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Journal articles on the topic 'Implementation of unary classifier'

Author: Grafiati

Published: 6 September 2021

Last updated: 18 February 2022

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1

Rangkuti, Rizki Perdana, Vektor Dewanto, Aprinaldi, and Wisnu Jatmiko. "Utilizing Google Images for Training Classifiers in CRF-Based Semantic Segmentation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 3 (May 19, 2016): 455–61. http://dx.doi.org/10.20965/jaciii.2016.p0455.

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One promising approach to pixel-wise semantic segmentation is based on conditional random fields (CRFs). CRF-based semantic segmentation requires ground-truth annotations to supervisedly train the classifier that generates unary potentials. However, the number of (public) annotation data for training is limitedly small. We observe that the Internet can provide relevant images for any given keywords. Our idea is to convert keyword-related images to pixel-wise annotated images, then use them as training data. In particular, we rely on saliency filters to identify the salient object (foreground) of a retrieved image, which mostly agrees with the given keyword. We utilize saliency information for back-and-foreground CRF-based semantic segmentation to further obtain pixel-wise ground-truth annotations. Experiment results show that training data from Google images improves both the learning performance and the accuracy of semantic segmentation. This suggests that our proposed method is promising for harvesting substantial training data from the Internet for training the classifier in CRF-based semantic segmentation.

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Efremides, O. B., M. P. Bekakos, and D. J. Evans. "Integrated classifier simulator and neurochip VHDL implementation." International Journal of Computer Mathematics 80, no. 11 (November 2003): 1343–50. http://dx.doi.org/10.1080/0020716031000148223.

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3

Tran, Hoai Linh, Van Nam Pham, and Duc Thao Nguyen. "A hardware implementation of intelligent ECG classifier." COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 34, no. 3 (May 5, 2015): 905–19. http://dx.doi.org/10.1108/compel-05-2014-0119.

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Purpose – The purpose of this paper is to design an intelligent ECG classifier using programmable IC technologies to implement many functional blocks of signal acquisition and processing in one compact device. The main microprocessor also simulates the TSK neuro-fuzzy classifier in testing mode to recognize the ECG beats. The design brings various theoretical solutions into practical applications. Design/methodology/approach – The ECG signals are acquired and pre-processed using the Field-Programmable Analog Array (FPAA) IC due to the ability of precise configuration of analog parameters. The R peak of the QRS complexes and a window of 300 ms of ECG signals around the R peak are detected. In this paper we have proposed a method to extract the signal features using the Hermite decomposition algorithm, which requires only a multiplication of two matrices. Based on the features vectors, the ECG beats are classified using a TSK neuro-fuzzy network, whose parameters are trained earlier on PC and downloaded into the device. The device performance was tested with the ECG signals from the MIT-BIH database to prove the correctness of the hardware implementations. Findings – The FPAA and Programmable System on Chip (PSoC) technologies allow us to integrate many signal processing blocks in a compact device. In this paper the device has the same performance in ECG signal processing and classifying as achieved on PC simulators. This confirms the correctness of the implementation. Research limitations/implications – The device was fully tested with the signals from the MIT-BIH databases. For new patients, we have tested the device in collecting the ECG signals and QRS detections. We have not created a new database of ECG signals, in which the beats are examined by doctors and annotated the type of the rhythm (normal or abnormal, which type of arrhythmia, etc.) so we have not tested the classification mode of the device on real ECG signals. Social implications – The compact design of an intelligent ECG classifier offers a portable solution for patients with heart diseases, which can help them to detect the arrhythmia on time when the doctors are not nearby. This type of device not only may help to improve the patients’ safety but also contribute to the smart, inter-networked life style. Originality/value – The device integrate a number of solutions including software, hardware and algorithms into a single, compact device. Thank to the advance of programmable ICs such as FPAA and PSoC, the designed device can acquire one channel of ECG signals, extract the features and classify the arrhythmia type (if detected) using the neuro-fuzzy TSK network in online mode.

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Ren, Jian Si. "Research and Implementation of Text Classification Algorithm." Applied Mechanics and Materials 644-650 (September 2014): 2395–98. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2395.

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The development of Internet and digital library has triggered a lot of text categorization methods. How to find desired information accurately and timely is becoming more and more important and automatic text categorization can help us achieve this goal. In general, text classifier is implemented by using some traditional classification methods such as Naive-Bayes (NB). ARC-BC (Associative Rule-based Classifier by Category) can be used for text categorization by dividing text documents into subsets in which all documents belong to the same category and generate associative classification rules for each subset. This classifier differs from previous methods in that it consists of discovered association rules between words and categories extracted from the training set. In order to train and test this classifier, we constructed training data and testing data respectively by selecting documents from Yahoo. The experimental result shows that the performance of ARC-BC based text categorization is very pretty efficient and effective and it is comparable to Naïve Bayesian algorithm based text categorization.

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MONSERRAT, M., F. ROSSELLÓ, and J. TORRENS. "WHEN IS A CATEGORY OF MANY-SORTED PARTIAL ALGEBRAS CARTESIAN-CLOSED?" International Journal of Foundations of Computer Science 06, no. 01 (March 1995): 51–66. http://dx.doi.org/10.1142/s0129054195000056.

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In this paper we study the cartesian closedness of the five most natural categories with objects all partial many-sorted algebras of a given signature. In particular, we prove that, from these categories, only the usual one and the one having as morphisms the closed homomorphisms can be cartesian closed. In the first case, it is cartesian closed exactly when the signature contains no operation symbol, in which case such a category is a slice category of sets. In the second case, it is cartesian closed if and only if all operations are unary. In this case, we identify it as a functor category and we show some relevant constructions in it, such as its subobjects classifier or the exponentials.

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Lee, Hansoo, and Sungshin Kim. "Black-Box Classifier Interpretation Using Decision Tree and Fuzzy Logic-Based Classifier Implementation." International Journal of Fuzzy Logic and Intelligent Systems 16, no. 1 (March 31, 2016): 27–35. http://dx.doi.org/10.5391/ijfis.2016.16.1.27.

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Chang, Yong Hoon, Kwon Soon Lee, and Gye Rok Jun. "The Implementation of Pattern Classifier for Karyotype Classification." Journal of Korean Society of Medical Informatics 3, no. 2 (1997): 207. http://dx.doi.org/10.4258/jksmi.1997.3.2.207.

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8

Sulistiyo, Mahmud Dwi, and Rita Rismala. "Implementation of Evolution Strategies for Classifier Model Optimization." Indonesian Journal on Computing (Indo-JC) 1, no. 2 (December 30, 2016): 13. http://dx.doi.org/10.21108/indojc.2016.1.2.43.

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<span style="font-size: 9.0pt; mso-bidi-font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Classification becomes one of the classic problems that are often encountered in the field of artificial intelligence and data mining. The problem in classification is how to build a classifier model through training or learning process. Process in building the classifier model can be seen as an optimization problem. Therefore, optimization algorithms can be used as an alternative way to generate the classifier models. In this study, the process of learning is done by utilizing one of Evolutionary Algorithms (EAs), namely Evolution Strategies (ES). Observation and analysis conducted on several parameters that influence the ES, as well as how far the general classifier model used in this study solve the problem. The experiments and analyze results show that ES is pretty good in optimizing the linear classification model used. For Fisher’s Iris dataset, as the easiest to be classified, the test accuracy is best achieved by 94.4%; KK Selection dataset is 84%; and for SMK Major Election datasets which is the hardest to be classified reach only 49.2%.</span>

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Jajoo, Gaurav, Yogesh Kumar, Ashok Kumar, and Sandeep Kumar Yadav. "Implementation of modulation classifier over software defined radio." IET Communications 14, no. 9 (June 2, 2020): 1467–75. http://dx.doi.org/10.1049/iet-com.2019.0922.

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10

Zhang, Bin, Cunpeng Wang, Yonglin Shen, and Yueyan Liu. "Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks." Remote Sensing 10, no. 12 (November 27, 2018): 1889. http://dx.doi.org/10.3390/rs10121889.

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The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNN for LULC detection is required. To improve the classification accuracy for high resolution remote sensing images, it is necessary to use another feature descriptor and to adopt a classifier for post-processing. A fully connected conditional random fields (FC-CRF), to use the fine-tuned CNN layers, spectral features, and fully connected pairwise potentials, is proposed for image classification of high-resolution remote sensing images. First, an existing CNN model is adopted, and the parameters of CNN are fine-tuned by training datasets. Then, the probabilities of image pixels belong to each class type are calculated. Second, we consider the spectral features and digital surface model (DSM) and combined with a support vector machine (SVM) classifier, the probabilities belong to each LULC class type are determined. Combined with the probabilities achieved by the fine-tuned CNN, new feature descriptors are built. Finally, FC-CRF are introduced to produce the classification results, whereas the unary potentials are achieved by the new feature descriptors and SVM classifier, and the pairwise potentials are achieved by the three-band RS imagery and DSM. Experimental results show that the proposed classification scheme achieves good performance when the total accuracy is about 85%.

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Patel, Jayeshkumar J., and Amisha P. Naik. "Design and implementation of 4 bit binary weighted current steering DAC." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 5642. http://dx.doi.org/10.11591/ijece.v10i6.pp5642-5649.

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A compact current-mode Digital-to-Analog converter (DAC) suitable for biomedical application is repesented in this paper .The designed DAC is binary weighted in 180nm CMOS technology with 1.8V supply voltage. In this implementation, authors have focused on calculaton of Non linearity error say INL and DNL for 4 bit DAC having various type of switches: NMOS, PMOS and Transmission Gate. The implemented DAC uses lower area and power compared to unary architecture due to absence of digital decoders. The desired value of Integrated non linearity (INL) and Differential non linearity (DNL) for DAC for are within a range of +0.5LSB. Result obtained in this works for INL and DNL for the case DAC using Transmission Gate is +0.34LSB and +0.38 LSB respectively with 22mW power dissipation.

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12

Liang, Faming. "An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine." Neural Computation 15, no. 8 (August 1, 2003): 1959–89. http://dx.doi.org/10.1162/08997660360675107.

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We propose a new Bayesian neural network classifier, different from that commonly used in several respects, including the likelihood function, prior specification, and network structure. Under regularity conditions, we show that the decision boundary determined by the new classifier will converge to the true one. We also propose a systematic implementation for the new classifier. In our implementation, the tune of connection weights, the selection of hidden units, and the selection of input variables are unified by sampling from the joint posterior distribution of the network structure and connection weights. The numerical results show that the new classifier consistently outperforms the commonly used Bayesian neural network classifier and the support vector machine in terms of generalization performance. The reason for the inferiority of the commonly used Bayesian neural network classifier and the support vector machine is discussed at length.

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Wang, Guang Hui, Zhong Yan Liu, and Shao Bing Zhang. "Research and Implementation of Multidimensional Customer Behavior Segmentation Model Based on Bayes and Fisher." Advanced Materials Research 945-949 (June 2014): 2455–58. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2455.

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an improved algorithm of Bayesian classifier combined with Fisher Linear Discriminant Analysis is proposed. This algorithm is the key to search the projection space of maximum separation. The original samples are projected to maximum separation space and new samples are obtained. These new samples are classifed by Bayes classifier. Experimental results show that improved Bayesian classifier has higher accuracy of classification and better performance of classification in the given data collection.

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14

Choi, Sun-Wook, and Chong Ho Lee. "A FPGA-based parallel semi-naive Bayes classifier implementation." IEICE Electronics Express 10, no. 19 (2013): 20130673. http://dx.doi.org/10.1587/elex.10.20130673.

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15

Sayed, Ratshih, Haytham Azmi, Amin M. Nassar, and Heba Shawkey. "Design Automation and Implementation of Machine Learning Classifier Chips." IEEE Access 8 (2020): 192155–64. http://dx.doi.org/10.1109/access.2020.3032658.

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16

Rane, Ankita, and Vadivel Sangili. "Implementation of Improved Ship-Iceberg Classifier Using Deep Learning." Journal of Intelligent Systems 29, no. 1 (July 24, 2019): 1514–22. http://dx.doi.org/10.1515/jisys-2018-0271.

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Abstract The application of synthetic aperture radar (SAR) for ship and iceberg monitoring is important to carry out marine activities safely. The task of differentiating the two target classes, i.e. ship and iceberg, presents a challenge for operational scenarios. The dataset comprising SAR images of ship and iceberg poses a major challenge, as we are provided with a small number of labeled samples in the training set compared to a large number of unlabeled test samples. This paper proposes a semisupervised learning approach known as pseudolabeling to deal with the insufficient amount of training data. By adopting this approach, we make use of both labeled data (supervised learning) and unlabeled data (unsupervised learning) to build a robust convolutional neural network model that results in a superior binary classification performance of the proposed method.

