The purpose of this paper is to solve the stochastic demand for the unbalanced transport problem using heuristic algorithms to obtain the optimum solution, by minimizing the costs of transporting the gasoline product for the Oil Products Distribution Company of the Iraqi Ministry of Oil. The most important conclusions that were reached are the results prove the possibility of solving the random transportation problem when the demand is uncertain by the stochastic programming model. The most obvious finding to emerge from this work is that the genetic algorithm was able to address the problems of unbalanced transport, And the possibility of applying the model approved by the oil products distribution company in the Iraqi Ministry of Oil to m

Among a variety of approaches introduced in the literature to establish duality theory, Fenchel duality was of great importance in convex analysis and optimization. In this paper we establish some conditions to obtain classical strong Fenchel duality for evenly convex optimization problems defined in infinite dimensional spaces. The objective function of the primal problem is a family of (possible) infinite even convex functions. The strong duality conditions we present are based on the consideration of the epigraphs of the c-conjugate of the dual objective functions and the ε-c-subdifferential of the primal objective functions.

The Traveling Salesman Problem is a story application of Atom Swarm Optimizations in this research. We have developed several novel techniques intended for solving TSP with PSO. Additionally, we introduced the notions to Swap Operative and Swap Chronological sequence and redefining the remaining operatives their foundation; that way, the study created unique PSO. Research prove it can produce satisfactory outcomes. The aim of this paper be there to assess the functioning of particle swarm optimization, for the going salesman issue TSP. The solution to this trouble is common to be NP-hard, it has N! permutations. The study's goal is to examine the capacity of both algorithms to solve intercontinental and other benchmark problems. Overall, th

This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown t

The geophysical testing is increasingly being employed in many geotechnical applications. It is preferred in monitoring the mechanical characteristics of the ground because of its economy, not time consuming and non-destructive nature. Seismic wave test is one of the geophysical methods which showed a potential in observing the general behaviour of the reinforced soil with stone columns. Findings in most cases showed that the seismic wave measurements was integrated with or compared to the conventional tests such as standard penetration test or cone penetration test. There was a noticeable success in identifying the enhancement achieved to the ground upon the strengthening with the column, specifically when the associated surveys can produc

Fibroepithelioma of Pinkus (FEP) is a slowly growing, low-grade malignant tumor with very low metastatic potential that is considered a distinct variant of basal cell carcinoma (BCC). It usually manifests as sessile or polypoidal lesions on the trunk of middle-aged patients. However, it may present in younger age groups, even in children. In this case, we present a rare case of FEP atypically presenting as a scaly plaque on the lower back for several years in an elderly female who was eventually diagnosed by excisional biopsy and histopathology.

Face recognition and identity verification are now critical components of current security and verification technology. The main objective of this review is to identify the most important deep learning techniques that have contributed to the improvement in the accuracy and reliability of facial recognition systems, as well as highlighting existing problems and potential future research areas. An extensive literature review was conducted with the assistance of leading scientific databases such as IEEE Xplore, ScienceDirect, and SpringerLink and covered studies from the period 2015 to 2024. The studies of interest were related to the application of deep neural networks, i.e., CNN, Siamese, and Transformer-based models, in face recogni

The rapid development of telemedicine services and the requirements for exchanging medical information between physicians, consultants, and health institutions have made the protection of patients’ information an important priority for any future e-health system. The protection of medical information, including the cover (i.e. medical image), has a specificity that slightly differs from the requirements for protecting other information. It is necessary to preserve the cover greatly due to its importance on the reception side as medical staff use this information to provide a diagnosis to save a patient's life. If the cover is tampered with, this leads to failure in achieving the goal of telemedicine. Therefore, this work provides an in