Counterfeit medicines are fake medicines which are either contaminated or contain the wrong or no active ingredient. Up to 30% of medicines in developing countries are counterfeit. Using Supervised Machine learning techniques we build a predictive model for predicting sales figures given other information related to counterfeit medicine selling operations. Thus, by predicting the values we can identify these illegal operations and counter them. In this paper we have also mentioned the importance of Data mining and Machine Learning algorithms with some comparison analysis.

This article shows how Newton's iterative methods for finding root of a polynomial equation can be used to create fractals in spreadsheets. Newton's method has served as one of the most fruitful paradigms in the development of complex iteration theory. The process of iteration is impossible to carry out by hand but extremely easy to carry out with a computer. By doing such experiments students get a feeling that they have the power to explore the uncharted wilderness of the dynamics of Newton's method. It gives mathematics an experimental component. It also illustrates a symbiotic relationship between technology and mathematics [1]. Technology can be used to develop our intuition, and mathematics is used to prove that our intuition is correct. The article explores Innovative use of Microsoft Excel's What-if Analysis tool to do automation of repeated computation. The method employed can also be used for Neural Network training and data clustering [9] in Excel. A wide variety of fractals can be created by using different polynomial equations [2][3][4][5][6][7].

Background/Objectives: In this progressive Hi-Tech ecosystem, the cuttingedge technologies in the Deep Learning techniques for Vehicle Detection and Classification engendered swift paradigm shifts in diverse operations through the deployment of convolutional neural models in the Traffic Surveillance System. The fundamental element of the Traffic management system constitutes a real-time dynamic image, which forms the base input for vehicle recognition systems. The deep model functionalities on these base static images are highly pragmatic, and a radical approach leads to its successful applicability. Methods: This study proposes Faster Region-based Convolutional Neural Network (R-CNN) technique for image-based vehicle detection with significant performance benefits. Essentially, the base network of a pre-trained deep model, fine-tuned VGG-16 is transformed into Faster R-CNN. At this stage, the framework is constructed for a customized finitecapacity vehicle dataset. Subsequently, it is applied to train and test the system. From the performance lens, for further system enhancement, the speedup Bottleneck, and Data Augmentation implementation improve training speed and accuracy. Findings: The Experiments demonstrate that the sensitivity factor is 93.5% which provides acceptable results of 87.6% with 0.42s in vehicle detection in aspects of accuracy and execution time. Novelty : For our customized dataset, the performance-enhanced detection framework shows an increase of 4% in sensitivity and 3.23s with respect to time as compared to the other existing models. The proposed research is designed for a novel Faster RCNN algorithm that is fine-tuned detection algorithm of vehicles integrating sophisticated approaches for dynamic transformation of the live traffic video stream recording by transposing these real-time traffic videos to image inputs to this optimized detection framework achieving a high sensitivity factor with an efficient computation stack benefiting cost and time.https://www.indjst.org/

The Mandelbrot Set is the most complex object in mathematics; its admirers like to say. An eternity would not be enough time to see it all, its disks studded with prickly thorns, its spirals and filaments curling outward and around, bearing bulbous molecules that hang, infinitely variegated, like grapes on God's personal vine [1]. In this article we show how it is drawn in spread sheet. The methodology employed is same as the one used for Newton's fractal. Since it is the daddy of all fractals, a separate article is devoted to it. The same principle is extended to draw fractals based on transcendental functions.

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