Web Intell.最新文献_第4页

Impact of self adaptive-elephant herding optimization towards neural network for facial emotion recognition 自适应象群优化对面部情绪识别神经网络的影响

Web Intell. Pub Date : 2022-05-12 DOI: 10.3233/web-210481

B. Devi, M. Preetha

{"title":"Impact of self adaptive-elephant herding optimization towards neural network for facial emotion recognition","authors":"B. Devi, M. Preetha","doi":"10.3233/web-210481","DOIUrl":"https://doi.org/10.3233/web-210481","url":null,"abstract":"FACIAL expression is one of the most efficient, universal and fundamental indicators to identify their emotions and intentions in humans. Various experiments have already been performed on automatic Facial Emotion Recognition (FER) owing to useful significance in medical diagnosis, stress monitoring for drivers, sociable robots, and other human-computer interface devices. Here, this proposed framework consists of two processes namely; “(i) proposed feature extraction and (ii) classification”. Here, a major novelty relies in the initial phase (i.e. feature extraction phase), where the Proposed Local Vector Pattern (Proposed- LVP) based features are extracted. In addition to the proposed-LVP, the other Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM) based features are also extracted. Besides, the Principal Component Analysis (PCA) method is used for reducing the dimension of the features. Further, they are subjected to classification process, where Optimized Neural Network (NN) is used. More particularly, a new Improved Elephant Herding Optimization (EHO) model termed as Self Adaptive-EHO (SA-EHO) is used to train the NN model via selecting the optimal weights. At last, the proposed work performance is computed over the other traditional systems with respect to the positive measures like “accuracy, sensitivity, specificity and precision”; negative measures like “False Positive Rate (FPR), False Negative Rate (FNR) and False Discovery Rate (FDR)”; other measures like “Negative Predictive Value (NPV), F1-score and Matthew’s Correlation Coefficient (MCC)”, respectively.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116103372","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

Conflict detection in Task Heterogeneous Information Networks 任务异构信息网络中的冲突检测

Web Intell. Pub Date : 2022-04-14 DOI: 10.3233/web-210478

Zhonghui Hu, Rui Zhang, Xichang Li, Zhi-ting Yu, Xiaojie Li, Wenfeng Zhao, Xudong Zhang, Lin Li

引用次数: 0

MLIM: A CTR prediction model describing evolution law of user interest 一种描述用户兴趣演变规律的点击率预测模型

Web Intell. Pub Date : 2022-04-13 DOI: 10.3233/web-210479

Zilong Jiang, Wei Deng, Wei Dai

引用次数: 0

Causal inference for the impact of economic policy on financial and labour markets amid the COVID-19 pandemic COVID-19大流行期间经济政策对金融和劳动力市场影响的因果推理

Web Intell. Pub Date : 2022-04-08 DOI: 10.3233/web-210477

Nhi N. Y. Vo, Guandong Xu, D. Le

引用次数: 0

A comprehensive study of data intelligence in the context of big data analytics 大数据分析背景下的数据智能综合研究

Web Intell. Pub Date : 2022-04-07 DOI: 10.3233/web-210480

C. Banchhor, N. Srinivasu

引用次数: 1

Advanced ant colony algorithm for high dimensional abnormal data mining in Internet of things 基于蚁群算法的物联网高维异常数据挖掘

Web Intell. Pub Date : 2022-01-07 DOI: 10.3233/web-210471

Huixian Wang, Hongjiang Zheng

引用次数: 0

Research on dynamic and secure storage of financial data based on cloud platform 基于云平台的金融数据动态安全存储研究

Web Intell. Pub Date : 2022-01-07 DOI: 10.3233/web-210472

Jingyi Li

引用次数: 1

A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges 云计算中容器资源分配方法的综合调查:最新技术和研究挑战

Web Intell. Pub Date : 2022-01-06 DOI: 10.3233/web-210474

K. Vhatkar, G. Bhole

{"title":"A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges","authors":"K. Vhatkar, G. Bhole","doi":"10.3233/web-210474","DOIUrl":"https://doi.org/10.3233/web-210474","url":null,"abstract":"The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124000959","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

A federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean 基于多层标签均值增强的联邦学习半监督信用预测方法

Web Intell. Pub Date : 2021-12-30 DOI: 10.3233/web-210476

Lin Li, Sijie Long, Jianxiu Bi, Guowei Wang, Jianwei Zhang, Xiaohui Tao

{"title":"A federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean","authors":"Lin Li, Sijie Long, Jianxiu Bi, Guowei Wang, Jianwei Zhang, Xiaohui Tao","doi":"10.3233/web-210476","DOIUrl":"https://doi.org/10.3233/web-210476","url":null,"abstract":"Learning based credit prediction has attracted great interest from academia and industry. Different institutions hold a certain amount of credit data with limited users to build model. An institution has the requirement to obtain data from other institutions for improving model performance. However, due to the privacy protection and subject to legal restrictions, they encounter difficulties in data exchange. This affects the performance of credit prediction. In order to solve the above problem, this paper proposes a federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean, which can aggregate parameters of each institution via joint training while protecting the data privacy of each institution. Moreover, in actual production and life, there are usually more unlabeled credit data than labeled ones, and the distribution of their feature space presents multiple data-dense divisions. To deal with these, local meanNet model is proposed with a multi-layer label mean based semi-supervised deep learning network. In addition, this paper introduces a cost-sensitive loss function in the supervised part of the local mean model. Conducted on two public credit datasets, experimental results show that our proposed federated learning based approach has achieved promising credit prediction performance in terms of Accuracy and F1 measures. At the same time, the framework design mode that splits data aggregation and keys uniformly can improve the security of data privacy and enhance the flexibility of model training.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129641430","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

Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing 云计算中隐私保护的指数反狮骑士优化

Web Intell. Pub Date : 2021-12-28 DOI: 10.3233/web-210473

Nagaraju Pamarthi, N. N. Rao

{"title":"Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing","authors":"Nagaraju Pamarthi, N. N. Rao","doi":"10.3233/web-210473","DOIUrl":"https://doi.org/10.3233/web-210473","url":null,"abstract":"The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.","PeriodicalId":245783,"journal":{"name":"Web Intell.","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128441493","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