Summary The Internet is connected with everyone for sharing and monitoring digital information. However, securing network resources from malicious activities is critical for several applications. Numerous studies have recently used deep learning‐based models in detecting intrusions and received relatively robust recognition outcomes. Nevertheless, most investigations have operated old datasets, so they could not detect the most delinquent attack information. Therefore, the current research proposes the long short‐term memory (LSTM)‐based near real‐time multiclass network intrusion detection system (NIDS) utilizing complex cloud CSE‐CICIDSS2018 datasets to secure and detect the network anomalous. The proposed strategy utilizes a random forest algorithm for dimensionality reduction and feature selection. In addition, the selected best suitable features were used in a deep learning‐based LSTM model developed for detecting network intrusions. The experimental outcomes reveal that the presented LSTM model obtained 99.66% testing accuracy with 0.12% loss. Thus, the suggested approach can detect network intrusions with the highest precision and lowest rate over the earlier designs.

The Sign Language Recognition system intends to recognize the Sign language used by the hearing and vocally impaired populace. The interpretation of isolated sign language from static and dynamic gestures is a difficult study field in machine vision. Managing quick hand movement, facial expression, illumination variations, signer variation, and background complexity are amongst the most serious challenges in this arena. While deep learning-based models have been used to accomplish the entirety of the field's state-of-the-art outcomes, the previous issues have not been fully addressed. To overcome these issues, we propose a Hybrid Neural Network Architecture for the recognition of Isolated Indian and Russian Sign Language. In the case of static gesture recognition, the proposed framework deals with the 3D Convolution Net with an atrous convolution mechanism for spatial feature extraction. For dynamic gesture recognition, the proposed framework is an integration of semantic spatial multi-cue feature detection, extraction, and Temporal-Sequential feature extraction. The semantic spatial multi-cue feature detection and extraction module help in the generation of feature maps for Full-frame, pose, face, and hand. For face and hand detection, GradCam and Camshift algorithm have been used. The temporal and sequential module consists of a modified auto-encoder with a GELU activation function for abstract high-level feature extraction and a hybrid attention layer. The hybrid attention layer is an integration of segmentation and spatial attention mechanism. The proposed work also involves creating a novel multi-signer, single, and double-handed Isolated Sign representation dataset for Indian and Russian Sign Language. The experimentation was done on the novel dataset created. The accuracy obtained for Static Isolated Sign Recognition was 99.76%, and the accuracy obtained for Dynamic Isolated Sign Recognition was 99.85%. We have also compared the performance of our proposed work with other baseline models with benchmark datasets, and our proposed work proved to have better performance in terms of Accuracy metrics.