Selection Of Parameter Sets Research Articles

MicroBundleCompute: Automated segmentation, tracking, and analysis of subdomain deformation in cardiac microbundles.

Advancing human induced pluripotent stem cell derived cardiomyocyte (hiPSC-CM) technology will lead to significant progress ranging from disease modeling, to drug discovery, to regenerative tissue engineering. Yet, alongside these potential opportunities comes a critical challenge: attaining mature hiPSC-CM tissues. At present, there are multiple techniques to promote maturity of hiPSC-CMs including physical platforms and cell culture protocols. However, when it comes to making quantitative comparisons of functional behavior, there are limited options for reliably and reproducibly computing functional metrics that are suitable for direct cross-system comparison. In addition, the current standard functional metrics obtained from time-lapse images of cardiac microbundle contraction reported in the field (i.e., post forces, average tissue stress) do not take full advantage of the available information present in these data (i.e., full-field tissue displacements and strains). Thus, we present "MicroBundleCompute," a computational framework for automatic quantification of morphology-based mechanical metrics from movies of cardiac microbundles. Briefly, this computational framework offers tools for automatic tissue segmentation, tracking, and analysis of brightfield and phase contrast movies of beating cardiac microbundles. It is straightforward to implement, runs without user intervention, requires minimal input parameter setting selection, and is computationally inexpensive. In this paper, we describe the methods underlying this computational framework, show the results of our extensive validation studies, and demonstrate the utility of exploring heterogeneous tissue deformations and strains as functional metrics. With this manuscript, we disseminate "MicroBundleCompute" as an open-source computational tool with the aim of making automated quantitative analysis of beating cardiac microbundles more accessible to the community.

Handling Imbalanced Data With Weighted Logistic Regression and Propensity Score Matching methods

The adoption of empirical methods for secondary data analysis has witnessed a significant surge in IS research. However, the secondary data is often incomplete, skewed, and imbalanced at best. Consequently, there is a growing recognition of the importance of empirical techniques and methodological decisions made to navigate through such issues. However, there is not enough methodological guidance, especially in the form of a worked case study that demonstrates the challenges of imbalanced datasets and offers prescriptive on how to deal with them. Using data on P2P money transfer services, this article presents a running example by analyzing the same dataset using several different methods. It then compares the outcomes of these choices and explicates the rationale behind some decisions such as inclusion and categorization of variables, parameter setting, and model selection. Finally, the article discusses certain regressions models such as weighted logistic regression and propensity matching, and when they should be used.

An adaptive evolutionary multi-objective clustering based on the data properties of the base partitions

Evolutionary multi-objective clustering (EMOC) is a modern clustering technique in which the general concepts of evolutionary multi-objective optimization are applied to the clustering problem. The design and definition of clustering are difficult problems, and the choice of the objective functions and parameter setting of the algorithms are still challenges. In this paper, we propose the adaptive evolutionary multi-objective clustering approach based on data properties (AEMOC). AEMOC considers a new metric to evaluate the base partitions (candidate solutions used in the initial population) generated by minimum spanning tree clustering. This metric is applied to: (1) determine properties of the data, such as separation and overlap; and (2) define an offline selection of objective functions and parameter settings of the multi-objective algorithm. The information regarding the data properties allows AEMOC to avoid unnecessary data processing in datasets in which the initialization provides optimal solutions and optimization is not required. AEMOC presented promising results considering a diverse set of artificial and real-life datasets, based on two aspects: (1) it succeeded in the determination of the data properties of the base partitions and verifying the potential clustering quality, presenting a correlation of 0.8 with a reference metric, and (2) it provided better clustering results than reference EMOC approaches, achieving a general gain in the clustering performance of 3% in real-life datasets and 7% in artificial datasets.

Predicting the length-of-stay of pediatric patients using machine learning algorithms

The management of hospitals’ resource capacity has a strong impact on the quality of care, and the length-of-stay (LOS) of patients is an indicator that reflects its efficiency and effectiveness. This study aims at predicting the LOS of pediatric patients (LOS-P) in hospitals to assist in decision-making regarding resource utilisation. LOS-P forecasting presents additional challenges to the analyst compared to other medical specialties since Pediatrics comprises several other subspecialties (e.g. pediatric oncology and traumatology). Pediatric patients within subspecialties compete for the same hospital resources, and aggregate LOS-P predictions are more useful for resource planning. However, aggregate pediatric LOS datasets are harder to model and result in lower forecasting accuracy. To address that problem, we propose a forecasting model based on Machine Learning algorithms. The method for LOS-P forecasting comprises five steps (data visualisation, data pre-processing, sample partitioning, model testing, and model definition through parameter setting and variable selection) and is tested using a dataset of hospitalisations of pediatric patients from a large Brazilian University hospital. Multiple linear regression, random forest, support vector regression, ridge regression, and partial least squares algorithms are applied and compared to determine the best forecasting model. Results indicate that all forecasting models yield satisfactory accuracy, with the best algorithms being random forest and support vector regressor. After refining the model through variable selection and using a Grid Search to find the best parameters, the random forest algorithm yielded an R2 of 65.67%, with an average absolute error of 3.51 days. Highlights Prediction of the length of stay of pediatric patients (LOS-P) in hospitals based on Machine Learning algorithms Multiple linear regression, random forest, support vector regression, ridge regression, and partial least squares algorithms were applied and compared Random forest algorithm yielded an R 2 of 65.67%, with an average absolute error of 3.51 days

DAYLIGHTING PERFORMANCE SIMULATION: PREDICTION ACCURACY/PROCESSING SPEED TRADE-OFF

ABSTRACT In daylighting performance simulations of façade systems, a trade-off is often required between processing speed and prediction accuracy. This is particularly relevant at design onset, where plausible simulation outcomes are essential to drive decisions between several alternative façade configurations. To help address this trade-off, this paper presents a sensitivity analysis evaluating the influence of key input parameter settings, namely ambient bounces and grid size, on the convergence of performance outcomes and on simulation run times. The results provide statistical evidence that, although lower precision settings mostly accelerate calculations, they decrease the accuracy of prediction estimates, particularly for complex façades. Conversely, the relative increased accuracy resulting from higher precision simulations might reach a point where differences have a negligible practical impact. The paper concludes with a range of recommendations to support the early-stage selection of parameter settings and contributes to more robust simulation outcomes towards reducing the gap between simulated and measured data.

Addressing Parameter Uncertainty in a Health Policy Simulation Model Using Monte Carlo Sensitivity Methods

We present a practical guide and step-by-step flowchart for establishing uncertainty intervals for key model outcomes in a simulation model in the face of uncertain parameters. The process starts with Powell optimization to find a set of uncertain parameters (the optimum parameter set or OPS) that minimizes the model fitness error relative to historical data. Optimization also helps in refinement of parameter uncertainty ranges. Next, traditional Monte Carlo (TMC) randomization or Markov Chain Monte Carlo (MCMC) is used to create a sample of parameter sets that fit the reference behavior data nearly as well as the OPS. Under the TMC method, the entire parameter space is explored broadly with a large number of runs, and the results are sorted for selection of qualifying parameter sets (QPS) to ensure good fit and parameter distributions that are centrally located within the uncertainty ranges. In addition, the QPS outputs are graphed as sensitivity graphs or box-and-whisker plots for comparison with the historical data. Finally, alternative policies and scenarios are run against the OPS and all QPS, and uncertainty intervals are found for projected model outcomes. We illustrate the full parameter uncertainty approach with a (previously published) system dynamics model of the U.S. opioid epidemic, and demonstrate how it can enrich policy modeling results.

