Eye-Clustering: An Enhanced Centroids Prediction for K-means Algorithm by Nasser, Youssef

“…Unsupervised machine learning is a powerful technique for performing clustering, which involves identifying patterns or similarities within a dataset and grouping them into distinct clusters or subgroups. …”
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Animal migration optimization algorithm: novel optimizer, analysis, and applications by Abualigah, Laith

“…Optimization algorithms are applied to find the optimal solutions in many domains and fields such as image processing, machine learning, and others. …”
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Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach by Sarif, Bambang

“…Multiple-Valued Logic (MVL) has been used in the design of a number of logic systems, including memory, multi-level data communication coding, and a number of special purpose digital processors. …”
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Adaptive Secure Pipeline for Attacks Detection in Networks with set of Distribution Hosts by ALSHAMSI, SUROUR

“…The research work carried out, in relation to attack detection ensemble learning, mainly aims to increase the performance of machine learning algorithms by combining their results. …”
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A conjugate self-organizing migration (CSOM) and reconciliate multi-agent Markov learning (RMML) based cyborg intelligence mechanism for smart city security by S. Shitharth (12017480)

“…Moreover, the Reconciliate Multi-Agent Markov Learning (RMML) based classification algorithm is used to predict the intrusion with its appropriate classes. …”

A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System by Ekinci, Serdar

“…CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm. …”
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DRL-Based UAV Path Planning for Coverage Hole Avoidance: Energy Consumption and Outage Time Minimization Trade-Offs by Bahareh Jafari (22501715)

“…By deploying a deep reinforcement learning algorithm, we find optimal UAV paths based on the two families of trajectories: spiral and oval curves, to tackle different design considerations and constraints, in terms of QoS, energy consumption and coverage hole avoidance. …”

Evolutionary algorithms for state justification in sequential automatic test pattern generation by El-Maleh, Aiman H.

“…A common search operation in sequential Automatic Test Pattern Generation is to justify a desired state assignment on the sequential elements. State justification using deterministic algorithms is a difficult problem and is prone to many backtracks, which can lead to high execution times. …”
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Target Detection Using Learning Methods by Sahnoon, Mohammad Moufeed

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Machine Learning Techniques for Pharmaceutical Bioinformatics by SULTAN, AHMED ATTA AHMED

“…A predictive model is developed to predict drug indication as well as to predict new DDIs using multiple machine learning algorithms. This dissertation presents a case study of predicted anti-cancer activity for 38 drugs. …”
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A method for optimizing test bus assignment and sizing for system-on-a-chip by Harmanani, Haidar M.

“…We present experimental results that demonstrate the effectiveness of our method while outperforming reported techniques.…”
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The role of Reinforcement Learning in software testing by Amr Abo-eleneen (17032284)

“…</p><h3>Results</h3><p dir="ltr">This study highlights different software testing types to which RL has been applied, commonly used RL algorithms and architecture for learning, challenges faced, advantages and disadvantages of using RL, and the performance comparison of RL-based models against other techniques.…”

Assessment of static pile design methods and non-linear analysis of pile driving by Abou-Jaoude, Grace G.

“…The pile/soil interaction system is described by a mass/spring/dashpot system where the properties of each component are derived from rigorous analytical solutions or finite element analysis. The outcome of this research is an algorithm that can be used to predict pile displacement and driving stresses. …”
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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. …”

The Detection of Dysarthria Severity Levels Using AI Models: A Review by Afnan Al-Ali (16888695)

A New Hamiltonian Semi-Analytical Approach to Vibration Analysis of Piezoelectric Multi-Layered Plates by Andrianarison, O.

“…The whole piezoelectric multilayered plate’s dynamic stiffness is then built, from which its circular frequencies are computed with the help of the Wittrick-Williams algorithm. A detailed discussion is provided on the implementation aspects, followed by some numerical examples to assess the robustness, accuracy and effectiveness of the proposed method. …”
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Identification of phantom movements with an ensemble learning approach by Akhan Akbulut (17380285)

“…The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. …”

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review by Zainab Jan (17306614)

“…A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. </p><h3>Conclusions </h3><p dir="ltr">This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. …”

Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis by Sami Elzeiny (16891521)

EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach by Muhammad Adeel Asghar (6724982)

“…<p dir="ltr">Much attention has been paid to the recognition of human emotions with the help of electroencephalogram (EEG) signals based on machine learning technology. Recognizing emotions is a challenging task due to the non-linear property of the EEG signal. …”