The tremendous growth of cyberspace has magnified the risk of advanced cyber-attacks, making the conventional security system deficient. An intrusion detec

The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.

When individuals interact with one another to accomplish specific goals, they learn from others’ experiences to achieve the tasks at hand. The same holds for learning in virtual environments, such as video games. Deep multiagent reinforcement learning shows promising results in terms of completing many challenging tasks. To demonstrate its viability, most algorithms use value decomposition for multiple agents. To guide each agent, behavior value decomposition is utilized to decompose the combined Q-value of the agents into individual agent Q-values. A different mixing method can be utilized, using a monotonicity assumption based on value decomposition algorithms such as QMIX and QVMix. However, this method selects individual agent actions through a greedy policy. The agents, which require large numbers of training trials, are not addressed. In this paper, we propose a novel hybrid policy for the action selection of an individual agent known as Q-value Selection using Optimization and DRL (QSOD). A grey wolf optimizer (GWO) is used to determine the choice of individuals’ actions. As in GWO, there is proper attention among the agents facilitated through the agents’ coordination with one another. We used the StarCraft 2 Learning Environment to compare our proposed algorithm with the state-of-the-art algorithms QMIX and QVMix. Experimental results demonstrate that our algorithm outperforms QMIX and QVMix in all scenarios and requires fewer training trials.

Cryptocurrencies function as a digital exchange medium operating on network-based technology, where records are secured using cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5). These cryptocurrencies utilize blockchain technology to provide transparent, reliable, and immutable transactions. Consequently, cryptocurrencies have gained significant traction across multiple sectors, particularly finance. However, their value is still prone to considerable fluctuations, which raises concerns about the risks associated with investments. The emerging discipline of cryptocurrency forecasting has gained popularity worldwide, and academics are employing a variety of deep learning (DL) and machine learning (ML) techniques to investigate the elements that influence cryptocurrency values. Among the various DL methods, LSTM has demonstrated noteworthy efficiency. Nevertheless, there are intrinsic downsides to LSTM, notably due to its sequential nature, which hinders parallelization and complicates the modeling of both short- and long-term dependencies. To address these shortcomings, the Transformer architecture has emerged as a potent solution. The Transformer is widely used in DL for its exceptional parallelization capabilities and its capacity to extract broad, distant data dependencies. Recent studies have explored Transformer-based approaches for cryptocurrency price forecasting, particularly for modeling long-term dependencies. However, these models often exhibit limitations in capturing high-frequency, short-term fluctuations, making them less suitable for short-term prediction tasks. Our proposed methodology introduces a novel Transformer-based hybrid framework designed to enhance forecasting accuracy across various time scales. We evaluate the forecasting accuracy for 10 cryptocurrencies at hourly, daily, and yearly frequencies. The findings show that, in comparison to other DL techniques such as LSTM, RNN, and baseline Autoformer, our model achieves superior accuracy. Furthermore, we benchmark our method against prominent Transformer variants such as Informer and FEDformer, and observe improved performance in both short- and long-term forecasting scenarios. These results indicate that our proposed model consistently outperforms existing state-of-the-art Transformer-based approaches in cryptocurrency price prediction.

In the rapidly advancing Reinforcement Learning (RL) field, Multi-Agent Reinforcement Learning (MARL) has emerged as a key player in solving complex real-world challenges. A pivotal development in this realm is the introduction of the mixing network, representing a significant leap forward in the capabilities of multi-agent systems. Drawing inspiration from COMA and VDN methodologies, the mixing network overcomes limitations in extracting combined Q-values from joint state-action interactions. Previous approaches like COMA and VDN faced constraints in fully utilising the state-provided information during training, limiting their effectiveness. QMIX and QVMinMax addressed this issue by employing neural networks to convert centralized states into weights for a second neural network, akin to hyper-networks. However, these solutions presented challenges such as computational intensity and susceptibility to local minima. To overcome these hurdles, our proposed methodology introduces three key contributions. First, we introduce the state- fusion network, an innovative alternative to traditional mixing, with a self-attention mechanism . Second, to address the local optima problem in MARL algorithms, we leverage the Grey Wolf Optimizer for weight and bias selection, adding a stochastic element for improved optimization. Finally, we comprehensively compare with QMIX, evaluating performance under two optimization methods: Gradient Descent and Stochastic Optimizer. Using the StarCraft II Learning Environment (SC2LE) as our experimental platform, our results demonstrate the superiority of our methodology over QMIX, QVMinMax, and QSOD in absolute performance, particularly when operating under resource constraints. Our proposed methodology contributes to the ongoing evolution of MARL techniques, showcasing advancements in attention mechanisms and optimization strategies for enhanced multi-agent system capabilities.