Keeb kwm thiab keeb kwm

1. Machine Learning Algorithms

Tshooj lus no muab cov ntsiab lus dav dav ntawm cov kev siv tshuab kev kawm algorithms feem ntau hauv kev kuaj kab mob.

1.1 Cov ntoo txiav txim siab

Kev txiav txim siab ntoo (DT) algorithms ua raws li txoj cai ntawm kev faib tawm. Hauv DT qauv, tus cwj pwm yuav muaj ntau yam tseem ceeb hu ua cov ntoo faib; nplooj sawv cev rau cov chav kawm sib txawv, thaum cov ceg ntoo qhia txog kev sib xyaw ua ke ntawm cov yam ntxwv uas ua rau cov ntawv teev npe hauv chav kawm. DT, ntawm qhov tod tes, yuav siv cov kev hloov pauv txuas ntxiv hu ua regression ntoo. C4.5 thiab EC4.5 yog ob qho ntawm cov paub zoo thiab feem ntau siv DT algorithms.

1.2 Txhawb vector tshuab

Rau kev faib tawm thiab cov teeb meem ntsig txog kev thim rov qab, kev txhawb nqa vector tshuab (SVMs) yog txoj kev kawm tshuab nrov. Kev them nyiaj yug vector tshuab tau qhia los ntawm Vapnik thaum xyoo pua 20th. Ntxiv nrog rau kev kuaj kab mob, kev txhawb nqa vector tshuab tau siv ntau yam kev qhuab qhia, suav nrog kev paub txog lub ntsej muag, kev ua kom muaj protein ntau, nrhiav pom homolog nyob deb, kev paub hais lus, thiab kev faib cov ntawv nyeem. Rau cov ntaub ntawv tsis muaj npe, saib xyuas ML algorithms ua tsis tau. Siv kev tshawb pom hyperplane rau kev sib cais ntawm cov ntaub ntawv, kev txhawb nqa vector tshuab tuaj yeem faib cov ntaub ntawv tsis muaj npe. Txawm li cas los xij, cov zis ntawm lub tshuab vector txhawb nqa tsis yog-linearly txawv. Txhawm rau kov yeej cov teeb meem no, kev xaiv cov kernel uas tsim nyog thiab tsis yog ob qho tseem ceeb rau kev siv SVMs hauv kev txheeb xyuas cov ntaub ntawv.

1.3 k - Tus Neeg Zej Zog

(KNN) kev faib tawm yog cov txheej txheem kev faib tawm uas tsis yog parametric tsim los ntawm Evelyn Fix thiab Joseph Hodges hauv xyoo 1951. kNN yog qhov tsim nyog rau ob qho tib si kev faib tawm thiab kev txheeb xyuas rov qab. Kev faib tawm kNN ua rau muaj kev koom tes hauv chav kawm. Ib txoj kev pov npav yog siv los faib cov khoom. Cov txheej txheem Euclidean nrug yog siv los txiav txim qhov kev ncua deb ntawm ob cov ntaub ntawv kuaj. Tus nqi kwv yees rau kev ntsuas kev rov qab yog qhov nruab nrab ntawm KNN qhov tseem ceeb.

Nyem qhov no kom tau txais Cov nyhuv ntawm tshuaj ntsuab Cistanches

1.4 Naïve Bayes

Tus naïve Bayesian (NB) classifier yog Bayesian-based probabilistic classifier. Raws li cov ntaub ntawv muab los yog cov ntaub ntawv taw qhia, nws kwv yees qhov tshwm sim ntawm kev ua tswv cuab hauv txhua chav kawm. Cov chav kawm uas muaj feem ntau yog qhov uas muaj qhov tshwm sim siab tshaj plaws. NB classifier yog siv los kwv yees qhov tshwm sim, tsis yog kev kwv yees.

1.5 Logistic regression

Logistic regression (LR) yog ib txoj kev kawm tshuab siv los daws cov teeb meem kev faib tawm. Cov qauv LR muaj cov qauv tsim nyog nrog qhov kev kwv yees txij li 0 mus rau 1. Piv txwv ntawm LR-raws li ML suav nrog kev txheeb xyuas spam, kev tshawb nrhiav kev dag ntxias hauv online, thiab kev kuaj pom tsis zoo. Txoj haujlwm surrogate, feem ntau hu ua sigmoid muaj nuj nqi, yog siv los ntawm LR. sigmoid ua haujlwm hloov txhua tus lej tiag ntawm 0 thiab 1.

