Enhancing multilayer perceptron neural network using archive-based harris hawks optimizer to predict gold prices by Abu Doush, Iyad

“…Harris Hawks optimizer (HHO) is a recently proposed metaheuristic technique that mimics the harris’ hawks behavior to chase and catch prey. In this paper, the algorithm search process is enhanced by introducing an external archive that saves the best solutions to be used in the next iterations. …”
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Island artificial bee colony for global optimization by Abu Doush, Iyad

“…This paper proposes an efficient version of artificial bee colony (ABC) algorithm based on the island model concepts. The new version is called the island artificial bee colony (iABC) algorithm. …”
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Dynamic performance evaluation and machine learning-assisted optimization of a solar-driven system integrated with PCM-based thermal energy storage: A case study approach by Haitham Osman (11737057)

“…A comprehensive techno-economic analysis is conducted, supported by a machine learning-assisted optimization framework that combines artificial neural networks with genetic algorithms. Considering optimum conditions, the system attains an exergetic efficiency of 30.13 % and a power generation of 7.24 MW, with a cost rate of 232.06 $/h and a payback period of 4.09 years. …”

ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-Time Constraints by Mohammed Al Disi (16855407)

“…Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. …”

Predicting Compression Modes and Split Decisions for HEVC Video Coding Using Machine Learning Techniques by Hassan, Mahitab Alaaeldin

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Detecting Arabic Cyberbullying Tweets in Arabic Social Using Deep Learning by ALFALASI, FARIS Jr

“…The data needs to be initially prepared so that deep learning algorithms may be trained on it before cyberbullying analysis can be done. …”
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Binary B-Hill Climbing Optimizer with S-shape Transfer Function for Feature Selection by Abou Doush, Iyad

“…In this paper, a β β -hill climbing optimizer is applied to solve the feature selection problem. β β -hill climbing is recently introduced as a local-search based algorithm that can obtain pleasing solutions for different optimization problems. …”
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Reputation-based cooperative detection model of selfish nodes in cluster-based QoS-OLSR protocol. (c2014) by Moati, Nadia

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Enhancing Healthcare Systems With Deep Reinforcement Learning: Insights Into D2D Communications and Remote Monitoring by Zina Chkirbene (16869987)

“…The core of DRLLVT is its novel algorithm that leverages Deep Reinforcement Learning (DRL) to dynamically adapt to changing environmental conditions, facilitating real-time decisions that consider node capacities, latency, and the overall network dynamics. …”

Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes by Muhammad Mohsin Khan (22150360)

“…Feature selection was done using a Random Forest algorithm to identify the top 20 features for the SAH severity prediction. …”

Integration of Artificial Intelligence in E-Procurement of the Hospitality Industry: A Case Study in the UAE by Mathew, Elezabeth

“…The novel LSTM time series algorithm proved to work best for demand forecasting. …”
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A residual-accelerated Jacobian method for rapid convergence in reservoir simulation by Ali Asif (23717367)

“…<p dir="ltr">Reservoir simulation requires efficient algorithms for complex, nonlinear systems. We introduce a Residual–Accelerated Jacobian (RAJ) for fully implicit reservoir simulation. …”

Malicious URL and Intrusion Detection using Machine Learning by Hamza, Amr

“…Different ML algorithms were applied, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). …”
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Economic Production Lot-Sizing For An Unreliable Machine Under Imperfect Age-Based Maintenance Policy by El-Ferik, S

“…Some useful properties of the cost function are developed to characterize the optimal policy. An algorithm is also proposed to find the optimal solutions to the problem at hand. …”
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Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights by Malekloo, Arman

“…Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. …”
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A Comprehensive Review of AI’s Current Impact and Future Prospects in Cybersecurity by Abdullah Al Siam (22304047)

“…We examine cutting-edge AI methodologies and principal models across many domains, including machine learning algorithms, deep learning architectures, natural language processing techniques, and anomaly detection algorithms, emphasizing their distinct contributions to enhancing security. …”

A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems by Raka Jovanovic (17947838)

“…To be more precise, a greedy randomized adaptive search procedure (GRASP) algorithm is developed and its performance is evaluated against optimal solutions acquired using the MIP. …”

Multi-Classifier Tree With Transient Features for Drift Compensation in Electronic Nose by Atiq Ur Rehman (8843024)

“…These electronic instruments rely on Machine Learning (ML) algorithms for recognizing the sensed odors. The effect of long-term drift influences the performance of ML algorithms and the models those are trained on drift free data fail to perform on the drifted data. …”

Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives by Yassine Himeur (14158821)

“…Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. …”

Building power consumption datasets: Survey, taxonomy and future directions by Yassine Himeur (14158821)

“…Accordingly, a novel visualization strategy based on using power consumption micro-moments has been presented along with an example of deploying machine learning algorithms to classify the micro-moment classes and identify anomalous power usage.…”