In this era of digitalization, huge amount of traffic is exchanged all over the world. Whether mobile, laptop or any other device, even the smallest of transferred data will leave a trace. This is a very good and enhancing approach towards detecting criminal activities by analyzing the network traffic of the source computer. Network data is however more unpredictable and volatile as compared to the preserved data. Investigators mostly analyze the firewall, packet transfer and other details. Generally, there are two type of systems used to analyze the network traffic like “catch me if you can” in which packets are captured and stored in the system in a batch mode or “stop, look and listen” where the data is analyzed sequentially in memory and only useful data is stored for further. This study can efficiently be done by packet analyzer which is an open source tool built to study the packets and various aspects of their behavior. The sole purpose of this paper is to understand how important network forensic is and how can this packet analyzer can help in finding out the suspicious packets in network and hence providing secure network and also conducting network forensics in various aspects such as port filtering, following up a packet, collecting raw data, tracing up a network threat, converting channels etc.

Quantum algorithms for predictive analytics is important across various sectors such as finance, healthcare, marketing and supply chain management where accurate predictions can be used to optimise operations and decision-making processes. This chapter addresses Specific quantum machine learning algorithms such as Quantum Support Vector Machines (QSVM), Quantum K-nearest Neighbors (QKNN) and Quantum Neural Networks (QNN). These algorithms enhance classification, regression and complex predictive tasks by accelerating training processes and managing large feature spaces effectively. And, to this, quantum optimization algorithms like Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are mentioned for their role in optimising model parameters and improving prediction accuracy. In addition, it discusses quantum data encoding methods, such as Quantum Fourier Transform (QFT) and amplitude encoding. Along with this, it acknowledges significant challenges such as hardware limitations, algorithmic complexity and practical integration issues.

Purpose: This study aims to investigate the effect of varying load and rotational speed on the pressure distribution characteristics of mild steel-based hydrodynamic journal bearings. Understanding these influences is crucial for optimizing lubrication performance, minimizing friction and enhancing bearing durability. By analyzing pressure variations under different operational conditions, the research provides insights into improving bearing design and predictive maintenance strategies. The findings contribute to the advancement of tribology and mechanical engineering by offering a better understanding of fluid film behavior, ultimately leading to more efficient and reliable bearing systems in industrial applications. Design/methodology/approach: This study employs an experimental approach to analyze the performance characteristics of mild steel-based hydrodynamic journal bearings under varying load and speed conditions. A test rig was designed to measure circumferential pressure distribution using SAE 20W40 lubricant at rotational speeds of 250, 500 and 750 RPM and loads of 0, 0.25 and 0.50 kg. Optical Emission Spectroscopy (OES) was used for material characterization. Taguchi optimization with an L18 orthogonal array and analysis of variance (ANOVA) analysis were applied to identify key influencing factors. The findings provide insights into lubrication dynamics and bearing efficiency, contributing to improved predictive maintenance and bearing design. Findings: The study reveals that rotational speed significantly influences the pressure distribution in hydrodynamic journal bearings compared to applied load. Maximum pressure is observed at higher speeds, with peak values occurring between 120° and 150° bearing angles. Increased speeds enhance lubricant film stability, reducing metal-to-metal contact and improving bearing efficiency. Load variations shift the pressure peak but have a lesser impact on overall pressure distribution. Taguchi optimization and ANOVA confirm that speed is the dominant factor affecting bearing performance. These findings provide valuable insights for optimizing journal bearing design and improving lubrication strategies in rotating machinery applications. Research limitations/implications: This study is limited to experimental investigations under controlled laboratory conditions, focusing on mild steel-based hydrodynamic journal bearings using SAE 20W40 lubricant. Variations in environmental factors, bearing materials and lubricant properties may influence real-world performance. Originality/value: This study provides a novel experimental investigation into the pressure distribution characteristics of mild steel-based hydrodynamic journal bearings under varying load and speed conditions. Unlike previous research, it integrates Taguchi optimization and ANOVA to systematically analyze the influence of operational parameters. The findings offer new insights into lubrication dynamics, highlighting the dominant role of rotational speed in enhancing bearing efficiency. The study’s practical implications contribute to optimizing journal bearing design, reducing frictional losses, and improving predictive maintenance strategies. These results are valuable for researchers and engineers working on tribology, mechanical systems and industrial lubrication technologies.