Journal of Intelligent & Fuzzy Systems最新文献_第9页

Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniques 利用遗传优化技术，通过 HDFS 的映射还原调度和副本放置提高大数据分析的数据处理速度

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-11 DOI: 10.3233/jifs-240069

M.R. Sundara Kumar, H.S. Mohan

{"title":"Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniques","authors":"M.R. Sundara Kumar, H.S. Mohan","doi":"10.3233/jifs-240069","DOIUrl":"https://doi.org/10.3233/jifs-240069","url":null,"abstract":"Big Data Analytics (BDA) is an unavoidable technique in today’s digital world for dealing with massive amounts of digital data generated by online and internet sources. It is kept in repositories for data processing via cluster nodes that are distributed throughout the wider network. Because of its magnitude and real-time creation, big data processing faces challenges with latency and throughput. Modern systems such as Hadoop and SPARK manage large amounts of data with their HDFS, Map Reduce, and In-Memory analytics approaches, but the migration cost is higher than usual. With Genetic Algorithm-based Optimization (GABO), Map Reduce Scheduling (MRS) and Data Replication have provided answers to this challenge. With multi objective solutions provided by Genetic Algorithm, resource utilization and node availability improve processing performance in large data environments. This work develops a novel creative strategy for enhancing data processing performance in big data analytics called Map Reduce Scheduling Based Non-Dominated Sorting Genetic Algorithm (MRSNSGA). The Hadoop-Map Reduce paradigm handles the placement of data in distributed blocks as a chunk and their scheduling among the cluster nodes in a wider network. Best fit solutions with high latency and low accessing time are extracted from the findings of various objective solutions. Experiments were carried out as a simulation with several inputs of varied location node data and cluster racks. Finally, the results show that the speed of data processing in big data analytics was enhanced by 30–35% over previous methodologies. Optimization approaches developed to locate the best solutions from multi-objective solutions at a rate of 24–30% among cluster nodes.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252096","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

Transformer-CNN hybrid network for crowd counting 用于人群计数的变压器-CNN 混合网络

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-11 DOI: 10.3233/jifs-236370

Jiamao Yu, Ying Yu, Jin Qian, Xing Han, Feng Zhu, Zhiliang Zhu

{"title":"Transformer-CNN hybrid network for crowd counting","authors":"Jiamao Yu, Ying Yu, Jin Qian, Xing Han, Feng Zhu, Zhiliang Zhu","doi":"10.3233/jifs-236370","DOIUrl":"https://doi.org/10.3233/jifs-236370","url":null,"abstract":"Efficient feature representation is the key to improving crowd counting performance. CNN and Transformer are the two commonly used feature extraction frameworks in the field of crowd counting. CNN excels at hierarchically extracting local features to obtain a multi-scale feature representation of the image, but it struggles with capturing global features. Transformer, on the other hand, could capture global feature representation by utilizing cascaded self-attention to capture remote dependency relationships, but it often overlooks local detail information. Therefore, relying solely on CNN or Transformer for crowd counting has certain limitations. In this paper, we propose the TCHNet crowd counting model by combining the CNN and Transformer frameworks. The model employs the CMT (CNNs Meet Vision Transformers) backbone network as the Feature Extraction Module (FEM) to hierarchically extract local and global features of the crowd using a combination of convolution and self-attention mechanisms. To obtain more comprehensive spatial local information, an improved Progressive Multi-scale Learning Process (PMLP) is introduced into the FEM, guiding the network to learn at different granularity levels. The features from these three different granularity levels are then fed into the Multi-scale Feature Aggregation Module (MFAM) for fusion. Finally, a Multi-Scale Regression Module (MSRM) is designed to handle the multi-scale fused features, resulting in crowd features rich in high-level semantics and low-level detail. Experimental results on five benchmark datasets demonstrate that TCHNet achieves highly competitive performance compared to some popular crowd counting methods.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253708","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

Multi-Level interaction network for temporal sentence grounding in videos 用于视频中时态句子接地的多级交互网络

