no code implementations • 6 Aug 2022 • Prakrit Joshi, Omar Hisham Alsadoon, Abeer Alsadoon, Nada AlSallami, Tarik A. Rashid, P. W. C. Prasad, Sami Haddad
Conclusion: The proposed system focuses on the accurate classification of oral cancer cells of different anatomical locations from the CLE images.
no code implementations • 5 Aug 2022 • Nitesh Banskota, Abeer Alsadoon, P. W. C. Prasad, Ahmed Dawoud, Tarik A. Rashid, Omar Hisham Alsadoon
The simulation results show significant improvements in accuracy and processing time, making the model suitable for the video analysis process.
no code implementations • 5 Aug 2022 • Bhoj Raj Pandit, Abeer Alsadoon, P. W. C. Prasad, Sarmad Al Aloussi, Tarik A. Rashid, Omar Hisham Alsadoon, Oday D. Jerew
The aim of this work is to increase the overall prediction accuracy along with reducing processing time by using multispace image in pooling layer of convolution neural network.
no code implementations • 5 Aug 2022 • Marmik Shrestha, Omar Hisham Alsadoon, Abeer Alsadoon, Thair Al-Dala'in, Tarik A. Rashid, P. W. C. Prasad, Ahmad AlRubaie
The proposed system consists of a deep learning technique that uses the Support Vector Machine (SVM) algorithm along with the Radial Base Function (RBF) along with the Long Short-term Memory Layer (LSTM) for prediction of onset of Type 2 Diabetes.
no code implementations • 4 Mar 2022 • Hui Yang, Abeer Alsadoon, P. W. C. Prasad, Thair Al-Dala'in, Tarik A. Rashid, Angelika Maag, Omar Hisham Alsadoon
The proposed solution improves accuracy and reduces the processing time of text emotion classification.
no code implementations • 21 Aug 2021 • Kshitiz Shrestha, Omar Hisham Alsadoon, Abeer Alsadoon, Tarik A. Rashid, Rasha S. Ali, P. W. C. Prasad, Oday D. Jerew
Methodology: The proposed system consists of Convolutional Neural Network (CNN) to enhance the accuracy of classification and prediction by using Elastic Net Regularization.
no code implementations • 1 Jul 2021 • Ashu Thapa, Abeer Alsadoon, P. W. C. Prasad, Simi Bajaj, Omar Hisham Alsadoon, Tarik A. Rashid, Rasha S. Ali, Oday D. Jerew
Methodology: The proposed system consists of a Deep Convolutional Neural Network (DCNN) that helps in enhancing and increasing the accuracy of the classification process.