Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

Effect of lindane on testicular antioxidant system and steroidogenic enzymes in adult rats

Aim: To find out the effect of lindane on testicular antioxidant system and testicular steroidogenesis in adult malerats. Methods: Adult male rats were orally administered with lindane at a dose of 5.0 mg/kg body weight per dayfor 30 days. Twenty-four hours after the last treatment the rats were killed using anesthetic ether. Testes, epididymis,seminal vesicles and ventral prostate were removed and weighed. A 10% testicular homogenate was prepared and cen-trifuged at 4℃. The supernatant was used for various biochemical estimations. Results; The body weight and theweights of testes, epididymis, seminal vesicles and ventral prostate were reduced in lindane-treated rats. There was asignificant decline in the activities of antioxidant enzymes superoxide dismutase (SOD), catalase and glutathione reduc-tase while an increase in hydrogen peroxide (H_2O_2) generation was observed. The specific activities of testicularsteroidogenic enzymes 3β-hydroxysteroid dehydrogenase and 17β-hydroxysteroid dehydrogenase were decreased. Thelevels of DNA, RNA and protein were also decreased in lindane-treated rats. Conclusion: Lindane induces oxida-tive stress and decreases antioxidant enzymes in adult male rats.

Effect of lindane on antioxidant enzymes in epididymis and epididymal sperm of adult rats

Aim: To find out the changes induced by lindane on the antioxidant enzymes in epididymis and epididymal sperm ofadult rats. Methods: Adult male rats were orally administered lindane at a dose of 5.0 mg/kg body weight per dayfor 30 days. At the end of the treatment, the rats were sacrificed. The epididymis was removed and weighed and spermwere collected for sperm count, motility and biochemical studies. A 1% homogenate of epididymis was prepared andused for biochemical estimations. Results; In lindane-treated rats, there were significant reductions in the epididy-mal weight, epididymal sperm count and motility compared with the controls. Significant decreases in the superoxidedismutase (SOD), catalase, glutathione reductase and glutathione peroxidase activities and significant increases in theH_2O_2 generation and lipid peroxidation were also observed in the epididymis and epididymal sperm of lindane-treatedrats. Conclusion; Lindane decreases the levels of antioxidant enzymes in the epididymis and epididymal sperm ofadult rats thereby inducing oxidative stress. (Asian J Androl 2001 Sep; 3 : 205 - 208)

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

The digital technologies that run based on users’content provide a platform for users to help air their opinions on various aspects of a particular subject or product.The recommendation agents play a crucial role in personalizing the needs of individual users.Therefore,it is essential to improve the user experience.The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites.In Context-Aware Recommender Systems(CARS),several influential and contextual variables are identified to provide an effective recommendation.A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation.The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users.However,the key issue is how contextual information is used to create good and intelligent recommender systems.This paper proposes an Artificial Neural Network(ANN)to achieve contextual recommendations based on usergenerated reviews.The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS.Thus,the most appropriate contexts in which a user should choose an item or service are achieved.This work converts every label set into a Multi-Label Classification(MLC)problem to enhance recommendations.Experimental results show that the proposed ANN performs better in the Binary Relevance(BR)Instance-Based Classifier,the BR Decision Tree,and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset.Furthermore,the accuracy of the proposed ANN achieves better results by 1.1%to 6.1%compared to other existing methods.

Optimal Adaptive Genetic Algorithm Based Hybrid Signcryption Algorithm for Information Security

The functions of digital signature and public key encryption are simultaneously fulfilled by signcryption,which is a cryptographic primitive.To securely communicate very large messages,the cryptographic primitive called signcryption efficiently implements the same and while most of the public key based systems are suitable for small messages,hybrid encryption(KEM-DEM)provides a competent and practical way.In this paper,we develop a hybrid signcryption technique.The hybrid signcryption is based on the KEM and DEM technique.The KEM algorithm utilizes the KDF technique to encapsulate the symmetric key.The DEM algorithm utilizes the Adaptive Genetic Algorithm based Elliptic curve cryptography algorithm to encrypt the original message.Here,for the security purpose,we introduce the three games and we proved the attackers fail to find the security attributes of our proposed signcryption algorithm.The proposed algorithm is analyzed with Daniel of Service(DOS),Brute Force attack and Man In Middle(MIM)attacks to ensure the secure data transaction.