Papers by Rihab Habeeb Sahib
Karbala International Journal of Modern Science, 2021
Regular E-voting systems for elections may count the votes in less time,less cost,save the privac... more Regular E-voting systems for elections may count the votes in less time,less cost,save the privacy of citizens,but still considered risky as votes can be tampered.E-voting systems based on a network distributed ledger show fast results,more trusted,save privacy,cannot be tampered,and distributed in which no central organization controls the system.This paper illustrate an e-voting system to solve the challenge of a massive ledger that is distributed among network-nodes using a data reduction technique as a security-matching-tool,singular value decomposition(SVD) that handle a copy of election results in another form and matched with the SQL-database results to announce a successful election-event representing a transparency-powerful-secured-system
3rd International Conference on Engineering and Science, 2024
A big challenge that faces many applications in different fields suffers in dealing with datasets... more A big challenge that faces many applications in different fields suffers in dealing with datasets of massive size. Additionally, retrieving and casting this data is somewhat time-consuming. Applications such as government or any institution election, surveys, healthcare …etc., leverage techniques of data reduction, dimensionality reduction, matrix decomposition, or compression such as the Singular Value Decomposition Technique. Our paper shows the use of this technique as a method in certain circumstances where data is of binary type and can be retrieved, cast, or updated in less time and in a smaller size without losing any information. In other words, we prove practically that the massive size of binary values can be managed in a form of matrices with low rank (low rank is one of the bases used in the Singular Value Decomposition technique) to return the exact matrix of information instead of dealing with the original large matrix of data. The experimental results are implemented on a Lenovo machine, Intel Corei5, CPU 2.5GH with 8GB of RAM, using visual basic, C#, in Visual Studio 2019 environment
3 rd International Conference on Engineering and Science (ICES2023), 2024
A big challenge that faces many applications in different fields suffers in dealing with datasets... more A big challenge that faces many applications in different fields suffers in dealing with datasets of massive
size. Additionally, retrieving and casting this data is somewhat time-consuming. Applications such as government or any
institution election, surveys, healthcare …etc., leverage techniques of data reduction, dimensionality reduction, matrix
decomposition, or compression such as the Singular Value Decomposition Technique. Our paper shows the use of this
technique as a method in certain circumstances where data is of binary type and can be retrieved, cast, or updated in less
time and in a smaller size without losing any information. In other words, we prove practically that the massive size of
binary values can be managed in a form of matrices with low rank (low rank is one of the bases used in the Singular
Value Decomposition technique) to return the exact matrix of information instead of dealing with the original large
matrix of data. The experimental results are implemented on a Lenovo machine, Intel Corei5, CPU 2.5GH with 8GB of
RAM, using visual basic, C#, in Visual Studio 2019 environment.
Although the prospect of the Internet of Things (IoT) is almost unlimited, developing IoT techniq... more Although the prospect of the Internet of Things (IoT) is almost unlimited, developing IoT techniques can look discouraging, demanding a multidomain expertise and complex web infrastructure. Combining machine learning and data analytics by smart connected devices can enable a large variety of applications including sophisticated predictive maintenance systems, home-grown traffic monitors, and futuristic user goods (such as the Google Nest and the Amazon Echo). This work develops a novel method to estimate tide levels integrating wind records by means of two neural network architectures. We merge past measurements of tide level with wind data that available to be downloaded. All the data are pre-processed by sorting out tidal harmonics and wind-induced surges. Correlations between surge variations and wind stress are then computed. Next, fitting neural network and NARX network are trained after preparing data that include actual tide levels, estimated harmonic tide altitudes, and wind stress elements. We evaluate both models using the error performance and show the deployment of the selected model with MATLAB ThingSpeak visualization environment. The results demonstrated that the performance of input–output Neural Fitting was 0.0592, while it was 0.0039 for the NARX neural network.
journal "Computing Technologies", 2024
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Papers by Rihab Habeeb Sahib
size. Additionally, retrieving and casting this data is somewhat time-consuming. Applications such as government or any
institution election, surveys, healthcare …etc., leverage techniques of data reduction, dimensionality reduction, matrix
decomposition, or compression such as the Singular Value Decomposition Technique. Our paper shows the use of this
technique as a method in certain circumstances where data is of binary type and can be retrieved, cast, or updated in less
time and in a smaller size without losing any information. In other words, we prove practically that the massive size of
binary values can be managed in a form of matrices with low rank (low rank is one of the bases used in the Singular
Value Decomposition technique) to return the exact matrix of information instead of dealing with the original large
matrix of data. The experimental results are implemented on a Lenovo machine, Intel Corei5, CPU 2.5GH with 8GB of
RAM, using visual basic, C#, in Visual Studio 2019 environment.
size. Additionally, retrieving and casting this data is somewhat time-consuming. Applications such as government or any
institution election, surveys, healthcare …etc., leverage techniques of data reduction, dimensionality reduction, matrix
decomposition, or compression such as the Singular Value Decomposition Technique. Our paper shows the use of this
technique as a method in certain circumstances where data is of binary type and can be retrieved, cast, or updated in less
time and in a smaller size without losing any information. In other words, we prove practically that the massive size of
binary values can be managed in a form of matrices with low rank (low rank is one of the bases used in the Singular
Value Decomposition technique) to return the exact matrix of information instead of dealing with the original large
matrix of data. The experimental results are implemented on a Lenovo machine, Intel Corei5, CPU 2.5GH with 8GB of
RAM, using visual basic, C#, in Visual Studio 2019 environment.