Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lag...Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lags behind adequate language sources for enabling the SA tasks.Thus,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct cultures.It has gained lesser effort than the other languages.This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic Corpus.The presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus.Firstly,the MVODRL-AA model follows data pre-processing and word embedding.Next,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic corpus.At last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model.The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.展开更多
BACKGROUND The outcome of surgical treatment for colorectal cancer(CRC)remains unsatis-factory and warrants further exploration and optimization.AIM To clarify the impact of chemotherapy plus cellular immunotherapy[de...BACKGROUND The outcome of surgical treatment for colorectal cancer(CRC)remains unsatis-factory and warrants further exploration and optimization.AIM To clarify the impact of chemotherapy plus cellular immunotherapy[dendritic cell-cytokine-induced killer(DC-CIK)cell immunotherapy]on patients after CRC surgery and to explore the mediating variables.METHODS A total cohort of 121 patients who underwent CRC surgery between January 2019 and April 2022 were selected.The sample comprised a control group of 55 pa-tients who received the XELOX chemotherapy regimen and a research group of 66 patients who received XELOX+DC-CIK immunotherapy.We performed compa-rative analyses of the clinical and pathological data of the two groups,including efficacy(2-year disease-free survival[DFS]rate),the incidence of adverse events(diarrhea,myelosuppression,gastrointestinal reactions,and peripheral neuritis),serum levels of tumor markers[carcinoembryonic antigens and carbohydrate an-tigens(CA)19-9 and CA242],and T-cell subsets[cluster of differentiation(CD)3+,CD3+CD4+,CD3+CD8+,natural killer(NK),and NK T cells].We also conducted preliminary univariate and mul-tivariate analyses of the variables that affected the efficacy of the treatments.RESULTS We found a significantly higher 2-year DFS rate of treatment efficacy in the research group than in the control group,with a statistically lower incidence of adverse events.Both groups showed a reduction in serum tumor markers after treatment but there was no marked intergroup difference.After treatment,the various T-cell subgroup indicators in the control group were significantly lower than those in the research group.The indices of T-cell subsets in the research group showed no significant change from preoperative levels.Univariate analysis revealed a significant correlation between TNM staging,tumor differentiation,and the rates of nonresponse to treatment in CRC patients after surgery.Multivariate results indicated that the treatment approach significantly affected the efficacy of postoperative CRC treatment.CONCLUSION We concluded that XELOX+DC-CIK immunotherapy for postsurgical CRC patients offers reduced rates of treatment-induced adverse events,extended 2-year DFS,enhanced immunity,and increased physiological antitumor responses.展开更多
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R263)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Ara-bia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR38.
文摘Sentiment analysis(SA)of the Arabic language becomes important despite scarce annotated corpora and confined sources.Arabic affect Analysis has become an active research zone nowadays.But still,the Arabic language lags behind adequate language sources for enabling the SA tasks.Thus,Arabic still faces challenges in natural language processing(NLP)tasks because of its structure complexities,history,and distinct cultures.It has gained lesser effort than the other languages.This paper developed a Multi-versus Optimization with Deep Reinforcement Learning Enabled Affect Analysis(MVODRL-AA)on Arabic Corpus.The presented MVODRL-AAmodelmajorly concentrates on identifying and classifying effects or emotions that occurred in the Arabic corpus.Firstly,the MVODRL-AA model follows data pre-processing and word embedding.Next,an n-gram model is utilized to generate word embeddings.A deep Q-learning network(DQLN)model is then exploited to identify and classify the effect on the Arabic corpus.At last,the MVO algorithm is used as a hyperparameter tuning approach to adjust the hyperparameters related to the DQLN model,showing the novelty of the work.A series of simulations were carried out to exhibit the promising performance of the MVODRL-AA model.The simulation outcomes illustrate the betterment of the MVODRL-AA method over the other approaches with an accuracy of 99.27%.
文摘BACKGROUND The outcome of surgical treatment for colorectal cancer(CRC)remains unsatis-factory and warrants further exploration and optimization.AIM To clarify the impact of chemotherapy plus cellular immunotherapy[dendritic cell-cytokine-induced killer(DC-CIK)cell immunotherapy]on patients after CRC surgery and to explore the mediating variables.METHODS A total cohort of 121 patients who underwent CRC surgery between January 2019 and April 2022 were selected.The sample comprised a control group of 55 pa-tients who received the XELOX chemotherapy regimen and a research group of 66 patients who received XELOX+DC-CIK immunotherapy.We performed compa-rative analyses of the clinical and pathological data of the two groups,including efficacy(2-year disease-free survival[DFS]rate),the incidence of adverse events(diarrhea,myelosuppression,gastrointestinal reactions,and peripheral neuritis),serum levels of tumor markers[carcinoembryonic antigens and carbohydrate an-tigens(CA)19-9 and CA242],and T-cell subsets[cluster of differentiation(CD)3+,CD3+CD4+,CD3+CD8+,natural killer(NK),and NK T cells].We also conducted preliminary univariate and mul-tivariate analyses of the variables that affected the efficacy of the treatments.RESULTS We found a significantly higher 2-year DFS rate of treatment efficacy in the research group than in the control group,with a statistically lower incidence of adverse events.Both groups showed a reduction in serum tumor markers after treatment but there was no marked intergroup difference.After treatment,the various T-cell subgroup indicators in the control group were significantly lower than those in the research group.The indices of T-cell subsets in the research group showed no significant change from preoperative levels.Univariate analysis revealed a significant correlation between TNM staging,tumor differentiation,and the rates of nonresponse to treatment in CRC patients after surgery.Multivariate results indicated that the treatment approach significantly affected the efficacy of postoperative CRC treatment.CONCLUSION We concluded that XELOX+DC-CIK immunotherapy for postsurgical CRC patients offers reduced rates of treatment-induced adverse events,extended 2-year DFS,enhanced immunity,and increased physiological antitumor responses.
