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Journal articles on the topic 'Probabilistic neural network (PNN) algorithm'

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Published: 3 June 2025

Last updated: 31 July 2025

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1

Alweshah, Mohammed, Mustafa Alessa, Saleh Alkhalaileh, Sofian Kassaymeh, and Bilal Abu-Salih. "Hybrid Aquila optimizer for efficient classification with probabilistic neural networks." Multiagent and Grid Systems 20, no. 1 (2024): 41–68. http://dx.doi.org/10.3233/mgs-230065.

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The model of a probabilistic neural network (PNN) is commonly utilized for classification and pattern recognition issues in data mining. An approach frequently used to enhance its effectiveness is the adjustment of PNN classifier parameters through the outcomes of metaheuristic optimization strategies. Since PNN employs a limited set of instructions, metaheuristic algorithms provide an efficient way to modify its parameters. In this study, we have employed the Aquila optimizer algorithm (AO), a contemporary algorithm, to modify PNN parameters. We have proposed two methods: Aquila optimizer bas

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Shakya, Subarna. "Probabilistic Neural Network based Managing Algorithm for Building Automation System." December 2021 3, no. 4 (2021): 272–83. http://dx.doi.org/10.36548/jaicn.2021.4.001.

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A building automation system is a centralized intelligent system, which controls the operation of energy, security, water, and safety by the help of hardware and software modules. The general software modules employed for automation process have an algorithm with pre-determined decisions. However, such pre-determined decision algorithms won’t work in a proper manner at all situations like a human brain. Therefore a human biological inspired algorithms are developed in recent days and termed as neural network algorithms. The Probabilistic Neural Network (PNN) is a kind of artificial neural netw

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Wang, Benyou, and Li Gu. "Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model." Information Technology and Control 48, no. 4 (2019): 618–25. http://dx.doi.org/10.5755/j01.itc.48.4.24036.

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With the popularity of the Internet, people's lives are becoming more and more convenient. However, the network security problems are becoming increasingly serious. This paper, aiming to better protect users’ network security from the internal and external malicious attacks, briefly introduces the probabilistic neural network and principal component analysis method, and combines them for detection of network intrusion data. Simulation analysis of Probabilistic Neural Network (PNN) and Principal Component Analysis-Probabilistic Neural Network (PCA-PNN) are carried out in MATLAB software. The re

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Li, Xin Guang, Min Feng Yao, Li Rui Jian, and Zhen Jiang Li. "The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering." Applied Mechanics and Materials 263-266 (December 2012): 2173–78. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2173.

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A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The

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Butusov, A. V., A. V. Kiselev, E. V. Petrunina, R. I. Safronov, V. V. Pesok, and A. E. Pshenichniy. "Algorithms for Monitoring the Effectiveness of Therapeutic and Rehabilitation Procedures Based on Clinical Blood Analysis Indicators in the Medical Decision Support System." Proceedings of the Southwest State University. Series: IT Management, Computer Science, Computer Engineering. Medical Equipment Engineering 13, no. 1 (2023): 170–90. http://dx.doi.org/10.21869/2223-1536-2023-13-1-170-190.

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The purpose of research is development of algorithms for a computer system for monitoring the effectiveness of therapeutic procedures in terms of clinical blood analysis.Methods. A set of algorithms has been developed for a computer system for monitoring the effectiveness of medicinal prescriptions based on the results of a clinical blood test, including an algorithm for analyzing the dynamics of intercellular ratios in a clinical blood test, an algorithm for filling in a database, an algorithm for forming a base of decisive rules, an algorithm for analyzing the sensitivity of a decisive rule.

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Li, Li-li, Kun Chen, Jian-min Gao, and Hui Li. "Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network." Shock and Vibration 2020 (November 11, 2020): 1–11. http://dx.doi.org/10.1155/2020/6694732.

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Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern

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Ji, Xin Xin, and Zhong Min Wang. "Faults Diagnosis of Ball Bearing Based on Probabilistic Neural Network." Applied Mechanics and Materials 543-547 (March 2014): 1149–52. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1149.