1.6 AdaBoost algorithm

Yoav Freund thiab Robert Schapire tsim Adaptive Boosting, feem ntau hu ua AdaBoost. AdaBoost yog ib tug classifier uas muab ntau yam tsis muaj zog classifiers mus rau hauv ib tug classifier. AdaBoost ua haujlwm los ntawm kev muab qhov hnyav dua rau cov qauv uas nyuaj rau kev faib tawm thiab muab cov uas twb tau muab faib ua tau zoo AdaBoost ua haujlwm los ntawm kev muab qhov hnyav ntxiv rau cov qauv uas nyuaj rau kev faib tawm thiab qhov hnyav dua rau cov uas twb tau muab faib ua tau zoo. Nws tuaj yeem siv rau kev faib tawm raws li kev txheeb xyuas regression.

Cov txiaj ntsig ntawm Cistanche tubulosa

2. Kev Kawm tob tob

Kev kawm tob (DL) yog subfield ntawm kev kawm tshuab (ML) uas siv ntau theem los rho tawm cov ntaub ntawv siab dua thiab qis dua (piv txwv li cov duab, cov lej lej, cov txiaj ntsig kev faib tawm) los ntawm cov tswv yim. Cov qauv kev kawm sib sib zog nqus feem ntau yog tsim los ntawm cov khoom siv hluav taws xob neural networks (ANNs), tshwj xeeb hauv kev sib txuas ntawm neural networks (CNNs), uas tuaj yeem ua ke nrog lwm cov qauv kev kawm sib sib zog nqus, suav nrog cov qauv tsim, kev ntseeg sib sib zog nqus, thiab Boltzmann tshuab. Kev kawm tob tuaj yeem muab faib ua peb hom: saib xyuas, ib nrab saib xyuas, thiab tsis saib xyuas. Sib sib zog nqus neural tes hauj lwm (DNNs), kev kawm ntxiv, thiab rov ua dua neural networks (RNNs) yog qee qhov tseem ceeb tshaj plaws DL architectures (RNNs).

Txhua txheej hauv kev kawm tob kawm cov ntaub ntawv sib txawv thaum kawm hloov nws cov ntaub ntawv nkag mus rau cov txheej txheem tom ntej. Piv txwv li, hauv daim ntawv thov kev paub txog duab, thawj cov tswv yim yuav yog matrix ntawm pixels thiab thawj txheej yuav ntes cov npoo ntawm daim duab. Qhov thib ob txheej, ntawm qhov tod tes, yuav tsim thiab encode lub qhov ntswg thiab qhov muag, thiab txheej thib peb yuav paub txog lub ntsej muag los ntawm kev sib txuas tag nrho cov ntaub ntawv sau los ntawm thawj ob txheej.

Hauv kev kho mob, DL tau cog lus zoo. Radiology thiab pathology yog ob txoj haujlwm kho mob tseem ceeb uas tau siv kev kawm tob hauv kev kuaj mob rau ntau xyoo. Tsis tas li ntawd, kev sau cov ntaub ntawv muaj txiaj ntsig los ntawm cov xeev molecular thiab txiav txim siab txog kev kis kab mob lossis kev kho mob raug mob yog kev siv DL, uas feem ntau lees paub los ntawm tib neeg kev tshawb fawb.

Cistanche ntxiv

Tus nqi ntawm cov tshuaj ntsuab Cistanche extract rau lub raum:

Cov kev tshawb fawb kho mob tau pom tias cov khoom xyaw nquag muaj nyob hauvCistanche extracttuaj yeem txhawb tib neeg lub cell rov tsim dua tshiab thiab cov metabolism, txhim khu kev tiv thaiv kab mob, thiab muaj qhov pom tseebtshuaj tiv thaiv kab mob, antiviral, thiab anti-aging teebmeem. Nyob rau hauv xyoo tas los no, kev kho mob ntawmkab mob raumnrog Cistanche tau ua tiav cov txiaj ntsig zoo. Cistanche yog ib qho ntxiv rau cov txiv neej thiab poj niam thiab tuaj yeem kho ob tus txiv neej impotence thiab poj niam infertility. Ntxiv mus, tonify lub raum tsis ua mob rau yin, thiab siv sij hawm ntev feem ntau tsis ua rau cov tsos mob xws li cua sov thiab lub qhov ncauj qhuav.

