Atmospheric temperature affects the growth and development of plants and has an important impact on the sustainable development of forest ecological systems. Predicting atmospheric temperature is crucial for forest management planning.Artificial neural network (ANN) and deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network (CNN), CNN-GRU, and CNN-LSTM, were utilized to predict the change of monthly average and extreme atmospheric temperatures in Zhengzhou City. Average and extreme atmospheric temperature data from 1951 to 2022 were divided into training data sets (1951–2000) and prediction data sets (2001–2022), and 22 months of data were used as the model input to predict the average and extreme temperatures in the next month.The number of neurons in the hidden layer was 14. Six different learning algorithms, along with 13 various learning functions, were trained and compared. The ANN model and deep learning models were evaluated in terms of correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), and good results were obtained. Bayesian regularization (trainbr) in the ANN model was the best performing algorithm in predicting average, minimum and maximum atmospheric temperatures compared to other algorithms in terms of R (0.9952, 0.9899, and 0.9721), and showed the lowest error values for RMSE (0.9432, 1.4034, and 2.0505), and MAE (0.7204, 1.0787, and 1.6224). The CNN-LSTM model showed the best performance. This CNN-LSTM method had good generalization ability and could be used to forecast average and extreme atmospheric temperature in other areas. Future climate changes were projected using the CNN-LSTM model. The average atmospheric temperature, minimum atmospheric temperature, and maximum atmospheric temperature in 2030 were predicted to be 17.23 °C, −5.06 °C, and 42.44 °C, whereas those in 2040 were predicted to be 17.36 °C, −3.74 °C, and 42.68 °C, respectively. These results suggest that the climate is projected to continue warming in the future.

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Deep residual convolutional neural network based on hybrid attention mechanism for ecological monitoring of marine fishery

The ever-increasing growth of online services and smart connectivity of devices have posed the threat of malware to computer system, android-based smart phones, Internet of Things (IoT)-based systems. The anti-malware software plays an important role in order to safeguard the system resources, data and information against these malware attacks. Nowadays, malware writers used advanced techniques like obfuscation, packing, encoding and encryption to hide the malicious activities. Because of these advanced techniques of malware evasion, traditional malware detection system unable to detect new variants of malware. Cyber security has attracted many researchers in the past for designing of Machine Learning (ML) or Deep Learning (DL) based malware detection models. In this study, we present a comprehensive review of the literature on malware detection approaches. The overall literature of the malware detection is grouped into three categories such as review of feature selection (FS) techniques proposed for malware detection, review of ML-based techniques proposed for malware detection and review of DL-based techniques proposed for malware detection. Based on literature review, we have identified the shortcoming and research gaps along with some future directives to design of an efficient malware detection and identification framework. 

Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

The overuse of thiamethoxam (THM) has threatened the survival of living organisms and it is necessary to find an environmentally friendly material to remove THM frequently detected in water. Biochar prepared from cow manure modified with ZnCl2 (Zn-CBC) was used to remove THM. Compared to the unmodified cow manure biochar (CBC), the removal ratio of THM by Zn-CBC was enhanced 35 times. In the mechanistic analysis, SEM and BET showed that Zn-CBC had a good pore structure and its specific surface area (166.502 m2 g-1) increased to 17 times that of CBC, indicating that Zn-CBC had good pore adsorption properties. The adsorption kinetic and isotherm implied that the main mechanism was chemisorption including π-π interaction and H-bonding. Furthermore, the stable graphitized structure of Zn-CBC allowed for efficient adsorption and reusability. In addition, this study constructed an intelligent prediction model using batch experiment data, and the high R2 (0.978) and low RMSE (0.057) implied that the model could accurately and quantitatively predict the adsorption efficiency. This paper provides a novel perspective to simultaneously remove the neonicotinoid insecticides and realize the resource utilization of cow manure. 

Thiamethoxam adsorption by ZnCl2 modified cow manure biochar: Mechanism and quantitative prediction.

