Optimal Set Of Parameters Research Articles

Numerical evaluation of wear parameters using meta-models

AbstractWear parameters identified by linear friction tester (LFT) experiments overestimate the wear mass loss when applied on a laboratory abrasion & skid tester 100 (LAT100) simulation. On the other hand, the identification of wear parameters directly from the LAT100 experiment can be challenging since variables such as the contact area and the slip velocity are not measured experimentally and need to be assumed. To improve the identification process, numerical calibration is used as a more suitable approach. This approach relies on meta-models as a target function for minimization. The meta-models are generated using a sample of wear parameters applied to an LAT100 finite element modeling (FEM) to calculate the corresponding wear mass.. In this model, a transport velocity is defined for the rolling simulation, and an arbitrary lagrangian–eulerian (ALE) adaptive meshing approach is adopted for the wear modeling. For the wear model, a combination of archard’s wear model and schallamach’s abrasion law is used. This model contains a pair of wear parameters to be identified. The ALE adaptive meshing technique moves the nodes independently of the material. Since the mesh topology remains the same, failure of the simulation occurs if the wear volume loss exceeds that of the element. Meta-models are created to extend wear modeling beyond this failure. Once the meta-models are created, they are used as a target function for the minimization algorithm. The minimization algorithm aims to find the optimal wear parameters by minimizing the difference between experimentally observed and numerically produced wear mass loss. The minimization algorithm inputs a set of wear parameters into the meta-models which in turn yield a prediction of the wear mass loss. The process is carried out until an optimum parameter set is identified. Such an approach has a lower accuracy if the parameters are identified directly from the experiment using assumptions regarding the contact shear stress and the sliding velocity. Nonetheless, the main advantage of parameters identified using the meta-model approach is the usability of these parameters in an LAT100 model.

Mechanical properties and corrosion behavior of SiC reinforced high entropy alloy (Ni-Co-Fe-Ti) matrix composites produced by microwave and conventional sintering techniques

Abstract The aim of this work is to synthesize SiC reinforced Ni-Co-Fe-Ti composite using powder metallurgy (PM) method. Two sintering methods, conventional sintering (CS) using a tubular furnace and microwave sintering (MWS), are employed to synthesize three different weight percentages of SiC (3 wt.%, 6 wt.%, and 9 wt.%) within a Ni-Co-Fe-Ti matrix. The densification behavior of synthesized Ni-Co-Fe-Ti-SiC composites are studied using sinterability function. The highest sinterability of 0.89 was achieved for Ni-Co-Fe-Ti-0 wt.wt.% SiC alloy. The densification rate was found to be higher in MW sintered composites compared to those sintered conventionally. 3.5 wt.% NaCl solution was considered for corrosion examination and it was determined that Ni-Co-Fe-Ti- 9 wt.% SiC MW sintered composite exhibit highest corrosion resistance. Ni-Co-Fe-Ti-9 wt.%SiC MW sintered composite exhibits higher compressive strength of 1050 MPa than conventional sintered Ni-Co-Fe-Ti-9 wt.%SiC (625MPa). Ni-Co-Fe-Ti-6wt.% SiC MW sintered composite possess highest hardness of 58 HRC among other prepared composites. Interestingly, decreasing trend in hardness was observed for further inclusion of SiC with Ni-Co-Ti-Fe matrix. Scanning electron microscopy (SEM) and microstructural characterization technique shows the formation of pores in the conventional sintered composites. But, there is no formation of pores in the MW sintered composite of all the composition. Tribological studies were conducted using pin on disc method. Worn surface morphology was analyzed and it shows that severe pullout was observed in the worn surface of conventional sintered Ni-Co-Fe-Ti- 6wt.%SiC.The best optimum set of parameters (Sliding Speed 1146 rpm, SiC inclusion 6 wt.%, Applied Load 20 N) were found out using Taguchi method.

Data-driven roughness estimation of additively manufactured samples using build angles

Achieving control of Laser Powder Bed Fusion (L-PBF) over the quality of the print is the main motivation for finding an optimum set of parameters in the process. Surface roughness is one of the characteristics of the print that impacts the performance of the desired functionality. This research focus is to relate the build angle with the surface roughness on the L-PBF printed specimens and utilize machine learning methods for roughness estimation of geometric features with varying build angles. The EOS M290 L-PBF printer was used to print Inconel-718 coupons using standard process parameters while varying build angles from 20 to 90 degrees at fixed 5-degree intervals. The specimens’ surface was analyzed using metrology tools and the data obtained was used for training the machine learning models. Machine learning methods are used to create regression models for estimating the roughness of the specimens using the build angle and location of the sample on the substrate. The findings of this study provide build angle-based predictive estimation of surface roughness of printed L-PBF parts. The machine learning model will help to make reliable decisions on choosing the build angle of a complex part based on the desired surface roughness of its geometric features.

