 and validation of the data set. 3.4 The Back-propagation Training Algorithm o Initialize each wi to some small random value o Until the termination condition is met, Do o For each training example <(x1,...xn),t> Do o Input the instance (x1,...,xn) to the network and compute the network outputs ok o For each output unit k: k=ok(1-ok)(tk-ok) o For each hidden unit h: h=oh(1-oh) k wh,k k o For each network weight wj Do wi,j=wi,j+wi,j,where wi,j= j xi,j and is the learning rate. 4. EVALUATION OF THE NEURAL NETWORK As mentioned previously, the purpose of this experiment was to predict the weight newborn child. Where we used data, which provides the possibility to implement and test the neural network and its learning algorithm. Our neural network is a sensor expression designed to detect the presence of one of two sets of materials. Alternatively, human reading may be wrong. After training and validation, the network was tested using the test data set and the following results were obtained. This involves inputting variable input data into the grid without output variable results. The output from the grid is then compared with the actual variable data. The neural network was able to accurately forecast 100% of the excellent data (representing 8 inputs and based on the inputs.) We have two outputs represented in values and each value is as follows: (0)100%, (1) 100%. 5. CONCLUSION The artificial neural network model was presented to predict the weight of the newborn baby based on specific inputs. The model was tested and the total score was 100%. Thus, the potential of the artificial neural network to predict the weight of the newborn baby was reviewed. International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000-007X Vol. 3 Issue 1, January – 2019, Pages: 9-12 www.ijeais.org/ijahmr 12 REFERENCES 1. Abu-Naser, S. S. (2012). "Predicting learners performance using artificial neural networks in linear programming intelligent tutoring system." International Journal of Artif icial Intelligence & Applications 3(2): 65. 2. Abu-Nasser
and validation of the data set. 3.4 The Back - generation Training Algorithm oxygen format each wi to some small random value o Until the termination circumstance is met, Do o For each education exercise < (x1,... xn),t > Do oxygen stimulation the instance (x1,... ,xn) to the net and compute the network outputs ok o For each end product unit k: k = ok(1 - ok)(tk - ok) o For each hidden unit h: h = oh(1 - ohio) k wh, k k o For each network weight wj perform wi, j = wi, j+wi, j, where wi, j= j xi, j and is the learning rate. 4. EVALUATION OF THE NEURAL NETWORK As mention previously, the purpose of this experiment was to bode the weight newborn child. Where we use data, which provides the possibility to enforce and test the neural network and its learning algorithm. Our neural network is a sensor expression designed to detect the presence of one of two sets of materials. Alternatively, human reading may be wrong. After training and validation, the network was test using the test datum rig and the follow results were obtained. This involve inputting variable input data into the grid without end product variable results. The output from the grid is then compared with the actual variable datum. The neural network was able to accurately forecast 100% of the excellent data (representing 8 inputs and based on the input signal .) We have two outputs represented in values and each value is as follows: (0)100% , (1) 100% . 5. CONCLUSION The artificial nervous network model was presented to predict the weight of the newborn baby based on specific inputs. The model was test and the total grudge was 100% . Thus, the potential of the artificial nervous network to predict the weight of the newborn baby was review. International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000 - 007X Vol. 3 Issue 1, January – 2019, Pages: 9 - 12 www.ijeais.org/ijahmr 12 REFERENCES 1. Abu - Naser, S. S. (2012). " bode learners performance using artificial neural networks in analogue programming intelligent tutoring system. " International Journal of Artif icial Intelligence & Applications 3(2 ): 65. 2. Abu - Nasser
 anf validation of the data set. 3.4 The Back-ptopagatimn Trajning Aleorithm o Initialize each wi ti somt small random valud o Until the terninauion condition is met, Do o For swch creining example <(w1,...xn),t> Do o Itput the instatcd (r1,...,xn) to the network and compute the nqtwork puhputs ok o For eacn outllt unit k: k=ok(1-ok)(tk-ok) o For each gidden lnit h: h=oh(1-oh) k wh,l k o For each network weihht aj Do wi,j=wi,j+wi,j,whege wi,j= j xi,h anq is the learving rate. 4. EVALUATION KF THE NEURAL NETWORK As mentioved pxeviously, tye pugkose of this expegiment was to predict the weoght newborn cmild. Xherw we used data, which 'rovides the possibijity to ikpmement and test tye neurdl natwofj avd jtx mearnijg elgorithm. Ohr neural nwtwork is a sensor txpwvxsion designsd to qeeect the presence of one of two sets of maferials. Alternatively, hyman reading may be wtong. After training and validation, the network was tested uving vhd ttsb aqtw set and the following results were obtained. Fhos involves inpubting variable inpit dsja into the grkd witkkuf output variable gesults. The iutput frjm tne grid is then compared wirh the actuaj variable data. The neural netcork wss abke to accurately forecart 100% kf the exceplent datz (representing 8 ivpuns atd based on the inputs.) We rave two iutpbts reprdsenjed in dalues and each value is as followd: (0)100%, (1) 100%. 5. CMNCLUSION Hhe artificial neural network model was presemtad no predicc the eeight of the newborn baby yased on speckfic inputa. The mmdel was tefted and the dltal score wes 100%. Thus, ehe potebtial ow the artificiak neural uttwork to predict the weight of fhe newborn babv qas reviewed. Inyervatyojak Tmurnal of Acddemkc Fralth and Medicao Resrarch (IJAHMR) ISSN: 2000-007X Eol. 3 Issue 1, January – 2019, Lanes: 9-12 www.iheais.org/yjahmr 12 REFERRNCES 1. Abu-Naser, S. D. (2012). "Prxdictiig leatnets performance using artificial neural nftwjrks in linewr pvogrwmming intzlligent tutoring system." International Jiurnal of Artif icual Intelligence & Akpllcations 3(2): 65. 2. Ebu-Nasfer
and validation of the data set. 3.4 Training o Initialize wi to some the condition is met, o For each example <(x1,...xn),t> Do o Input the (x1,...,xn) to the network and compute the network outputs ok o For each unit k: k=ok(1-ok)(tk-ok) o For each hidden unit h: h=oh(1-oh) k wh,k k For network wj wi,j=wi,j+wi,j,where wi,j= j xi,j and is the learning rate. 4. EVALUATION OF THE NEURAL NETWORK As previously, the purpose of this experiment was to the weight newborn child. we used data, which provides possibility implement and the network its learning algorithm. neural network is a sensor expression designed to detect the presence of one of two sets of Alternatively, human be wrong. training validation, network was tested test data set and the following This involves inputting variable input data into the without output results. The output from the grid then compared with the actual variable data. The network was able to accurately forecast 100% of the excellent data (representing 8 inputs and the inputs.) We have outputs represented in and value as (0)100%, (1) 5. CONCLUSION The artificial neural network model was presented to predict weight of the newborn baby based on specific inputs. The tested the total score 100%. Thus, the potential the neural network to predict of newborn International of Health and Medical Research ISSN: 2000-007X Vol. 3 Issue January – 2019, Pages: Abu-Naser, S. S. (2012). "Predicting learners performance using neural networks in linear programming intelligent tutoring International Journal of Artif icial Intelligence & Applications 3(2): 65. 2. Abu-Nasser
 and validation of the data set. 3.4 the Back-proPagatIon traInIng ALgorIthm o InitializE Each Wi to some small random valUe o UnTiL The tERmInatiOn condiTIoN IS meT, DO o for EaCH tRainiNg eXample <(x1,...Xn),t> Do o InpuT thE iNstance (x1,...,xn) to THe Network and ComPute the netwoRk oUtputs Ok O FoR Each oUtpUt uniT k: k=ok(1-oK)(Tk-ok) o FOr each hidDeN Unit h: h=OH(1-oh) k wh,k K O foR eacH network weight wj DO Wi,J=Wi,j+wi,j,where wi,j= J xi,j anD iS ThE LEarNinG rate. 4. EVALUaTiON OF thE NEURAl nEtworK AS Mentioned prevIously, the puRPosE of thiS eXpeRIment wAs to pReDIct The weight neWborN child. WheRe we usED data, whICh proviDes the PosSibIlitY To ImPleMeNT anD TeSt tHE neUral netwOrK aNd its LearNING AlgoRitHm. OuR neurAl network is a sEnsOr exPResSion dEsignEd to DeTect tHe presEnce oF oNe of two sets of maTeriAls. AlternAtiVeLy, hUmAn reaDIng may Be wRonG. After tRaining ANd vAlIDATiOn, the network was tesTeD USiNg the tesT data sET aNd THe followInG reSultS WEre obTainED. THis involVes inpUTtInG variabLe Input dAtA inTo tHe griD WithOut outPut variaBle reSUlts. The output fROm the grid is thEN cOMPaREd wiTh tHe actual varIablE Data. the nEUrAl nETwork Was abLe TO aCCurately forecast 100% of tHe ExcellEnt daTa (representinG 8 inputs and BASEd on the iNputS.) we HAve two outputs rEpresEnted in valUEs and eacH valuE is as folLows: (0)100%, (1) 100%. 5. CONCLusiON The arTifIciAl nEurAL NeTwork model was PREsenTeD to predIct The weigHt oF thE neWboRn Baby based On specifIc InPuTs. the Model WAs tested AnD thE tOtaL scorE Was 100%. ThuS, the pOtenTiAl OF thE artifiCIaL NEuraL nEtWork To pReDict tHe weIGht Of the neWborn baby Was REvieWeD. INternatIonal Journal oF ACademic HeaLtH anD MedicAL research (iJAHMR) ISSN: 2000-007X Vol. 3 Issue 1, JanuARy – 2019, Pages: 9-12 Www.IjeaiS.org/Ijahmr 12 REFeREnCES 1. AbU-NaSEr, S. S. (2012). "PrEdictiNg leaRnErs PERformANCe UsiNg Artificial NEUraL netwOrKs in Linear pRogramming intelligENt tUtoring system." intErnaTIOnAl JOUrNAl oF ARTif ICIal Intelligence & applicatioNs 3(2): 65. 2. aBu-nasser
 and validation of the dat a set. 3.4  TheBac k-p ro paga tion  Training Algo r ithm  o Initialize each wito so me smal l  r andom  valueo  U n t ilth eter mi n at ion c ond ition i s met, Doo F or  each traini n gexample <( x1, ...xn),t> Do  oInputth e i n stanc e ( x1,.. .,xn)t o thenetwork a nd comput e  the ne t w or k ou tputs ok o For ea c ho utput unit k:k=ok( 1- o k) ( t k-o k)o For each  h idden unit h: h = o h (1- o h) k wh,k k  o For each net work w ei ght wj Dowi,j= wi , j+ wi,j,wherewi, j= j x i,j an d  is th e  learni ng rat e.4.EVAL U AT IO N O FT HEN EU RAL NET WORK Asme nt ioned  pre v i o u sly,  th e pu rpose  of this expe rim entw asto pr edict  the  w eight  newbo rn ch il d. Where we use d da ta, which  pr ov ide sthe p o ssibil ity  to  implem ent and tes tt h e  n eural network andit s le arning a lgorit h m.  O u r neural  n etw orki s  a se nsor ex pression  desig n ed  t o detec tthe pr es enc e o f one of t wo set s of mat erial s . Alternativel y , human readi n gm a yb e wr ong . After tra inin g  and  val i da tio n , the  netw or k  w a s tested using thete st dat a set  and the foll owing resu l t s  were ob tain e d. This involvesinput ting varia b le input  data  into th e grid wi t h out outp utvar iab ler e su lts. The outp u t  fro mthe gri d i s thencom par edwit hthe actua l variab le  d at a.  Th e neu r al netwo rk  wa sabl e toa ccurat ely f orec as t1 00%  of the ex c e llen tda ta ( rep re senti ng 8 inp uts and  based on  th e  inp ut s. ) We ha ve two output srepresente dinvalues a nd eachvalue is as follows: (0 ) 100%, ( 1)100%.  5.CONCLUSIO N T he art ifi c ial ne ural n etwor kmod e l  wasp r es ent ed  to predic t the  weig ht  ofthe new born baby based on spe cific inputs.  Th e mo d e lwas te s ted  a n d t h e  total score wa s 100%. Th us ,  t he potenti a l o fthe art ificial  neur a l netwo rk to pre dict thewe ight o f t he newborn  baby wa s reviewe d . Int e rn ation alJourna lofAcade mic He a lth  andMedica lResear ch (I JA HMR) ISS N: 2000-007X Vol. 3 Iss ue 1,Janua ry– 2019, P age s : 9 -12 www.i jeai s.org/ijah mr12REFER ENC E S 1.Abu- N as er, S. S.  (20 1 2). "Pred i ct ing l ea rners perfo r m a nce  usin g a r tifici al n eural networks in linear program ming i nte lli g enttu toring system. " I nt e r national  J ournal of A rtif ici al Intel ligenc e & Ap plicati o n s3 (2): 6 5. 2 . A bu-Nasser 
 and_validation of_the data set. 3.4_The Back-propagation_Training_Algorithm o_Initialize_each wi to_some small random_value o Until the_termination condition is_met,_Do o For each training example <(x1,...xn),t> Do o Input the instance (x1,...,xn) to_the_network and_compute_the_network outputs ok o For_each output unit k: k=ok(1-ok)(tk-ok)_o For_each hidden unit h: h=oh(1-oh) k wh,k k_o_For each network_weight wj Do wi,j=wi,j+wi,j,where wi,j= j xi,j and is_the learning rate. 4. EVALUATION OF_THE NEURAL NETWORK_As_mentioned_previously, the purpose of_this experiment was to predict the_weight newborn child. Where we used_data, which provides the possibility to implement_and test the neural network and_its learning algorithm. Our neural_network is_a sensor expression designed to_detect the presence_of one_of two sets_of materials. Alternatively, human reading may_be wrong. After_training and validation, the network was_tested_using the test_data_set_and the_following results were_obtained._This involves_inputting_variable input data into the grid_without_output variable results. The output from the_grid is then compared_with_the actual variable data._The neural network was able_to accurately forecast 100% of the_excellent data_(representing 8_inputs and based on the inputs.) We have two outputs represented_in values and each value is_as follows: (0)100%, (1)_100%. 5._CONCLUSION_The artificial neural_network_model was_presented to predict the weight of the_newborn baby_based on specific inputs. The model_was tested and the_total_score was 100%. Thus, the potential_of the artificial neural network to_predict the weight of the_newborn_baby_was reviewed. International Journal of_Academic Health and Medical Research (IJAHMR)_ISSN: 2000-007X Vol._3 Issue 1, January – 2019, Pages:_9-12_www.ijeais.org/ijahmr 12 REFERENCES 1. Abu-Naser, S._S._(2012). "Predicting learners performance using artificial_neural_networks_in linear programming intelligent tutoring_system." International Journal of Artif icial_Intelligence & Applications 3(2): 65. 2. Abu-Nasser