N. Narayanan Prasanth

Intelligent transport systems are increasing in application for real-time communication between vehicles and the infrastructure, and along with that are increasing the popularity of vehicular ad-hoc networks (VANETs). However, the very open and dynamic environment gives rise to varied kinds of DDoS attacks that can disrupt safety–critical services. The existing mechanisms for detection of DDoS attacks in VANETs have been found to suffer from low efficacy of detection, high magnitude of false alarm rates, and poor adaptability to evolving patterns of attacks. To address this challenge, this paper introduces VANET-DDoSNet++, a novel, multi-layered defense framework that uniquely integrates optimized feature selection, advanced deep learning detection, adaptive reinforcement learning mitigation, and secure blockchain-based reporting. The preprocessing step ensures high quality of data by dealing with missing values, removing outliers, augmenting the data, and detecting outliers effectively, preparing for analysis. The features including network traffic statistics, spatiotemporal data, deep traffic embeddings, and behavioural patterns are extracted. To improve the detection performance, a hybrid selection strategy is introduced featuring an adaptive dragonfly algorithm (ADA) and an Enhanced grasshopper optimization algorithm (EGOA) for feature selection where the optimal features are determined. Finally, the detection part applies a hybrid architecture of deep learning referred to as VANET-DDoSNet++, where convolutional LSTM networks, attention layers, and residual/dense connections are used for reliable DDoS detection. An adaptive reinforcement learning-based intrusion mitigation approach with reward shaping tailors defense strategies dynamically with evolving attack vectors by all means. The decentralized trust management mechanism based on blockchain is intended for a secure and verifiable real-time threat reporting from vehicles. The CIC-DDoS2019 dataset, which includes real-world vehicular traffic data with modern reflective DDoS attacks, is utilized for evaluation. The experimental results show that VANET-DDoSNet++ surpasses other currently existing methodologies achieving 98.04% accuracy with 70% training data and 99.18% with 80% training data besides dramatically reducing false positive and negative rates as well as improving overall precision, F1-score, sensitivity, and specificity. The factor deals with the evolution of DDoS attacks whereas VANET networks offer a dynamic and secure intrusion detection and mitigation framework.

Publishing highlights after a sports game is a common practice in the broadcast industry, providing viewers with a quick summary of the game and highlighting interesting events. However, the manual process of compiling all the clips into a single video can be time-consuming and cumbersome for video editors. Therefore, the development of an artificial intelligence (AI) model for sports highlight generation would significantly reduce the time and effort required to create these videos and improve the overall efficiency and accuracy of the process. This would benefit not only the broadcast industry but also sports fans who are looking for a quick and engaging way to catch up on the latest games. The objective of the paper is to develop an AI model that automates the process of sports highlight generation by taking a match video as input and returning the highlights of the game. The approach involves creating a list of words (wordnet) that indicate a highlight and comparing it with the commentary audio’s transcript to find a similarity, making use of a speech-to-text conversion, followed by some pre-processing of the extracted text, vectorization and finally measurement of the cosine similarity metric between the text and the wordnet. However, this process can become time-consuming too, in case of longer match videos, as the computation times of the AI models become inefficient. So, we used a parallel processing technique to counter the time required by the AI models to compute the outputs on large match videos, which can decrease the overall time complexity and increase the overall throughput of the model.

A new array P system variant, parallel 8-directional array P systems, is introduced in this paper. In addition, we have found an application for such a system when moving a robot in a rectangular grid from source to destination without colliding with obstacles. The following four cases of robot motion have been discussed. In the grid, we start with a fixed number of static obstacles, followed by a fixed number of dynamic obstacles moving with constant speed, a fixed number of dynamic obstacles moving with varying speeds, and finally, varying numbers of dynamic obstacles moving with constant speed. All these algorithms were solved in polynomial time.

Bitcoin is the world’s most traded cryptocurrency and highly popular among cryptocurrency investors and miners. However, its volatility makes it a risky investment, which leads to the need for accurate and fast price-prediction models. This article proposes a Bitcoin price-prediction model using a long short-term memory (LSTM) network in a distributed environment. A tensor processing unit (TPU) has been used to provide the distributed environment for the model. The results show that the TPU-based model performed significantly better than a conventional CPU-based model.