2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE)最新文献_第2页

Features of Metaheuristic Algorithm for Integration with ANFIS Model 与ANFIS模型集成的元启发式算法的特点

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009722

Aref Yelghi, Shirmohammad Tavangari

引用次数: 1

Solve the Inverse Kinematics of Robot Arms using Sand Cat Swarm Optimization (SCSO) Algorithm 利用沙猫群优化(SCSO)算法求解机械臂逆运动学

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009772

Amir Seyyedabbasi

{"title":"Solve the Inverse Kinematics of Robot Arms using Sand Cat Swarm Optimization (SCSO) Algorithm","authors":"Amir Seyyedabbasi","doi":"10.1109/ICTACSE50438.2022.10009772","DOIUrl":"https://doi.org/10.1109/ICTACSE50438.2022.10009772","url":null,"abstract":"Inverse kinematics of robot arms is one of the optimization problems. The six joints of the Six degrees of freedom PUMA 560 robot arm are considered as an inverse kinematics system in this study. There are many possibilities for joint angles in this problem, making the analysis difficult to determine using deterministic rules. Several metaheuristic algorithms are presented in this paper for solving the inverse kinematics problem of robot arms, including the sand cat swarm optimization algorithm (SCSO). Additionally, we compare the particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) optimization algorithms to see which is most efficient. In this study, meta-heuristic algorithms are used to determine the inverse kinematics of the robotic arm, which are essential to tracking a rectangular trajectory in three dimensions. A cost function analysis was conducted in order to further analyze the results. In addition, the results of the comparison of the meta-heuristic algorithms to the inverse kinematics task showed that the SCSO algorithm performed better than the competitors.","PeriodicalId":301767,"journal":{"name":"2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124260042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

2D Vector Representation of Binomial Hierarchical Tree Items 二项层次树项的二维矢量表示

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009738

Ilknur Dönmez, Seda Karateke, M. Zontul

{"title":"2D Vector Representation of Binomial Hierarchical Tree Items","authors":"Ilknur Dönmez, Seda Karateke, M. Zontul","doi":"10.1109/ICTACSE50438.2022.10009738","DOIUrl":"https://doi.org/10.1109/ICTACSE50438.2022.10009738","url":null,"abstract":"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 \"living 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.","PeriodicalId":301767,"journal":{"name":"2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE)","volume":"35 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120860307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of URL-based Phishing Attacks Using Neural Networks 基于url的网络钓鱼攻击的神经网络检测

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009645

J. Novakovic, S. Marković

引用次数: 0

Cooperative Multi-Instance Multi-Application Routing for the Smart Grid 面向智能电网的协同多实例多应用路由

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009886

Milad Ayoub

引用次数: 0

Web Service-Based Two-Dimensional Vehicle Pallet Loading with Routing for a Real-World Problem 基于Web服务的二维车辆托盘装载路径问题

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009852

Ilker Nacakli, Sadi Guzel, M. Zontul

引用次数: 0

Investigation of Lateral Scattering Profiles of Heavy Ions with Monte Carlo Simulation System 用蒙特卡罗模拟系统研究重离子横向散射分布

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009871

Fatih Ekinci

引用次数: 0

Offering New Routing Method in Ad hoc Networks using Ant Colony Algorithm 利用蚁群算法在Ad hoc网络中提供新的路由方法

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009849

Shirmohammad Tavangari, Aref Yelghi, M. S. Darafshani

引用次数: 0

A Novel Fuzzy-MRAS Observer of Wind Turbines Conversion Systems Based on DFIG 一种基于DFIG的风电机组转换系统模糊mras观测器

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009878

L. Saihi, Y. Bakou, F. Ferroudji, Azzedinne Tayebi, Abdelkader Hadidi, Oulimar Ibrahim

引用次数: 0

Examining the Effect of Feature Normalization and Feature Selection for Logistic Regression Based Multimodal Stress Detection 检验特征归一化和特征选择在基于逻辑回归的多模态应力检测中的效果

2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE) Pub Date : 2022-09-29 DOI: 10.1109/ICTACSE50438.2022.10009720

M. Fauzi, Bian Yang, P. Yeng

{"title":"Examining the Effect of Feature Normalization and Feature Selection for Logistic Regression Based Multimodal Stress Detection","authors":"M. Fauzi, Bian Yang, P. Yeng","doi":"10.1109/ICTACSE50438.2022.10009720","DOIUrl":"https://doi.org/10.1109/ICTACSE50438.2022.10009720","url":null,"abstract":"Automated multimodal stress detection using smartwatches and machine learning (ML) has been very popular nowadays. One of the processes in ML-based classification is preprocessing, which includes feature normalization and feature selection because it can enhance classification performance. In this study, we construct a multimodal-based stress detection system using Logistic Regression and investigate the effects of feature normalization and feature selection on performance. The experiment results show that the stress classification system with feature normalization performs better than without feature normalization. The results also show that the use of the fewest features gives the worst performance. The performance of the stress classification system increases as the number of features increases but the performance slightly declines at a particular point. The best performance was obtained when Min-Max normalization and ANOVA-based feature selection were employed with accuracy, precision, recall, and F1-measure of 0.894, 0.819, 0.859, and 0.817, respectively. This best result was achieved when 90% of the total features (378 features) were used.","PeriodicalId":301767,"journal":{"name":"2022 International Conference on Theoretical and Applied Computer Science and Engineering (ICTASCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127018585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0