Related references
Note: Only part of the references are listed.
Article
Agronomy
Rafael Rodrigues de Souza et al.
Summary: Ignoring the violation of the error normality assumption is a common mistake that can cause inconsistencies in the results of the analysis of variance. This study aimed to analyze the response of the Shapiro-Wilk test to sample size for soybean grain yield and determine the sample size for optimizing the W statistic estimate. The findings suggest that at least 17 plants per experimental unit should be used for reliably estimating error normality for soybean grain yield.
EUROPEAN JOURNAL OF AGRONOMY
(2023)
Article
Computer Science, Artificial Intelligence
Hansu Kim et al.
Summary: This paper proposes a variable three-term conjugate gradient (VTTCG) method to enhance the search direction and achieve improved convergence stability in the training of artificial neural networks. Experimental results show that the performance of the VTTCG method is superior to that of four conventional methods, including SGD, Adam, AMSGrad, and AdaBelief.
Article
Computer Science, Artificial Intelligence
Sunanda Das et al.
Summary: Sign language serves as a comprehensive medium of communication for individuals with hearing and speaking impairments. This paper presents a hybrid model and a background elimination algorithm for the automatic recognition of Bangla Sign Language. The proposed system achieves high accuracy and precision in character and digit recognition.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Weijie Zhou et al.
Summary: This paper proposes a hybrid ensemble forecasting technique that combines the advantages of AGO, LSSVR, dummy variable, and time trend item to predict seasonal time series with nonlinearity and uncertainty. The proposed framework, GSTLSSVR, allows for the easy addition of arbitrary seasonality to any feature, significantly improving model realism. The empirical results show that the proposed model outperforms other benchmarks in both case studies, indicating promising prospects for identifying seasonal patterns in energy production sequences.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Alex X. Wang et al.
Summary: k-nearest neighbors (k-NN) is a well-known classification algorithm that is widely used in different domains. We proposed the Centroid Displacement-based k-NN algorithm to address the issue of class determination in standard k-NN algorithm. Our experimental results demonstrate that our algorithm is able to enhance the classification performance of the standard k-NN algorithm and its variants and also improve the computational efficiency.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Ju Chen et al.
Summary: This article introduces a method called Probabilistic 2 Satisfiability to control the distribution of negative and positive literals in Artificial Neural Networks by assigning probabilities to variables in logical rules. Experimental results show that the proposed model outperforms existing methods with a probability of at least 81.8%.
ADVANCES IN ENGINEERING SOFTWARE
(2023)
Article
Computer Science, Information Systems
Nur'Afifah Rusdi et al.
Summary: In this paper, a novel logic mining model is proposed, which selects optimal attributes through statistical analysis in the pre-processing phase and enhances the satisfaction of logical rules by using a permutation operator. The performance of the proposed model is validated by comparing it with existing works in logic mining using various performance metrics.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Moch Saiful Umam et al.
Summary: This paper combines tabu search with a genetic algorithm, using a new partial opposed-based technique for population initialization to minimize makespan. By combining these two algorithms, a new algorithm is obtained that balances searches for higher quality solutions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Agriculture, Multidisciplinary
Yongliang Qiao et al.
Summary: Monitoring and classifying dairy behaviours is crucial for improving animal health, fertility, and production yield. This study proposes a deep learning framework using C3D network and ConvLSTM to intelligently monitor and classify five common behaviours of cows. The proposed approach achieves high classification accuracy and outperforms state-of-the-art methods.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Review
Computer Science, Software Engineering
Tim Sonnekalb et al.
Summary: Due to the continuous digitalization of our society, the importance of making distributed and web-based applications more secure has increased. Deep learning and its representation learning approach have been proposed for program code analysis to reduce vulnerability. This systematic literature review aims to analyze and compare 32 primary studies on DL-based vulnerability analysis of program code. The review discusses various analysis approaches, code embeddings, and network topologies, and identifies the current state of research and future directions. It also provides an overview of publicly available datasets for benchmarking approaches. This SLR serves as an overview and starting point for researchers interested in deep vulnerability analysis on program code.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Information Systems
Jun Dou et al.
Summary: This study introduces a feature selection method that can address imbalanced class and incomplete data simultaneously, and its effectiveness and universal applicability are demonstrated through experiments.
INFORMATION SCIENCES
(2022)
Article
Gastroenterology & Hepatology
Mazen Noureddin et al.
Summary: The study introduced a highly specific MRI-based score (MAST) to identify patients with Fibro-NASH, outperforming previous scores in non-invasively identifying patients at higher risk of liver disease progression.
JOURNAL OF HEPATOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Man-Fai Leung et al.
Summary: This paper presents a collaborative neurodynamic optimization approach for cardinality-constrained portfolio selection, solving the mixed-integer optimization problem using multiple recurrent neural networks and particle swarm optimization. Experimental results demonstrate the superior performance of this approach in handling stock data compared to other methods.
