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Journal articles on the topic 'FNN Classifier'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Njah, M., and R. El Hamdi. "A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers." Engineering, Technology & Applied Science Research 7, no. 3 (2017): 1685–93. http://dx.doi.org/10.48084/etasr.968.

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This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN) based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones a

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Njah, M., and Hamdi R. El. "A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers." Engineering, Technology & Applied Science Research 7, no. 3 (2017): 1685–93. https://doi.org/10.5281/zenodo.809269.

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This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN) based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones a

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Tomo, Tito Pradhono, Alexander Schmitz, Guillermo Enriquez, Shuji Hashimoto, and Shigeki Sugano. "Wayang Robot with Gamelan Music Pattern Recognition." Journal of Robotics and Mechatronics 29, no. 1 (2017): 137–45. http://dx.doi.org/10.20965/jrm.2017.p0137.

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[abstFig src='/00290001/13.jpg' width='245' text='Wayang robot' ] This paper proposes a way to protect endangered wayang puppet theater, an intangible cultural heritage from Indonesia, by turning a robot into a puppeteer successor. We developed a seven degrees-of-freedom (DOF) manipulator to actuate the sticks attached to the wayang puppet body and hands. The robot can imitate 8 distinct human puppeteer’s manipulations. Furthermore, we developed a gamelan music pattern recognition, towards a robot that can perform based on the gamelan music. In the offline experiment, we extracted energy (time

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Petrunina, E. V., S. A. Filist, L. V. Shulga, V. V. Pesok, Hayder Ali H. Alawsi, and A. V. Butusov. "Hybrid Neuro-Fuzzy Classifier for Monitoring the Effectiveness of Treatment of Diseases of the Respiratory System, Taking into Account Comorbidity." Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering 13, no. 4 (2024): 27–53. http://dx.doi.org/10.21869/2223-1536-2023-13-4-27-53.

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The purpose of research is to develop a hybrid neuro-fuzzy classifier for remote monitoring of the severity of community-acquired pneumonia, taking into account the risk of concomitant diseases.Methods. To assess the severity of community-acquired pneumonia and determine the effectiveness of its treatment plan, a hybrid neural network is included in the hybrid neuro-fuzzy classifier, which contains three macrolayers: PNNFNN-FNN*. The number of decisive blocks of the PNN macrolayer is equal to the number of segments allocated in the space of informative features, and the output of each PNN bloc

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Takke, Akshay. "Intrusion Detection System by using K-Means Clustering, C 4.5, FNN, SVM Classifier." International Journal for Research in Applied Science and Engineering Technology V, no. IV (2017): 636–38. http://dx.doi.org/10.22214/ijraset.2017.4113.

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AYO, F. E., O. FOLORUNSO, A. ABAYOMI-ALLI, and A. C. OLUBIYI. "PREDICTING SOCIAL NETWORK ADDICTION USING VARIANT SIGMOID TRANSFER FEED-FORWARD NEURAL NETWORKS (FNN-SNA)." Journal of Natural Sciences Engineering and Technology 19, no. 1 (2021): 131–48. http://dx.doi.org/10.51406/jnset.v19i1.2110.

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Researchers have reflected on personal traits that may predict Social Networking Sites (SNS) addiction. However, most of the researchers involved in the findings of personality traits predictor for social networking addiction either postulate or based their conclusions on analytical tools. Moreso, a review of the literature reveals that the prediction of social networking addiction using classifiers have not been well researched. We examined the prediction of SNS addiction from a well-structured questionnaire consisting of sixteen (16) personality traits. The questionnaire was administered on

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Attestog, Sveinung, Jagath Sri Lal Senanayaka, Huynh Van Khang, and Kjell G. Robbersmyr. "Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors." Sensors 22, no. 3 (2022): 1216. http://dx.doi.org/10.3390/s22031216.

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This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from r

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Patro, Pramoda, Krishna Kumar, and G. Suresh Kumar. "Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2755–62. http://dx.doi.org/10.1166/jctn.2020.9117.

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Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning

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BENALI, R., N. DIB, and F. REGUIG BEREKSI. "CARDIAC ARRHYTHMIA DIAGNOSIS USING A NEURO-FUZZY APPROACH." Journal of Mechanics in Medicine and Biology 10, no. 03 (2010): 417–29. http://dx.doi.org/10.1142/s021951941000354x.

