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

"…In this study, we introduce an innovative method for the multi-classification of breast cancer histopathological images utilizing Bidirectional Recurrent Neural Networks (BRNN). The BRNN structure consists of four unique elements: the backbone branch for transfer learning, the Gated Recurrent Unit (GRU), the residual collaborative branch, and the feature fusion module. …"

Design of acoustic absorbing metasurfaces using a data-driven approach حسب Hamza Baali (14603380)

الموضوعات:

Nonlinear analysis of shell structures using image processing and machine learning حسب M.S. Nashed (16392961)

"…We show that the results of the trained network agree well with the results of the nonlinear finite element analysis. …"

Intelligent Rapidly-Exploring Random Tree Star Algorithm حسب Ahmed, Khidir Galal Eldin Khidir

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Fuzzy simulated evolution algorithm for topology design of campusnetworks حسب Youssef, H.

"…We present an approach based on the simulated evolution algorithm for the design of campus network topology. …"
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Synthesis of MVL Functions - Part I: The Genetic Algorithm Approach حسب 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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The role of Reinforcement Learning in software testing حسب 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.…"

Optimized FPGA Implementation of PWAM-Based Control of Three—Phase Nine—Level Quasi Impedance Source Inverter حسب Syed Rahman (569240)

"…Since, PWAM control algorithm is more complex than PSCPWM, FPGA based implementation for PWAM control is discussed. …"

Deep Reinforcement Learning for Resource Constrained HLS Scheduling حسب Makhoul, Rim

"…In this work, we present a resource constrained scheduling approach that minimizes latency and subject to resource constraints using a deep Q learning algorithm. The actions and rewards for the proposed algorithm are selected carefully to guide the agent to its objective. …"
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Reinforcement Learning-Based School Energy Management System حسب Yassine Chemingui (18891757)

"…After cloning the baseline strategy, the agent learns with proximal policy optimization in an actor-critic framework. …"

Predicting Patient ICU Readmission Using Recurrent Neural Networks With Long Short-Term Memory حسب Nadia Rasheed (22997257)

Drones Tracking Adaptation Using Reinforcement Learning: Proximal Policy optimization حسب Alhadhrami, Esra Ebrahim

"…Our results demonstrate the successful learning capability of the PPO agent over time, enabling it to suggest the optimal Q value by effectively capturing the policy of appropriate rewards under varying environmental conditions. …"
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Recent advances on artificial intelligence and learning techniques in cognitive radio networks حسب Abbas, Nadine

"…The literature survey is organized based on different artificial intelligence techniques such as fuzzy logic, genetic algorithms, neural networks, game theory, reinforcement learning, support vector machine, case-based reasoning, entropy, Bayesian, Markov model, multi-agent systems, and artificial bee colony algorithm. …"
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Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars حسب Abathar Al-Hamrani (16494884)

"…Finally, a design equation that can predict the shear capacity of one-way BFRC-BFRP slabs was proposed based on genetic algorithm. The proposed model showed the best prediction accuracy compared to the available design codes and guidelines with a mean of predicted to experimental shear capacities (V_pred/V_exp) ratio of 0.97 and a coefficient of variation of 17.91%.…"

Enhancing Student Services: Machine Learning Chatbot Intent Recognition for High School Inquiries حسب Assayed, Suha Khalil

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

"…In this paper, a novel speech separation algorithm is proposed that integrates the twin-delayed deep deterministic (TD3) policy gradient reinforcement learning (RL) agent with the expectation maximization (EM) algorithm for clustering the spatial cues of individual sources separated on azimuth. …"

Advanced Quantum Control with Ensemble Reinforcement Learning: A Case Study on the XY Spin Chain حسب Farshad Rahimi Ghashghaei (20880995)

"…<p dir="ltr">This research presents an ensemble Reinforcement Learning (RL) approach that combines Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms to tackle quantum control problems. …"

Edge Caching in Fog-Based Sensor Networks through Deep Learning-Associated Quantum Computing Framework حسب 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). …"

Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle حسب Reza Jafari (3494018)

"…To evaluate the generalizability of the algorithms, the agents are tested across various velocities, tire–road friction coefficients, and additional scenarios implemented in IPG CarMaker, a high-fidelity vehicle dynamics simulator. …"

A comprehensive review of deep reinforcement learning applications from centralized power generation to modern energy internet frameworks حسب Sakib Mahmud (15302404)

"…We present a structured taxonomy covering value-based, policy-based, actor-critic, model-based, and advanced multi-agent and multi-objective approaches, and link algorithms to tasks such as dispatch, microgrid coordination, real-time pricing, load balancing, and demand–response. …"