Weather prediction is an important aspect of modern society, with implications for everything from agriculture to disaster response. However, accurately predicting the weather remains a challenging task due to the complexity of atmospheric processes and the limitations of existing technologies. In recent years, advancements in futuristic technologies such as artificial intelligence and machine learning, high-resolution weather models, climate modeling, remote sensing, and citizen science have offered new opportunities to improve weather prediction. These technologies have the potential to provide more detailed and accurate data, identify patterns and relationships in large data sets, and improve communication and education strategies. Continued research and development in these areas may lead to more accurate and timely weather predictions, ultimately improving public safety and quality of life.
The advent of deep learning has revolutionized the technology industry and has made Deep Neural Networks (DNNs) the powerhouse of many modern day software applications. Well-trained DNNs are able to perform complex tasks such as speech recognition, object detection and image classification with high precision and accuracy. However, the task of training such complex networks at times requires enormous amount of computational resources for which the task is often outsourced to third parties. Recent work suggests that outsourcing the training task can act as a favourable gateway for a malicious trainer to induce a backdoor in the model, which when triggered can force the model into performing in a predefined way which has been set up by the malicious trainer. This paper starts by giving an overview on how such attacks are induced and consequently discusses and provides experimental proof on the various strategies which can be used to neutralise such attacks. We use the l1 and l2 norm to identify weights which are susceptible to be poisoned and prune them away by setting their value to zero. We further inspect the efficiency of layer wise and global pruning. We infer from our experiments that fine-tuning the model for a few epochs after the fine-pruning stage has been completed helps the model to regain its lost accuracy and provides better test time accuracy. During this study, we understand that performing fine-pruning in the later layers is more effective. By pruning the last layer along with fine-tuning the model after fine-pruning has been completed, we achieve 99.96% and 86.05% accuracy for the clean validation and test dataset respectively and consequently witness a drop in attach success rate from 99 to 0 and near 0 in some cases.
The prevalence of picture tampering and alteration has grown significantly in recent years across several industries. The objective of this research work is to identify and draw attention to violations committed when manipulating digital photographs nowadays. The system identifies areas of the image that are likely to be altered using a combination of feature extraction, image segmentation, and machine learning approaches. Its effectiveness is then tested using a publicly accessible dataset of altered photos.
Due to certain unique qualities and capabilities, Blockchain is a highly beneficial approach that may be used to safely manage various gadgets in a smart city. It has several uses, particularly in dispersed situations where elements such as wireless sensor nodes must be confident of the server's legitimacy. Because modern blockchain solutions that handle post-quantum problems have not been developed, we explore a blockchain in the quantum-resistant cryptography context and strive to find how it might withstand quantum computing assaults throughout this work. Furthermore, the newly born Proof of Stake (PoS) provides faster and cheaper transactions, but its security is not proven in comparison to its forefather Proof of Work (PoW). As a result, a new quantum-resistant proof of stake (Quantum-Resistant PoS) agreement technique for smart city application services has been developed. The proposed model has the ability to protect a distributed ledger system against a quantum assault and also provides scalability and inexpensive transactions. Additionally, user-based post-quantum authentication is included in the transaction process to create a simple payment verification node. Following that, we present a detailed rundown of how to execute a post-quantum simple payment verification and transaction on a blockchain. Thus, our study will contribute to imminent quantum-resistant blockchain exploration along with the design or structure of potentially distributed ledger technology-based ubiquitous computing.
Cybersecurity has emerged as one of the most crucial facets of the Internet of Things (IoT) due to the increased possibility of cyberattacks. IoT cybersecurity aims to lower cybersecurity risk for businesses and users by safeguarding IoT resources and privacy. IoT security management could be improved by using new cybersecurity technology and solutions. However, efficient IoT cyber risk management frameworks are lacking for organizations. This paper presents the most significant proposals of cybersecurity in IoT by describing their objectives, working principles, applications, features, and approaches referred. The paper also presents cybersecurity in IoT using five optimization algorithms such as particle swarm optimization, ant colony optimizations, an artificial bee colony, genetic algorithm, and AdaBoost algorithm. The paper concludes with research gaps and limitations, which urge for further study for subsequent research work.