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The ventricular premature contractions (VPC) are cardiac arrhythmias that are widely encountered in the cardiologic field. They can be detected using the electrocardiogram (ECG) signal parameters. A novel method for detecting VPC from the ECG signal is proposed using a new algorithm (Slope) combined with a fuzzy-neural network (FNN). To achieve this objective, an algorithm for QRS detection is first implemented, and then a neuro-fuzzy classifier is developed. Its performances are evaluated by computing the percentages of sensitivity (SE), specificity (SP), and correct classification (CC). This

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Ko, Jun-Hyun, Chan-Seok Song, and Sung-Kwun Oh. "Design of Meteorological Radar Echo Classifier Using Fuzzy Relation-based Neural Networks : A Comparative Studies of Echo Judgement Modules." Journal of Korean Institute of Intelligent Systems 24, no. 5 (2014): 562–68. http://dx.doi.org/10.5391/jkiis.2014.24.5.562.

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Lee, Ji-Yeoun. "Experimental Evaluation of Deep Learning Methods for an Intelligent Pathological Voice Detection System Using the Saarbruecken Voice Database." Applied Sciences 11, no. 15 (2021): 7149. http://dx.doi.org/10.3390/app11157149.

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This work is focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for pathological voice detection using mel-frequency cepstral coefficients (MFCCs), linear prediction cepstrum coefficients (LPCCs), and higher-order statistics (HOSs) parameters. In total, 518 voice data samples were obtained from the publicly available Saarbruecken voice database (SVD), comprising recordings of 259 healthy and 259 pathological women and men, respectively, and using /a/, /i/, and /u/ vowels at normal pitch. Significant differences were observed betwe

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Pawar, Mohandas V., and Anuradha J. "Opposition-Based Deer Hunting Optimization-Based Hybrid Classifier for Intrusion Detection in Wireless Sensor Networks." International Journal of Distributed Systems and Technologies 13, no. 5 (2022): 1–29. http://dx.doi.org/10.4018/ijdst.300356.

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This paper tempts to implement a new machine-learning algorithm for detecting attacks in WSN. The developed model involves three main phases (a) Data Acquisition, (b) Feature Extraction, and (c) Detection. Next to the data acquisition from different benchmark datasets, the attributes in the form of features are extracted. Further, a new hybrid machine learning algorithm with the integration of Neural Network (NN), and Fuzzy Classifier is used for detection, and it is termed as FNN. As an improvement to the developed hybrid model, the number of hidden neurons in NN, and the membership function

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Palani, Muthukumar, and Velumani Thiyagarajan. "An Intelligent Early Detection of Melanoma Using Fuzzy Neural Networks with Java Optimization (FNN-JO) Classifier." International Journal of Engineering Trends and Technology 72, no. 7 (2024): 75–82. http://dx.doi.org/10.14445/22315381/ijett-v72i7p108.

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Fang, HaiFeng, Jin Cao, LiHua Cai, Ta Zhou, and MingQiang Wang. "The recognition of plastic bottle using linear multi hierarchical SVM classifier." Journal of Intelligent & Fuzzy Systems 40, no. 6 (2021): 11509–22. http://dx.doi.org/10.3233/jifs-202729.

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Both classification rate and accuracy are crucial for the recyclable PET bottles, and the existing combination methods of SVM all simply use SVM as the unit classifier, ignoring the improvement of SVM’s classification performance in the training process of deep learning. A linear multi hierarchical deep structure based on Support Vector Machine (SVM) is proposed to cover this problem. A novel definition of the input matrix in each layer enhances the optimization of Lagrange multipliers in Sequential Minimal Optimization (SMO) algorithm, thus the datapoint in maximum interval of SVM hyperplane

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Elmenabawy, Nermeen, Mervat El-Seddek, Hossam El-Din Moustafa, and Ahmed Elnakib. "Deep segmentation of the liver and the hepatic tumors from abdomen tomography images." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 303. http://dx.doi.org/10.11591/ijece.v12i1.pp303-310.

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A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver t

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Bahar, Entaz, and Hyonok Yoon. "Modeling and Predicting the Cell Migration Properties from Scratch Wound Healing Assay on Cisplatin-Resistant Ovarian Cancer Cell Lines Using Artificial Neural Network." Healthcare 9, no. 7 (2021): 911. http://dx.doi.org/10.3390/healthcare9070911.

