Extremely boosted neural network for more accurate multi-stage Cyber attack prediction in cloud computing environment by Surjeet Dalal (4906894)

“…This paper demonstrated the results of executing various machine learning algorithms and proposed an enormously boosted neural network. …”

The buffered work-pool approach for search-tree based optimization algorithms by Abu-Khzam, Faisal N.

“…Different algorithms adopt different branching and pruning techniques in order to reduce the unavoidable exponential growth in run time. …”
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Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms by Joao Palotti (8479842)

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces by Uzair Sajjad (19646296)

“…In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. …”

Detection of Depression in Arabic Social Media: A Comparison of Traditional and Modern Machine Learning Algorithms by ALSHEHHI, OMAR KHALID HAMAD

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Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems by Amal Hichri (21383672)

“…This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. …”

A critical review and performance comparisons of swarm-based optimization algorithms in maximum power point tracking of photovoltaic systems under partial shading conditions by Muhammad Shahid Wasim (16883984)

“…The swarm-based MPPT algorithms are stochastic meta-heuristic approaches that have become very popular recently in various applications owing to the drawbacks of conventional MPPT algorithms under different operating conditions. …”

Oversampling techniques for imbalanced data in regression by Samir Brahim Belhaouari (9427347)

“…For such high-dimension data our approach outperforms the Synthetic Minority Oversampling Technique for Regression (SMOTER) algorithm for the IMDB-WIKI and AgeDB image datasets. …”

Defense against adversarial attacks: robust and efficient compressed optimized neural networks by Insaf Kraidia (19198012)

“…First, introducing a pioneering batch-cumulative approach, the exponential particle swarm optimization (ExPSO) algorithm was developed for meticulous parameter fine-tuning within each batch. …”

Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches by Mohammad, Areej Osama

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Performance Prediction Using Classification by MOOLIYIL, GITA

“…A comprehensive evaluation requires that multiple models with different algorithms were analyzed using key performance measures. …”
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Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review by Ruba, Sulaiman

“…Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. The most employed ML algorithms were random forest, logistics regression, and gradient boosting. …”
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Network-based identification of key master regulators associated with an immune-silent cancer phenotype by Raghvendra Mall (581171)

“…We use a consensus of four different pipelines consisting of two state-of-the-art gene regulatory network inference techniques, regularized gradient boosting machines and ARACNE to determine TR regulons, and three separate enrichment techniques, including fast gene set enrichment analysis, gene set variation analysis and virtual inference of protein activity by enriched regulon analysis to identify the most important TRs affecting immunologic antitumor activity. …”

Data-Driven Electricity Demand Modeling for Electric Vehicles Using Machine Learning by Far, Reza Davoodi

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Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review by Ruba Sulaiman (17734065)

“…Most of the included studies focused on mortality prediction, utilizing datasets of varying sizes and diverse ML algorithms. The most employed ML algorithms were random forest, logistics regression, and gradient boosting. …”

Optimized FPGA Implementation of PWAM-Based Control of Three—Phase Nine—Level Quasi Impedance Source Inverter by Syed Rahman (569240)

“…Since, PWAM control algorithm is more complex than PSCPWM, FPGA based implementation for PWAM control is discussed. …”

Positive Unlabelled Learning to Recognize Dishes as Named Entity by TAREK, AIMAN

“…This research has the potential to be extended to other topics other than food and dish names, also it acts as a framework and algorithm independent.…”
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A multi-pretraining U-Net architecture for semantic segmentation by Cagla Copurkaya (22502042)

“…<p dir="ltr">Pathological cancer research relies heavily on different domain-specific applications including nucleus segmentation from histopathology images. …”

Application of Data Mining to Predict and Diagnose Diabetic Retinopathy by Haniyeh, Maryam

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Ensemble Deep Random Vector Functional Link Neural Network for Regression by Minghui Hu (2457952)

“…To address this problem, we propose a random skip connection-based edRVFL, which can keep the diversity in the latent space. esc-RVFL is an ensemble scheme that utilizes several edRVFL-RSC models trained on the different folds of the training dataset. The esc-edRVFL is identified as the best-performing algorithm through a comprehensive evaluation of 31 UCI datasets.…”