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17

Binyamin, Muhammad Ahsan, Junaid Alam Khan, and Hasan Mahmood. "A Classifier For Ideal Unimodular Singularities." Analele Universitatii "Ovidius" Constanta - Seria Matematica 23, no. 2 (June 1, 2015): 59–69. http://dx.doi.org/10.1515/auom-2015-0025.

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Abstract In this article we characterize the ideal unimodular singularities in terms of their invariants. On the basis of this characterization we give an implementation of a classifier for ideal unimodular singularities in the computer algebra system SINGULAR.

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18

Shabir, Muhammad, Rimsha Mushtaq, and Munazza Naz. "An algebraic approach to N-soft sets with application in decision-making using TOPSIS." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 819–39. http://dx.doi.org/10.3233/jifs-202717.

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In this paper, we focus on two main objectives. Firstly, we define some binary and unary operations on N-soft sets and study their algebraic properties. In unary operations, three different types of complements are studied. We prove De Morgan’s laws concerning top complements and for bottom complements for N-soft sets where N is fixed and provide a counterexample to show that De Morgan’s laws do not hold if we take different N. Then, we study different collections of N-soft sets which become idempotent commutative monoids and consequently show, that, these monoids give rise to hemirings of N-soft sets. Some of these hemirings are turned out as lattices. Finally, we show that the collection of all N-soft sets with full parameter set E and collection of all N-soft sets with parameter subset A are Stone Algebras. The second objective is to integrate the well-known technique of TOPSIS and N-soft set-based mathematical models from the real world. We discuss a hybrid model of multi-criteria decision-making combining the TOPSIS and N-soft sets and present an algorithm with implementation on the selection of the best model of laptop.

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Diaz, Carlos, Giovanny Sanchez, Gonzalo Duchen, Mariko Nakano, and Hector Perez. "An efficient hardware implementation of a novel unary Spiking Neural Network multiplier with variable dendritic delays." Neurocomputing 189 (May 2016): 130–34. http://dx.doi.org/10.1016/j.neucom.2015.12.086.

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20

Bagui, Sikha, Keerthi Devulapalli, and Sharon John. "MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier." International Journal of Intelligent Information Technologies 16, no. 2 (April 2020): 1–23. http://dx.doi.org/10.4018/ijiit.2020040101.

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This study presents an efficient way to deal with discrete as well as continuous values in Big Data in a parallel Naïve Bayes implementation on Hadoop's MapReduce environment. Two approaches were taken: (i) discretizing continuous values using a binning method; and (ii) using a multinomial distribution for probability estimation of discrete values and a Gaussian distribution for probability estimation of continuous values. The models were analyzed and compared for performance with respect to run time and classification accuracy for varying data sizes, data block sizes, and map memory sizes.

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Lee, Tae-Ju, Seung-Min Park, Kwang-Eun Ko, Won-Ki Sung, and Kwee-Bo Sim. "Implementation of Unsupervised Nonlinear Classifier with Binary Harmony Search Algorithm." Journal of Korean Institute of Intelligent Systems 23, no. 4 (August 25, 2013): 354–59. http://dx.doi.org/10.5391/jkiis.2013.23.4.354.

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22

Ali, Wisam H., Sammar Jaafar Ismail, and Reem Jaafar Ismail. "A Hardware Implementation of Neuro-PMD Model classifier based FPGA." IOP Conference Series: Materials Science and Engineering 765 (March 17, 2020): 012016. http://dx.doi.org/10.1088/1757-899x/765/1/012016.

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23

Torres, L. C. B., C. L. Castro, F. Coelho, F. Sill Torres, and A. P. Braga. "Distance‐based large margin classifier suitable for integrated circuit implementation." Electronics Letters 51, no. 24 (November 2015): 1967–69. http://dx.doi.org/10.1049/el.2015.1644.

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24

Habibi, Muhammad. "Implementation of Cosine Similarity in an automatic classifier for comments." JISKA (Jurnal Informatika Sunan Kalijaga) 3, no. 2 (June 11, 2019): 110. http://dx.doi.org/10.14421/jiska.2018.32-05.

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Classification of text with a large amount is needed to extract the information contained in it. Student comments containing suggestions and criticisms about the lecturer and the lecture process on the learning evaluation system are not well classified, resulting in a difficult assessment process. So from that, we need a classification model that can classify comments automatically into classification categories. The method used is the Cosine Similarity method, which is a method for calculating similarities between two objects expressed in two vectors. The data used in this study were 1,630 comment data with several different categories. The test in this study uses k-fold cross-validation with k = 10. The results showed that the percentage accuracy of the classification model was 80.87%.

