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An Automatic Threshold Selection Using ALO for Healthcare duplicate Record detection with Reciprocal Neuro-Fuzzy Inference System

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Computers, Materials & Continua 2023年第3期74卷 5821-5836页

作者： Ala Saleh Alluhaidan Pushparaj Anitha Subbappa Ved Prakash Mishra P.V.Chandrika Anurika Vaish Sarthak Sengupta Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhKSA.P.O.Box:8442811671 Department of Electronics and Communication Engineering National Institute of Technical Teacher Training and ResearchChandigarh160019India Department of Periodontology JSS Dental College&HospitalJSS Academy of Higher Education and ResearchMysuru570015India Department of Computer Science and Engineering Amity UniversityDubai345019United Arab Emirates Department of Management/Economics Prin LN Welingkar Institute of Management Development and ResearchMumbai400019India Department of Management Studies Indian Institute of Information TechnologyAllahabad211012India

ESystems based on EHRs(Electronic health records)have been in use for many years and their amplified realizations have been felt *** still have been pioneering collections of massive volumes of health *** detections involve discovering records referring to the same practical components,indicating tasks,which are generally dependent on several input parameters that experts *** linkage specifies the issue of finding identical records across various data *** similarity existing between two records is characterized based on domain-based similarity functions over different ***-duplication of one dataset or the linkage of multiple data sets has become a highly significant operation in the data processing stages of different data mining *** objective is to match all the records associated with the same *** measures have been in use for representing the quality and complexity about data linkage algorithms,and many other novel metrics have been *** outline of the problem existing in themeasurement of data linkage and de-duplication quality and complexity is *** article focuses on the reprocessing of health data that is horizontally divided among data custodians,with the purpose of custodians giving similar features to sets of *** first step in this technique is about an automatic selection of training examples with superior quality from the compared record pairs and the second step involves training the reciprocal neuro-fuzzy inference system(RANFIS)*** the Optimal Threshold classifier,it is presumed that there is information about the original match status for all compared record pairs(i.e.,Ant Lion Optimization),and therefore an optimal threshold can be computed based on the respective ***,Clinical Decision(CD),and Cork Open Research Archive(CORA)data repository help analyze the proposed method with evaluated benchmarks with current techniques.

关键词： duplicate detection healthcare record linkage dataset pre-processing reciprocal neuro-fuzzy inference system and ant lion optimization fuzzy system

Detecting duplicate Contributions in Pull-Based Model CombiningTextual and Change Similarities

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Journal of Computer Science & Technology 2021年第1期36卷 191-206页

作者： Zhi-Xing Li Yue Yu Tao Wang Gang Yin Xin-Jun Mao Huai-Min Wang Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology Changsha 410073China Laboratory of Software Engineering for Complex Systems College of ComputerNational University of Defense TechnologyChangsha 410073China

Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging *** pull-based development model,as the state-of-art collaborative development mechanism,provides high openness and transparency to improve the visibility of contributors'***,duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this *** not detected in time,duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant *** this paper,we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission *** a new-arriving contribution,we first compute textual similarity and change similarity between it and other existing *** then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change *** evaluation shows that 83.4%of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8%using only textual similarity and 78.2%using only change similarity.

关键词： Pull-request duplicate detection textual similarity change similarity

Improved Approximate detection of duplicates for Data Streams Over Sliding Windows

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Journal of Computer Science & Technology 2008年第6期23卷 973-987页

作者：沈鸿张育 Department of Computer Science and Technology University of Science and Technology of China School of Computer Science University of Adelaide

Detecting duplicates in data streams is an important problem that has a wide range of applications. In general, precisely detecting duplicates in an unbounded data stream is not feasible in most streaming scenarios, and, on the other hand, the elements in data streams are always time sensitive. These make it particular significant approximately detecting duplicates among newly arrived elements of a data stream within a fixed time frame. In this paper, we present a novel data structure, Decaying Bloom Filter （DBF）, as an extension of the Counting Bloom Filter, that effectively removes stale elements as new elements continuously arrive over sliding windows. On the DBF basis we present an efficient algorithm to approximately detect duplicates over sliding windows. Our algorithm may produce false positive errors, but not false negative errors as in many previous results. We analyze the time complexity and detection accuracy, and give a tight upper bound of false positive rate. For a given space G bits and sliding window size W, our algorithm has an amortized time complexity of O（√G/W）. Both analytical and experimental results on synthetic data demonstrate that our algorithm is superior in both execution time and detection accuracy to the previous results.

关键词： data stream duplicate detection bloom filter approximate query sliding window

Learning to combine multiple string similarity metrics for effective toponym matching

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International Journal of Digital Earth 2018年第9期11卷 913-938页

作者： Rui Santos Patricia Murrieta-Flores Bruno Martins Instituto Superior Técnico and INESC-ID University of LisbonLisbonPortugal Digital Humanities Research Center University of ChesterChesterUK

Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching,that is,the problem of matching place names that share a common *** this article,we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching *** also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics,which has the natural advantage of avoiding the manual tuning of similarity *** with a very large dataset show that the performance differences for the individual similarity metrics are relatively small,and that carefully tuning the similarity threshold is important for achieving good *** methods based on supervised machine learning,particularly when considering ensembles of decision trees,can achieve good results on this task,significantly outperforming the individual similarity metrics.

关键词： Toponym matching supervised learning string similarity metrics duplicate detection ensemble learning geographic information retrieval

Leach： an automatic learning cache for inline primary deduplication system

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Frontiers of Computer Science 2014年第2期8卷 175-183页

作者： Bin LIN Shanshan LI Xiangke LIAO Jing ZHANG Xiaodong LIU School of Computer National University of Defense Technology Changsha 410073 China

Deduplication technology has been increasingly used to reduce storage costs. Though it has been successfully applied to backup and archival systems, existing techniques can hardly be deployed in primary storage systems due to the associated latency cost of detecting duplicated data, where every unit has to be checked against a substantially large fin- gerprint index before it is written. In this paper we introduce Leach, for inline primary storage, a self-learning in-memory fingerprints cache to reduce the writing cost in deduplica- tion system. Leach is motivated by the characteristics of real- world I/O workloads： highly data skew exist in the access patterns of duplicated data. Leach adopts a splay tree to or- ganize the on-disk fingerprint index, automatically learns the access patterns and maintains hot working sets in cache mem- ory, with a goal to service a majority of duplicated data de- tection. Leveraging the working set property, Leach provides optimization to reduce the cost of splay operations on the fin- gerprint index and cache updates. In comprehensive experi- ments on several real-world datasets, Leach outperforms con- ventional LRU （least recently used） cache policy by reducing the number of cache misses, and significantly improves write performance without great impact to cache hits.

关键词： deduplication duplicate detection splay tree,cache

HisTrace:A System for Mining on News-Related Articles instead of Web Pages

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HisTrace:A System for Mining on News-Related Articles instea...