Radar Pulse Interleaving For Multi-Target Tracking by Elshafei, M.

“…Interleaving algorithms developed to operate radars exploit the dead-times between the transmitted and the received pulses to allocate new tracking tasks that might involve transmitting or receiving pulses, thus increasing the capacity of the system. …”
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Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm by De Rochechouart, Maxence

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

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

“…The sensors are unbiased radars with fixed locations. The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. …”
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Drones Tracking Adaptation Using Reinforcement Learning: Proximal Policy optimization by Alhadhrami, Esra Ebrahim

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Modified arithmetic optimization algorithm for drones measurements and tracks assignment problem by Abu Zitar, Raed

“…In many cases, several radars are collectively used to track drones efficiently, generating measurements and several tracks under different circumstances. …”
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A Tabu Search algorithm for mapping data to multicomputers. (c1997) by Diab, Kamal M.

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Allocating data to distributed-memory multiprocessors by genetic algorithms by Mansour, Nashat

“…We present three genetic algorithms (GAs) for allocating irregular data sets to multiprocessors. …”
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A genetic algorithm for testable data path synthesis by Harmanani, H.

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A neural networks algorithm for data path synthesis by Harmanani, Haidar M.

“…This paper presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. …”
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Distributed dimension reduction algorithms for widely dispersed data by Abu-Khzam, F.N.

“…It runs in linear time and requires very little data transmission. A series of experiments is conducted to gauge how the algorithm’s emphasis on minimal data transmission affects solution quality. …”
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Data damage assessment and recovery algorithm from malicious attacks in healthcare data sharing systems by Haraty, Ramzi A.

“…In this paper, we present an efficient data damage assessment and recovery algorithm to delete malicious transactions and recover affected transactions in a data source in a health care system based on the concept of the matrix. …”
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Public health utility of cause of death data: applying empirical algorithms to improve data quality by Sarah Charlotte Johnson (9715415)

Physical optimization algorithms for mapping data to distributed-memory multiprocessors by Mansour, Nashat

“…We present three parallel physical optimization algorithms for mapping data to distributed-memory multiprocessors, concentrating on irregular loosely synchronous problems. …”
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Parallel physical optimization algorithms for allocating data to multicomputer nodes by Mansour, Nashat

“…Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes are presented. …”
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Sensitivity to parameters and general applicability of genetic algorithms and simulated annealing algorithms for mapping data to multicomputers. (c1994) by Kawash, Jalal Y.

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An enhanced k-means clustering algorithm for pattern discovery in healthcare data by Haraty, Ramzi A.

“…This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. …”
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DG-Means – A Superior Greedy Algorithm for Clustering Distributed Data by Assaf, Ali

“…In this work, we present DG-means, which is a greedy algorithm that performs on distributed sets of data. …”
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Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization by Abu Zitar, Raed

“…This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. …”
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On the steiner walk problem. (c2009) by Maarouf, Amina

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