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To solve the difficulties of establishing precise mathematical model of ball bearing fault diagnosis, a classification method based on probabilistic neural network (PNN) used for ball bearing fault mode classification is proposed. Firstly, this paper analyzed the basic theory of PNN, and then a mapping relationship between feature vector and fault mode is set up based on PNN. Secondly, selections of ball bearing fault features and practical procedure of neural network setting and training are discussed. Experiments and compared with the algorithm of back propagation neural network (BPNN) prove

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Siregar, Siti Julianita, Ahmadi Irmansyah Lubis, and Erika Fahmi Ginting. "Penerapan Neural Network Dalam Klasifikasi Citra Permainan Batu Kertas Gunting dengan Probabilistic Neural Network." Building of Informatics, Technology and Science (BITS) 3, no. 3 (2021): 420–25. http://dx.doi.org/10.47065/bits.v3i3.1143.

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In this research, an image classification model was developed to distinguish hand objects pointing at rock, paper, and scissors using one of the popular image classification methods, namely the Probabilistic Neural Network. Probabilistic Neural Network is a method in an artificial neural network that is used to classify a category based on the results of calculating the distance between the density function and the probability. PNN has 4 stages of processing, namely Input Layer, Pattern Layer, Summation Layer, and Output Layer. Tests in the study were carried out with a total of 60 testing dat

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Pambudi, Yoyok Dwi Setyo. "PARTICLE SWARM OPTIMIZATION BASED PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF SEVERE ACCIDENT OF NUCLEAR REACTOR." JURNAL TEKNOLOGI REAKTOR NUKLIR TRI DASA MEGA 23, no. 3 (2021): 99. http://dx.doi.org/10.17146/tdm.2021.23.3.6247.

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Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard probabilistic neural network (PNN) and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB. The exp

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Pambudi, Yoyok Dwi Setyo. "PARTICLE SWARM OPTIMIZATION BASED PROBABILISTIC NEURAL NETWORK FOR CLASSIFICATION OF SEVERE ACCIDENT OF NUCLEAR REACTOR." JURNAL TEKNOLOGI REAKTOR NUKLIR TRI DASA MEGA 23, no. 3 (2021): 99. http://dx.doi.org/10.17146/tdm.2021.23.3.6247.

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Due to its danger and complexity, the identification and prediction of major severe accident scenarios from an initiating event of a nuclear power plant remains a challenging task. This paper aims to classify severe accident at the Advanced Power Reactor (APR) 1400, which includes the loss of coolant accidents (LOCA), total loss of feedwater (TLOFW), station blackout (SBO), and steam generator tube rupture (SGTR) using a standard probabilistic neural network (PNN) and Particle Swarm Optimization Based Probabilistic Neural Network (PSO PNN). The algorithm has been implemented in MATLAB. The exp

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Wang, Yanyu, Peng Qiu, Yang Liu, Yishen Guo, and Cheng Peng. "IGSA-PNN-based Methods for Power Transformer Fault Diagnosis." Journal of Physics: Conference Series 2694, no. 1 (2024): 012082. http://dx.doi.org/10.1088/1742-6596/2694/1/012082.

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Abstract To enhance the precision of power transformer fault diagnosis, it is necessary to make improvements. Aiming at the shortcomings of Probabilistic Neural Network (PNN) network experience selection smoothing factor and avoiding the shortcomings of traditional Gravitational Search Algorithm (GSA) easy 0to fall into local optimum and convergence speed slow, a Probabilistic Neural Network (PNN) model using chaos sequence to improved GSA for power transformer fault diagnosis is proposed. Firstly, chaos sequence is used to increase the diversity of gravitational particles to avoid falling int

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Liu, Xiyuan, Liying Wang, Hongyan Yan, Qingjiao Cao, Luyao Zhang, and Weiguo Zhao. "Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features." Biomimetics 9, no. 1 (2024): 32. http://dx.doi.org/10.3390/biomimetics9010032.

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To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four o

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Wen, Jianping, Haodong Zhang, Zhensheng Li, and Xiurong Fang. "Research on Electric Vehicle Braking Intention Recognition Based on Sample Entropy and Probabilistic Neural Network." World Electric Vehicle Journal 14, no. 9 (2023): 264. http://dx.doi.org/10.3390/wevj14090264.

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The accurate identification of a driver’s braking intention is crucial to the formulation of regenerative braking control strategies for electric vehicles. In this paper, a braking intention recognition model based on the sample entropy of the braking signal and a probabilistic neural network (PNN) is proposed to achieve the accurate recognition of different braking intentions. Firstly, the brake pedal travel signal is decomposed to extract the effective components via variational modal decomposition (VMD); then, the features of the decomposed signal are extracted using sample entropy to obtai

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Patel, Manoj, Dr Maneesh Shrivastava, and Kavita Deshmukh. "Simulation And Auditing Of Network Security Based On Probabilistic Neural Network Approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (2012): 91–94. http://dx.doi.org/10.24297/ijct.v3i1b.2746.