Raws li Dr. Xu Jing, Department of Nephrology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, cov neeg mob uas muaj kab mob hauv qab no yuav tsum tau saib xyuas tshwj xeeb:

1. Ntshav Qab Zib: Mob ntshav qab zib yuav ua rau kuv cov piam thaj ntau ntxiv, txawm tias nws yog hom 1 lossis hom 2 mob ntshav qab zib, ntshav qab zib nephropathy tuaj yeem tshwm sim, thiab qhov ntev ntawm tus kab mob, qhov kev pheej hmoo ntau dua. Thaum cov kab mob hauv lub raum mus rau theem kawg, nws yuav nyuaj rau tswj.

2. Cov ntshav siab: ntshav siab ntshav siab thiab kuv lub npe menyuam yaus "cov kwv tij nyuaj". Tsis tsuas yog ntshav siab ua rau kuv lub siab thiab ua rau kuv noj qab haus huv, tab sis thaum kuv ua tsis taus pa, nws kuj ua rau ntshav siab thib ob. Ob peb "sib koom" ua ke, nws yog qhov nyuaj!

3. Hyperuricemia: Nrog rau txoj kev ua neej hloov, qhov tshwm sim ntawm gout (hyperuricemia) maj mam nce. Urate crystals tso rau hauv ob lub raum, ua kom lub raum ua haujlwm tsis zoo; Kev ua haujlwm ntawm lub raum tsis zoo yuav txo qis lub peev xwm ntawm ob lub raum kom tshem tawm uric acid, ua rau hyperuricemia dua, thiab tsim lub voj voog vicious.

4. Kev ua neej nyob tsis zoo: Kev ua neej nyob ntev thiab tsis muaj kev tawm dag zog ua rau rog rog, rog dhau, lossis ib txwm nyob lig, haus cawv thiab haus luam yeeb, thiab lwm yam, yuav ua rau kuv ua haujlwm ntev thiab so tsis tau, yog li ua rau kuv lub nra hnyav thiab ua rau kuv ua haujlwm ntev. kev puas tsuaj.

Cistanche tubulosa extract

6. Ua kom tawm hws

Thaum twg peb tawm hws, peb ua rau lub nra ntawm peb lub raum vim peb ntxiv dag zog rau lwm txoj hauv kev tshem cov kua dej ntawm lub cev. Hauv qhov kev nkag siab no, kev tawm hws tuaj yeem yog "kev kho" zoo los saib xyuas lub raum ua haujlwm. Nruab nrab lossis siab siv lub cev ua si, lossis los ntawm ib ntus. Qhuav thiab ntub dej sauna. Noj cov txuj lom kub, xws li qhiav los yog cayenne kua txob.

7. Kev siv cov tshuaj ntxuav tes tsis tu ncua

Ib zaug lossis ob zaug hauv ib xyoos, tshwj xeeb tshaj yog thaum lub caij hloov pauv, nws yog ib lub tswv yim zoo los ua qee qhov kev ntxuav lossis ntxuav kom huv los txhawb kev tshem tawm cov co toxins thiab txhim kho daim siab thiab lub raum ua haujlwm. Qhov no suav nrog kev txhim kho kev noj zaub mov zoo, feem ntau ua rau lub raum ntxuav, uas koj tuaj yeem sim:

a: Ntxiv qej thiab dos kua zaub rau koj noj ntau zaus.

b: Hloov cov dej haus nrog ntsuab pear, celery, thiab kua txiv apple.

c: Siv natural infusions ntawm dandelion, burdock, thiab horsetail los txhawb kev tshem tawm cov kua.

8. Siv cov cua sov hauv zos

Cov cua sov hauv zos uas siv lub raj mis dej kub los yog lub tshuab cua sov tuaj yeem muab kev pabcuam thaum muaj kev tsis xis nyob hauv qab ntawm lub raum. Ntawm qhov tod tes, zam qhov txias txias hauv cheeb tsam pob tw, tshwj xeeb tshaj yog cua txias hauv lub caij ntuj no yog qhov tseem ceeb los tiv thaiv (los yog txo qis, raws li qhov xwm txheej) tsis xis nyob hauv cheeb tsam no, txawm tias muaj kab mob raum.

9. Tsis txhob haus cawv thiab noj ntau dhau

Tswv yim los saib xyuas tus txiav: tsis txhob haus cawv. Tau kawg, tsis txhob haus cawv yog qhov tseem ceeb rau kev saib xyuas lub raum noj qab haus huv. Ntau yam kev tshawb fawb tau pom tias cawv tuaj yeem ua rau lub raum ua haujlwm tsis zoo rau qhov zoo thiab qhov ntau dua cov cawv haus, qhov kev pheej hmoo ntawm kev tsim ntau yam teeb meem kev noj qab haus huv. Ntawm qhov tod tes, nws yog ib qho tseem ceeb kom nco ntsoov tias txhua yam ntau dhau yog qhov tsis zoo rau koj txoj kev noj qab haus huv, tsis yog cawv lossis lwm yam tshuaj.

