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3-Multi ranked encryption with enhanced security in cloud computing

Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio artificial intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of computer science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and computer science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina dept.of computer science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when requested.We refer to a model that satisfies all of the conditions a 3-multi ranked search model.However,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation center.That is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real life.In this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security assumptions.The proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same keyword.Moreover,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different key.Our evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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Automated classification technique for edge-on galaxies based on mathematical treatment of brightness data

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Research in Astronomy and Astrophysics 2021年第10期21卷 231-238页

作者： Mohamed Eassa Ibrahim Mohamed Selim Walid Dabour Passent Elkafrawy computer science Department Integrated Thebes InstituteCairoEgypt Faculty of computer&artificial intelligence Sadat City UniversitySadat CityEgypt Math and computer science dept. Faculty of ScienceMenoufia UniversityEgypt computer science dept. Taibah UniversityAlula BranchKingdom of Saudi Arabia School of Information Technology and computer science(ITCS) Nile UniversityEgypt

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy.Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels.In the current work,a novel technique for the classification of edge-on galaxies has been developed.This technique is based on the mathematical treatment of galaxy brightness data from their images.A special treatment for galaxies’brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars.A novel slimness weighting factor is developed to classify edge-on galaxies based on their slimness.The technique has the capacity to be optimized for different catalogs with different brightness levels.In the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this catalog.Upon classification of the full set of 4458 galaxies from the EFIGI catalog,an accuracy of 97.5% has been achieved,with an average processing time of about 0.26 seconds per galaxy on an average laptop.

关键词： techniques:image processing methods:data analysis galaxies:formation

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Survey of feature selection and extraction techniques for stock market prediction

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Financial Innovation 2023年第1期9卷 667-691页

作者： Htet Htet Htun Michael Biehl Nicolai Petkov Bernoulli Institute for Mathematics Computer ScienceArtificial IntelligenceUniversity of GroningenGroningenThe Netherlands

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

关键词： Feature selection Feature extraction Dimensionality reduction Stock market forecasting Machine learning

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Expression Recognition Method Based on Convolutional Neural Network and Capsule Neural Network

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computers, Materials & Continua 2024年第4期79卷 1659-1677页

作者： Zhanfeng Wang Lisha Yao School of computer science and artificial intelligence Chaohu UniversityHefei238000China School of Big Data and artificial intelligence Anhui Xinhua UniversityHefei230088China

Convolutional neural networks struggle to accurately handle changes in angles and twists in the direction of images,which affects their ability to recognize patterns based on internal feature levels. In contrast, CapsNet overcomesthese limitations by vectorizing information through increased directionality and magnitude, ensuring that spatialinformation is not overlooked. Therefore, this study proposes a novel expression recognition technique calledCAPSULE-VGG, which combines the strengths of CapsNet and convolutional neural networks. By refining andintegrating features extracted by a convolutional neural network before introducing theminto CapsNet, ourmodelenhances facial recognition capabilities. Compared to traditional neural network models, our approach offersfaster training pace, improved convergence speed, and higher accuracy rates approaching stability. Experimentalresults demonstrate that our method achieves recognition rates of 74.14% for the FER2013 expression dataset and99.85% for the CK+ expression dataset. By contrasting these findings with those obtained using conventionalexpression recognition techniques and incorporating CapsNet’s advantages, we effectively address issues associatedwith convolutional neural networks while increasing expression identification accuracy.

关键词： Expression recognition capsule neural network convolutional neural network

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Remarks on “A new non-specificity measure in evidence theory based on belief intervals”

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Chinese Journal of Aeronautics 2018年第3期31卷 529-533页

作者： Joaquín ABELLAN Department of computer science and artificial intelligence University of Granada

Two types of uncertainty co-exist in the theory of evidence： discord and non-specificity.From 90s, many mathematical expressions have arisen to quantify these two parts in an evidence.An important aspect of each measure presented is the verification of a coherent set of properties.About non-specificity, so far only one measure verifies an important set of those properties. Very recently, a new measure of non-specificity based on belief intervals has been presented as an alternative measure that quantifies a similar set of properties（Yang et al., 2016）. It is shown that the new measure really does not verify two of those important properties. Some errors have been found in their corresponding proofs in the original publication.

关键词： Additivity Imprecise probabilities Non-specificity Subadditivity Theory of evidence Uncertainty measures

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EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

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computers, Materials & Continua 2023年第10期77卷 557-582页

作者： Mohammed Aly Abdullah Shawan Alotaibi Department of artificial intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBadr City11829Egypt computer science Department Shaqra UniversityShaqra City11961Saudi Arabia

Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes.Manual segmentation is crucial but time-consuming.Deep learning methods have emerged as key players in automating brain tumor segmentation.In this paper,we propose an efficient modified U-Net architecture,called EMU-Net,which is applied to the BraTS 2020 dataset.Our approach is organized into two distinct phases:classification and segmentation.In this study,our proposed approach encompasses the utilization of the gray-level co-occurrence matrix(GLCM)as the feature extraction algorithm,convolutional neural networks(CNNs)as the classification algorithm,and the chi-square method for feature selection.Through simulation results,the chi-square method for feature selection successfully identifies and selects four GLCM features.By utilizing the modified U-Net architecture,we achieve precise segmentation of tumor images into three distinct regions:the whole tumor(WT),tumor core(TC),and enhanced tumor(ET).The proposed method consists of two important elements:an encoder component responsible for down-sampling and a decoder component responsible for up-sampling.These components are based on a modified U-Net architecture and are connected by a bridge section.Our proposed CNN architecture achieves superior classification accuracy compared to existing methods,reaching up to 99.65%.Additionally,our suggested technique yields impressive Dice scores of 0.8927,0.9405,and 0.8487 for the tumor core,whole tumor,and enhanced tumor,respectively.Ultimately,the method presented demonstrates a higher level of trustworthiness and accuracy compared to existing methods.The promising accuracy of the EMU-Net study encourages further testing and evaluation in terms of extrapolation and generalization.

关键词： MR images GLCM SVM segmentation U-Net CNN

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Strong fault prognosability of partially-observed discrete event systems

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science China(Information sciences) 2023年第3期66卷 301-302页

作者： Yingrui ZHOU Zengqiang CHEN Zhongxin LIU Zhipeng ZHANG College of artificial intelligence Nankai University School of computer science and Engineering Tianjin University of Technology

Dear editor, As an important model to analyze cyber-physical systems,the study of discrete event systems has always been prevailing [1–4]. Especially, the problem of fault prognosis becomes a crucial subject for its applications in security and maintenance [5]. In decentralized fault prognosis, the given system can be monitored by several agents and each agent sends its local observation to the global prognoser,

关键词： PROGNOSIS

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A Novel Predictive Model for Edge Computing Resource Scheduling Based on Deep Neural Network

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computer Modeling in Engineering & sciences 2024年第4期139卷 259-277页

作者： Ming Gao Weiwei Cai Yizhang Jiang Wenjun Hu Jian Yao Pengjiang Qian School of artificial intelligence and computer science Jiangnan UniversityWuxi214122China The Center for Intelligent Systems and Applications Research School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi214122China School of Information Engineering Huzhou UniversityHuzhou313000China

Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.

关键词： Edge computing resource scheduling predictive models

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Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception

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Big Data Mining and Analytics 2021年第4期4卷 223-232页

作者： Changjie Wang Zhihua Li Benjamin Sarpong Department of computer science and Technology School of Artificial Intelligence and Computer ScienceJiangnan UniversityWuxi 214122China

Identity-recognition technologies require assistive equipment,whereas they are poor in recognition accuracy and expensive.To overcome this deficiency,this paper proposes several gait feature identification algorithms.First,in combination with the collected gait information of individuals from triaxial accelerometers on smartphones,the collected information is preprocessed,and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset;then,with the multimodal characteristics of the collected biological gait information,a Convolutional Neural Network based Gait Recognition(CNN-GR)model and the related scheme for the multimodal features are developed;at last,regarding the proposed CNN-GR model and scheme,a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed.Experimental results show that the proposed algorithms perform well in recognition accuracy,the confusion matrix,and the kappa statistic,and they have better recognition scores and robustness than the compared algorithms;thus,the proposed algorithm has prominent promise in practice.

关键词： gait recognition person identification deep learning multimodal feature fusion

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Densely nested top-down flows for salient object detection

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science China(Information sciences) 2022年第8期65卷 57-70页

作者： Chaowei FANG Haibin TIAN Dingwen ZHANG Qiang ZHANG Jungong HAN Junwei HAN School of artificial intelligence Xidian University School of Mechano-Electronic Engineering Xidian University Brain and artificial intelligence Laboratory School of AutomationNorthwestern Polytechnical University Department of computer science Aberystwyth University

With the goal of identifying pixel-wise salient object regions from each input image, salient object detection(SOD) has been receiving great attention in recent years. One kind of mainstream SOD method is formed by a bottom-up feature encoding procedure and a top-down information decoding procedure. While numerous approaches have explored the bottom-up feature extraction for this task, the design of top-down flows remains under-studied. To this end, this paper revisits the role of top-down modeling in salient object detection and designs a novel densely nested top-down flows(DNTDF)-based framework. In every stage of DNTDF, features from higher levels are read in via the progressive compression shortcut paths(PCSPs). The notable characteristics of our proposed method are as follows.(1) The propagation of high-level features which usually have relatively strong semantic information is enhanced in the decoding procedure.(2) With the help of PCSP, the gradient vanishing issues caused by non-linear operations in top-down information flows can be alleviated.(3) Thanks to the full exploration of high-level features, the decoding process of our method is relatively memory-efficient compared to those of existing methods. Integrating DNTDF with EfficientN et, we construct a highly light-weighted SOD model, with very low computational complexity. To demonstrate the effectiveness of the proposed model, comprehensive experiments are conducted on six widely-used benchmark datasets. The comparisons to the most state-of-the-art methods as well as the carefully-designed baseline models verify our insights on the top-down flow modeling for SOD.

关键词： salient object detection top-down flow densely nested framework convolutional neural networks

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