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: Probabilistic Neural Network approach used for mobile adhoc network is more efficient way to estimate the network security. In this paper, we are using an Adhoc On Demand Distance Vector (AODV) protocol based mobile adhoc network. In our Proposed Method we are considering the multiple characteristics of nodes. In this we use all the parameter that is necessary in AODV. For simulation purpose we use the probabilistic neural network approach that gives more efficient and accurate results as comparison to the clustering algorithm in the previous systems was used. The performance of PNN (probabi

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Xie, Chun Ling. "Diagnosis of Nuclear Power Plant Based on Probabilistic Neural Network." Applied Mechanics and Materials 513-517 (February 2014): 3992–95. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3992.

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This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimize possible nuclear disasters. Probabilistic Neural Network (PNN) overcame the shortcomings of entrapment in local optimum, slow convergence rate which was in BP algorithm. PNN is fit to diagnose the fault of nuclear power plant and has auto-adaptability. The method not only makes the original neural network smaller in terms of computation and realization, but also improves diagnosis speed and accuracy of nuclear power plant.

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Zoqi Sarwani, Mohammad. "CAMPUS SENTIMENT ANALYSIS E-COMPLAINT USING PROBABILISTIC NEURAL NETWORK ALGORITHM." Kursor 8, no. 3 (2017): 135. http://dx.doi.org/10.28961/kursor.v8i3.88.

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E-complaint is one of the technologies which is used to collect feedback from customers in the form of criticism and suggestions using electronic systems. For some companies or agencies, ecomplaint is used to provide better services to its customers. This study is aimed to perform sentiment analysis of an e-complaint service, with the case of Brawijaya University. There are three main stages for the proposed system, i.e. Text Preprocessing, Text Weighting, and PNN forthe classification. Tokenization, filtering, and stemming are done in the text preprocessing. Resulted text from the preprocessi

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Omar Hatem Zaidan, Al-Dulaimi. "Using a probabilistic neural network algorithm to measure the level of education quality." T-Comm 17, no. 3 (2023): 27–33. http://dx.doi.org/10.36724/2072-8735-2023-17-3-27-33.

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The traditional method of determining the quality of education is too unambiguous and unreasonable, which is not suitable for a comprehensive assessment of students' abilities. The purpose of the article is to justify the use of a probabilistic neural network algorithm. Research methods. The reliability of the presented results is ensured by the analysis of scientific literature, modeling of a probabilistic neural network, comparative analysis of models and evaluation of the effectiveness of the model. Research results. In this paper, a probabilistic neural network (PNN) algorithm is used to d

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Li, Ning, Fuxing He, Wentao Ma, Ruotong Wang, Lin Jiang, and Xiaoping Zhang. "The Identification of ECG Signals Using Wavelet Transform and WOA-PNN." Sensors 22, no. 12 (2022): 4343. http://dx.doi.org/10.3390/s22124343.

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Electrocardiogram (ECG) signal identification technology is rapidly replacing traditional fingerprint, face, iris and other recognition technologies, avoiding the vulnerability of traditional recognition technologies. This paper proposes an ECG signal identification method based on the wavelet transform algorithm and the probabilistic neural network by whale optimization algorithm (WOA-PNN). Firstly, Q, R and S waves are detected by wavelet transform, and the P and T waves are detected by local windowed wavelet transform. The characteristic values are constructed by the detected time points, a

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Kusy, Maciej. "Dimensionality Reduction for Probabilistic Neural Network in Medical Data Classification Problems." International Journal of Electronics and Telecommunications 61, no. 3 (2015): 289–300. http://dx.doi.org/10.1515/eletel-2015-0038.

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Abstract This article presents the study regarding the problem of dimensionality reduction in training data sets used for classification tasks performed by the probabilistic neural network (PNN). Two methods for this purpose are proposed. The first solution is based on the feature selection approach where a single decision tree and a random forest algorithm are adopted to select data features. The second solution relies on applying the feature extraction procedure which utilizes the principal component analysis algorithm. Depending on the form of the smoothing parameter, different types of PNN

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Zhang, Jiancheng, Rendong Pi, Xiaohong Ma, Jianqing Wu, Hongtao Li, and Ziliang Yang. "Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network." Electronics 10, no. 7 (2021): 803. http://dx.doi.org/10.3390/electronics10070803.

