An ant colony optimization algorithm to improve software quality prediction models by Azar, D.

“…For this purpose, software quality prediction models have been extensively used. However, building accurate prediction models is hard due to the lack of data in the domain of software engineering. …”
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A Genetic Algorithm for Improving Accuracy of Software Quality Predictive Models by Azar, Danielle

“…In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.…”
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Impact of AI and Dynamic Ensemble Techniques in Enhancing Healthcare Services: Opportunities and Ethical Challenges by Haseeb Javed (18576340)

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Using Machine Learning Algorithms to Forecast Solar Energy Power Output by Ali Jassim Lari (22597940)

“…The data forecasting horizon used was a 24-h window in steps of 30 min. …”

Predictive Model of Psychoactive Drugs Consumption using Classification Machine Learning Algorithms by Almahmood, Mothanna

“…Eighteen classification models were built using different classification algorithms such as Gaussian Naive Bais, Logistic Regression, k-nearest neighbors, Random Forest, and Decision Tree. …”
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Time-varying volatility model equipped with regime switching factor: valuation of option price written on energy futures by Leduc, Guillaume

“…To determine the parameters of the regime switching model and identify when economic states change, we employ the EM algorithm, utilizing real gas futures price data. …”
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A data envelopment analysis model for opinion leaders’ identification in social networks by Hamed Baziyad (19273738)

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

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A hybrid heuristic approach to optimize rule based software quality estimation models. (c2008) by Korkmaz, Rita

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Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems by Majdi Mansouri (16869885)

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GenDE: A CRF-Based Data Extractor by Kayed, Mohammed

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Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition by Dabeeruddin Syed (16864260)

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Intelligent Fusion of Deep Features for Improved Waste Classification by Kashif Ahmad (12592762)

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An Effective Hash Based Assessment and Recovery Algorithm for Healthcare Systems by Boukhari, Bahia

“…Finally, the experimental results prove the improvements provided by our hash based algorithm over previously suggested models.…”
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Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain by Farshad Rahimi Ghashghaei (20880995)

“…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. This research aims to use the complementary strengths of DQN and PPO algorithms to develop robust and adaptive control policies for noisy and uncertain quantum systems. …”

Noise-Robust Parameter Estimation Of Linear Systems by Emara-Shabaik, Husam

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Forecasting the nearly unforecastable: why aren’t airline bookings adhering to the prediction algorithm? by Saravanan Thirumuruganathan (11038038)

“…The resulting model achieves an 89% predictive accuracy using historical data. …”

Deploying model obfuscation: towards the privacy of decision-making models on shared platforms by Sadhukhan, Payel

“…The implementation nuances involve data and model sharing among allies and partners working on the same domain. …”
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Design of acoustic absorbing metasurfaces using a data-driven approach by Hamza Baali (14603380)

Predicting Dropouts among a Homogeneous Population using a Data Mining Approach by BILQUISE, GHAZALA

“…Our research relies solely on pre-college and college performance data available in the institutional database. Our research reveals that the Gradient Boosted Trees is a robust algorithm that predicts dropouts with an accuracy of 79.31% and AUC of 88.4% using only pre-enrollment data. …”
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