Log in

Relevant bibliographies by topics / Backpropagation and Boltzmann Machine algorithms / Journal articles

To see the other types of publications on this topic, follow the link: Backpropagation and Boltzmann Machine algorithms.

Journal articles on the topic 'Backpropagation and Boltzmann Machine algorithms'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Backpropagation and Boltzmann Machine algorithms.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

D., T. V. Dharmajee Rao, and V. Ramana K. "A Novel Approach for Efficient Training of Deep Neural Networks." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 954–61. https://doi.org/10.11591/ijeecs.v11.i3.pp954-961.

Full text

Abstract:

Deep Neural Network training algorithms consumes long training time, especially when the number of hidden layers and nodes is large. Matrix multiplication is the key operation carried out at every node of each layer for several hundreds of thousands of times during the training of Deep Neural Network. Blocking is a well-proven optimization technique to improve the performance of matrix multiplication. Blocked Matrix multiplication algorithms can easily be parallelized to accelerate the performance further. This paper proposes a novel approach of implementing Parallel Blocked Matrix multiplicat

APA, Harvard, Vancouver, ISO, and other styles

2

Dharmajee Rao, D. T. V., and K. V. Ramana. "A Novel Approach for Efficient Training of Deep Neural Networks." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 954. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp954-961.

Full text

Abstract:

<p style="text-indent: 1.27cm; margin-bottom: 0.35cm; line-height: 115%;" align="justify"><span style="font-family: Arial,serif;"><span style="font-size: small;"><em>Deep Neural Network training algorithms consumes long training time, especially when the number of hidden layers and nodes is large. Matrix multiplication is the key operation carried out at every node of each layer for several hundreds of thousands of times during the training of Deep Neural Network. Blocking is a well-proven optimization technique to improve the performance of matrix multiplication. Block

APA, Harvard, Vancouver, ISO, and other styles

3

Pearlmutter, Barak A. "Fast Exact Multiplication by the Hessian." Neural Computation 6, no. 1 (1994): 147–60. http://dx.doi.org/10.1162/neco.1994.6.1.147.

Full text

Abstract:

Just storing the Hessian H (the matrix of second derivatives δ2E/δwiδwj of the error E with respect to each pair of weights) of a large neural network is difficult. Since a common use of a large matrix like H is to compute its product with various vectors, we derive a technique that directly calculates Hv, where v is an arbitrary vector. To calculate Hv, we first define a differential operator Rv{f(w)} = (δ/δr)f(w + rv)|r=0, note that Rv{▽w} = Hv and Rv{w} = v, and then apply Rv{·} to the equations used to compute ▽w. The result is an exact and numerically stable procedure for computing Hv, wh

APA, Harvard, Vancouver, ISO, and other styles

4

XU, LEI, STAN KLASA, and ALAN YUILLE. "RECENT ADVANCES ON TECHNIQUES OF STATIC FEEDFORWARD NETWORKS WITH SUPERVISED LEARNING." International Journal of Neural Systems 03, no. 03 (1992): 253–90. http://dx.doi.org/10.1142/s0129065792000218.

Full text

Abstract:

The rediscovery and popularization of the backpropagation training technique for multilayer perceptrons as well as the invention of the Boltzmann machine learning algorithm has given a new boost to the study on supervised learning networks. In recent years, besides widely spread applications and various further improvements of the classical backpropagation technique, many new supervised learning models, techniques as well as theories, have also been proposed in a vast number of publications. This paper tries to give a rather systematic review on the recent advances on supervised learning techn

APA, Harvard, Vancouver, ISO, and other styles

5

O'Reilly, Randall C. "Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm." Neural Computation 8, no. 5 (1996): 895–938. http://dx.doi.org/10.1162/neco.1996.8.5.895.

Full text

Abstract:

The error backpropagation learning algorithm (BP) is generally considered biologically implausible because it does not use locally available, activation-based variables. A version of BP that can be computed locally using bidirectional activation recirculation (Hinton and McClelland 1988) instead of backpropagated error derivatives is more biologically plausible. This paper presents a generalized version of the recirculation algorithm (GeneRec), which overcomes several limitations of the earlier algorithm by using a generic recurrent network with sigmoidal units that can learn arbitrary input/o

APA, Harvard, Vancouver, ISO, and other styles

6

S. Manoha. "A Deep Dive into Training Algorithms for Deep Belief Networks." Journal of Information Systems Engineering and Management 10, no. 13s (2025): 178–86. https://doi.org/10.52783/jisem.v10i13s.2021.

