Confusion matrix for a binary outcome (adapted from Fawcett [30]). by João Albuquerque (12936572)

Homomorphic binary tree. by Jianhua He (341366)

Proposed Algorithm. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<b>(B)</b> Focusing on the positive class only, the algorithm exploits FIM (<i>apriori</i> algorithm) to build a list of candidate interactions of any desired order, retaining those that have an empirical frequency above a given threshold <i>δ</i>. …”

Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model by Getachew S. Molla (6416744)

“…A particle swarm optimization algorithm is incorporated to preregress conceptual segment parameters of solutes. …”

Improved support vector machine classification algorithm based on adaptive feature weight updating in the Hadoop cluster environment by Jianfang Cao (1881379)

“…The MapReduce parallel programming model on the Hadoop platform is used to perform an adaptive fusion of hue, local binary pattern (LBP) and scale-invariant feature transform (SIFT) features extracted from images to derive optimal combinations of weights. …”

An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Related Work Summary. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Simulation parameters. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Training losses for N = 10. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

“…Hence, an Energy-Harvesting Reinforcement Learning-based Offloading Decision Algorithm (EHRL) is proposed. EHRL integrates Reinforcement Learning (RL) with Deep Neural Networks (DNNs) to dynamically optimize binary offloading decisions, which in turn obviates the requirement for manually labeled training data and thus avoids the need for solving complex optimization problems repeatedly. …”

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Generalized Tensor Decomposition With Features on Multiple Modes by Jiaxin Hu (1327875)

“…An efficient alternating optimization algorithm with provable spectral initialization is further developed. …”

Contextual Dynamic Pricing with Strategic Buyers by Pangpang Liu (18886419)

“…<p>Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. …”

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports by Olivier Q. Groot (9370461)

“…The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases.…”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…Optimization with GridSearchCV corroborated this stagnation, identifying a simple linear model (C=0.05, gamma='scale') as the optimal configuration, indicating that the additional complexity of nonlinear kernels did not confer predictive gains. …”

Spectral estimation of large stochastic blockmodels with discrete nodal covariates by Angelo Mele (10809478)

“…We show that computing our estimator is much faster than standard variational expectation–maximization algorithms and scales well for large networks. …”