• Inventy@editormails.com

 

Volume 11 ~ Issue 5

 

 


Volume 11 ~ Issue 5

Research Article  open access
An Analysis on Minutes of the Goals in Football Matches with the Nonhomogeneous Poisson Processes
Şenol Çelik
Turkey
Research Inventy: International Journal of Engineering & Science , Volume 11 ~ Issue 5

Abstract

This study analyses the counts of the goals scored by Galatasaray, Fenerbahçe, Beşiktaş, and Trabzonspor between the minutes 0-15, 16-30, 31-45, 46-60, 61-75, and 76-90 in Turkey during Super League between 2013-2014 season and 2021 season. The probability functions were developed accordingly. By analyzing the goal counts with the management of the nonhomogeneous Poisson processes, teams' probability of scoring 0, 1, 2 goals in each time frame was calculated. As a result, it is seen that the teams' probability of scoring a goal with the nonhomogeneous Poisson processes with respect to time periods and the probability of two combined Poisson teams scoring goals can be calculated.

Keywords: Poisson process, probability functions, goal


Research Article  open access
Chemical Analysis of Summer Honeys Collected From Apis dorsata hives of Pombhurna Tahsil of Chandrapur District of Maharashtra State (India)
Laxmikant N. Borkar, Devendra M. Mate
India
Research Inventy: International Journal of Engineering & Science , Volume 11 ~ Issue 5

Abstract

The present investigation was undertaken to determine the chemical analysis of 5 squeezed summer honey samples (CHN-POM-YER, CHN-POM-CHI, CHN-POM-DIG, CHN-POM-DEV, CHN-POM-BEM) collected from forest area of Pombhurna Tahsil of Chandrapur District of Maharashtra State (India). These samples were analysed for several parameters such as moisture, total reducing sugar, Levulose or Fructose, Dextrose or Glucose, L/D ratio, Sucrose, Acidity. This type of chemical analysis favours the utilization of the honey for good quality in this area.

Keywords: Chemical Analysis, Summer Honey, Pombhurna Tahsil


Research Article  open access
Design, Construction and Performance Evaluation of a Motorised and Manually Operated Groundnut Sheller
Ipilakyaa Tertsegha, Iorbee Michael, Tyohemba Imoter
Nigeria
Research Inventy: International Journal of Engineering & Science , Volume 11 ~ Issue 5

Abstract

The design, construction and performance evaluation of a motorized and manually operated groundnut sheller was carried out. It consists of a feed hopper, frame, beaters mounted on the shaft drum, blower (fan) and a delivery chute. The machine is powered by an auxiliary engine for the motorized part and also by a paddling system for the manually operated part. Tests were carried out at cylinder speeds of 2.00, 3.50, 5.00m/s, moisture content of 8.00, 10.00, 12.00% dry base and feed rate of 1.5, 3.00 and 4.5kg/min. Performance evaluation of the shelling machine was carried out in terms of shelling capacity (SC), mechanical damage (MD), shelling efficiency (SE) and cleaning efficiency (CE). The peak shelling capacity was 211.65kg/hr at an operating cylinder speed of 3.5m/s, moisture content of 10% and feed rate of 4.5kg/min. At the optimum condition, mechanical damage, shelling efficiency, and cleaning efficiency were recorded as 6.93%, 92.41% and 84.46% respectively. From the performance indices of the shelling machine, it can be concluded that the sheller can be used by both small industries and local farmers using the electrically and manually operated parts respectively.

Keywords: Shelling machine, mechanical damage, shelling efficiency, cleaning efficiency.


Research Article  open access
Deep Neural Networks based Classical Painting Style Prediction
Rabeeah Ahmed Meelad, Marina Maranovic
Serbia
Research Inventy: International Journal of Engineering & Science , Volume 11 ~ Issue 5

Abstract

How does deep convolution neural network classify painting style? Since the deep Convolution Neural Network(CNN or ConvNet) have been introduced in early of 90's, from that date to until now, the convolution neural network undergone many improvements, and it have consistently been competitive with other techniques for image classification and recognition,Several studies have shown the ability of the deep convolution neural network to learn and predict painting style. In this paper we are going to representing deep neural network,and use different deep neural networks methods, such as (AlexNet,VGG16,ResNet,LeNet5,Inception), and we are going to built in our classifier to classification Art Style of different art works for the famous Painters,such as [Abstract Expression-ism,Analytical Cubism,Art Nouveau Modern,Color Field Paint-ing, Cubism,Early Renaissance,Expressionism,Fauvism, High Re-naissance, Impressionism].

Keywords: Painting style prediction, deep neural networks, image processing, colour information, painting evaluation.