Titlebook: Applied Computing for Software and Smart Systems; Proceedings of ACSS Rituparna Chaki,Agostino Cortesi,Nabendu Chaki Conference proceeding

猛然一拉

Image Binarization with Hybrid Adaptive Thresholdsues, local technique gives a result similar to other local techniques, while the hybrid technique gives a result which is not similar to the previous two but is a very effective one. So it is convenient to apply in degraded document image binarization. This technique is compared with other global as

GAVEL

BEN-CNN-BiLSTM: A Model of Consequential Document Set Identification of Bengali Textusing the test dataset to calculate recall, precision, F-score, and accuracy. Compared to other standard classification algorithms in Bengali text classification, our proposed BEN-CNN-BiLSTM model achieved 93.94% accuracy. Thus, it can be said that the proposed BEN-CNN-BiLSTM model can be a new docu

問到了燒瓶

A Machine Learning Model for Automatic Sleep Staging Based on Single-Channel EEG Signalsd method were superior to those of state-of-the-art methods centered on the different machine learning classifiers. In this paper, automatic sleep data staging was realized, effectively improving the accuracy.

縮減了

Deep Learning-Based Prediction of Time-Series Single-Cell RNA-Seq Dataoint is also necessary when data is degradable or missing. Hence, in this work, we have attempted to develop a deep neural network (DNN)-based prediction model for estimating gene expression values in time-series scRNA-seq data. The DNN regressor is capable of estimating data at advanced time-points

Density

Stress Analysis Using Machine Learningctive area of research and achieved high performance of models, those were based on signal and speech which were computationally costlier and text-based research work using a state-of-the-art model called the BERT has achieved an f1-score i.e. 80.65%. This project focuses on text-domain and uses ope

AGOG

SLUMP

Classification of Kathakali Asamyuktha Hasta Mudras Using Naive Bayes Classifier and Convolutional N

微塵

織物

巫婆

978-981-19-6790-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor