iConDet2: An Improved Conjunctivitis Detection Portable Healthcare App Powered by Artificial Intelligence

Conjunctivitis is one of the common and contagious ocular diseases which affects the conjunctiva of the human eye. Both the bacterial and viral types of it can be treated with eye drops and other medicines. It is important to diagnose the disease at its early stage to realise the connection between it and other diseases, especially COVID-19. Mobile applications like iConDet is such a solution that performs well for the initial screening of Conjunctivitis. In this work, we present with iConDet2 which provides an advanced solution than the earlier version of it. It is faster with a higher accuracy level (95%) than the previously released iConDet. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Classifying the Students' Behavior on e-Learning System using Fine-Tuning K-NN Method

monitoring the student's behavior is challenging for teachers in online learning, which is crucial to solving. It is because, in this pandemic period, online learning is required to minimize the spreading of coronavirus. However, research in this domain is not much. This study provides an alternative to this problem by classifying students' behavior in the e-Learning system, where the k-NN is applied to mine the students' behavior. In addition, this paper also tests the proper parameters to improve the performance of k-NN: k and distance. The experimental result shows that the best performance on the cross-validation technique is reached by Euclidean distance and, on the percentage-split, is achieved by distance-Manhattan. These are indicated by the highest accuracy level obtained by neighbors of five and 20 fold, about 96.9% on the cross-validation technique. On the percentage split technique, the highest accuracy level, about 95.3%, is reached by neighbors of four and split 50%. In this best performance, four students are misclassified on the cross-validation and six on the percentage split. © 2022 IEEE.

Stock Prediction Using Machine Learning Algorithms with Special Reference to Technical Indicators

Machine learning algorithms have increasingly become chosen tools for stock price prediction. Using a variety of financial news as an input to compare various algorithms for accuracy level has been extensively studied. However, taking some of the prominent technical indicators as an input to test the algorithms’ prediction accuracy for a stock price has remained less explored. This study focuses on using chosen seventeen technical indicators to compare selected algorithms to test the prediction accuracy for six Indian stocks as a sample. This study covers the critical time period of the outbreak of the Covid-19 pandemic and attempts to capture the impact on accuracy levels of algorithms. Three algorithms are tested, and among them random forest algorithm has demonstrated superior results. Based on these results, this study proposes a framework to create a platform for further application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.