Search Results - (( learning implementation swarm algorithm ) OR ( java application mining algorithm ))

Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

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Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

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Multi-Robot Learning with Bat Algorithm With Mutation (Bam) by Chandrathevan, Sathiamurthy

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Pressure vessel design simulation: Implementing of multi-swarm particle swarm optimization by Salih, Sinan Q., Alsewari, Abdulrahman A., Yaseen, Zeher M.

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Particle Swarm Optimization in Machine Learning Prediction of Airbnb Hospitality Price Prediction by Masrom, S., Baharun, N., Razi, N.F.M., Rahman, R.A., Abd Rahman, A.S.

“…This paper presents the research design and implementation of using Particle Swarm Optimization to automate the features selections in the machine learning models for Airbnb price prediction. …”
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Artificial fish swarm optimization for multilayer network learning in classification problems by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam

“…Nature-Inspired Computing (NIC) has always been a promising tool to enhance neural network learning. Artificial Fish Swarm Algorithm (AFSA) as one of the NIC methods is widely used for optimizing the global searching of ANN.In this study, we applied the AFSA method to improve the Multilayer Perceptron (MLP) learning for promising accuracy in various classification problems.The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. …”
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Artificial Fish Swarm Optmization for Multilayernetwork Learning in Classification Problems by Hasan, Shafaatunnur, Tan, Swee Quo, Shamsuddin, Siti Mariyam, Sallehuddin, Roselina

“…The parameters of AFSA: AFSA prey, AFSA swarm and AFSA follow are implemented on the MLP network for improving the accuracy of various classification datasets from UCI machine learning. …”
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A hybrid particle swarm optimization - extreme learning machine approach for intrusion detection system by M.H., Ali, Mohamad, Fadlizolkipi, Ahmad Firdaus, Zainal Abidin, Nik Zulkarnaen, Khidzir

“…This work proposes the extreme learning machine (ELM) is one of the poplar machine learning algorithms which, easy to implement with excellent learning performance characteristics. …”
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Machine failure prediction technique using recurrent neural network long short-term memory-particle swarm optimization algorithm by Rashid, N.A., Abdul Aziz, I., Hasan, M.H.B.

“…To optimize between the accuracy and execution time, a population-inspired Particle Swarm Optimization (PSO) algorithm is employed. PSO will be utilized to select the optimal RNN-LSTM topology specifically the learning rate instead of using manual search. …”
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Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management by almahameed, Bader aldeen, Bisharah, Majdi

“…This study emphasizes the importance of Machine Learning and Particle Swarm Optimization (PSO) in the context of predictive modeling and cost optimization within the field of construction project management. …”
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Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

“…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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A fast learning network with improved particle swarm optimization for intrusion detection system by Ali, Mohammed Hasan

“…This situation makes the detection of cyber-based attacks on computer networks a relevant and challenging area of research. The Fast Learning Network (FLN) is one of the new machine learning algorithms that are easy to implement, computationally efficient, and with excellent learning performance characteristics. …”
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A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition by Koh J.S., Tan R.H.G., Lim W.H., Tan N.M.L.

“…Therefore, a modified PSO hybridized with adaptive local search (MPSO-HALS) is designed as a robust, real-time MPPT algorithm. A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. …”

Advancements and challenges in mobile robot navigation: a comprehensive review of algorithms and potential for self-learning approaches by Al Mahmud, Suaib, Kamarulariffin, Abdurrahman, Mohd Ibrahim, Azhar, Haja Mohideen, Ahmad Jazlan

“…With the goal of enhancing the autonomy in mobile robot navigation, numerous algorithms (traditional AI-based, swarm intelligence-based, self-learning-based) have been built and implemented independently, and also in blended manners. …”
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Malicious URL classification using artificial fish swarm optimization and deep learning by Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, K. Nour, Mohamed, M. Asiri, Mashael, M. Al-Sharafi, Ali, Othman, Mahmoud, Motwakel, Abdelwahed

“…With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. …”
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Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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A web-based implementation of k-means algorithms by Lee, Quan

“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks by Shing, Chiang Ta, Mohammed Al-Andoli, Mohammed Nasser, Wooi, Ping Cheah

“…Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. …”
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The importance of data classification using machine learning methods in microarray data by Jaber, Aws Naser, Moorthy, Kohbalan, Machap, Logenthiran, Safaai, Deris

“…To unleash the full potential of microarrays, machine-learning algorithms and gene selection methods can be implemented to facilitate processing on microarrays and to overcome other potential challenges. …”
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