Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

Ahmed, Shameem; Ghosh, Kushal Kanti; Bera, Suman Kumar; Schwenker, Friedhelm; Sarkar, Ram

doi:10.1109/access.2020.3024095

Cited by 20 publications

(12 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Large pl-values imply further exploration and small pl-values imply further exploitation. When exploration isn't accurately taken care of, the algorithms might be trapped in local optimal [23]. Researches have conducted complete experiment for determining the optimal value for pl, and pl = 0.6 • N is fixed in this study, whereas N means the population size.…”

Section: Design Of Proposed Ems For Avsmentioning

confidence: 99%

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Hamza¹,

Alajmi²,

Hassine³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Recent advancements of the intelligent transportation system (ITS) provide an effective way of improving the overall efficiency of the energy management strategy (EMSs) for autonomous vehicles (AVs). The use of AVs possesses many advantages such as congestion control, accident prevention, and etc. However, energy management and traffic flow prediction (TFP) still remains a challenging problem in AVs. The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs. In this view, this paper presents novel sustainable energy management with traffic flow prediction strategy (SEM-TPS) for AVs. The SEM-TPS technique applies type II fuzzy logic system (T2FLS) energy management scheme to accomplish the desired engine torque based on distinct parameters. In addition, the membership functions of the T2FLS scheme are chosen optimally using the barnacles mating optimizer (BMO). For accurate TFP, the bidirectional gated recurrent neural network (Bi-GRNN) model is used in AVs. A comprehensive experimental validation process is performed and the results are inspected with respect to several evaluation metrics. The experimental outcomes highlighted the supreme performance of the SEM-TPS technique over the recent state of art approaches.

show abstract

Section: Design Of Proposed Ems For Avsmentioning

confidence: 99%

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Hamza¹,

Alajmi²,

Hassine³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Although the previous studies have achieved good performance, we think that better performance and accuracy with a fewer number of informative genes can still be achieved. For instance, Authors, in [29], have considered the image contrast enhancement to be an optimization problem. They have adopted the Barnacles Mating Optimizer (BMO) algorithm to address this problem.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Barnacles Mating Optimizer Algorithm With Support Vector Machines for Gene Selection of Microarray Cancer Classification

et al. 2021

View full text Add to dashboard Cite

These days, the classification between normal and cancerous tissues and between different types of cancers represents a very important issue. Selecting the little informative number of genes is considered the main challenge in the cancer diagnosis issue. Therefore, Gene selection is usually the preliminary step for solving the cancer classification problems. Bio-inspired metaheuristic optimization algorithms, when used to solve gene selection and classification problems, they demonstrate their effectiveness. Barnacles Mating Optimizer (BMO) algorithm, which imitates the behavior of mating barnacles in nature for solving optimization problems, is considered one of these algorithms. In this paper, Barnacles Mating Optimizer (BMO) algorithm augmented with Support Vector Machines (SVM) called BMO-SVM is proposed for a microarray gene expression profiling in order to select the most predictive and informative genes for cancer classification. Conducting a comparative experimental study among a set of the most common bio-inspired optimization techniques to specify the most effective. A binary microarray dataset (i.e., leukemia1) and a multi-class microarray dataset (i.e., SRBCT, lymphoma, and leukemia2) are used for testing the performance of the proposed model. The experimental results revealed the superiority of the proposed BMO-SVM approach against several well-known meta-heuristic optimization algorithms, such as the Tunicate Swarm Algorithm (TSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). It is worth mentioning that our proposed algorithm achieves a high informational superiority percentage compared to other algorithms.

show abstract

“…9 Meta-heuristic algorithms 42,43 are computational intelligence paradigms mainly used for solving different complex optimization problems. Owing to their computational efficiency as well as superior performance in resource-constrained environments, meta-heuristic algorithms have been extensively used across the domains, including feature selection, 44 neural architecture search, 45,46 task scheduling, 47 handwritten script classification, 48 image contrast enhancement, [49][50][51] data clustering, 52 multilevel image thresholding, 53,54 and solving class imbalance problem 55,56 among others. Mostly, these algorithms are inspired from: (1) theory of evolution, such as Genetic Algorithm (GA) 57 and Differential Evolution 58 ; (2) natural behavior of organisms, such as the Whale Optimization Algorithm (WOA), 59 Cuckoo Search (CS) 60 and Flower Pollination Algorithm 61 ; (3) swarm intelligence, such as the Particle Swarm Optimization (PSO) 62 and the Grey Wolf Optimizer (GWO) 63 ; and (4) physical or scientific phenomena, such as the Gravitational Search Algorithm (GSA), 64 and the Multiverse Optimizer, 65 to name a few.…”

Section: Related Workmentioning

confidence: 99%

Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

Chattopadhyay

Kundu

Singh

et al. 2021

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and timeefficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of "Pneumonia" and "Normal" X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the

show abstract

Gray Level Image Contrast Enhancement Using Barnacles Mating Optimizer

Cited by 20 publications

References 37 publications

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

A Hybrid Barnacles Mating Optimizer Algorithm With Support Vector Machines for Gene Selection of Microarray Cancer Classification

Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

Contact Info

Product

Resources

About