International journal of hybrid intelligent systems最新文献_第2页

A genetic scheduling strategy with spatial reuse for dense wireless networks 密集无线网络空间复用遗传调度策略

International journal of hybrid intelligent systems Pub Date : 2023-07-11 DOI: 10.3233/his-230015

Vinicius Fulber-Garcia, F. Engel, E. P. Duarte

引用次数: 0

Adaptive action-prediction cortical learning algorithm under uncertain environments 不确定环境下自适应动作预测皮质学习算法

International journal of hybrid intelligent systems Pub Date : 2023-07-07 DOI: 10.3233/his-230013

Kazushi Fujino, Takeru Aoki, K. Takadama, Hiroyuki Sato

{"title":"Adaptive action-prediction cortical learning algorithm under uncertain environments","authors":"Kazushi Fujino, Takeru Aoki, K. Takadama, Hiroyuki Sato","doi":"10.3233/his-230013","DOIUrl":"https://doi.org/10.3233/his-230013","url":null,"abstract":"The cortical learning algorithm (CLA) is a time series prediction algorithm. Memory elements called columns and cells discretely represent data with their state combinations, whereas linking elements called synapses change their state combinations. For tasks requiring to take actions, the action-prediction CLA (ACLA) has an advantage to complement missing state values with their predictions. However, an increase in the number of missing state values (i) generates excess synapses negatively affect the action predictions and (ii) decreases the stability of data representation and makes the output of action values difficult. This paper proposes an adaptive ACLA using (i) adaptive synapse adjustment and (ii) adaptive action-separated decoding in an uncertain environment, missing multiple input state values probabilistically. (i) The proposed adaptive synapse adjustment suppresses unnecessary synapses. (ii) The proposed adaptive action-separated decoding adaptively outputs an action prediction separately for each action value. Experimental results using uncertain two- and three-dimensional mountain car tasks show that the proposed adaptive ACLA achieves a more robust action prediction performance than the conventional ACLA, DDPG, and the three LSTM-assisted reinforcement learning algorithms of DDPG, TD3, and SAC, even though the number of missing state values and their frequencies increase. These results implicate that the proposed adaptive ACLA is a way to making decisions for the future, even in cases where information surrounding the situation partially lacked.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48562858","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

A hybrid approach: Uncertain configurable QoT-IoT composition based on fuzzy logic and genetic algorithm 一种混合方法:基于模糊逻辑和遗传算法的不确定可配置QoT-IoT组合

International journal of hybrid intelligent systems Pub Date : 2023-07-07 DOI: 10.3233/his-230014

Soura Boulaares, S. Sassi, D. Benslimane, S. Faiz

引用次数: 0

GA evolved CGP configuration data for digital circuit design on embryonic architecture 遗传算法将CGP组态数据演化为基于胚胎结构的数字电路设计

International journal of hybrid intelligent systems Pub Date : 2023-07-02 DOI: 10.3233/his-230012

Gayatri Malhotra, P. Duraiswamy

{"title":"GA evolved CGP configuration data for digital circuit design on embryonic architecture","authors":"Gayatri Malhotra, P. Duraiswamy","doi":"10.3233/his-230012","DOIUrl":"https://doi.org/10.3233/his-230012","url":null,"abstract":"Embryonic architecture that carries self-evolving design with fault tolerant feature is proposed for deep space missions. Fault tolerance is achieved in the embryonic architecture due to its homogeneous structure. The cloning of configuration data or genome data to all the embryonic cells makes each cell capable of selecting required cell function using selective gene. The primary digital circuits of avionics are implemented on the fabric, where the configuration data in Cartesian Genetic Programming (CGP) format is evolved through customized GA. The CGP format is preferred over LUT format for the circuit configuration data due to its fixed data size in case of modular design. Further the CGP format enables fault detection at embryonic cell level as well as logic gate level. The various combinational and sequential circuits like adder, comparator, multiplier, register and counter are designed and implemented on embryonic fabric using Verilog. The circuit performance is evaluated using simulation. The proposed PHsClone genetic algorithm (GA) design with parallel-pipeline approach is to achieve faster convergence. Four concurrent PHsClone GA executions (four parallel threads) achieve convergence for the 10 times faster for a 1-bit adder, and 3 times faster for a 2-bit comparator.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45686686","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

Conceptual modeling of Big Data extraction phase 大数据提取阶段概念建模

International journal of hybrid intelligent systems Pub Date : 2023-05-25 DOI: 10.3233/his-230008

Hana Mallek, Faïza Ghozzi, F. Gargouri

{"title":"Conceptual modeling of Big Data extraction phase","authors":"Hana Mallek, Faïza Ghozzi, F. Gargouri","doi":"10.3233/his-230008","DOIUrl":"https://doi.org/10.3233/his-230008","url":null,"abstract":"As the amount of information exceeds the management and storage capacity of traditional data management systems, several domains need to take into account this growth of data, in particular the decision-making domain known as Business Intelligence (BI). Since the accumulation and reuse of these massive data stands for a gold mine for businesses, several insights that are useful and essential for effective decision making have to be provided. However, it is obvious that there are several problems and challenges for the BI systems, especially at the level of the ETL (Extraction-Transformation-Loading) as an integration system. These processes are responsible for the selection, filtering and restructuring of data sources in order to obtain relevant decisions. In this research paper, our central focus is especially upon the adaptation of the extraction phase inspired from the first step of MapReduce paradigm in order to prepare the massive data to the transformation phase. Subsequently, we provide a conceptual model of the extraction phase which is composed of a conversion operation that guarantees obtaining NoSQL structure suitable for Big Data storage, and a vertical partitioning operation for presenting the storage mode before submitting data to the second ETL phase. Finally, we implement through Talend for Big Data our new component which helps the designer extract data from semi-structured data.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41582986","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: 1

