The exponential expansion of the Internet has exhausted the IPv4 addresses provided by IANA. The new IP edition, i.e. IPv6 introduced by IETF with new features such as a simplified packet header, a greater address space, a different address sort, improved encryption, powerful section routing, and stronger QoS. ISPs are slowly seeking to migrate from current IPv4 physical networks to new generation IPv6 networks. The move from actual IPv4 to software-based IPv6 is very sluggish, since billions of computers across the globe use IPv4 addresses. The configuration and actions of IP4 and IPv6 protocols are distinct. Direct correspondence between IPv4 and IPv6 is also not feasible. In terms of the incompatibility problems, all protocols can coexist throughout the transformation for a few years. Compatibility, interoperability, and stability are key concerns between IP4 and IPv6 protocols. After the conversion of the network through an IPv6, the move causes several issues for ISPs. The key challenges faced by ISPs are packet traversing, routing scalability, performance reliability, and protection. Within this study, we meticulously analyzed a detailed overview of all aforementioned issues during switching into ipv6 network.

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An imbalanced dataset is commonly found in at least one class, which are typically exceeded by the other ones. A machine learning algorithm (classifier) trained with an imbalanced dataset predicts the majority class (frequently occurring) more than the other minority classes (rarely occurring). Training with an imbalanced dataset poses challenges for classifiers; however, applying suitable techniques for reducing class imbalance issues can enhance classifiers' performance. In this study, we consider an imbalanced dataset from an educational context. Initially, we examine all shortcomings regarding the classification of an imbalanced dataset. Then, we apply data-level algorithms for class balancing and compare the performance of classifiers. The performance of the classifiers is measured using the underlying information in their confusion matrices, such as accuracy, precision, recall, and F measure. The results show that classification with an imbalanced dataset may produce high accuracy but low precision and recall for the minority class. The analysis confirms that undersampling and oversampling are effective for balancing datasets, but the latter dominates.