Enhancing Forensic Accuracy with IFDenseNet-138 from the Sensor Data Analysis using Evidence Collector (EC) Mobile Application - Aloni , Sukhada; Shekhawat , Divya (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In this research, we present the development of an open-source mobile application called Evidence Collector (EC) for collecting sensor data from mobile devices. The EC app was built using the Thunkable platform and is available for public use. In addition, we propose a deep neural network algorithm, IFDenseNet- 138, which is designed to analyze the sensor data collected by the EC app. The IFDenseNet-138 algorithm is capable of accurately detecting up to 10 different types of occurrences at a crime scene. An IFDenseNet-138 model achieved an accuracy of 96.32% with high precision and recall scores, indicating its effectiveness in performing 10-class classification. The primary objective of this research is to aid forensic teams in solving mystery cases where the victim is deceased but their mobile device data is available. The utilization of such data can help forensic teams in reconstructing the events leading to the crime, identifying suspects, and gathering evidence for legal proceedings.