SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification

Attaallah, Abdulaziz; Khan, Raees Ahmad

doi:10.32604/cmc.2022.021968

Cited by 57 publications

(20 citation statements)

References 28 publications

(33 reference statements)

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“…The work of Haq [15] tries to fill in these blanks and improve the accuracy of air pollution organisation and prediction. A total of five ML models were created to classify air pollution, with one of them being the innovative SMOTEDNN.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Macherla,

Kotapati,

Sunitha

et al. 2023

ISI

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In many ways, our everyday lives depend on having access to reliable data about the state of the air around us. If you can predict the air quality ahead of time, you can put in place the right warnings and safety measures to keep people from getting hurt. The Control Boards in India set up the National Air Monitoring Programme (NAMP), which checks the air in 342 locations in 240 cities. There are a few distinct categories for the Air Quality Index (AQI). The AQI in Chennai was predicted using data that was collected and preprocessed to account for missing values and eliminate duplicates. Air quality forecasting using deep learning technology is investigated using a huge dataset describing the surrounding environment. This study suggests a scheme for classifying AQI values using multi-output and (MMS) based on long short-term memory (LSTM). Increased classification precision is achieved by using Chaotic Hunger Games Search (CHGS) in the hyper-parameter tuning process. When compared to conventional methods, the AQI value provided by the proposed deep learning model is more precise and accurate for a given location within a metropolis. The suggested deep learning algorithm improves forecast accuracy, serving as a public service announcement to bring levels down to a safe level. The AQI values can be reliably predicted by the deep learning method, which aids in sustainable urban development planning. By coordinating road traffic signals and encouraging people to take public transit in strategic areas, the AQI target value can lessen pollution.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Macherla,

Kotapati,

Sunitha

et al. 2023

ISI

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show abstract

“…Deep learning techniques have shown a lot of promise in improving speech enhancement performance in non-stationary noisy environments, where the characteristics of the noise may change over time [ 4 – 6 ], and show its effectiveness in other applications [ 7 – 10 ]. Deep neural networks (DNNs) are effective models for speech enhancement because they can learn the nonlinear relationship between input and output features.…”

Section: Introductionmentioning

confidence: 99%

Deep causal speech enhancement and recognition using efficient long-short term memory Recurrent Neural Network

Li,

Basit,

Daraz

et al. 2024

PLoS ONE

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Long short-term memory (LSTM) has been effectively used to represent sequential data in recent years. However, LSTM still struggles with capturing the long-term temporal dependencies. In this paper, we propose an hourglass-shaped LSTM that is able to capture long-term temporal correlations by reducing the feature resolutions without data loss. We have used skip connections in non-adjacent layers to avoid gradient decay. In addition, an attention process is incorporated into skip connections to emphasize the essential spectral features and spectral regions. The proposed LSTM model is applied to speech enhancement and recognition applications. The proposed LSTM model uses no future information, resulting in a causal system suitable for real-time processing. The combined spectral feature sets are used to train the LSTM model for improved performance. Using the proposed model, the ideal ratio mask (IRM) is estimated as a training objective. The experimental evaluations using short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) have demonstrated that the proposed model with robust feature representation obtained higher speech intelligibility and perceptual quality. With the TIMIT, LibriSpeech, and VoiceBank datasets, the proposed model improved STOI by 16.21%, 16.41%, and 18.33% over noisy speech, whereas PESQ is improved by 31.1%, 32.9%, and 32%. In seen and unseen noisy situations, the proposed model outperformed existing deep neural networks (DNNs), including baseline LSTM, feedforward neural network (FDNN), convolutional neural network (CNN), and generative adversarial network (GAN). With the Kaldi toolkit for automated speech recognition (ASR), the proposed model significantly reduced the word error rates (WERs) and reached an average WER of 15.13% in noisy backgrounds.

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“…In the field of transportation, deep learning can be used to achieve object detection in traffic scenes [ 8 ] and lane detection in intelligent driving [ 9 ]. Deep learning can help realize climate change forecast [ 10 ], air pollution classification and forecast [ 11 ], detection or classification of botnets [ 12 ]. What’s more, the supervised image classification algorithm is used to realize the classification of forest areas [ 13 ] and Convolutional Neural Network is applied to automatic weed detection system [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

An object detection algorithm combining self-attention and YOLOv4 in traffic scene

Lü

Zhao

et al. 2023

PLoS ONE

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Automobile intelligence is the trend for modern automobiles, of which environment perception is the key technology of intelligent automobile research. For autonomous vehicles, the detection of object information, such as vehicles and pedestrians in traffic scenes is crucial to improving driving safety. However, in the actual traffic scene, there are many special conditions such as object occlusion, small objects, and bad weather, which will affect the accuracy of object detection. In this research, the SwinT-YOLOv4 algorithm is proposed for detecting objects in traffic scenes, which is based on the YOLOv4 algorithm. Compared with a Convolutional neural network (CNN), the vision transformer is more powerful at extracting vision features of objects in the image. The CNN-based backbone in YOLOv4 is replaced by the Swin Transformer in the proposed algorithm. The feature-fusing neck and predicting head of YOLOv4 is remained. The proposed model was trained and evaluated in the COCO dataset. Experiments show that our method can significantly improve the accuracy of object detection under special conditions. Equipped with our method, the object detection precision for cars and person is improved by 1.75%, and the detection precision for car and person reach 89.04% and 94.16%, respectively.

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SMOTEDNN: A Novel Model for Air Pollution Forecasting and AQI Classification

Cited by 57 publications

References 28 publications

Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Deep Learning Framework-Based Chaotic Hunger Games Search Optimization Algorithm for Prediction of Air Quality Index

Deep causal speech enhancement and recognition using efficient long-short term memory Recurrent Neural Network

An object detection algorithm combining self-attention and YOLOv4 in traffic scene

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