DPCF: A framework for imputing missing values and clustering data in drug discovery process

The advent of modern Internet of Things (IoT) architectures has led to ease in data collection and availability. The data generated from such architectures are of large volume and dimensionality. As a result, data missingness and data labeling are the commonly occurring problems in the data collection process when data volume is too large. Data clustering is a commonly used unsupervised pattern classification technique that helps in identifying the hidden structure of the datasets and clusters or groups similar data items together. In context to chemometrics, clustering techniques play a significant role in identifying the structure-property relationships and structure-activity relationships among different compounds in drug discovery process. The quantitative structure-property relationships (QSPR) are based on the similar property principle which states that if the compounds are clustered together based on structural descriptors, the compounds within the same cluster will have similar properties. The quantitative structure-activity relationships (QSAR) help in determining the empirical relationships between chemical structure and biological activity of a set of identical compounds. Hence, a variety of compounds can be divided into homogeneous subsets using cluster analysis. The effectiveness of any clustering algorithm mostly depends on how efficiently the initial cluster centers are identified. Traditional techniques that mostly rely on the random selection of initial cluster centers and different parameter settings may result in distinct clusters for the same dataset. Moreover, if the initial cluster centers selected are outliers then the clusters formed are of poor quality. The quality of each cluster can be determined by using distinct cluster validation indices (CVIs). This paper aims to solve the problems of data missingness as well as data labeling for unsupervised datasets. In continuation to the previous study where the NMVI (Nullify the Missing Values before Imputation) technique efficiently imputes the missing values in different datasets, in this paper the proposed DPCF (Data Partitioning-based Clustering Framework) framework utilize the NMVI technique to impute the missing values and a novel Z-Clust clustering algorithm is proposed that efficiently clusters the unlabeled data samples. The integration of the NMVI technique and Z-Clust clustering technique in the proposed framework makes it well suited for the analysis of unsupervised datasets having missing values. The performance evaluation of the proposed Z-Clust clustering technique is done by using five standard CVIs and the results are compared with the existing clustering techniques. The experimental results depict that the proposed Z-Clust clustering technique shows better cluster formation as compared to the existing clustering techniques. Henceforth, the proposed DPCF framework is well suited for the analysis of datasets without labels and having missing values.

Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high-throughput experiments.

High-throughput biological experiments are essential tools for identifying biologically interesting candidates in large-scale omics studies. The results of a high-throughput biological experiment rely heavily on the operational factors chosen in its experimental and data-analytic procedures. Understanding how these operational factors influence the reproducibility of the experimental outcome is critical for selecting the optimal parameter settings and designing reliable high-throughput workflows. However, the influence of an operational factor may differ between strong and weak candidates in a high-throughput experiment, complicating the selection of parameter settings. To address this issue, we propose a novel segmented regression model, called segmented correspondence curve regression, to assess the influence of operational factors on the reproducibility of high-throughput experiments. Our model dissects the heterogeneous effects of operational factors on strong and weak candidates, providing a principled way to select operational parameters. Based on this framework, we also develop a sup-likelihood ratio test for the existence of heterogeneity. Simulation studies show that our estimation and testing procedures yield well-calibrated type I errors and are substantially more powerful in detecting and locating the differences in reproducibility across workflows than the existing method. Using this model, we investigated an important design question for ChIP-seq experiments: How many reads should one sequence to obtain reliable results in a cost-effective way? Our results reveal new insights into the impact of sequencing depth on the binding-site identification reproducibility, helping biologists determine the most cost-effective sequencing depth to achieve sufficient reproducibility for their studygoals.

Design and Simulation Study of the Induction Heated Injection Mold with Sliders.

In order to increase the quality of the products manufactured by injection molding, RTC technology can be used to achieve higher mold temperatures. As a result, the path of the injected melt can be extended, allowing the production of parts with more complex shapes and greater length. Induction heating allows heating only selected forming surfaces of the mold which increases the speed and efficiency of the process. This paper presents the concept of a detachable inductor integrated with sliders to enable the application of this technology in an injection mold with sliders, along with the theoretical model used to perform the tests. First, the effect of the magnetic concentrator shape on the process was analyzed. This was followed by a simulation study of the influence of process parameters: heating time, frequency, and electric current. An extensive analysis of the test results of the temperature distribution on the insert allowed for the selection of parameter sets that would enable obtaining the desired surface temperature without a major increase in process time. The results of simulation studies confirm the possible applications and present the range of parameters for obtaining the optimal process.

