Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

Estimation of Peanut Maturity Using Color Image Analysis

Peanuts pods grow underground and mature unevenly, resulting that choosing the correct time to harvest is more complicated than other crops. Pod maturity can be determined by blasting with a pressure washer to remove outer skin of the pod (exocarp) to expose the color of the middle layer (mesocarp). The mesocarp color changes with maturity from white to yellow, orange, brown and finally black. The sum of percentage from orange, brown, and black mesocarp (OBB) color and black color (BL) represents the kernels that are mature enough to harvest. The goal of this research is to identify methodologies to estimate OBB and BL of the pods using RGB images taken in the field and validate the proposed model using other pod images. The Mahalanobis distance classification method was used to process sets of images and calculate pod area (number of pixels) corresponding to two classes (mesocarp and background) with nine different color groups. The results showed a performance of 94% effectiveness for mesocarp using Mahalanobis distance classification. Statistical regression for OBB and BL was developed based on 315 images of peanut pods taken from the field. The R2 and root mean square error of predicted and actual OBB were 0.93 and 4.1%, respectively. The R2 and root mean square error of predicted and actual BL were 0.88 and 1.8%, respectively. The validation of OBB using other images provided reasonable estimation (R2 = 0.98 and RMSE = 2.73%). This study introduces a novel, cost-effective, and non-destructive method for estimating peanut maturity using RGB imagery and Mahalanobis distance classification in the field. This innovative approach addresses the limitations of traditional methods and offers a robust alternative for real-time maturity assessment.

Quadrature Kalman particle fitler

In order to resolve the state estimation problem of nonlinear/non-Gaussian systems, a new kind of quadrature Kalman particle filter （QKPF） is proposed. In this new algorithm, quadrature Kalman filter （QKF） is used for generating the impor- tance density function. It linearizes the nonlinear functions using statistical linear regression method through a set of Gaussian- Hermite quadrature points. It need not compute the Jacobian matrix and is easy to be implemented. Moreover, the importantce density function integrates the latest measurements into system state transition density, so the approximation to the system poste- rior density is improved. The theoretical analysis and experimen- tal results show that, compared with the unscented partcle filter （UPF）, the estimation accuracy of the new particle filter is improved almost by 18%, and its calculation cost is decreased a little. So, QKPF is an effective nonlinear filtering algorithm.

Understanding groundwater table using a statistical model

In this study, a statistical model was established to estimate the groundwater table using precipitation, evaporation, the river stage of the Liangduo River, and the tide level of the Yellow Sea, as well as to predict the groundwater table with easily measurable climate data in a coastal plain in eastern China. To achieve these objectives, groundwater table data from twelve wells in a farmland covering an area of 50 m ~ 150 m were measured over a 12-month period in 2013 in Dongtai City, Jiangsu Province. Trend analysis and correlation analysis were conducted to study the patterns of changes in the groundwater table. In addition, a linear regression model was established and regression analysis was conducted to understand the relationships between precipitation, evaporation, river stage, tide level, and groundwater table. The results are as follows： （1） The groundwater table was strongly affected by climate factors （e.g., precipitation and evaporation）, and river stage was also a significant factor affecting the groundwater table in the study area （p 〈 0.01, where p is the probability value）. （2） The groundwater table was especially sensitive to precipitation. The significance of the factors of the groundwater table were ranked in the following descending order： precipitation, evaporation, and river stage. （3） A triple linear regression model of the groundwater table, precipitation, evaporation, and river stage was established. The linear relationship between the groundwater table and the main factors was satisfied by the actual values versus the simulated values of the groundwater table （R^2 = 0.841, where R^2 is the coefficient of determination）.