Parallel swarm-based algorithms for scheduling independent tasks 基于并行群的独立任务调度算法

International journal of hybrid intelligent systems Pub Date : 2023-05-25 DOI: 10.3233/his-230006

Robert Dietze, Maximilian Kränert

引用次数: 0

Recent advances of ML and DL approaches for Arabic handwriting recognition: A review 阿拉伯语手写识别的ML和DL方法的最新进展:综述

International journal of hybrid intelligent systems Pub Date : 2023-05-25 DOI: 10.3233/his-230005

Anis Mezghani, R. Maalej, M. Elleuch, M. Kherallah

引用次数: 0

An optimized efficient combinatorial learning using deep neural network and statistical techniques 使用深度神经网络和统计技术的优化有效组合学习

International journal of hybrid intelligent systems Pub Date : 2023-05-25 DOI: 10.3233/his-230007

Jyothi V K, Guda Ramachandra Kaladhara Sarma

{"title":"An optimized efficient combinatorial learning using deep neural network and statistical techniques","authors":"Jyothi V K, Guda Ramachandra Kaladhara Sarma","doi":"10.3233/his-230007","DOIUrl":"https://doi.org/10.3233/his-230007","url":null,"abstract":"Research work is to discover the rapid requirement of Artificial Intelligence and Statistics in medical research. Objective is to design a diagnostic prediction system that can detect and predict diseases at an early stage from clinical data sets. Some of major diseases leading reasons of death globally are heart disease and cancer. There are different kinds of cancer, in this study we focused on breast cancer and heart disease. Prediction of these diseases at a very early stage is curable and preventive diagnosis can control death rate. Designed two Artificial Intelligence systems for prediction of above-mentioned diseases using statistics and Deep neural networks (i) Combinatorial Learning (CLSDnn) and (ii) an optimized efficient Combinatorial Learning (eCLSDnn). To evaluate the performance of the proposed system conducted experiments on three different data sets, in which two data sets are of breast cancer namely, Wisconsin-data set of UCI Machine Learning repository and AI for Social Good: Women Coders’ Bootcamp data set and Cleveland heart disease data set of UCI Machine Learning repository. The proposed architectures of binary classification are validated for 70%–30% data splitting and on K-fold cross validation. Recognition of Malignant cancerous tumors CLSDnn model achieved maximum accuracy of 98.53% for Wisconsin data set, 95.32% for AI for Social Good: Women Coders’ data set and 96.72% for Cleveland data set. Recognition of Malignant cancerous tumors eCLSDnn model achieved 99.36% for Wisconsin data set, 97.12% for AI for Social Good: Women Coders’ data set and 99.56% for the Cleveland heart disease data set.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47394224","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

Destructive computing with winner-lose-all competition in multi-layered neural networks 多层神经网络中输者全输竞争的破坏性计算

International journal of hybrid intelligent systems Pub Date : 2023-05-25 DOI: 10.3233/his-230011

R. Kamimura

{"title":"Destructive computing with winner-lose-all competition in multi-layered neural networks","authors":"R. Kamimura","doi":"10.3233/his-230011","DOIUrl":"https://doi.org/10.3233/his-230011","url":null,"abstract":"The present paper aims to propose a new learning method based on destructive computing, contrary to the conventional progressive computing or the steady-step learning. In spite of the existence of a large amount of biased or distorted information in inputs, the conventional learning methods fundamentally aim to gradually acquire information that is as faithful as possible to inputs, which has prevented us from acquiring intrinsic information hidden in the deepest level of inputs. At this time, it is permitted to suppose a leap to that level by changing information at hand not gradually but drastically. In particular, for the really drastic change of information, we introduce the winner-lose-all (WLA) to drastically destroy the supposedly most important information for immediately reaching or leaping to intrinsic information, hidden in complicated inputs. The method was applied to a target-marketing problem. The experimental results show that, with the new method, multi-layered neural networks had an ability to disentangle complicated network configurations into the simplest ones with simple and independent correlation coefficients between inputs and targets. This was realized by drastically changing the information content in the course of learning and, correspondingly, by mixing regular and irregular properties over connection weights.","PeriodicalId":88526,"journal":{"name":"International journal of hybrid intelligent systems","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46847788","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

A rapid literature review on ensemble algorithms for COVID-19 classification using image-based exams 基于图像检查的COVID-19分类集成算法的快速文献综述

International journal of hybrid intelligent systems Pub Date : 2023-05-25 DOI: 10.3233/his-230009

Elaine Pinto Portela, O. Cortes, Josenildo Costa da Silva

引用次数: 0