Decision-level Gait Fusion for Human Identification at a Distance by Al-Tayyan, Amer

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Decision-level fusion for single-view gait recognition with various carrying and clothing conditions by Al-Tayyan, Amer

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Intelligent Fusion of Deep Features for Improved Waste Classification by Kashif Ahmad (12592762)

Teaching–learning-based optimization algorithm: analysis study and its application by Abualigah, Laith

“…The teaching–learning-based optimization (TLBO) algorithm is a novel nature-based optimization approach that has attracted a lot of interest from researchers because of its great capacity to handle optimization problems. …”
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Design and implementation of a deep learning-empowered m-Health application by Akhan Akbulut (17380285)

Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network by Mohammad Reza Chalak Qazani (13893261)

“…Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. …”

A combined algorithm based on finite elements and a modified methodof characteristics for the analysis of the corona in wire-ductelectrostatic precipitators by Al-Hamouz, Z.M.

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On the Optimization of Band Gaps in Periodic Waveguides by Jamil Renno (14070771)

Subjects: “…Nature-inspired optimization algorithms…”

Extended Behavioral Modeling of FET and Lattice-Mismatched HEMT Devices by Khawam, Yahya Bader

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Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering by Abu Zitar, Raed

“…This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. …”

An Optimized Feature Selection Technique in Diversified Natural Scene Text for Classification Using Genetic Algorithm by Ghulam Jillani Ansari (16896342)

“…First after preprocessing input images, the whole feature space (population) is built using a multimodal feature representation technique. Second, a feature level fusion approach is used to combine the features. …”

Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars by Abathar Al-Hamrani (16494884)

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DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins by Samir Brahim, Belhaouari

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Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information by M. Ghoniem, Rania

“…This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. …”
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Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique by J. Jayapradha (21323774)

Design of adaptive arrays based on element position perturbations by Dawoud, M.M.

“…The authors report on the design of a digital feedback control system to provide null steering by controlling the array element positions automatically. The array comprises a signal processor, digital control algorithm (PID), stepper motors, shaft encoders, actuators and multiplexers. …”
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Day-Ahead Load Demand Forecasting in Urban Community Cluster Microgrids Using Machine Learning Methods by Sivakavi Naga Venkata Bramareswara Rao (15944992)

“…Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. …”

Artificial neural network algorithms. (c1999) by Ramadan, Samer N.

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Adaptive bias simulated evolution algorithm for placement by Youssef, H.

“…A new solution is evolved from current solution by relocating some of the solution elements. Elements with lower goodnesses have higher probabilities of getting selected for perturbation. …”
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An improved kernelization algorithm for r-Set Packing by Abu-Khzam, Faisal N.

“…Such parameterized reductions are known as kernelization algorithms, and a reduced instance is called a problem kernel. …”
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