Search Results - (( java implementation path algorithm ) OR ( using stations learning algorithm ))

Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

“…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment by Shaliza Hayati A. Wahab, Nordin Saad, Azali Saudi, Ali Chekima

“…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

“…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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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.

“…Therefore, this study uses hybrid deep learning models to forecast air pollution based on the concentration of particulate matter with diameter size of less than 2.5 ?…”

Sustainable Management Of River Water Quality Using Artificial Intelligence Optimisation Algorithms by Chia, See Leng

“…Among the hybrid models, in terms of accuracy, the best optimisation algorithm at station 1K06 was the AMFO while the best optimisation algorithm at station 1K07 was the HPSOGA. …”
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Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia by Hindia, Mhd Nour

“…The selection is based on the user preferences since it uses a self-learning algorithm to determine triggers and handover thresholds dynamically. …”
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Machine learning modeling for radiofrequency electromagnetic fields (RF-EMF) signals from mmWave 5G signals by Al-Jumaily, Abdulmajeed, Sali, Aduwati, Riyadh, Mohammed, Wali, Sangin Qahtan, Li, Lu, Osman, Anwar Faizd

“…Additionally, the ICNIRP standard was used and compared with the results and validation values of the algorithms.…”
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Extreme Learning Machines: A new approach for prediction of reference evapotranspiration by Abdullah S.S., Malek M.A., Abdullah N.S., Kisi O., Yap K.S.

Predicting user trajectories using deep learning algorithms / Ahmad Zaki Aiman Abdul Rashid, Azita Laily Yusof and Norsuzila Ya’acob by Abdul Rashid, Ahmad Zaki Aiman, Yusof, Azita Laily, Ya’acob, Norsuzila

“…In order to produce seamless handover performance, a user’s trajectory acts as a catalyst in determining the exact time and position of making the handover from one base station to another base station. Due to this, this paper predicts user’s future trajectory from past trajectory utilizing deep learning (DL) algorithms which are Long-Short Term Memory (LSTM), BiDirectional LSTM, and Gated Recurrent Unit (GRU). …”
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Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models by Elbeltagi A., Pande C.B., Kumar M., Tolche A.D., Singh S.K., Kumar A., Vishwakarma D.K.

“…Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. …”

Prediction of atmospheric carbon monoxide concentration utilizing different machine learning algorithms: A case study in Kuala Lumpur, Malaysia by Latif S.D., Almalayih M., Yafouz A., Ahmed A.N., Zaini N., Irwan D., AlDahoul N., Sherif M., El-Shafie A.

A Survey Of Supervised Machine Learning In Wireless Sensor Network: A Power Management Perspective by Ul haq, Riaz, Norrozila, Sulaiman, Muhammad, Alam

“…This survey paper is focused on the discussion of best optimal path routing algorithms in wireless sensor networks by using supervised machine learning approaches. …”
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Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm by Mohamadi S., Sammen S.S., Panahi F., Ehteram M., Kisi O., Mosavi A., Ahmed A.N., El-Shafie A., Al-Ansari N.

Creating Air Temperature Models for High-Elevation Desert Areas Using Machine Learning by Forooshani, Massoud, Gegov, Alexander, Pepin, Nick, Adda, Mo

“…In this study, we estimate Ta in the high-elevation desert zone of Kilimanjaro (>4500m) using four models (five models including the benchmark model) with unique sets of inputs using five machine learning (ML) algorithms. …”
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Robust Data Fusion Techniques Integrated Machine Learning Models For Estimating Reference Evapotranspiration by Chia, Min Yan

“…As for the NNE, a novel meta-learner based on the stochastic-enabled extreme learning machine integrated with whale optimisation algorithm (WOA-ELM) was developed and used in such an application for the first time. …”
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Sales prediction for Adha Station by using predictive analytics by Mohd Mokhid, Muhammad Amier Latieff

“…This project addresses the existing issues in the business processes at Adha Station, a Thai restaurant in Malaysia. This research presented a technique for projecting sales utilising current data through a machine learning algorithm. …”
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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.

Machine learning versus linear regression modelling approach for accurate ozone concentrations prediction by Jumin E., Zaini N., Ahmed A.N., Abdullah S., Ismail M., Sherif M., Sefelnasr A., El-Shafie A.

“…Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. …”

Prediction of biochemical oxygen demand in Mexican surface waters using machine learning / Maximiliano Guzmán-Fernández ... [et al.] by Maximiliano, Guzmán-Fernández, Misael, Zambrano-de la Torre, Claudia, Sifuentes-Gallardo, Oscar, Cruz-Dominguez, Carlos, Bautista-Capetillo, Juan, Badillo-de Loera, Efrén, González Ramírez, Héctor, Durán-Muñoz

“…Two groups were formed and used as input to four machine learning algorithms. …”
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