Efficient Approximate Conformance Checking Using Trie Data Structures by Awad, Ahmed

Subjects: “…Estimation error,Runtime,Computational modeling,Data structures,Approximation algorithms,Encoding,Computational efficiency…”
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An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm by M Baritha Begum (17541483)

The unified effect of data encoding, ansatz expressibility and entanglement on the trainability of HQNNs by Muhammad Kashif (3923483)

“…We focus on the combined influence of data encoding, qubit entanglement, and ansatz expressibility in hybrid quantum neural networks (HQNNs) for multi-class classification tasks. …”

Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams by Eduardo Feo-Flushing (23276023)

“…Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. …”

CoLoSSI: Multi-Robot Task Allocation in Spatially-Distributed and Communication Restricted Environments by Ishaq Ansari (22047902)

“…<p dir="ltr">In our research, we address the problem of coordination and planning in heterogeneous multi-robot systems for missions that consist of spatially localized tasks. Conventionally, this problem has been framed as a task allocation problem that maps tasks to robots. …”

Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars by Abathar Al-Hamrani (16494884)

“…A 3D finite element model was first developed and validated against available experimental results. …”

STEM: spatial speech separation using twin-delayed DDPG reinforcement learning and expectation maximization by Muhammad Salman Khan (7202543)

“…For stationary sources, the proposed system gives satisfactory performance in terms of quality, intelligibility, and separation speed, and generalizes well with the test data from a mismatched speech corpus. Its perceptual evaluation of speech quality (PESQ) score is 0.55 points better than a self-supervised learning (SSL) model and almost equivalent to the diffusion models at computational cost and training data which is many folds lesser than required by these algorithms. …”

On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

“…<h3 dir="ltr">Purpose</h3><p dir="ltr">This work applies a computational framework for vibration attenuation in periodic structures by combining the established wave and finite element (WFE) method with nature-inspired optimization algorithms. …”

A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks by Umesh Kumar Lilhore (17727684)

“…The proposed energy-efficient routing protocol is based on an enhanced genetic algorithm and data fusion technique. In the proposed energy-efficient routing protocol, an existing genetic algorithm is enhanced by adding an encoding strategy, a crossover procedure, and an improved mutation operation that helps determine the nodes. …”

Enhancing Breast Cancer Diagnosis With Bidirectional Recurrent Neural Networks: A Novel Approach for Histopathological Image Multi-Classification by Rajendra Babu Chikkala (22330876)

“…<p dir="ltr">In recent years, deep learning methods have dramatically improved medical image analysis, though earlier models faced difficulties in capturing intricate spatial and contextual details. …”

Spectrum Sensing Algorithms for Cooperative Cognitive Radio Networks by Hassan, Yasmin Adel

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MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network by Sakib Mahmud (15302404)

“…Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. …”

A Parallel Neural Networks Algorithm for the Clique Partitioning Problem by Harmanani, Haidar M.

“…This paper presents a parallel algorithm to solve the Clique Partitioning Problem, an NP-complete problem. …”
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Leveraging Machine and Deep Learning Algorithms for hERG Blocker Prediction by Syed Mohammad (21075689)

“…It is noted that spatial relationships within molecules are crucial in predicting hERG blockers. …”

A Novel BIPV Reconfiguration Algorithm for Maximum Power Generation under Partial Shading by Saoud A. Al-Janahi (18877213)

“…The system is optimised for maximum yield to determine the optimal configuration and number of modules for each string using a genetic algorithm. The outcomes from the algorithm are based on clustering the solar insolation values and then applying a genetic algorithm optimisation to indicate the optimum BIPV array layout for maximum yield.…”

Optimization of Interval Type-2 Fuzzy Logic System Using Grasshopper Optimization Algorithm by Saima Hassan (14918003)

“…The antecedent part parameters (Gaussian membership function parameters) are encoded as a population of artificial swarm of grasshoppers and optimized using its algorithm. …”

Inertial Navigation system of In-pipe Inspection Robot by Al-Masri, Wasim

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Monitoring Bone Density Using Microwave Tomography of Human Legs: A Numerical Feasibility Study by Alkhodari, Mohanad Ahmed

“…This study was performed using an in-house finite-element method contrast source inversion algorithm (FEM-CSI). …”
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Improving MRI Resolution: A Cycle Consistent Generative Adversarial Network-Based Approach for 3T to 7T Translation by Zakaria Shams Siam (22048001)

Image-Based Air Quality Estimation Using Convolutional Neural Network Optimized by Genetic Algorithms: A Multi-Dataset Approach by Arshad Ali Khan (23152516)

“…To mitigate overfitting, we implemented dropout layers, batch normalization, and early stopping, significantly enhancing the model’s generalization capability. Specifically, three different open-access datasets were combined into a single training dataset, capturing extensive temporal, spatial, and environmental variability. …”