Full text

Abstract:

Deep Belief Networks (DBNs) have emerged as powerful tools for feature learning, representation, and generative modeling. This paper presents a comprehensive exploration of the various training algorithms employed in the training of DBNs. DBNs, composed of multiple layers of stochastic hidden units, have found applications in diverse domains such as computer vision, natural language processing, and bioinformatics. The paper begins by delving into the pre-training phase, where Restricted Boltzmann Machines (RBMs) play a central role. We review the Contrastive Divergence (CD) and Persistent Cont

APA, Harvard, Vancouver, ISO, and other styles

7

Abdel-Jaber, Hussein, Disha Devassy, Azhar Al Salam, Lamya Hidaytallah, and Malak EL-Amir. "A Review of Deep Learning Algorithms and Their Applications in Healthcare." Algorithms 15, no. 2 (2022): 71. http://dx.doi.org/10.3390/a15020071.

Full text

Abstract:

Deep learning uses artificial neural networks to recognize patterns and learn from them to make decisions. Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data. It was found that deep learning approaches can be used for big data analysis successfully. Applications include virtual assistants such as A

APA, Harvard, Vancouver, ISO, and other styles

8

Ajay, A. Gidd, and S. Shewale Ajinkya. "One Look at Deep Learning Algorithms." Recent Innovations in Wireless Network Security 2, no. 1 (2020): 1–5. https://doi.org/10.5281/zenodo.3819806.

Full text

Abstract:

<em>Deep learning is a mainly an area of machine learning. The intention of this paper is to give a quick overview of deep learning algorithms. In this paper the author is discussed the most popular deep learning algorithm which are used most frequent in the current scenario. Now a days deep learning has caught special attention because it can solve very complex problem with less computation.</em>

APA, Harvard, Vancouver, ISO, and other styles

9

Wu, Zhiyong, Xiangqian Ding, and Guangrui Zhang. "A Novel Method for Classification of ECG Arrhythmias Using Deep Belief Networks." International Journal of Computational Intelligence and Applications 15, no. 04 (2016): 1650021. http://dx.doi.org/10.1142/s1469026816500218.

Full text

Abstract:

In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph (ECG) arrhythmias classification is proposed. The construction process of ECG classification model consists of two steps: features learning for ECG signals and supervised fine-tuning. In order to deeply extract features from continuous ECG signals, two types of restricted Boltzmann machine (RBM) including Gaussian–Bernoulli and Bernoulli–Bernoulli are stacked to form DBN. The parameters of RBM can be learned by two training algorithms such as contrastive divergence and persistent contrastive divergence.

APA, Harvard, Vancouver, ISO, and other styles

10

Reddy, G. Vinoda, Sreedevi Kadiyala, Chandra Srinivasan Potluri, et al. "An Intrusion Detection Using Machine Learning Algorithm Multi-Layer Perceptron (MlP): A Classification Enhancement in Wireless Sensor Network (WSN)." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 2s (2022): 139–45. http://dx.doi.org/10.17762/ijritcc.v10i2s.5920.

Full text

Abstract:

During several decades, there has been a meteoric rise in the development and use of cutting-edge technology. The Wireless Sensor Network (WSN) is a groundbreaking innovation that relies on a vast network of individual sensor nodes. The sensor nodes in the network are responsible for collecting data and uploading it to the cloud. When networks with little resources are deployed harshly and without regulation, security risks occur. Since the rate at which new information is being generated is increasing at an exponential rate, WSN communication has become the most challenging and complex aspect

APA, Harvard, Vancouver, ISO, and other styles

11

Handari, Bevina D., Dewi Wulandari, Nessa A. Aquita, Shafira Leandra, Devvi Sarwinda, and Gatot F. Hertono. "Comparing Restricted Boltzmann Machine – Backpropagation Neural Networks, Artificial Neural Network – Genetic Algorithm and Artificial Neural Network – Particle Swarm Optimization for Predicting DHF Cases in DKI Jakarta." International Journal on Advanced Science, Engineering and Information Technology 12, no. 6 (2022): 2476. http://dx.doi.org/10.18517/ijaseit.12.6.16226.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Singarimbun, Roy Nuary, Ondra Eka Putra, N. L. W. S. R. Ginantra, and Mariana Puspa Dewi. "Backpropagation Artificial Neural Network Enhancement using Beale-Powell Approach Technique." Journal of Physics: Conference Series 2394, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1742-6596/2394/1/012007.

Full text

Abstract:

Abstract Machine learning algorithms can study existing data to perform specific tasks. One of the well-known machine learning algorithms is the backpropagation algorithm, but this algorithm often provides poor convergence speed in the training process and a long training time. The purpose of this study is to optimize the standard backpropagation algorithm using the Beale-Powell conjugate gradient algorithm so that the training time needed to achieve convergence is not too long, which later can be used as a reference and information for solving predictive problems. The Beale-Powell conjugate g

APA, Harvard, Vancouver, ISO, and other styles

13

Li, Yu, Yuan Zhang, and Yue Ji. "Privacy-Preserving Restricted Boltzmann Machine." Computational and Mathematical Methods in Medicine 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/138498.

Full text

Abstract:

With the arrival of the big data era, it is predicted that distributed data mining will lead to an information technology revolution. To motivate different institutes to collaborate with each other, the crucial issue is to eliminate their concerns regarding data privacy. In this paper, we propose a privacy-preserving method for training a restricted boltzmann machine (RBM). The RBM can be got without revealing their private data to each other when using our privacy-preserving method. We provide a correctness and efficiency analysis of our algorithms. The comparative experiment shows that the a

APA, Harvard, Vancouver, ISO, and other styles

14

Apolloni, Bruno, and Diego de Falco. "Learning by Asymmetric Parallel Boltzmann Machines." Neural Computation 3, no. 3 (1991): 402–8. http://dx.doi.org/10.1162/neco.1991.3.3.402.

Full text

Abstract:

We consider the Little, Shaw, Vasudevan model as a parallel asymmetric Boltzmann machine, in the sense that we extend to this model the entropic learning rule first studied by Ackley, Hinton, and Sejnowski in the case of a sequentially activated network with symmetric synaptic matrix. The resulting Hebbian learning rule for the parallel asymmetric model draws the signal for the updating of synaptic weights from time averages of the discrepancy between expected and actual transitions along the past history of the network. As we work without the hypothesis of symmetry of the weights, we can incl

APA, Harvard, Vancouver, ISO, and other styles

15

Santharooban, S., and S. P. Abeysundara. "Machine Learning Approach to Classify Breast Tissues: A Case Study Using Six-classed Breast Tissue Data." Sri Lankan Journal of Applied Statistics 23, no. 3 (2022): 133–53. http://dx.doi.org/10.4038/sljastats.v23i3.8081.

Full text

Abstract:

The present study investigates the effectiveness of six Machine Learning (ML) algorithms in classifying the breast tissue dataset generated using the electrical impedance spectroscopy method. This study used the breast tissue dataset available at the UCI machine learning repository, consisting of 106 spectral records with ten variables. The data were partitioned into train and test datasets. Sixty six percentage of data was allocated for the train dataset and balance for the test dataset. Six ML algorithms were tested for effectiveness using accuracy, Cohen’s Kappa, sensitivity and specificity

APA, Harvard, Vancouver, ISO, and other styles

16

Srinivas, V., K. Aditya, G. Prasanth, R. G.Babukarthik, S. Satheeshkumar, and G. Sambasivam. "A Novel Approach for Prediction of Heart Disease: Machine Learning Techniques." International Journal of Engineering & Technology 7, no. 2.32 (2018): 108. http://dx.doi.org/10.14419/ijet.v7i2.32.15380.

Full text

Abstract:

Heart disease and machine learning are the two different words where one is related to medical field and another one to artificial intelligence. In medical filed most of them are facing the problems with the heart disease and machine learning is developing area in computer science. Heart disease is general called cardiac disease where it gives the more data or information, it is to be collected to give the reports for the patients and the machine learning also requires the data for predicting and to solve the problems. Machine learning techniques are used in prediction of heart diseases where

APA, Harvard, Vancouver, ISO, and other styles

17

Falah, Miftahul, Dian Palupi Rini, and Iwan Pahendra. "Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 1 (2021): 90. http://dx.doi.org/10.30865/mib.v5i1.2597.

Full text

Abstract:

Predicting disease is usually done based on the experience and knowledge of the doctor. Diagnosis of such a disease is traditionally less effective. The development of medical diagnosis based on machine learning in terms of disease prediction provides a more accurate diagnosis than the traditional way. In terms of predicting disease can use artificial neural networks. The artificial neural network consists of various algorithms, one of which is the Backpropagation Algorithm. In this paper it is proposed that disease prediction systems use the Backpropagation algorithm. Backpropagation algorith

APA, Harvard, Vancouver, ISO, and other styles

18

Meir, Ronny. "ON DERIVING DETERMINISTIC LEARNING RULES FROM STOCHASTIC SYSTEMS." International Journal of Neural Systems 02, no. 04 (1991): 283–89. http://dx.doi.org/10.1142/s012906579100025x.

Full text

Abstract:

We discuss the derivation of deterministic learning rules from an underlying stochastic system. We focus on the symmetrically connected Boltzmann machine and show how various approximations give rise to different learning algorithms. In particular, we show how to derive a symmetrized form of the recurrent back propagation learning algorithm from the Boltzmann machine. We also discuss the connection between the different deterministic learning algorithms focusing on the probability distributions from which they originate. It will also be shown that inspite of the fact that two probability distr

APA, Harvard, Vancouver, ISO, and other styles

19

Wiederman, Meagan. "Biological Faithfulness is Unnecessary for Machine Learning." University of Western Ontario Medical Journal 87, no. 2 (2019): 27–29. http://dx.doi.org/10.5206/uwomj.v87i2.1134.