Establishing a process route for additive manufacturing of NiCu-based Alloy 400: an alignment of gas atomization, laser powder bed fusion, and design of experiments

Alloy 400 is a corrosion-resistant, NiCu-based material which is used in numerous industrial applications, especially in marine environments and the high-temperature chemical industry. As conventional manufacturing limits geometrical complexity, additive manufacturing (AM) of the present alloy system promises great potential. For this purpose, a robust process chain, consisting of powder production via gas atomization and a design of experiment (DoE) approach for laser powder bed fusion (LPBF), was developed. With a narrow particle size distribution, powders were found to be spherical, flowable, consistent in chemical composition, and, hence, generally applicable to the LPBF process. Copper segregations at grain boundaries were clearly detected in powders. For printed parts instead, low-intensity micro-segregations at cell walls were discovered, being correlated with the iterative thermal stress applied to solidified melt-pool-near grains during layer-by-layer manufacturing. For the production of nearly defect-free LPBF structures, DoE suggested a single optimum parameter set instead of a broad energy density range. The latter key figure was found to be misleading in terms of part densities, making it an outdated tool in modern, software-based process parameter optimization. On the microscale, printed parts showed an orientation of melt pools along the build direction with a slight crystallographic [101] texture. Micro-dendritic structures were detected on the nanoscale being intersected by a high number of dislocations. Checked against hot-extruded reference material, the LPBF variant performed better in terms of strength while lacking in ductility, being attributed to a finer grain structure and residual porosity, respectively.

Wristband Personal Passive Samplers and Suspect Screening Methods Highlight Gender Disparities in Chemical Exposures.

Wristband personal samplers enable human exposure assessments for a diverse range of chemical contaminants and exposure settings with a previously unattainable scale and cost-effectiveness. Paired with nontargeted analyses, wristbands can provide important exposure monitoring data to expand our understanding of the environmental exposome. Here, a custom scripted suspect screening workflow was developed in the R programming language for feature selection and chemical annotations using gas chromatography-high-resolution mass spectrometry data acquired from the analysis of wristband samples collected from five different cohorts. The workflow includes blank subtraction, internal standard normalization, prediction of chemical uses in products, and feature annotation using multiple library search metrics and metadata from PubChem, among other functionalities. The workflow was developed and validated against 104 analytes identified by targeted analytical results in previously published reports of wristbands. A true positive rate of 62 and 48% in a quality control matrix and wristband samples, respectively, was observed for our optimum set of parameters. Feature analysis identified 458 features that were significantly higher on female-worn wristbands and only 21 features that were significantly higher on male-worn wristbands across all cohorts. Tentative identifications suggest that personal care products are a primary driver of the differences observed.

Enhancing High-Alloy Steel Cutting with Abrasive Water Injection Jet (AWIJ) Technology: An Approach Using the Response Surface Methodology (RSM).

The common machining technologies for difficult-to-machine materials do not remarkably ensure acceptable efficiency and precision in bulk materials cutting. High-energy abrasive water injection jet (AWIJ) treatment can cut diverse materials, even multi-layer composites characterized by divergent properties, accurately cutting complex profiles and carrying them out in special circumstances, such as underwater locations or explosion hazard areas. This work reports research on the AWIJ machining quality performance of X22CrMoV12-1 high-alloy steel. The response surface method (RSM) was utilized in modeling. The most influencing process control parameters on cut kerf surface roughness-abrasive flow rate, pressure, and traverse speed-were tested. The result is a mathematical model of the process in the form of a three-variable polynomial. The key control parameter affecting the cut slot roughness turned out to be the traverse speed. In contrast, pressure has a less significant effect, and the abrasive mass flow rate has the slightest impact on the cut slot roughness. Under the optimal conditions determined as a result of the tests, the roughness of the intersection surface Sq does not exceed 2.3 μm. Based on the ANOVA, we confirmed that the model fits over 96% appropriately with the research outcomes. This method reduces the computations and sharply determines the optimum set of control parameters.

