Blockchain being a leading technology in the 21st century is revolutionizing each sector of life. Services are being provided and upgraded using its salient features and fruitful characteristics. Businesses are being enhanced by using this technology. Countries are shifting towards digital currencies i.e., an initial application of blockchain application. It omits the need of central authority by its distributed ledger functionality. This distributed ledger is achieved by using a consensus mechanism in blockchain. A consensus algorithm plays a core role in the implementation of blockchain. Any application implementing blockchain uses consensus algorithms to achieve its desired task. In this paper, we focus on provisioning of a comparative analysis of blockchain’s consensus algorithms with respect to the type of application. Furthermore, we discuss the development platforms as well as technologies of blockchain. The aim of the paper is to provide knowledge from basic to extensive from blockchain architecture to consensus methods, from applications to development platform, from challenges and issues to blockchain research gaps in various areas.

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data’s characteristics. The proposed novel approach model RFMT analyzed Pakistan’s largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky–Harabasz, Davies–Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.

Intelligent Transportation System is the future for safe and secure transportation. Vehicles in the ITS share basic safety information which can prompt the disclosure of the real identity of the vehicles. Thus, adversaries can misuse these safety messages. Pseudonyms are alias granted to vehicles by trusted authorities to conceal their original identities. To avoid linkability, various pseudonym generation and distribution protocols have been proposed. Such protocols pose overheads in the system as they are performed by Central Authorities. Therefore, re- utilizing the existing pseudonyms through shuffling is the most optimal mechanism for ITS. The Blockchain is a digital ledger and tamper-resistant record of transactions. It eliminates the need for central authority as well as provides anonymity of transactions resulting in more secure and privacy-protected solutions. To handle distribution optimization issues in the pseudonym shuffling process without a central authority, the blockchain is used with its distributed consensus. The shuffling results are logged in blocks as transactions. Pseudonym shuffle randomness is achieved via blockchain and it provides robustness in the structure. When one system fails, the rest would continue to work. The method also provides a fully traceable record in case of certification revocation. The existing blockchain-based pseudonym shuffling mechanism uses traditional consensus algorithms to support the cryptography operation. This leads to overhead in terms of execution time and memory usage. This research proposes Proof of Pseudonym consensus protocol for the shuffling scheme to improve the efficiency of consensus as compared to Proof of Work, Proof of Kernel Work and, Proof of Elapsed Time in terms of time and memory. The execution time of Proof of Pseudonym is shorter than other algorithms. The security and privacy analysis revealed that our scheme achieves identity privacy, unlinkability, and non-repudiation properties. Threat analysis evaluates the proposed protocol in terms of both internal and external attacks.

An incident, in the perception of information technology, is an event that is not part of a normal process and disrupts operational procedure. This research work particularly focuses on software failure incidents. In any operational environment, software failure can put the quality and performance of services at risk. Many efforts are made to overcome this incident of software failure and to restore normal service as soon as possible. The main contribution of this study is software failure incidents classification and prediction using machine learning. In this study, an active learning approach is used to selectively label those data which is considered to be more informative to build models. Firstly, the sample with the highest randomness (entropy) is selected for labeling. Secondly, to classify the labeled observation into either failure or no failure classes, a binary classifier is used that predicts the target class label as failure or not. For classification, Support Vector Machine is used as a main classifier to classify the data. We derived our prediction models from the failure log files collected from the ECLIPSE software repository.

The increase in digital payments has become more prominent and challenging in this short period of time and poses a great threat to both banks and customers. The traditional rule-based fraud detection systems cannot handle the evolving attack patterns, large volumes of data, and significantly biased transaction datasets. This work proposed a comprehensive machine learning-based fraud detection system that integrates advanced data preparation, efficient resampling approaches, and a wide variety of supervised learning algorithms to overcome these challenges. It compares seven machine learning models using Random Under-Sampling and Synthetic Minority Over-Sampling Technique. All the models went through the same preprocessing pipeline, which was used to enhance the reliability of the model through feature scaling and anomaly control. This paper used the Latency-Aware Accuracy Index, a metric that considers latency and outperforms the traditional metrics. It provides empirical evidence that boosting-based models such as LightGBM, CatBoost and XGBoost using SMOTE outperform classical classifiers with respect to prediction performance and inference time efficiency. It illustrates that accuracy alone is not sufficient for practical implementation; latency plays an equally important role in minimizing financial loss by ensuring a smooth customer experience. The results indicate that ensemble learning models much exceed the performance of standard linear classifiers in balanced datasets. XGBoost demonstrated superior performance with SMOTE oversampling, getting an accuracy of 0.9898, an F1-score of 0.9898, and a LAAI of 0.9889. This shows that it is strong across different balancing approaches. On the other hand, Logistic Regression and Naïve Bayes always got accuracy values around 0.50, which shows that they could not find the complex non-linear patterns that are typical of fraud detection.