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Bellini, A., A. Leone, R. Rovatti, E. Franchi, and N. Manaresi. "Analog fuzzy implementation of a perceptual classifier for videophone sequences." IEEE Transactions on Consumer Electronics 42, no. 3 (1996): 787–94. http://dx.doi.org/10.1109/30.536186.

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26

Celenk, M., and S. R. Datari. "Hypercube concurrent processor implementation of a position invariant object classifier." IEE Proceedings E Computers and Digital Techniques 138, no. 2 (1991): 73. http://dx.doi.org/10.1049/ip-e.1991.0009.

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27

Wang, Lishan. "Research and Implementation of Machine Learning Classifier Based on KNN." IOP Conference Series: Materials Science and Engineering 677 (December 10, 2019): 052038. http://dx.doi.org/10.1088/1757-899x/677/5/052038.

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Lee, Janggoon, Chanhee Park, and Heejun Roh. "Revisiting Adaptive Frequency Hopping Map Prediction in Bluetooth with Machine Learning Classifiers." Energies 14, no. 4 (February 10, 2021): 928. http://dx.doi.org/10.3390/en14040928.

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Thanks to the frequency hopping nature of Bluetooth, sniffing Bluetooth traffic with low-cost devices has been considered as a challenging problem. To this end, BlueEar, a state-of-the-art low-cost sniffing system with two Bluetooth radios proposes a set of novel machine learning-based subchannel classification techniques for adaptive frequency hopping (AFH) map prediction by collecting packet statistics and spectrum sensing. However, there is no explicit evaluation results on the accuracy of BlueEar’s AFH map prediction. To this end, in this paper, we revisit the spectrum sensing-based classifier, one of the subchannel classification techniques in BlueEar. At first, we build an independent implementation of the spectrum sensing-based classifier with one Ubertooth sniffing radio. Using the implementation, we conduct a subchannel classification experiment with several machine learning classifiers where spectrum features are utilized. Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.Our results show that higher accuracy can be achieved by choosing an appropriate machine learning classifier and training the classifier with actual AFH maps.

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29

Lindahl, Lise M., Søren Besenbacher, Anne H. Rittig, Pamela Celis, Andreas Willerslev-Olsen, Lise M. R. Gjerdrum, Thorbjørn Krejsgaard, et al. "Prognostic miRNA classifier in early-stage mycosis fungoides: development and validation in a Danish nationwide study." Blood 131, no. 7 (February 15, 2018): 759–70. http://dx.doi.org/10.1182/blood-2017-06-788950.

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Key Points A validated 3-miRNA classifier can effectively predict progression from early- to advanced-stage MF and survival at time of diagnosis. This classifier outperforms existing clinical prognostic factors and paves the way for implementation of personalized treatment in MF.

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30

Janjua, Faira, and Gerhard Pfister. "A classifier for simple space curve singularities." Studia Scientiarum Mathematicarum Hungarica 51, no. 1 (March 1, 2014): 92–104. http://dx.doi.org/10.1556/sscmath.51.2014.1.1267.

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The classification of Bruce and Gaffney respectively Gibson and Hobbs for simple plane curve singularities respectively simple space curve singularities is characterized in terms of invariants. This is the basis for the implementation of a classifier in the computer algebra system singular.

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Shaul, Hayim, Dan Feldman, and Daniela Rus. "Secure k-ish Nearest Neighbors Classifier." Proceedings on Privacy Enhancing Technologies 2020, no. 3 (July 1, 2020): 42–61. http://dx.doi.org/10.2478/popets-2020-0045.

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AbstractThe k-nearest neighbors (kNN) classifier predicts a class of a query, q, by taking the majority class of its k neighbors in an existing (already classified) database, S. In secure kNN, q and S are owned by two different parties and q is classified without sharing data. In this work we present a classifier based on kNN, that is more efficient to implement with homomorphic encryption (HE). The efficiency of our classifier comes from a relaxation we make to consider κ nearest neighbors for κ ≈k with probability that increases as the statistical distance between Gaussian and the distribution of the distances from q to S decreases. We call our classifier k-ish Nearest Neighbors (k-ish NN). For the implementation we introduce double-blinded coin-toss where the bias and output of the toss are encrypted. We use it to approximate the average and variance of the distances from q to S in a scalable circuit whose depth is independent of |S|. We believe these to be of independent interest. We implemented our classifier in an open source library based on HElib and tested it on a breast tumor database. Our classifier has accuracy and running time comparable to current state of the art (non-HE) MPC solution that have better running time but worse communication complexity. It also has communication complexity similar to naive HE implementation that have worse running time.

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Afzal, Deeba, and Gerhard Pfister. "A classifier for simple isolated complete intersection singularities." Studia Scientiarum Mathematicarum Hungarica 52, no. 1 (March 1, 2015): 1–11. http://dx.doi.org/10.1556/sscmath.52.2015.1.1293.

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M. Giusti’s classification of the simple complete intersection singularities is characterized in terms of invariants. This is a basis for the implementation of a classifier in the computer algebra system Singular.

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Shi, Chun Lei, Dan Dan Yang, and Guang Yuan Jiang. "Research of AdaBoost Face Detection and OpenCV Implementation." Applied Mechanics and Materials 651-653 (September 2014): 482–85. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.482.

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We introduce the principle of AdaBoost face detection algorithm based on Haar-like feature in detail, and use OpenCV face detection module to realize AdaBoost algorithm on CMU face library. Experimental results show that the algorithm receives good effects. Meanwhile we analyze the relationship between the number of simple classifier and face detection result.

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Sychou, Uladzislau. "A Single-Node Classifier Implementation on Chua Oscillator within a Physical Reservoir Computing Framework." International Journal of Bifurcation and Chaos 31, no. 11 (September 2, 2021): 2150161. http://dx.doi.org/10.1142/s0218127421501613.

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The study lies in the field of physical reservoir computing and aims to develop a classifier using Fisher Iris dataset for benchmark tasks. Single Chua chaotic oscillator acts as a physical reservoir. The study was performed using computer simulation. The features of Iris flowers are represented as the consequence of short pulses at a constant level of a control parameter, which is fed to the oscillator, changing its dynamics. During the classification of flowers, the oscillator works without being reset, so each pulse on the input changes the phase trajectory and makes it unique for each Iris flower. Finally, the estimation of the symmetry of an attractor makes it possible to connect each species of Iris with the properties of the attractor. The resulting architecture of the classifier includes a single-node externally-driven Chua oscillator with time-delayed input. The classifier shows two mistakes in classifying the dataset with 75 samples working in chaotic mode.

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Alhazov, Artiom, Alberto Leporati, Luca Manzoni, Giancarlo Mauri, and Claudio Zandron. "Alternative space definitions for P systems with active membranes." Journal of Membrane Computing 3, no. 2 (April 16, 2021): 87–96. http://dx.doi.org/10.1007/s41965-021-00074-2.

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AbstractThe first definition of space complexity for P systems was based on a hypothetical real implementation by means of biochemical materials, and thus it assumes that every single object or membrane requires some constant physical space. This is equivalent to using a unary encoding to represent multiplicities for each object and membrane. A different approach can also be considered, having in mind an implementation of P systems in silico; in this case, the multiplicity of each object in each membrane can be stored using binary numbers, thus reducing the amount of needed space. In this paper, we give a formal definition for this alternative space complexity measure, we define the corresponding complexity classes and we compare such classes both with standard space complexity classes and with complexity classes defined in the framework of P systems considering the original definition of space.

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Kitson, Ezra, and Curtis A. Suttle. "VHost-Classifier: virus-host classification using natural language processing." Bioinformatics 35, no. 19 (March 1, 2019): 3867–69. http://dx.doi.org/10.1093/bioinformatics/btz151.

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Abstract Motivation When analyzing viral metagenomic sequences, it is often desired to filter the results of a BLAST analysis by the host species of the virus. VHost-Classifier automates this procedure using a natural language processing algorithm written in Python 3, which takes a list of taxonomic identifiers (taxids) returned from a BLAST query using viral sequences as input. The taxid output is binned by the evolutionary lineage of their host, based on string matching the words in their English names. If VHost-Classifier cannot identify a host, it attempts to bin the sequences by the environment from which the sample originated. VHost-Classifier predicts the evolutionary lineage of the host from the virus name and does not rely on referencing taxids against a database; therefore, it is not constrained by the size of a database and can host classify newly characterized viruses. Results Benchmarked on a test dataset of 1000 randomly selected viral taxids on the NCBI taxonomy database, VHost-Classifier assigned, with 100% accuracy, a host to the rank of Class for >93% of viruses, and to the rank of Family for >37% of viruses. Availability and implementation For more information about VHost-Classifier as well as implementation instructions, visit https://github.com/Kzra/VHost-Classifier. Supplementary information Supplementary data are available at Bioinformatics online.