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Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of four classes: Pedestrian, bicycle, passenger car, and truck. Five features calculated from the point cloud generated from the roadside LiDAR were selected to represent the difference between different classes. A total of 2736 records (2062 records for training, and 674 records for testing) we

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HASEENA, H., PAUL K. JOSEPH, and ABRAHAM T. MATHEW. "ARTIFICIAL NEURAL NETWORK BASED ECG ARRHYTHMIA CLASSIFICATION." Journal of Mechanics in Medicine and Biology 09, no. 04 (2009): 507–25. http://dx.doi.org/10.1142/s0219519409003103.

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Reliable and computationally efficient means of classifying electrocardiogram (ECG) signals has been the subject of considerable research effort in recent years. This paper explores the potential applications of a talented, versatile computation model called the Artificial Neural Network (ANN) in the field of ECG signal classification. Two types of ANNs: Multi-Layered Feed Forward Network (MLFFN) and Probabilistic Neural Networks (PNN) are used to classify seven types of ECG beats. It includes six types of arrhythmia data and normal data. Here, parametric modeling strategies are used in conjun

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Azzahrah, Diah Siti Fatimah, and Alamsyah Alamsyah. "Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification." Recursive Journal of Informatics 1, no. 2 (2023): 73–82. http://dx.doi.org/10.15294/rji.v1i2.66078.

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Purpose: This study aims to compare algorithms to determine the accuracy of the algorithm and determine the speed of the algorithm used for diabetes classification. Methods: There are two algorithms used in this study, namely Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN). The data used is the Pima Indians Diabetes Database. The data contains 768 data with 8 attributes and 1 target class, namely 0 for no diabetes and 1 for diabetes. The dataset has been divided into 80% training data and 20% testing data. Result: Accuracy is obtained after implementing k-fold cros

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Azlah, Muhammad Azfar Firdaus, Lee Suan Chua, Fakhrul Razan Rahmad, Farah Izana Abdullah, and Sharifah Rafidah Wan Alwi. "Review on Techniques for Plant Leaf Classification and Recognition." Computers 8, no. 4 (2019): 77. http://dx.doi.org/10.3390/computers8040077.

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Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extract

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Javier-A-Rodríguez-Herrejón, Enrique-Reyes-Archundia, Jose-A.-Gutiérrez-Gnecchi, Arturo-Mendez-Patiño, and Juan-C.-Olivares-Rojas. "Detection of counterfeit images using SIFT and PNN." Global Journal of Engineering and Technology Advances 20, no. 1 (2024): 171–78. https://doi.org/10.5281/zenodo.13694513.

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Manipulation of digital images presents a significant challenge, primarily due to the continuous advancement and sophistication of image enhancement tools. As a consequence, image feature extraction and matching have become pivotal areas of research in the field of image processing. Among the various techniques developed, the Scale-Invariant Feature Transform algorithm has gained widespread recognition for its robustness and superior performance in feature matching tasks when compared to other existing methods. In this context, our proposed method, which integrates the Scale-Invariant Feature

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Tao, Lingyu, Xiaohui Yang, Yichen Zhou, and Li Yang. "A Novel Transformers Fault Diagnosis Method Based on Probabilistic Neural Network and Bio-Inspired Optimizer." Sensors 21, no. 11 (2021): 3623. http://dx.doi.org/10.3390/s21113623.

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Since it is difficult for the traditional fault diagnosis method based on dissolved gas analysis (DGA) to meet today’s engineering needs in terms of diagnostic accuracy and stability, this paper proposes an artificial intelligence fault diagnosis method based on a probabilistic neural network (PNN) and bio-inspired optimizer. The PNN is used as the basic classifier of the fault diagnosis model, and the bio-inspired optimizer, improved salp swarm algorithm (ISSA), is used to optimize the hidden layer smoothing factor of PNN, which stably improves the classification performance of PNN. Compared

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ГАВРИЛЮК, МИРОСЛАВ, та НАЗАРІЙ ГОВДИШ. "ДОСЛІДЖЕННЯ ЕФЕКТИВНОСТІ ВИКОРИСТАННЯ PNN ДЛЯ РОЗВ’ЯЗАННЯ ЗАДАЧ МЕДИЧНОЇ ДІАГНОСТИКИ В УМОВАХ АНАЛІЗУ МАЛИХ ДАНИХ ВИСОКОЇ РОЗМІРНОСТІ". Herald of Khmelnytskyi National University. Technical sciences 331, № 1 (2024): 248–51. http://dx.doi.org/10.31891/2307-5732-2024-331-37.

