Titlebook: Machine Learning and Metaheuristics Algorithms, and Applications; Second Symposium, So Sabu M. Thampi,Selwyn Piramuthu,Dhananjay Singh Conf

結束

獨裁政府

Exam Seating Allocation to Prevent Malpractice Using Genetic Multi-optimization Algorithm,

證明無罪

Machine Learning and Metaheuristics Algorithms, and ApplicationsSecond Symposium, So

就職

ITCH

Deep Neural Networks with Multi-class SVM for Recognition of Cross-Spectral Iris Images,Poly-U database, which is from 209 subjects. CNN with softmax cross-entropy loss gives the most accurate matching of testing images. This method gives better results in terms of EER. We analyzed the proposed architecture on other publicly available databases through various experiments.

medium

Emotion Recognition from Facial Expressions Using Siamese Network,t recognizes emotions using our in-house developed dataset AED-2 (Amrita Emotion Dataset-2) which has 56 images of subjects expressing seven basic emotions viz., disgust, sad, fear, happy, neutral, anger, and surprise. It involves the implementation of the Siamese network which estimates the similar

sultry

Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models,ression models using the training data that consisted of NIFTY 50 index records from December 29, 2014 till December 28, 2018. Using these regression models, we predicted the . values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forec

finale

rheumatism

Analysis of UNSW-NB15 Dataset Using Machine Learning Classifiers,s, Logistic Regression, SMO, J48 and Random Forest. Experimental results give out its noticeable classification accuracy of 0.99 with the random forest classifier having 0.998 recall and specificity 0.999 respectively. Research studies reveal the fact that threat diagnosis using conventional dataset

散布

Concept Drift Detection in Phishing Using Autoencoders,concept drift. We use ADD to detect drift in a phishing detection data set which contains drift as it was collected over one year. We also show that ADD is competitive within ±24% with popular streaming drift detection algorithms on benchmark drift datasets. The average accuracy on the phishing data