Full text

Abstract:

Artificial intelligence (AI) is the ability of any device to take an input, like that of its environment, and work to achieve a desired output. Some advancements in AI have focused n replicating the human brain in machinery. This is being made possible by the human connectome project: an initiative to map all the connections between neurons within the brain. A full replication of the thinking brain would inherently create something that could be argued to be a thinking machine. However, it is more interesting to question whether a non-biologically faithful AI could be considered as a thinking

APA, Harvard, Vancouver, ISO, and other styles

20

Albagmi, Faisal Mashel, Mehwish Hussain, Khurram Kamal, et al. "Predicting Multimorbidity Using Saudi Health Indicators (Sharik) Nationwide Data: Statistical and Machine Learning Approach." Healthcare 11, no. 15 (2023): 2176. http://dx.doi.org/10.3390/healthcare11152176.

Full text

Abstract:

The Saudi population is at high risk of multimorbidity. The risk of these morbidities can be reduced by identifying common modifiable behavioural risk factors. This study uses statistical and machine learning methods to predict factors for multimorbidity in the Saudi population. Data from 23,098 Saudi residents were extracted from the “Sharik” Health Indicators Surveillance System 2021. Participants were asked about their demographics and health indicators. Binary logistic models were used to determine predictors of multimorbidity. A backpropagation neural network model was further run using t

APA, Harvard, Vancouver, ISO, and other styles

21

Tamuntuan, Virginia, Kusrini Kusrini, and Kusnawi Kusnawi. "Analisis Perbandingan Kinerja Algoritma Klasifikasi Pada Mahasiswa Berpotensi Dropout." Building of Informatics, Technology and Science (BITS) 6, no. 2 (2024): 847–55. https://doi.org/10.47065/bits.v6i2.5658.

Full text

Abstract:

This research aims to compare the performance levels of two data mining classification algorithms, namely Support Vector Machine and Neural Network Backpropagation, using the K-fold cross-validation method. The data used consists of graduates from 2019 to 2023 at STMIK Multicom Bolaang Mongondow. A total of 80% of the 200 data points were used as training data, while the remaining 20% were used as testing data. K-fold cross-validation was conducted with K set to 5. The results of the study indicate that the Support Vector Machine algorithm achieved an accuracy of 80%, recall of 80%, and precis

APA, Harvard, Vancouver, ISO, and other styles

22

Sharma, Arvind Kumar, and Amrita Puri. "Study of machine learning based algorithms for active noise control of machinary noise." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 268, no. 5 (2023): 3414–25. http://dx.doi.org/10.3397/in_2023_0490.

Full text

Abstract:

Machine learning is becoming a part of every field. After the discovery of deep learning, its application in various fields are increasing day by day. This paper presents a study of three machine learning based active noise control algorithms to reduce machinary noise. Three algorithms studied in this work are: filtered-x backpropagation neural network (FxBPNN), radial basis function (RBF) and deep recurrent neural network (DRNN). Experimentally recorded noises of band saw, CNC and compressor are used in the simulation study. Two cases of primary path are considered: a) linear path and b) nonl

APA, Harvard, Vancouver, ISO, and other styles

23

Xu, Jiang, Siqian Liu, Zhikui Chen, and Yonglin Leng. "A Hybrid Imputation Method Based on Denoising Restricted Boltzmann Machine." International Journal of Grid and High Performance Computing 10, no. 2 (2018): 1–13. http://dx.doi.org/10.4018/ijghpc.2018040101.

Full text

Abstract:

Data imputation is an important issue in data processing and analysis which has serious impact on the results of data mining and learning. Most of the existing algorithms are either utilizing whole data sets for imputation or only considering the correlation among records. Aiming at these problems, the article proposes a hybrid method to fill incomplete data. In order to reduce interference and computation, denoising restricted Boltzmann machine model is developed for robust feature extraction from incomplete data and clustering. Then, the article proposes partial-distance and co-occurrence ma

APA, Harvard, Vancouver, ISO, and other styles

24

Chaikovska, Maryna, and Oleksandr Shkeda. "Machine learning algorithm for an artificial neural network for building a model of managerial decision-making when developing a marketing strategy." Marketing and Digital Technologies 7, no. 2 (2023): 137–46. http://dx.doi.org/10.15276/mdt.7.2.2023.10.

