Search Results - (( variable interaction system algorithm ) OR ( variable active learning algorithm ))

Optimization of Lipase Catalysed Synthesis of Sugar Alcohol Esters Using Taguchi Method and Neural Network Analysis by Adnani, Seyedeh Atena

“…In order to make the synthesis process more environmentally friendly, the reactions between xylitol and capric acid, caprylic acid, and caproic acid, were also performed in solvent-free system. In this system,similar insolvent system, three methods including one variable at a time, Taguchi method and ANN were used for optimization and prediction of percentage of conversion. …”
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A variable combinatorial test suite strategy based on modified greedy algorithm by Homaid, Ameen A. Ba, Alsewari, Abdulrahman A.

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CTJ: Input-output based relation combinatorial testing strategy using Jaya algorithm by Ng, Yeong Khang

“…Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). …”
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Fuzzy Adaptive Tuning of a Particle Swarm Optimization Algorithm for Variable-Strength Combinatorial Test Suite Generation by Kamal Z., Zamli, Ahmed, Bestoun S., Mahmoud, Thair, Afzal, Wasif

“…Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact, variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. …”
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Taguchi?s T-method with Normalization-Based Binary Bat Algorithm by Marlan Z.M., Jamaludin K.R., Harudin N.

“…Therefore, a variable selection technique using a swarm-based Binary Bat algorithm is proposed. …”

Algorithm enhancement for host-based intrusion detection system using discriminant analysis by Dahlan, Dahliyusmanto

“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm by Mohd Riduwan, Ghazali

“…Meanwhile, in the MIMO systems, many control variables available that interact with each other, and the single node hormone regulation of NEPID is still inadequate in producing better control accuracy of nonlinear MIMO systems. …”
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Parameter estimation of multivariable system using Fuzzy State Space Algorithm / Razidah Ismail … [et al.] by Ismail, Razidah, Ahmad, Tahir, Harish, Noor Ainy, A. Halim, Rosenah

“…The proposed new approach is expected to provide a faster and innovative tool for simulation and analysis of multi variable system.…”
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HABC: Hybrid artificial bee colony for generating variable T-way test sets by Alazzawi, A.K., Rais, H.M., Basri, S.

“…The input variables for a realworld software may diverge in how they strongly influence variable strength (VS) interaction can achieve that effectively. …”
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HABC: Hybrid artificial bee colony for generating variable T-way test sets by Alazzawi, A.K., Rais, H.M., Basri, S.

“…The input variables for a realworld software may diverge in how they strongly influence variable strength (VS) interaction can achieve that effectively. …”
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CTJ: Input-output based relation combinatorial testing strategy using Jaya algorithm by Alsewari, Abdulrahman A., Ng, Yeong Khang, Kamal Z., Zamli, Mohammed E., Younis

“…Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). …”
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A study on advanced statistical analysis for network anomaly detection by Ngadi, Md. Asri, Idris, Mohd. Yazid, Abdullah, Abd. Hanan

“…Anomaly detection algorithms model normal behavior. Anomaly detection models compare sensor data to normal patterns learned from the training data by using statistical method and try to detect activity that deviates from normal activity. …”
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The performance of Taguchi�s T-method with binary bat algorithm based on great value priority binarization for prediction by Marlan Z.M., Ramlie F., Jamaludin K.R., Harudin N.

“…This, however, leads to sub-optimality of prediction accuracy as the orthogonal array design lacks in offering higher-order variable interactions, in addition to its fixed and limited variable combinations to be assessed and evaluated. …”

A parallel genetic algorithm-based TSK-Fuzzy system for dynamic car-following modeling by Purnomo, Muhammad Ridwan Andi, Abdul Wahab, Dzuraidah, Hassan, Azmi, Rahmat, Riza Atiq

“…The proposed model is used for the tire-road slippage index determination which influences the car's speed. Since the car interact with each other on the road and the driver progressive level is different, three interaction variables, that are current car speed, distance to the car ahead and driver progressive level, are defined and an indication of their influence on the tire-road slippage index is analysed. …”
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Examining the potential of machine learning for predicting academic achievement: A systematic review by Nazir, M., Noraziah, Ahmad, Rahmah, M., Sharma, Aditi

“…Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. …”
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Examining the potential of machine learning for predicting academic achievement: A systematic review by Nazir, M., Noraziah, Ahmad, Rahmah, M., Sharma, Aditi

“…Predicting student academic performance is a critical area of education research. Machine learning (ML) algorithms have gained significant popularity in recent years. …”
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Classification of students' performance in computer programming course according to learning style by Norwawi N.M., Abdusalam S.F., Hibadullah C.F., Shuaibu B.M.

Enhancing Harmony Search Metaheuristic Algorithm for Coverage Efficiency, Test Suite Reduction, and Running Time in Combinatorial Interaction Testing by Muazu, Aminu Aminu, Hashim, Ahmad Sobri

“…Combinatorial Interaction Testing (CIT) is an efficient technique for detecting faults caused by interactions among system factors. …”
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

“…The performance of ANNs depend on many factors, including the network structure, the selection of activation function, the learning rate of the training algorithm, and initial synaptic weight values, the number of input variables, and the number of units in the hidden layer. …”
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Classification of Students' Performance in Computer Programming Course According to Learning Style by Norwawi, NM, Abdusalam, SF, Hibadullah, CF, Shuaibu, BM

“…The findings show that that student's good performance in programming courses has a visual, active and sequential learning style.…”