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  • Open Access


    Micro-Locational Fine Dust Prediction Utilizing Machine Learning and Deep Learning Models

    Seoyun Kim1,#, Hyerim Yu2,#, Jeewoo Yoon1,3, Eunil Park1,2,*

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 413-429, 2024, DOI:10.32604/csse.2023.041575

    Abstract Given the increasing number of countries reporting degraded air quality, effective air quality monitoring has become a critical issue in today’s world. However, the current air quality observatory systems are often prohibitively expensive, resulting in a lack of observatories in many regions within a country. Consequently, a significant problem arises where not every region receives the same level of air quality information. This disparity occurs because some locations have to rely on information from observatories located far away from their regions, even if they may be the closest available options. To address this challenge, a novel approach that leverages machine… More >

  • Open Access


    Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

    SangSeok Lee1, HaeWon Moon1, Lee Sael1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 319-336, 2024, DOI:10.32604/cmes.2023.031150

    Abstract How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data? Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors. For example, air quality tensor data consists of multiple sensory values gathered from wide locations for a long time. Such data, accumulated over time, is redundant and consumes a lot of memory in its raw form. We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks. To… More > Graphic Abstract

    Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

  • Open Access



    Hussien Aziz Saheb,*, Ala'a Abbas Mahdi, Qusay Rasheed Al-amir

    Frontiers in Heat and Mass Transfer, Vol.16, pp. 1-15, 2021, DOI:10.5098/hmt.16.9

    Abstract In this study, indoor air quality and thermal comfort were investigated for two persons sitting inside an office room of dimensions (3×2.5×2.5m). The office room is equipped with personal ventilation systems positioned 50 cm from the person's face. These systems are characterized by the ability to change the rates of airflow (ATD). Experimental studies and results were conducted on a thermal manikin that simulates the human body in a sitting position, and the results are compared with CFD analysis using the k-epsilon and the RNG turbulent models. The experimental study focused on measuring the speed and temperature of the air… More >

  • Open Access


    Airstacknet: A Stacking Ensemble-Based Approach for Air Quality Prediction

    Amel Ksibi1, Amina Salhi1, Ala Saleh Alluhaidan1,*, Sahar A. El-Rahman2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2073-2096, 2023, DOI:10.32604/cmc.2023.032566

    Abstract The quality of the air we breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals. Unfortunately, personal air quality measurement remains challenging. In this study, we investigate the use of first-person photos for the prediction of air quality. The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction. AirStackNet consists of two layers and four regression models, where the first layer generates meta-data from… More >

  • Open Access


    Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework

    Manar Ahmed Hamza1,*, Hadil Shaiba2, Radwa Marzouk3, Ahmad Alhindi4, Mashael M. Asiri5, Ishfaq Yaseen1, Abdelwahed Motwakel1, Mohammed Rizwanullah1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3235-3250, 2022, DOI:10.32604/cmc.2022.029604

    Abstract Environmental sustainability is the rate of renewable resource harvesting, pollution control, and non-renewable resource exhaustion. Air pollution is a significant issue confronted by the environment particularly by highly populated countries like India. Due to increased population, the number of vehicles also continues to increase. Each vehicle has its individual emission rate; however, the issue arises when the emission rate crosses the standard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop prediction approaches to monitor and control pollution using real time data. With the development of… More >

  • Open Access


    Monitoring and Prediction of Indoor Air Quality for Enhanced Occupational Health

    Adela POP (Puscasiu), Alexandra Fanca*, Dan Ioan Gota, Honoriu Valean

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 925-940, 2023, DOI:10.32604/iasc.2023.025069

    Abstract The amount of moisture in the air is represented by relative humidity (RH); an ideal level of humidity in the interior environment is between 40% and 60% at temperatures between 18° and 20° Celsius. When the RH falls below this level, the environment becomes dry, which can cause skin dryness, irritation, and discomfort at low temperatures. When the humidity level rises above 60%, a wet atmosphere develops, which encourages the growth of mold and mites. Asthma and allergy symptoms may occur as a result. Human health is harmed by excessive humidity or a lack thereof. Dehumidifiers can be used to… More >

  • Open Access


    Air Quality Predictions in Urban Areas Using Hybrid ARIMA and Metaheuristic LSTM

    S. Gunasekar*, G. Joselin Retna Kumar, G. Pius Agbulu

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1271-1284, 2022, DOI:10.32604/csse.2022.024303

    Abstract Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air quality leads to rising health issues in the human’s life in many ways especially respiratory infections, heart disease, asthma, stroke and lung cancer. The contaminated air comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter of PM10, PM2.5, and an Air Quality Index (AQI). These pollutant ingredients are very harmful to human’s health and also leads to death. So, it is necessary to develop a prediction model for air quality as regular on… More >

  • Open Access


    Multi-Site Air Pollutant Prediction Using Long Short Term Memory

    Chitra Paulpandi*, Murukesh Chinnasamy, Shanker Nagalingam Rajendiran

    Computer Systems Science and Engineering, Vol.43, No.3, pp. 1341-1355, 2022, DOI:10.32604/csse.2022.023882

    Abstract The current pandemic highlights the significance and impact of air pollution on individuals. When it comes to climate sustainability, air pollution is a major challenge. Because of the distinctive nature, unpredictability, and great changeability in the reality of toxins and particulates, detecting air quality is a puzzling task. Simultaneously, the ability to predict or classify and monitor air quality is becoming increasingly important, particularly in urban areas, due to the well documented negative impact of air pollution on resident’s health and the environment. To better comprehend the current condition of air quality, this research proposes predicting air pollution levels from… More >

  • Open Access


    Air Pollution Prediction Using Dual Graph Convolution LSTM Technique

    R. Saravana Ram1, K. Venkatachalam2, Mehedi Masud3, Mohamed Abouhawwash4,5,*

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1639-1652, 2022, DOI:10.32604/iasc.2022.023962

    Abstract In current scenario, Wireless Sensor Networks (WSNs) has been applied on variety of applications such as targets tracking, natural resources investigation, monitoring on unapproachable place and so on. Through the sensor nodes, the information for the applications is gathered and transferred. The physical coordination of these sensor nodes is determined, and it is called as localization. The WSN localization methods are studied widely for recent research with the study of small proportion of the sensor node called anchor nodes and their positions are determined through the GPS devices. Sometimes sensor nodes can be a IoT device in the network. With… More >

  • Open Access


    Accurate Multi-Site Daily-Ahead Multi-Step PM2.5 Concentrations Forecasting Using Space-Shared CNN-LSTM

    Xiaorui Shao, Chang Soo Kim*

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5143-5160, 2022, DOI:10.32604/cmc.2022.020689


    Accurate multi-step PM2.5 (particulate matter with diameters 2.5um) concentration prediction is critical for humankinds’ health and air population management because it could provide strong evidence for decision-making. However, it is very challenging due to its randomness and variability. This paper proposed a novel method based on convolutional neural network (CNN) and long-short-term memory (LSTM) with a space-shared mechanism, named space-shared CNN-LSTM (SCNN-LSTM) for multi-site daily-ahead multi-step PM2.5 forecasting with self-historical series. The proposed SCNN-LSTM contains multi-channel inputs, each channel corresponding to one-site historical PM2.5 concentration series. In which, CNN and LSTM are used to extract each… More >

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