A Specialized System for Arrhythmia Detection for Basic Research in Cardiology

The detection of cardiac arrhythmias has a long history in medicine, with current developments focusing on early detection using mobile devices. In basic research, however, the use cases and data differ greatly from the experimental setup. We developed a Python-based system to ease detection and analysis of arrhythmic sections in signals measured on extracted and stimulated cardiac myocytes. Multiple algorithms were integrated into the system, tested and evaluated. The best algorithm resulted in an F1-score of 0.97 and was primarily provided in the application.

Log Replication in Raft vs Kafka

The implementation of a fault-tolerant system requires some type of consensus algorithm for correct operation. From Paxos to View-stamped Replication and Raft multiple algorithms have been developed to handle this problem. This paper presents and compares the Raft algorithm and Apache Kafka, a distributed messaging system which, although at a higher level, implements many concepts present in Raft (strong leadership, append-only log, log compaction, etc.).This shows that mechanisms conceived to handle one class of problems (consensus algorithms) are very useful to handle a larger category in the context of distributed systems.

Comparative Prediction of Wine Quality and Protein Synthesis Using ARSkNN

The amount of data available and information over the past few decades has grown manifold and will only increase exponentially. The ability to harvest and manipulate information from this data has become a crucial activity for effective and faster development. Multiple algorithms and approaches have been developed in order to harvest information from this data. These algorithms have different approaches and therefore result in varied outputs in terms of performance and interpretation. Due to their functionality, different algorithms perform differently on different datasets. In order to compare the effectiveness of these algorithms, they are run on different datasets under a given set of fixed restrictions (e.g., hardware platform, etc.). This paper is an in-depth analysis of different algorithms based on trivial classifier algorithm, kNN, and the newly developed ARSkNN. The algorithms were executed on three different datasets, and analysis was done by evaluating their performance taking into consideration the accuracy percentage and execution time as performance measures.