Search Results - (( developing work svm algorithm ) OR ( java implication based algorithm ))

Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems by Mohammad Khamees Khaleel, Alsajri

“…Hence, a hybrid classifier called ITLBO-IPJAYA-SVM was developed in this work for the improvement of the efficiency of network intrusion on data sets that contain multiple types of attacks. …”
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Hand gesture recognition for autism diagnosis using Support Vector Machine (SVM) Algorithm / Muhammad Asyraf Mohamad Zain by Mohamad Zain, Muhammad Asyraf

“…For the future work, this system need to be improved by using dataset that are related to the ASD and by using other classification algorithm.…”
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Prediction of COVID-19 outbreak using Support Vector Machine / Muhammad Qayyum Mohd Azman by Mohd Azman, Muhammad Qayyum

“…A prototype architecture and a user-friendly graphical interface tailored for SVM-based outbreak predictions are developed, accompanied by detailed code snippets elucidating essential steps in data loading, encoding, scaling, and SVM model training. …”
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Simultaneous fault diagnosis based on multiple kernel support vector machine in nonlinear dynamic distillation column by Taqvi, S.A.A., Zabiri, H., Uddin, F., Naqvi, M., Tufa, L.D., Kazmi, M., Rubab, S., Naqvi, S.R., Maulud, A.S.

“…In the developed MK-SVM algorithm, multilabel approach based on various kernel functions has been utilized for the classification of simultaneous faults. …”
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SVM modelling of agarwood oil quality grading using radial basis function (RBF) and sequential minimal optimization (SMO) learning algorithms by Muhammad Haziq Haikal, Hisham, Nurlaila, Ismail, Nor Azah, Mohd Ali, Saiful Nizam, Tajuddin, Mohd Nasir, Taib

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A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant by Chun T.S., Malek M.A., Ismail A.R.

A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant by Ting, Sie Chun, Abdul Malik, Marlinda, Ismail, Amelia Ritahani

“…The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. …”
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Classification of imbalanced travel mode choice to work data using adjustable svm model by Qian, Y., Aghaabbasi, M., Ali, M., Alqurashi, M., Salah, B., Zainol, R., Moeinaddini, M., Hussein, E.E.

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A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification by Madanan M., Gunasekaran S.S., Mahmoud M.A.

Agarwood oil quality classification using one versus all strategies in multiclass on SVM model / Aqib Fawwaz Mohd Amidon … [et al.] by Mohd Amidon, Aqib Fawwaz, Mahabob, Noratikah Zawani, Ismail, Nurlaila, Mohd Yusoff, Zakiah, Taib, Mohd Nasir

“…Support vector machine (SVM) has been chosen as a main model and for the specific function algorithm was multiclass function. …”
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Spectral discrimination and index development of roofing materials and conditions using field spectroscopy and worldview-3 satellite image by Samsudin, Sarah Hanim

“…Three feature selection algorithms of Genetic Algorithm (GA), Support Vector Machine (SVM) and Random Forest (RF) were used to select the most significant wavelengths since the algorithms works well with large size of data and widely applied for feature selection of hyperspectral remote sensing data. …”
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Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

“…Many optimisation-based intrusion detection algorithms have been developed and are widely used for intrusion identification. …”
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AI recommendation penetration testing tool for cross-site scripting: support vector machine algorithm by Salim, Nur Saadah, Saad, Shahadan

“…This research introduces a new approach to enhancing cybersecurity by integrating Support Vector Machine (SVM) algorithms with penetration testing to develop a recommendation system focused on Cross-Site Scripting (XSS) attack detection. …”
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Polycystic Ovary Syndrome (PCOS) prediction system using PSO-SVM / Lukman Hakim Shaufee, Hamidah Jantan and Ummu Fatihah Mohd Bahrin by Shaufee, Lukman Hakim

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Development of track-driven agriculture robot with terrain classification functionality / Khairul Azmi Mahadhir by Mahadhir, Khairul Azmi

“…For instance, a terrain covered with gravel produces high vibration to robot when traversing on the surface. In this work, an agricultural robot is embedded with machine learning algorithm based on Support Vector Machine (SVM). …”
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Fault classification in smart distribution network using support vector machine by Chuan O.W., Ab Aziz N.F., Yasin Z.M., Salim N.A., Wahab N.A.

“…In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. …”

Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data by Mohammad Aman Ullah

Diabetic retinopathy detection using Gray-Level Co-Occurrence Matrix / Aliff Azfar Aris by Aris, Aliff Azfar

“…The classification was performed by using Support Vector Machine (SVM) to generate the cross-validation accuracy to determine the learning algorithm’s performance. …”
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Gesture recognition system for Nigerian tribal greeting postures using support vector machine / Segun Aina …[et al.] by Aina, Segun, V. Sholesi, Kofoworola, R. Lawal, Aderonke, D. Okegbile, Samuel, I. Oluwaranti, Adeniran

“…This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. …”
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Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar M.A.A.A., Ker P.J., Tang S.G.H., Baharuddin M.Z., Lee H.J., Omar A.R.

“…Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chicken�s comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. …”