Full text

Abstract:

The article describes the application of the machine learning method, namely the backpropagation algorithm, in order to optimize managerial decision making when developing a marketing strategy. A qualitative analysis of data processing has been carried out, which proves the relevance of using the backpropagation method in marketing interpretation. Using the example of a task with the choice of hashtags for social media, a five-step model has been built step by step, which, after passing through many iterations of machine learning algorithms, could automate the solution of problems similar to t

APA, Harvard, Vancouver, ISO, and other styles

25

Gómez Ramos, Marcos Yamir, J. Sergio Ruíz García, and Farid García Lamont. "Classification of corn plants and weed based on characteristics of color and texture using methods of segmentation Otsu and PCA." International Journal of Combinatorial Optimization Problems and Informatics 12, no. 3 (2021): 98–108. https://doi.org/10.61467/2007.1558.2021.v12i3.218.

Full text

Abstract:

The corn crop is very important in Mexico. Corn is fertilized manually or with machinery. When fertilization is manual, it consists of depositing fertilizer to each corn plant. Whereas machine fertilization, involve of dropping fertilizer along the furrow continuously. Manual fertilization is effective, but it is expensive and time-consuming. Machine fertilization can be inefficient, because fertilizer is deposited in the weeds or where there is no corn plant. When the fertilizer is not absorbed by the plant, it can damage the aquifers. This project presents algorithms to classify corn plants

APA, Harvard, Vancouver, ISO, and other styles

26

Wang, Qianglong, Xiaoguang Gao, Kaifang Wan, Fei Li, and Zijian Hu. "A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy." Mathematical Problems in Engineering 2020 (March 20, 2020): 1–19. http://dx.doi.org/10.1155/2020/4206457.

Full text

Abstract:

The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning. Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages. RBM training algorithms are sampling algorithms essentially based on Gibbs sampling. Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms. To address the above problem, in this paper, we propose a f

APA, Harvard, Vancouver, ISO, and other styles

27

Leema N., Khanna H. Nehemiah, Elgin Christo V. R., and Kannan A. "Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms." International Journal of Operations Research and Information Systems 11, no. 4 (2020): 62–85. http://dx.doi.org/10.4018/ijoris.2020100104.

Full text

Abstract:

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 diffe

APA, Harvard, Vancouver, ISO, and other styles

28

Shaikh, Rumana M. "Cardiovascular Diseases Prediction Using Machine Learning Algorithms." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 1083–88. http://dx.doi.org/10.17762/turcomat.v12i6.2426.

Full text

Abstract:

A broad variety of health conditions are involved in heart disease. Several illnesses and disorders come under the heart disease umbrella. Heart disease forms include: In arrhythmia, abnormality of the heart rhythm. Arteriosclerosis, Hardening of the arteries is atherosclerosis. Via cardiomyopathy, this disorder causes muscles in the heart to harden or grow weak. Defects of the congenital heart, heart abnormalities that are present at birth are congenital heart defects. Disease of the coronary arteries (CAD), the accumulation of plaque in the heart's arteries triggers CAD. It's called ischemic

APA, Harvard, Vancouver, ISO, and other styles

29

Pratiwi, Heny, and Kusno Harianto. "Perbandingan Algoritma ELM Dan Backpropagation Terhadap Prestasi Akademik Mahasiswa." J-SAKTI (Jurnal Sains Komputer dan Informatika) 3, no. 2 (2019): 282. http://dx.doi.org/10.30645/j-sakti.v3i2.147.

Full text

Abstract:

Extreme Learning Machine and Backpropagation Algorithms are used in this study to find out which algorithm is most suitable for knowing student academic achievement. The data about students are explored to get a pattern so that the characteristics of new students can be known every year. The evaluation process of this study uses confusion matrix for the introduction of correctly recognized data and unknown data. Comparison of this algorithm uses student data at the beginning of the lecture as early detection of students who have problems with academics to be anticipated. The variables used are

APA, Harvard, Vancouver, ISO, and other styles

30

Sunil Basnet. "AI-ML algorithm for enhanced performance management: A comprehensive framework using Backpropagation (BP) Algorithm." International Journal of Science and Research Archive 11, no. 1 (2024): 1111–27. http://dx.doi.org/10.30574/ijsra.2024.11.1.0118.

Full text

Abstract:

In the era of economic globalization and heightened market competition, organizations face the imperative to establish robust performance evaluation mechanisms that drive both organizational development and individual employee motivation. This article delves into the multifaceted factors influencing employee performance, encompassing personal attributes, interpersonal relations, and work standards. The study takes a deep dive into the transformative integration of AI-ML algorithms, proposing a comprehensive framework for elevated performance management. Through the application of machine learn

APA, Harvard, Vancouver, ISO, and other styles

31

Holloway, Ian, Aihua Wood, and Alexander Alekseenko. "Acceleration of Boltzmann Collision Integral Calculation Using Machine Learning." Mathematics 9, no. 12 (2021): 1384. http://dx.doi.org/10.3390/math9121384.

