanese, Korean), South Asia (Hindi, Bengali, Tamil), Southeast Asia (Malay, Thai, Vietnamese), European languages (Spanish, French) and the Middle East (Arabic). Our comprehensive study examines available resources and recent advances in low-resourced languages for different aspects of SM, IA, and NM to discover new frontiers as potential field of research.

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges.

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.

DEVELOPMENT OF COMPRESSIVE STRENGTH PREDICTIVE MODELS OF SELF-COMPACTING CONCRETE CURED USING DIFFERENT CURING METHODS

Curing is the process of controlling the rate and extent of moisture loss, relative humidity and temperature from newly poured concrete for a certain period of time after it has been cast or finished to ensure that the cement has been properly hydrated and the concrete has hardened. The concrete strength, durability and other physical properties are affected by curing and application of the various types as it relates to the prevailing weather conditions in a particular locality, as curing is one of many requirements for concrete production, as such it is important to study the effect of different curing method. The concrete cube specimens produced with cement, fine aggregate, and coarse aggregate mix-ratio of 1:2.23:1.62 were prepared with a water-cement ratio of 0.5 and superplasticizer (SP) dosages of 0.5%, 1.0%, and 1.5%. The SP dosages were computed as percentages by weight of the cement content. The cubes were tested for compressive strength after curing for 7, 14, 21, 28, and 56 days using three curing methods namely; Immersion, open air, and wet burlap curing methods. This study assessed the effect of different curing methods on compressive strength of self-compacting concrete through the development of a mathematical method to model and analyze the effect of the curing methods used on the compressive strength of the SCC and also to validate the reliability of the method used. Data Fit software was used in the model development, the curing age and super-plasticizer dosage were used as independent variables while the compressive strength...

Development and validation of a novel model to predict pulmonary embolism in cardiology suspected patients: A 10-year retrospective analysis.

As there are no predictive models for pulmonary embolism (PE) in patients with suspected PE at cardiology department. This study developed a predictive model for the probability of PE development in these patients. This retrospective analysis evaluated data from 995 patients with suspected PE at the cardiology department from January 2012 to December 2021. Patients were randomly divided into the training and validation cohorts (7:3 ratio). Using least absolute shrinkage and selection operator regression, optimal predictive features were selected, and the model was established using multivariate logistic regression. The features used in the final model included clinical and laboratory factors. A nomogram was developed, and its performance was assessed and validated by discrimination, calibration, and clinical utility. Our predictive model showed that six PE-associated variables (age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease). The area under the curve - receiver operating characteristic curves of the model were 0.721 and 0.709 (95% confidence interval: 0.676-0.766 and 0.633-0.784), respectively, in both cohorts. We also found good consistency between the predictions and real observations in both cohorts. In decision curve analysis, the numerical model had a good net clinical benefit. This novel model can predict the probability of PE development in patients with suspected PE at cardiology department.

Assessment of the bone biological age using multiple regression and neural network analysis

Background. Nowadays, many studies confirm the increased risk of osteoporosis in the elderly. Strategies for optimizing diagnosis include a combined use of various methods, so calculating bone biological age (BA) can also be important for disease prediction. Recently, some new methodological approaches to BA calculation with the mathematical methods implementation were proposed. However, studies utilizing advanced approaches, particularly neural network (NN) in Ukraine, are limited. The purpose of this study was to develop a bone BA model and compare its accuracy using stepwise multiple regression (MLR) and NN analysis methods. Materials and methods. In a single-center cohort study, we retrospectively analyzed the data of 3,071 healthy women aged 40–90 years. The analysis of the study indices included the anthropometric parameters, dual-energy X-ray absorptiometry indices (DXA), and the parameters of the 10-year probability of major osteoporotic fractures (FRAX). For the development of bone BA models, MLR methods were used and the construction of the NN model was performed using a multilayer perceptron model. Results. As a result of the calculation, we received the MLR formula for bone BA determining bone mineral density (BMD) of lumbar spine and radius, minimal femoral and hip BMD as well as Trabecular Bone Score, and FRAX. The MLR equation allowed to calculation of the bone BA with an error of less than 4.9 years for study and control groups and demonstrated the high connection between calculated and chronological (ChrA) ages (R = 0.77; p < 0.00001). The use of NN analysis showed the best results using 6 input variables and 1 internal layer of 7 neurons. The assessment of the connection between BA and ChrA demonstrated a high coefficient of correlation (R = 0.88; p < 0.000001) with an average error of age calculation of less than 3.7 years for the study and control groups. Conclusions. A comparison of the accuracy of both models in bone BA estimation revealed a significant advantage of the deep learning NN, however, the use trained NN model requires specialized software, whereas the MLR formula can be used without additional costs.