Delineating modelling uncertainty in river flow indicators with representative parameter sets

For mitigating negative effects of floods and droughts, estimates of flow indicators and their uncertainties are essential. The recently introduced concept of the representative parameter sets (RPSs) enables modelling uncertainty to be represented in the flow frequency space at low computational cost, using a small subset of pre-selected model parameter sets. This concept is here adapted to assess hazards of three flow indicators: annual maximal flow, annual 7-day-average low flow, and annual mean flow. An additional in-depth analysis assesses the RPS transferability to other flow indicators and to hydrological signatures. RPS-based simulations are benchmarked with a random selection of parameter sets. The results show that i) RPSs can be successfully transferred between flow indicators with only a small drop in model performance; and ii) RPSs can be used to represent modelling uncertainty in hydrological signatures. The RPS concept has thus great potential for delineating modelling uncertainty of any environmental model.

Multi-Objective Optimization of WEDM of Aluminum Hybrid Composites Using AHP and Genetic Algorithm

Aluminum hybrid composites have the potential to satisfy emerging demands of lightweight materials with enhanced mechanical properties and lower manufacturing costs. There is an inclusion of reinforcing materials with variable concentrations for the preparation of hybrid metal matrix composites to attain customized properties. Hence, it is obligatory to investigate the impact of different machining conditions for the selection of optimum parameter settings for aluminum-based hybrid metal matrix composite material. The present study aims to identify the optimum machining parameters during wire electrical discharge machining of samples prepared with graphite, ferrous oxide, and silicon carbide. In the present research work, five different process parameters and three response parameters such as material removal rate, surface roughness, and spark Gap are considered for process optimization. Energy-dispersive spectroscopy and scanning electron microscopy analysis reported the manifestation of the recast layer. Analytical hierarchy process and genetic algorithm have been successfully implemented to identify the best machining conditions for hybrid composites.

Backside Chippings Improvement through Wafer Dicing Parameter Optimization and Understanding the Anistropic Silicon Properties

Semiconductor Companies and Industries soar high as the trend for electronic gadgets and devices increases. Transition from “manual” to “fully automatic” application is one of the advantages why consumer adapt to changes and prefer electronic devices as one of daily answers. Individuals who admire these electronic devices often ask how they are made.
 As we look inside each device, we can notice interconnected microchips commonly called IC (Integrated Circuit). These are specially prepared silicon wafers where integrated circuit are developed. Commonly, each device is composed of numerous microchips depending on the design and functionality
 IC production is processed from “front-end” to “back-end” assembly. Front-end assembly includes wafer fabrication where electrical circuitry is prepared and integrated to every single silicon wafers. Back-end assembly covers processing the wafer by cutting into smaller individual and independent components called “dice”. Each dice will be placed into Leadframe, bonded with wires prior encapsulating with mold compounds. After molding, each IC will be cut through a process called singulation. Afterwards, all molded units are subjected for functional testing.
 Dice is central to each IC; it is where miniature transistor, resistor and capacitor are integrated to form complex small circuitry in microchips.
 Pre-assembly (Pre-assy) stations have the first hand prior to all succeeding stations. Live wafers are primary direct materials processed in these stations. Robust work instruction and parameter must be practiced during handling and processing to avoid gross rejection and possible work-related defects.
 The paper is all about the challenges to resolve and improved the backside chippings in 280um wafer thickness in mechanical dicing saw. The conventional Mechanical dicing process induce a lot of mechanical stress and vibration during the cutting process which oftentimes lead to backside chipping and die crack issues. However, backside chippings can mitigate with proper selection of parameter settings and understand the silicon wafer properties.

Neural network modeling of consumer satisfaction in mobile commerce: An empirical analysis

The mobile commerce (m-commerce) industry has rapidly grown in value in recent years, as has the number of m-commerce service providers and interest in it from consumers and academia alike. In order to ensure customer loyalty, providers must determine which factors influence consumer satisfaction in m-commerce. Therefore, the objective of this study is to determine and rank the significant predictors of satisfaction in m-commerce. The paper also develops a procedure for artificial neural network model design and parameter setting in technology acceptance studies. Data was collected from 224 users of m-commerce services. The results presented are based on a combination of structural equation modeling (SEM) and artificial neural network (ANN) analyses. A multi-layer perceptron was used for ANN modeling. The results show that the optimal ANN model has one hidden layer and a sigmoid as an activation function in both layers, while the number of hidden nodes should be determined using a recommended rule-of-thumb. In addition, mobility and trust were found to be the most significant determinants of consumer satisfaction in m-commerce. The results of the study are significant as they have important implications for both academia and companies, due to the fact that some of the factors investigated in the study, such as mobility, have rarely been explored in previous consumer satisfaction studies, but were proved to be very significant. Another important result of the study is the proposal of a detailed procedure of ANN model design and the recommendations made for the selection of ANN model architecture and parameter settings.

Area optimized design of tetrad flip-flop using new tetrad value logic gate’s

Logic levels in digital systems are predefined voltage and current range and a defined difference in voltage and current value signify binary information’s in digital systems. There are many logic families available to define binary data. In binary logic, there are two logic levels 1 and 0. This paper defines a new tetrad value logic (TVL) which can have four logic levels 0,1,2 and 3. This work presented novel designs of tetrad value logic NOT gate, AND gate and OR gate. With the help of these tetrad logic gates, a tetrad logic Flip-flop has been designed and tested on Tanner Electronic Design Automation (EDA) tool. The proposed tetrad flip-flop is a memory cell of tetrad Value Logic (TVL) which can store Logic 0, Logic 1, Logic 2, and Logic 3 when the input clock is off, and on the positive edge of the input clock, it can change the past stored logic with new input logic. This work discusses the possible application of tetrad logic. Simulation of tetrad logic gates and flip flop performed with help of tanner tool and verifies expected output correctly for all possible test cases. Parameters like Voltage levels, power consumption, noise margin, MOS count, propagation time, fan-out, noise margin, clock frequency, sink, source current, setup and hold time, are analyzed for proposed tetrad logic gates and flip flop. The S-edit 9.0 is used for the schematic design and T-spice 9.0 is used for parameter setting and design technology selection and W-edit 9.0 is used for simulation observation. The obtained results have been compared with other similar work and found better.

Early Recognition and Discrimination of COVID-19 Severity Using Slime Mould Support Vector Machine for Medical Decision-Making

COVID-19 has spread rapidly across the world, leading to the insufficiency of medical resources in many regions. Early detection and identification of high-risk COVID-19 patients will contribute to early intervention and optimize medical resource allocation. Using the clinical data from the Affiliated Yueqing Hospital of Wenzhou Medical University (Yueqing, China), an evolutionary support vector machine model is designed to recognize and discriminate the severity of the COVID-19 by patients basic information and hematological indexes. The support vector machine is a frequently used pattern classification tool affected by both the kernel parameter setting and feature selection for its classification accuracy. This study recommends an enhanced Slime Mould Algorithm (ESMA), mixing a new movement strategy of white holes, black holes, and wormholes, to perform parameter optimization and feature selection simultaneously for SVM. Therefore, the proposed SVM framework (ESMA-SVM) can also obtain high-quality classification results, and it is less prone to stagnation in the classification process. To verify the capabilities of the proposed methodology, first, the performance of the ESMA is thoroughly verified by using IEEE CEC2017 benchmark functions and the diversity and compared with other similar methods experimentally using these standard benchmark functions. Moreover, the balance between diversification and intensification capability of the enhanced ESMA and the original SMA is also investigated statistically. Finally, the designed model ESMA-SVM and other competitive SVM models based on other optimization algorithms are applied to early recognition and discrimination of COVID-19 severity. Through the analysis of experimental results, the core compensations of ESMA are confirmed, and the ESMA-SVM can obtain strong performance in terms of several performance evaluation indexes on discrimination of COVID-19 severity.

