Search Results - (( java implementation force algorithm ) OR ( basic pre selection algorithm ))

Application of genetic algorithm and JFugue in an evolutionary music generator by Tang, Jia Rou

“…The appliance of harmonic principles, rhythmic patterns, and user feedback provides the basic for fitness assessment in ensuring that the generated music meets pre-set requirements. …”
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Classification of herbs plant diseases via hierarchical dynamic artificial neural network by Abdullah, Lili Nurliyana, Khalid, Fatimah, Borhan, N.M.

“…The image processing is divided into four stages: Image Pre-Processing to remove image noises (Fixed-Valued Impulse Noise, Random-Valued Impulse Noise and Gaussian Noise), Image Segmentation to identify regions in the image that were likely to qualify as diseased region, Image Feature Extraction and Selection to extract and select important image features and Image Classification to classify the image into different herbs diseases classes. …”
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Classification of herbs plant diseases via hierachical dynamic artificial neural network after image removal using kernel regression framework by Abdullah, Lili Nurliyana, Khalid, Fatimah, Borhan, N.M.

“…The image processing is divided into four stages: Image Pre-Processing to remove image noises (Fixed-Valued Impulse Noise, Random-Valued Impulse Noise and Gaussian Noise), Image Segmentation to identify regions in the image that were likely to qualify as diseased region, Image Feature Extraction and Selection to extract and select important image features and Image Classification to classify the image into different herbs diseases classes. …”
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Improving neural networks training using experiment design approach by Chong, Wei Kean

“…Randomly select the m data set for conventional training algorithm. …”
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Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive by Al-Bared, M.A.M., Mustaffa, Z., Armaghani, D.J., Marto, A., Yunus, N.Z.M., Hasanipanah, M.

“…Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. …”
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Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive by Al-Bared, M.A.M., Mustaffa, Z., Armaghani, D.J., Marto, A., Yunus, N.Z.M., Hasanipanah, M.

“…Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. …”
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Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad by Ahmad, Khairul Adilah

“…Feature extraction and selection reduces the number of features. In this research, the shape and size feature were extracted using aspect ratio of selected morphological features. …”
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Al-Hams and Al-Jahr Sifaat evaluation using classification approach by Altalmas, Tareq, M., Ahmad, Salmiah, Sediono, Wahju, Nik Hashim, Nik Nur Wahidah, Embong, Abd Halim, Hassan, Surul Shahbudin

“…Mel-frequency Cepstral Coefficients (MFCC) technique was used as features extraction, obtained from the pre-processed audio signal. Features selection technique was then implemented to reduce the size of the features vector, where later, K-nearest Neighbor (KNN) algorithm was used as the classification technique. …”
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Real-time oil palm fruit bunch ripeness grading system using image processing techniques by Alfatni, Meftah Salem M.

“…In this research, a real time oil palm grading system was built and an image processing techniques algorithm was developed based on the external features of oil palm fresh fruit bunches (FFB) such as colour, texture, and thorns. …”
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Incremental learning for large-scale stream data and its application to cybersecurity by Ali, Siti Hajar Aminah

“…In Chapter 1, the concept of in�cremental learning is briefly described and basic theories on Resource Allocating Network (RAN) and conventional data selection method are discussed in this chapter. …”
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