The two articles strive to solve real-life problems and have presented unique perspectives of authors.
The article Application of Transformer-based Language Models to Detect Hate Speech in Social Media by Swapnanil Mukherjee and Dr. Sujit Das aims at detecting and removing hate speech on social platforms efficiently.
“Due to this substantial degree of freedom granted to users, they often misuse such open platforms to pejorate, demean, and intimidate other users through the usage of offensive and hateful language.”
The performance of two Transformer language models, RoBERTa and XLNet on different datasets have been investigated.
“On four different datasets three of which were from different social media platforms and compared them to the existing deep learning and classical architectures. Both the models surpassed the existing baseline scores on three of the four datasets and performed nearly as well on the fourth dataset, which used a RoBERTa model."
“These state-of-the-art models, which have revolutionized the domain of natural language processing, prove to be equally effective and promising for the task of hate speech detection.”
The other article Reliability and Performance Analysis of a Series-Parallel System using Gumbel–Hougaard Family Copula by Dr. Anas Sani Maihulla, Dr. Ibrahim Yusuf and Dr. Saminu I. Bala looks into the reliability metrics that are used to assess the strength of a solar system’s serial system.
“The new study might help homes overcome some of the problems experienced by electric generation systems operating in hostile locations or under adverse weather conditions.”
“Scientists are the light to this world, as a publisher, we are responsible to ensure their light is not blocked by any obstacles. There is no end in the exploration of truth, only by supporting each other can we go farther. ”Managing editor Mr. Chen says.
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