Strengthen EEG-based emotion recognition using firefly integrated optimization algorithm

Emotion recognition is helpful for human to enhance self-awareness and respond appropriately towards the happenings around them. Due to the complexity and diversity of emotions, EEG-based emotion recognition is still a challenging task in pattern recognition. In order to recognize diverse emotions, we propose a novel firefly integrated optimization algorithm (FIOA) in this paper. It can simultaneously accomplish multiple tasks, i.e. the optimal feature selection, parameter setting and classifier selection according to different EEG-based emotion datasets. The FIOA utilizes a ranking probability objection function to guarantee the high accuracy recognition with less features. Moreover, the hybrid encoding expression and the dual updating strategy are developed in the FIOA so as to realize the optimal selection of feature subset and classifier without stagnating in the local optimum. In addition to the public DEAP datasets, we also conducted an EEG-based music emotion experiment involving 20 subjects for the validation of the proposed FIOA. After filtering and segmentation, three categories of features were extracted from every EEG signal. Then FIOA was applied to every subject dataset for two pattern recognition of emotions. The results show that the FIOA can automatically find the optimal features, parameter and classifier for different emotion datasets, which greatly reduces the artificial selection workload. Furthermore, comparing with the binary particle swarm optimization (PSObinary) and the binary firefly (FAbinary), the FIOA can achieve the higher accuracy with less features in the emotion recognition.

Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis

Support vector machine (SVM) is a widely used pattern classification method that its classification accuracy is greatly influenced by both kernel parameter setting and feature selection. Therefore, in this study, to perform parameter optimization and feature selection simultaneously for SVM, we propose an improved whale optimization algorithm (CMWOA), which combines chaotic and multi-swarm strategies. Using several well-known medical diagnosis problems of breast cancer, diabetes, and erythemato-squamous, the proposed SVM model, termed CMWOAFS-SVM, was compared with multiple competitive SVM models based on other optimization algorithms including the original algorithm, particle swarm optimization, bacterial foraging optimization, and genetic algorithms. The experimental results demonstrate that CMWOAFS-SVM significantly outperformed all the other competitors in terms of classification performance and feature subset size.

Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning.

MotivationSelecting the optimal machine learning (ML) model for a given dataset is often challenging. Automated ML (AutoML) has emerged as a powerful tool for enabling the automatic selection of ML methods and parameter settings for the prediction of biomedical endpoints. Here, we apply the tree-based pipeline optimization tool (TPOT) to predict angiographic diagnoses of coronary artery disease (CAD). With TPOT, ML models are represented as expression trees and optimal pipelines discovered using a stochastic search method called genetic programing. We provide some guidelines for TPOT-based ML pipeline selection and optimization-based on various clinical phenotypes and high-throughput metabolic profiles in the Angiography and Genes Study (ANGES).ResultsWe analyzed nuclear magnetic resonance-derived lipoprotein and metabolite profiles in the ANGES cohort with a goal to identify the role of non-obstructive CAD patients in CAD diagnostics. We performed a comparative analysis of TPOT-generated ML pipelines with selected ML classifiers, optimized with a grid search approach, applied to two phenotypic CAD profiles. As a result, TPOT-generated ML pipelines that outperformed grid search optimized models across multiple performance metrics including balanced accuracy and area under the precision-recall curve. With the selected models, we demonstrated that the phenotypic profile that distinguishes non-obstructive CAD patients from no CAD patients is associated with higher precision, suggesting a discrepancy in the underlying processes between these phenotypes.Availability and implementationTPOT is freely available via http://epistasislab.github.io/tpot/.Supplementary information Supplementary data are available at Bioinformatics online.

Real-Time Human-In-The-Loop Simulation with Mobile Agents, Chat Bots, and Crowd Sensing for Smart Cities.

Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods.

A theoretical and numerical investigation of a family of immersed finite element methods

In this article we consider the widely used immersed finite element method (IFEM), in both explicit and implicit form, and its relationship to our more recent one-field fictitious domain method (FDM). We review and extend the formulation of these methods, based upon an operator splitting scheme, in order to demonstrate that both the explicit IFEM and the one-field FDM can be regarded as particular linearizations of the fully implicit IFEM. However, the one-field FDM can be shown to be more robust than the explicit IFEM and can simulate a wider range of solid parameters with a relatively large time step. In addition, it can produce results almost identical to the implicit IFEM but without iteration inside each time step. We study the effect on these methods of variations in viscosity and density of fluid and solid materials. The advantages of the one-field FDM within the IFEM framework are illustrated through a selection of parameter sets for two benchmark cases.