Optimizing Multi Characteristics in Drilling of GFRP Composite Using Utility Concept with Taguchi's Approach

AbstractIn the drilling process of chopped glass fibre reinforced polyester composites; the delamination is a major problem. The delamination reduces the structural integrity of the material, results in poor assembly tolerance and has the potential for long term performance deterioration. Surface roughness is also an important aspect of drilling fibre reinforced polyester which can cause high stress on rivets and bolts, leading to failure. In the modern competitive manufacturing scenario, it is very important to optimize the process parameters to arrive at its full utility. In this present study the utility concept has been applied for multi characteristics optimization and identification of the optimal setting condition of process parameters at various relative weightage values of characteristics. Delamination factor and average surface roughness were taken as the measure of performances for this material and feed rate (f), drill diameter (D), spindle speed (N) and material thickness (t) were chosen as drilling parameters. Experiments were conducted based on Taguchi L9 (34) orthogonal array design. Analysis was performed based on utility method varying the importance of quality characteristics or responses in drilling process. The optimal setting of parameters is expected to be useful for process engineers

An Improved Model Ensembled of Different Hyper-parameter Tuned Machine Learning Algorithms for Fetal Health Prediction

Fetal health is a critical concern during pregnancy as it can impact the well-being of both the mother and the baby. Regular monitoring and timely interventions are necessary to ensure the best possible outcomes. While there are various methods to monitor fetal health in the mother's womb, the use of artificial intelligence (AI) can improve the accuracy, efficiency, and speed of diagnosis. In this study, we propose a robust ensemble model called ensemble of tuned Support Vector Machine and ExtraTrees (ETSE) for predicting fetal health. Initially, we employed various data preprocessing techniques such as outlier rejection, missing value imputation, data standardization, and data sampling. Then, seven machine learning (ML) classifiers including Support Vector Machine (SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), Decision Tree (DT), Random Forest (RF), ExtraTrees (ET), and K-Neighbors were implemented. These models were evaluated and then optimized by hyperparameter tuning using the grid search technique. Finally, we analyzed the performance of our proposed ETSE model. The performance analysis of each model revealed that our proposed ETSE model outperformed the other models with 100% precision, 100% recall, 100% F1-score, and 99.66% accuracy. This indicates that the ETSE model can effectively predict fetal health, which can aid in timely interventions and improve outcomes for both the mother and the baby.Comment: 23 pages, 6 Tables, 5 Figure

A Simple Procedure for Searching Pareto Optimal Front in Machining Process: Electric Discharge Machining

Optimum control parameter setting in complex and stochastic type processes is one of the most challenging problems to the process engineers. As such, effective model development and determination of optimal operating conditions of electric discharge machining process (EDM) are reasonably difficult. In this apper, an easy to handle optimization procedure, weight-varying multiobjective simulated annealing, is proposed and is applied to optimize two conflicting type response parameters in EDM—material removal rate (MRR) and average surface roughness (Ra) simultaneously. A solution set is generated. The Pareto optimal front thus developed is further modeled. An inverse solution procedure is devised so that near-optimum process parameter settings can be determined for specific need based requirements of process engineers. The results are validated

IoMT-Blockchain based Secured Remote Patient Monitoring Framework for Neuro-Stimulation Device

Biomedical Engineering's Internet of Medical Things (IoMT) is helping to improve the accuracy, dependability, and productivity of electronic equipment in the healthcare business. Real-time sensory data from patients may be delivered and subsequently analyzed through rapid development of wearable IoMT devices, such as neuro-stimulation devices with a range of functions. Data from the Internet of Things is gathered, analyzed, and stored in a single location. However, single-point failure, data manipulation, privacy difficulties, and other challenges might arise as a result of centralization. Due to its decentralized nature, blockchain (BC) can alleviate these issues. The viability of establishing a non-invasive remote neurostimulation system employing IoMT-based transcranial Direct Current Stimulation is investigated in this work (tDCS). A hardware-based prototype tDCS device has been developed that can be operated over the internet using an android application. Our suggested framework addresses the problems of IoMTBC-based systems, meets the criteria of real-time remote patient monitoring systems, and incorporates literature best practices in the relevant fields.Comment: 8 Figures and 2 Table

Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections

The early identification of bacteremia is critical for ensuring appropriate treatment of nosocomial infections in intensive care unit (ICU) patients. The aim of this study was to use flow cytometric data of myeloid cells as a biomarker of bloodstream infection (BSI). An eight-color antibody panel was used to identify seven monocyte and two dendritic cell subsets. In the learning cohort, immunophenotyping was applied to (1) control subjects, (2) postoperative heart surgery patients, as a model of noninfectious inflammatory responses, and (3) blood culture-positive patients. Of the complex changes in the myeloid cell phenotype, a decrease in myeloid and plasmacytoid dendritic cell numbers, increase in CD14(+)CD16(+) inflammatory monocyte numbers, and upregulation of neutrophils CD64 and CD123 expression were prominent in BSI patients. An extreme gradient boosting (XGBoost) algorithm called the “infection detection and ranging score” (iDAR), ranging from 0 to 100, was developed to identify infection-specific changes in 101 phenotypic variables related to neutrophils, monocytes and dendritic cells. The tenfold cross-validation achieved an area under the receiver operating characteristic (AUROC) of 0.988 (95% CI 0.985–1) for the detection of bacteremic patients. In an out-of-sample, in-house validation, iDAR achieved an AUROC of 0.85 (95% CI 0.71–0.98) in differentiating localized from bloodstream infection and 0.95 (95% CI 0.89–1) in discriminating infected from noninfected ICU patients. In conclusion, a machine learning approach was used to translate the changes in myeloid cell phenotype in response to infection into a score that could identify bacteremia with high specificity in ICU patients