Search Results - (( rayleigh distribution path algorithm ) OR ( parameters pollution learning algorithm ))

Air quality prediction by machine learning models: A predictive study on the indian coastal city of Visakhapatnam by Ravindiran G., Hayder G., Kanagarathinam K., Alagumalai A., Sonne C.

Subjects:

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

Impact of air pollutants on climate change and prediction of air quality index using machine learning models by Ravindiran G., Rajamanickam S., Kanagarathinam K., Hayder G., Janardhan G., Arunkumar P., Arunachalam S., AlObaid A.A., Warad I., Muniasamy S.K.

“…Particulate matter, gaseous pollutants, and meteorological parameters were used to predict AQI, and the heat map generated showed that of all the parameters, PM2.5 has the greatest impact on AQI, with a value of 0.91. …”

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.

Subjects:

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…”

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.

“…The proposed models predict the hourly air pollutant concentration based on 4-h historical input based on six air pollutant data, meteorology parameters, and PM2.5 concentration data from the neighboring air quality monitoring stations. …”

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. …”

Enhancing quality of service in Multi-Radio Multi-Channel Wireless Mesh Networks by Raja Bongsu, Raja Hasyifah

“…The COngestion Reduction Routing Algorithm (CORRA) tackles this challenge by evaluating link quality and interference to find optimal data paths. …”
Get full text
Get full text
Get full text

Modeling the effect of process parameters on the photocatalytic degradation of organic pollutants using artificial neural networks by Ayodele B.V., Alsaffar M.A., Mustapa S.I., Cheng C.K., Witoon T.

An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models by Jalil-Masir H., Fattahi R., Ghanbari-Adivi E., Asadi Aghbolaghi M., Ehteram M., Ahmed A.N., El-Shafie A.

A stacked ensemble deep learning model for water quality prediction / Wong Wen Yee by Wong , Wen Yee

“…Any resampling algorithm is not a necessity in the case of this proposed algorithm. …”
Get full text
Get full text
Get full text

Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan by Ziyad Sami B.F., Latif S.D., Ahmed A.N., Chow M.F., Murti M.A., Suhendi A., Ziyad Sami B.H., Wong J.K., Birima A.H., El-Shafie A.

The prediction of diesel engine NOx emissions using artificial neural network / Mohd. Mahadzir Mohammud and Khairil Faizi Mustafa by Mohammud, Mohd. Mahadzir, Mustafa, Khairil Faizi

“…The modelling algorithm implemented, takes a large set of measurements to learn how to predict the NOx emission from four operating parameters. …”
Get full text
Get full text
Get full text

The prediction of diesel engine NOx emissions using artificial neural network / Mohd Mahadzir Mohammud and Khairil Faizi Mustafa by Mohammud, Mohd Mahadzir, Mustafa, Khairil Faizi

“…The modelling algorithm implemented, takes a large set of measurements to learn how to predict the NOx emission from four operating parameters. …”
Get full text
Get full text
Get full text

Predicting noise-induced hearing loss (NIHL) in TNB workers using GDAM algorithm by Rehman Gillani, Syed Muhammad Zubair

“…This research proposed an algorithm for improving the current working performance of Back-propagation algorithm by adaptively changing the momentum value and at the same time keeping the ‘gain’ parameter fixed for all nodes in the neural network. …”
Get full text
Get full text
Get full text
Get full text

Ozone Concentration Forecasting Based on Artificial Intelligence Techniques: A Systematic Review by Yafouz A., Ahmed A.N., Zaini N., El-Shafie A.

“…Decision trees; Forecasting; Multilayer neural networks; Ozone; Predictive analytics; Support vector machines; Artificial intelligence techniques; Machine learning techniques; Multi layer perceptron; Optimization approach; Ozone concentration forecasting; Prediction accuracy; Stand-alone algorithm; Tropospheric ozone concentration; Learning systems; ozone; air quality; algorithm; concentration (composition); machine learning; optimization; ozone; prediction; theoretical study; air pollutant; air quality; artificial intelligence; artificial neural network; concentration (parameter); decision tree; feed forward neural network; forecasting; fuzzy system; human; measurement accuracy; multilayer perceptron; prediction; random forest; recurrent neural network; Review; support vector machine; systematic review…”

Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction by Yafouz A., AlDahoul N., Birima A.H., Ahmed A.N., Sherif M., Sefelnasr A., Allawi M.F., Elshafie A.

Prediction of oil and gas pipeline failures through machine learning approaches: A systematic review by Al-Sabaeei, A.M., Alhussian, H., Abdulkadir, S.J., Jagadeesh, A.

“…This review article mainly focuses on the novelty of using machine and deep learning techniques, specifically artificial neural networks (ANNs), support vector machines (SVMs) and hybrid machine learning (HML) algorithms, for predicting different pipeline failures in the oil and gas industry. …”
Get full text
Get full text

Water quality monitoring using machine learning and IoT: a review by Hasan, Tahsin Fuad, Kabbashi, Nassereldeen Ahmed, Saleh, Tanveer, Alam, Md. Zahangir, Abd Wahab, Mohd Firdaus, Nour, Abdurahman Hamid

“…The paper explores various ML algorithms, including supervised and unsupervised learning and deep learning, along with their applications, and discusses the use of IoT sensors for real-time monitoring of water quality parameters such as pH, dissolved oxygen, temperature, and turbidity.…”
Get full text
Get full text
Get full text
Get full text

Channel Modeling and Direction-of-Arrival Estimation in Mobile Multiple-Antenna Communication Systems by Ravari, Arastoo Rostami

“…The model can be applied to evaluate smart antenna systems and beamforming algorithms in the uplink by generating uncorrelated multipaths Rayleigh fading waveforms with certain spatio-temporal correlation and spatial coordinates relative to base stations to simulate received signals from mobiles and interferers. …”
Get full text
Get full text