Search Results - (( java classification learning algorithm ) OR ( wolf optimization path algorithm ))

Development of an improved GWO algorithm for solving optimal paths in complex vertical farms with multi-robot multi-tasking by Shen, Jiazheng, Hong, Tang Sai, Fan, Luxin, Zhao, Ruixin, Mohd Ariffin, Mohd Khairol Anuar, As’arry, Azizan

“…The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). …”
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Development of Hybrid Algorithm for Integrated Aircraft Routing Problem and Crew Pairing Problem by NurulFarihan Mohamed, ZaitulMarlizawati Zainuddin, Said Salhi, Nurul Huda Mohamed, NurulAkmal Mohamed

Enhancing performance of global path planning for mobile robot through Alpha–Beta Guided Particle Swarm Optimization (ABGPSO) algorithm by Ahmad, Javed, Ab Wahab, Mohd Nadhir, Ramli, Ahmad, Misro, Md Yushalify, Ezza, Wan Zafira, Wan Hasan, Wan Zuha

“…Through extensive simulations across various static environment maps, we demonstrate that the ABGPSO algorithm outperforms existing state-of-the-art optimization techniques, including Genetic Algorithms (GA), Grey Wolf Optimization (GWO), and modern optimizers like the Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO) and Reptile search algorithm (RSA). …”
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Continuous path planning of Kinematically redundant manipulator using Particle Swarm Optimization by Machmudah, A., Parman, S., Baharom, M.B.

“…Based on a geometrical analysis, feasible postures of a self-motion are mapped into an interval so that there will be an angle domain boundary and a redundancy resolution to track the desired path lies within this boundary. To choose a best solution among many possible solutions, meta-heuristic optimizations, namely, a Genetic Algorithm (GA), a Particle Swarm Optimization (PSO), and a Grey Wolf Optimizer (GWO) will be employed with an optimization objective to minimize a joint angle travelling distance. …”
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Continuous path planning of Kinematically redundant manipulator using Particle Swarm Optimization by Machmudah, A., Parman, S., Baharom, M.B.

“…Based on a geometrical analysis, feasible postures of a self-motion are mapped into an interval so that there will be an angle domain boundary and a redundancy resolution to track the desired path lies within this boundary. To choose a best solution among many possible solutions, meta-heuristic optimizations, namely, a Genetic Algorithm (GA), a Particle Swarm Optimization (PSO), and a Grey Wolf Optimizer (GWO) will be employed with an optimization objective to minimize a joint angle travelling distance. …”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…Recently, various techniques based on different algorithms have been developed. However, the classification accuracy and computational cost are not satisfied. …”
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A Hybrid Metaheuristic Technique Based on Grey Wolf Optimisation, Symbiotic Organism Search, and Ant Colony Optimisation for Solving Multi-Objective Vehicle Routing Problems by Ary, Maxsi

“…This study developed a hybrid metaheuristic algorithm involving grey wolf optimization, symbiotic organism search, and ant colony optimization (HMGSA) to address the multi-objective VRP. …”
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Adoption of machine learning algorithm for analysing supporters and non supporters feedback on political posts / Ogunfolajin Maruff Tunde by Ogunfolajin Maruff , Tunde

“…This thesis is based on the application of sentiment classification algorithm to tweet data with the goal of classifying messages based on the polarity of sentiment towards a particular topic (or subject matter). …”
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Energy-efficient routing using novel optimization with Tabu techniques for Wireless Sensor Network by Ahmed Hamza, Manar, Hassan Abdalla Hashim, Aisha, H. Elkamchouchi, Dalia, Nemri, Nadhem, S. Alzahrani, Jaber, Sayed A. Aziz, Amira, Ahmed Ibrahim, Mnahel, Motwakel, Abdelwahed

“…In the proposed method, a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques (GW-IPSO-TS) was used. …”
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Face classification for three major ethnic of Orang Asli using Back Propagation Neural Network / Nor Intan Shafini Nasaruddin by Nasaruddin, Nor Intan Shafini

“…The image classification prototype is developed by using JAVA programming language which is based on supervised learning algorithm, Backpropagation Neural Network. …”
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Development Of Machine Learning User Interface For Pump Diagnostics by Lee, Zhao Yang

“…The features extracted of time domain and frequency domain in vibration and acoustic will use as database of a Support Vector Machine (SVM) algorithms by using MATLAB R2021a. The result from the SVM algorithms will be used as database for the machine learning in Microsoft Azure. …”
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Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning by Solihin M.I., Yanto, Hayder G., Maarif H.A.-Q.

Object-Oriented Programming semantics representation utilizing agents by Mohd Aris, Teh Noranis

“…Generally, these source codes go through the preprocessing, comparison, extraction, generate program semantics and classification processes. A formal algorithm that can be applied to any two related Java-based source codes examples is invented to generate the semantics of these source codes. …”
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Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…The results reveal that the proposed hybrid algorithm is capable of achieving classification accuracy values of (95.82 % and 97.68 %), detection rates values of (95.8 % and 99.3 %) and false alarm rates values of (0.083 % and 0.045 %) on both KDD CUP 99 and NSL KDD. …”
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Cybernetics approaches in intelligent systems for crops disease detection with the aid of IoT by Rathinam R., Kasinathan P., Govindarajan U., Ramachandaramurthy V.K., Subramaniam U., Garrido S.

Anfis Modelling On Diabetic Ketoacidosis For Unrestricted Food Intake Conditions by Saraswati, Galuh Wilujeng

“…Future work should be focusing on data collection of the E-Nose sensors and the improvement of the learning algorithm robustness towards environmental noise during data acquisition, such as evaporation and contamination of odor samples.…”
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