Engineering design problems are usually large-scale constrained optimization problems, and metaheuristic algorithms are vital for solving such complex problems. Therefore, this paper introduces a new ...nature-inspired metaheuristic algorithm: the gannet optimization algorithm (GOA). The GOA mathematizes the various unique behaviors of gannets during foraging and is used to enable exploration and exploitation. GOA’s U-shaped and V-shaped diving patterns are responsible for exploring the optimal region within the search space, with sudden turns and random walks ensuring better solutions are found in this region. In order to verify the ability of the GOA to find the optimal solution, we compared it with other comparison algorithms in multiple dimensions of 28 benchmark functions. We found that the GOA has a shorter running time in high dimensions and can provide a better solution. Finally, we apply the GOA to five engineering optimization problems. The experimental results show that the GOA is suitable for many constrained engineering design problems and can provide better solutions in most cases.
•To design a new attention based hybrid deep learning model for classifying the brain tumor in the dataset images.•To eliminate the noises in the images for enhancing the image quality through an ...effective pre-processing stage.•To reduce the complexity, brain tumor regions are accurately segmented using threshold based approach.•To enhance the classification accuracy, most discriminative features are selected through two varied mechanism.•To validate the performance of proposed model, different parameters are evaluated and the obtained results are compared with other existing methods.
Brain cancer is a life-threatening disease that affects many people and is caused by an abnormal growth of tissue in or around the brain. Therefore, early diagnosis and treatment of brain tumor are necessary. Detecting medical diseases is one of the crucial tasks in the clinical field as it helps improve the lives of patients. Magnetic resonance imaging (MRI) is generally used to diagnose brain tumors. However, the existing MRI-based deep learning (DL) model is a time-consuming process and produces less accurate results. To solve this problem, the proposed study uses a twin attention-based dense convolutional autoencoder (TA-CAE) to identify brain tumors in the MRI images. The input image is first captured from the dataset and then pre-processed through enhanced average filtering to remove unwanted noise and improve image quality through image resizing and HSV color channel conversion. The pre-processed images are then segmented to find the affected region. Here, segmentation is performed using Gannet-based Kapurs Thresholding (G-KaT) techniques. Oriented gradient pyramidal histograms and grayscale run length matrix (PHOG-GLRLM) feature extraction techniques were used to extract the shape and texture features of the MRI after segmentation. Depending on these features, the novel TA-CAE model diagnosed the brain tumor and classified it into three different brain tumor types such as glioma, pituitary tumor and meningioma. The Python tool is used for the simulation process, and the TA-CAE model is evaluated based on several performance metrics. The simulated results demonstrate that the proposed TA-CAE provides an accuracy of 97.28%, which is a better performance compared to other existing brain tumor classification techniques.
We compared long-term (1977 to 2014) trends in concentrations of PFAS in eggs of the marine sentinel species, the Northern gannet (Morus bassanus), from the Irish Sea (Ailsa Craig) and the North Sea ...(Bass Rock). Concentrations of eight perfluorinated carboxylic acids (PFCAs) and three perfluorinated sulfonates (PFSAs) were determined and we report the first dataset on PFAS in UK seabirds before and after the PFOS ban. There were no significant differences in ∑PFAS or ∑PFSAs between both colonies. The ∑PFSAs dominated the PFAS profile (>80%); PFOS accounted for the majority of the PFSAs (98–99%). In contrast, ∑PFCAs concentrations were slightly but significantly higher in eggs from Ailsa Craig than in those from Bass Rock. The most abundant PFCAs were perfluorotridecanoate (PFTriDA) and perfluoroundecanoate (PFUnA) which, together with PFOA, comprised around 90% of the ∑PFCAs.
