A Novel Hybrid Spotted Hyena-Swarm Optimization (HS-FFO) Framework for Effective Feature Selection in IOT Based Cloud Security Data

Internet of Things (IoT) has gained its major insight in terms of its deployment and applications. Since IoT exhibits more heterogeneous characteristics in transmitting the real time application data, these data are vulnerable to many security threats. To safeguard the data, machine and deep learning based security systems has been proposed. But this system suffers the computational burden that impedes threat detection capability. Hence the feature selection plays an important role in designing the complexity aware IoT systems to defend the security attacks in the system. This paper propose the novel ensemble of spotted hyena with firefly algorithm to choose the best features and minimise the redundant data features that can boost the detection system's computational effectiveness.  Firstly, an effective firefly optimized feature correlation method is developed.  Then, in order to enhance the exploration and search path, operators of fireflies are combined with Spotted Hyena to assist the swarms in leaving the regionally best solutions. The experimentation has been carried out using the different IoT cloud security datasets such as NSL-KDD-99 , UNSW and CIDCC -001 datasets and contrasted with ten cutting-edge feature extraction techniques, like PSO (particle swarm optimization), BAT, Firefly, ACO(Ant Colony Optimization), Improved PSO, CAT, RAT, Spotted Hyena, SHO and  BOC(Bee-Colony Optimization) algorithms. Results demonstrates the proposed hybrid model has achieved the better feature selection mechanism with less convergence  time and aids better for intelligent threat detection system with the high performance of detection

Smart Multi-Model Emotion Recognition System with Deep learning

Emotion recognition is added a new dimension to the sentiment analysis. This paper presents a multi-modal human emotion recognition web application by considering of three traits includes speech, text, facial expressions, to extract and analyze emotions of people who are giving interviews. Now a days there is a rapid development of Machine Learning, Artificial Intelligence and deep learning, this emotion recognition is getting more attention from researchers. These machines are said to be intelligent only if they are able to do human recognition or sentiment analysis. Emotion recognition helps in spam call detection, blackmailing calls, customer services, lie detectors, audience engagement, suspicious behavior. In this paper focus on facial expression analysis is carried out by using deep learning approaches with speech signals and input text

Integrated publish/subscribe and push-pull method for cloud based IoT framework for real time data processing

Cloud-based IoT is a platform that connects smart devices to the cloud for real-time data analysis. Any device on the IoT platform can connect to the cloud through messaging. The integrated publish/subscribe and push/pull methods for a cloud-based IoT framework that is scalable for connecting IoT devices and processing the real-time data are proposed. The proposed framework uses a publish/subscribe messaging broker and a push-pull method for transmitting the data from the device to the cloud. The IoT devices publish the data via the broker, to which the cloud service providers subscribe. This publishing and transferring of data from the broker to the cloud is implemented with the help of a push-pull mechanism. In this mechanism, the broker makes the computations required to select the cloud service provider. Hence, the overhead of the device is reduced. All computations go in parallel, which reduces the latency of the system. The system is flexible for any number of devices, brokers, and cloud service providers, which shows that the system is scalable. The results demonstrated the effectiveness of the model, which was developed using a cloud-based IoT framework with a focus on scalability and latency