Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.

Recently, digital transformation is supposed to affect all aspects of human life profoundly. Nevertheless, there is a lack of summaries map digital transformation in the environmental sustainability domain. To address this knowledge gap, this study examines the impacts of digital transformation on environmental sustainability, including both positive and negative effects. Furthermore, the results highlight the transformations that preserve the environment in three main areas: waste management and handling, pollution prevention and control, and sustainable resource management. Based on the literature summary, this study also discusses the opportunities and challenges in this field, which attempts to offer a vision for further research.

In the current globalization trend, digital transformation is an indispensable requirement that affects all aspects of life. Within the education domain, it creates opportunities for tertiary education institutions to replace traditional methods of teaching, learning, research, and operation with more innovative, creative, and cost-effective methods. This article aims to examine recent technological advancements to promote the transformation in tertiary education. Our research methodology consists of two main activities: (1) identifying relevant literature on the use of technology to promote transformation in tertiary education by adopting PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guildelines; (2) conducting a thematic analysis based on the findings of the literature review; to explore the relationships between them in order to better understand the link between technological trends and digital transformation in tertiary education. The findings indicate that the current technology trends that are being concerned and deployed in the educational environment are Artificial intelligence, the Internet of Things, blockchain technology, and other relevant platforms and technologies such as Social networks, Mobile platforms, Big data analytics, Cloud computing, Robotic Process Automation, Virtual reality and Augmented reality and Additive manufacturing. The article also discusses the adjustment of tertiary education in the process of digital transformation with the support of technology and points out the shortcomings as well as research directions in the coming time.

Recent developments in Artificial intelligence (AI) have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and will probably try to use it in their activities. This survey aims at providing an overview on the way artificial intelligence can be used to power a malicious program that is: intelligent evasion techniques, autonomous malware, AI against itself, and applying bio-inspired computation and swarm intelligence.

Digital speech recognition is a challenging problem that requires the ability to learn complex signal characteristics such as frequency, pitch, intensity, timbre, and melody, which traditional methods often face issues in recognizing. This article introduces three solutions based on convolutional neural networks (CNN) to solve the problem: 1D-CNN is designed to learn directly from digital data; 2DS-CNN and 2DM-CNN have a more complex architecture, transferring raw waveform into transformed images using Fourier transform to learn essential features. Experimental results on four large data sets, containing 30,000 samples for each, show that the three proposed models achieve superior performance compared to well-known models such as GoogLeNet and AlexNet, with the best accuracy of 95.87%, 99.65%, and 99.76%, respectively. With 5-10% higher performance than other models, the proposed solution has demonstrated the ability to effectively learn features, improve recognition accuracy and speed, and open up the potential for broad applications in virtual assistants, medical recording, and voice commands.