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Parkinson's disease is one of the illnesses that cause certain difficulties at the stage of diagnosis. Recently, there has been a tendency to increase the popularity of the use of artificial intelligence methods as an auxiliary diagnostic tool. A probabilistic neural network (PNN) has become widely used for solving problems in the field of medicine. Despite the rather high efficiency of its use for certain tasks, some aspects of its functioning remain insufficiently researched in practice. Existing scientific works do not pay due attention to the issue of using the optimal distance as a measur

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Javier-A-Rodríguez-Herrejón, Enrique-Reyes-Archundia, Jose-A.-Gutiérrez-Gnecchi, Arturo-Mendez-Patiño, and Juan-C.-Olivares-Rojas. "Detection of counterfeit images using SIFT and PNN." Global Journal of Engineering and Technology Advances 20, no. 1 (2024): 171–78. http://dx.doi.org/10.30574/gjeta.2024.20.1.0134.

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Manipulation of digital images presents a significant challenge, primarily due to the continuous advancement and sophistication of image enhancement tools. As a consequence, image feature extraction and matching have become pivotal areas of research in the field of image processing. Among the various techniques developed, the Scale-Invariant Feature Transform algorithm has gained widespread recognition for its robustness and superior performance in feature matching tasks when compared to other existing methods. In this context, our proposed method, which integrates the Scale-Invariant Feature

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Jian, Xiao Gang, and Jian Gxin Huang. "Performance Assessment on Two Kinds of GA-Based Neural Networks in Fault Diagnosis." Advanced Materials Research 403-408 (November 2011): 3090–94. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3090.

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In this paper, we analyze characteristics of two kinds of GA-Based neural networks. For large scale neural networks, it is necessary to optimize the initial network parameters. Using the global optimum ability of GA(Genetic Algorithm), we optimize the initial weights and biases of BPNN (Back-Propagation Neural Networks), which can avoid the local minimum. And we also optimize the spread coefficient of Gaussian Radial Basis Function of PNN (Probabilistic Neural Networks). Then the results in transformer fault diagnosis are compared. Experimental results based on Matlab show that the method of G

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Roy, Prasenjit, and Baher Abdulhai. "GAID: Genetic Adaptive Incident Detection for Freeways." Transportation Research Record: Journal of the Transportation Research Board 1856, no. 1 (2003): 96–105. http://dx.doi.org/10.3141/1856-10.

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Extensive research on point-detector-based automatic traffic-impeding incident detection indicates the potential superiority of neural networks over conventional approaches. All approaches, however, including neural networks, produce detection algorithms that are location specific—that is, neither transferable nor adaptive. A recently designed and ready-to-implement freeway incident detection algorithm based on genetically optimized probabilistic neural networks (PNN) is presented. The combined use of genetic algorithms and neural networks produces GAID, a genetic adaptive incident detection l

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Johaira, U. Lidasan, and P. Tagacay Martina. "Mushroom Recognition using Neural Network." International Journal of Computer Science Issues 15, no. 5 (2018): 52–57. https://doi.org/10.5281/zenodo.1467659.

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An application would be beneficial if it is real time and could give its users enough information. This would be of greater advantage for mobile applications. Mushroom Recognition using Neural Network is a mobile-based application that combined the power of neural network with image processing to recognize mushroom image based on its order and family and if it is edible or inedible/poisonous. It is a multi-class classification program that recognizes mushroom image from 3 orders and 8 families defined in this research. The application used the GrabCut algorithm for image segmentation and Proba

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Inas Azizah, Prily Dwi. "Penerapan Probabilistic Neural Network pada Klasifikasi Berat Bayi Baru Lahir." Jurnal Riset Statistika 1, no. 2 (2022): 152–59. http://dx.doi.org/10.29313/jrs.v1i2.524.

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Abstract. Based on the Government Agency Performance Report of the Banjar City Health Office in 2018, there were 32 cases of Infant Mortality Rate (IMR) with the main causes being Low Birth Weight (LBW) of 56%, Asphyxia of 30%, and congenital defects of 14 %. Infant mortality due to LBW has a 4 times greater risk compared to babies born weighing more than 2500 grams. If the possibility of giving birth to LBW can be done early, then the incidence of LBW can be minimized. Probabilistic Neural Network (PNN) is an algorithm that uses a probability function. PNN is often used in classification beca

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V.S, BAKKIALAKSHMI, Pornpimol Chawengsaksopark, and Mithileysh Sathiyanarayanan. "Body gesture recognition using crow search algorithm enhanced probabilistic neural network for human- computer interaction." F1000Research 14 (February 3, 2025): 149. https://doi.org/10.12688/f1000research.160816.1.

