Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally.Parkinson’s affects about 1%of people over 60 and its symptoms increase with age.The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech signals.With the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this data.Several classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many fields.This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based.The dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 patients.The experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

Selection Method of Production Enterprises by Large Pharmaceutical Commercial Companies Based on AHP

Objective To study the policies of integrating medical resources and centralized drug procurement in China from 2018 to 2020,and to provide a reference for large pharmaceutical commercial companies to select partners.Methods Analytic hierarchy process(AHP)and fuzzy synthesis evaluation method were used to establish the index evaluation system and assign values to each index.Results and Conclusion According to the questionnaire survey data,the weight of each evaluation index was determined,and the evaluation results were obtained by using the fuzzy synthesis evaluation method.The selection of production enterprises by large pharmaceutical commercial companies includes five first-level indicators and 11 second-level indicators.They can provide a favorable reference for the selection of production enterprises by large pharmaceutical commercial companies against the background of complex pharmaceutical industry.

Extremum selection method of random variable for nonlinear dynamic reliability analysis of turbine blade deformation

To effectively select random variable in nonlinear dynamic reliability analysis,the extremum selection method(ESM)is proposed.Firstly,the basic idea was introduced and the mathematical model was established for the ESM.The nonlinear dynamic reliability analysis of turbine blade radial deformation was taken as an example to verify the ESM.The results show that the analysis precision of the ESM is 99.972%,which is almost kept consistent with that of the Monte Carlo method;moreover,the computing time of the ESM is shorter than that of the traditional method.Hence,it is demonstrated that the ESM is able to save calculation time and improve the computational efficiency while keeping the calculation precision for nonlinear dynamic reliability analysis.The present study provides a method to enhance the nonlinear dynamic reliability analysis in selecting the random variables and offers a way to design structure and machine in future work.

Mango internal defect detection based on optimal wavelength selection method using NIR spectroscopy

A non-destructive technique should be developed for performance analysis of mango fruits because the spongy tissue or internal defects could lower the quality of mango fruit and incur a lack of productivity.In this study,wavelength selection methods were proposed to identify the range of wavelengths for the classification of defected and healthy mango fruits.Feature selection methods were adopted here to achieve a significant selection of wavelengths.To measure the goodness of themodel,the datasetwas collected using the NIR(Near Infrared)spectroscopy with wavelength ranging from 673 nm–1900 nm.The classification was performed using Euclidean distance measure both in the original feature space and in FLD(Fisher's Linear Discriminant)transformed space.The experimental results showed that the lower range wavelength(673 nm–1100 nm)was the efficient wavelength for the detection of internal defects in mangoes.Further to express the effectiveness of the model,different feature selection techniques were investigated and found that the Fisher's criterion based technique appeared to be the best method for effective wavelength selection useful for classification of defected and healthy mango fruits.The optimal wavelengths were found in the range of 702.72 nm to 752.34 nm using Fisher's criterionwith a classification accuracy of 84.5%.This study showed that NIR systemis a useful technology for the automaticmango fruit assessmentwhich has the potential to be used for internal defects in online sorting,easily distinguishable by those who do not meet minimum quality requirements.

Monte Carlo Analytic Hierarchy Process (MAHP) approach to selection of optimum mining method

One of the most critical and complicated steps in mine design is a selection of suitable mining method based upon geological,geotechnical,geographical,safety and economical parameters.The aim of this study is developing a Monte Carlo simulation to selection the optimum mining method by using effective and major criteria and at the same time,taking subjective judgments of decision makers into consideration.Proposed approach is based on the combination of Monte Carlo simulation with conventional Analytic Hierarchy Process(AHP).Monte Carlo simulation is used to determine the confdence level of each alternative’s score,is calculated by AHP,with the respect to the variance of decision makers’opinion.The proposed method is applied for Jajarm Bauxite Mine in Iran and eventually the most appropriate mining methods for this mine are ranked.

Selection of surgical methods for thoracic ossification of ligamentum flavum combined with cervical spondylotic myelopathy

Objective To investigate the difference between different surgical methods for thoracic ossification of ligamentum flavum(OLF) combined with cervical spondylotic myelopathy(CSM) . Methods From January 1991 to January 2003,56 cases

Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System(CPS)applications.Edge devices enable limited computational capacity and energy availability that hamper end user performance.We designed a novel performance measurement index to gauge a device’s resource capacity.This examination addresses the offloading mechanism issues,where the end user(EU)offloads a part of its workload to a nearby edge server(ES).Sometimes,the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources(such as storage and computation).The manuscript aims to reduce the service offloading rate by selecting a potential device or server to accomplish a low average latency and service completion time to meet the deadline constraints of sub-divided services.In this regard,an adaptive online status predictive model design is significant for prognosticating the asset requirement of arrived services to make float decisions.Consequently,the development of a reinforcement learning-based flexible x-scheduling(RFXS)approach resolves the service offloading issues,where x=service/resource for producing the low latency and high performance of the network.Our approach to the theoretical bound and computational complexity is derived by formulating the system efficiency.A quadratic restraint mechanism is employed to formulate the service optimization issue according to a set ofmeasurements,as well as the behavioural association rate and adulation factor.Our system managed an average 0.89%of the service offloading rate,with 39 ms of delay over complex scenarios(using three servers with a 50%service arrival rate).The simulation outcomes confirm that the proposed scheme attained a low offloading uncertainty,and is suitable for simulating heterogeneous CPS frameworks.