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Lin, Chia-Hung, and Guo-Wei Lin. "FPGA implementation of fractal patterns classifier for multiple cardiac arrhythmias detection." Journal of Biomedical Science and Engineering 05, no. 03 (2012): 120–32. http://dx.doi.org/10.4236/jbise.2012.53016.

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Kaur, Gurjantpal. "Implementation of Color Face Recognition using LTP and KNN-LR Classifier." International Journal for Research in Applied Science and Engineering Technology 6, no. 7 (July 31, 2018): 585–90. http://dx.doi.org/10.22214/ijraset.2018.7101.

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Minghua Shi and A. Bermak. "An Efficient Digital VLSI Implementation of Gaussian Mixture Models-Based Classifier." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 14, no. 9 (September 2006): 962–74. http://dx.doi.org/10.1109/tvlsi.2006.884048.

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Achmad, Rifki, and Abba Suganda Girsang. "Implementation of Naive Bayes Classifier Algorithm in Classification of Civil Servants." Journal of Physics: Conference Series 1485 (March 2020): 012018. http://dx.doi.org/10.1088/1742-6596/1485/1/012018.

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Shu, Haiyan, Rongshan Yu, Wenyu Jiang, and Wenxian Yang. "Efficient Implementation of $k$-Nearest Neighbor Classifier Using Vote Count Circuit." IEEE Transactions on Circuits and Systems II: Express Briefs 61, no. 6 (June 2014): 448–52. http://dx.doi.org/10.1109/tcsii.2014.2320031.

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Shah, Sahil, and Jennifer Hasler. "SoC FPAA Hardware Implementation of a VMM+WTA Embedded Learning Classifier." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8, no. 1 (March 2018): 28–37. http://dx.doi.org/10.1109/jetcas.2017.2777784.

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Saeh, Ibrahim, Wazir Mustafa, and Nasir Al-geelani. "New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 870. http://dx.doi.org/10.11591/ijece.v6i2.9572.

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This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.

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Saeh, Ibrahim, Wazir Mustafa, and Nasir Al-geelani. "New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 870. http://dx.doi.org/10.11591/ijece.v6i2.pp870-876.

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This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design.

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Setianingrum, Anif Hanifa, Dea Herwinda Kalokasari, and Imam Marzuki Shofi. "IMPLEMENTASI ALGORITMA MULTINOMIAL NAIVE BAYES CLASSIFIER." JURNAL TEKNIK INFORMATIKA 10, no. 2 (January 26, 2018): 109–18. http://dx.doi.org/10.15408/jti.v10i2.6822.

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ABSTRAK Informasi diperkirakan lebih dari 80% tersimpan dalam bentuk teks tidak terstruktur. Oleh karena itu, dibutuhkan sistem pengelolaan teks yaitu dengan metode text mining yang diyakini memiliki potensial nilai komersial tinggi. Salah satu implementasi dari text mining yaitu klasifikasi teks. Tidak hanya dokumen, pemanfaatan klasifikasi juga digunakan pada surat. Peneliti mengkaji Multinomial Naive Bayes Classifier untuk mengklasifikasi surat keluar sehingga dapat menentukan nomor surat secara otomatis. Sistem klasifikasi didukung dengan confix-stripping stemmer untuk menemukan kata dasar dan TF-IDF untuk pembobotan kata. Pengujian diukur dengan menggunakan confusion matrix. Dari hasil pengujian menunjukkan bahwa implementasi Multinomial Naive Bayes Classifier pada sistem klasifikasi surat memiliki tingkat accuracy, precision, recall, dan F-measure berturut-turut sebesar 89,58%, 79,17%, 78,72%, dan 77,05%. ABSTRACT The information estimated that more than 80% is stored in the form of unstructured text. Therefore, it takes a text management system, namely text mining method is believed to have high potential commercial. One of text mining implementation is text classification. Not only documents, the use of classification is also used in official letter. Researcher examined Multinomial Naive Bayes Classifier to classify the letter so it can determine the letters classification code automatically. The classification system is supported by confix-stripping stemmer to find root and TF-IDF for term weighting. The test used by confusion matrix of a classified as a measure of its quality. The test results showed that the implementation of Multinomial Naive Bayes Classifier on letter classification system has a level of accuracy, precision, recall, and F-measure respectively for 89.58%, 79.17%, 78.72% and 77.05%.How to Cite : Setianingrum, A. H. Kalokasari, D.H . Shofi. I. M. (2017). IMPLEMENTASI ALGORITMA MULTINOMIAL NAIVE BAYES CLASSIFIER. Jurnal Teknik Informatika, 10(2), 109-118. doi: 10.15408/jti.v10i2.6822Permalink/DOI: http://dx.doi.org/10.15408/jti.v10i2.6822