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Background Body gesture recognition has become a fundamental technique in Human-Computer Interaction (HCI). As human-machine interaction evolves, there is an increasing need for precise and efficient gesture detection systems. However, current methods face limitations such as accuracy constraints, high computational complexity, and limited adaptability. This study addresses these challenges by proposing an innovative approach to enhance the accuracy and efficiency of body gesture recognition systems. Methods The proposed system integrates advanced algorithms and techniques to improve performan

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Zhou, Yichen, Xiaohui Yang, Lingyu Tao, and Li Yang. "Transformer Fault Diagnosis Model Based on Improved Gray Wolf Optimizer and Probabilistic Neural Network." Energies 14, no. 11 (2021): 3029. http://dx.doi.org/10.3390/en14113029.

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Dissolved gas analysis (DGA) based in insulating oil has become a more mature method in the field of transformer fault diagnosis. However, due to the complexity and diversity of fault types, the traditional modeling method based on oil sample analysis is struggling to meet the industrial demand for diagnostic accuracy. In order to solve this problem, this paper proposes a probabilistic neural network (PNN)-based fault diagnosis model for power transformers and optimizes the smoothing factor of the pattern layer of PNN by the improved gray wolf optimizer (IGWO) to improve the classification acc

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Lin, Guo Fu. "A New Image Segmentation Method Based on Three-Dimensional Neural Network." Advanced Materials Research 490-495 (March 2012): 157–61. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.157.

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In this paper, a three-dimensional probabilistic approach for MR brain image segmentation is proposed. Based on the noise-free representative reference vectors provided by SOM, the results of the 3D-PNN method are superior to other traditional algorithms. In addition to the 3D-PNN architecture, a fast three-step training method is proposed. The proposed approach also incorporates structure tensor to find appropriate feature sets for the 3D-PNN with respect to resulting classification accuracy. Computational results with simulated MR brain images have shown the promising performance of the prop

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Dzulfiqar, Mohamad Alif, and Ahmadi Irmansyah Lubis. "Media Pembelajaran Pengenalan Citra Pesawat Udara Dengan Memanfaatkan Metode Jaringan Saraf Tiruan." Jurnal Teknologi dan Riset Terapan (JATRA) 6, no. 2 (2024): 1–7. https://doi.org/10.30871/jatra.v6i2.8885.

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This study developed an image classification model to help learn the recognition of aircraft types using the Probabilistic Neural Network (PNN) method, one of the techniques in artificial neural networks that is often used for image classification. PNN works by classifying categories based on the calculation of the distance between the concentration and probability functions. In the process, PNN consists of four main stages: Input Layer, Pattern Layer, Summation Layer, and Output Layer. This study used 90 test data from three different object classes taken from the available data sets. The tes

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Kumar, Jullius, Dharmendra Lal Gupta, and Lokendra Singh Umrao. "Fault-Tolerant Algorithm for Software Preduction Using Machine Learning Techniques." International Journal of Software Science and Computational Intelligence 14, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/ijssci.309425.

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Many software reliability algorithms have been used to predict and approximate the reliability of software. One general expectation of these traditional algorithms is to predict the fault and automatically delete the observed faults. This presumption will not be reasonable in practice and may not always exist. In this paper, the various algorithms have been used such as probabilistic neural network (PNN), generalized neural network (GRNN), linear regression, support vector machine (SVM), bagging, decision trees (DTs), and k-nearest neighbor (KNN) to measure the accuracy of various data and com

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Alarfaj, Fawaz Khaled, and Nayeem Ahmad Khan. "Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks." Applied Sciences 13, no. 7 (2023): 4365. http://dx.doi.org/10.3390/app13074365.

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SQL injection attack is considered one of the most dangerous vulnerabilities exploited to leak sensitive information, gain unauthorized access, and cause financial loss to individuals and organizations. Conventional defense approaches use static and heuristic methods to detect previously known SQL injection attacks. Existing research uses machine learning techniques that have the capability of detecting previously unknown and novel attack types. Taking advantage of deep learning to improve detection accuracy, we propose using a probabilistic neural network (PNN) to detect SQL injection attacks

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Chen, Kaixuan, and Guangqiang Wu. "The Vehicle Intention Recognition with Vehicle-Following Scene Based on Probabilistic Neural Networks." Vehicles 5, no. 1 (2023): 332–43. http://dx.doi.org/10.3390/vehicles5010019.

