Search Results - (( java implementation path algorithm ) OR ( using wi 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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MFA-OSELM algorithm for WiFi-based indoor positioning system by Al-Khaleefa, Ahmed Salih, Mohd Riduan, Ahmad, Azmi Awang, Md Isa, Al-Saffar, Ahmed Ali Mohammed, Mona Riza, Mohd Esa, Reza Firsandaya, Malik

“…Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. …”
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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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MFA-OSELM Algorithm For Wifi-Based Indoor Positioning System by AL-Khaleefa, Ahmed Salih, Mohd Riduan, Ahmad, Awang Md Isa, Azmi, AL-Saffar, Ahmed, Mohd Esa, Mona Riza, Malik, Reza Firsandaya

“…Facilitating the use of WiFi for this purpose requires fingerprint formation and the implementation of a learning algorithm with the aim of using the fingerprint to determine locations. …”
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Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media Anugerah, Md. Tap, Abu Osman

“…Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. …”
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Extreme learning machine for user location prediction in mobile environment by Mantoro, Teddy, Olowolayemo, Akeem, Olatunji, Sunday O., Ayu, Media A., Abu Osman, Md. Tap

“…Purpose – Prediction accuracies are usually affected by the techniques and devices used as well as the algorithms applied. This work aims to attempt to further devise a better positioning accuracy based on location fingerprinting taking advantage of two important mobile fingerprints, namely signal strength (SS) and signal quality (SQ) and subsequently building a model based on extreme learning machine (ELM), a new learning algorithm for single-hidden-layer neural networks. …”
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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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Wifi-based location-independent human activity recognition and localization using deep learning by Abuhoureyah, Fahd Saad Amed

“…A trajectory mapping approach using CSI-Triangulation with deep learning is proposed to refine the localization capabilities of WiFi-based HAR, offering an accurate and robust solution for localization in diverse real-world scenarios. …”
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WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques by Daw Abdulsalam Ali Daw

Evolutionary multi-objective optimization of autonomous mobile robots in neural-based cognition for behavioural robustness by Chin, Kim On, Teo, Jason Tze Wi, Azali Saudi

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Self-organizing map approach for determining mobile user location using IEEE 802.11 signals by Mantoro, Teddy, Ayu, Media Anugerah, Nuraini, Asma, Amin, Sulafa Mohd

“…With the novel use of Wi-Fi based on the IEEE 802.11 standards: inferring the location of a wireless client from signal quality measures, the Kohonen SOM paradigms were used. …”
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A New Algorithm For Prediction WIMAX Traffic Based On Artificial Neural Network Models by Daw Abdulsalam Ali Daw, Kamaruzzaman Bin Seman, Madihah Mohd Saudi

“…Testing the different configurations using real traffic data recorded at base stations (A, B and AB) that belong to a Libyan WiMAX Network. …”
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Automating commercial video game development using computational intelligence by Tse, Guan Tan, Teo, Jason Tze Wi, Patricia Anthony

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Spatiotemporal extraction of aquaculture ponds under complex surface conditions based on deep learning and remote sensing indices by Qin, Weirong, Ismail, Mohd Hasmadi, Ramli, Mohammad Firuz, Deng, Junlin, Wu, Ning

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A new LoRa based positioning algorithm utilizing sequence based deep learning technique by Suseenthiran, Kavetha

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Mobility, Residual Energy, and Link Quality Aware Multipath Routing in MANETs with Q-learning Algorithm by Tilwari, Valmik, Dimyati, Kaharudin, Hindia, Mhd Nour, Fattouh, Anas, Amiri, Iraj

“…The proposed scheme makes routing decisions by determining the optimal route with energy efficient nodes to maintain the stability, reliability, and lifetime of the network over a sustained period of time. The MRLAM scheme uses a Q-Learning algorithm for the selection of optimal intermediate nodes based on the available status of energy level, mobility, and link quality parameters, and then provides positive and negative reward values accordingly. …”
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Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq by Yaseen, Z.M., Jaafar, O., Deo, R.C., Kisi, O., Adamowski, J., Quilty, J., El-Shafie, A.

“…The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. …”
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A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem by Ahmed A.N., Van Lam T., Hung N.D., Van Thieu N., Kisi O., El-Shafie A.