function final_result=objective_process(datapass,elechoose)

traindata=datapass{1};
trainclass=datapass{2};
testdata=datapass{3};
lengthdata=datapass{5};
encode_word=datapass{6};
flag=datapass{21};
networkiter=datapass{22};
datanum=find(elechoose==0);
traindata_matrix=cell2mat(traindata);
loc=find(ismember(traindata_matrix,datanum));
traindata_matrix(loc)=0;
[rr,cc]=size(traindata_matrix);
for kr=1:rr
traindata1{kr}=traindata_matrix(kr,:);
end
traindata=traindata1;
datain_size=1;
dim_data=50;
hidden_len=80;
total_word=encode_word.NumWords;
no_of_class=3;
network_layer_infor=[ ...
    sequenceInputLayer(datain_size)
    wordEmbeddingLayer(dim_data,total_word)
    lstmLayer(hidden_len,'OutputMode','last')
    fullyConnectedLayer(no_of_class)
    softmaxLayer
    classificationLayer];

if(flag==1)
    train_opt=trainingOptions('adam', ...
    'MiniBatchSize',16, ...
    'GradientThreshold',2, ...
    'Shuffle','every-epoch', ...
    'Plots','training-progress', ...
    'Verbose',false);

else

train_opt=trainingOptions('adam', ...
    'MiniBatchSize',16, ...
    'GradientThreshold',2, ...
    'Shuffle','every-epoch', ...
    'Plots','none', ...
     'Verbose',false);

end
train_opt.MaxEpochs=networkiter;
net=trainNetwork(traindata,trainclass,network_layer_infor,train_opt);
resultout=predict(net,testdata);
[maxval,maxlc]=max(((round(resultout.'))));
ypred1=categorical(maxlc).';
sin=double(trainclass);
sout=double(ypred1);
tardata=[];
resdata=[];
for km=1:length(sin)
    tardata=[tardata double(ismember([1;2;3],sin(km)))];
    resdata=[resdata double(ismember([1;2;3],sout(km)))];
end
[~,confu_result]=confusion(tardata,resdata); 
%% find accuracy 
accuracy=(sum(diag(confu_result))/sum(confu_result(:)))*100;
final_result{1}=accuracy;
final_result{2}=confu_result;
final_result{3}=tardata;
final_result{4}=resdata;