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International Core Journal of Engineering 2022年第8期8卷 867-876页

作者： Jiyun Wu Yaping Cui Qiangqiang Gao Lu Lin Yan Ou Yanrong Zhan

Objective To explore the law of Chinese herbal fumigation and washing in the treatment of perianal eczema through data mining. Methods China National Knowledge Network (CNKI), VIP Chinese Journal Server (VIP), Wanfang Statistical Information Public Service network platform (Wanfang Statistics), National Biomedical Literature Research Information Service Network Management System (SinoMed) were retrieved from the database construction to May 2020 On the research paper on the treatment of perianal eczema by traditional Chinese medicine fumigating and washing. According to the prescriptions recorded in the collected articles, Microsoft Excel or WPS was used to establish a database to analyze the drug frequency, sexual taste and meridian return, etc. IBM SPSS Statistics 18.0 statistical software was used to analyze the data of association rules based on association rules and Apriori *** 286 articles were included, including 283 prescriptions, including 155 traditional Chinese medicines, 22 of which were more frequently used than 35. Sophora flavescens was the most frequently used drug. The medicinal taste of 155 is mainly bitter cold and simmering temperature, and mainly goes to the liver meridian. The efficacy classification is mainly heat-clearing medicine. In the association rule, the core drug pair of Sophora flavescens and Phellodendron phellodendri was the association rule (support = 73.85).Conclusion TCM fumigating and washing prescription drugs for perianal eczema are mainly heat-clearing drugs, accompanied by poison, insecticide and antipruritic drugs, antipruritic drugs, etc. Sophora flavescens, Phellodendron phellodendri, Cnidium fruit, Difuzi fruit, Fructus diorrhizae, rhizoma dioscoreae, parsnip and so on are frequently used.

关键词： Perianal Eczema Fumigation of Traditional Chinese Medicine Data Mining clustering algorithm

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Network embedding architecture using laplace regularization-non-negative matrix factorization for virtualization

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MICROPROCESSORS AND MICROSYSTEMS 2021年 81卷 103616-103616页

作者： Sudhakar, Sengan Kanmani, P. Amudha, K. Raja, P. Vishnu Dubey, Anil Kumar Sulthana, A. Razia Subramaniyaswamy, V Priya, V Sree Sakthi Engn Coll Dept Comp Sci & Engn Coimbatore 641104 Tamil Nadu India SRM Inst Sci & Technol Dept Comp Sci Chennai 603203 Tamil Nadu India Kongunadu Coll Engn & Technol Dept Elect & Commun Engn Tiruchy 621215 Tamil Nadu India Kongu Engn Coll Dept Comp Sci & Engn Perundurai 638060 Tamil Nadu India ABES Engn Coll Dept Comp Sci & Engn Ghaziabad Ncr India Birla Inst Sci & Technol Pilani Dept Comp Sci & Engn Dubai Campus Dubai 45055 U Arab Emirates SASTRA Deemed Univ Sch Comp Thanjavur 613401 Tamil Nadu India Mahendra Inst Technol Dept Comp Sci & Engn Namakkal 637503 Tamil Nadu India

While modeling the applications for a problem in cloud computing, researchers and scientists frequently use graphs as abstractions. Graphs provide structural models that make it possible to analyze and understand how many separate systems act together. The omnipresence in cloud computing systems is increasing information networks. The graph embedding algorithms preserve the microscopic structure over the cloud, and many of them miss the mesoscopic structure of the networks. In this paper, asymmetric non-negative Laplace regularization for cloud platform and matrix factorization is implemented for network embedding. The proposed algorithm preserves the mesoscopic structure in cloud computing, the learned model from the Laplace, and matrix factorization. The embedded cloud network can be used for link prediction, vertex recommendation, node clustering. It is a scalable algorithm for higher proximity preserving along with community structure. The correctness and convergence are measures as performance parameters in the network. Based factorization is used for updating the rules. The experimental study shows that the proposed system is well-organized compared to the existing process in structure preservation in cloud computing.

