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Gene selection for microarray data classification based on Gray Wolf Optimizer enhanced with TRIZ-inspired operators
Published 2021“…Datasets with such characteristics pose a challenge to machine learning algorithms. This is because they impede the training and testing process and entail high resource computations that deteriorate the classification performance. …”
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103
ML-Based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks
Published 2022“…The proposed scheme for handover prediction outperforms traditional methods i.e. received signal strength method and traveling distance method by reducing the number of unnecessary handovers by 60% and 50% respectively. Similarly, in AP selection, the proposed scheme outperforms the strongest signal first and least loaded first algorithms by achieving higher throughput gains up to 9.2% and 8% respectively.…”
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Application of Data Mining to Predict and Diagnose Diabetic Retinopathy
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doctoralThesis -
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Computer-aided detection of Melanoma using geometric features
Published 2017“…The k-Nearest Neighbors (k-NN) machine learning algorithm is used to classify 15 lesions based on their ABD features. …”
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Type 2 Diabetes Mellitus Automated Risk Detection Based on UAE National Health Survey Data: A Framework for the Construction and Optimization of Binary Classification Machine Learn...
Published 2020“…Machine Learning (ML) saw a great increase in general and domain specific research. …”
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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review
Published 2023“…Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). …”
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Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm
Published 2024“…This work aims to enhance the performance of the K-means algorithm by introducing a novel method for selecting the initial centroids, thereby minimizing randomness and reducing the number of iterations needed to reach optimal results. …”
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Multi Self-Organizing Map (SOM) Pipeline Architecture for Multi-View Clustering
Published 2024“…A self-organizing map is one of the well-known unsupervised neural network algorithms used for preserving typologies during mapping from the input space (high-dimensional) to the display (low-dimensional).An algorithm called Local Adaptive Receptive Field Dimension Selective Self-Organizing Map 2 is a modified form of a self-organizing Map to cater different data types in the dataset. …”
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Enhancing building sustainability: A Digital Twin approach to energy efficiency and occupancy monitoring
Published 2024“…Our data-driven occupancy detection approach utilized Machine Learning (ML) algorithms to intelligently determine room occupancy, allowing for precise energy management based on real-time usage patterns. …”
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Generation and Detection of Sign Language Deepfakes - A Linguistic and Visual Analysis
Published 2025“…We also apply machine learning algorithms to establish a baseline for deepfake detection on this dataset, contributing to the detection of fraudulent sign language videos.…”
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PROVOKE: Toxicity trigger detection in conversations from the top 100 subreddits
Published 2022“…Implications are that toxicity trigger detection algorithms can leverage generic approaches but must also tailor detections to specific communities.…”
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Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine
Published 2025“…Machine learning and deep learning technologies improve seizure monitoring, automate EEG analysis, and facilitate tailored therapeutic strategies, addressing the complexities of epilepsy management. …”
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Identification of phantom movements with an ensemble learning approach
Published 2022“…Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. …”
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Generic metadata representation framework for social-based event detection, description, and linkage
Published 2020“…SEDDaL consists of four main modules for: i) describing social media objects in a generic Metadata Representation Space Model (MRSM) consisting of three composite dimensions: temporal, spatial, and semantic, ii) evaluating the similarity between social media objects’ descriptions following MRSM, iii) detecting events from similar social media objects using an adapted unsupervised learning algorithm, where events are represented as clusters of objects in MRSM, and iv) identifying directional, metric, and topological relationships between events following MRSM’s dimensions. …”
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