Kalman filter algorithms have been widely used in dust environment concentration detection systems. However, in industrial environments, methods such as KF and median filtering usually require about 10 s of detection time, which cannot meet the requirements of online real-time detection. For this reason, this present study proposes a framework that combines a gated recirculation unit (GRU) with the KF method to achieve online real-time detection of dust concentration. In this framework, the GRU is mainly responsible for handling dynamic and nonlinear characteristics and capturing instantaneous concentration trends. On the other hand, the Kalman filter utilizes its superior state estimation capability to provide more accurate system state estimation by fusing real-time predictions from GRU and sensor measurements. The results show that the KFGRU method is superior to the conventional linear filtering method with a response time of less than 2 s and can detect dust concentration online in real-time. In terms of prediction accuracy, the deviation value of the curve processed by the KFGRU method is only 0.334, which is a significant breakthrough compared with the Kalman filter algorithm 0.755, the sliding average method 0.843, and the median filter method 0.849 (the smaller the deviation value, the higher the prediction accuracy). This study provides a comprehensive and innovative approach for dust concentration monitoring in dust reduction and explosion suppression systems, which not only meets the real-time requirement but also makes important progress in explosion safety management. This will provide more reliable and advanced technical support for dust control and safety in industrial production processes. 

Real-time Concentration Detection of Al Dust Using GRU-based Kalman Filtering Approach

PM2.5 is a significant environmental pollutant that damages the environment and endangers human health. Precise forecast of PM2.5 concentrations is very important to control air pollution and improve people’s life quality. In the subway indoor air quality (IAQ) system, the data collected by telemonitoring systems is frequently lost due to many reasons. A deep learning model called RF-CNN-GRU, which combines random forest (RF), convolutional neural network (CNN) and gated recurrent unit (GRU), is proposed to predict atmospheric PM2.5 concentrations with incomplete original data. The RF-CNN-GRU model employs the RF to fill in missing values in the data and subsequently applies the CNN to extract features from the imputed data. The data is finally sent to the GRU network to train and predict PM2.5 concentrations. Comparing with single CNN, GRU and long short-term memory (LSTM) models, the predictive accuracy of the RF-CNN-GRU model is significantly improved. The RF-CNN-GRU model shows a slight improvement in prediction results when compared to models such as CNN-GRU, RF-CNN, RF-GRU, and RF-LSTM. The findings demonstrate that the RF-CNN-GRU model has excellent accuracy in PM2.5 concentration prediction when the original data is incomplete. 

A Novel Deep Learning Model Integrating CNN and GRU to Predict Particulate Matter Concentrations

A novel deep learning framework with variational auto-encoder for indoor air quality prediction

During the long operation of wastewater treatment systems, there is a risk of various failures due to aging equipment and environmental climate change. Hence, to ensure the stability of the wastewater treatment procedure, it is necessary to perform prompt and efficient fault diagnosis. This paper presents an optimized fusion deep learning network designed for feature extraction and fault diagnosis within wastewater treatment processes. The method combines knowledge-based fault diagnosis advantages and creates a reliable model using a multi-scale convolutional neural network to capture spatio-temporal features. The proxy-nearest neighborhood component analysis optimizes model parameters and improves fault classification performance compared to traditional cross entropy and the fault classification performance of the model was validated using faults based on the wastewater treatment process system BSM1. It achieves a 81.52% average accuracy for 11 fault types, outperforming conventional models and single sub-models with different loss functions. This study demonstrates the greater potential of the proposed model approach for fault diagnosis in wastewater treatment. 

Fault Diagnosis Using Deep Learning for Wastewater Treatment Processes

An optimized TCN-LSTM model for predicting PM2.5 in metro systems.

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A Novel Deep Learning Model Integrating CNN and GRU to Predict Particulate Matter Concentrations

A novel deep learning framework with variational auto-encoder for indoor air quality prediction

Fault Diagnosis Using Deep Learning for Wastewater Treatment Processes

An optimized TCN-LSTM model for predicting PM2.5 in metro systems.