关键词： Cloud computing Graph embedding network Homogenous network clustering algorithm Embedded System

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Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System

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Procedia Computer Science 2015年 60卷 750-759页

作者： Siti Noraini Sulaiman Nor Ashidi Mat-Isa Nor Hayati Othman Fadzil Ahmad Faculty of Electrical Engineering Universiti Teknologi MARA Penang Campus 13500 Permatang Pauh Penang MALAYSIA. Imaging and Intelligent System Research Team (ISRT) School of Electrical & Electronics Engineering Universiti Sains Malaysia Engineering Campus 14300 Nibong Tebal Penang MALAYSIA. Clinical Research Platform School of Medical Sciences Universiti Sains Malaysia 16150 Kubang Kerian Kelantan MALAYSIA

Cervical cancer has caused many deaths each year. Screening tests, such as Pap smear test used for the detection of the precancerous stage are able to avoid the occurrence of cervical cancer. However, the Pap smear test has several disadvantages such as less effective slides preparation and human error. Therefore, a computer-aided diagnosis system is introduced as a solution to the problem. One of the diagnostic systems that has been built is NeuralPap. However, the NeuralPap performance is limited by several constraints. This research proposed several new image processing algorithms to reduce these constraints. The Adaptive Fuzzy-k-Means (AFKM) clustering algorithm is proposed to replace the Moving k-Means (MKM) to segment Pap smear images into the nucleus, cytoplasm and background regions. Next, the feature extraction algorithm based on pseudo colouring called the Pseudo Colour Feature Extraction (PCFE) manual and Semi-Automatic PCFE are designed to replace the Region Growing Based Feature Extraction (RGBFE) which uses monochromatic images. This research is a step forward compared with the NeuralPap system by proposing the feature extraction algorithm for overlapping cells by combining the concept of colour space with Semi-Automatic PCFE algorithm. In addition, this research has also suggested the AFKM algorithm as a new centre positioning algorithm for the Radial Basis Function (RBF) and Hybrid RBF (HRBF) networks replacing the MKM algorithm. The entire proposed algorithm has been proven to produce better performance than the corresponding algorithm used in the NeuralPap. In addition, the combination of all algorithms has managed to increase the accuracy of the classification of cervical cancer to 76.35%, compared with 73.40% which is obtained from the previous NeuralPap system.

关键词： Diagnosis System Neural Network Region Growing Feature Extraction Cervical Cancer clustering algorithm

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Cluster-Based Quality-Aware Adaptive Data Compression for Streaming Data

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ACM JOURNAL OF DATA AND INFORMATION QUALITY 2017年第1期9卷 2-2页

作者： Basheer, Aseel Sha, Kewei Univ Baghdad Coll Sci Women Dept Comp Sci Baghdad Iraq Univ Houston Dept Comp Sci Houston TX 77058 USA Univ Houston Cyber Secur Inst Houston TX 77058 USA

Wireless sensor networks (WSNs) are widely applied in data collection applications. Energy efficiency is one of the most important design goals of WSNs. In this article, we examine the tradeoffs between the energy efficiency and the data quality. First, four attributes used to evaluate data quality are formally defined. Then, we propose a novel data compression algorithm, Quality-Aware Adaptive data Compression (QAAC), to reduce the amount of data communication to save energy. QAAC utilizes an adaptive clustering algorithm to build clusters from dataset;then a code for each cluster is generated and stored in a Huffman encoding tree. The encoding algorithm encodes the original dataset based on the Haffman encoding tree. An improvement algorithm is also designed to reduce the information loss when data are compressed. After the encoded data, the Huffman encoding tree and parameters used in the improvement algorithm have been received at the sink, a decompression algorithm is used to retrieve the approximation of the original dataset. The performance evaluation shows that QAAC is efficient and achieves a much higher compression ratio than lossy and lossless compression algorithms, while it has much smaller information loss than lossy compression algorithms.

