An authenticated encrypted routing protocol against attacks in mobile ad-hoc networks

Mobile Ad hoc Network is stated as a cluster that contains Digital data terminals and they are furnished with the wireless transceivers which are able to communicate with each other with no need of any fixed architecture or concentrated authority. Security is one of the major issues in MANETs because of vast applications such as Military Battlefields, emergency and rescue operations[10]. In order to provide anonymous communications and to identify the malicious nodes in MANETs, many authors have proposed different secure routing protocols but each protocol have their own advantages and disadvantages. In MANTE’s each and every node in the communicating network functions like router and transmits the packets among the networking nodes for the purpose of communication[11]. Sometimes nodes may be attacked by the malicious nodes or the legitimate node will be caught by foemen there by controlling and preventing the nodes to perform the assigned task or nodes may be corrupted due to loss of energy. So, due to these drawbacks securing the network under the presence of adversaries is an important thing. The existing protocols were designed with keeping anonymity and the identification of vicious nodes in the network as the main goal. For providing better security, the anonymity factors such as Unidentifiability and Unlinkability must be fully satisfied[1]. Many anonymous routing schemes that concentrate on achieving anonymity are proposed in the past decade and they provides the security at different levels and also provides the privacy protection that is of different cost. In this paper we consider a protocol called Authenticated Secure Routing Protocol proposed which provides both security & anonymity. Anonymity is achieved in this protocol using Group signature. Over all by using this protocol performance in terms of throughput as well as the packet dropping rate is good compared to the other living protocols

Audiovisual speech recognition based on a deep convolutional neural network

Audiovisual speech recognition is an emerging research topic. Lipreading is the recognition of what someone is saying using visual information, primarily lip movements. In this study, we created a custom dataset for Indian English linguistics and categorized it into three main categories: (1) audio recognition, (2) visual feature extraction, and (3) combined audio and visual recognition. Audio features were extracted using the mel-frequency cepstral coefficient, and classification was performed using a one-dimension convolutional neural network. Visual feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural networks. Finally, integration was performed using a deep convolutional network. The audio speech of Indian English was successfully recognized with accuracies of 93.67% and 91.53%, respectively, using testing data from 200 epochs. The training accuracy for visual speech recognition using the Indian English dataset was 77.48% and the test accuracy was 76.19% using 60 epochs. After integration, the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67% and 91.75%, respectively

Fault detection and state estimation in robotic automatic control using machine learning

In the commercial and industrial sectors, automatic robotic control mechanisms, which include robots, end effectors, and anchors containing components, are often utilized to enhance service quality. Robotic systems must be installed in manufacturing lines for a variety of industrial purposes, which also increases the risk of a robot, end controller, and/or device malfunction. According to its automated regulation, this may hurt people and other items in the workplace in addition to resulting in a reduction in quality operation. With today's advanced systems and technology, security and stability are crucial. Hence, the system is equipped with fault management abilities for the identification of developing defects and assessment of their influence on the system's activity in the upcoming utilizing fault diagnostic methodologies. To provide adaptive control, fault detection, and state estimation for robotic automated systems intended to function dependably in complicated contexts, efficient techniques are described in this study. This paper proposed a fault detection and state estimation using Accelerated Gradient Descent based support vector machine (AGDSVM) and gaussian filter (GF) in automatic control systems. The Proposed system is called (AGDSVM + GF). The proposed system is evaluated with the following metrics accuracy, fault detection rate, state estimation rate, computation time, error rate, and energy consumption. The result shows that the proposed system is effective in fault detection and state estimation and provides intelligent control automatic control