The ∑PFSAs and ∑PFCAs had very different temporal trends. ∑PFSAs concentrations in eggs from both colonies increased significantly in the earlier part of the study but later declined significantly, demonstrating the effectiveness of the phasing out of PFOS production in the 2000s. In contrast, ∑PFCAs concentrations in eggs were constant and low in the 1970s and 1980s, suggesting minimal environmental contamination, but residues subsequently increased significantly in both colonies until the end of the study. This increase appeared driven by rises in long chain compounds, namely the odd chain numbered PFTriDA and PFUnA. PFOA, had a very different temporal trend from the other dominant acids, with an earlier rise in concentrations followed by a decline in the last 15 years in Ailsa Craig; later temporal trends in Bass Rock eggs were unclear.
Although eggs from both colonies contained relatively low concentrations of PFAS, the majority had PFOS residues that exceeded a suggested Predicted No Effect Concentration and ~ 10% of the eggs exceeded a suggested Lowest-Observable-Adverse-Effect.
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•Long-term (>35 years) analyses of PFAS concentrations in gannet eggs from a North Sea and a Irish Sea colony.•PFOS dominated the PFAS profile in eggs from both colonies.•Overtime, the ∑PFSAs first rose and then fell and ∑PFCAs remained unchanged and then rose.•PFOS and PFOA concentrations increased in early years but are now declining.•Long-chain odd PFCAs concentrations in eggs are still increasing.
Seabirds are marine predators known to forage in association with fisheries, however detailed knowledge on seabird-fishery interactions remains scarce in several regions of the world. We quantified ...seabird-fishery interactions and bycatch in central Portuguese coastal waters (NE Atlantic) between 2016 and 2018 in four gears: purse-seines, longlines, gillnets, and fishing traps. We mapped gear-specific fishing effort and seabird bycatch events and characterized fishery catches. Specific objectives were to determine separately for seabird-fishery interactions and bycatch (i) the gear with the highest rates, (ii) the most abundant species, and (iii) to assess the main drivers (i.e. year, season, gear, and fishery catch) of seabird-fishery interactions. Purse-seines had the highest seabird-fishery interactions, and the most abundant species were Yellow-legged and Lesser black-backed gulls, Northern gannet, and Cory's shearwater. Total seabird-fishery interactions varied inter-annually but not seasonally, indicating high total seabird numbers at fishing boats year-round. In contrast, higher fishery interactions were found during spring for Yellow-legged gulls. Age classes of individuals varied according to species, and fishery catches had a positive effect on seabird-fishery interactions. Seabird bycatch occurred mostly in longlines and within the ‘Ilhas Berlengas’ Special Protection Area. Northern gannet and Cory's shearwater were the most bycaught species, and species ecological traits seemed important in determining gear-specific bycatch. Our results suggest a strong influence of purse-seine and artisanal fisheries on seabirds in the NE Atlantic coast, and future studies should investigate the effects of these fisheries on seabird populations in other regions of the world.
•Purse-seine had the highest seabird-fishery interactions.•Longline had the highest seabird bycatch rate.•Yellow-legged and Lesser black-backed gulls interacted more with fisheries.•Northern gannet and Cory's shearwater were the most bycaught seabird species.•Purse-seine and artisanal fisheries influence seabirds in the NE Atlantic coast.
Bycatch is one of the main threats to seabird conservation. In Portugal, there are alarming estimates of seabirds bycaught per year in bottom gillnet fisheries, which get entangled during fishing ...operations, when the gear is close to the surface. In this study, a visual deterrent (scarybird) was tested in a fishing vessel operating bottom gillnets near and within Berlengas Islands SPA (Special Protection Area). Trials were conducted between 2019 and 2020, with 18 control and experimental fishing trips monitored by a trained observer. The scarybird proved to be an effective bird deterrent measure, affecting both the numbers and distribution of seabirds and keeping a large portion of birds away from the vessel during fishing operations, making them potentially less vulnerable to bycatch. The deterrent device was more effective in the closest area to the vessel (0–20 m) where there was a significant reduction in the number of gulls (Larus michahellis/fuscus, −56 %) and northern gannets (Morus bassanus, −72 %) close to the vessel, by comparison to control fishing trips. The use of this aerial deterrent device had no impact on the fishery's target catches and revenue, which contributed to a good acceptance by fishermen. This simple and “easy to implement” deterrent device has potential to be further tested as a bycatch mitigation measure in bottom gillnets but also in other gears, whenever interactions with birds occur mainly close to the surface.