Full text

Abstract:

The Boltzmann equation is essential to the accurate modeling of rarefied gases. Unfortunately, traditional numerical solvers for this equation are too computationally expensive for many practical applications. With modern interest in hypersonic flight and plasma flows, to which the Boltzmann equation is relevant, there would be immediate value in an efficient simulation method. The collision integral component of the equation is the main contributor of the large complexity. A plethora of new mathematical and numerical approaches have been proposed in an effort to reduce the computational cost

APA, Harvard, Vancouver, ISO, and other styles

32

Vinayakumar, Gopika. "A Comparison of KNN Algorithm and MNL Model for Mode Choice Modelling." European Transport/Trasporti Europei, no. 92 (March 2023): 1–14. http://dx.doi.org/10.48295/et.2023.92.3.

Full text

Abstract:

Mode choice modelling helps to identify potential users of traffic and plays an important role in policy and decision-making by the government. With the advancement of artificial intelligence and machine learning techniques, several studies were carried out to analyse the performance of mode choice models in which the backpropagation algorithm was used. However, for faster convergence of parameters, it would be interesting to explore other efficient algorithms of machine learning as the conjugated gradient search in spite of the backpropagation algorithm. The present study adds to the literatu

APA, Harvard, Vancouver, ISO, and other styles

33

Edi, Ismanto, Ab Ghani Hadhrami, Izrin Md Saleh Nurul, Al Amien Januar, and Gunawan Rahmad. "Recent systematic review on student performance prediction using backpropagation algorithms." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 3 (2022): 597–606. https://doi.org/10.12928/telkomnika.v20i3.21963.

Full text

Abstract:

A comprehensive systematic study was carried out in order to identify various deep learning methods developed and used for predicting student academic performance. Predicting academic performance allows for the implementation of various preventive and supportive measures earlier in order to improve academic performance and reduce failure and dropout rates. Although machine learning schemes were once popular, deep learning algorithms are now being investigated to solve difficult predictions of student performance in larger datasets with more data attributes. Deep neural network prediction metho

APA, Harvard, Vancouver, ISO, and other styles

34

Yang, Yanhua, Guiyong Liu, Haihong Zhang, Yan Zhang, and Xiaolong Yang. "Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms." Buildings 14, no. 1 (2024): 190. http://dx.doi.org/10.3390/buildings14010190.

Full text

Abstract:

Machine learning (ML) algorithms have been widely used in big data prediction and analysis in terms of their excellent data regression ability. However, the prediction accuracy of different ML algorithms varies between different regression problems and data sets. In order to construct a prediction model with optimal accuracy for fly ash concrete (FAC), ML algorithms such as genetic programming (GP), support vector regression (SVR), random forest (RF), extremely gradient boost (XGBoost), backpropagation artificial neural network (BP-ANN) and adaptive network-based fuzzy inference system (ANFIS)

APA, Harvard, Vancouver, ISO, and other styles

35

Abdurrakhman, Arief, Lilik Sutiarso, Makhmudun Ainuri, Mirwan Ushada, and Md Parvez Islam. "Prediction of Biogas Production from Agriculture Waste Biomass Based on Backpropagation Neural Network." BIO Web of Conferences 165 (2025): 06001. https://doi.org/10.1051/bioconf/202516506001.

Full text

Abstract:

An integral aspect of sustainable agriculture involves the implementation of a meticulously planned waste management infrastructure. One strategy to achieve this objective is the utilization of agricultural waste, specifically in the form of biomass, to generate sustainable energy such as biogas. This study aims to provide valuable prediction model for biogas production with many variables which is influenced. The study identifies four variables, namely pH, moisture content, Organic Loading Rate (OLR) and temperature which significantly impact on the biogas production, especially in Indonesia.

APA, Harvard, Vancouver, ISO, and other styles

36

Durve, Mihir, Andriano Tiribocchi, Andrea Montessori, Marco Lauricella, and Sauro Succi. "Machine learning assisted droplet trajectories extraction in dense emulsions." Communications in Applied and Industrial Mathematics 13, no. 1 (2022): 70–77. http://dx.doi.org/10.2478/caim-2022-0006.

Full text

Abstract:

Abstract This work analyzes trajectories obtained by YOLO and DeepSORT algorithms of dense emulsion systems simulated via lattice Boltzmann methods. The results indicate that the individual droplet’s moving direction is influenced more by the droplets immediately behind it than the droplets in front of it. The analysis also provide hints on constraints of a dynamical model of droplets for the dense emulsion in narrow channels.

APA, Harvard, Vancouver, ISO, and other styles

37

Zhong, Wei, Shuangli Wang, Tan Wu, Xiaolei Gao, and Tianshui Liang. "Optimized Machine Learning Model for Fire Consequence Prediction." Fire 7, no. 4 (2024): 114. http://dx.doi.org/10.3390/fire7040114.