Reliability Sensitivity-based Correlation Coefficient Calculation in Structural Reliability Analysis

The correlation coefficients of random variables of mechanical structures are generally chosen with experience or even ignored,which cannot actually reflect the effects of parameter uncertainties on reliability.To discuss the selection problem of the correlation coefficients from the reliability-based sensitivity point of view,the theory principle of the problem is established based on the results of the reliability sensitivity,and the criterion of correlation among random variables is shown.The values of the correlation coefficients are obtained according to the proposed principle and the reliability sensitivity problem is discussed.Numerical studies have shown the following results：（1） If the sensitivity value of correlation coefficient ρ is less than（at what magnitude 0.000 01）,then the correlation could be ignored,which could simplify the procedure without introducing additional error.（2） However,as the difference between ρs,that is the most sensitive to the reliability,and ρR,that is with the smallest reliability,is less than 0.001,ρs is suggested to model the dependency of random variables.This could ensure the robust quality of system without the loss of safety requirement.（3） In the case of ｜Eabs｜ρ0.001 and also ｜Erel｜ρ0.001,ρR should be employed to quantify the correlation among random variables in order to ensure the accuracy of reliability analysis.Application of the proposed approach could provide a practical routine for mechanical design and manufactory to study the reliability and reliability-based sensitivity of basic design variables in mechanical reliability analysis and design.

Detection of Campylobacter sp. from Poultry Feces in Ouagadougou, Burkina Faso

Background: Campylobacter contamination in poultry and poultry product has been reported worldwide. The present study aims to determine the prevalence of Campylobacter in poultry feces using selective enrichment Bolton broth and multiplex PCR. Method: Two methods were used in this study: the first was direct plating of poultry feces into mCCDA agar plates. The second, three antibiotics were used at different concentrations to add in Bolton broth supplemented. These antibiotics were Rifampicin (Oxoid, Nepean, Ontario) with 10 mg/L, colistin (Oxoid, Nepean, Ontario) with 1 mg/mL and 2 mg/mL;trimethoprim (Oxoid, Nepean, Ontario) with 10 mg/L. The colonies with typical campylobacter morphology on blood agar (little, red and ring colonies) were further identified to the species level by multiplex polymerase chain reaction (PCR). Results: The addition of colistin (2 mg/mL) to the Bolton broth with selective supplements enhanced the selective isolation of Campylobacter strains. Out of the 52 feces samples, 18 (34.61%) were positive for campylobacter and direct plating on mCCDA 11 (21.15%) campylobacter strains (p < 0.05). The PCR results have shown that 17 (94.45%) of the campylobacter strains detected belonged to Campylobacter coli and 1 (5.55%) strain to Campylobacter jejuni. Conclusion: Although it is known to be difficult to isolate Campylobacter from animal feces samples, this study shows that antibiotic selective pressure improves the isolation efficiency of Campylobacter from poultry feces.

Optimization of multicomponent solvent selection in high-performance liquid chromatography using a statistical method

A computer-assisted method is presented for optimization of multicomponent solvent mobile phase selection for separation of O-ethyl-N-isopropyl phosphoro(thioureido)thioates in reversed-phase HPLC and four geometric isomers of pesticides Decis in normal-phase HPLC.The method is based on Snyder's solvent selection triangle concept using a statistical method.The optimization of the separation over the experimental region is based on a special polynomial esti- mation from seven experimental runs,and resolution(R_s)is used as the selection criterion.Excellent agreement was obtained between predicted data and experimental results.

Optimization of two-factor(pH and ion concentration)simultaneous selection in HPLC using a statistical method

A computer-assisted method is presented for simultaneous optimization of pH and ion con- centration selection for the optimal separation in reversed-phase HPLC.The method is based on a polynomial estimation from nine preliminary experiments according to two-factor rectangular design. This is followed by a two-dimension computer scanning technique.Resolution is used as the selection criterion.Good agreement was obtained between predicted data and experimental results.

Nyquist Stability Analysis and Capacitance Selection for DC Current Flow Controllers in Meshed Multi-terminal HVDC Grids

Controllability of DC current/power flow is essentialin multi-terminal HVDC (MTDC) grids, particularly for theMTDC grids in a meshed topology. In this paper, consideringmeshed MTDC (M2TDC) grids with the installation of twoline/multi-lineDC current flow controllers (CFCs), a small-signalmodel of the DC CFCs integrated M2TDC grids is derived,studying the impact of the power losses of the DC CFC andtheir influence on the analysis of energy exchanges. The systemstability analysis is analysed using the Nyquist diagram, which ismore suitable for analyzing complex nonlinear systems with morecompact and reliable indicators of stability in comparison withgain/phase margins shown in the Bode diagram. In addition, aselection method of the interconnected capacitor of the DC CFCis proposed under different operating conditions. The impact ofthe switching frequencies of the DC CFC on the control ranges ofthe DC current flows is analyzed. The effectiveness of the Nyquistanalysis and the capacitance selection method is verified bysimulation studies using PSCAD/EMTDC. The obtained control ranges of the DC CFC with different switching frequenciesand capacitances would be useful for practical engineeringapplications.

