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generation algorithm » genetic algorithm (Expand Search), detection algorithm (Expand Search)
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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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Defense against adversarial attacks: robust and efficient compressed optimized neural networks
Published 2024“…First, introducing a pioneering batch-cumulative approach, the exponential particle swarm optimization (ExPSO) algorithm was developed for meticulous parameter fine-tuning within each batch. …”
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Matheuristic Fixed Set Search Applied to the Two-Stage Capacitated Facility Location Problem
Published 2025“…We propose a twofold contribution: first, an adaptive greedy algorithm that generates high-quality initial solutions, achieving markedly better results than traditional constructive heuristics at comparable computational costs; and second, the adaptation of the Matheuristic Fixed Set Search (MFSS) to the TSCFLP. …”
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TIDCS: A Dynamic Intrusion Detection and Classification System Based Feature Selection
Published 2020“…TIDCS reduces the number of features in the input data based on a new algorithm for feature selection. Initially, the features are grouped randomly to increase the probability of making them participating in the generation of different groups, and sorted based on their accuracy scores. …”
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An Effective Hybrid NARX-LSTM Model for Point and Interval PV Power Forecasting
Published 2021“…First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. …”
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Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition
Published 2021“…It investigates the gain in training time and the performance in terms of accuracy when clustering-based deep learning modeling is employed for STLF. A k-Medoid based algorithm is employed for clustering whereas the forecasting models are generated for different clusters of load profiles. …”
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A Digital DNA Sequencing Engine for Ransomware Analysis using a Machine Learning Network
Published 2020“…A newly collected dataset after feature selection is used to generate the DNA sequence. …”
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29
GenDE: A CRF-Based Data Extractor
Published 2020“…Moreover, it gives a high performance result when tested on the SWDE benchmark dataset (84.91%).…”
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30
Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine
Published 2024“…Our employed scheme achieved the best results for both datasets obtaining accuracy of 93.25% (G1020 dataset) and 96.75% (ORIGA dataset), respectively. …”
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Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework
Published 2022“…<div><p>Fog computing (FC) based sensor networks have emerged as a propitious archetype for next-generation wireless communication technology with caching, communication, and storage capacity services in the edge. …”
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An Artificial Intelligence Approach for Predictive Maintenance in Electronic Toll Collection System
Published 2019“…Meaning no “perfect” machine learning algorithm that will produce good results at particular problem, in fact for each type of problem a specific algorithm is suited and might achieves good outcome, while another algorithm fails heavily. …”
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K Nearest Neighbor OveRsampling approach: An open source python package for data augmentation
Published 2022“…The KNNOR algorithm has outperformed the state-of-the-art augmentation algorithms by enabling classifiers to achieve much higher accuracy after injecting artificial minority datapoints into imbalanced datasets. …”
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Bootstrap-based Aggregations and their Stability in Feature Selection
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Using Text Mining and Clustering Techniques on Tweets to Discover Trending Topics in Dubai
Published 2015“…Tweets corpus of Dubai were collected, they were presented through the bag of words model using TF-IDF weighting scheme, after that the output of text transformation was introduced to k-means clustering algorithm with cosine similarity measure. Findings indicate that heuristic evaluation techniques are not so helpful in this domain; also, the model has generated interesting clusters about trending topics and events in Dubai. …”
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Optimizing ADWIN for Steady Streams
Published 2022“…With the ever-growing data generation rates and stringent con straints on the latency of analyzing such data, stream analytics is overtaking. …”
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Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining
Published 2024“…This implementation leverages an extensive dataset generated in a recent experimental study by the authors. …”
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A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models
Published 2024“…First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. …”
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Sentiment Analysis of the Emirati Dialect text using Ensemble Stacking Deep Learning Models
Published 2023“…The findings reveal that the overall Fleiss Kappa coefficient is = 0.93, indicating an almost-perfect agreement amongst the three annotators. Once the dataset was constructed and validated, I then conducted a performance evaluation and comparison of various basic Machine Learning algorithms, Deep Learning models, and stacking deep learning models on different datasets of Sentiment Analysis of Arabic Dialects. …”
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A collaborative filtering recommendation framework utilizing social networks
Published 2023“…The framework is evaluated using a dataset of movie ratings and social connections between users. …”