Efficient Dynamic Cost Scheduling Algorithm for Data Batch Processing by Al Sadawi, Alia

“…A Master of Science thesis in Engineering Systems Management by Alia Al Sadawi entitled, "Efficient Dynamic Cost Scheduling Algorithm for Data Batch Process," submitted in May 2016. …”
Get full text

Efficient Dynamic Cost Scheduling Algorithm for Financial Data Supply Chain by Al Sadawi, Alia

“…An iterative dynamic scheduling algorithm (DCSDBP) was developed to address the data batching process. …”
Get full text

Sensitivity to parameters and general applicability of genetic algorithms and simulated annealing algorithms for mapping data to multicomputers. (c1994) by Kawash, Jalal Y.

Get full text
Get full text

DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data by Assaf, Ali

Subjects: Get full text
Get full text
Get full text

A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT by Dhananjay Bisen (19482454)

“…This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. …”

Efficient Approximate Conformance Checking Using Trie Data Structures by Awad, Ahmed

Subjects: “…Estimation error,Runtime,Computational modeling,Data structures,Approximation algorithms,Encoding,Computational efficiency…”
Get full text
Get full text
Get full text

Data of simulation model for photovoltaic system's maximum power point tracking using sequential Monte Carlo algorithm by Odat, Alhaj-Saleh A.

“…Additionally, these data can be readily applied to compare algorithmic results referenced by (Babu, T.S. et al., 2015; PrasanthRam, J. et al., 2017) [2,3], and contribute to the development of new processes for practical applications.…”
Get full text
Get full text
Get full text

Estimating Construction Project Duration Using a Machine Learning Algorithm by Nasr, Joshua

Get full text
Get full text
Get full text

Graph Computing Systems and Partitioning Techniques: A Survey by Tewodros Alemu Ayall (19273717)

Subjects:

A Multiprocessing-Based Sensitivity Analysis of Machine Learning Algorithms for Load Forecasting of Electric Power Distribution System by Ameema Zainab (16864263)

Subjects:

Correlation Clustering via s-Club Cluster Edge Deletion by Makarem, Norma

Subjects: “…Cluster analysis -- Data processing…”
Get full text
Get full text
Get full text

Genetic and heuristic algorithms for regrouping service sites. (c2000) by Tabbara, Hiba

Subjects: “…Genetic algorithms -- Data processing…”
Get full text
Get full text

On the steiner walk problem. (c2009) by Maarouf, Amina

Subjects: “…Euclidean algorithm -- Data processing…”
Get full text
Get full text

Parallel Algorithms for Distinguishing Nondeterministic Finite State Machines by Ali, Mustafa

Get full text

A genetic algorithm for corrective retesting. (c1995) by Fakih, Khaled A.

Subjects: Get full text
Get full text

A Novel Non-Invasive Estimation of Respiration Rate From Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model by Md. Nazmul Islam Shuzan (16888827)

Subjects:

An ant colony optimization algorithm to improve software quality prediction models by Azar, D.

“…For this purpose, software quality prediction models have been extensively used. However, building accurate prediction models is hard due to the lack of data in the domain of software engineering. …”
Get full text
Get full text
Get full text

Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark by Ameema Zainab (16864263)

“…However, with the huge increase in data size, sophisticated models have to be created which require big data platforms. …”

Three-phase approach for curriculum-based course timetabling problem. (c2012) by Jazzar, Hanaa Salem El-

Subjects: “…Scheduling -- Data processing…”
Get full text
Get full text

Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms by Arafat Rahman (8065562)

“…A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. …”