SIVA SATYA SREEDHAR P
@gecgudlavalleru.ac.in
Assistant Professor and Information Technology
Gudlavalleru Engineering College
RESEARCH INTERESTS
Image Processing, Machine Learning, Deep Learning, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
M. M., , , , , , , , , M. S. Minu,et al.
ASPG Publishing LLC
Recently, Emotion detection utilizing EEG signals develops popularity in domain of Human-Computer Interaction (HCI). EEG (electroencephalography) is a non-invasive approach, which processes electrical action from the brain through electrodes located in the scalp. An emotion recognition approach could not only be significant for healthy people among them disabled persons for detecting emotional changes and is utilized for different applications. It is significant to realize that emotion recognition in EEG indications is a difficult task owing to difficult and subjective nature of emotions. In recent times, Machine learning (ML) algorithms like Random Forests or Support Vector Machines (SVM) and Deep Learning (DL) systems namely Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN) are trained on EEG feature extracted and connected emotional labels for classifying the user emotional state. This study presents an Automated EEG-based Emotion Detection using Bonobo Optimizer with Deep Learning (AEEGED-BODL) technique on HCI applications. The goal of the study is to analyze the EEG signals for the classification of several kinds of emotions in HCI applications. To achieve this, the AEEGED-BODL technique uses Higuchi fractal dimension (HFD) approach for extracting features in the EEG signals. Besides, the AEEGED-BODL technique makes use of the quasi-recurrent neural network (QRNN) approach for the detection and classification of distinct kinds of emotions. Furthermore, the BO system was demoralized for optimum hyperparameter selection of QRNN model, which helps in attaining an improved detection rate. The simulation validation of AEEGED-BODL algorithm was simulated on EEG signal database. The comprehensive result stated best outcome of the AEEGED-BODL algorithm over other recent approaches
R. Rajkumar, , , , , , , , , Dınesh Valluru,et al.
ASPG Publishing LLC
Recently, healthcare systems integrate the power of deep learning (DL) models with the connectivity and data processing capabilities of the Internet of Things (IoT) to enhance the early recognition and diagnosis of disease. Oral cancer diagnosis comprises the detection of cancerous or pre-cancerous abrasions in the oral cavity. Timely identification is essential for successful treatment and enhanced prognosis. Here is an overview of the key aspects of oral cancer diagnosis. One potential benefit of utilizing DL for oral cancer detection is that it analyses huge counts of data fast and accurately, and it could not need clear programming of the rules for recognizing abnormalities. This can create the procedure of detecting oral cancer more effective and efficient. Thus, the study presents an Enhanced Jaya Optimization Algorithm with Deep Learning Based Oral Cancer Classification (EJOADL-OCC) method. The presented EJOADL-OCC method aims to classify and detect the existence of oral cancer accurately and effectively. To accomplish this, the presented EJOADL-OCC method initially exploits median filtering for the noise elimination. Next, the feature vector generation process is performed by the residual network (ResNetv2) model with EJOA as a hyperparameter optimizer. For accurate classification of oral cancer, a continuously restricted Boltzmann machine with a deep belief network (CRBM-DBN) model. The simulated validation of the EJOADL-OCC algorithm is tested by the series of simulations and the outcome demonstrates its supremacy over present DL approaches.
S. Rajakumar, P. Siva Satya Sreedhar, S. Kamatchi, and G. Tamilmani
Elsevier BV
Meduri V. N. S. S. R. K. Sai Somayajului, Balaji Tedla, and P. Siva Satya Sreedhar
Springer Nature Singapore
Dinesh Valluru, Mohammed Ahmed Mustafa, Hind Yasin Jasim, Kandula Srikanth, M.V.L.N. RajaRao, and Purilla Siva Satya Sreedhar
IEEE
Augmented Reality (AR), a unique method of integrating the virtual world into the real world, has the potential to increase academic attainment in the classroom. This research work focuses on developing and evaluating a strategy for enhancing student education with AR in the classroom. AR enables unique human-computer interactions in real time between the physical and digital worlds. The effectiveness of AR in the classroom will depend on its development, deployment, and integration into both standard and nontraditional teaching environments. Throughout the creation and implementation of an AR classroom, collaborative learning practices and other methodologies were taken into account. Collaboration occurs when two or more individuals work together, share information, and gain insights from one another. This research offers a succinct summary of the promise and challenges of adopting AR to transform the classroom.
Siva Satya Sreedhar P, Sravani Velpula, Rishwitha Parise, Naidu Krishna Vamsi, and Sakhamuri Krishna Chaitanya
IEEE
Phishing attacks are a prevalent form of social engineering that target individuals through emails to obtain confidential and sensitive information. These attacks can lead to larger security breaches in both corporate and government networks. There have been several attempts to counter phishing assaults, but so far none have proven successful. For this reason, improved strategies for identifying phishing attempts are desperately needed. The proposed fix is a deep learning-based strategy for identifying malicious phishing attempts. By analyzing more than 5,000 phishing emails sent at the University of Malaysia’s Department of Computer Science and Information Technology, the authors hoped to create a model that reliably detects phishing assaults to achieve this, they selected relevant features through feature engineering and used the Random Forest models to extract feature importance at different levels. Finally, the model was trained using Convolutional Neural Networks (CNN), leading to improved detection and accuracy.
S. Navaneethan, P. Siva Satya Sreedhar, S. Padmakala, and C. Senthilkumar
Computers, Materials and Continua (Tech Science Press)
Melam Nagaraju, Adilakshmi Yannam, Siva Satya Sreedhar P, and Maridu Bhargavi
Informa UK Limited
ABSTRACT A Double optimization based convolution network model is introduced in the proposed video based facial expression recognition framework. The proposed model comprises U-shaped network, Residual-Network architecture, and Coot optimization. Before performing expression recognition, the input video is subjected to pre-processing, and face detection is performed over the extracted frames using the viola jones algorithm. The U-shaped network has the advantage of improving the processing speed of the convolution network, whereas the residual network can reduce the error that occurs during the frame encoding and gradient dissipation avoidance. Due to this merit, these two networks are combined and introduced in the proposed framework for facial expression recognition. The experimental evaluation is performed using a matrix laboratory tool over the three datasets: Affectiva-MIT Facial Expression Dataset, BAUM-1s and Real-world affective faces database. The comparative analysis shows that the proposed network has attained an efficient recognition rate than other existing network architecture.
Siva Satya Sreedhar, R Anitha, Priya Rachel, S Suganya, C Ramesh Babu Durai, and G S Uthayakumar
IEEE
Energy distribution is vital in an IoT-based Wireless Sensor Network (WSN).There is no other fuel source for WSN since they deal with battery systems. This means that when the battery runs out, they have no option except to replace it on a regular basis, which isn't always possible. Information may be lost during transmission as another problem with WSNs. Despite the fact that information disasters are rare, it remains a constant threat. The greatest danger lies in a loss of data. B) CH-to-sink data lost. This article saves energy by forecasting missing node values.
P. Siva Satya Sreedhar and N. Nandhagopal
Computers, Materials and Continua (Tech Science Press)