Numerical and experimental study on effect of induction heating parameters on the coating of Zn layer on mild steel

The objective of the study is to determine the most effective induction heating parameters for melting pure zinc selectively over the mild steel substrate surface. The numerical simulation on effect of induction heating parameters on the mild steel substrate is studied using COMSOL Multiphysics 5.6 software. The optimised effective parameters are used for the experimental analysis for the validation. Variables such as coil configuration, stand-off distance (SOD), coil current and frequency were systematically changed, and optimal settings were chosen on the basis of time temperature contour curve and the heating rate within the material. Results of the numerical analysis portrayed that a pancake coil geometrical configuration, SOD of 2 mm, coil current of 300 A and alternating frequency of 300 kHz were found as the optimum set of parameters for the effective surface repair. An experimental investigation was carried out by depositing a thin layer of zinc on mild steel substrate using the optimized parameters. The surface integrity of the coated specimen was examined through the high-resolution optical microscopy, X-ray diffraction and electron back scattered diffraction (EBSD) techniques. The hardness property of the coated specimen was examined with a Vickers microhardness tester. The microstructure analysis and hardness measurement showed that consistent coating is achieved without affecting the base metal/substrate properties. The experimental results very much coincide with the numerical simulation.

A fast and robust sliding mode controller for automatic voltage regulators in electrical power systems

In this paper, a fast and robust sliding mode controller (SMC) based on a novel multi-variable sliding function is proposed for Automatic Voltage Regulators (AVRs) in electrical power systems. Lyapunov stability theorem is used to guarantee the asymptotic stability of the proposed controller. As the proposed controller is multi-variable, there may be different optimum controller parameter sets that result in performance close to each other. Hence, two optimum sets of parameters are explored by two well-known algorithms, namely, Sine Cosine Algorithm and Grey Wolf Optimization. A new objective function is considered for the optimizations, including an overshoot constraint. The performance of the proposed controller with the optimum parameter sets is evaluated for five different parameter uncertainties and load disturbance scenarios, and the resulting time domain measurements are compared with the results of recently published SMCs. A 38.36 % improvement in settling time was observed compared to the most recent study using the same realistic AVR model. In addition, the proposed controller suppressed different load disturbances, including parasitic ones, and quickly recovered the AVR output. All results revealed that the proposed controller achieved the best performance in terms of speed and robustness compared to existing controllers, despite the controller and exciter limitations.

Process parameter optimization for laser powder directed energy deposition of Inconel 738LC

Processing nickel-based superalloys is one of the major challenges in the additive manufacturing (AM) field. Nickel-based superalloys consist of several alloying elements, which make them prone to the formation of undesired phases and cracking due to the complex thermodynamics during the metal AM processes. In order to produce defect-free parts from nickel-based superalloys via metal AM processes, optimization of process parameters is required. In this study, the effect of laser powder directed energy deposition (LP-DED) process parameters on the geometrical properties and porosity ratio of Inconel 738 LC (low carbon) is investigated. Geometrical specifications, including the track height, width, and wetting angle are the major factors that indicate the quality of the final product. The analysis of variance (ANOVA) and response surface methodology (RSM) were developed in order to predict the geometrical specifications of single tracks from the key process parameters, namely laser power, scan speed, and powder feed rate. An optimum set of process parameters was obtained through multi-objective optimization by minimizing the porosity ratio, crack formation and maximizing the deposition rate. The results were verified and a good agreement with the experimental measurements was achieved. The produced bulk sample showed less than one percent porosity content, which indicates the effectiveness of single-track optimization prior to bulk sample production. The overall microstructure of the bulk sample showed a high content of MC carbides resulting from the abundance of alloying elements.

Prediction and optimization of hobbing parameters for minimizing energy consumption and gear geometric deviations

Improving gear precision and achieving green sustainability in gear machining are two important aspects of the gear manufacturing process. However, to achieve these two goals simultaneously, it may be necessary to make trade-offs when selecting the gear processing parameters. In this work, both energy consumption and gear geometric deviations were considered simultaneously to optimize the hobbing parameters. The relationships between the hobbing parameters, energy consumption, and gear geometric deviations were modeled using the response surface method (RSM). The statistical significance of the model was tested using analysis of variance (ANOVA). An improved multi-objective particle swarm optimization (IMOPSO) was then performed to solve optimization problems that involved multiple and conflicting objectives in the hobbing process. The results obtained indicate that both the energy consumption ( E) and the gear geometric deviations are parameter-dependent. The feed rate ( f) and the spindle speed ( n) have opposing effects on both energy consumption E and the gear geometric deviations. The optimum hobbing parameter sets obtained from the calculated Pareto frontier can provide a feasible solution for manufacturers to solve the trade-off problems that occur in the hobbing process, and the experimental results confirmed the effectiveness of the IMOPSO approach.

Application of Machine Learning Methods for Process Optimization in Electronic Packaging Processes

Epoxy resins are commonly used as encapsulation materials in electronic packaging processes. Fluctuations in the materials lead to both changes in processability and to varying quality. Ideally, these variations should be identified, and measures taken as quickly as possible to reduce scrap parts and thus costs. A promising optimization approach for encapsulation processes are machine learning models, which have already shown good results in quality predictions, especially for injection molding. Subsequent quality measurements are avoided with good quality prediction models. With this type of models, not only predictions can be made, but also optimal parameter combinations can be found. In this paper, models for predicting quality criteria warpage and residual enthalpy of the epoxy molding compound were set up, trained and validated. Time series of in-situ sensors were used, from which relevant features were extracted and which, together with machine parameters, provide a dataset for prediction. The most promising prediction models are random forest regression and gradient boosting regression. T hey predict warpage with an accuracy of 90% to 91% and the residual enthalpy with an accuracy of 95%. Subsequently, optimization models of the machine parameters were set up. All relevant target variables were considered in a cost function, through the minimization of which an optimal parameter set was found. The gradient boosted tree and Bayesian optimization were determined to be the most promising models, as they lead to the lowest values of the respective cost function.

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs' parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN's outputs to predict the daily closing gold price.

Friction and wear analysis of basalt micro-filler loaded various epoxies and esters based thermoset polymer composites

In this study, thermoset polymers (synthetic epoxy, bio-epoxy, polyester, and vinyl ester) and basalt-reinforced (30 wt%) polymer composites were examined for their hardness and tribological characteristics. Under two levels of basalt weight percentage (0% and 30%), and three levels of load (3, 6, and 9 N), speed (100, 150, and 200 rpm), and distance (100, 150, and 200 m) parameters, the coefficient of friction (COF) and specific wear rate (SWR) are examined. According to the L18 orthogonal array design, which was created using the statistical software MINITAB-19, the tests are carried out using Pin-on-Disk Tribometer as per ASTM G99 standard. The optimum parameters are determined for COF and SWR using Taguchi statistical method. The significance of each parameter and its contribution is analyzed using the ANOVA technique. For synthetic epoxy samples, the optimum set of parameters for the COF response is 30 wt% basalt (2nd rank), 9 N load (4th rank), 100 rpm speed (1st rank), and 150 m distance (3rd rank); for the SWR response, the parameters are 30 wt% basalt (1st rank), 3 N load (2nd rank), 100 rpm speed (4th rank), and 200 m distance (3rd rank). For bio-epoxy samples, the optimum set of parameters for the COF response is 30 wt% basalt (4th rank), 9 N load (1st rank), 150 rpm speed (3rd rank), and 100 m distance (2nd rank); for the SWR response, the parameters are 30 wt% basalt (4th rank), 6 N load (2nd rank), 200 rpm speed (3rd rank), and 200 m distance (1st rank). For polyester samples, the optimum set of parameters for the COF response are 30 wt% basalt (2nd rank), 6 N load (4th rank), 200 rpm speed (1st rank), and 200 m distance (3rd rank); for the SWR response, the parameters are 30 wt% basalt (1st rank), 3 N load (4th rank), 100 rpm speed (3rd rank), and 150 m distance (2nd rank). For vinyl ester samples, the optimum set of parameters for the COF response is 30 wt% basalt (1st rank), 9 N load (3rd rank), 100 rpm speed (4th rank), and 100 m distance (2nd rank); for the SWR response, the parameters are 30 wt% basalt (2nd rank), 6 N load (3rd rank), 100 rpm speed (1st rank), and 100 m distance (4th rank). The ANOVA method demonstrated that the addition of 30 wt% basalt considerably reduced both COF and SWR. The optimum parameters that are not included in the proposed array underwent confirmation tests, and the results showed that the optimum parameters had an error rate that was less than 10% and within the bounds of statistical acceptability. According to the SEM morphology, the basalt particles reduced the material's plastic flow under different processing conditions.

Multiobjective optimization of process parameters of AZ91D/AgNPs/TiO2 composite fabricated by friction stir processing using response surface methodology and desirability

PurposeThis paper aims to determine the optimum parametric settings for yielding superior mechanical properties, namely, ultimate tensile strength (UTS), yield strength (YS) and percentage elongation (EL) of AZ91D/AgNPs/TiO2 hybrid composite fabricated by friction stir processing.Design/methodology/approachAn empirical model has been developed to govern crucial influencing parameters, namely, rotation speed (RS), tool transverse speed (TS), number of passes (NPS) and reinforcement fraction (RF) or weight percentage. Box Behnken design (BBD) with four input parameters and three levels of each parameter was used to design the experimental work, and analysis of variance (ANOVA) was used to check the acceptability of the developed model. Desirability function analysis (DFA) for a multiresponse optimization approach is integrated with response surface methodology (RSM). The individual desirability index (IDI) was calculated for each response, and a composite desirability index (CDI) was obtained. The optimal parametric settings were determined based on maximum CDI values. A confirmation test is also performed to compare the actual and predicted values of responses.FindingsThe relationship between input parameters and output responses (UTS, YS, and EL) was investigated using the Box-Behnken design (BBD). Silver nanoparticles (AgNPs) and nano-sized titanium dioxide (TiO2) enhanced the ultimate tensile strength and yield strength. It was observed that the inclusion of AgNPs led to an increase in ductility, while the increase in the weight fraction of TiO2 resulted in a decrease in ductility.Practical implicationsAZ91D/AgNPs/TiO2 hybrid composite finds enormous applications in biomedical implants, aerospace, sports and aerospace industries, especially where lightweight materials with high strength are critical.Originality/valueIn terms of optimum value through desirability, the experimental trials yield the following results: maximum value of UTS (318.369 MPa), maximum value of YS (200.120 MPa) and EL (7.610) at 1,021 rpm of RS, 70 mm/min of TS, 4 NPS and level 3 of RF.

Direct model parameter identification of twisted string actuators using Nelder-Mead simplex method

A twisted string actuator (TSA) is a simple rotary-to-linear motion converter with a large transmission ratio. This paper presents a simple procedure for direct model parameters identification of TSAs, which accepts the model in initial ODE-based form, while the previous techniques required a complex reformulation of the model. Also, the identification process estimates all unknown or impossible-to-measure parameters directly (not in an arithmetic combination) in the original form of the mathematical model of the TSA, which is a benefit in comparison with common and classic adaptive parameter estimators. The proposed nonlinear identification procedure employs the Nelder–Mead simplex method to minimize the cost function as the core. Moreover, the identification results are verified in different working frequencies. The presented model parameter optimization procedure reduced the deviation between the mathematical model and the real TSA behavior for a set of optimum parameters up to [Formula: see text] in terms of the defined cost function. As an application for the mathematical model of the TSA with the identified parameters, a feedback linearization controller is designed for the position control of the actuator. The simulation and implementation of the controller prove the proper performance of the controller.

Simulation study of electron beam optics for a distributed X-ray source toward stationary CT architecture

For computed tomography (CT) imaging to be considered “real time”, one set of tomographic projections are to be acquired in less than 30 ms. Current conventional CT systems are limited to approximately 300 ms because of mechanical and material limitations. To bypass the mechanical limitations of a conventional gantry system, there is an open design challenge to develop a distributed X-ray source that is tightly packed and bright. The work presented here reports a design for a distributed X-ray source based on a rotating cylindrical anode. In particular, this work focuses on designing the electron beam optics for said X-ray source and refining these optics via multi-physics simulation studies. We designed these studies to investigate the electron beam behavior for switching, steering, and focusing. We demonstrated that the high-energy electron beam could be turned off and on via the grid-switching technique, could be steered, and could be dynamically focused for distinct positions along the cylindrical anode. We also report the optimal set of parameters that result in the desired electronic focal spot size (1 mm × 7 mm) and shape at each source position for our system.

Optimization of Charpy Impact Strength of Tough PLA Samples Produced by 3D Printing Using the Taguchi Method.

