STATISTICAL MODEL BASED OPTIMAL PREDICTION ON DRILLING PARAMETERS

The drilling is an imperative machining practice in the mechanical field for fitting or cutting the materials devoid of any disturbance. Various elements are basically employed within the automobile applications on account of the light weight, exceptional firmness and the moderate cheapness. The effectiveness of the drilled opening for the material shields is expanded by minimizing the eccentricity factor. The eccentricity is a degree of the nature of a drilled hole, and the process is based on input parameters. The significant intention of the suggested procedure is to built a mathematical modeling   with the support  of the optimization techniques. The mathematical modeling is done by minimizing the time consumed in the case of extension of the real time experiment. It is utilized to predict the diameter of the drill whole entry and exit, material removal rate and the eccentricity factor for the drilling process. Different optimization algorithms are utilized to find the optimal weights α and β of the mathematical modeling. All the optimum results demonstrate that the attained error values between the output of the experimental values and the predicted values are near equal to zero in the designed model. From the results, the minimum error 97.2% is determined by the mathematical modeling attained in the Artificial Fish Swarm Optimization (AFSO) process

Prospective single blind placebo-controlled randomized clinical trial to assess the efficacy and safety of metformin in promoting wound healing and weight reduction in non-diabetic overweight post-operative female patients

Background: Obesity is an important preventable risk factor that can affect wound healing. Oral hypoglycemic drug metformin apart from its antihyperglycemic and not hypoglycemic property has been reported to promote wound healing in non-diabetic animal studies and weight reduction in non-diabetic individuals. Hence, this: prospective randomized study single-blind placebo-controlled study was designed with the aim to assess the efficacy and safety of metformin in wound healing and weight reduction in a tertiary care hospital in Pondicherry during the period between December 2012 and January 2014.Methods: 215 non-diabetic post-operative patients with body mass index (BMI) of 25-29.9 kg/m2 from the Department of Obstetrics and Gynecology included after obtaining informed consent received tablet metformin 500 mg or placebo B.D from 2nd post-operative day up-to 30 days. Fasting blood sugar, postprandial blood sugar, BMI were recorded initially, and at the end. Clinical evaluation of wound was done on 8th, 15th and 30th post-operative days. Unpaired t-test was applied to compare the two groups for quantitative analysis and Chi-square test to analyze the qualitative outcome by using GraphPad prism - 6 software. p<0.05 was considered to be statistically significant.Results: Surgical site infection of 13.3% and 3.3% was observed in control and metformin groups respectively. Wound healing promoting effect of metformin was evidenced by p=0.0087 and 0.01 on 8th and on 15th day. Weight reducing effect was evidenced by p=0.0001 on comparing BMI. No significant hypoglycemia was observed. No adverse drug reaction was reported.Conclusions: Our study has shown metformin having best wound healing and weight reducing effect without producing hypoglycemia. Long-term studies on all types of surgeries in both males and females including emergencies are suggested

Certain Investigation of Real power flow control of Artificial Neural Network based Matrix converter-Unified Power Flow Controller in IEEE 14 Bus system

The power consumption is rapid increased due to ASD (Adjustable Speed Drives) and automation in industries and large consumption of electricity in domestic regions increased the concern of the power quality. The quality of the power received in the Distribution system is altered because of the losses in the transmission system. The losses in the transmission system is mitigated using the FACTS (Flexible AC Transmission System)controller among these controllers UPFC (Unified Power Flow Controller) plays a vital role in controlling the shunt and series reactive powers in the bus of the power system. The conventional topology of the UPFC consists of AC-DC converter and energy stored in the DC link and DC-AC converter injected a voltage in series to the bus which as to be controlled. Whereas a new topology based on matrix converter can replace the dual converters and perform the required task. The construction of 2-bus, 7-bus and IEEE-14-bus power system is designed and modeled. MC-UPFC (Matrix Converter Based Unified Power Flow Controller) is designed and constructed. The MC-UPFC is the rich topology in the FACTS which is capable of controlling both the transmission parameters simultaneously with the switching technique of Direct power control by the smooth sliding control which gives less ripple in the injecting control parameters such as control voltage [Vc] and voltage angle [α]. By implementing MC-UPFC the real and reactive power can be controlled simultaneously and independently. The control techniques were designed based on the Proportional Integral derivative(PID) with sliding surface power control, FLC (Fuzzy Logic Controller) and ANN (Artificial Neural Network)&nbsp; and&nbsp; the performance of&nbsp; Vc and α of the controllers are investigated. Hence the sliding surface and relevant control switching state of the MC can be controlled by the FLC which gives the robust and autonomous decision making in the selection of the appropriate switching state for the effective real power control in the power system. The work has been carried out in the MATLAB Simulink simulator which gives the finest controlling features and simple design procedures and monitoring of the output

