Journal of AI, Data Science and Cyber Systems

The Journal of AI, Data Science, and Cyber Systems is an international, peer-reviewed, open-access journal that focuses on the development of intelligent algorithms, data science techniques, and cyber-enabled systems. It is a key journal publication of the IEEE Computer Society. The aim of the journal is to offer a one-stop platform for publishing research work that combines artificial intelligence, machine learning, data analytics, computing infrastructure, and secure cyber systems in innovative ways. The research should focus on theoretical aspects, as well as implementation scenarios, so that scalable, trusted, and resilient digital systems can be built. Knowing the fast convergence of AI, big data, cloud and edge computing, and cyber physical systems, the journal strongly encourages interdisciplinary and inter-industry research that combines computer science and engineering fields with information systems and application domains. A special focus will be placed on research that contributes to the ethical, explainable, and trustworthy use of AI.

Artificial Intelligence and Machine Learning
AI is a broad topic. You’ve got machine learning, deep learning, and reinforcement learning, all of which are fueling how computers can learn from data and experience. You see foundation models, large language models, generative AI all over the place, all of which are informing how we use technology. You’ve got natural language processing, which is making it possible for computers to understand us and communicate with us the way we communicate. Computer vision is all about how we can analyze images, videos, to enable a computer to see. You’ve got knowledge representation, or planning, which is how systems can reason, make decisions. We need trustworthy AI, so we need explanations, so we need explanations. It is a huge topic, ethical, fairness, bias – we don’t just care what we can do, but how we do it. In the background, we’ve got evolutionary computation, optimization, all of which is looking to make this technology perform better, faster.

Data Science & Intelligent Analytics
Big data analytics and large-scale processing are fundamental to modern data science. Mining into the data to spot deep patterns, predictive analytics help one to look into what's coming next. Statistical learning and probabilistic modelling provide some necessary mathematical muscle to the mix. The data engineering, integration, and solid management of data are what keep it all running behind the scenes. With streaming, real-time, and spatiotemporal analytics, you may make flying decisions, whichever and whenever the data comes in from. Decision support systems and intelligent information systems chart choices, while data visualization makes all those numbers make sense.

Cyber Systems and Cybersecurity
Cyber physical systems and digital twins represent a zone where the distinctions between the digital and the real world tend to dwindle away. Networked and distributed cyber systems connect everything and everyone, everywhere. Cybersecurity, privacy, and secure communication are always in vogue no one wants their data insecure. Encryption and authentication keep everything securely locked down. But with AI on the radar, you get the security of AI and adversarial machine learning, where it is always a never-ending race to be one step ahead of the attackers. Intrusion detection, malware analysis, and cybersecurity intelligence keep the threats away. Blockchain and decentralized ledgers disrupt the way and mean of trust and verification of cyber transactions.

Computing Systems & Intelligent Infrastructure
Cloud computing, edge computing, fog computing, and server less computing are the various paradigms for processing and storing your data based on your requirements. The Internet of Things (IoT), sensor networks, enable intelligence in common objects. Distributed, parallel, high-performance computing does the heavy lifting for complex computational tasks. Intelligent software systems and system architectures maintain flexibility and scalability for everything. Real-time and autonomous systems based on embedded systems are the driving forces behind autonomous vehicles and smart devices. Software engineering for AI systems is the final piece that mixes all these and ensures everything is working correctly.

Applied AI & Interdisciplinary Domains
AI and data science applications have permeated the healthcare, bioinformatics, and medical imaging fields you can observe this in the form of diagnoses and research on a daily basis. Smart finance, fintech, and risk analysis impact the way we manage finances and detect financial risks. Smart cities and smart transportation integrate well into our daily life and make everything smoother and more efficient. Also in industry, artificial intelligence and robotics and Industry 4.0 and 5.0 keep everything smart and going within the factories and the whole chain. AI doing good stuff in the sustainability and energy sectors and keeping an eye on things that matter and ways to make improvements. Additionally, the more AI that is involved with everyday life, the more human interaction with AI is relevant.