Development of Machine Learning Models for the Identification of Elevated Ketone Bodies During Hyperglycemia in Patients with Type 1 Diabetes.

Aims: Diabetic ketoacidosis (DKA) is a serious life-threatening condition caused by a lack of insulin, which leads to elevated plasma glucose and metabolic acidosis. Early identification of developing DKA is important to start treatment and minimize complications and risk of death. The aim of the present study is to develop and test prediction model(s) that gives an alarm about their risk of developing elevated ketone bodies during hyperglycemia. Methods: We analyzed data from 138 type 1 diabetes patients with measurements of ketone bodies and continuous glucose monitoring (CGM) data from over 30,000 days of wear time. We utilized a supervised binary classification machine learning approach to identify elevated levels of ketone bodies (≥0.6 mmol/L). Data material was randomly divided at patient level in 70%/30% (training/test) dataset. Logistic regression (LR) and random forest (RF) classifier were compared. Results: Among included patients, 913 ketone samples were eligible for modeling, including 273 event samples with ketone levels ≥0.6 mmol/L. An area under the receiver operating characteristic curve from the RF classifier was 0.836 (confidence interval [CI] 90%, 0.783-0.886) and 0.710 (CI 90%, 0.646-0.77) for the LR classifier. Conclusions: The novel approach for identifying elevated ketone levels in patients with type 1 diabetes utilized in this study indicates that CGM could be a valuable resource for the early prediction of patients at risk of developing DKA. Future studies are needed to validate the results.

Development of a Self-Evaluation Model Based on an Intelligent Tutoring System to Increase Student Learning Motivation in Basic Programming Subjects

Learning evaluation is a series of efforts to improve the quality of learning. Evaluation activities will have a good impact on students to encourage themselves in making improvements that affect their learning achievement. However, the implementation of learning evaluation in the field is not always easy. As the results of field studies conducted at SMK BPI Bandung, there are obstacles in the continuity of learning evaluations which the implementation has not been optimal. This study aims to develop a self-evaluation model based on an intelligent learning system (ITS) as a support in the learning process, where students can actively and independently evaluate their abilities, thereby increasing learning motivation which has an impact on their learning achievement. From the results of the research conducted using a nonequivalent pretest-posttest control group design, it was concluded that: 1) The evaluation media based on the ITS got a percentage value of 97.78% in the "very good" category and was feasible to use. 2) ITS evaluation media proved to increase learning motivation in the experimental class, marked by an increase in the average value of pre-test to post-test of 0.83% and showed differences in motivation with the control class. 3) Students respond to the ITS evaluation media, namely the media has a "very good" category with a percentage of 83.58%.

Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures

This paper introduces a three-dimensional (3D) semi-explicit probabilistic numerical model for simulating crack propagation within the framework of the finite element method. The model specifically addresses macrocrack propagation using linear volume elements. The criteria governing the macrocrack propagation is based on the softening behavior observed in concrete under uniaxial tension. This softening behavior corresponds to a dissipated cracking energy that is equal to the mode I critical fracture energy (GIC) used in the Linear Elastic Fracture Mechanics theory (LEFM). The probabilistic nature of this model revolves around the random distribution of two mechanical properties: tensile strength (ft) and fracture energy, which varies based on the volume of finite elements. The scattering of the fracture energy increases as the volume of finite elements decreases in order to consider the strong heterogeneity of the material. This work primarily aims to estimate the relationship between the standard deviation of GIC and the volume of finite elements through the development of the numerical model. For this purpose, an inverse analysis is conducted based on a fracture mechanical test simulation. This test involves macrocrack propagation in a large Double Cantilever Beam (DCB) specimen with a crack length exceeding two meters. The proposed inverse analysis procedure yields highly significant results, indicating that the numerical model effectively evaluates both crack length and crack opening during propagation.

Dynamics of changes in the temperature coefficient of electrical conductivity of distilled water in conductometric cells during heating and cooling

