Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network

Maintaining the cattle farm along with the wellbeing of every heifer has been the major concern in dairy farm. A robust system is required which can tackle the problem of continuous monitoring of cows. the computer vision techniques provide a new way to understand the challenges related to the identification and welfare of the cows. This paper presents a state-of-art instance segmentation mask RCNN algorithm to train and build a model on a very challenging cow dataset that is captured during the winter season. The dataset poses many challenges such as overlapping of cows, partial occlusion, similarity between cows and background, and bad lightening. An attempt is made to improve the accuracy of the segmenter and the performance is measured after fine tuning the baseline model. The experiment result shows that fine tuning the mask RCNN algorithm helps in significantly improving the accuracy of instance segmentation of cows. this work is a contribution towards the real time monitoring of cows in cattle farm environment with the purpose of behavioural analysis of the cattle

The Private School Advantage: Evidence from School Vouchers and Educational Leadership

School choice is becoming increasingly popular around the globe. Broadly the term ‘school choice’ is used to describe the options available for families to send children to school(s) other than the one they are residentially assigned to. Private school choice interventions known as ‘school vouchers,’ offer public or private funding to enable families to send their children to private school. Research in 1970s and 80s by James Coleman and his colleagues showed a private school advantage in student achievement and graduation rates, in comparison to traditional public schools. Competing evidence was presented by Christopher Lubienski and Sarah Lubienski in 2013, claiming a public school advantage in student achievement. The debates surrounding a particular school sector advantage can be better addressed using causal evidence and using large datasets to understand possible mechanisms that differentiate the school sectors. This dissertation reports on four analyses of the possibility of a private school advantage, using a variety of data. The first study looks at overall evidence on student achievement in math and reading scores from causal studies on private school vouchers around the globe. The second study offers a supplemental cost-effectiveness evaluation of the same set of voucher programs. In the third study, nationally representative data on public and private school principals is analyzed to study principal autonomy over seven school-level activities across school sectors. Using the same dataset, the fourth study examines the determinants of principal attrition across school sectors. Principals’ stated responses to stay in the profession in the baseline year are compared to their revealed status a year later. Some contributions of this dissertation are evidence of vouchers increasing reading test scores more in comparison to math test scores and a larger test score impact in developing countries than in the U.S. The dissertation finds more autonomy over school-level activities and more likelihood to remain in the profession for the private school principal in comparison to the traditional public school principal. Hence, future studies may test the role of principal autonomy and principals’ remaining in the profession as a mediator of school choice outcomes

Empowering Recommendations with NLP: Exploiting Textual Reviews for Enhanced Rating-Based Systems

This research paper proposes a rating-based recommender system that leverages Natural Language Processing (NLP) techniques to enhance the accuracy and effectiveness of recommendations. Traditional recommender systems primarily rely on numerical ratings provided by users to make predictions. However, these ratings often lack detailed information about user preferences and suffer from sparsity and inconsistency issues. By incorporating NLP, we aim to extract valuable insights from textual reviews and improve the recommendation process. Our system utilizes sentiment analysis, topic modelling, and text embeddings to capture the implicit information in reviews and generate more personalized and context-aware recommendations. The experimental results demonstrate the superior performance of the proposed rating-based recommender system compared to conventional approaches

Machine Learning for Cardiovascular Disease Risk Assessment: A Systematic Review

Accurate diagnosis and early detection of heart disease can help save lives because it is the primary cause of mortality. If a forecast is inaccurate, patients could potentially suffer significant harm. Today, it is challenging to predict and identify heart disease. 24 hour monitoring is not practical due to the extensive equipment and time required. Heart disease treatments can be both expensive and challenging. In order to obtain the data from databases and use this information to successfully forecast cardiac illness, a variety of data mining techniques and machine learning algorithms are now accessible. We have used every technique to put the heart disease prognosis into practise. The algorithms used in SVM, NAIVE BAYER, REGRESSION, KNN, ADABOOST, DECISION TREE, and XG-BOOST And Voting Ensemble Method

Computational investigation of cavitating flow around two dimensional NACA 4424 and MHKF-240 hydrofoil

This study focuses on the comparison of the performance of two unsymmetrical hydrofoils, NACA 4424 and MHKF-240 at 60 angle of attack under cavitation. The Schnerr and Sauer cavitation model along with Realizable k-ε turbulence model is used for numerical computation in commercial software ANSYS Fluent. The lift, drag and pressure coefficients for different cavitation numbers were studied. Among both the hydrofoils MHKF-240 gives a higher lift coefficient which is the parameter of better performance

Support vector regression: A novel soft computing technique for predicting the removal of cadmium from wastewater

43-50The presence of toxic heavy metals in the wastewater coming from industries is of great concern across the world. In the present work, a novel soft computing technique support vector regression (SVR)technique has been used to predict the removal of cadmium ions from wastewater with agricultural waste ‘rice polish’ as a low-cost adsorbent, with contact time, initial adsorbate concentration, pH of the medium, and temperature as the independent parameters. The developed SVR-based model has been compared with the widely used multiple regression (MR) model based on the statistical parameters such as coefficient of determination (R2), average relative error (AARE) etc. The prediction performance of SVR-based model has been found to be more accurate and generalized in comparison to MR model with low AARE values of 0.67% and high R2 values of 0.9997 while MR model gives an AARE value of 29.27% and 0.2161 as coefficient of determination (R2). Furthermore, it has also been observed that the SVR model effectively predicts the behavior of the complex interaction process of cadmium ions removal from waste water under various experimental conditions

The Evolution of Resin-Based Dental Materials/Composites: The Way Forward

Composites have come a long way since replacement of silicates as the material of choice in restorative and esthetic dentistry. The qualities of the resin-based material have immensely increased in the context of bonding and curing mechanisms. Due to its esthetic qualities, the composite has left behind many restorative materials and become the number one choice in the field of esthetic dentistry. Still, a lot needs to be done to reduce the limitations of composites like expansion or shrinkage, optimization of composites according to amorphous calcium phosphate, incorporation of antibacterial properties in composites and enhancement of self-adhesive properties. Fortuitously, an ample amount of research is done in this area in recent years and as a result, an improvement of restorative properties is also seen. This article discusses the evolution of the resin-based composite, the recent advancements and the way forward to achieve better materials

Numerical modelling of shear thickening fluid in nanosilica dispersion

In this paper, a numerical model of the shear thickening fluid (STF) is generated and the rheological properties are compared with the experimental data. Power Law model has been used to fit the rheological data for STF. Experimental data is taken from a performed study and a user defined function (UDF) has been written to develop the shear thickening behavior. The purpose of this study is to exactly model the behavior of shear thickening fluids by using UDF, to explain the shear-thickening mechanisms under different shear rates. Different parameters like viscosity, shear stress and velocity of the STF have also been reported

Nonclassical Symmetry Analysis of Boundary Layer Equations

The nonclassical symmetries of boundary layer equations for two-dimensional and radial flows are considered. A number of exact solutions for problems under consideration were found in the literature, and here we find new similarity solution by implementing the SADE package for finding nonclassical symmetries