19 research outputs found
Engineering applications and analysis of vibratory motion fourth order fluid film over the time dependent heated flat plate
No full text
Time dependent second grade fluid between two vertical oscillating plates with heat transfer effect
No full textSome properties of two-fold symmetric analytic functions
No full textIn this paper, we introduce a new class of two-fold symmetric functions analytic in the unit disc. We prove such results as subordination and superordination properties, convolution properties, distortion theorems, and inequality properties of this new class
Time dependent Oldroyd-B liquid film flow over an oscillating and porous vertical plate with the effect of thermal radiation
No full textTime dependent second grade fluid between two vertical oscillating plates with heat transfer effect
No full textIn this paper, the flow of unsteady second grade fluid problem is examined between two vertical and oscillating plates. The parallel plates are oscillating. The heat transfer effect is taken on the plates. Optimal Homotopy Asymptotic Method (OHAM) is used for solution. Analytical solution is obtained. Effect of several physical parameters are studied as well as discussed in details
Vibratory motion of fourth order fluid film over a unsteady heated flat
No full textAnalysis of heat transfer is studied in magnetohydrodynamic (MHD) thin layer flow of an unsteady fourth grade fluid past a moving and oscillating vertical plate for lift and drainage problem. The governing equations are modelled in terms of nonlinear partial differential equations with some physical boundary conditions. Two different analytical methods, namely Adomian Decomposition Method (ADM) and the Optimal Homotopy Asymptotic Method (OHAM) are used for finding the series solution of the problem. The solutions obtained through two different techniques are compared using graphs and tables and found an excellent agreement. The variants of embedded flow parameters in the solution are analyzed through graphical illustrations
Emotion recognition from occluded facial images using deep ensemble model.
No full textFacial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications
Retracted: Advancements in intrusion detection: A lightweight hybrid RNN-RF model
No full textComputer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats
