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A two-level authentication scheme for clone node detection in smart cities using Internet of things

COMPUTATIONAL INTELLIGENCE 2020年第3期36卷 1200-1220页

作者： Shanmugam, Anitha Paramasivam, Jayanthi Kongu Engn Coll Dept Informat Technol Perundurai Tamil Nadu India Kongu Engn Coll Dept Comp Sci & Engn Perundurai India

Clone node attack in IoT sensor devices remains a grave security concern as it paves the way for sinkhole, wormhole, and selective forwarding attacks. In this paper, a two-level authentication scheme named Fingerprint-based Zero-Knowledge Authentication (FZKA) algorithm is proposed to improve the detection rate of clone node among the sensor devices. In the fingerprint generation phase, the base station calculates a distinct fingerprint value for each and every node in the network by gathering neighborhood information, represented in the form of superimposed s-disjunct code matrix. The calculated fingerprint is considered as a secret value and distributed to each cluster nodes for the process of authentication. The FZKA algorithm improves the cloned node detection accuracy with minimal detection time. The simulation results highlight the cloned node detection rate of the proposed scheme by a margin of 92.5% against the existing Exponential Smoothing algorithm (ETS), Position Verification Method, and Message Verification and Passing algorithms.

关键词： authentication clone node clustering algorithm Internet of things security

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A New Intuitionistic Fuzzy c-means clustering algorithm

A New Intuitionistic Fuzzy c-means Clustering Algorithm

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International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

作者： Jiang, Hui Zhou, Xiaoguang Feng, Baisheng Zhang, Mingdong Naval Aviat Inst Flight Simulat & Training Ctr Hu Ludao 125001 LiaoNing Peoples R China

ISBN: (纸本)9781479925650

This paper presents a new intuitionistic fuzzy cmeans (IFCM) clustering algorithm by adapting a new method to calculate the hesitation degree of data point in cluster. From the definition of fuzzy entropy, if a clustering result of a data point has bigger fuzzy entropy, the clustering result should have more uncertainty. It means that we have insufficient information to deal with the clustering of a data point, so the hesitation degree of clustering result of the data point should be greater. Form this opinion, a mathematical model is applied to calculate the hesitation degree of clustering of data point based on fuzzy entropy is given. An IFCM clustering algorithms is present. Experiments are performed using two-dimensional synthetic data-sets referred from previous papers. Results have shown that proposed algorithm is not only effective for linear and nonlinear separation, but also able to describe more information comparing to fuzzy c-means clustering algorithm.

关键词： intuitionistic fuzzy set clustering algorithm fuzzy entropy

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Examining plant uptake and translocation of emerging contaminants using machine learning: Implications to food security

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 698卷 133999页

作者： Bagheri, Majid Al-jabery, Khalid Wunsch, Donald Burken, Joel G. Missouri Univ Sci & Technol Civil Architectural & Environm Engn Dept 1401 N Pine St Rolla MO 65409 USA Missouri Univ Sci & Technol Appl Computat Intelligence Lab Elect & Comp Engn Dept Rolla MO 65409 USA

When water and solutes enter the plant root through the epidermis, organic contaminants in solution either cross the root membranes and transport through the vascular pathways to the aerial tissues or accumulate in the plant roots. The accumulation of contaminants in plant roots and edible tissues is measured by root concentration factor (RCF) and fruit concentration factor (FCF). In this paper, 1) a neural network (NN) was applied to model RCF based on physicochemical properties of organic compounds, 2) correlation and significance of physicochemical properties were assessed using statistical analysis, 3) fuzzy logic was used to examine the simultaneous impacts of significant compound properties on RCF and FCF, 4) a clustering algorithm (k-means) was used to identify unique groups and discover hidden relationships within contaminants in various parts of the plants. The physicochemical cutoffs achieved by fuzzy logic for the RCF and the FCF were compared versus the cutoffs for compounds that crossed the plant root membranes and found their way into transpiration stream (measured by transpiration stream concentration factor, TSCF). The NN predicted the RCF with improved accuracy compared to mechanistic models. The analysis indicated that log K-ow, molecular weight, and rotatable bonds are the most important properties for predicting the RCF. These significant compound properties are positively correlated with RCF while they are negatively correlated with TSCF. Comparing the relationships between compound properties in various plant tissues showed that compounds detected in the edible parts have physicochemical cutoffs that are more like the compounds crossing the plant root membranes (into xylem tissues) than the compounds accumulating in the plant roots, with clear relationships to food security. The cluster analysis placed the contaminants into three meaningful groups that were in agreement with the results of fuzzy logic. (C) 2019 Elsevier B.V. All rights reserved

关键词： Plant uptake Food security Neural network Fuzzy logic clustering algorithm

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An Effective Student Grouping and Course Recommendation Strategy Based on Big Data in Education

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INFORMATION 2022年第4期13卷

作者： Guo, Yu Chen, Yue Xie, Yuanyan Ban, Xiaojuan Univ Sci & Technol Beijing Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528399 Peoples R China

Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students' cooperation ability and lifelong learning ability. Based on students' interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency-inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy.

关键词： study group division course recommendation feature vectors semantic analysis clustering algorithm

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A Mental clustering algorithm based on the Embedded Agent in Wireless Sensor Network

A Mental Clustering Algorithm based on the Embedded Agent in...

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3rd International Conference on Advanced Design and Manufacturing Engineering (ADME 2013)

作者： Jian, Yu-Mei Chen, Ming Feng, Guo-Fu Shanghai Ocean Univ Coll Informat Technol Shanghai 201306 Peoples R China

ISBN: (纸本)9783037858431

In this paper, a clustering protocol for Embedded Agent sensor network is proposed. In LEACH protocol, the selection probability P of candidate cluster head is constant without thinking about residual energy, which caused the lower residual energy easy to be the candidate cluster head. Compared with traditional structure of wireless sensor network, parameter was optimized. The confidence of the node, select probability and residual energy are the parameter of candidate cluster head, communication cost as the parameter of final cluster node. Compared with LEACH and ASCH, EACA take more parameters than LEACH. Results show that EACA can balance the energy load in the network, and effectively reduce more energy consumption.

关键词： WSN Mental factor Agent clustering algorithm

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Using Machine Learning to Efficiently Vaccinate Homebound Patients Against COVID-19: A Real-time Immunization Campaign

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JOURNAL OF MEDICAL INTERNET RESEARCH 2022年第7期24卷 e37744页

作者： Kumar, Anish Ren, Jennifer Ornstein, Katherine A. Gliatto, Peter M. Icahn Sch Med Mt Sinai New York NY USA Icahn Sch Med Mt Sinai Dept Geriatr & Palliat Med New York NY USA Icahn Sch Med Mt Sinai Inst Care Innovat Home New York NY USA Icahn Sch Med Mt Sinai Dept Med New York NY USA

In March 2021, the New York City Department of Health and Mental Hygiene (NYCDOHMH) received a supply of single-dose Janssen COVID-19 vaccines to vaccinate the city's patients who are homebound, but they needed assistance to identify and reach this population [1,2]. Mount Sinai Visiting Doctors (MSVD) is a large home-based primary care program serving more than 1200 patients who are homebound throughout Manhattan. We partnered with NYCDOHMH to vaccinate patients in our program. The administrative team generally schedules routine home visits based on zip codes, using 12 unique catchment areas covering all of Manhattan. This existing zoning system was inadequate for vaccination purposes for several reasons, mainly because these zones were too large. Furthermore, the additional task of manually scheduling patients by zone would be overwhelming for administrative staff, especially given the temporal constraints of the Janssen vaccine (ie, doses expired in 6 hours). In response, we developed a system to geographically cluster patients to efficiently vaccinate our homebound patients against COVID-19. © Anish Kumar, Jennifer Ren, Katherine A Ornstein, Peter M Gliatto.

关键词： home care covid vaccination COVID-19 machine learning vaccine geographic cluster patient data clustering algorithm geospatial digital surveillance public health logistic operation electronic health record integration

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Application of K-means algorithm to Werner deconvolution solutions for depth and image estimations

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HELIYON 2022年第11期8卷 e11665页

作者： Eshimiakhe, Daniel Lawal, Kola Ahmadu Bello Univ Dept Phys Zaria Nigeria

One of the most popular techniques for computer-assisted solution estimates for magnetics and gravity field data is Werner deconvolution. The approaches frequently produce erratic results and may not always forecast the maximum number of the geologic entity that produces them due to the intrinsic instability of potential field data. This led to the application of the K-means machine learning algorithm to further enhance the detection of the geologic potential field-generated bodies. Two substances that resembled dikes were combined to form a synthetic magnetic model. Random noise was added to the synthetic data, to make the solutions a bit more complex. Werner deconvolution technique was applied to the synthetic model to generate solutions. K-means unsupervised machine learning algorithm was applied to the generated solutions created by the synthetic data. We further applied this algorithm to real data sets from a mining site. The clustering result shows a good spatial corre-spondence with the geologic model, and the method was able to estimate the precise location and depth of the dike bodies. The proposed method is entirely data-driven and has proven to work in the presence of noise.

关键词： clustering algorithm Potential field Werner deconvolution

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Multiscale topology optimization for non-uniform microstructures with hybrid cellular automata

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2020年第2期62卷 757-770页

作者： Jia, Jiao Da, Daicong Loh, Cha-Liang Zhao, Haibin Yin, Sha Xu, Jun Beihang Univ Vehicle Energy & Safety Lab VESL Beijing 100191 Peoples R China Beihang Univ Sch Transportat Sci & Engn Dept Automot Engn Beijing 100191 Peoples R China Univ Paris Est Lab Modelisat & Simulat Multi Echelle MSME UMR CNRS 8208 5 Bd Descartes F-77454 Marne La Vallee France Changshu Automot Trim Co Ltd Changshu 215500 Peoples R China

Based on hybrid cellular automata (HCA), we present a two-scale optimization model for heterogeneous structures with non-uniform porous cells at the microscopic scale. The method uses theK-means clustering algorithm to achieve locally nonperiodicity through easily obtained elemental strain energy. This energy is used again for a two-scale topological optimization procedure without sensitivity analysis, avoiding drastically the computational complexity. Both the experimental tests and numerical results illustrate a significant increase in the resulting structural stiffness with locally nonperiodicity, as compared to using uniform periodic cells. The effects of parameters such as clustering number and adopted method versus classical Optimality Criteria (OC) are discussed. Finally, the proposed methodology is extended to 3D two-scale heterogeneous structure design.

关键词： Concurrent optimization Hybrid cellular automata clustering algorithm Non-uniform microstructure

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MULTI-CORE BASED PARALLEL N-PATH LABELING HKM clustering algorithm

MULTI-CORE BASED PARALLEL N-PATH LABELING HKM CLUSTERING ALG...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Liao, Kaiyang Liu, Guizhong Qiao, Zhen Liu, Chaoteng Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

ISBN: (纸本)9781479916030

The detection of useful patterns in large datasets has attracted considerable interest recently. The hierarchical K-means clustering algorithm (HKM) is very efficient in large scale data analysis. It has been extensively used for building visual vocabulary in large scale image/video retrieval. However, the accuracy and speed of HKM still have room for improvement. In this paper, we propose a Parallel N-path labeling HKM clustering algorithm (PNHKM) which improves on the HKM clustering algorithm in the following ways. Firstly, we adopt a Greedy N-best Paths Labeling (GNPL) method to improve the clustering accuracy. Secondly, we focus on developing a parallel clustering algorithm for multicore processors. Our results confirm that the PNHKM is much faster and more effective.

关键词： Parallel algorithm clustering algorithm video retrieval

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Investigating populations in generalized Darwinism

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BIOLOGY & PHILOSOPHY 2020年第1期35卷 1-27页

作者： Baraghith, Karim Heinrich Heine Univ Dusseldorf Dusseldorf Ctr Log & Philosophy Sci Dept Philosophy Univ Str 1 D-40225 Dusseldorf Germany

Darwinian evolution is a population-level phenomenon. This paper deals with a structural population concept within the framework of generalized Darwinism (GD), resp. within a generalized theory of evolution. According to some skeptical authors, GD is in need of a valid population concept in order to become a practicable research program. Populations are crucial and basic elements of any evolutionary explanation-biological or cultural-and have to be defined as clearly as possible. I suggest the "causal interactionist population concept" (CIPC), by R. Millstein for this purpose, and I will try to embed the approach into a generalized evolutionary perspective by mathematically formalizing its key definitions. Using graph-theory, (meta-) populations as described in the CIPC can serve as proper clusters of evolutionary classification based on the rates of interactions between their elements. I will introduce the concept of a cohesion index (CI) as a measurement of possible population candidates within a distribution of elements. The strength of this approach lies in its applicability and interactions are relatively easy to observe. Furthermore, problems of clustering tokens (e.g. of cultural information) via typicality, e.g. their similarity in intrinsic key characteristics, can be avoided, because CIPC is a (mainly) external approach. However, some formal problems and conceptual ambiguities occur within a simple version of this CI, which will be addressed in this paper as well as some possible applications.

关键词： Population concept Generalized Darwinism Cultural evolution CIPC Graph theory clustering algorithm

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