The problem of taking into account the influence of air on the properties of distilled water is considered, namely the lack of a single generally accepted method for calculating such an influence. The sensitivity of the structure of water to the influence of external factors is described and the possibility of recording and studying such factors by changes in the temperature coefficient of electrical conductivity of water is shown. The dependences of the temperature coefficient of electrical conductivity of distilled water on the rate of change in water temperature, the degree of filling of conductometric cells, as well as on the intensity of the exchange of carbon dioxide between water and air across their interface have been studied. It is noted that these metabolic processes are currently insufficiently studied. A hardware-software measuring complex has been developed and manufactured to study the temperature coefficient of electrical conductivity of water when its temperature changes within the range of 20–55 °C. The temperature coefficient of electrical conductivity of water in sealed conductometric cells was measured at different degrees of filling the cells with distilled water and the rate of heating and cooling of water. The degree of filling of the cells varied within the range of 10–100 %, the rate of change in water temperature varied within the range of 0.04–2.00 °C/min. With a constant heating and cooling time of 15 minutes in all experiments, the change in speed was achieved by changing the temperature of the heating element. The integral temperature coefficient of electrical conductivity is calculated based on the initial and final values of electrical conductivity and water temperature in each measurement cycle. The dependences of the temperature coefficient of electrical conductivity on the rate of change in water temperature at several constant degrees of cell filling were obtained. It has been shown that with a constant ratio of the volumes of water and air in the cell and an increase in the rate of heating of water, the temperature coefficient of electrical conductivity of water decreases by 19–22 %. It has been established that at a constant rate of water heating, with a decrease in the volume of water in the cell, the temperature coefficient of electrical conductivity of water decreases by 40–42 %. The results obtained can be used to quantify the dissociation coefficient of carbonic acid, the mobility of hydrogen ions, as well as the intensity of the gas exchange process under various external influences on water. Refinement of data on the electrical conductive properties of water and processes at the water/air interface is necessary for the development of models of atmospheric phenomena and climate change, as well as for the creation of sensors for weak changes in environmental parameters for the purposes of both environmental monitoring and medical diagnostics.

Model design choices impact biological insight: Unpacking the broad landscape of spatial-temporal model development decisions.

Computational models enable scientists to understand observed dynamics, uncover rules underlying behaviors, predict experimental outcomes, and generate new hypotheses. There are countless modeling approaches that can be used to characterize biological systems, further multiplied when accounting for the variety of model design choices. Many studies focus on the impact of model parameters on model output and performance; fewer studies investigate the impact of model design choices on biological insight. Here we demonstrate why model design choices should be deliberate and intentional in context of the specific research system and question. In this study, we analyze agnostic and broadly applicable modeling choices at three levels-system, cell, and environment-within the same agent-based modeling framework to interrogate their impact on temporal, spatial, and single-cell emergent dynamics. We identify key considerations when making these modeling choices, including the (i) differences between qualitative vs. quantitative results driven by choices in system representation, (ii) impact of cell-to-cell variability choices on cell-level and temporal trends, and (iii) relationship between emergent outcomes and choices of nutrient dynamics in the environment. This generalizable investigation can help guide the choices made when developing biological models that aim to characterize spatial-temporal dynamics.

Development and verification of mechanistic vaginal absorption and metabolism model to predict systemic exposure after vaginal ring and gel application.

The current work describes the development of mechanistic vaginal absorption and metabolism model within Simcyp Simulator to predict systemic concentrations following vaginal application of ring and gel formulations. Vaginal and cervix physiology parameters were incorporated in the model development. The study highlights the model assumptions including simulation results comparing systemic concentrations of 5 different compounds, namely, dapivirine, tenofovir, lidocaine, ethinylestradiol and etonogestrel, administered as vaginal ring or gel. Due to lack of data, the vaginal absorption parameters were calculated based on assumptions or optimized. The model uses release rate/in vitro release profiles with formulation characteristics to predict drug mass transfer across vaginal tissue into the systemic circulation. For lidocaine and tenofovir vaginal gel, the predicted to observed AUC0-t and Cmax ratios were well within 2-fold error limits. The average fold error (AFE) and absolute AFE indicating bias and precision of predictions range from 0.62 to 1.61. For dapivirine, the pharmacokinetic parameters are under and overpredicted in some studies due to lack of formulation composition details and relevance of release rate used in ring model. The predicted to observed AUC0-t and Cmax ratios were well within 2-fold error limits for etonogestrel and ethinylestradiol vaginal ring (AFEs and absolute AFEs from 0.84 to 1.83). The current study provides first of its kind physiologically based pharmacokinetic framework integrating physiology, population and formulation data to carry out in silico mechanistic vaginal absorption studies, with the potential for virtual bioequivalence assessment in the future.

Updated Predictive Models for Permanent Seismic Displacement of Slopes for Greece and Their Effect on Probabilistic Landslide Hazard Assessment

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope’s yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed.