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In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time—frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the vari

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Liang, Shitong, and Jie Ma. "Compound Fault Diagnosis of Gearbox Based on RLMD and SSA-PNN." Mathematical Problems in Engineering 2021 (September 29, 2021): 1–9. http://dx.doi.org/10.1155/2021/3716033.

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In order to solve the difficulty in the classification of gearbox compound faults, a gearbox fault diagnosis method based on the sparrow search algorithm (SSA) improved probabilistic neural network (PNN) is proposed. Firstly, the gearbox fault signal is decomposed into a series of product functions (PFs) by robust local mean decomposition (RLMD). Then, the permutation entropy of PFs, which contains much fault information, is calculated to construct the feature vector and input it into the SSA-PNN model. The experimental results show that compared with the traditional fault diagnosis methods ba

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B, Sivaranjani, and Kalaiselvi C. "SOBEL OPERATOR AND PCA FOR NEAREST TARGET OF RETINA IMAGES." ICTACT Journal on Image and Video Processing 11, no. 4 (2021): 2483–91. http://dx.doi.org/10.21917/ijivp.2021.0353.

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In eye, innermost layer is retina. Various important anatomical structures are available in this. Different eye diseases like diabetic retinopathy, glaucoma, etc are indicated by this. For clinical study, patient screening, and diagnosing ocular diseases, physicians are assisted by vascular intersections and blood vessels extraction in retinal images. Retina image’s nearest template are detected using fuzzy neural network (FNN), Probabilistic neural network (PNN) and Adaptive Neuro Fuzzy Inference System (ANFIS) classifier’s ensemble in recent work. However, various factors like low contrast a

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Liu, Fangrong. "Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks." Computational Intelligence and Neuroscience 2022 (August 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/8935906.

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A brand-new kind of flexible logic system called universal logic aims to address a variety of uncertain problems. In this study, the role of convolutional neural networks in assessing probabilistic pan-logic algorithms is investigated. A generic logic probability algorithm analysis based on a convolutional neural network is suggested due to the unpredictable outputs of the probabilistic algorithm and the difficulty of its analysis. The stochastic gradient descent technique and the error backpropagation algorithm are used to investigate the broad logic probability algorithm (SGD). The experimen

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Xu, Haoxiang, Tongyao Ren, Zhuangda Mo, and Xiaohui Yang. "A Fault Diagnosis Model for Tennessee Eastman Processes Based on Feature Selection and Probabilistic Neural Network." Applied Sciences 12, no. 17 (2022): 8868. http://dx.doi.org/10.3390/app12178868.

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Since the classification methods mentioned in previous studies are currently unable to meet the accuracy requirements for fault diagnosis in large-scale chemical industries, these methods are gradually being eliminated and rarely used. This research offers a probabilistic neural network (PNN) based on feature selection and a bio-heuristic optimizer as a fault diagnostic approach for chemical industries using artificial intelligence. The sample characteristics are initially simplified using heuristic feature selection and support vector machine recursive feature elimination (SVM-RFE). Using PNN

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Manoharan, Dr Samuel, and Prof. Sathish. "Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network." Journal of Soft Computing Paradigm 2, no. 1 (2020): 36–46. http://dx.doi.org/10.36548/jscp.2020.1.004.

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The most vital step in mining data’s in order to have a proper decision making is the classification, it is remains important in multiple of human activities such as the industrial applications, marketing campaigns, research process and the scientific endeavors. The process of classifying involves the objects categorization into classes that are already defined. These categorizations are developed according to the identical attributes of the items or the objects. Multitudes of methods were devised to improve the accuracy in the classification to devour an enhanced performance in terms of faste

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Nsour, Heba Al, Mohammed Alweshah, Abdelaziz I. Hammouri, Hussein Al Ofeishat, and Seyedali Mirjalili. "A Hybrid Grey Wolf Optimiser Algorithm for Solving Time Series Classification Problems." Journal of Intelligent Systems 29, no. 1 (2018): 846–57. http://dx.doi.org/10.1515/jisys-2018-0129.

