A5018111131- Neural Networks and Applications
- Machine Learning and ELM
- Smart Agriculture and AI
- Advanced Memory and Neural Computing
- Leaf Properties and Growth Measurement
- Spectroscopy and Chemometric Analyses
- Numerical Methods and Algorithms
- Music and Audio Processing
- Polynomial and algebraic computation
- Ancient Egypt and Archaeology
- IoT and Edge/Fog Computing
- Advanced Neural Network Applications
- Islamic Studies and History
- Iterative Methods for Nonlinear Equations
- Internet Traffic Analysis and Secure E-voting
- Speech and Audio Processing
- Network Security and Intrusion Detection
- Archaeology and Historical Studies
- Cloud Computing and Resource Management
- EEG and Brain-Computer Interfaces
- Advanced Malware Detection Techniques
- Heat Transfer and Numerical Methods
- Model Reduction and Neural Networks
- Distributed and Parallel Computing Systems
- Advanced Numerical Analysis Techniques
University of Winnipeg
2021-2024
Qatar University
2020-2021
Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional (CNNs) such as network homogeneity with sole linear neuron model. ONNs are heterogeneous networks a generalized However operator search method in is not only computationally demanding, but heterogeneity also limited since same set operators will then be used for all neurons each layer. Moreover, performance directly depends on library used, which...
Abstract The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional (CNNs) that are homogenous only with a linear neuron model. As heterogenous ONNs based on generalized model encapsulate any set of non-linear operators to boost diversity and learn highly complex multi-modal functions or spaces minimal complexity training data. However, default search method find optimal in ONNs, so-called Greedy Iterative Search (GIS) method,...
Cloud computing is a paradigm that provides multiple services over the internet with high flexibility in cost-effective way. However, growth of cloud-based comes major security issues. Recently, machine learning techniques are gaining much interest applications as they exhibit fast processing capabilities real-time predictions. One challenge implementation these available training data for each new potential attack category. In this paper, we propose model secure network based on algorithms....
Energy efficiency is a major topic in every scientific field, since being energy efficient means producing more for smaller cost. Data centers are no exception to this rule as their use represents large portion of the global consumption, and it needless say that they ought perform optimally while eco-friendly order preserve natural resources much possible providing high quality service users. In paper, we propose an algorithm allocating users pool servers energy-efficient way. Our allocation...
Recently, the EAGL-I system was developed to rapidly create massive labeled datasets of plants intended be commonly used by farmers and researchers AI-driven solutions in agriculture. As a result, publicly available plant species recognition dataset composed 40,000 images with different sizes consisting 8 created order demonstrate its capabilities. This paper proposes novel method, called Variably Overlapping Time-Coherent Sliding Window (VOTCSW), that transforms variable size 3D...
De l'iqta' etatique a militaire : transition economique et changements sociaux Baghdad, 247-447 de l'Hegire/861-1055 ap. J.
In this paper, we develop a direct formula for determining the coefficients in canonical basis of best polynomial degree $M$ that approximates $N>M$ on symmetric interval $\mathcal{L}^2$-norm. We also formally prove using is more computationally efficient than classical matrix multiplication approach and provide an example to illustrate it numerically stable approach.
The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional (CNNs) that are homogenous only with a linear neuron model. As heterogenous ONNs based on generalized model encapsulate any set of non-linear operators to boost diversity and learn highly complex multi-modal functions or spaces minimal complexity training data. However, default search method find optimal in ONNs, so-called Greedy Iterative Search (GIS) method, usually takes...
Operational Neural Networks (ONNs) have recently been proposed to address the well-known limitations and drawbacks of conventional Convolutional (CNNs) such as network homogeneity with sole linear neuron model. ONNs are heterogenous networks a generalized model that can encapsulate any set non-linear operators boost diversity learn highly complex multi-modal functions or spaces minimal complexity training data. However, Greedy Iterative Search (GIS) method, which is search method used find...