Search Results - (( based classification using algorithm ) OR ( problem representation using algorithm ))

Extremal region detection and selection with fuzzy encoding for food recognition by Razali @ Ghazali, Mohd Norhisham

“…Three algorithms were used to accomplish the task of feature representation. …”
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Document classification based on kNN algorithm by term vector space reduction by Moldagulova A., Sulaiman R.B.

Computing irreducible representations of two-generator groups by Sarmin, Nor Haniza, Robert F., Morse, Ahmad, Satapah, Ahmad, Azhana, Mohd. Ali, Nor Muhainiah

“…Groups, Algorithms and Programming (GAP) software has been used for the calculations. …”
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Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences by Rajesvary Rajoo R., Salam R.A.

Sentiment analysis for malay newspaper (SAMNews) using negative selection algorithm / Nur Amalina Redzuan by Redzuan, Nur Amalina

“…However, for the machine learning text representation it cause some problems because of the noisy text. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning by Safa, Soodabeh

“…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification by Sobhan Sheykhivand, Tohid Yousefi Rezaii, Zohreh Mousavi, Azra Delpak, Ali Farzamnia

“…In this research, a fully automated system is presented to automatically detect the various states of the epileptic seizure. This study is based on sparse representation-based classification (SRC) theory and the proposed dictionary learning using electroencephalogram (EEG) signals. …”
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Deep plant: A deep learning approach for plant classification / Lee Sue Han by Lee , Sue Han

“…Besides using solely a single leaf organ to recognize plant species, numerous studies have employed DL methods to solve multi-organ plant classification problem. …”
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Malicious URL Detection with Distributed Representation and Deep Learning by Do N.Q., Selamat A., Lim K.C., Krejcar O.

MODIFIED AIS-BASED CLASSIFIER FOR MUSIC GENRE CLASSIFICATION by Muda, N. A.

“…Various music contents, for example beat, pitch, timbral and many others were used and analyzed to represent the music. To be able to manipulate these content representations for recognition: feature extraction and classification are two major focuses of investigation in this area. …”
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Discriminative feature representation for Malay children’s speech recognition / Seyedmostafa Mirhassani by Mirhassani, Seyedmostafa

“…In case of multiple filterbanks the cepstral features are used in different experts for performing classification based on different representation of speeches. …”
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Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system by Seyed Mohammad Hussein, Ahmad, Siti Anom, Hassan, Mohd Khair, Ishak, Asnor Juraiza

“…Usually shape and texture based techniques are used at the same time in object recognition problems. …”
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Software metrics selection model for predicting maintainability of object-oriented software using genetic algorithms by Bakar, Abubakar Diwani

“…The latest effort to solve this selection problem is the development of the metrics selection model that uses genetic algorithm (GA). …”
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Sentiment analysis for airline services on Twitter using deep learning with word embedding / Mawada Mohamed Nour El Daim El Khalifa by Mawada Mohamed , Nour El Daim El Khalifa

“…The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. However, existing approaches such as the Bag of Words (BOW) model is frequently used for text classification, where a document is mapped to a feature vector before the construction of the actual model, using machine learning techniques, like Logistical Regression and Support Vector algorithms. …”
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On equivalence of FIS and ELM for interpretable rule-based knowledge representation by Wong S.Y., Yap K.S., Yap H.J., Tan S.C., Chang S.W.

k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data by Alfred, Rayner, Shin, Kung Ke, Sainin, Mohd Shamrie, On, Chin Kim, Pandiyan, Paulraj Murugesa, Ag Ibrahim, Ag Asri

“…This paper proposes a two-layered genetic algorithm-based feature selection in order to improve the classification performance of learning relational database using a k-NN ensemble classifier.The proposed method involves the task of omitting less relevant features but retaining the diversity of the classifiers so as to improve the performance of the k-NN ensemble. …”
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A review: radar-based fall detection sensor / Hidayatusherlina Razali ... [et al.] by Razali, Hidayatusherlina, Abd. Rashid, Nur Emileen, Nasarudin, Muhammad Nazrin Farhan, Ismail, Nor Najwa, Ismail Khan, Zuhani, Enche Ab Rahim, Siti Amalina

“…However, the radar system is the most reliable detection method since it avoids the downsides of other sensors, such as discomfort, heavy computational processing, and privacy problems. Radar-based sensors can accurately capture Doppler frequency variations from human motion by utilising time-frequency representations and classification algorithms. …”
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Rough Neural Networks Architecture For Improving Generalization In Pattern Recognition by Ali Adlan, Hanan Hassan

“…The RSNN provides a novel methodology for designing nonlinear filters without prior knowledge of the problem domain. The RNN was used to detect patterns present in satellite image. …”
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Edge assisted crime prediction and evaluation framework for machine learning algorithms by Adhikary, Apurba, Murad, Saydul Akbar, Munir, Md Shirajum, Choong Seon, Hong Seong

“…Criminal risk is predicted using classification models for a particular time interval and place. …”
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Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer�s Disease by Sadiq, A., Yahya, N., Tang, T.B., Hashim, H., Naseem, I.

“…The classification algorithm developed based on the nonfractal connectivity feature and support vector machine classifier has shown an excellent performance, with an accuracy of 90.3 and 83.3 for the XHSLF dataset and ADNI dataset, respectively. …”
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