Analysis of fractional order systems using newton iteration-based approximation technique

Fractional differential equations play a major role in expressing mathematically the real-world problems as they help attain good fit to the experimental data. It is also known that fractional order controllers are more flexible than integer order controllers. But when it comes to the numerical approximation of fractional order functions inaccuracies arise if the conversion technique is not chosen properly. So, when a fractional order plant model is approximated to an integer order system, it is required that the approximated model be accurate, as the overall system performance is based on the estimated integer order model. Nitisha-Pragya-Carlson (NPC) is a recent approximation technique proposed in 2018 to derive the rational approximation of fractional order differ-integrators. In this paper, three fractional order plant models having fractional powers 3.1, 1.25 and 1.3 is analyzed in frequency domain in terms of magnitude and phase response. The performance of approximated third and second order NPC based integer model is studied and compared with the integer models developed using other existing technique. The approximation error is calculated by comparing the frequency response of the developed models with the ideal response. It has been found that in all the three examples NPC based models are very much close to the ideal values. Hence proving the efficacy of NPC technique in approximation of fractional order systems

How Distance to School and Study Hours after School Influence Students’ Performance in Mathematics and English: A Comparative Analysis

Mathematics and English are essential subjects of the education system globally, as they provide skills for everyday human life. Mathematics and English are core subjects in the senior secondary education program in Botswana. The performance of students in Botswana in mathematics and English over the years has been poor. Most students travel long distances to school and have to return home every day. The distance to school and the hours after school for studying may contribute to the poor performance of students in these subjects. This quantitative study determined the influence of the distance to school and after school study hours on the performance of senior secondary students in mathematics and English in Botswana. Data were collected through a survey of a random sample of 168 students learning mathematics and English in senior secondary schools in Botswana. Findings of analyses of variance indicated that study hours after school and the distance to school have a significant influence on the performance of students in mathematics, whereas no influence was determined on student performance in English. Further, Post Hoc analysis determined that the long travelling distance and low number of hours of after school study had a sizeable adverse influence on student performance in mathematics. To improve student performance in mathematics, it was recommended that stakeholders should ensure that students stay closer to school and had better, more reliable transport. The former can be achieved by increasing the amount of hostel accommodation in schools

Financial Tracker using NLP

NLP (Natural Language Processing) is a mechanism that helps computers to know natural languages like English. In general, computers can understand data, tables etc. which are well formed. But when it involves natural languages, it's unacceptable for computers to spot them. NLP helps to translate the tongue in such a fashion which will be easily processed by modern computers. Financial Tracker is an approach which will use NLP as a tool and can differentiate the user messages in various categories. the appliance of the approach will be seen at multiple levels. At a personal level, this permits users to filtrate useful financial messages from an large junk of text messages. On the opposite hand, from an industrial point of view, this can be useful in services like online loan disbursal, which are hitting the market nowadays. These services attempt to provide online loans to individuals in an exceedingly faster and quicker manner. But when it involves business view, loan recovery from customers becomes a really important & crucial aspect. As most such services can’t take strict legal actions against the fraud customers, it becomes a requirement that loan should be provided only to those customers who deserve it. At that time, this model can come under the image. As a business we will find the user’s messages from their inbox (after taking permission from the users). These messages are often filtered using NLP which might help to differentiate various types of messages within the user's inbox which might further be used as a content for further prediction and analysis on user’s behaviour in terms of cash related transactions

A Review of Short Term Load Forecasting using Artificial Neural Network Models

AbstractThe electrical short term load forecasting has been emerged as one of the most essential field of research for efficient and reliable operation of power system in last few decades. It plays very significant role in the field of scheduling, contingency analysis, load flow analysis, planning and maintenance of power system. This paper addresses a review on recently published research work on different variants of artificial neural network in the field of short term load forecasting. In particular, the hybrid networks which is a combination of neural network with stochastic learning techniques such as genetic algorithm(GA), particle swarm optimization (PSO) etc. which has been successfully applied for short term load forecasting (STLF) is discussed thoroughly