Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features by Mariam Bahameish (19255789)

“…<p dir="ltr">Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. …”

CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth, Kunhoth

“…Three machine learning algorithms support vector machine (SVM), AdaBoost, and Random forest are employed to assess the performance of the CNN features and fused CNN features. …”
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CNN feature and classifier fusion on novel transformed image dataset for dysgraphia diagnosis in children by Jayakanth Kunhoth (14158908)

“…Three machine learning algorithms support vector machine (SVM), AdaBoost, and Random forest are employed to assess the performance of the CNN features and fused CNN features. …”

Microwave brain imaging system to detect brain tumor using metamaterial loaded stacked antenna array by Amran Hossain (570706)

“…The antenna is comprised of metamaterial-loaded with three substrate layers, including two air gaps. One 1 × 4 MTM array element is used in the top layer and middle layer, and one 3 × 2 MTM array element is used in the bottom layer. …”

Integration of Textural and Material Information into BIM Using Spectrometry and Infrared Sensing by Zabin, Asem Ahmad

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Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review by Arfan Ahmed (17541309)

“…WDs coupled with artificial intelligence (AI) algorithms show promise to help understand and conclude meaningful information from the gathered data and provide advanced and clinically meaningful analytics.…”

LDSVM: Leukemia Cancer Classification Using Machine Learning by Abdul Karim (417009)

“…The main aim was to predict the initial leukemia disease. Machine learning algorithms such as decision tree (DT), naive bayes (NB), random forest (RF), gradient boosting machine (GBM), linear regression (LinR), support vector machine (SVM), and novel approach based on the combination of Logistic Regression (LR), DT and SVM named as ensemble LDSVM model. …”

Artificial intelligence models for predicting the mode of delivery in maternal care by Rawan AlSaad (14159019)

“…Five machine learning algorithms were evaluated: XGBoost, AdaBoost, random forest, decision tree, and multi-layer perceptron (MLP) classifier. …”

Inception voltage of corona in bipolar ionized fields-effect oncorona power loss by Al-Hamouz, Z.

“…In this paper, an iterative finite element based algorithm is presented as a numerical tool for the solution of the bipolar ionized field around high voltage direct current (HVDC) transmission lines. …”
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An Evolutionary Meta-Heuristic for State Justification in Sequential Automatic Test Pattern Generation by El-Maleh, Aiman H.

“…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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An evolutionary meta-heuristic for state justification insequential automatic test pattern generation by El-Maleh, A.H.

“…In this work, we propose a hybrid approach which uses a combination of evolutionary and deterministic algorithms for state justification. A new method based on Genetic Algorithms is proposed, in which we engineer state justification sequences vector by vector. …”
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Study of the Impact of Land Use and Land Cover (LULC) Changes on local climate in Six Mega Global Cities in the 21st Century by Sabobeh, Lara Ali

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Modeling and Guidance of an Underactuated Autonomous Underwater Vehicle by Wadi, Ali H M

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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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Combining offline and on-the-fly disambiguation to perform semantic-aware XML querying by Tekli, Joe

“…Dedicated weighting functions and various search algorithms have been developed for that purpose and will be presented here. …”
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Corona power loss computation in bundled bipolar conductors by Al-Hamouz, Z.M.

“…In this paper, a finite element (FE) based algorithm devoted for the computation of the corona current and hence the corona power loss associated with bundled bipolar high voltage direct current (HVDC) conductors is presented. …”
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Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices by Neder Karmous (19743430)

“…IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. …”

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. …”

A hybrid approach for XML similarity by Tekli, Joe

“…In this paper, we integrate IR semantic similarity assessment in an edit distance algorithm, seeking to amend similarity judgments when comparing XML-based documents. …”
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Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort by Sergio Márquez-Sánchez (19437985)

“…Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. …”