Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)

Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained

Optimizing Tumor Classification Through Transfer Learning and Particle Swarm Optimization-Driven Feature Extraction

Brain tumors pose a significant threat, especially when not detected early. The Inception v3 machine learning model has found extensive applications in computer vision and related fields. This study aims to develop a robust transfer learning model for classification, adaptable to various data modalities through neural networks. However, the training process for these neural networks is complex, being both demanding and computationally intensive. To tackle this challenge, we introduce an innovative training approach for Inception v3 referred to as ‘PSO-INCEPT’ (Particle Swarm Optimization-based Inception v3 training). In this method, the weight vectors for each Inception v3 model are analogized to particle positions in a phase space. The PSO cooperates with the ADAM optimizer in achieving the purpose of training with the best performance and generalization. This research is composed of two main parts, the first stage is being performed by the model independently using the ADAM optimizer. In the following stage, PSO-INCEPT models share the latest weight vectors or particle coordinates and loss function approximations via training. The optimization function then uses them to improve the validation accuracy. The effectiveness of PSO-INCEPT was evaluated through experiments that were conducted on Kaggle datasets that provide a common base ground by having four distinct classes. Experimental studies have proven the extraordinary ability of the proposed model by providing 99.33% validation accuracy and 99.95% training accuracy which shows exceptional performance

Optimizing lung cancer classification through hyperparameter tuning

Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection