Low-Complexity Machine Learning-based Behavioral Modeling of Power Amplifiers by Aziz, Mohammad Rabih

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Competitive learning/reflected residual vector quantization for coding angiogram images by Mourn, W.A.H.

“…This paper deals with employing competitive learning neural network in RRVQ design algorithm that results in competitive learning RRVQ algorithm for the RVQ structure. …”
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Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework by Tayyabah Hasan (18427887)

“…The framework is basically a merger of a deep learning (DL) agent deployed at the network edge with a quantum memory module (QMM). …”

Advancing Coherent Power Grid Partitioning: A Review Embracing Machine and Deep Learning by Mohamed Massaoudi (16888710)

“…This article provides an updated review of the cutting-edge machine learning and data-driven techniques used for PGP in networked PSs. …”

A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architectures by Bo Lyu (16522643)

“…<p dir="ltr">In recent years, there has been a significant advancement in memristor-based neural networks, positioning them as a pivotal processing-in-memory deployment architecture for a wide array of deep learning applications. …”

Probabilistic AutoRegressive Neural Networks for Accurate Long-Range Forecasting by Panja, Madhurima

“…While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and modern neural network models. …”
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When geoscience meets generative AI and large language models: Foundations, trends, and future challenges by Hadid, Abdenour

“…This survey discusses several GAI models that have been used in geoscience comprising generative adversarial networks (GANs), physics‐informed neural networks (PINNs), and generative pre‐trained transformer (GPT)‐based structures. …”
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Binary-NeRV: Hybrid-Precision Weights Binarization for Efficient Neural Video Representation by Shanableh, Tamer

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LungVision: X-ray Imagery Classification for On-Edge Diagnosis Applications by Aldamani, Raghad

“…We propose a simple deep learning architecture that experiments with six different convolutional neural networks. …”
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COVID-19 infection localization and severity grading from chest X-ray images by Anas M. Tahir (16870077)

“…An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). …”

Hydrogen Sulfide (H₂S) Sensor: A Concept of Physical Versus Virtual Sensing by Ahmed Alsarraj (16876014)

“…The merits of the proposed system are as follows: 1) a virtual sensing concept is combined with a physical sensing platform to enhance the proposed model’s estimation power in quantifying H₂S in air samples; 2) a new feature extraction method based on fractional derivatives is proposed to further enhance the model’s learning capabilities; 3) an array of four gas sensors is fabricated in the in-house foundry to record and analyze the signature of H₂S at various concentration levels; 4) a shallow neural network (NN) model is trained and tested on the recorded data, and based on the NN’s input–output relation, a mathematical model is presented for the quantification of H₂S; and 5) the proposed model is a highly sensitive and reliable H₂S gas sensing scheme with the ability to detect the gas instantaneously. …”

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)

“…Cyborg intelligence is one of the most popular and advanced technologies suitable for securing smart city networks against cyber threats. Various machine learning and deep learning-based cyborg intelligence mechanisms have been developed to protect smart city networks by ensuring property, security, and privacy. …”

An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data: Background, Applications, and Ongoing Challenges by Tekli, Joe

“…Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity.…”
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