Variable Selection in Data Analysis: A Synthetic Data Toolkit by Mitra, Rohan

“…Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). …”
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How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation by Joni Salminen (7434770)

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

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UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data by Behrouz Ahadzadeh (19757022)

“…To overcome these challenges, a new FS algorithm named Uniform-solution-driven Binary Feature Selection (UniBFS) has been developed in this study. …”

Social spider optimization algorithm: survey and new applications by Abualigah, Laith

“…This algorithm has been developed over time, resulting in many versions besides theories and findings. …”
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Salp swarm algorithm: survey, analysis, and new applications by Abualigah, Laith

“…This chapter offers the sea salmon-associated polyp (SALP) swarm algorithm (SSA) and multipurpose SSA (MSSA) as new optimization algorithms for solving optimization problems with single and multiple objectives. …”
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Auto-indexing Arabic texts based on association rule data mining. (c2015) by Rouba G. Nasrallah

“…Our model denotes extracting new relevant words by relating those chosen by the previous classical methods, to new words using data mining rules. …”
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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.…”
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A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method by Amit Kumar Balyan (18288964)

“…To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. …”

A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems by Wali Khan Mashwani (14590504)

“…In general, EAs are mainly categorized into nature-inspired and swarm-intelligence- (SI-) based paradigms. All these developed algorithms have some merits and also demerits. …”

Indexing Arabic texts using association rule data mining by Haraty, Ramzi A.

“…The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. …”
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A parallel ant colony optimization to globally optimize area in high-level synthesis. (c2011) by Kozhaya, David Elias

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Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence by Al Rayhi, Nasser

“…One of the best algorithms in terms of the result is the Long Short Term Memory (LSTM) since it is based on recurrent neural networks which uses loop as a method to learn from heuristics data. …”
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Allocation and re-allocation of data in a grid using an adaptive genetic algorithm by Mansour, N.

“…Allocation and re-allocation of data in a grid using an adaptive genetic algorithm. …”
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An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm by M Baritha Begum (17541483)

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

“…By encoding the proxy behavior using a trie data structure, we obtain a logarithmically reduced search space for alignment computation compared to a set-based representation. …”
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Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm by Abu Zitar, Raed

“…Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. …”
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Joint Inversion of Direct Current Resistivity and Seismic Refraction Data, using Local and Global Optimization Algorithms with Synthetic and Real Data Examples by unknown

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Using Machine Learning Algorithms to Forecast Solar Energy Power Output by Ali Jassim Lari (22597940)

“…We focused on the first 30-min, 3-h, 6-h, 12-h, and 24-h windows to gain an appreciation of the impact of forecasting duration on the accuracy of prediction using the selected machine learning algorithms. The study results show that Random Forest outperformed all other tested algorithms. …”

Limiting the Collection of Ground Truth Data for Land Use and Land Cover Maps with Machine Learning Algorithms by Usman Ali (6586886)

“…This was accomplished by (1) extracting reliable LULC information from Sentinel-2 and Landsat-8 s images, (2) generating remote sensing indices used to train ML algorithms, and (3) comparing the results with ground truth data. …”