Performance prediction of expressway pavement in high maintenance level areas based on cosine deterioration equation: A case study of Zhejiang Province in China

Accurate prediction of performance decay law is an important basis for long-term planning of maintenance strategy.The statistical regression prediction model is the most widely employed method to calculate pavement performance due to its advantages such as the small amount of calculation and good accuracy,but the traditional prediction model seems not applicable to the high maintenance level areas with excellent pavement conditions.In this paper,the service life and the cumulative number of the axle load were determined as the independent variables of prediction models of pavement performance.The pavement condition index(PCI)and rutting depth index(RDI)were selected as maintenance decision control indexes to establish the unified prediction model of PCI and RDI respectively by applying the cosine deterioration equation.Results reveal that the deterioration law of PCI presents an anti-S type or concave type and the deterioration law of RDI shows an obvious concave type.The prediction model proposed in this study added the pavement maintenance standard factor d,which brings the model parameterα(reflecting the road life)and the deterioration equations are more applicable than the traditional standard equations.It is found that the fitting effects of PCI and RDI prediction models with different traffic grades are relatively similar to the actual service state of the pavements.

Comparative Analysis of Curing Methods:Natural and Activated Lateritic Concrete

This work was on non-activated and activated lateritic soil used in proportions of 0%to 30%,to replace fine sand by wt.%,in the production of lateritic concrete.A mix of 1:2:4 was used,and the cube samples were cured in four(4)curing media of water,sand,polythene,and sawdust.The aim was to evaluate the effects of these curing methods on the mechanical strengths,and other properties of lateritic concrete.The sensitivity of the generated data was characterized statistically and developing linear regression models for predictions.For the Non-Activated Laterite soil(NALS,control mix(0%)),the design strength of 20 MPa was achieved by all the curing methods(standard and non-standard).However,for other replacement levels,water curing was adequate for 10%and 30%,sand at 10%,and sawdust for 20%and 30%,respectively.On the other hand,for the Activated Laterite soil(ALS),the 20 MPa design strength was met only at 0%replacement for all curing methods.Sawdust medium at 10%also satisfied the 20 MPa strength.

Enhanced piecewise regression based on deterministic annealing

Regression is one of the important problems in statistical learning theory. This paper proves the global convergence of the piecewise regression algorithm based on deterministic annealing and continuity of global minimum of free energy w.r.t temperature, and derives a new simplified formula to compute the initial critical temperature. A new enhanced plecewise regression algorithm by using ＂migration of prototypes＂ is proposed to eliminate ＂empty cell＂ in the annealing process. Numerical experiments on several benchmark datasets show that the new algorithm can remove redundancy and improve generalization of the piecewise regression model.

THE STUDY OF RETRIEVAL THEORY AND METHODS FROM SATELLITE REMOTE SENSING FOR METEOROLOGICAL PARAMETERS OVER EASTERN ASIA-PARTI:ISPRM AND SRRM

A review of ten-year's practice in developing the improved simultaneous physical retrieval method(ISPRM)is given in the hope that some creative ideas can be drawn from it.The improvement upon the SPRM is associated with the under-determinedness of this ill-posed inverse problem.In our experiment,the precondition is observed that prior information must be independent of the satellite measurements.The well-posed retrieval theory has told us that the forward process is fundamental for the retrieval,and it is the bridge between the input of satellite radiance and the output of retrievals.In order to obtain a better result from the forward process. the full advantage of every prior information available must be taken.It is necessary to turn the ill- posed inverse problem into the well-posed one.Then by using the Ridge regression or Bayes algorithm to find the optimal combination among the first guess,the theoretical analogue information and the satellite observations,the impact of the under-determinedness of this inverse problem on the numerical solution is minimized.

A Research of China Stock Market by Capital-Asset Pricing Model

Capital Asset Pricing Model (CAPM) is an important investment portfolio model,which is developmented from Markowitz’s investment portfolio theory. This paper initially verifies CAPM by means of the statistical regression analysis on the data in Shanghai stock exchange, including 164 kinds of going public stocks, from September 1992 to October 1994. The paper analyzes the current situation of China stock exchange and suggests how to develop its trade.