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)

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

“…UniBFS exploits the inherent characteristic of binary algorithms-binary coding-to search the entire problem space for identifying relevant features while avoiding irrelevant ones. …”

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

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Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks by S. N. V. Bramareswara Rao (21768302)

“…Nowadays, deep learning algorithms are essential for ensuring the reliable, safe, and efficient operation of these complex energy systems. …”

A systematic review of text classification research based on deep learning models in Arabic language by Wahdan, Ahlam

“…The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. …”
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Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory by Nadia Rasheed (22997257)

Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection by Alok Singh Chauhan (8264985)

YOLO-DefXpert: An Advanced Defect Detection on PCB Surfaces Using Improved YOLOv11 Algorithm by Prabu Selvam (22330264)

Soft Sensor for NOx Emission using Dynamical Neural Network by Shakil, M.

“…Lag space for the model is found by using genetic algorithm to find the best time delayed model. Lag space obtained from the linear model is then used for constriction of the dynamical neural network. …”
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Intelligent Rapidly-Exploring Random Tree Star Algorithm by Ahmed, Khidir Galal Eldin Khidir

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Optimizing Document Classification: Unleashing the Power of Genetic Algorithms by Ghulam Mustafa (458105)

“…Additionally, our proposed model optimizes the features using a genetic algorithm. Optimal feature selection performances a crucial role in this domain, enhancing the overall accuracy of the document classification system while reducing the time complexity associated with selecting the most relevant features from this large-dimensional space. …”

Mining airline data for CRM strategies. (c2006) by Maalouf, Lena

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Tensile Test Optimization Using the Design of Experiment and Soft Computing by Mehdi Moayyedian (14880358)

Multiclass feature selection with metaheuristic optimization algorithms: a review by Abu Zitar, Raed

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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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Modified arithmetic optimization algorithm for drones measurements and tracks assignment problem by Abu Zitar, Raed

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Efficient convex-elastic net algorithm to solve the Euclideantraveling salesman problem by Al-Mulhem, M.

“…This paper describes a hybrid algorithm that combines an adaptive-type neural network algorithm and a nondeterministic iterative algorithm to solve the Euclidean traveling salesman problem (E-TSP). …”
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Artificial intelligence-based methods for fusion of electronic health records and imaging data by Farida Mohsen (16994682)

“…In our analysis, a typical workflow was observed: feeding raw data, fusing different data modalities by applying conventional machine learning (ML) or deep learning (DL) algorithms, and finally, evaluating the multimodal fusion through clinical outcome predictions. …”