Subject matter: Speech emotion recognition (SER) is an ongoing interesting research topic. Its purpose is to establish interactions between humans and computers through speech and emotion. To recognize speech emotions, five deep learning models: Convolution Neural Network, Long-Short Term Memory, Artificial Neural Network, Multi-Layer Perceptron, Merged CNN, and LSTM Network (CNN-LSTM) are used in this paper. The Toronto Emotional Speech Set (TESS), Surrey Audio-Visual Expressed Emotion (SAVEE) and Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets were used for this system. They were trained by merging 3 ways TESS+SAVEE, TESS+RAVDESS, and TESS+SAVEE+RAVDESS. These datasets are numerous audios spoken by both male and female speakers of the English language. This paper classifies seven emotions (sadness, happiness, anger, fear, disgust, neutral, and surprise) that is a challenge to identify seven emotions for both male and female data. Whereas most have worked with male-only or female-only speech and both male-female datasets have found low accuracy in emotion detection tasks. Features need to be extracted by a feature extraction technique to train a deep-learning model on audio data. Mel Frequency Cepstral Coefficients (MFCCs) extract all the necessary features from the audio data for speech emotion classification. After training five models with three datasets, the best accuracy of 84.35 % is achieved by CNN-LSTM with the TESS+SAVEE dataset.

The widespread deployment of the Internet of Things in any smart city provides a regular flow of huge amount of data in server(s) that poses challenges for effective and efficient management to improve the quality of citizens’ life. To maintain the privacy and security of these data, a proper and secured identification and authentication process is very essential. In this paper, we propose a cluster-based identification and authentication process for the users, edge servers, and service servers, which are engaged in storing, processing, and accessing data. The proposed identification and authentication process is secured due to some codes (values), which are not possible to compute except by the concerned entities. For the proposed trust evaluation method (which actually strengthens the proposed authentication process), we consider major components and their integration in the model very carefully so that the simulation results become credible. Hence, we hope that the simulation results will be useful for the readership. As a whole, the proposed approach has potentials of being implemented in real-time applications.