Deep learning is a machine learning approach that produces excellent
performance in various applications, including natural language processing,
image identification, and forecasting. Deep learning network performance
depends on the hyperparameter settings. This research attempts to optimize the
deep learning architecture of Long short term memory (LSTM), Convolutional
neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks
using Particle swarm optimization (PSO), a swarm intelligence-based
metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN),
and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the
performance of the proposed models. PM2.5 as a target variable was affected by
dew point, pressure, temperature, cumulated wind speed, hours of snow, and
hours of rain. The deep learning network inputs consist of three different
scenarios: daily, weekly, and monthly. The results show that the proposed M-1
with three hidden layers produces the best results of RMSE and MAPE compared to
the proposed M-2, M-3, and all the baselines. A recommendation for air
pollution management could be generated by using these optimized models

研究使用 PSO 优化深度学习的架构，以进行北京 PM 2.5 数据集的预测，结果表明 PSO-LSTM 是最好的模型，可用于空气污染管理推荐。

基于 PSO 调节的三种深度学习模型在北京 PM2.5 预测中的参数优化

Optimized Three Deep Learning Models Based-PSO Hyperparameters for  Beijing PM2.5 Prediction

This paper focuses on an important environmental challenge; namely, water
quality by analyzing the potential of social media as an immediate source of
feedback. The main goal of the work is to automatically analyze and retrieve
social media posts relevant to water quality with particular attention to posts
describing different aspects of water quality, such as watercolor, smell,
taste, and related illnesses. To this aim, we propose a novel framework
incorporating different preprocessing, data augmentation, and classification
techniques. In total, three different Neural Networks (NNs) architectures,
namely (i) Bidirectional Encoder Representations from Transformers (BERT), (ii)
Robustly Optimized BERT Pre-training Approach (XLM-RoBERTa), and (iii) custom
Long short-term memory (LSTM) model, are employed in a merit-based fusion
scheme. For merit-based weight assignment to the models, several optimization
and search techniques are compared including a Particle Swarm Optimization
(PSO), a Genetic Algorithm (GA), Brute Force (BF), Nelder-Mead, and Powell's
optimization methods. We also provide an evaluation of the individual models
where the highest F1-score of 0.81 is obtained with the BERT model. In
merit-based fusion, overall better results are obtained with BF achieving an
F1-score score of 0.852.
We also provide comparison against existing methods, where a significant
improvement for our proposed solutions is obtained. We believe such rigorous
analysis of this relatively new topic will provide a baseline for future
research.

本研究旨在通过使用社交媒体的数据作为及时反馈，探究社交媒体作为水质监测的潜在价值，同时提出了一个使用多个神经网络结构的框架来分析与检索与水质相关的社交媒体帖子，其中最高的 F1-score 值为 0.81，而总体融合结果最高的 F1-score 值为 0.852。