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The study of artificial neural networks (ANN) has undergone a tremendous revolution in recent years, boosted by deep learning tools. The presence of a greater number of learning tools and their applications, in particular, favors this revolution. However, there is a significant need to deal with the issue of implementing a systematic method during the development phase of the ANN to increase its performance. A multilayer feedforward neural network (FNN) was proposed in this paper to predict the cell migration assay on cisplatin-sensitive and cisplatin-resistant (CisR) ovarian cancer (OC) cell

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Atassi, Reem, and Fuad Alhosban. "Fusion Optimization and Classification Model for Blockchain Assisted Healthcare Environment." Fusion: Practice and Applications 9, no. 2 (2022): 62–73. http://dx.doi.org/10.54216/fpa.090205.

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Healthcare transformation is becoming one of the highest priorities in a world whereby remarkable advances in technology are taking place. Recent healthcare data fusion management systems are centralized, which possess the probability of failure in case of a natural disaster. Blockchain has expanded fast to be the most widely spoken innovation that could address a large number of present data management problems in the health care sector. The usage of blockchain technology for the distribution of secure and safe health care datasets has received all the attention. This article presents a Bat O

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Mosavi, M. R., and M. Khishe. "Training a Feed-Forward Neural Network Using Particle Swarm Optimizer with Autonomous Groups for Sonar Target Classification." Journal of Circuits, Systems and Computers 26, no. 11 (2017): 1750185. http://dx.doi.org/10.1142/s0218126617501857.

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Feed-Forward Neural Networks (FFNNs), as one of the wide-spreading Artificial NNs, has been used to solve many practical problems such as classification of the sonar dataset. Improper selection of the training method, which is an important part of the design process, results in slow convergence rate, entrapment in local minima, and sensitivity to initial conditions. To overcome these issues, the recently proposed method known as “Particle Swarm Optimizer with Autonomous Groups (AGPSO)” has been used in this paper. It is known that the FNNs are very sensitive to the problem’s dimension, so clea

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Nermeen, Elmenabawy, El-Seddek Mervat, El-Din Moustafa Hossam, and Elnakib Ahmed. "Deep segmentation of the liver and the hepatic tumors from abdomen tomography images." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 303–10. https://doi.org/10.11591/ijece.v12i1.pp303-310.

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A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two outputclassified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the li

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Krivokuća, Milan, Mihalj Bakator, Dragan Ćoćkalo, et al. "Data Mining Approaches in Predicting Entrepreneurial Intentions Based on Internet Marketing Applications." Applied Sciences 14, no. 24 (2024): 11778. https://doi.org/10.3390/app142411778.

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Amidst the globalization of markets, there has been a continuous intensification of competitiveness between enterprises. The modern business environment has caused a shift in how business is conducted. Opportunities and challenges arise, which put a tremendous pressure on enterprises regardless of size and industry. Entrepreneurship in enterprises plays an important role in obtaining a competitive edge in the market. Thus, entrepreneurial intentions in enterprises can often shape the future and survival of the enterprise. In this paper, the prediction of entrepreneurial intentions in enterpris

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Delimata, Pawel, and Zbigniew Suraj. "Feature Selection Algorithm for Multiple Classifier Systems: A Hybrid Approach." Fundamenta Informaticae 85, no. 1-4 (2008): 97–110. https://doi.org/10.3233/fun-2008-851-408.

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Many problems in pattern classification and knowledge discovery require a selection of a subset of attributes or features to represent the patterns to be classified. The approach presented in this paper is designed mostly for multiple classifier systems with homogeneous (identical) classifiers. Such systems require many different subsets of the data set. The problem of finding the best subsets of a given feature set is of exponential complexity. The main aim of this paper is to present ways to improve RBFS algorithm which is a feature selection algorithm. RBFS algorithm is computationally quit

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Ling, Ping, Xiang Sheng Rong, Yong Quan Dong, and Guo Sheng Hao. "Develop Local Fuzzy Classifier to Modify Low-Confidence Output of Global Classifier." Advanced Materials Research 989-994 (July 2014): 1762–65. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1762.

