INTELLIGENT SURVEILLANCE SYSTEM FOR FIRE DETECTION USING YOLOV8

This study describes a lightweight deep learning model trained on a self-made image dataset taken inside farms and open areas of the Holy Shrine of Al-Hussainiya in the City of Karbala, Iraq. This dataset includes fire and smoke images taken using a Samsung A52S camera in different weather conditions. The overall goal is to create a fire detection system model that can successfully replace the existing physical sensor-based fire detectors and lessen the issues that come with such fire detectors, including false and delayed triggering. Another goal is to control fires on farms or open areas and prevent crop damage as much as possible. Previous studies were reviewed. Moreover, the architecture of the You Only Look Once version 8 (YOLOv8) model was briefly explained, and the results it achieved were compared with those achieved by previous versions. Then, the proposed system was trained and evaluated with the YOLOv8 large model. Results showed that the proposed system outperformed the rest of the current systems in mAP, which reached 98.5%

Mathematical simulation of memristive for classification in machine learning

Over the last few years, neuromorphic computation has been a widely researched topic. One of the neuromorphic computation elements is the memristor. The memristor is a high density, analogue memory storage, and compliance with Ohm's law for minor potential changes. Memristive behaviour imitates synaptic behaviour. It is a nanotechnology that can reduce power consumption, improve synaptic modeling, and reduce data transmission processes. The purpose of this paper is to investigate a customized mathematical model for machine learning algorithms. This model uses a computing paradigm that differs from standard Von-Neumann architectures, and it has the potential to reduce power consumption and increasing performance while doing specialized jobs when compared to regular computers. Classification is one of the most interesting fields in machine learning to classify features patterns by using a specific algorithm. In this study, a classifier based memristive is used with an adaptive spike encoder for input data. We run this algorithm based on Anti-Hebbian and Hebbian learning rules. These investigations employed two of datasets, including breast cancer Wisconsin and Gaussian mixture model datasets. The results indicate that the performance of our algorithm that has been used based on memristive is reasonably close to the optimal solution