Search Results - (((( self learning algorithm ) OR ( pattern machine algorithm ))) OR ( patterns bat algorithm ))

Naive Bayes-guided bat algorithm for feature selection by Taha A.M., Mustapha A., Chen S.-D.

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Naive bayes-guided bat algorithm for feature selection. by Taha, Ahmed Majid, Mustapha, Aida, Chen, Soong Der

“…Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. …”
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Bat algorithm for rough set attribute reduction by Taha A.M., Tang A.Y.C.

“…In this paper, a new optimization method has been introduced called bat algorithm for attribute reduction (BAAR), the proposed method is based mainly on the echolocation behavior of bats. …”

BIOLOGICAL INSPIRED INTRUSION PREVENTION AND SELF-HEALING SYSTEM FOR CRITICAL SERVICES NETWORK by MOHAMED AHMED ELSHEIK, MUNA ELSADIG

“…The design features of the proposed system are inspired by the human immune system, integrated with pattern recognition nonlinear classification algorithm and machine learning. …”
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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.

Survival versus non-survival prediction after acute coronary syndrome in Malaysian population using machine learning technique / Nanyonga Aziida by Nanyonga , Aziida

“…Self-Organizing Feature (SOM) was used to visualize and identify the relationship and pattern between factors affecting mortality after ACS. …”
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Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm by Mohd Sharif, Sharifah, Mohd Kusin, Faradiella, Asha’ari, Zulfa Hanan, Aris, Ahmad Zaharin

“…The self organizing map (SOM) combined with the K-means algorithm arranged the data based on the relationships of 25 variables. …”
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Mobile app of mood prediction based on menstrual cycle using machine learning algorithm / Nur Hazirah Amir by Amir, Nur Hazirah

“…It implemented Supervised Learning algorithm with Bayes’ Theorem model for the calculation of mood prediction using Python programming language. …”
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Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction by Al-Himyari, Bayadir Abbas

“…The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. …”
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An Improved Network Intrusion Detection Method Based On CNN-LSTM-SA by Bian, Hui

“…As cyber threats grow in complexity and frequency, the importance of Network Intrusion Detection Systems in modern cybersecurity defense becomes increasingly critical. Traditional machine learning algorithms, such as Decision Trees, Naive Bayes, Random Forest, Random Trees, Multi-Layer Perceptron, and Support Vector Machines, have been extensively applied to address these threats. …”
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Analyzing enrolment patterns: Stacked ensemble statistical learning-based approach to educational decision making by Chuan, Zun Liang, Chong, Teak Wei, Japashov, Nursultan, Soon, Kien Yuan, Tan, Wei Qing, Noriszura, Ismail, Liong, Choong-Yeun, Tan, Ee Hiae

“…Moreover, the introduction of the novel stacked ensemble machine learning algorithm had improved predictive accuracy compared to traditional dichotomous logistic regression algorithms on average, particularly at optimal training-to-test ratios of 70:30, 80:20, and 90:10. …”
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APPLICATION OF LINK GRAMMAR IN SEMI-SUPERVISED NAMED ENTITY RECOGNITION FOR ACCIDENT DOMAIN by SARI, YUNITA SARI

“…For the third contribution, we have applied the Self-Training algorithm which is one of the semi-supervised machines learning technique. …”
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DeepIoT.IDS: Hybrid deep learning for enhancing IoT network intrusion detection by A. Mostafa, Salama, Al-Azzawi, Ziadoon Kamil Maseer, Bahaman, Nazrulazhar, Yusof, Robiah, Musa, Omar, Al-rimy, Bander Ali Saleh

“…Recently, researchers have suggested deep learning (DL) algorithms to define intrusion features through training empirical data and learning anomaly patterns of attacks. …”
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Computer-aided system for extending the performance of diabetes analysis and prediction by Murad, Saydul Akbar, Zafril Rizal, M Azmi, Zaid Hafiz, Hakami, Prottasha, Nusrat Jahan, Kowsher, Md

“…The machine learning algorithms used to predict the risks of Type 2 diabetes. …”
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Bat Algorithm for Complex Event Pattern Detection in Sentiment Analysis by Kabir Ahmad, Farzana, Kamaruddin, Siti Sakira, Yusof, Yuhanis, Yusoff, Nooraini

“…Thus, this study proposed a Bat Algorithm (BA) to address the complex learning structure of DBN in detecting sentiment patterns. …”
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Teaching and learning via chatbots with immersive and machine learning capabilities by Nantha Kumar Subramaniam

“…These chatbots acquired its intelligence through a hybrid approach that combines pattern-matching technique and machine learning algorithm in order to formulate its responses. …”
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An early warning system for students at risk using supervised machine learning by Yam, Zheng Hong, Mohd Norshahriel, Abd Rani, Nabilah Filzah, Mohd Radzuan, Lim, Huay Yen, Sarasvathi, Nagalingam

“…For that, this study will perform a study of the current issues, factors, and solutions in education student’s data, determine the supervised machine learning algorithms, compare which model is the best predict the students’ performances, develop an early warning system for the educators to make an early decision in order to assist and consult the at-risk students, and finally conduct testing and evaluation of the system. …”
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Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman by Che Osman, Siti Eshah

“…Due to the center biased nature of the videos, the HPSO algorithm uses an initial pattern (hexagon-shaped) to speed up the convergence of the algorithm. …”
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An algorithm for Elliott Waves pattern detection by Vantuch, T., Zelinka, I., Vasant, P.

“…The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. …”
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An algorithm for Elliott Waves pattern detection by Vantuch, T., Zelinka, I., Vasant, P.

“…The Random Decision Forest and the Support Vector Machine are the machine learning algorithms employed for this task. …”
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