Sexual segregation, common in many species, is usually attributed to intra-specific competition or habitat choice. However, few studies have simultaneously quantified sex-specific foraging behaviour ...and habitat use. We combined movement, diving, stable isotope and oceanographic data to test whether sexual segregation in northern gannets Morus bassanus results from sex-specific habitat use. Breeding birds foraging in a seasonally stratified shelf sea were tracked over 3 consecutive breeding seasons (2010–2012). Females made longer trips, foraged farther offshore and had lower δ13C values than males. Male and female foraging areas overlapped only slightly. Males foraged more in mixed coastal waters, where net primary production (NPP) was relatively high (>3 mg C m−2 d−1) and sea-surface temperature (SST) was relatively low (<10°C). Males also tended to use areas with higher SSTs (>15°C) more than females, possibly as a consequence of foraging in productive mixed waters over offshore banks. Females foraged most frequently in stratified offshore waters, of intermediate SST (12–15°C), but exhibited no consistent response to NPP. Sex-specific differences in diving behaviour corresponded with differences in habitat use: males made more long and deep U-shaped dives. Such dives were characteristic of inshore foraging, whereas shorter and shallower V-shaped dives occurred more often in offshore waters. Heavier birds attained greater depths during V-shaped dives, but even when controlling for body mass, females made deeper V-shaped dives than males. Together, these results indicate that sexual segregation in gannets is driven largely by habitat segregation between mixed and stratified waters, which in turn results in sex-specific foraging behaviour and dive depths.
The trade off between energy gained and expended is the foundation of understanding how, why and when animals perform any activity. Based on the concept that animal movements have an energetic cost, ...accelerometry is increasingly being used to estimate energy expenditure. However, validation of accelerometry as an accurate proxy for field metabolic rate in free-ranging species is limited. In the present study, Australasian gannets (Morus serrator) from the Pope's Eye colony (38°16'42″S 144°41'48″E), south-eastern Australia, were equipped with GPS and tri-axial accelerometers and dosed with doubly labelled water (DLW) to measure energy expenditure during normal behaviour for 3-5 days. The correlation between daily energy expenditure from the DLW and vectorial dynamic body acceleration (VeDBA) was high for both a simple correlation and activity-specific approaches (R2=0.75 and 0.80, respectively). Varying degrees of success were observed for estimating at-sea metabolic rate from accelerometry when removing time on land using published energy expenditure constants (R2=0.02) or activity-specific approaches (R2=0.42). The predictive capacity of energy expenditure models for total and at-sea periods was improved by the addition of total distance travelled and proportion of the sampling period spent at sea during the night, respectively (R2=0.61-0.82). These results indicate that accelerometry can be used to estimate daily energy expenditure in free-ranging gannets and its accuracy may depend on the inclusion of movement parameters not detected by accelerometry.
Surface Electromyogram (sEMG) signals, like other electrophysiological measurements, get corrupted by several artefacts; much critical helpful information regarding a person’s clinical conditions may ...alter or be lost entirely. Therefore, the sEMG signal must be filtered to minimise such artefacts. The drive of this paper is to propose an efficient adaptive Volterra noise mitigation architecture (AVNMA) for the sEMG signal. Further, the optimal coefficients for designing the Volterra filter architecture are achieved by applying the recently proposed metaheuristic algorithm, gannet optimisation algorithm and compared to the performance of other benchmark optimisation algorithms, namely harmony search optimisation algorithm and teaching learning-based optimisation algorithm to the proposed architecture. The quantitative analysis based on the proposed architecture is measured in terms of several metrics such as mean squared error, normalised root mean square error, peak reconstruction error, mean difference, maximum error and signal-to-noise ratio at various noisy environments in the presence of additive white Gaussian noise, including artefacts such as muscle noise, baseline wandering, electrode misplacements, and electrical interference. The outcomes of experimental research (SNR = 102.236 dB andMSE = 6.71E-09 for healthy-sEMG) ensure the superiority of the gannet optimisation algorithm based-AVNMA over the other metaheuristic algorithm based-AVNMAs. The proposed approach is verified parametrically and non-parametrically in a statistical sense with the help of two sample t-tests and the Mann-Whitney U test, respectively.
