Speech Emotion Recognition (SER) is an active research area with wide range of applications like medical, entertainment, monitoring in the field of Human Computer Interface. Generally, speech signals include high dimensionality of the feature that degrades the performance of SER. This research paper concentrated on SER using hybrid network that is composed of Convolutional Neural Network and Bidirectional Long Short Term Memory Networks (CNN-BLSTM). The major objective of this study is to recognize more relevant temporal features rather than traditional feature learning. The proposed CNN-BLSTM techniques increases the precision, recall and accuracy of emotion recognition in speech signal significantly. IEMOCAP dataset has been used in experimental analysis of the proposed approach to classify the different emotions of human; those are happy, angry, sad, and neutral. The performance of the CNN-BLSTM method has been measured using parameters like precision, recall and accuracy. Compare to the existing Support Vector Machine (SVM), the proposed CNN-BLSTM achieved approximately 9.83% of true positives proportion enhancement in SER.

In our current generation we are very much habituated to many mobile services like communication, ecommerce etc. In mobile communication services SMS’s (Short Message Service’s) are very common and important services which we are using in personal purposes and profession. In these services some messages may cause spam attacks which is trap to users to access their personal information or attracting them to purchase a product from unauthorized websites. It is very easy for companies send any information or service or alert to their customers/users with these SMS API’s. Based on these services it is also possible for sending spam messages. So in this system we are using advance Machine Learning concepts for detection of the spam filtering in the SMS’s. In this system we are importing the dataset from UCI repository and for spam SMS detection we implementing machine learning classifiers like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Neural Networks (NN) algorithms and with their metrics like accuracy, precision, recall and f-score. We calculate performances between there algorithms as well as we show the experiment results with visualization techniques and analyses which algorithm is best for spam SMS detection.