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This paper proposes an assembling classifier consisting of a global classifier and a local classifier, named as GCLC. To this end, we present a weighted Support Vector Machine (wSVM) that serves as the global classifier, and a fuzzy k-nearest neighbor (fkNN) that serves as the local one. When a query arrives, wSVM labels it firstly. If the global decision is below some threshold, the local fkNN works to provide an improved decision. Extensive experiments on real datasets demonstrate the performance of GCLC compared with the state of the art.

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Saha, Suman, C. A. Murthy, and Sankar K. Pal. "Rough set Based Ensemble Classifier forWeb Page Classification." Fundamenta Informaticae 76, no. 1-2 (2007): 171–87. https://doi.org/10.3233/fun-2007-761-211.

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Combining the results of a number of individually trained classification systems to obtain a more accurate classifier is a widely used technique in pattern recognition. In this article, we have introduced a rough set based meta classifier to classify web pages. The proposed method consists of two parts. In the first part, the output of every individual classifier is considered for constructing a decision table. In the second part, rough set attribute reduction and rule generation processes are used on the decision table to construct a meta classifier. It has been shown that (1) the performance

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Widyadhana, Arya, Cornelius Bagus Purnama Putra, Rarasmaya Indraswari, and Agus Zainal Arifin. "A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier." Jurnal Ilmu Komputer dan Informasi 14, no. 1 (2021): 65–71. http://dx.doi.org/10.21609/jiki.v14i1.959.

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K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that assigns class label of a query sample dependent on the nearest local centroid mean vector to better represent the underlying statistic of the dataset. The proposed classifier is

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Worasawate, Denchai, Panarit Sakunasinha, and Surasak Chiangga. "Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach." AgriEngineering 4, no. 1 (2022): 32–47. http://dx.doi.org/10.3390/agriengineering4010003.

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Most mango farms classify the maturity stage manually by trained workers using external indicators such as size, shape, and skin color, which can lead to human error or inconsistencies. We developed four common machine learning (ML) classifiers, the k-mean, naïve Bayes, support vector machine, and feed-forward artificial neural network (FANN), all of which were aimed at classifying the ripeness stage of mangoes at harvest. The ML classifiers were trained on biochemical data and then tested on physical and electrical data.The performance of the ML models was compared using fourfold cross valida

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Garcia, Heriberto A., Trenton Couture, Amit Galor, et al. "Comparing Performances of Five Distinct Automatic Classifiers for Fin Whale Vocalizations in Beamformed Spectrograms of Coherent Hydrophone Array." Remote Sensing 12, no. 2 (2020): 326. http://dx.doi.org/10.3390/rs12020326.

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A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a larg

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Góra, Grzegorz, and Arkadiusz Wojna. "RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning." Fundamenta Informaticae 51, no. 4 (2002): 369–90. https://doi.org/10.3233/fun-2002-51403.

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The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decision is predicted not on the basis of the whole support set of all rules matching a test case, but the support set restricted to a neighbourhood of a test case. The size of the optimal neighbourhood is automatically induced during the learning phase. The empirical study shows the interesting fact that it is enough to consider a small neighbourhood to achieve classification accuracy comparable to an algorithm considerin

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Saminu, Sani, Guizhi Xu, Shuai Zhang, et al. "Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals." ELEKTRIKA- Journal of Electrical Engineering 19, no. 2 (2020): 1–11. http://dx.doi.org/10.11113/elektrika.v19n2.219.

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These Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction tech

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Labourier, E., M. Lloyd, B. Andruss, A. Adai, and A. Schwarzbach. "An miRNA assay for the classification of benign and neoplastic lesions in pancreatic fine-needle aspirates." Journal of Clinical Oncology 29, no. 4_suppl (2011): 163. http://dx.doi.org/10.1200/jco.2011.29.4_suppl.163.

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163 Background: Improved diagnosis of pancreatic ductal adenocarcinoma (PDAC) versus benign pancreatic lesions is an urgent clinical need. We previously reported a laboratory-developed test based on the differential expression of two miRNAs, miR-196a and miR-217, with a sensitivity and specificity of 95% in formalin-fixed, paraffin-embedded (FFPE) specimens with ≥60% tumor content. Here, we report the development and validation of an miRNA-based research test optimized for low % tumor specimens and fine needle aspirates (FNA). Methods: RNA was extracted using methods developed and validated by

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Pechenizkiy, Mykola, Alexey Tsymbal, Seppo Puuronen, and David Patterson. "Feature Extraction for Dynamic Integration of Classifiers." Fundamenta Informaticae 77, no. 3 (2007): 243–75. https://doi.org/10.3233/fun-2007-77304.

