Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence medical imaging has enabled remarkable developments. The presented framework classifies patients with brain high accuracy efficiency using CNN, pre-trained models, the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray MRI images. Additionally, CNN Transfer Learning (TL) hyperparameters will optimized through MRFO, resulting in improved performance of model. Two public...

Optimising the shape and size of large-scale truss frames is challenging because there a nonlinear interaction between cross-sectional nodal coordinate forces structures. Meanwhile, combining bar variables creates multi-modal search space with dynamic constraints, making an expensive optimisation engineering problem. Besides, most real problems are large-scale, algorithms faced issue scalability by increasing This paper proposed novel Cooperative Coevolutionary marine predators algorithm...

This paper presents a comparative study between four techniques recently used to improve the wind energy conversion system (WECS) water pumping systems. The WECS is renewable source which has developed rapidly in recent years. use of field free solution (economically) compared electricity grid supply. control WECS, equipped with permanent magnet synchronous generator, objective carefully maximising power generation. A proposed Fuzzy Logic Control, optimised using genetic algorithm and...

The Artificial Bee Colony (ABC) algorithm is a relatively new for function optimization. inspired by the foraging behavior of honey bees. In this work, performance ABC enhanced introducing concept generalized opposition-based learning. This introduced through initialization step and generation jumping. proposed (GOABC) compared to (OABC) using CEC05 benchmarks library.

Fire is one of the critical problems that have not been solved yet despite technological development. Human losses are most important aspect related to fires. Since it may be impossible prevent fire from occurring, would helpful minimize its impact in terms human losses. The fact unmanned aerial vehicles (UAVs) can handle dangerous and risky tasks as well their fast efficient performance allows them used such entering exploring disastrous zones. Hence, an indoor fireproof vehicle enables...

The Artificial Bee Colony (ABC) algorithm is a powerful continuous optimization tool that has been proposed in the past few years. Many studies have shown ABC superiority terms of performance when compared to other well-known algorithms. In this paper, implementation Cooperative (CABC) based on explicit space decomposition approach investigated. Both and its cooperative versions are applied set classical benchmark functions.

In this paper we test a hybrid Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithm on the CEC13 testbed. The hybridization technique is component-based one, where PSO augmented with an ABC component to improve personal bests of particles.

The Artificial Bee Colony (ABC) algorithm is a relatively new for function optimization. inspired by the foraging behavior of honey bees. In this work, performance ABC enhanced introducing concept opposition-based learning. This introduced through initialization step and generation jumping. proposed (OABC) compared to Differential Evolution (ODE) when applied Black-Box Optimization Benchmarking (BBOB) library in previous two GECCO conferences.

The harvesting robot is a that created for cherry tomatoes in households and greenhouses. For households, the project aims to protect privacy of people who have their own gardens at home, do not prefer workers coming home harvest fruits. In addition, weather Kuwait very hot, which makes it difficult harvesters work long periods time. This creates problem because if gardener does finish on time; fruits become rotten causing wastage loss. Therefore, using provides an excellent solution such...

In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The technique is a component-based one where PSO algorithm augmented with an ABC component to improve personal bests particles. Two different hybrid algorithms are tested in work based on method which applied All well-known CEC05 benchmark functions compared three metrics.

The field of firefighting has long been a dangerous one, and there have numerous devastating losses because lack in technological advancement. Additionally, the current methods applied are inadequate inefficient relying heavily on humans who prone to error, no matter how extensively they trained. A recent trend that become popular is use robots instead handle fire hazards. This mainly can be used situations too for any individual involve themselves in. In our project, we develop robot able...

Brain Storm Optimization (BSO) is a recently developed population-based algorithm to mimic the brainstorming process in humans. It has been successfully applied domain of non-linear continuous optimization. In this work, we propose enhancing performance BSO by introducing re-initialization mechanism triggered current state population. addition, also modify step-size equation order take search space size into consideration. The proposed improved compared with two most recent variants based on...

This paper benchmarks the Particle Swarm Optimization (PSO) algorithm using noise-free BBOB 2009 testbed.

A capstone design project is an extensive piece of work that requires creative activity and thinking. It provides a unique opportunity for students to demonstrate their abilities, skills, experiences are attained throughout bachelor engineering program. The learning outcomes projects mostly map all student at the program level. This paper presents unified assessment framework courses which allows sound evaluations performance qualities in addition assessing outcomes. developed comprises...