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RETRACTED ARTICLE: Construction of feature analysis model for demeanor evidence investigation based on data mining algorithm
Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimo...
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Published in: | The Journal of supercomputing 2023, Vol.79 (16), p.18605-18626 |
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description | Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice. |
doi_str_mv | 10.1007/s11227-023-05370-5 |
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With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. 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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c1595-87800db6035bc3ebcdb4a2f0d8d4128897bd3b6c562367eabdbeec1264f6c5293</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Zhang, Mengxing</creatorcontrib><creatorcontrib>Qi, Lin</creatorcontrib><creatorcontrib>Guo, Yulong</creatorcontrib><title>RETRACTED ARTICLE: Construction of feature analysis model for demeanor evidence investigation based on data mining algorithm</title><title>The Journal of supercomputing</title><addtitle>J Supercomput</addtitle><description>Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice.</description><subject>Algorithms</subject><subject>Compilers</subject><subject>Computer Science</subject><subject>Data mining</subject><subject>Gene expression</subject><subject>Interpreters</subject><subject>Interrogation</subject><subject>Litigation</subject><subject>Processor Architectures</subject><subject>Programming Languages</subject><issn>0920-8542</issn><issn>1573-0484</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kEFrGzEQhUVpoK6bP9CTIOdtR9JqJedmtk4aMBSMcxbSatZR8EqJtA4E-uOjxoXeepo3w_uGmUfIVwbfGID6XhjjXDXARQNSKGjkB7JgUtW21e1HsoAVh0bLln8in0t5BIBWKLEgv3eb_W7d7zc_6Hq3v-u3m2vap1jmfBrmkCJNIx3RzqeM1EZ7fC2h0Cl5PNIxZepxQhurwJfgMQ5IQ3zBMoeDfaedLehpFd7Olk4hhnig9nhIOcwP0xdyMdpjwcu_dUnubzb7_mez_XV716-3zcDkSjZaaQDvOhDSDQLd4F1r-Qhe-5ZxrVfKeeG6QXZcdAqt8w5xYLxrxzrkK7EkV-e9Tzk9n-p55jGdcv2mGK67DlT16OriZ9eQUykZR_OUw2Tzq2Fg_qRszimbmrJ5T9nICokzVKo5HjD_W_0f6g0UboBu</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Zhang, Mengxing</creator><creator>Qi, Lin</creator><creator>Guo, Yulong</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>2023</creationdate><title>RETRACTED ARTICLE: Construction of feature analysis model for demeanor evidence investigation based on data mining algorithm</title><author>Zhang, Mengxing ; Qi, Lin ; Guo, Yulong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1595-87800db6035bc3ebcdb4a2f0d8d4128897bd3b6c562367eabdbeec1264f6c5293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Compilers</topic><topic>Computer Science</topic><topic>Data mining</topic><topic>Gene expression</topic><topic>Interpreters</topic><topic>Interrogation</topic><topic>Litigation</topic><topic>Processor Architectures</topic><topic>Programming Languages</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Mengxing</creatorcontrib><creatorcontrib>Qi, Lin</creatorcontrib><creatorcontrib>Guo, Yulong</creatorcontrib><collection>CrossRef</collection><jtitle>The Journal of supercomputing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Mengxing</au><au>Qi, Lin</au><au>Guo, Yulong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RETRACTED ARTICLE: Construction of feature analysis model for demeanor evidence investigation based on data mining algorithm</atitle><jtitle>The Journal of supercomputing</jtitle><stitle>J Supercomput</stitle><date>2023</date><risdate>2023</risdate><volume>79</volume><issue>16</issue><spage>18605</spage><epage>18626</epage><pages>18605-18626</pages><issn>0920-8542</issn><eissn>1573-0484</eissn><abstract>Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11227-023-05370-5</doi><tpages>22</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Compilers Computer Science Data mining Gene expression Interpreters Interrogation Litigation Processor Architectures Programming Languages |
title | RETRACTED ARTICLE: Construction of feature analysis model for demeanor evidence investigation based on data mining algorithm |
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