The integration of Blockchain and Machine Learning (ML) technologies offers a transformative approach to combating fraud across various sectors, including finance, healthcare, and cybersecurity. Blockchain's decentralized and immutable nature ensures data integrity and transparency, while Machine Learning algorithms enable the detection of intricate fraud patterns through predictive analytics and anomaly detection. This synergistic combination provides a robust mechanism for identifying fraudulent activities in real time, minimizing human error, and optimizing decision-making processes. In the financial sector, Blockchain enhances the security and transparency of transactions, while ML models analyze transaction data to identify unusual patterns that may indicate fraud. In healthcare, Blockchain ensures the secure sharing of medical records, and ML assists in detecting fraudulent claims and potential identity theft. Cybersecurity applications leverage Blockchain for secure communication and data storage, with ML identifying potential threats or vulnerabilities. By combining these two cutting-edge technologies, organizations can strengthen their fraud detection systems, improve trust, and mitigate the risks associated with financial losses, data breaches, and privacy violations. This paper explores the multidisciplinary synergy of Blockchain and ML, illustrating their potential to revolutionize fraud detection mechanisms across multiple domains, providing a comprehensive overview of current advancements, challenges, and future directions for their integration in the fight against fraud.

Data losses in wireless sensor network (WSN) commonly occur due to diverse transmission errors such as hardware or software limitations, channel congestion, network coverage constraint and transmission delay. Another important cause for data loss is distinct security attacks caused by illegal interferences of illicit third parties. Apart from that data loss may occur due to some unforeseen causes too. A number of efforts have been made in WSN to control such types of data loss during the transmission process individually or along with various combinations. However, none of them are capable of addressing each of the mentioned cause of data loss in WSN environment. Henceforth, we have proposed an error resistant technique for WSN to address all of the mentioned causes for data loss. The proposed technique also offers a backup system for the accidental data losses. The experimental results shows that the proposed technique offers minimum data loss during the communication process by offering higher Signal to Noise Ratio (SNR) and low Information Loss compared to the other existing error control techniques. The time efficiency can also be justified by its high Throughput and complexity can be verified by measuring Cyclomatic Complexity.

These days, machine learning (ML) models are used across many complex fields, including bioinformatics, medicine, and other disciplines. Their black-box nature, however, may make it difficult to understand and trust the outcomes they provide. As a result, there is now a greater need for trustworthy visualisation tools to promote confidence in machine learning models. This topic has gained popularity in the visualisation community over the past few decades. To offer an overview of current research on the subject, as well as the frontiers of that study. The Internet of Things (IoT) enables billions of objects, in both physical and virtual contexts, to intelligently communicate with one another. Smartphones, for example, have evolved into valuable personal assistants and an integral part of people’s daily lives and work. Machine learning, such as reinforcement learning and deep learning, is increasingly commonly used in IoT to improve performance and efficiency. Machine learning, on the other hand, has several flaws that could jeopardise the security, trust, and privacy of IoT environments. One of the most serious threats is adversarial learning, in which attackers try to trick the learning algorithm with carefully crafted training examples, leading to incorrect outputs. This Special Issue will feature cutting-edge research from academia and industry, with a focus on debates over the security, trust, and privacy issues in machine learning-based IoT.

As the amount of data keeps adding at an exponential rate, Big Data Analytics is an increasingly critical field that needs such advanced machine learning-based data mining methods to efficiently find patterns. In this study, deep learning architectures, namely Convolutional Neural Networks (CNN) and Fully Connected Neural Networks (FCNN), are evaluated and compared regarding high dimensional feature extractions and classification with traditional Support Vector Machine (SVM) techniques. The implementation of the above-proposed framework was presented by training validated models on a high-dimensional dataset in TensorFlow and PyTorch. Classification effectiveness was assessed using performance metrics of accuracy, precision, recall, and F1-score. A PCA-based visualization was performed to analyze whether each model would extract the features well. Also CNN model has the highest accuracy i.e 93.5% compared to the accuracy of FCNN i.e 89.1 and SVM i.e 85.2 which proves its better hierarchical feature learning. It was also found that CNNs converged faster with 25 epochs, with SVM taking too long to converge and offering bad separability of the features, thus CNN towards FCNN models proved to be more effective for complex pattern recognition tasks for Big Data Analytics. Nevertheless, more research is needed to create computationally viable XAI and hybrid models for their real-world use.