Machine learning and artificial intelligence in the detection of moving objects using image processing K. Janagi, Devarajan Balaji, P. Renuka, S. Bhuvaneswari Mathematical Models Using Artificial Intelligence for Surveillance Systems, 2024 Digital technology plays a major role in various fields like real life, science and engineering. This chapter deals with the uses of digital technology in detecting and tracking of objects. In particular, it applies LBF algorithm, background subtraction algorithm, GMM model (Gaussian Mixture Model), GANN (Generative adversarial neural networks), Kalman filter, Fuzzy c-mean, End-of-Queue (EOQ), Delaunay triangulation, robust approaches, RPCA (Robust Principal Component Analysis), Semi-Automatic Vehicle Detection System (SAVDS), and 3D LiDAR (Light Detection and Ranging). Based on the above-mentioned algorithms one can easily detect the moving object or track the object (in most of the cases a human being) more accurately. Generally, all the algorithms including RPCA, NLTFN (Non-Convex Logarithm Fraction Norms), RNLTFN (Robust Non-Convex Logarithm Fraction Norms) are mostly used to segregate the human images from other images, detection of images in poor weather conditions, monitoring the traffic in the vibrant work places and so on. These algorithms can be combined to provide the solutions as human intelligence. It can be envisaged to predict the technology based on the trend being observed by literatures.
IoT incorporated deep learning model combined with SmartBin technology for real-time solid waste management Muthuramalingam Sivakumar, Perumal Renuka, Pandian Chitra, Sundararajan Karthikeyan Computational Intelligence, 2022 With a view of the massive human resources and time requirements, the need for an automated, more accurate, and quicker method for handling the classification of solid wastes is felt more than ever worldwide. In this work, an attempt has been made to develop a model named SmartBin. Two different approaches have been followed to classify solid wastes as biodegradable and non‐biodegradable efficiently. The first approach is based on convolutional neural network (CNN) and Internet of Things (IoT), while the second approach adds several sensors to the model developed using the first approach. CNN‐based IoT is applied on datasets collected using three methods. The first one is Images from Kaggle; the second method adopted searches through Google and Bing, whereas the third one involved captured manually under a controlled environment. It is observed that the second approach has proved to be better, with an accuracy level of 98.57, which is a significantly improved performance over the first approach with an accuracy of 95.24%.
Slip effects on ohmic dissipative non-newtonian fluid flow in the presence of aligned magnetic field A. Hakeem, B. Ganga, R. Kalivanan, P. Renuka Journal of Applied and Computational Mechanics, 2020 The present paper deals with the effects of Ohmic dissipative Casson fluid flow over a stretching sheet in the presence of aligned magnetic field. The present phenomenon also includes the interaction of thermal radiation and velocity slip. The governing boundary layer equations are transformed into a set of ordinary differential equations using the similarity transformations. The dimensionless velocity and temperature profiles are solved analytically using hypergeometric function and numerically by using fourth order Runge-Kutta method with shooting technique. It is noted that the increasing values of Eckert number increases the temperature profile and decreases the local Nusselt number.
Effects of Aligned Magnetic Field on Slip Flow of Casson Fluid over a Stretching Sheet R. Kalaivanan, P. Renuka, N. Vishnu Ganesh, A.K. Abdul Hakeem, B. Ganga, S. Saranya Procedia Engineering, 2015 The present article investigates the inclined magnetic field effects on slip flow of Casson fluid over a stretching sheet. The governing boundary layer non-linear PDEs are converted into non-linear ODEs using similarity transformation. The analytical solutions for the present problem are obtained in terms of confluent hypergeometric function. The effects of various non-dimensional parameters onvelocity, temperature, skin friction and Nusselt number are discussed in detail through plots.