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-10 DOI: 10.3233/jifs-234800

Guangli Wu, Zhijun Yang, Jing Zhang

{"title":"Multi-Level interaction network for temporal sentence grounding in videos","authors":"Guangli Wu, Zhijun Yang, Jing Zhang","doi":"10.3233/jifs-234800","DOIUrl":"https://doi.org/10.3233/jifs-234800","url":null,"abstract":"Temporal sentence grounding in videos (TSGV), which aims to retrieve video segments from an untrimmed videos that semantically match a given query. Most previous methods focused on learning either local or global query features and then performed cross-modal interaction, but ignore the complementarity between local and global features. In this paper, we propose a novel Multi-Level Interaction Network for Temporal Sentence Grounding in Videos. This network explores the semantics of queries at both phrase and sentence levels, interacting phrase-level features with video features to highlight video segments relevant to the query phrase and sentence-level features with video features to learn more about global localization information. A stacked fusion gate module is designed, which effectively captures the temporal relationships and semantic information among video segments. This module also introduces a gating mechanism to enable the model to adaptively regulate the fusion degree of video features and query features, further improving the accuracy of predicting the target segments. Extensive experiments on the ActivityNet Captions and Charades-STA benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art methods.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254673","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

Research on probabilistic language multi-attribute group decision-making method based on correlation coefficient and improved entropy 基于相关系数和改进熵的概率语言多属性群体决策方法研究

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-10 DOI: 10.3233/jifs-235593

Junwei Li, Mengmeng Lian, Yong Jin, Miaomiao Xia, Huaibin Hou

{"title":"Research on probabilistic language multi-attribute group decision-making method based on correlation coefficient and improved entropy","authors":"Junwei Li, Mengmeng Lian, Yong Jin, Miaomiao Xia, Huaibin Hou","doi":"10.3233/jifs-235593","DOIUrl":"https://doi.org/10.3233/jifs-235593","url":null,"abstract":"To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255334","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

An improved loci method for outlier detection in fuzzy datasets based on fractional distance metric and outlierness degree 基于分数距离度量和离群度的模糊数据集离群点检测改进定位方法

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-10 DOI: 10.3233/jifs-234448

Mehdi Hajiloei, A. F. Jahromi, Somayeh Zolmani

引用次数: 0

Least squares parameter estimation for uncertain fractional differential equations and application to stock model 不确定分数微分方程的最小二乘参数估计及其在种群模型中的应用

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-10 DOI: 10.3233/jifs-237977

Liu He, Yuanguo Zhu, Tingqing Ye

引用次数: 0

Synergizing enterprise resource management with technology through driving innovation and growth in business models 通过推动业务模式的创新和增长，实现企业资源管理与技术的协同效应

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-10 DOI: 10.3233/jifs-235379

Mengtong Xie, Huaqi Chai

{"title":"Synergizing enterprise resource management with technology through driving innovation and growth in business models","authors":"Mengtong Xie, Huaqi Chai","doi":"10.3233/jifs-235379","DOIUrl":"https://doi.org/10.3233/jifs-235379","url":null,"abstract":"A human resources management plan is presently recognised as one of the most important components of a corporate technique. This is due to the fact that its major purpose is to interact with people, who are the most precious asset that an organisation has. It is impossible for an organisation to achieve its objectives without the participation of individuals. An organisation may effectively plan as well as manage individual processes to support the organization’s objectives and adapt nimbly to any change if it has well-prepared HR techniques and an action plan for its execution. This investigation puts up a fresh way for the board of directors of a private firm to increase their assets and advance their growth by using cloud programming that is characterised by networks. The small company resource has been improved by strengthening human resource management techniques, and the cloud SDN network is used for job scheduling using Q-convolutional reinforcement recurrent learning. The proposed technique attained Quadratic normalized square error of 60%, existing SDN attained 55%, HRM attained 58% for Synthetic dataset; for Human resources dataset propsed technique attained Quadratic normalized square error of 62%, existing SDN attained 56%, HRM attained 59% ; proposed technique attained Quadratic normalized square error of 64%, existing SDN attained 58%, HRM attained 59% for SyriaTel dataset.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140255271","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