Establishment of a selective evaluation method for DPP4 inhibitors based on recombinant human DPP8 and DPP9 proteins

Dipeptidyl peptidase 4(DPP4)is recognised as an attractive anti-diabetic drug target,and several DPP4 inhibitors are already on the market.As members of the same gene family,dipeptidyl peptidase 8(DPP8)and dipeptidyl peptidase 9(DPP9)share high sequence and structural homology as well as functional activity with DPP4.However,the inhibition of their activities was reported to cause severe toxicities.Thus,the development of DPP4 inhibitors that do not have DPP8 and DPP9 inhibitory activity is critical for safe anti-diabetic therapy.To achieve this goal,we established a selective evaluation method for DPP4 inhibitors based on recombinant human DPP8 and DPP9 proteins expressed by Rosetta cells.In this method,we used purified recombinant 120 kDa DPP8 or DPP9 protein from the Rosetta expression system.The optimum concentrations of the recombinant DPP8 and DPP9 proteins were 30 ng/mL and 20 ng/mL,respectively,and the corresponding concentrations of their substrates were both 0.2 mmol/L.This method was highly reproducible and reliable for the evaluation of the DPP8 and DPP9 selectivity for DPP4 inhibitor candidates,which would provide valuable guidance in the development of safe DPP4 inhibitors.

An adaptive turbo-shaft engine modeling method based on PS and MRR-LSSVR algorithms

In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.

Unsupervised Nonlinear Adaptive Manifold Learning for Global and Local Information

In this paper,we propose an Unsupervised Nonlinear Adaptive Manifold Learning method(UNAML)that considers both global and local information.In this approach,we apply unlabeled training samples to study nonlinear manifold features,while considering global pairwise distances and maintaining local topology structure.Our method aims at minimizing global pairwise data distance errors as well as local structural errors.In order to enable our UNAML to be more efficient and to extract manifold features from the external source of new data,we add a feature approximate error that can be used to learn a linear extractor.Also,we add a feature approximate error that can be used to learn a linear extractor.In addition,we use a method of adaptive neighbor selection to calculate local structural errors.This paper uses the kernel matrix method to optimize the original algorithm.Our algorithm proves to be more effective when compared with the experimental results of other feature extraction methods on real face-data sets and object data sets.

Predicting the true density of commercial biomass pellets using near-infrared hyperspectral imaging

The use of biomass is increasing because it is a form of renewable energy that provides high heating value.Rapid measurements could be used to check the quality of biomass pellets during production.This research aims to apply a near-infrared(NIR)hyperspectral imaging system for the evaluation of the true density of individual biomass pellets during the production process.Real-time measurement of the true density could be beneficial for the operation settings,such as the ratio of the binding agent to the raw material,the temperature of operation,the production rate,and the mixing ratio.The true density could also be used for rough measurement of the bulk density,which is a necessary parameter in commercial production.Therefore,knowledge of the true density is required during production in order to maintain the pellet quality as well as operation conditions.A prediction model was developed using partial least squares(PLS)regression across different wavelengths selected using different spectral pre-treatment methods and variable selection methods.After model development,the performance of the models was compared.The best model for predicting the true density of individual pellets was developed with first-derivative spectra(D1)and variables selected by the genetic algorithm(GA)method,and the number of variables was reduced from 256 to 53 wavelengths.The model gave R_(cal)^(2),R_(val)^(2),SEC,SEP,and RPD values of 0.88,0.89,0.08 g/cm^(3),0.07 g/cm^(3),and 3.04,respectively.The optimal prediction model was applied to construct distribution maps of the true density of individual biomass pellets,with the level of the predicted values displayed in colour bars.This imaging technique could be used to check visually the true density of biomass pellets during the production process for warnings to quality control equipment.

Enhanced flow injection analysis for measurements of S-nitrosothiols species in biological samples using highly selective amperometric nitric oxide sensor

A highly selective nitric oxide（NO） sensor is fabricated and applied to devise an enhanced flow injection analysis（FIA） system for S-nitrosothiols（RSNOs） measurement in biological samples.The NO sensor is prepared using a polytetrafluoroethylene（PTFE） gas-permeable membrane loaded with Teflon AF？ solution,a copolymer of tetrafluoroethylene and 2,2-bis（trifluoroethylene）-4,5-difluoro -l,3-dioxole,to improve selectivity.This method is much simpler and possesses good performance over a wide range of RSNOs concentrations.Standard deviation for three parallel measurements of blood plasma is 4.0%.The use of the gas sensing configuration as the detector enhances selectivity of the FIA measurement vs.using less selective electrochemical detectors that do not use PTFE/Teflon type outer membranes.