Data Science at IgMin Research | Technology Group

In this paper, we present a deep CNN-based approach for multi-class classification of three-dimensional (3-D) objects using phase-only digital holographic information. The 3-D objects considered for the multi-class (four-class) classification task are ‘triangle-square’, ‘circle-square’, ‘square-triangle’, and ‘triangle-circle’. The 3-D object ‘triangle-square’ is considered for Class-1 and the remaining 3-D objects ‘circle-square’, ‘square-circle’, and ‘tr...iangle-circle’ are considered for Class-2, Class-3, and Class-4. The digital holograms of 3-D objects were created using the two-step Phase-Shifting Digital Holography (PSDH) technique and were computationally post-processed to obtain phase-only digital holographic data. Subsequently, a deep CNN was trained on a phase-only image dataset consisting of 2880 images to produce the results. The loss and accuracy curves are presented to validate the performance of the model. Additionally, the results are validated using metrics such as the confusion matrix, classification report, Receiver Operating Characteristic (ROC) curve, and precision-recall curve.Read more

Energy Valley Optimizer (EVO) is one of the recent metaheuristic algorithms. It draws inspiration from advanced principles in physics related to particle stability and decay modes. This paper presents a new Energy Valley Optimizer (EVO) and levy flights that are hybrid to improve the EVO in solving optimization problems. Levy flight is one of the most important randomization techniques. Fifteen mathematical test functions (five unimodal functions, four multimodal functions, and six composite functions) are solved with the proposed algorithm. We... also compare our results with previous results of metaheuristic algorithms. The statistical results show that the results of the Levy Energy Valley Optimizer (LEVO) outperform other algorithms in almost all mathematical test functions.Read more

People are suffering from” data obesity” as a result of the expansion and quick development of various Artificial Intelligence (AI) technologies and machine learning fields. The management of the current techniques is becoming more challenging due to the data created in the Smart-Health and Fintech service sectors. To provide stable and reliable methods for processing the data, several Machine Learning (ML) techniques were applied. Due to privacy-related issues with the aforementioned two providers, ML cannot fully use the data, whi...ch becomes difficult since it might not give the results that were expected. When the misuse and exploitation of personal data were gaining attention on a global scale and traditional machine learning (CML) was facing difficulties, Google introduced the concept of Federated Learning (FL). In order to enable the cooperative training of machine learning models among several organizations under privacy requirements, federated learning has been a popular research area. The expectation and potential of federated learning in terms of smart-health and fintech services are the main topics of this research.Read more