In recent years, the exponential growth of both structured and unstructured data has highlighted the importance of efficient knowledge discovery techniques. Among these, image mining and data mining play pivotal roles in extracting meaningful patterns and insights from different forms of information. At the same time, data mining focuses on structured datasets such as databases and transaction records, while image mining deals with unstructured visual content, requiring advanced feature extraction, pattern recognition, and machine learning methods. This paper presents a comparative analysis of image mining and data mining techniques, emphasising their methodologies, applications, and challenges. The comparison explores key dimensions, including data preprocessing, feature selection, algorithmic approaches, and application domains such as healthcare, security, business intelligence, and multimedia. Experimental results show clustering accuracy of 87% for data mining and image classification accuracy of 92% for image mining, highlighting the effectiveness of specialised approaches. The study highlights both the similarities and differences in knowledge discovery processes, demonstrating how integrating image mining and data mining can enhance decision-making in diverse fields. The findings provide a comprehensive understanding of their complementary roles, offering valuable insights for researchers and practitioners aiming to develop hybrid approaches to knowledge discovery.

This paper provides a comparative study of dark web crypto networks, focusing on their economic aspects. This study is observing how the operational models of mainstream cryptocurrencies, such as Bitcoin and Monero, function in dark web illicit markets. Researchers are contrasting the structural and transactional gap of such cryptocurrencies, making them differently adopted and operational for criminal purposes. Among its primary objectives is to quantify the economic impact of said networks by comparing transaction volumes, price volatility, and the market capitalization of cryptocurrencies traded on the dark web. The dataset was artificially created to emulate the nature of transactions and economic values observed on dark websites. Statistical analysis was conducted using a range of software, including network programs for visualizing transactional flows, statistical packages, and economic modelling tools. The results indicate a heightened, intensive financial footprint of dark web cryptocurrency networks, underscoring the challenges they pose to international financial regulation and law enforcement. The paper concludes with an evaluation of how these networks will develop in the future and how they will become part of the growing role in the international legal economy over the next few years.

Brain-inspired neuromorphic computing, which is based on the neuronal structure of the brain, provides a revolutionary paradigm for real-time edge computing that is efficient in terms of energy consumption. The purpose of this work is to investigate the role of synaptic plasticity, specifically spike-timing-dependent plasticity (STDP), in increasing computational efficiency in neuromorphic edge device topologies. A new architecture is shown here that uses STDP to facilitate on-chip learning and adaptation to a wide range of sensory inputs. Additionally, this design features reduced cloud processing, lower latency, and improved energy efficiency. The classification of streams online is the primary focus of our efforts. This is a fundamental operation performed in most edge operations, including autonomous navigation and health monitoring on wearable devices. Our model is trained and evaluated in a Python-based simulation environment, and Brian2 is used to simulate neuronal dynamics. The performance of the novel architecture is evaluated using the Spiking Heidelberg Digits (SHD) benchmark, an appropriate metric for spike-based classification of auditory samples. This architecture demonstrates higher processing speed and energy efficiency than traditional von Neumann architectures, achieving 96.4% classification accuracy and a mean power consumption of 2.7 milliwatts.

The 5G wireless technology outlines the new age in telecommunication technologies in terms of higher speed data, low-latency secure communication, and mass-scale device connection. This paper is shortly to address issues such as the basics of 5G technologies, diversity, and possible challenges. In answering this research question, a systematic assumption is made, along with the accumulation and analysis of sufficient information to focus on inquiring into and establishing how the evolution towards new levels of technology, resulting in 5G, and its effects are envisioned across most industries and society. They have vast data that they use to estimate regression equations and forecast the adoption of 5G technology across various areas. Accordingly, the paper focuses on P Select Gradient Boosting and Random Forest as the main variables, along with GDP per capita, population in urban areas, and the tech readiness index. Moreover, this paper provides generalisations of the major data characteristics and model performance, resulting in a general presentation of more pro-5G factors. Since this research outlines the opportunities and threats of 5 G, it aims to contribute to the discussion on the evolution of international connectivity.

This Quantum Cryptography, as a future generation cyber security protocol research, includes experimental data that have been gathered from publicly accessible quantum cryptographic experiments done by university research facilities and cyber security facilities. The data comprises performance records of Quantum Key Distribution (QKD) deployments across various network environments and networks. Critical parameters, including encryption success rates, error rates, latency, throughput, and QKD efficiency, were studied to ensure the security and reliability of quantum cryptographic protocols being implemented. Experimental setups, such as Qiskit and QuTech, are utilized in experiments that offer the functionality to simulate and test quantum cryptographic systems. Quantum circuit building, quantum protocol simulation, and performance monitoring against various network topologies are facilitated by these platforms. Moreover, the experimental setup utilized actual quantum key distribution hardware and secure communication channels to facilitate real-time experimentation. Visualization employed Matplotlib to produce histograms and 3D plots of the success rates, throughput, and latency. Statistical modeling was performed to estimate the correlation between network latency, quantum key efficiency, and error rates, thereby plotting the contribution of quantum cryptography to the security of modern communication infrastructure in the future. Theory and practice, as exemplified in experimentation with quantum-computing software and numerical modelling, converge in this article to introduce possible future limits on using quantum cryptography as a nascent next-generation paradigm for cybersecurity.

The swift nature of cybersecurity threats necessitates a thorough understanding of user behaviour in defending against threats. In this research, the behaviour of end-users while online and its implications for cybersecurity threats are considered. Based on common users' behaviours, risk disposition, and protective measures, the research identifies the causes of security failures. The research is a population survey conducted across diverse demographic populations to determine their awareness levels of security protocols. Measures utilised in the analysis are 500 responses from diverse demographics, phishing attack simulation metrics, and observational metrics of password management. The measures were sourced from open-source cybersecurity data sets and anonymised user data obtained during the research conducted. Packages such as Python (statistical analysis and data visualisation). This report contains the tools (Python, Graphviz, Microsoft Excel) for creating diagrams, designing data, and tabulating to represent and analyse results. The research suggests that less security-conscious users are more susceptible to phishing attacks and social engineering scams. The suggested framework consists of robust security controls and tailored training interventions to address such threats. A holistic behaviour-focused approach is encouraged to maximise threat prevention and security awareness practice. The results are applicable in the development of dynamic security solutions that react to user behaviour patterns for organisational and individual cybersecurity enhancement practices.