This research employs the Taguchi method and analysis of variance (ANOVA) to investigate, analyze, and optimize the impact strength of tough polylactic acid (PLA) material produced through fused deposition modeling (FDM). This study explores the effect of key printing parameters-specifically, infill density, raster angle, layer height, and print speed-on Charpy impact strength. Utilizing a Taguchi L16 orthogonal array experimental design, the parameters are varied within defined ranges. The results, analyzed through signal-to-noise (S/N) ratios and ANOVA, reveal that infill density has the most substantial impact on Charpy impact strength, followed by print speed, layer height, and raster angle. ANOVA identifies infill density and print speed as the most influential factors, contributing 38.93% and 36.51%, respectively. A regression model was formulated and this model predicted the impact strength with high accuracy (R2 = 98.16%). The optimized parameter set obtained through the Taguchi method, namely, a 100% infill density, 45/-45° raster angle, 0.25 mm layer height, and 75 mm/s print speed, enhances the impact strength by 1.39% compared to the experimental design, resulting in an impact strength of 38.54 kJ/m2. Validation experiments confirmed the effectiveness of the optimized parameters.

Process parametric optimization of fused deposition modeling for manufacturing of acrylonitrile butadiene styrene parts

Additive manufacturing has been a revolution in the last decade and has led to a number of product innovations. Rapid prototyping (RP) has significantly reduced the product development cycle time and RP techniques are now replacing many conventional plastic processing techniques where production volume is not an issue. In order to ascertain the utility of additive manufacturing techniques for the development of fully functional parts, it’s important to establish the optimal set of process parameters for achieving maximum mechanical performance. In the current experimental investigation, the dogbone-shaped parts of acrylonitrile butadiene styrene (ABS) were fabricated by the fused deposition modeling (FDM) process. The process parameters were optimized for dogbone-shaped specimens fabricated by the FDM process. Among the parameters, it has been found that orientation and infill density are the most dominant factors affecting the tensile strength of FDM parts printed with ABS material.

Solving Robotic Trajectory Sequential Writing Problem via Learning Character's Structural and Sequential Information.

The writing sequence of numerals or letters often affects aesthetic aspects of the writing outcomes. As such, it remains a challenge for robotic calligraphy systems to perform, mimicking human writers' implicit intention. This article presents a new robot calligraphy system that is able to learn writing sequences with limited sequential information, producing writing results compatible to human writers with good diversity. In particular, the system innovatively applies a gated recurrent unit (GRU) network to generate robotic writing actions with the support of a prelabeled trajectory sequence vector. Also, a new evaluation method is proposed that considers the shape, trajectory sequence, and structural information of the writing outcome, thereby helping ensure the writing quality. A swarm optimization algorithm is exploited to create an optimal set of parameters of the proposed system. The proposed approach is evaluated using Arabic numerals, and the experimental results demonstrate the competitive writing performance of the system against state-of-the-art approaches regarding multiple criteria (including FID, MAE, PSNR, SSIM, and PerLoss), as well as diversity performance concerning variance and entropy. Importantly, the proposed GRU-based robotic motion planning system, supported with swarm optimization can learn from a small dataset, while producing calligraphy writing with diverse and aesthetically pleasing outcomes.

A Systematic Review on: Polycystic Ovary Syndrome Using Machine Learning

Abstract: Polycystic Ovary Syndrome (PCOS) is a medical condition which causes hormonal disorder in women in their childbearing years. The hormonal imbalance leads to a delayed or even absent menstrual cycle. Women with PCOS majorly suffer from excessive weight gain, facial hair growth, acne, hair loss, skin darkening and irregular periods leading to infertility in rare cases. Early detection and diagnosis of PCOS are crucial for timely intervention and management. The existing methodologies and treatments are insufficient for early-stage detection and prediction. Machine learning can play a significant role in automating PCOS detection based on medical data and patient information. To deal with this problem, we propose a system which can help in early detection and prediction of PCOS treatment from an optimal and minimal set of parameters. In this study, we present an online application that enables women to conveniently estimate their likelihood of having the condition while remaining in the comfort of their own homes until assistance becomes accessible. Using feature selection approaches, we select only the non-invasive, readily measurable characteristics at home, resulting in a minimal but optimal set of clinical data for the same prediction. We employ the random-forest classifier, which has demonstrated encouraging outcomes when used in conjunction with machine learning techniques to diagnose PCOS. The Kaggle dataset, which consists of data points with clinical variables such as weight increase, LHS levels, hair loss, acne, BMI, and follicles in the left and right ovary, is used in this work.