Assessing the risk and effect of climate change with two-layer fuzzy logic-SWARA: A comparative practice in Turkiye 用双层模糊逻辑--SWARA 评估气候变化的风险和影响：土耳其的比较实践

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-10 DOI: 10.3233/jifs-236298

Elanur Adar-Yazar, Buket Karatop, Selim Gökcan Karatop

{"title":"Assessing the risk and effect of climate change with two-layer fuzzy logic-SWARA: A comparative practice in Turkiye","authors":"Elanur Adar-Yazar, Buket Karatop, Selim Gökcan Karatop","doi":"10.3233/jifs-236298","DOIUrl":"https://doi.org/10.3233/jifs-236298","url":null,"abstract":"Many factors such as population growth, development of industry/technology, and increase in production-consumption disrupt the ecological balance and cause climate change, which is a global problem. Determining the criteria that cause climate change is very important in finding effective solutions to the problem. In the study, the criteria were determined, weighted with a new method, Step-wise Weight Assessment Ratio Analysis (SWARA), and ranked according to their priorities with two-layer fuzzy logic model. The Fuzzy SWARA method allows the evaluation process, which becomes complicated due to the difficulties and factors experienced in decision-making, to be carried out more effectively and realistically. The risk and effect of climate change in Turkiye were evaluated regionally. However, the developed model also has a wide application area. Research findings revealed that the highest risk/effect of climate change have the Marmara and Central Anatolia regions. The lowest risk region is the Eastern Anatolia. Air pollution, population growth and deforestation have the highest weights. Important suggestions have presented especially for priority criteria. In this way, the factors that should be prioritized in climate change environmental problem solutions have been revealed and will make it easier for researchers and managers to provide more effective management.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140254934","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

New concept of centrality measurement in fuzzy social networks 模糊社交网络中中心度测量的新概念

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-09 DOI: 10.3233/jifs-232602

Rupkumar Mahapatra, Sovan Samanta, M. Pal

引用次数: 0

Enhancing spectrum utilization and primary user detection in cognitive radio networks through greedy cooperative spectrum sensing 通过贪婪合作频谱感知提高认知无线电网络的频谱利用率和主用户检测能力

Journal of Intelligent & Fuzzy Systems Pub Date : 2024-03-09 DOI: 10.3233/jifs-239871

S. Dhivya, A. Rajeswari

{"title":"Enhancing spectrum utilization and primary user detection in cognitive radio networks through greedy cooperative spectrum sensing","authors":"S. Dhivya, A. Rajeswari","doi":"10.3233/jifs-239871","DOIUrl":"https://doi.org/10.3233/jifs-239871","url":null,"abstract":"The utilization of the spectrum is optimized through which primary users of modern wireless communication technologies might obtain a higher chance of detection. The research aims to study how the NI-USRP hardware platform can be used to set up greedy cooperative spectrum sensing for cognitive radio networks. Research primarily deals with energy detection and eigenvalue-based detection approaches, both of which are highly recognized for their capacity to sense the spectrum without having prior knowledge of the primary user signals. In the hardware arrangement, there is one transmitter and two cognitive radio receivers. LABVIEW makes it simple to deploy and maximizes the detection probability across a large sample. Here, it was demonstrated that cooperative spectrum sensing is superior to non-cooperative spectrum sensing, which results in a reduction in the risk of errors occurring during detection. The research discovered that the OR combination rule has a higher detection probability than the AND rule at the same time. The research emphasizes the significance of expanding cooperative spectrum sensing to improve overall detection capabilities. SNRs that are more than 10 dB allow the energy detector to operate, and the eigenvalue detector continues to work when the SNR drops to –9 dB.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140256230","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

Journal of Intelligent &amp; Fuzzy Systems最新文献

Journal of Intelligent & Fuzzy Systems最新文献