Full text

Abstract:

This article focuses on using machine learning to predict the distance at which a chemical storage tank fire reaches a specified thermal radiation intensity. DNV’s Process Hazard Analysis Software Tool (PHAST) is used to simulate different scenarios of tank leakage and to establish a database of tank accidents. Backpropagation (BP) neural networks, random forest models, and the optimized random forest model K-R are used for model training and consequence prediction. The regression performance of the models is evaluated using the mean squared error (MSE) and R2. The results indicate that the K-

APA, Harvard, Vancouver, ISO, and other styles

38

Shao, Wei, Wenhan Yue, Ye Zhang, et al. "The Application of Machine Learning Techniques in Geotechnical Engineering: A Review and Comparison." Mathematics 11, no. 18 (2023): 3976. http://dx.doi.org/10.3390/math11183976.

Full text

Abstract:

With the development of data collection and storage capabilities in recent decades, abundant data have been accumulated in geotechnical engineering fields, providing opportunities for the usage of machine learning approaches. Thus, a rising number of scholars are adopting machine learning techniques to settle geotechnical issues. In this paper, the application of three popular machine learning algorithms, support vector machine (SVM), artificial neural network (ANN), and decision tree (DT), as well as other representative algorithms in geotechnical engineering, is reviewed. Meanwhile, the appl

APA, Harvard, Vancouver, ISO, and other styles

39

Sahu, Ranu, and Khushboo Choubey. "Comparative Analysis of Supervised and Unsupervised Learning Methods for Pattern Classification." International Journal of Innovative Research in Computer and Communication Engineering 12, Special Is (2024): 58–63. http://dx.doi.org/10.15680/ijircce.2024.1203509.

Full text

Abstract:

In the higher learning system, this article compares and contrasts supervised and unsupervised learning approaches to see which is more effective for classifying patterns. Among the most significant uses of machine learning algorithms is classification. Our research shows that, although the supervised learning algorithm, Backpropagation learning with errors, does a great deal of nonlinear real-time assignments, the unsupervised learning algorithm, Kohonen Self-Organizing Map (KSOM), performs very well in our study's classification tasks.

APA, Harvard, Vancouver, ISO, and other styles

40

Panagiotou, Dimitrios K., and Anastasios I. Dounis. "Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network." Energies 15, no. 17 (2022): 6453. http://dx.doi.org/10.3390/en15176453.

Full text

Abstract:

Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is ben

APA, Harvard, Vancouver, ISO, and other styles

41

Paturi, Uma Maheshwera Reddy, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, and Nagireddy Gari Subba Reddy. "Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys." Crystals 15, no. 5 (2025): 404. https://doi.org/10.3390/cryst15050404.

Full text

Abstract:

This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. Among the ML methods explored, a backpropagation neural network (BPNN) model with a sigmoid activation function exhibited superior predictive accuracy compared to other algorithms. The BPNN model achieved excellent correlation coefficients (R2) of 99.54% and 96.39% for training (116 datasets) and cross-validation (39 datasets), respectively. Testing of the BPNN model on an independent dataset

APA, Harvard, Vancouver, ISO, and other styles

42

Rasna, Rasna, Moh Rahmat Irjii Matdoan, Junaidi Salat, Fitria Fitria, and Seno Lamsir. "Weather Classification and Prediction on Imagery Using Boltzmann Machine." International Journal of Engineering, Science and Information Technology 5, no. 2 (2025): 182–89. https://doi.org/10.52088/ijesty.v5i2.806.

Full text

Abstract:

Weather is a physical process or event that occurs in the atmosphere at a specific time and place, as well as its changes over a short period in a particular location on Earth. To produce weather forecast information, there is a series of processes that must be carried out until the weather information is conveyed accurately. The stages involved in the feature extraction process are carried out first. This process is carried out to obtain specific characteristics or features from a dataset. After the feature extraction process has been completed, the next step is to predict the weather based o

APA, Harvard, Vancouver, ISO, and other styles

43

Jammoussi, Imen, and Mounir Ben Nasr. "A Hybrid Method Based on Extreme Learning Machine and Self Organizing Map for Pattern Classification." Computational Intelligence and Neuroscience 2020 (August 25, 2020): 1–9. http://dx.doi.org/10.1155/2020/2918276.

Full text

Abstract:

Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In order to select a suitable number of hidden neurons, this paper proposes a novel hybrid learning based on a two-step process. First, the parameters of hidden layer are adjusted by a self-organized learning algorithm. Next, the weights matrix of the output layer is determined using the Moore–Penrose inverse method. Nine classification datasets are co

APA, Harvard, Vancouver, ISO, and other styles

44

Manasa K, Patta Sai, D Sai Balaji, and D Abhiram. "Phishing Website Detection using Machine Learning." international journal of engineering technology and management sciences 9, Special Issue 1 (2025): 90–95. https://doi.org/10.46647/ijetms.2025.v09si01.014.