关键词： Wireless sensor networks data quality adaptive clustering algorithm energy efficiency data compression

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Meshless numerical manifold method with novel subspace tracking and CSS locating techniques for slope stability analysis

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COMPUTERS AND GEOTECHNICS 2024年 166卷

作者： Cao, Xitailang Lin, Shan Liang, Zenglong Guo, Hongwei Zheng, Hong Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China Hong Kong Polytech Univ PolyU Dept Civil & Environm Engn Hung Hom Kowloon Hong Kong Peoples R China

The meshless numerical manifold method (MNMM) inherits the advantages of handling continuous and discontinuous problems uniformly, improving interpolation precision, and avoiding linear dependence issues. This paper refines the MNMM method for slope stability analysis to overcome the dependence and sensitivity of the strength reduction method of the finite element method on the number of finite element meshes and the algorithm for solving the nonlinear equations when calculating slopes with soft and weak interlayers. First, the strength reduction method (SRM) was incorporated into MNMM to create the numerical model called MNMM-SRM. Based on the MNMM-SRM, a complementary Mohr-Coulomb (M-C) control equation compliant with Koiter's rule of multi-yielding surfaces was established. In the principal stress space, the subspace tracking method was used to track the eigenvalues and eigenvectors of the stress tensor, thereby addressing the corner problem of M-C multi-yield surfaces. Combining the k-means clustering algorithm with wavelet transformation and employing the slope's total nodal displacement field as an input variable, an intelligent and efficient critical slip surface (CSS) automatic extraction method was developed. Three typical cases were used to validate the accuracy of the numerical model. This numerical model has a wide range of potential applications in slope stability research.

关键词： Meshless Numerical Manifold Method Strength reduction method Slope stability analysis clustering algorithm Wavelet transformation

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The potential of Wi-Fi data to estimate bus passenger mobility

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2023年第1期192卷

作者： Lea, Fabre Caroline, Bayart Bonnel, Patrick Mony, Nicolas Univ Lumiere Lyon 2 Lab Amenagement Econ Transports 3 Rue Maurice Audin F-69120 Vaulx En Velin France Univ Lumiere Lyon 1 Lab Sci Actuarielles & Financieres 50 Ave Tony Garnier F-69007 Lyon France Explain 36 Bd Canuts F-69004 Lyon France

Last decades have been marked by deep socio-economic transformations, an uneven evolution of transport demand in main urban areas and the emergence of new and more sustainable modes of transportation (carpooling, self-services bicycles). These changes have strongly impacted the interaction between service supply and demand in the transport industry. In this context, passive data as Wi-Fi and Bluetooth become a key source of information to understand individual mobility behaviors and ensure the sustainable development of transport infrastructures. In this paper, we present a framework that uses disruptive technology to collect passive data in buses, continuously and at a lower cost than traditional mobility surveys. Previous research, conducted over a more limited spatial and temporal framework, uses filtering methods, which do not allow the results to be replicated. This study uses artificial intelligence to sort transmitted signals, get transit ridership and build Origin-Destination matrices. Its originality consists in providing a concrete, automatic and replicable method to transport operators. The comparison of the results with other data sources confirms the relevance of the presented algorithms in demand forecasting. Therefore, our findings provide interesting insights for data-driven decision making and service quality management in urban public transport.

关键词： Passive data analytics Wi-Fi sensors clustering algorithm Origin-Destination matrices Travel behavior Public transport demand

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Multi-resource scheduling and routing for emergency recovery operations

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2020年 50卷 101780页

作者： Bodaghi, Behrooz Shahparvari, Shahrooz Fadaki, Masih Lau, Kwok Hung Ekambaram, Palaneeswaran Chhetri, Prem Swinburne Univ Technol Fac Sci Engn & Technol Hawthorn Vic Australia RMIT Univ Sch Accounting Informat Syst & Supply Chain Melbourne Vic Australia

Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations.

关键词： Expendable resources Non-expendable resources Emergency recovery operations Multi-resource scheduling Heuristics algorithms clustering algorithm Coronavirus outbreak case

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Multicriteria Decision Making Approach for Cluster Validation

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Procedia Computer Science 2012年 9卷 1283-1291页

作者： Yi Peng Yong Zhang Gang Kou Jun Li Yong Shi School of Management and Economics University of Electronic Science and Technology of China Chengdu 610054 P. R. China CAS Research Center on Fictitious Economy and Data Sciences Beijing100080 China College of Information Science & Technology University of Nebraska at Omaha Omaha NE 68182 USA

This paper proposes a multiple criteria decision making (MCDM)-based framework to address two fundamental issues in cluster validation: 1) evaluation of clustering algorithms and 2) estimation of the optimal cluster number for a given data set. Since both issues involve more than one criterion, they can be modeled as multiple criteria decision making (MCDM) problems. The proposed framework is examined by an experimental study. The results suggest that MCDM methods are practical tools for the evaluation of clustering algorithms. In addition, the selected MCDM method, PROMETHEE II can estimate the optimal numbers of clusters for ten out of fifteen datasets by adjusting the weights of criteria.

关键词： clustering algorithm cluster validation multiple criteria decision making (MCDM)

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Change point estimation in a dynamic stochastic block model

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 4330-4388页

作者： Monika Bhattacharjee Moulinath Banerjee George Michailidis Informatics Institute University of Florida Gainesville Department of Statistics University of Michigan Ann Arbor Informatics Institute Department of Statistics University of Florida Gainesville

We consider the problem of estimating the location of a single change point in a network generated by a dynamic stochastic block model mechanism. This model produces community structure in the network that exhibits change at a single time epoch. We propose two methods of estimating the change point, together with the model parameters, before and after its occurrence. The first employs a least-squares criterion function and takes into consideration the full structure of the stochastic block model and is evaluated at each point in time. Hence, as an intermediate step, it requires estimating the community structure based on a clustering algorithm at every time point. The second method comprises the following two steps: in the first one, a least-squares function is used and evaluated at each time point, but ignoring the community structure and only considering a random graph generating mechanism exhibiting a change point. Once the change point is identified, in the second step, all network data before and after it are used together with a clustering algorithm to obtain the corresponding community structures and subsequently estimate the generating stochastic block model parameters. The first method, since it requires knowledge of the community structure and hence clustering at every point in time, is significantly more computationally expensive than the second one. On the other hand, it requires a significantly less stringent identifiability condition for consistent estimation of the change point and the model parameters than the second method; however, it also requires a condition on the misclassification rate of misallocating network nodes to their respective communities that may fail to hold in many realistic settings. Despite the apparent stringency of the identifiability condition for the second method, we show that networks generated by a stochastic block mechanism exhibiting a change in their structure can easily satisfy this condition under a multitude of scenar

关键词： stochastic block model Erőds-Rényi random graph change point edge probability matrix community detection estimation clustering algorithm convergence rate

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A Novel Methodology for Extracting and Evaluating Therapeutic Movements in Game-Based Motion Capture Rehabilitation Systems

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JOURNAL OF MEDICAL SYSTEMS 2018年第12期42卷 255-255页

作者： Yang, Zhichao Rafiei, Mohammad H. Hall, Alexis Thomas, Caroline Midtlien, Hali A. Hasselbach, Alexander Adeli, Hojjat Gauthier, Lynne V. Univ Massachusetts Coll Informat & Comp Sci Amherst MA 01003 USA Ohio State Univ Dept Phys Med & Rehabil Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Ohio State Univ Dept Neurol Columbus OH 43210 USA Ohio State Univ Dept Neurosci Columbus OH 43210 USA Univ Massachusetts Dept Phys Therapy Lowell MA 01854 USA

Virtual rehabilitation yields outcomes that are at least as good as traditional care for improving upper limb function and the capacity to carry out activities of daily living. Due to the advent of low-cost gaming systems and patient preference for game-based therapies, video game technology will likely be increasingly utilized in physical therapy practice in the coming years. Gaming systems that incorporate low-cost motion capture technology often generate large datasets of therapeutic movements performed over the course of rehabilitation. An infrastructure has yet to be established, however, to enable efficient processing of large quantities of movement data that are collected outside of a controlled laboratory setting. In this paper, a methodology is presented for extracting and evaluating therapeutic movements from game-based rehabilitation that occurs in uncontrolled and unmonitored settings. By overcoming these challenges, meaningful kinematic analysis of rehabilitation trajectory within an individual becomes feasible. Moreover, this methodological approach provides a vehicle for analyzing large datasets generated in uncontrolled clinical settings to enable better predictions of rehabilitation potential and dose-response relationships for personalized medicine.

关键词： Stroke Telerehabilitation Serious games Kinect Motion capture Motor rehabilitation Hemiparesis Signal processing clustering algorithm Probability density function

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