Emotion recognition from speech signals plays a crucial role in Human-Machine Interaction (HMI), particularly in the development of applications such as affective computing and interactive systems. This review seeks to provide an in-depth examination of current methodologies in speech emotion recognition (SER), with a focus on databases, feature extraction techniques, and classification models. It has been done in the past using low-level descriptors (LLDs) like Mel-Frequency Cepstral Coefficients (MFCCs), linear predictive coding (LPC), and pitch-based features in methods like Support Vector Machines (SVM), Random Forests (RF), and Gaussian Mixture Models (GMM). But the development of deep learning techniques has completely changed the field. Models like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have shown that they are better at capturing the complex temporal and spectral features of speech. This paper reviews prominent speech emotion datasets, exploring their linguistic diversity, annotation processes, and emotional labels. It also analyzes the efficacy of different speech features and classifiers in handling challenges such as data imbalance, limited data availability, and cross-lingual variations. The review highlights the need for future work to address real-time processing, context-sensitive emotion detection, and the integration of multi-modal data to enhance the performance of SER systems. By consolidating recent advancements and identifying areas for further research, this paper aims to provide a clearer path for optimizing feature extraction and classification techniques in the field of emotion recognition.

The paper introduces a pioneering taxonomy for 5G jamming attacks, meticulously crafted through diverse research methodologies, including literature review, case studies, incident database analysis, and network architecture assessment. This taxonomy is based on six dimensions: attack classification, attack techniques, jammer characteristics, impact and target analysis, countermeasure strategies, and security layers. It gives a full picture of what is needed to protect 5G networks from new security threats, especially jamming attacks. Real‐world case studies from regions like Ukraine, the South China Sea, and Cuba validate the taxonomy’s efficacy in analyzing varied jamming scenarios and offer practical insights into cyber threats and mitigation strategies. By looking closely at attack details, network effects, countermeasure strategies, and security layers, this research makes a big difference in improving the security of 5G networks. It gives everyone involved, the tools and knowledge they need to protect themselves from jamming attacks and make networks more resilient in a world where technology and connectivity are changing quickly. The key findings of this research include identifying key 5G network vulnerabilities to jamming attacks and presenting a detailed six‐dimensional taxonomy: attack classification, techniques, jammer characteristics, impact analysis, countermeasures, and security layers. It emphasizes the need for strong security strategies to improve 5G network resilience against evolving threats, which case studies from Cuba, the South China Sea, and Ukraine have validated.

Distributed Ledger Technology (DLT), encompassing blockchain and similar decentralized systems, is revolutionizing secure and scalable computing. This paper comprehensively surveys DLT, highlighting its core principles, cloud integration and enhanced data security/scalability. It examines various DLT systems, advanced consensus mechanisms and Layer 2 solutions to address scalability. It explores DLT’s innovative use cases in finance, healthcare, supply chains and IoT, emphasizing advantages and challenges. It also delves into DLT’s regulatory landscape, addressing compliance, data privacy and governance, while assessing the potential for regulatory clarity and international cooperation. The paper aims to guide researchers and practitioners navigating DLT’s evolving landscape, underscoring its transformative potential for secure and scalable computing in the digital age.

Dense segmentation tasks, including semantic, instance, and panoptic segmentation, are essential for improving our comprehension of urban landscapes. This paper examines various deep learning methodologies to enhance dense segmentation in urban scene parsing. Additionally, it delves into the challenges associated with adapting these deep learning models for practical, real-world applications and explores enhancements to address these challenges. Multiple datasets for urban scene parsing were analyzed, with the Cityscapes and Mapillary Vistas datasets identified as more suitable for urban scene parsing. Among them, the Mapillary Vistas dataset has greater diversity and complexity. Although Vision Transformers are computationally demanding, they exhibit superior performance in dense segmentation tasks. Prominent techniques that enhance the performance of Vision Transformers (ViTs), such as Encoder-Decoder architectures, Atrous convolutions, Atrous Spatial Pyramid Pooling (ASPP), dual decoders, and mask transformers are reviewed. Ensuring the performance of Vision Transformer models across various domains and fine-tuning them for practical, real-world applications present critical challenges in dense segmentation tasks for urban scene parsing. Among the various thematic attention approaches explored to ensure models concentrate on regions of interest, Masked Attention, and Visual Prompt Tuning mechanisms have shown superior results. Nonetheless, considerable potential remains for enhancing generalization across diverse urban datasets and improving overall efficiency.