Belief selection in point-based planning algorithms for POMDPs by Azar, Danielle

“…Current point-based planning algorithms for solving partially observable Markov decision processes (POMDPs) have demonstrated that a good approximation of the value function can be derived by interpolation from the values of a specially selected set of points. …”
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Distributed dimension reduction algorithms for widely dispersed data by Abu-Khzam, F.N.

“…Stress function measurements indicate that the distributed algorithm is highly competitive with the original FastMap heuristic.…”
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Machine Learning Solutions for the Security of Wireless Sensor Networks: A Review by Yazeed Yasin Ghadi (16667109)

“…Moreover, this study also addresses issues of adapting machine learning algorithms to accommodate the sensors’ functionalities in the network configuration. …”

Genetic and heuristic algorithms for regrouping service sites. (c2000) by Tabbara, Hiba

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

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Properties of simulated annealing and genetic algorithms for mapping data to multicomputers by Mansour, Nashat

“…We experimentally analyze some properties of simulated annealing algorithms (SA) and genetic algorithms (GA) for mapping data to multicomputers. …”
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Economic load dispatch using memetic sine cosine algorithm by Abu Zitar, Raed

“…The results show that the performance of the SCA-βHC algorithm is increased by tuning its parameters in proper value. …”
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AGEomics Biomarkers and Machine Learning—Realizing the Potential of Protein Glycation in Clinical Diagnostics by Naila Rabbani (291722)

“…AGEomics biomarkers have been used in diagnostic algorithms using machine learning methods. In this review, I describe the utility of AGEomics biomarkers and provide evidence why these are close to the phenotype of a condition or disease compared to other metabolites and metabolomic approaches and how to train and test algorithms for clinical diagnostic and screening applications with high accuracy, sensitivity and specificity using machine learning approaches.…”

Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants by Mohannad N. Khandakji (13885434)

“…<h3>Background</h3><p dir="ltr">Existing BRCA2-specific variant pathogenicity prediction algorithms focus on the prediction of the functional impact of a subtype of variants alone. …”

Economic Production Lot-Sizing For An Unreliable Machine Under Imperfect Age-Based Maintenance Policy by El-Ferik, S

“…Numerical results are provided to illustrate both the use of the algorithm in the study of the optimal cost function and the latter's sensitivity to different changes in cost factors. …”
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Evolutionary algorithms, simulated annealing and tabu search: a comparative study by Youssef, H.

“…The termevolutionary algorithmis used to refer to any probabilistic algorithmwhose design is inspired by evolutionary mechanisms found in biological species. Most widely known algorithms of this category are genetic algorithms (GA). …”
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A Multiswarm Intelligence Algorithm for Expensive Bound Constrained Optimization Problems by Wali Khan Mashwani (14590504)

“…Thirty bound constrained benchmark functions are used to evaluate the performance of the proposed algorithm. …”

Isolating Physical Replacement of Identical IoT Devices Using Machine and Deep Learning Approaches by Mohammad, Areej Osama

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Optimum Track to Track Fusion Using CMA-ES and LSTM Techniques by Fares, Samar

“…An objective function utilizing the covariance of the fused tracks is used by the first algorithm while a cost function based on the Kullback-Leibler (KL) divergence measure is used in the second case for training the LSTM. …”
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New Fast Arctangent Approximation Algorithm for Generic Real-Time Embedded Applications by Mohieddine Benammar (18103039)

“…This paper presents a novel technique that combines the advantages of both rational formulae and LUT approximation methods. The new algorithm exploits the pseudo-linear region around the tangent function zero point to estimate a reduced input arctangent through a modified rational approximation before referring this estimate to its original value using miniature LUTs. …”

Exploring New Parameters to Advance Surface Roughness Prediction in Grinding Processes for the Enhancement of Automated Machining by Mohammadjafar Hadad (21142499)

“…To evaluate a robust algorithm, a method is devised that involves different networks utilizing various activation functions and neuron sizes to distinguish and select the best architecture for this study. …”

A non-convex economic load dispatch problem using chameleon swarm algorithm with roulette wheel and Levy flight methods by Abu Zitar, Raed

“…In this paper, several enhancements were made to this algorithm. First, it’s position updating process was slightly tweaked and took advantage of the chameleons’ randomization as well as adopting several time-varying functions. …”
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Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System by Abualigah, Laith

“…This study proposes a novel approach for designing a fractional order proportional-integral-derivative (FOPID) controller that utilizes a modified elite opposition-based artificial hummingbird algorithm (m-AHA) for optimal parameter tuning. Our approach outperforms existing optimization techniques on benchmark functions, and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision. …”
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A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks by Saad, Walid

“…While most existing literature focuses on downlink-only or uplink-only scheduling algorithms, the proposed algorithm aims at ensuring a utility function that jointly captures the quality of service in terms of delay and channel quality on both links. …”
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Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise by Saab, Samer S.

“…This work also provides a recursive algorithm that generates the appropriate learning gain functions that meet the arbitrary high precision output tracking objective. …”
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