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Abstract One of the major objectives of any classification technique is to categorise the incoming input values based on their various attributes. Many techniques have been described in the literature, one of them being the probabilistic neural network (PNN). There were many comparisons made between the various published techniques depending on their precision. In this study, the researchers investigated the search capability of the grey wolf optimiser (GWO) algorithm for determining the optimised values of the PNN weights. To the best of our knowledge, we report for the first time on a GWO al

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Liu, Mei Hong, Zhen Hua Li, Yu Xian Li, and Jun Ruo Chen. "The Classification of Non-Asbestos Gasket Formulation Using Clustering Method." Applied Mechanics and Materials 233 (November 2012): 388–91. http://dx.doi.org/10.4028/www.scientific.net/amm.233.388.

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At present, study on the non-asbestos gasket materials is the hotspot research in static sealing field. The non-asbestos sealing gaskets research and development has made great strides into the practical phase. Formula is an important factor of material, which determines performance of material. In order to obtain well performance, it is needed to optimization formula to get optimal formula that not only improve performance of non-asbestos gasket, but also reduce development time accordingly reduce cost of non-asbestos gasket. Classification of raw materials can be transformed into a mathemati

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Hao, Min, Shuo Shi Ma, Xiao Dong Hao, Li Li Ma, and Li Juan Wang. "Feature Selection Based on GA and PNN." Advanced Materials Research 217-218 (March 2011): 1753–57. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.1753.

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A new image feature selection method with the combination of Genetic Algorithm(GA) and Probabilistic Neural Network(PNN) is proposed and applied to potato shape feature selection and classification. The classifier selecting principle is investigated by combining with the genetic algorithm. A new feature selection method based on GA and PNN is put forward firstly. Comprehensively considering the factor of classification accuracy,selected feature number and the impact of the two factors, a new fitness function is proposed. The initial Zernike moments parameters of potatoes are optimized using im

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Chiradeja, Pathomthat, Chaichan Pothisarn, Nattanon Phannil, et al. "Application of Probabilistic Neural Networks Using High-Frequency Components’ Differential Current for Transformer Protection Schemes to Discriminate between External Faults and Internal Winding Faults in Power Transformers." Applied Sciences 11, no. 22 (2021): 10619. http://dx.doi.org/10.3390/app112210619.

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Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a ¼ cycle DWT as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the ¼ cycle DWT is used to detect the maximum ratio and faults. The simulation system uses variou

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Anitha, V. "An Operative Acute Brain Tumor Recognition by Jointure Inward Unswerving Probabilistic Neural Network Classifier." Journal of Medical Imaging and Health Informatics 12, no. 2 (2022): 155–67. http://dx.doi.org/10.1166/jmihi.2022.3935.

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Brain tumors have to be predicted earlier to avoid the risk of being mortal. For an effective detection an adaptive segmentation with two-tier tumors region extraction is needed. This framework offers preprocessing to avoid noise occurrence by fusing median and wiener filter also utilizes adaptive pillar C-means algorithm for obtaining the essential feature set thus the processing time is reduced. Thus the attained essential feature sets are then classified by means of unswerving PNN (Probabilistic Neural network) classifier where classification is done twice initially to classify whether beni

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Men, Hong, Yu Ming Guo, Rui Xia Wen, and Bin Zhu. "Recognition of Mineral Water with an Electronic Tongue Based on PCA and PNN." Key Engineering Materials 467-469 (February 2011): 888–93. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.888.

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Electronic Tongue is a kind of intelligent equipment which is used to distinguish tastes. An electronic tongue composed of a sensor array of ion-selective electrodes has been developed and used for the qualitative analysis of five different brands of mineral water. The acquired original data has been optimized by principal component analysis (PCA) and then the probabilistic neural network (PNN) model is designed to process the data. The application results show that the performance of the proposed method has surpasses the traditional BP neural network algorithm, the speed of recognition is fas

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Edet, O. E., I. Tamunobereton- Ari, A. R. C. Amakiri, and J. Amonieah. "Enhancing reservoir characterization using deep learning aided model-based inversion: a case study of data from the coastal swamp zone in the Niger Delta." World Journal of Applied Science & Technology 15, no. 2 (2024): 340–46. http://dx.doi.org/10.4314/wojast.v15i2.27.

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Seismic Reservoir Characterization is a pivotal element in seismic data interpretation. This study describes a successful utilization of model-based seismic inversion methodology coupled with Probabilistic Neural Network for the identification of hydrocarbon reservoir zones within post-stack seismic data. The paper unfolds in two segments. Initially, Acoustic Impedance (AI) volume is extrapolated from seismic datasets via the application of the model-based inversion algorithm in the time domain. The strong correlation coefficient of 0.988 between synthetic and seismic data underscores the effe

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