Article
Public, Environmental & Occupational Health
Kirstin Roster et al.
Summary: This study developed a machine learning model to predict monthly dengue cases in Brazilian cities, with different models performing best in different cities. The random forests model showed the best overall performance. Machine learning and decision tree ensemble models are important for dengue surveillance in Brazil.
AMERICAN JOURNAL OF EPIDEMIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Yi Hu et al.
Summary: This paper proposes an efficient Two-Archive model based multimodal evolutionary algorithm to provide more elegant solutions and diverse decisions in multi-objective optimization problems. The algorithm expands two solution spaces with different evolutionary requirements by using two parallel offspring generation mechanisms based on competitive particle swarm optimizer and differential evolution. Furthermore, niching local search scheme and reverse vector mutation strategy are employed to improve convergence and diversity.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Biomedical
Aleka Melese Ayalew et al.
Summary: This paper presents a unique approach for quick diagnosis of COVID-19 using chest X-ray images. A hybrid model combining a convolutional neural network (CNN) and histogram of oriented gradients (HOG) method achieved high accuracy in detecting and classifying COVID-19. This method can help reduce inaccurate diagnosis and treatment of COVID-19, leading to improved patient outcomes.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Engineering, Electrical & Electronic
Kun Xie et al.
Summary: Copy number variation (CNV) is a common phenomenon in the human genome and accurate detection of CNVs using Next-generation sequencing (NGS) data is crucial for cancer research; various methods have been developed for CNV detection, but a comprehensive approach is needed; the proposed IhybCNV method outperforms peer methods in experimental evaluation, showing promise as a tool for detecting genome mutations.
DIGITAL SIGNAL PROCESSING
(2022)
Article
Engineering, Civil
Zongjun Wu et al.
Summary: Accurate estimation of maize evapotranspiration is crucial for improving crop water use efficiency. The Penman-Monteith model was used to simulate crop ET with a focus on canopy resistance accuracy. Factors like Rn, LAI, and theta were found to significantly impact canopy resistance. The study recommended the use of the Differential Evolution algorithm for optimizing the Jarvis model parameters in semi-humid regions of northern China.
JOURNAL OF HYDROLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Wei-Chao Lin et al.
Summary: This study compares the performance of multilayer perceptron (MLP) and deep belief networks (DBN) in missing value imputation, with DBN performing the best. In addition, the research finds that when considering the discretization of continuous data, the choice of imputation algorithm is more important than the order in which the two steps are combined.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Mechanical
Peiming Shi et al.
Summary: This paper proposes a new deep learning method for monitoring the health states of a rolling mill using multi-source sensing data. The method feeds 2D kurtogram images generated from vibration signals and acoustic signals to the improved one-dimensional Convolutional Neural Network (I1DCNN) and improved two-dimensional Convolutional Neural Network (I2DCNN) respectively, while introducing Group Normalization (GN) and Global averaging pooling (GAP) for better performance. Experimental results demonstrate that the proposed method achieves efficient and accurate health states monitoring compared to other state-of-the-art deep learning methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Computer Science, Artificial Intelligence
Sagnika Sen et al.
Summary: This study aims to predict the excess charge quartile of a healthcare provider and identify the features that predict their membership in that billing group. The analysis shows that billing patterns are characteristic of a provider's overall practice and socio-economic and demographic factors are significant predictors of a provider's billing patterns.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematics
Mohd Shareduwan Mohd Kasihmuddin et al.
Summary: This paper proposes a novel logic mining approach that combines association analysis and supervised learning to identify and obtain the optimal induced logic rule, thus improving the performance of logic mining.
Article
Mathematics
Siti Syatirah Muhammad Sidik et al.
Summary: This study proposes an additional optimization layer for Discrete Hopfield Neural Network to control the distribution of negative literals, using the Weighted Random 2 Satisfiability model and the binary Artificial Bee Colony algorithm. Experimental results and comparisons show that the proposed algorithm performs well in finding the correct logical structure.
Article
Mathematics
Yuan Gao et al.
Summary: One of the main challenges in representing information using nonsystematic logic is the lack of flexibility, which can lead to potential overfitting. This paper proposes a novel higher-order logical structure called G-Type Random k Satisfiability by incorporating random features into the formulation of first, second, and third-order logical clauses. The proposed logic was implemented in the Discrete Hopfield Neural Network and shown to be more flexible and generate a higher diversity of solutions.
Article
Energy & Fuels
Yujia Ma et al.
Summary: This study aims to apply the multi-objective optimization method to the energy-saving and NOx emission reduction problem of gas turbines operating at part-load conditions. By using a global optimization algorithm, Genetic Algorithm, the optimal working parameters of the gas turbine are obtained to achieve a balance between energy efficiency and environmental protection.
Article
Computer Science, Artificial Intelligence
Qian Bao et al.
