Search Results - (( developing pollution learning algorithm ) OR ( java implication based algorithm ))

A systematic literature review of deep learning neural network for time series air quality forecasting by Zaini N., Ean L.W., Ahmed A.N., Malek M.A.

“…air quality; algorithm; artificial neural network; industrial development; literature review; machine learning; public health; time series; urbanization; air pollution; forecasting; human; time factor; Air Pollution; Deep Learning; Forecasting; Humans; Neural Networks, Computer; Time Factors…”

Air quality forecasting and mapping in Malaysian urban areas: A hybrid deep learning approach by Nur'atiah Zaini

Forecasting of fine particulate matter based on LSTM and optimization algorithm by Zaini N., Ahmed A.N., Ean L.W., Chow M.F., Malek M.A.

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Air pollution forecasting in Kuala Terengganu using Artificial Neural Network (ANN) / Nur Raudzah Abdullah by Abdullah, Nur Raudzah

“…Existing researches on air pollution forecasting used a variety of machine learning algorithm. …”
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An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective by Mousavi, Seyed Mohsen, Sadeghi R., Kiarash, Lee, Lai Soon

“…To integrate machine learning and human interactions, this paper develops a new three-stage interactive algorithm in business analytics, called the interactive Nautilus-based algorithm, to address complex problems. …”
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Development of prediction model for phosphate in reservoir water system based machine learning algorithms by Latif S.D., Birima A.H., Ahmed A.N., Hatem D.M., Al-Ansari N., Fai C.M., El-Shafie A.

“…Decision trees; Eutrophication; Forecasting; Learning systems; Neural networks; Phosphate fertilizers; Predictive analytics; Reservoirs (water); Stochastic systems; Support vector machines; Water pollution; Water quality; Water supply; Conventional modeling; Cross validation; Developed model; Non-point source pollution; Prediction model; Primary sources; Statistical indices; Water quality parameters; Learning algorithms…”

GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms by Tella, A., Balogun, A.-L.

“…Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). …”
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Investigating the reliability of machine learning algorithms as an advanced tool for ozone concentration prediction by Ayman Mohammed Shaher Yafouz, Mr.

“…The hybrid technique has been developed by using deep learning algorithms with the structure of multiple layers (with several neurons) of CNN and LSTM. …”

Real-time and predictive analytics of air quality with IoT system: A review by Nurmadiha, Osman, Mohd Faizal, Jamlos, Fatimah, Dzaharudin, Aidil Redza, Khan, You, Kok Yeow, Khairil Anuar, Khairi

“…(ii) Very less integration of IoT together with predictive analytics, it is separately to study for air pollution monitoring system. (iv) Data analytics for Air Pollution Index (API) prediction along with IoT, with various communication protocols can as-sist in the development of real-time, and continuous high precision environmen-tal monitoring systems. v) Machine Learning (ML) Regression algorithm is suit-able for prediction and classification of concentration gas pollutant, while ANN and SVM algorithm is used for forecasting.…”
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Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan by Azha S.F., Sidek L.M., Ahmad Z., Zhang J., Basri H., Zawawi M.H., Noh N.M., Ahmed A.N.

“…The dynamic and non-linear characteristics of water quality parameters pose significant challenges for conventional machine learning algorithms like multi-linear regression, as they struggle to capture these complexities. …”

The implications for ahybrid detection technique against malicious sqlattacks on web applications by Bahjat Arif, Sarajaldeen Akram, Wani, Sharyar

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A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant by Aziz N.L.A.A., Yap K.S., Bunyamin M.A.

“…The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. …”

Predicting the onset of acute coronary syndrome events and in-hospital mortality using machine learning approaches / Song Cheen by Song , Cheen

“…Acute coronary syndrome (ACS) represents a significant health concern, and its risk increases with exposure to environmental factors, particularly air pollution. Understanding this association is crucial given the increasing prevalence of air pollution in many regions, particularly in Malaysia, which is affected by air pollution. …”
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Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches by Jui, Julakha Jahan, Mohd Ashraf, Ahmad, Molla, Md Mamun, Muhammad Ikram, Mohd Rashid

“…It highlights the shift towards integrating machine learning and artificial intelligence (AI) breakthroughs in EMSs development. …”
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Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review by Yafouz A., Ahmed A.N., Zaini N., El-Shafie A.

Waste Prediction in Gross Pollutant Trap Using Machine Learning Approach by Elpina, Sari, Tri Basuki, Kurniawan

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Development Of Construction Noise Prediction Method Using Deep Learning Model by Siew, Jun Teng

“…A simple prediction chart method was developed on top of a stochastic algorithm called Monte Carlo simulation by complying with the standard BS 5228 for the noise prediction in the environmental impact assessment during the planning stage of a construction project. …”
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Application Of Multi-Layer Perceptron Technique To Detect And Locate The Base Of A Young Corn Plant by Morshidi, Malik Arman

“…Results of studying color segmentation using machine-learning algorithm and color space analysis is presented in this thesis. …”
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GIS-Enhanced Crop Yield Modeling with Machine Learning by Venkatesh, S.D., Chitra, K., Harilakshami, V.M.

“…To address these issues, we have developed a system using machine learning algorithms aimed at helping farmers. …”
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Calibration Model of a Low-Cost Air Quality Sensor Using an Adaptive Neuro-Fuzzy Inference System by Alhasa, Kemal, Mohd Nadzir, Mohd, Olalekan, Popoola, Latif, Mohd, Yusup, Yusri, Iqbal Faruque, Mohammad, Ahamad, Fatimah, Abd Hamid, Haris, Aiyub, Kadaruddin, Md Ali, Sawal, Khan, Md, Samah, Azizan Abu, Yusuff, Imran, Othman, Murnira, Tengku Hassim, Tengku, Ezani, Nor

“…Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO2) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). …”
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