CEEB TOOM:

1.Brijain, M.; ib. Patel, R.; Kev, M.; Rana, K. Kev tshawb fawb txog kev txiav txim siab ntoo algorithm rau kev faib tawm. Int. J. Eng. Dev. Res. 2014. Muaj

2. Walse, RS; Kurundkar, GD; Khamitkar, SD; Muley, AA; Bhalchandra, PU; Lokhande, SN Siv Tau Zoo ntawm Naïve Bayes, Tsob Ntoo Txiav Txim Siab, thiab Random Forest Techniques for Analysis of Chronic Kidney Disease. Hauv Kev Ua Haujlwm ntawm Lub Rooj Sib Tham Thoob Ntiaj Teb ntawm Cov Ntaub Ntawv thiab Kev Sib Txuas Lus Technology rau Kev Txawj Ntse, Ahmedabad, Is Nrias teb, 15–16 Tsib Hlis 2020; Springer: Berlin/Heidelberg, Lub teb chaws Yelemees, 2020; Ib., 237–245.

3. Rajendran, K.; Jayabalan, M.; Thiruchelvam, V. Predicting mob cancer ntawm lub mis los ntawm kev saib xyuas tshuab kev kawm cov ntaub ntawv nyob rau hauv cov chav kawm ntawv imbalanced cov ntaub ntawv. Int. J. Adv. Tshuab xam zauv. Sci. Appl. 2020, 11, 54–63.

4. Tsao, HY; Chan, PY; Su, ECY Predicting ntshav qab zib retinopathy thiab txheeb xyuas cov ntsiab lus biomedical siv tshuab kev kawm algorithms. BMC Bioinform. 2018, 19, 111–121.

5.. Nurrohman, A.; Abdullah, S.; Murfifi, H. Parkinson's disease subtype classification: Application of decision tree, logistic regression, and logit leaf model. Hauv AIP Conference Proceedings; AIP Publishing LLC: Melville, NY, USA, 2020; Ib., 2242, p. 0 30015.

6.. Drucker, H.; Wu, D.; Vapnik, VN Txhawb nqa vector tshuab rau spam categorization. IEEE Trans. Neural Netw. Xyoo 1999, 10, 1048–1054.

7. Kho, E.; Hodges, JL Kev ntxub ntxaug. Nonparametric kev ntxub ntxaug: Cov khoom sib xws. Int. Stat. Rev. Int. De Stat. Xyoo 1989, 57, 238–247.

8. Wright, RE Logistic regression. Hauv Kev Nyeem Ntawv thiab nkag siab ntau yam kev txheeb cais; American Psychological Association: Washington, DC, USA, 1995.

9. Schapire, RE piav qhia AdaBoost. Nyob rau hauv Empirical Inference; Springer: Berlin/Heidelberg, Lub teb chaws Yelemees, 2013; ib., 37–52.

10. Nyob zoo, I.; Bengio, Y.; Courville, A. Kev Kawm tob; MIT Press: Cambridge, MA, USA, 2016.

11. Hayashi, Y. Txoj kev coj yog xav tau los txhim kho lub thawv dawb hauv kev kawm tob hauv hluav taws xob, kab mob, thiab kho qhov muag: Kev tshuaj xyuas luv luv. Pem hauv ntej. Neeg hlau. AI 2019, 6, 24.

12. Akkus, Z.; Galimzianova, A.; Hoog, A.; Rubin, DL; Erickson, BJ Kev kawm tob rau lub hlwb MRI segmentation: Lub xeev ntawm kev kos duab thiab cov lus qhia yav tom ntej. J. Tus lej. Cov duab 2017, 30, 449–459.

13. Ahsan, MM; E Alam, T.; Trafalis, T.; Huebner, P. Deep MLP-CNN tus qauv siv cov ntaub ntawv sib xyaw kom paub qhov txawv ntawm cov neeg mob COVID-19 thiab Non-COVID-19. Symmetry 2020, 12, 1526.

14. Houssein, EH; Yim, MM; Ali, UA; Suganthan, PN Deep thiab tshuab kev kawm txuj ci rau kev kho mob imaging-based cancer mis: Kev tshuaj xyuas dav. Kws tshaj lij Syst. Appl. Xyoo 2021, 167, 114161.