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

“…Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (<i>η</i>). …”

Modelling of pollutant transport in compound open channels by Chatila, Jean Georges

“…Different statistical methods were considered in evaluating the simulated results.…”
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Multimodal feature fusion and ensemble learning for non-intrusive occupancy monitoring using smart meters by Sakib Mahmud (15302404)

“…We combine features from all three modes of MMF-NIOM to achieve a state-of-the-art non-intrusive occupancy classification performance of 91.5 % accuracy and 91.5 % f1-score, approximately, by an ensemble of fine-tuned classifiers on the electricity consumption & occupancy (ECO) dataset. The proposed method is sustainable, robust, adaptable to various households, and can be mass-implemented within smart meters at a much lower cost and effort compared to the traditional internet of things (IoT)-based intrusive systems.…”

Con-Detect: Detecting adversarially perturbed natural language inputs to deep classifiers through holistic analysis by Hassan, Ali

“…We thus propose Con-Detect—a Contribution based Detection method—for detecting adversarial attacks against NLP classifiers. …”
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Con-Detect: Detecting Adversarially Perturbed Natural Language Inputs to Deep Classifiers Through Holistic Analysis by Hassan Ali (3348749)

“…We thus propose Con-Detect—a Contribution based Detection method—for detecting adversarial attacks against NLP classifiers. …”

Deep and transfer learning for building occupancy detection: A review and comparative analysis by Aya Nabil Sayed (17317006)

“…Moreover, the paper conducted a comparative study of the readily available algorithms for occupancy detection to determine the optimal method in regards to training time and testing accuracy. …”

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

“…Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. …”

Efficient Seismic Volume Compression using the Lifting Scheme by Khene, M. F.

“…In addition, the lifting scheme offers: 1) a dramatic reduction of the required auxiliary memory, 2) an efficient combination with parallel rendering algorithms to perform arbitrary surface and volume rendering for interactive visualization, and 3) an easy integration in the parallel I/O seismic data loading routines. …”
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Heuristics. (c2006) by Kallab, Chadi

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CubeSat Communication Subsystems: A Review of On-Board Transceiver Architectures, Protocols, and Performance by Amr Zeedan (17983750)

“…Nevertheless, several directions for improvements are proposed such as the use of improved channel coding algorithms, Field Programmable Gate Arrays (FPGAs), beamforming, advanced antennas, deployable solar panels, and transition to higher frequency bands. …”

Face Flow: Constrained Optical Flow Framework for Faces by Alkaddour, Muhannad

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Artificial Intelligence for Skin Cancer Detection: Scoping Review by Abdulrahman Takiddin (14153181)

“…Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks.…”

Application of Metastructures for Targeted Low-Frequency Vibration Suppression in Plates by Ratiba F. Ghachi (14152455)

“…The thin plate and the zigzag cutouts are modelled using the finite element method, and the optimal location and optimal tip mass of the zigzag cutouts are obtained using genetic algorithms through iterative simulations. …”

Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images by Rehan Raza (17019105)

“…Considering these shortcomings, computational methods especially machine learning and deep learning algorithms are leveraged as an alternative to accelerate the accurate detection of CT scans as cancerous, and non-cancerous. …”

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review by Mohamed Massaoudi (16888710)

“…This review article taxonomically dives into the nitty-gritty of the mainstream DL-based PVPF methods while showcasing their strengths and weaknesses. …”

Crown Structures for Vertex Cover Kernelization by Abu-Khzam, Faisal N.

“…Crown structures in a graph are defined and shown to be useful in kernelization algorithms for the classic vertex cover problem. Two vertex cover kernelization methods are discussed. …”
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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. …”

The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review by Zainab Jan (17306614)

“…We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning–based models (3, 9%). …”

Comparative Study on Arabic Text Classification: Challenges and Opportunities by Abualigah, Laith

“…Based on the reviewed researches, SVM and Naive Bayes were the most widely used classifiers for Arabic text classification, while more effort is needed to develop and to implement flexible Arabic text classification methods and classifiers.…”
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Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar (22046597)

“…We highlight the transition from traditional signature-based to anomaly-based detection methods, emphasizing the significant advantages of AI-driven approaches in identifying novel and sophisticated intrusions. …”