An integral part of every degree program leading to a professional certification, including engineering, is the campus interview. Having every program's student placed is the main objective. Efforts to improve students' placement achievements have led to the inclusion of communication skills in all engineering courses; nonetheless, the curriculum mostly concentrates on developing four language talents. Insufficient emphasis is placed on the significance of employability skills among students. Students should be self-aware about their skill sets, and this essay will make a case for that fact while also outlining why it is important and providing suggestions for how students might showcase their strongest qualifications. The three stages that make up this suggested method are feature selection, model training, and log preprocessing. Initial Preprocessing, Stopwords Removal, Stemming, Lemmatization, and Emoji Recognition are all parts of preprocessing. Picking out features Principal component analysis (PCA) creates a principal component as a linear combination of factor-related observed variables by applying an appropriate weight to each of the variables. A New-A-ResNet-LSTM Ensemble was used all the way through training the model. Compared to LSTM and ResNet, this novel method has a higher average accuracy of 92.95%. © 2024 IEEE.
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