Creating synthetic data, which closely resembles real data, using AI based techniques is becoming increasingly important in solving machine learning problems across the entire lifecycle of ML from training to tuning and testing. Synthetic data can solve multiple limitations like data being scarce or unavailable, data privacy concerns like in healthcare scenarios with PII and PHI data, or can just speed up the AI model development journey by providing fast access to data while the real data is being prepared.

Business-to-Business (B2B) payments are an area factor that affects traditional payments. The main issues affecting traditional payments include the fact that they are prone to making mistakes such as inefficiencies, errors, and delays, which affect operations. The evolvements of Artificial Intelligence (AI) and Robotic Process Automation (RPA) state the significance of revolutionary changes in this area.

Artificial Intelligence (AI) is reshaping industries, economies, and daily experiences, offering unprecedented opportunities while raising profound ethical questions as AI systems become more capable and integrated into society; ensuring they reflect human values is crucial for fostering trust, fairness, and long-term benefits. AI does not operate in isolation – it mirrors the data, assumptions, and goals that shape its development.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the healthcare landscape, ushering in a new era of possibilities for patients, providers, and researchers. These technologies are enhancing the efficiency of medical processes and redefining the boundaries of what’s possible in diagnosis, treatment, and patient care.

As artificial intelligence (AI) becomes deeply intertwined with our lives, the ethical and governance challenges it presents are becoming increasingly urgent. AI's "moral matrix" refers to the complex web of ethical dilemmas, societal expectations, and regulatory frameworks shaping its development and use. From algorithmic bias and data privacy to autonomous decision-making and accountability, navigating this matrix requires a balanced approach that prioritizes human well-being without stifling innovation.

The nature of cloud computing is such that infrastructure needs to be managed dynamically and anticipating to cater for new applications. Berkeley Packet Filter extended (eBPF), which was initially developed for packet filtering within Linux, has developed into a versatile means for monitoring, performance debugging, and protection in the cloud and beyond.

Enterprises steadily emerging into the borderless digital ecosystem, traditional security paradigms are in a poor position to cope with the increasing complexity of modern cyber threats. Zero Trust Architecture (ZTA) has become a new direction to enterprise security whereby the defense paradigm has moved from the perimeter focus to an adaptive identity focused one.

The arrival of automation in data engineering has rewritten the way organizations manage Big Data processing and analytics. Automation-powered smart pipelines lend themselves to automated ingestion, transformation, and loading processes without much automation. However, embedding these pipelines into the real world brings the challenges of tool integration, data quality assurance, real-time processing and maintainability.

AI and blockchain in the healthcare sector are slowly emerging as a modern and innovative approach towards the achievement of secure and accurate data management, as well as effective process optimization. AI has the ability to work with big data to help get insights into large sets of medical data; on the other hand, blockchain provides a decentralized place to store this data securely.

Reinforcement Learning (RL) has rapidly emerged as a powerful tool in many fields to provide intricate solutions for enhancing the decision-making process; applying RL to Large Language Models (LLMs) extended the ways of enhancing text generation. As for this paper, its primary concern is how RL, from the real-world application, deep reinforcement learning, policy gradient methods, and value-based methods, can go beyond conventional retraining for LLMs.

The gaming sector, characterized by its extensive digital framework and millions of simultaneous users, represents a significant target for cyber threats, including Distributed Denial of Service (DDoS) assaults, phishing initiatives, and data infringements. Traditional DNS scanning methodologies often depend on active techniques, which, although effective, may unintentionally disrupt ongoing services or elicit defensive responses. This paper presents a groundbreaking passive enumeration approach specifically designed for DNS scanning within the gaming industry. By utilizing publicly accessible DNS records, threat intelligence frameworks, and historical data, this method reduces disruptions while yielding vital insights into potential weaknesses.

This paper presents an ethical and bias-aware framework for object detection in images using a You only look once (YOLO) based deep learning model, integrating reinforcement learning (RL) and Internet of Things (IoT) data for real-time ethical decision-making in autonomous systems. The framework addresses bias concerns in autonomous systems by assigning ethical scores to different object classes (e.g., pedestrians, vehicles) based on predefined risk and ethical factors.

The convergence of Industry 4.0 and IoT has fueled a need for low-latency decision-making at the edge of production systems, while also leveraging centralized resources for global analytics and security. This paper proposes a Hybrid Edge-Central (HEC) Architecture that partitions Graph Convolutional Networks (GCNs) across on-premises devices and cloud data centers, supplemented by Generative Adversarial Networks (GANs) for adversarial testing. A hardware security module (HSM) on each edge device protects API keys and ensures tamper-resistant cryptographic operations.

Continuous Integration and Continuous Deployment (CI/CD) pipelines are a fundamental part of modern DevOps, helping teams deliver software quickly and reliably. However, making these pipelines as efficient as possible is not an easy task. Challenges like poor resource allocation, deployment failures, and performance slowdowns in everchanging environments can hold teams back. This paper dives into how Reinforcement Learning (RL) can step in to tackle these challenges, especially in Kubernetes-based setups. RL agents learn from both past data and real-time information, making smart decisions to improve resource usage, speed up deployments, and handle failures more effectively. The paper takes a deep look at how RL works, compares it to traditional methods, and explores real-world examples.

With the increasing speed and complexity of cyber attacks malware remains one of the most significant cybersecurity threats faced by organizations, individuals and governments. Traditional signature detection systems struggle to keep pace with evolving zero-day threats, making Machine Learning (ML) a crucial component of modern cybersecurity. With applications in intrusion detection malware analysis fraud prevention and real-time security response systems ML plays a key role in the detection of threats, prevention and incident response. However integrating ML into cybersecurity presents several challenges. The dynamic nature of cyber threats demands regular model updates.

In the data processing system, the data pipeline plays a crucial role. The scalability is a mandatory feature for processing enormous volume of data along with proper approaches for data management. Google cloud platform offers various services for efficient data processing and in this article, we are building a scalable data pipeline using Google cloud.

Artificial Intelligence (AI) is transforming industries, driving innovations in healthcare, finance, transportation, and beyond. Yet, as AI systems transition from controlled environments to real-world applications, their performance often falters. The unpredictable nature of real-world data introduces noise, inconsistencies, & adversarial threats that can undermine AI's reliability. This discrepancy between lab success and real-world deployment highlights the critical need for enhancing AI robustness.

Artificial Intelligence (AI) is driving a profound transformation across the financial sector, fundamentally changing how businesses manage accounting, taxation, and risk. From streamlining complex workflows to enhancing decision-making, AI has become an indispensable tool for financial professionals seeking to improve accuracy and efficiency.