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Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be processed. Generally, the whole space of original features is used to find the neighborhood of a new instance for local accuracy estimates in

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Wirth, Lori J., Mimi I.-Nan Hu, Steven G. Waguespack, et al. "NTRK, RET, BRAF, and ALK fusions in thyroid fine-needle aspirates (FNAs)." Journal of Clinical Oncology 39, no. 15_suppl (2021): 6083. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.6083.

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6083 Background: Receptor tyrosine kinase (RTK) fusions may be targeted by small molecule inhibitors to treat various advanced tumors, including thyroid cancer. Clinical trials have studied selective inhibitors of ALK, BRAF, NTRK and RET, leading to several FDA-approved therapies. The Afirma Genomic Sequencing Classifier (GSC) classifies cytologically indeterminate thyroid nodules as molecularly benign or suspicious. The Xpression Atlas reports 905 genomic variants and 235 fusion pairs on GSC Suspicious, Suspicious for Malignancy (SFM), and Malignant FNA samples at the time of diagnosis. Here

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Subbulakshmi, C. V., and S. N. Deepa. "Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/418060.

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Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM

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Al-ogaili, Hussein. "Deep Learning Based Hybrid Classifier for Analyzing Hepatitis C in Ultrasound Images." Wasit Journal of Computer and Mathematics Science 1, no. 4 (2022): 1–13. http://dx.doi.org/10.31185/wjcm.65.

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Although liver biopsy is the gold standard for identifying diffuse liver disorders, it is an intrusive procedure with a host of negative side effects. Physician subjectivity may affect the ultrasonography diagnosis of diffuse liver disease. As a result, there is still a clear need for an appropriate classification of liver illnesses. In this article, an unique deep classifier made up of deep convolutional neural networks (CNNs) that have already been trained is proposed to categories the liver condition. The variants of ResNet and AlexNet are a few networks that are combined with fully connect

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de Pretis, Nicolò, Stefano Francesco Crinò, and Luca Frulloni. "The Role of EUS-Guided FNA and FNB in Autoimmune Pancreatitis." Diagnostics 11, no. 9 (2021): 1653. http://dx.doi.org/10.3390/diagnostics11091653.

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Autoimmune pancreatitis (AIP) is an increasingly recognized disease classified into two different subtypes based on histology. According to the International Diagnostic Criteria (ICDC), the diagnosis is achieved using a combination of different criteria. In patients presenting with a typical imaging appearance, the diagnosis may be straightforward, and steroid treatment is recommended, even without histological confirmation. In patients with atypical imaging or mass-forming appearance, the differential diagnosis with pancreatic cancer is challenging and crucial for treatment strategy. Endoscop

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Jadhav, Hindavi Kishor, and Vinoth Babu Kumaravelu. "Deep Learning-Assisted Transmit Antenna Classifiers for Fully Generalized Spatial Modulation: Online Efficiency Replaces Offline Complexity." Applied Sciences 13, no. 8 (2023): 5134. http://dx.doi.org/10.3390/app13085134.

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In this work, deep learning (DL)-based transmit antenna selection (TAS) strategies are employed to enhance the average bit error rate (ABER) and energy efficiency (EE) performance of a spectrally efficient fully generalized spatial modulation (FGSM) scheme. The Euclidean distance-based antenna selection (EDAS), a frequently employed TAS technique, has a high search complexity but offers optimal ABER performance. To address TAS with minimal complexity, we present DL-based approaches that reframe the traditional TAS problem as a classification learning problem. To reduce the energy consumption a

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Midelfart, Herman, Jan Komorowski, Kristin Nørsett, Fekadu Yadetie, Arne K. Sandovik, and Astrid Lægreid. "Learning Rough Set Classifiers from Gene Expressions and Clinical Data." Fundamenta Informaticae 53, no. 2 (2002): 155–83. https://doi.org/10.3233/fun-2002-53204.

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Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capability opens a wide range of research opportunities in biology, but the technology generates a vast amount of data that cannot be handled manually. Computational analysis is thus a prerequisite for the success of this technology, and research and development of computational tools for microarray analysis are of great importance. One application of microarray technology is cancer studies where supervised learning may be us

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Hamzeh, Ali, and Adel Rahmani. "A New Architecture for Learning Classifier Systems to Solve POMDP Problems." Fundamenta Informaticae 84, no. 3-4 (2008): 329–51. https://doi.org/10.3233/fun-2008-843-403.

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Reinforcement Learning is a learning paradigm that helps the agent to learn to act optimally in an unknown environment through trial and error. An RL-based agent senses its environmental state, proposes an action, and applies it to the environment. Then a reinforcement signal, called the reward, is sent back from the environment to the agent. The agent is expected to learn how to maximize overall environmental reward through its internal mechanisms. One of the most challenging issues in the RL area arises as a result of the sensory ability of the agent, when it is not able to sense its current

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Bai, Junjie, Kan Luo, Jun Peng, et al. "Music Emotions Recognition by Machine Learning With Cognitive Classification Methodologies." International Journal of Cognitive Informatics and Natural Intelligence 11, no. 4 (2017): 80–92. http://dx.doi.org/10.4018/ijcini.2017100105.

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Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, physiology, psychology, arts and affective computing. In this article, music emotions are classified into four types known as those of pleasing, angry, sad and relaxing. MER is formulated as a classification problem in cognitive computing where 548 dimensions of music features are extracted and modeled. A set of classifications and machine learning algorithms are explored and comparatively studied for MER, which includes Support Vector Machine (SVM), k-Nea

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Hamzeh, Ali, and Adel Rahmani. "Approximating Arbitrary Reinforcement Signal by Learning Classifier Systems using Micro Genetic Algorithm." Fundamenta Informaticae 86, no. 1-2 (2008): 93–111. https://doi.org/10.3233/fun-2008-861-205.

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Learning Classifier Systems are Evolutionary Learning mechanisms which combine Genetic Algorithm and the Reinforcement Learning paradigm. Learning Classifier Systems try to evolve state-action-reward mappings to propose the best action for each environmental state to maximize the achieved reward. In the first versions of learning classifier systems, state-action pairs can only be mapped to a constant real-valued reward. So to model a fairly complex environment, LCSs had to develop redundant state-action pairs which had to be mapped to different reward values. But an extension to a well-known L

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Glowacz, Adam. "Recognition of Acoustic Signals of Commutator Motors." Applied Sciences 8, no. 12 (2018): 2630. http://dx.doi.org/10.3390/app8122630.

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Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an electric impact drill and the commutator motor of a blender. Acoustic signals were recorded by a smartphone. Five states of the electric impact drill and three states of the blender were analysed: for the electric impact drill, these states were healthy, damaged gear train, faulty fan with five broken rotor blades, faulty fan with 10 broken

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Al-Hassani, Raghad Tariq, Dogu Cagdas Atilla, and Çağatay Aydin. "Development of High Accuracy Classifier for the Speaker Recognition System." Applied Bionics and Biomechanics 2021 (May 19, 2021): 1–10. http://dx.doi.org/10.1155/2021/5559616.

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Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise im

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Roelandt, Jean-Luc. "Classifier/déclassifier sans stigmatiser en santé mentale : la fin de la « schizophrénie » ?" L'information psychiatrique 87, no. 3 (2011): 185. http://dx.doi.org/10.3917/inpsy.8703.0185.

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Li, Bing, Pei-lin Zhang, Shuang-shan Mi, Peng-yuan Liu, and Dong-sheng Liu. "Applying the fuzzy lattice neurocomputing (FLN) classifier model to gear fault diagnosis." Neural Computing and Applications 22, no. 3-4 (2011): 627–36. http://dx.doi.org/10.1007/s00521-011-0719-y.

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Roelandt, Jean-Luc. "Classifier/déclassifier sans stigmatiser en santé mentale : la fin de la « schizophrénie » ?" L'information psychiatrique Volume 87, no. 3 (2011): 185–89. http://dx.doi.org/10.1684/ipe.2011.0751.

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Cai, Jing, Ge Zhou, Mengkun Dong, Xinlei Hu, Guangda Liu, and Weiguang Ni. "Real-Time Arrhythmia Classification Algorithm Using Time-Domain ECG Feature Based on FFNN and CNN." Mathematical Problems in Engineering 2021 (May 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/6648432.