Crop Insurance Premium Recommendation System Using Artificial Intelligence Techniques

Purpose: The objective of this study is to build a crop insurance premium recommender model which will be fair to both crop insurance policy holders and crop insurance service providers.   Theoretical Framework: The Nonparametric Bayesian Model (modified) is the name of the proposed model suggested by Maulidi et al. (2021) and it consists of six variables which are regional risk, cultivation time period, land area, claim frequency, discount eligibility (local variable) and premium. Discount eligibility variable is introduced to encourage right farming practices among farmers.   Design/methodology/approach: Descriptive research method is used in this study as it is used to accurately represent the characteristics of a group of items. The population for this study is 943 respondents. The entire dataset is used for in-depth and accurate analysis. Five Artificial Intelligence models (Machine Learning models) are proposed for crop insurance premium prediction and they are Ada Boost Regressor, Gradient Boosting Regressor, Extra Trees Regressor, Support Vector Regressor and K-Neighbors Regressor. Among them Gradient Boosting Regression model has given the highest accuracy. Thus, Gradient Boosting Regression model is the most suitable model to be recommended for crop insurance premium prediction.   Findings and Suggestions: Regional risk, land area, claim frequency and cultivation time period is the order of independent variables from highest to least in terms of regression coefficient. This relative importance helps Non-Banking Financial Companies (NBFCs) to suggest farmers that they should concentrate most on the regional risk or chances of crop failure in a particular region in which they are doing agriculture and least on the cultivation time period of a crop or the season in which a crop is cultivated. Two suggestions for future researchers are to extend this research work to other parts of Tamil Nadu and to apply hybrid machine learning techniques to the proposed model.   Practical Implication: Unlike the existing formula-based traditional method used for calculating crop insurance premium, artificial intelligence models (machine learning models) can automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve its accuracy based on new data. Hence, the crop insurance premium suggested by the most accurate model among the artificial intelligence models used in this study will be fair to both NBFCs and farmers. Here, fair means moderate. On the other hand, the crop insurance premium suggested by the existing formula-based method may not be fair in the long term as they cannot automatically learn the changes that take place with respect to the nature of variables in the proposed model and improve.   Originality/value: In this research article, the relative importance of independent variables in the proposed model is determined and it helps NBFCs to suggest farmers that they should concentrate most on the region they are doing agriculture and least on the cultivation time period of a crop. Additionally, a machine learning model which can automatically learn and improve itself is used and hence the crop insurance premium predicted by it will be fair. Finally, the entire population containing 943 respondents details is analysed

PREDICTIVE TIME MODEL OF AN ANGLIA AUTOFLOW MECHANICAL CHICKEN CATCHING SYSTEM

In this project, a predictive time model was developed for an Anglia Autoflow mechanical chicken catching system. At the completion of poultry growout, hand labor is currently used to collect the birds from the house, although some integrators are beginning to incorporate mechanical catching equipment. Several regression models were investigated with the objective of predicting the time taken to catch the chicken. A regression model relating distance to total time (sum of packing time, catching time, movement to catching and movement to packing) provided the best performance. The model was based on data collected from poultry farms on the Delmarva Peninsula during a six-month period. Statistical Analysis System (SAS) and NeuroShell Easy Predictor were used to build the regression and neural network models respectively. Model adequacy was established by both visual inspection and statistical techniques. The models were validated with experimental results not incorporated into the initial model.Livestock Production/Industries,