Solid waste is a major issue in today's world. Which can be a contributing factor to pollution and the spread of vector-borne diseases. Because of its complicated nonlinear processes, this problem is difficult to model and optimize using traditional methods. In this study, a mathematical model was developed to optimize the cost of solid waste recycling and management. In the optimization phase, the salp swarm algorithm (SSA) is utilized to determine the level of discarded solid waste and reclaimed solid waste. An optimization technique SSA is a new method of finding the ideal solution for a mathematical relationship based on leaders and followers. It takes a lot of random solutions, as well as their outward or inward fluctuations, to find the optimal solution. This method also included multiple adaptive and random variables to guarantee that the solution space was explored and used in various optimization tasks. When all criteria are considered, the results of this study show that the SSA is efficient for least-distance path allocation. The simulation findings reveal a significant improvement over the well-known particle swarm optimization (PSO) algorithm, with recycling and disposal costs decreasing by 10% to 30%.
Gumbel distribution was dealt with great care by researchers and statisticians. There are traditional methods to estimate two parameters of Gumbel distribution known as Maximum Likelihood, the Method of Moments and recently the method of re-sampling called (Jackknife). However, these methods suffer from some mathematical difficulties in solving them analytically. Accordingly, there are other non-traditional methods, like the principle of the nearest neighbors, used in computer science especially, artificial intelligence algorithms, including the genetic algorithm, the artificial neural network algorithm, and others that may to be classified as meta-heuristic methods. Moreover, this principle of nearest neighbors has useful statistical featu
... Show MoreCredit risk assessment has become an important topic in financial risk administration. Fuzzy clustering analysis has been applied in credit scoring. Gustafson-Kessel (GK) algorithm has been utilised to cluster creditworthy customers as against non-creditworthy ones. A good clustering analysis implemented by good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK) algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial centres. Utilising similar degree between points to get similarity density, and then by means of maximum density points selecting; the modified Kohonen Network method generate clustering initial centres to get more reasonable clustering res
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The aim of this paper is to model and optimize the fatigue life and hardness of medium carbon steel CK35 subjected to dynamic buckling. Different ranges of shot peening time (STP) and critical points of slenderness ratio which is between the long and intermediate columns, as input factors, were used to obtain their influences on the fatigue life and hardness, as main responses. Experimental measurements of shot peening time and buckling were taken and analyzed using (DESIGN EXPERT 8) experimental design software which was used for modeling and optimization purposes. Mathematical models of responses were obtained and analyzed by ANOVA variance to verify the adequacy of the models. The resul
... Show MoreThis paper presents a brief study undertaken for improving the performance of information and communication management of construction projects through investing in information and communication technologies (ICT). The work aims at first to investigate and diagnose the problems, challenges, weaknesses, and inefficiencies related to information and communication management in projects in the construction industry of Iraq. Studying the diagnosed matters and the different solutions of ICT to improve project management performance is following the investigation process. The research presents a technological system suggested to process a lot of the diagnosed problems, challenges, weakness, and inefficiencies of the construction projects and t
... Show MoreMaximum power point tracking (MPPT) is used in photovoltaic (PV) systems to enhance efficiency and maximize the output power of PV module, regardless the variation of temperature, irradiation, and the electrical characteristics of the load. A new MPPT system has been presented in this research, consisting of a synchronous DC-DC step-down Buck converter controlled by an Arduino microcontroller based unit. The MPPT process with Perturb and Observe method is performed with a DC-DC converter circuit to overcome the problem of voltage mismatch between the PV modules and the loads. The proposing system has high efficiency, lower cost and can be easily modified to handle more energy sources. The test results indicate that the u
... Show MoreIn digital images, protecting sensitive visual information against unauthorized access is considered a critical issue; robust encryption methods are the best solution to preserve such information. This paper introduces a model designed to enhance the performance of the Tiny Encryption Algorithm (TEA) in encrypting images. Two approaches have been suggested for the image cipher process as a preprocessing step before applying the Tiny Encryption Algorithm (TEA). The step mentioned earlier aims to de-correlate and weaken adjacent pixel values as a preparation process before the encryption process. The first approach suggests an Affine transformation for image encryption at two layers, utilizing two different key sets for each layer. Th
... Show MoreText based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.
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