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To solve the problem of real-time arrhythmia classification, this paper proposes a real-time arrhythmia classification algorithm using deep learning with low latency, high practicality, and high reliability, which can be easily applied to a real-time arrhythmia classification system. In the algorithm, a classifier detects the QRS complex position in real time for heartbeat segmentation. Then, the ECG_RRR feature is constructed according to the heartbeat segmentation result. Finally, another classifier classifies the arrhythmia in real time using the ECG_RRR feature. This article uses the MIT-B

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Zhang, Yangqianhui, Chunyang Mo, Jiajun Ma, and Liang Zhao. "Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification." Applied Sciences 11, no. 22 (2021): 10957. http://dx.doi.org/10.3390/app112210957.

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Time series classification (TSC) task is one of the most significant topics in data mining. Among all methods for this issue, the deep-learning-based shows superior performance for its good adaption to raw series data and automatic extraction of features. However, rare eyes are kept on composing ensembles of these superior individual classifiers to achieve further breakthroughs. The existing deep learning ensembles NNE did a heavy work of combining 60 individuals but did not maximize the deserving improvement, since it merely pays attention to the diversity of individuals but ignores their acc

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Dinu, Liviu P., and Marius Popescu. "A Multi-Criteria Decision Method Based on Rank Distance." Fundamenta Informaticae 86, no. 1-2 (2008): 79–91. https://doi.org/10.3233/fun-2008-861-204.

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The multi-criteria decision making process can be summarized as follows. Given a pattern d and a set C = {c _1 , c _2 , …, c _m } of allmpossible categories of d, we are interested in predicting its class by using a set of n classifiers l _1 , l _2 , …, l _n . Each classifier produces a ranking of categories. In this paper we propose and test a decision method which combines the rankings by using a particular method, called rank distance categorization. This method is actually based on the rank distance, a metric which was successfully used in computational linguistics and bioinformatics. We d

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Najdet Nasret Coran, Ali, Prof Dr Hayri Sever, and Dr Murad Ahmed Mohammed Amin. "Acoustic data classification using random forest algorithm and feed forward neural network." International Journal of Engineering & Technology 9, no. 2 (2020): 582. http://dx.doi.org/10.14419/ijet.v9i2.30815.

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Speaker identification systems are designed to recognize the speaker or set of speakers according to their acoustic analysis. Many approach-es are made to perform the acoustic analysis in the speech signal, the general description of those systems is time and frequency domain analysis. In this paper, acoustic information is extracted from the speech signals using MFCC and Fundamental Frequency methods combi-nation. The results are classified using two different algorithms such as Random-forest and Feed Forward Neural Network. The FFNN classifier integration with the acoustic model resulted a r

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Wirth, Lori J., Steven G. Waguespack, Naifa Lamki Busaidy, et al. "Genomic landscape of FNAs positive for medullary thyroid cancer (MTC) and potential impact on systemic therapy." Journal of Clinical Oncology 37, no. 15_suppl (2019): 6087. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.6087.

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6087 Background: Systemic therapies targeting specific genomic alterations in advanced MTC are available or under investigation. The Afirma Genomic Sequencing Classifier (GSC) uses RNA sequencing to assess FNA specimens from cytologically indeterminate thyroid nodules, which are also tested for specific molecular aberrations associated with thyroid cancer via a suite of highly accurate malignancy classifiers. This suite can be applied independently to Bethesda V/VI nodules. The Afirma Xpression Atlas (XA) is an additional test that can be combined with Afirma GSC to report nucleotide variants

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Lee, Ji-Yeoun. "Classification between Elderly Voices and Young Voices Using an Efficient Combination of Deep Learning Classifiers and Various Parameters." Applied Sciences 11, no. 21 (2021): 9836. http://dx.doi.org/10.3390/app11219836.

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The objective of this research was to develop deep learning classifiers and various parameters that provide an accurate and objective system for classifying elderly and young voice signals. This work focused on deep learning methods, such as feedforward neural network (FNN) and convolutional neural network (CNN), for the detection of elderly voice signals using mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstrum coefficients (LPCCs), skewness, as well as kurtosis parameters. In total, 126 subjects (63 elderly and 63 young) were obtained from the Saarbruecken voice databa

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