Full text

Abstract:

Phishing is a common and cunning method used by attackers to rob users of their personal details. They assume the identity of trusted sources, getting users to divulge bank information, usernames, and passwords. It is critical for cyber experts to develop authentic methods of identifying and blocking such advanced threats. This paper discusses using machine learning to identify phishing URLs. We try to produce strong models that can discriminate between real and spurious URLs on the basis of varied features of both. Decision tree, random forest, (SVM) the Support Vector Machine, XGBoost, Back-

APA, Harvard, Vancouver, ISO, and other styles

45

Syaharuddin, Fatmawati, Herry Suprajitno, and Ibrahim. "Hybrid Algorithm of Backpropagation and Relevance Vector Machine with Radial Basis Function Kernel for Hydro-Climatological Data Prediction." Mathematical Modelling of Engineering Problems 10, no. 5 (2023): 1706–16. http://dx.doi.org/10.18280/mmep.100521.

Full text

Abstract:

Hydro-climatological data serves a pivotal role in monitoring climatic alterations and facilitating agricultural planning, inclusive of evapotranspiration estimation, water management, and crop pattern design. The necessity to accurately and expeditiously model and forecast this data underscores the need for effective methodologies. This paper introduces a hybrid algorithm, integrating backpropagation and relevance vector machine (BP-RVM) with a radial basis function (RBF) kernel. A comparative analysis was conducted between RBF and Logsig activation functions in conjunction with resilient bac

APA, Harvard, Vancouver, ISO, and other styles

46

JOHANSSON, THOMAS, and EWERT BENGTSSON. "DATA PARALLEL SUPERVISED CLASSIFICATION ALGORITHMS ON MULTISPECTRAL IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 10, no. 07 (1996): 751–67. http://dx.doi.org/10.1142/s021800149600044x.

Full text

Abstract:

In remote sensing the intensities from a multispectral image are used in a classification scheme to distinguish different ground cover from each other. An example is given where different soil types are classified. A digitized complete scene from a satellite sensor consists of a large amount of data and in future image sensors the resolution and the number of spectral bands will increase even further. Data parallel computers are therefore well-suited for these types of classification algorithms. This article will focus on three supervised classified algorithms: the Maximum Likelihood, the K-Ne

APA, Harvard, Vancouver, ISO, and other styles

47

Zhang, Huimin. "Machine Learning Algorithms for Predicting and Estimating Book Borrowing in University Libraries." Journal of Advanced Computational Intelligence and Intelligent Informatics 28, no. 5 (2024): 1204–9. http://dx.doi.org/10.20965/jaciii.2024.p1204.

Full text

Abstract:

Accurate prediction of borrowing volume of library books is conducive to the decision-making of the managers. This study briefly introduces the backpropagation neural network (BPNN) algorithm used to predict the borrowing volume of university libraries. The factor analysis method and genetic algorithm were employed to optimize the BPNN algorithm to improve its prediction performance. The book borrowing records of 2022 from Handan College Library were considered the subject of simulation experiments. The designed algorithm was compared with the extreme gradient boosting and traditional BPNN alg

APA, Harvard, Vancouver, ISO, and other styles

48

Leng, Qian, Honggang Qi, Jun Miao, Wentao Zhu, and Guiping Su. "One-Class Classification with Extreme Learning Machine." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/412957.

Full text

Abstract:

One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tu

APA, Harvard, Vancouver, ISO, and other styles

49

Liu, Qinghua, Lu Sun, Alain Kornhauser, Jiahui Sun, and Nick Sangwa. "Road roughness acquisition and classification using improved restricted Boltzmann machine deep learning algorithm." Sensor Review 39, no. 6 (2019): 733–42. http://dx.doi.org/10.1108/sr-05-2018-0132.

Full text

Abstract:

Purpose To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small. Design/methodology/approach The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power sp

APA, Harvard, Vancouver, ISO, and other styles

50

Mahecha-Gómez, Jorge E. "ARTIFICIAL INTELLIGENCE WITH NEURAL NETWORKS NOBEL PRIZES IN PHYSICS AND CHEMISTRY 2024." MOMENTO, no. 70 (January 30, 2025): I—XXI. https://doi.org/10.15446/mo.n70.118564.

Full text

Abstract:

John Joseph Hopfield began his career studying excitons in condensed matter physics, but his most important contributions were in the physics of computation and information, including his 1982 work on neural networks. Geoffrey Hinton, known as the “godfather” of artificial intelligence, laid the foundations for deep neural networks and developed the “backpropagation” method in 1986. These advances, along with Hopfield networks and the “Boltzmann machine”, constitute the beginning of artificial intelligence. David Baker is a pioneer in the design and prediction of three-dimensional protein stru

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!