Data model-based toolpath generation techniques for CNC milling machines

Introduction: With the development of computer technology and data modeling, the use of point cloud models to generate tool paths is particularly important for improving productivity and accuracy.Methods: This study proposes a new method that first preprocesses the point cloud data using four-point denoising and octree methods to improve processing efficiency. Subsequently, roughing tool paths were analyzed using the layer slicing method and finishing paths using the residual height method.Results and Discussion: The experimental results show that the layer slicing method has a minimum error close to 10% on the roughing path generation and the computation time is reduced to 35 s, while the residual height method has an error rate of 10.17% on the finishing path and the computation time is only 11.82 s, which reflects a high trajectory smoothness and accuracy. The above results show that the study not only optimizes the tool path generation process and improves the machining efficiency and accuracy, but also demonstrates the potential application of point cloud models in the machining of complex parts.Conclusion: The novel tool roughing and finishing methods provide more reliable path planning for actual machining operations, and future research will be devoted to further improving the performance of the data processing algorithms and exploring more efficient path planning strategies to facilitate automated production.

Creativity Through Embodied Movement in Expressive Activities: Conceptualising a New Pedagogical Model

ABSTRACT This work aimed to outline the first stages for the development of a pedagogical model for teaching-learning of expressive content within physical education (creative body movement). Drawing on several suggestions from previous models’ conceptualizations, we explore and present the theoretical foundations, practical considerations, and assumptions that align and support their core features. The major theme of the model is that students explore new expressive movements and develop capabilities through expressive games or tasks that nurture their creativity and ease their embodied movements. This implies that teachers should provide students with several resources and stimuli to nurture their creativity. Also, teachers should promote students’ embodiment through the exploration of new movements that allow knowing themselves in different movement situations. The affective and cognitive domains will be prominent in this model. The teachers’ and students’ assumptions, implementation needs, and enactment about how to apply this model are also proposed.

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation.

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with anormalized root mean squared error(N-RMSE)of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

Modeling spatial groundwater level patterns of Bangladesh using physio-climatic variables and machine learning algorithms

Groundwater monitoring is essential for sustainable groundwater resource management in a country like Bangladesh, where this precious resource is gradually declining due to over-extraction. However, traditional methods of acquiring groundwater level (GWL) data over a large area are time-consuming and expensive. To address this, this study proposes an alternative approach using freely available daily groundwater storage (GWS) gridded data from the Global Land Data Assimilation System (GLDAS) and other data sources such as population, rainfall, temperature, irrigation, and elevation. By employing different regression and machine learning models like multiple linear regression (MLR), regression trees, support vector machines (SVM), Gaussian process regression (GPR), and artificial neural networks (ANN), the study aimed to model GWL data for Bangladesh at a spatial resolution of 0.25° × 0.25°. In-situ weekly GWL data collected from 844 locations across the country were used for model development. The results indicated that GWS data alone was insufficient to estimate the spatial variability and trend of groundwater in Bangladesh. However, the comparison of different models showed that the ANN model performed better, with an overall correlation coefficient (R) of 0.95 and mean squared error (MSE) of 0.64 m2 when estimating GWL using GWS and other data. The study also identified population and rainfall as the most influential factors in determining GWL. The developed ANN model can be utilized to estimate GWL at locations where observation data are unavailable, enabling the monitoring of GWL for sustainable groundwater management in Bangladesh.

Bitcoin Price Prediction Model Development Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM)

Cryptocurrency is a virtual currency that can be used as a financial or economic standard, foreign currency reserve, and as a means of payment in some countries. The value that goes up and down every time is not easy to predict using logic. This is a problem for investors, besides that investors lack knowledge about the direction of crypto money movement. In addition, there is no system that can predict the price of Bitcoin, so this can cause investors to take the wrong steps in transactions and can cause losses. To avoid this risk, a system is needed that can predict bitcoin prices using data mining techniques, namely forecasting, the algorithms used are CNN and LSTM. The data used is Bitcoin closing price data from January 1, 2017, to April 26, 2023. The data is divided into 80% training data and 20% testing data. The prediction results are evaluated using MAPE which gets a MAPE value of 0.037 or 3.7% in the CNN algorithm, while the LSTM algorithm gets a value of 0.065 or 6.5%. The MAPE results of the two algorithms are in the MAPE range &lt;10%, so it can be said that the ability of the forecasting model is very good so that it can be used as a reference to determine the prediction of bitcoin prices in the next few periods.

Evolution in Development of a Predictive Deep-Learning Model for Total Hip Replacement Based on Radiographs: Commentary on an article by Yi Xu, MD, et al.: "Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs. The Total Hip Replacement Prediction (THREP) Model".

Evolution in Development of a Predictive Deep-Learning Model for Total Hip Replacement Based on Radiographs: Commentary on an article by Yi Xu, MD, et al.: "Development and Validation of a Deep-Learning Model to Predict Total Hip Replacement on Radiographs. The Total Hip Replacement Prediction (THREP) Model".