Diabetic retinopathy (DR) is an irreversible disease that damages blood vessels and results in permanent visual impairment. Timely detection and proper diagnosis can mitigate severe eye impacts and ...slow DR progression. Color fundus images are commonly used by eye specialists to diagnose DR. However, manual operations are tedious, laborious, time-consuming, and error-prone. As a result, automated techniques are gaining increased attention in the medical domain due to their ability to precisely analyze unstructured clinical notes. In this paper, a novel Gannet-optimized deep belief network-based wavelet kernel extreme learning machine (GO-DBN-WKELM) technique is proposed for detecting DR occurrences and assessing its progressive stages. The GO-DBN-WKELM approach detects and classifies DR severities into six distinct classes: no DR (i.e., normal), early DR, mild non-proliferative DR, moderate non-proliferative DR, severe non-proliferative DR, and proliferative DR. Initially, detailed information from the original datasets is extracted by reducing feature dimensions using the Deep Belief Network (DBN) model. Subsequently, the extracted images are analyzed by the proposed GO-DBN-WKELM classification model, which effectively detects and accurately classifies fundus images based on their severities into distinct classes. The enhanced detection performance is primarily due to the application of the GO algorithm with a wavelet kernel extreme learning machine (WKELM). The GO algorithm not only optimizes the kernel parameters of the WKELM but also increases the convergence speed of the classifier. The proposed classifier is analyzed using three different types of datasets, namely MESSIDOR, DIARETDB1, and IDRiD datasets. The effectiveness of the proposed classification model is determined by evaluating its ability to detect DR in terms of diverse performance metrics, such as accuracy, precision, recall, and F-measure. Simulation results reveal that the proposed GO-DBN-WKELM classifier achieved a high accuracy rate of approximately 98% for the MESSIDOR dataset and 97.8% for the DIARETDB1 dataset. These outcomes demonstrate the potential of the GO-DBN-WKELM approach in detecting and classifying diabetic retinopathy severities, offering an efficient, automated alternative to manual methods that can aid eye specialists in providing timely diagnoses and interventions for patients with this condition.
•The highlights of the article are given below for your kind perusal. Kindly, consider and forward my article for further processes.•U-Net is used for lesion segmentation of diabetic retinopathy ...fundus images.•Gannet Pelican Optimization Algorithm (GPOA)to identify various types of lesions.•Deep Q Network (DQN) is used for the detection of diabetic retinopathy.•EGFOA is used for the training of DQN.
The diabetes complication which causes various damage to the human eye lead to complete blindness is called diabetic retinopathy. The investigation of the optimization-based Deep Learning (DL) approach is introduced for the detection of diabetic retinopathy using fundus images. Here, the fundus images are pre-processed initially using a median filter and Region of Interest (RoI) extraction, to remove the noise in the image. U-Net is used for lesion segmentation and trained using the introduced Gannet Pelican Optimization Algorithm (GPOA) to identify various types of lesions where GPOA is the integration of the Gannet Optimization Algorithm (GOA) and Pelican Optimization Algorithm (POA). The data augmentation process is carried out using flipping, rotation, shearing, cropping, and translation of fundus images, and the data-augmented fundus image is allowed for a feature extraction process where the image and vector-based features of fundus images are extracted. In addition, Deep Q Network (DQN) is used for the detection of diabetic retinopathy and is trained using the introduced Exponential Gannet Pelican Optimization Algorithm (EGFOA). The EGFOA is the combination of Exponentially Weighted Moving Average (EWMA), Gannet Optimization Algorithm (GOA), and Firefly Optimization Algorithm (FFA). Experimental outcomes achieved a maximum of 91.6% of accuracy, 92.2% of sensitivity, and 91.9% of specificity.
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