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Novack, T., and U. Stilla. "DISCRIMINATION OF URBAN SETTLEMENT TYPES BASED ON SPACE-BORNE SAR DATASETS AND A CONDITIONAL RANDOM FIELDS MODEL." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W4 (March 11, 2015): 143–48. http://dx.doi.org/10.5194/isprsannals-ii-3-w4-143-2015.

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In this work we focused on the classification of Urban Settlement Types (USTs) based on two datasets from the TerraSAR-X satellite acquired at ascending and descending look directions. These data sets comprise the intensity, amplitude and coherence images from the ascending and descending datasets. In accordance to most official UST maps, the urban blocks of our study site were considered as the elements to be classified. The considered USTs classes in this paper are: Vegetated Areas, Single-Family Houses and Commercial and Residential Buildings. Three different groups of image attributes were utilized, namely: Relative Areas, Histogram of Oriented Gradients and geometrical and contextual attributes extracted from the nodes of a Max-Tree Morphological Profile. These image attributes were submitted to three powerful soft multi-class classification algorithms. In this way, each classifier output a membership value to each of the classes. This membership values were then treated as the potentials of the unary factors of a Conditional Random Fields (CRFs) model. The pairwise factors of the CRFs model were parameterised with a Potts function. The reclassification performed with the CRFs model enabled a slight increase of the classification’s accuracy from 76% to 79% out of 1926 urban blocks.

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Candeloro, Alessandro, Carlo Mereghetti, Beatrice Palano, Simone Cialdi, Matteo G. A. Paris, and Stefano Olivares. "An Enhanced Photonic Quantum Finite Automaton." Applied Sciences 11, no. 18 (September 21, 2021): 8768. http://dx.doi.org/10.3390/app11188768.

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In a recent paper we have described an optical implementation of a measure-once one-way quantum finite automaton recognizing a well-known family of unary periodic languages, accepting words not in the language with a given error probability. To process input words, the automaton exploits the degree of polarization of single photons and, to reduce the acceptance error probability, a technique of confidence amplification using the photon counts is implemented. In this paper, we show that the performance of this automaton may be further improved by using strategies that suitably consider both the orthogonal output polarizations of the photon. In our analysis, we also take into account how detector dark counts may affect the performance of the automaton.

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Urbanowicz, Ryan J., and Jason H. Moore. "Learning Classifier Systems: A Complete Introduction, Review, and Roadmap." Journal of Artificial Evolution and Applications 2009 (September 22, 2009): 1–25. http://dx.doi.org/10.1155/2009/736398.

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If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.

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C.S Kaushik, V., S. Kolangiammal, and B. E Manoj Kumar. "Multi Feature Based Classifier for Spectrum Sensing in Cognitive Radio." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 894. http://dx.doi.org/10.14419/ijet.v7i3.12.16557.

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Cognitive Radio (CR) is an important technology which can enable the implementation of Dynamic Spectrum Access, which is a paradigm shift from the static spectrum access model. It is an intelligent wireless communication system which can sense the environment and can take decisions to effectively use the available radio resource without creating any interference to the Licensed Primary Users. Hence sensing of the spectrum plays a very important role in the effective implementation of this technology. We propose a new spectrum sensing algorithm in this paper which is based on machine learning and uses a Multi Feature based Classifier (MFC) model for classification of the spectrum.

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Malani, Rheo, Arief Bramanto Wicaksono Putra, and Muhammad Rifani. "Implementation of the Naive Bayes Classifier Method for Potential Network Port Selection." International Journal of Computer Network and Information Security 12, no. 2 (April 8, 2020): 32–40. http://dx.doi.org/10.5815/ijcnis.2020.02.04.

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