Enhanced Chimp Optimization Algorithm Using Attack Defense Strategy and Golden Update Mechanism for Robust COVID-19 Medical Image Segmentation

Medical image segmentation is a powerful and evolving technology in medical diagnosis.In fact,it has been identified as a very effective tool to support and accompany doctors in their fight against the spread of the coronavirus(COVID-19).Various techniques have been utilized for COVID-19 image segmentation,including Multilevel Thresholding(MLT)-based meta-heuristics,which are considered crucial in addressing this issue.However,despite their importance,meta-heuristics have significant limitations.Specifically,the imbalance between exploration and exploitation,as well as premature convergence,can cause the optimization process to become stuck in local optima,resulting in unsatisfactory segmentation results.In this paper,an enhanced War Strategy Chimp Optimization Algorithm(WSChOA)is proposed to address MLT problems.Two strategies are incorporated into the traditional Chimp Optimization Algorithm.Golden update mechanism that provides diversity in the population.Additionally,the attack and defense strategies are incorporated to improve the search space leading to avoiding local optima.The experimental results were conducted by comparing WSChoA with recent and well-known algorithms using various evaluation metrics such as Feature Similarity Index(FSIM),Structural Similarity Index(SSIM),Peak signal-to-Noise Ratio(PSNR),Standard deviation(STD),Freidman Test(FT),and Wilcoxon Sign Rank Test(WSRT).The results obtained by WSChoA surpassed those of other optimization techniques in terms of robustness and accuracy,indicating that it is a powerful tool for image segmentation.

Mechanical Eye Trauma Epidemiology, Prognostic Factors, and Management Controversies—An Update

Purpose of Review: The management of eye injuries is both difficult and argumentative. This study attempts to highlight the management of ocular trauma using currently available information in the literature and author experience. This review presents a workable framework from the first presentation, epidemiology, classification, investigations, management principles, complications, prognostic factors, final visual outcome and management debates. Review Findings: Mechanical ocular trauma is a leading cause of monocular blindness and possible handicap worldwide. Among several classification systems, the most widely accepted is Birmingham Eye Trauma Terminology (BETT). Mechanical ocular trauma is a topic of unsolved controversy. Patching for corneal abrasion, paracentesis for hyphema, the timing of cataract surgery and intraocular lens implantation are all issues in anterior segment injuries. Regarding posterior segment controversies, the timing of vitrectomy, use of prophylactic cryotherapy, the necessity of intravitreal antibiotics in the absence of infection, the use of vitrectomy vs vitreous tap in traumatic endophthalmitis is the issues. The pediatric age group needs to be approached by a different protocol due to the risk of amblyopia, intraocular inflammation, and significant vitreoretinal adhesions. The various prognostic factors have a role in the final visual outcome. B scan is used to exclude R.D, Intraocular foreign body (IOFB), and vitreous haemorrhage in hazy media. Individual surgical strategies are used for every patient according to the classification and extent of the injuries. Conclusion: This article examines relevant evidence on the management challenges and controversies of mechanical trauma of the eye and offers treatment recommendations based on published research and the authors’ own experience.

Research on Adaptive TSSA-HKRVM Model for Regression Prediction of Crane Load Spectrum

For the randomness of crane working load leading to the decrease of load spectrum prediction accuracy with time,an adaptive TSSA-HKRVM model for crane load spectrum regression prediction is proposed.The heterogeneous kernel relevance vector machine model(HKRVM)with comprehensive expression ability is established using the complementary advantages of various kernel functions.The combination strategy consisting of refraction reverse learning,golden sine,and Cauchy mutation+logistic chaotic perturbation is introduced to form a multi-strategy improved sparrow algorithm(TSSA),thus optimizing the relevant parameters of HKRVM.The adaptive updatingmechanismof the heterogeneous kernel RVMmodel under themulti-strategy improved sparrow algorithm(TSSA-HKMRVM)is defined by the sliding window design theory.Based on the sample data of the measured load spectrum,the trained adaptive TSSA-HKRVMmodel is employed to complete the prediction of the crane equivalent load spectrum.Applying this method toQD20/10 t×43m×12mgeneral bridge crane,the results show that:compared with other prediction models,although the complexity of the adaptive TSSA-HKRVMmodel is relatively high,the prediction accuracy of the load spectrum under long periods has been effectively improved,and the completeness of the load information during thewhole life cycle is relatively higher,with better applicability.