Metin Zontul

Makale | 2022 | Conference Proceedings

Since increasing oil prices and vehicle costs increase transportation costs in order delivery systems, the optimal vehicle loading and routing is very crucial for the companies in competitive conditions. Although there are many studies related to optimal vehicle loading and routing by using linear programming and heuristic algorithms, there is not enough practical web service-based application in the literature. In this study, we propose a hybrid model to solve the problem of two-dimensional vehicle pallet loading with routing for a real-world data by combining Knapsack Problem solver algorithms such as MaxRects, Skyline and Guillot . . .ine with Dijkstra's algorithm for loading and routing respectively as a web service-based application. © 2022 IEEE Daha fazlası Daha az

AREF YELGHI

Makale | 2022 | Conference Proceedings

In recent years, many applications based on the Neural Network, Neuro-Fuzzy, and optimization algorithms have been more common for solving regression and classification problems. In the Adaptive Neuro-fuzzy inference system(ANFIS), many researchers used the adaption of metaheuristic algorithms with ANFIS to propose the best estimation model. However, many researchers only focused on the experiment without the demonstration mathematical or indicating which characteristic of optimization algorithm, during the run, affect and settable in coordination with ANFIS. The paper provides an adaption of metaheuristic algorithms with ANFIS whic . . .h has been performed by considering accuracy parameters in layer 1 and layer 4 for the estimation problem. It is integrated six well-known metaheuristic algorithms and extracting the characteristic of them. In the experiment, the metaheuristic algorithms based on the evolutionary computation have been demonstrated more stable than swarm intelligence methods in tuning parameters of ANFIS. © 2022 IEEE Daha fazlası Daha az

METİN ZONTUL | SEDA KARATEKE

Makale | 2022 | Conference Proceedings

Today Artificial Intelligence (AI) algorithms need to represent different kinds of input items in numeric or vector format. Some input data can easily be transformed to numeric or vector format but the structure of some special data prevents direct and easy transformation. For instance, we can represent air condition using humidity, pressure, and temperature values with a vector that has three features and we can understand the similarity of two different air measurements using cosine-similarity of two vectors. But if we are dealing with a general ontology tree, which has elements "entity"as the root element, its two children "livin . . .g things"and "non-living things"as first- level elements repeatedly children of "living things"that are "Animals", "Plants"as second level elements, it is harder to represent this kind of data with numeric values. The ontology tree starts from the general items and goes to specific items. If we want to represent an element of this tree with a vector; how can it be possible? And if we want the measured similarity using some methods like cosine-similarity, which one similarity is higher, ("Animal"and "non-living thing") or ("Animal"and "Living thing")? How should we select the values of this vector for each item of the hierarchical tree? In this paper, we propose an original and basic idea to represent the hierarchical tree items with 2D vectors and in the proposed method the cosine-similarity metric works for measuring the semantic similarity of represented items at the same level as parent items. There are two important results related to our representation: (1) The "y"values of the items give the hierarchical level of the item. (2) For the same level items, the cosine similarities between the parent item and child items are higher if the child belongs to this parent compared to other childrens'. In other words, the cosine similarity between the parent item and child items is highest if the child belongs to this parent. © 2022 IEEE Daha fazlası Daha az

AREF YELGHI

Makale | 2022 | Conference Proceedings

The aim of this study is to provide a novel method routing in ad hoc networks using ant colony algorithm. Hence for this study the researcher attempts to discover and create routes with less number of crossings, nodes sustainable and less energy transfer, to reduce latency end-to-end, save bandwidth and to extend the life and increase the lifetime of the network nodes. Research methodology for simulation algorithm has been OPNET software. Therefore, the proposed algorithm's performance was compared with one of the most routing algorithms in mobile ad hoc networks Ant Hoc Net. The results showed that the proposed algorithm compared w . . .ith Ant Hoc Net has more end-to-end delay, more package shipping, and less routing overhead can reduce energy consumption and thus increases the lifetime of the network nodes. The results of this study indicate that the latency end-to-end, saving bandwidth and increasing lifetime of nodes and network lifetime can be predicted by the proposed algorithm. © 2022 IEEE Daha fazlası Daha az