Enhancing the agro engineering system using game theory analytics

Abstract The advent of technologies and the impact of computer play an essential role in performing regular activities. The new era of Big Data analytics helps in analyzing the data through primary data and secondary data. By using modern tools to handle Distributed File systems enormous data can be handled and processed with high speed and accuracy in a stipulated time. The use of these emerging trends in all the fields gives better solution and strives the world in all aspects of digitization in a successful manner. To produce high yields in food crops, the agriculture adopted modern industrial systems. By using modern tools and techniques the agricultural production can be increased abundantly. The proposed system will help and attract the common people to invest their hard work and time in agricultural production, and make a huge contribution in the financial growth of the nation. The tractors and agro engineering tools for irrigation systems, are already used to improve the production of food crops. But the lack of advanced techniques such as smart system for monitoring the crop, to look for the strength of the soil through soil analyzer, checking for moisturisation in land and air, etc., are very much needed for proper yield. This system presents the novel framework and initial experimental results shows the feasibility of proposed system.</jats:p

Novel approach to enhance network security using key performance indicator in business analytics

Abstract In Recent days, the challenges in Business applications are more in analyzing the complexities of day to day activities. Key execution pointer (KPIs) are business estimations used by corporate bosses and various directors to follow and examine factors thought about noteworthy to the achievement of a Business. Reasonable KPIs base on the business cycles and limits that senior organization sees as commonly huge for assessing progress toward meeting imperative destinations and execution targets. KPIs differ from relationship to affiliation subject to business needs. One of the key display pointers for an open association will likely be its stock expense, while a KPI for a covertly held startup may be the amount of new customers incorporated each quarter. Surely, even direct adversaries in an industry are likely going to screen different plans of KPIs specially designed to their individual business procedures and the leader’s strategies for thinking.</jats:p

CNN based framework for identifying the Indian currency denomination for physically challenged people

Abstract One of the premier issues confronting the visual hindered individual is money, acknowledgment especially for cash. Be that as it may, the outwardly weakened individual may not think about the estimation of cash, and they endure part in cash trade related issues in their normal life. To address this issue, we have built up a framework for acknowledgment of money, notes, which might be the helpful device for an outwardly debilitated individual. Investigation and trials were done on the money informational collection, which encouraged CNN dependent on the key highlights, for example, watermarks, pictures printed on cash, esteemed composed as words and numbers and the total cash. This paper deals with the utilization of Convolutional Neural Networks (CNNs) for solving this society issues and investigations about the exhibition and evaluation of different CNN models. Here, Alexnet, Googlenet, and Vgg16 models have been considered for assessment. All the models were adjusted as far as preparing and testing the individuals of data sets. Among these three models, Alexnet accomplished better in preparing fulfillment, Vgg16 model indicated the better execution and accomplished 100%, Google net arrives at 88% as far as productivity.</jats:p

Smart Agricultural&ndash;Industrial Crop-Monitoring System Using Unmanned Aerial Vehicle&ndash;Internet of Things Classification Techniques

Unmanned aerial vehicles (UAVs) coupled with machine learning approaches have attracted considerable interest from academicians and industrialists. UAVs provide the advantage of operating and monitoring actions performed in a remote area, making them useful in various applications, particularly the area of smart farming. Even though the expense of controlling UAVs is a key factor in smart farming, this motivates farmers to employ UAVs while farming. This paper proposes a novel crop-monitoring system using a machine learning-based classification with UAVs. This research aims to monitor a crop in a remote area with below-average cultivation and the climatic conditions of the region. First, data are pre-processed via resizing, noise removal, and data cleaning and are then segmented for image enhancement, edge normalization, and smoothing. The segmented image was pre-trained using convolutional neural networks (CNN) to extract features. Through this process, crop abnormalities were detected. When an abnormality in the input data is detected, then these data are classified to predict the crop abnormality stage. Herein, the fast recurrent neural network-based classification technique was used to classify abnormalities in crops. The experiment was conducted by providing the present weather conditions as the input values; namely, the sensor values of temperature, humidity, rain, and moisture. To obtain results, around 32 truth frames were taken into account. Various parameters&mdash;namely, accuracy, precision, and specificity&mdash;were employed to determine the accuracy of the proposed approach. Aerial images for monitoring climatic conditions were considered for the input data. The data were collected and classified to detect crop abnormalities based on climatic conditions and pre-historic data based on the cultivation of the field. This monitoring system will differentiate between weeds and crops