Summary: This paper proposes an archive-based two-stage evolutionary algorithm (AT-CMOEA) for solving constrained multi-objective optimization problems (CMOPs), which aims to focus search resources on the region of interest through the exploration and exploitation stages. The method shows competitiveness in effectiveness and reliability in finding a set of well-distributed optimal solutions compared to other representative algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri et al.
Summary: This study addresses the overfitting problem of the existing Discrete Hopfield Neural Network in handling real-life optimization problems by introducing a non-systematic Satisfiability model that promotes diversification of solutions. Experimental results show that the Genetic Algorithm outperforms other algorithms in optimizing the logic and training phases.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Environmental
Nida Nasir et al.
Summary: This study evaluates various artificial intelligence algorithms to develop a reliable approach for accurately predicting water quality. The CATBoost model is found to be the most accurate classifier, with an accuracy of 94.51%. Furthermore, by applying stacking ensemble models with all classifiers, the accuracy reaches 100% in various Meta-classifiers. Therefore, the boosting algorithm is proposed as a reliable approach for water quality classification.
JOURNAL OF WATER PROCESS ENGINEERING
(2022)
Article
Mathematics
Syed Anayet Karim et al.
Summary: This paper presents a novel algorithm called Hybrid Election Algorithm (HEA), which combines evolutionary and swarm intelligence methods to solve optimization and combinatorial problems. By embedding RANkSAT representation into a diversified logical structure, the proposed HEA model improves the storage capacity of Discrete Hopfield Neural Network (DHNN) and outperforms other tested algorithms in terms of performance metrics.
Article
Computer Science, Interdisciplinary Applications
Yueling Guo et al.
Summary: The paper proposes a flexible logical rule called Y-Type Random 2 Satisfiability, which combines systematic and non-systematic logic in Discrete Hopfield Neural Network to optimize solution spaces. The results show that the proposed logical rule provides better solution capacity in terms of synaptic weight assessments, error analyses, energy evaluation, etc.
ADVANCES IN ENGINEERING SOFTWARE
(2022)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri et al.
Summary: This paper introduces a new class of non-systematic satisfiability logic called Weighted Random k Satisfiability, which fairly distributes the distribution of negative literals using Genetic Algorithm. The study demonstrates that different ratios of negative literals and advanced training algorithms positively impact synaptic weight management and production of global minima solutions.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Siti Zulaikha Mohd Jamaludin et al.
Summary: This paper proposes a new logic mining method using log-linear analysis and neural networks to improve performance by selecting the best attributes and formulating logical rules. Experimental results show that the proposed method has significant advantages in terms of accuracy, precision, and Mathews correlation coefficient.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Physics, Multidisciplinary
Siti Zulaikha Mohd Jamaludin et al.
Summary: The paper introduces an energy-based k-satisfiability reverse analysis combined with a Hopfield neural network to extract the relationships between factors in a recruitment dataset. Experimental simulations demonstrate the effectiveness and robustness of this method in extracting dominant attributes for positive recruitment in an insurance agency in Malaysia.
Article
Computer Science, Information Systems
Xiangjun Li et al.
Summary: This paper proposes a mixed data clustering algorithm with noise filtered distribution centroid and iterative weight adjustment strategy, which improves the clustering performance for mixed data.
INFORMATION SCIENCES
(2021)
Article
Engineering, Chemical
Muna Mohammed Bazuhair et al.
Summary: This study introduces higher-order Random k Satisfiability in neural networks to address the issue of representing non-systematic logical rules, optimizing the learning phase with an Election Algorithm. The results show the compatibility and performance of Random 3 Satisfiability with the Election Algorithm in optimizing the learning and retrieval phase, compared to the conventional Exhaustive Search model.
Article
Biotechnology & Applied Microbiology
Davide Chicco et al.
Article
Thermodynamics
Hamid Karimi et al.
Article
Computer Science, Information Systems
Irshad Hussain et al.
Article
Physics, Multidisciplinary
Nur Ezlin Zamri et al.
Article
Engineering, Chemical
Saratha Sathasivam et al.
Article
Multidisciplinary Sciences
Saratha Sathasivam et al.
Proceedings Paper
Computer Science, Artificial Intelligence
Ahmad Alzahrani et al.
DATA MANAGEMENT, ANALYTICS AND INNOVATION, ICDMAI 2019, VOL 1
(2020)
Article
Computer Science, Artificial Intelligence
C. Okan Sakar et al.
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Computer Science, Artificial Intelligence
Pauline Ong et al.
APPLIED SOFT COMPUTING
(2019)
Article
Mathematics
Mohd Shareduwan Mohd Kasihmuddin et al.
Article
Computer Science, Artificial Intelligence
Gerardo Marcos Tornez Xavier et al.
Article
Mathematical & Computational Biology
Viola Folli et al.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
(2017)
Article
Computer Science, Information Systems
Qiao Yu et al.
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
(2017)
Article
Computer Science, Artificial Intelligence
Mustafa Gocken et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2016)
Article
Business
Sergio Moro et al.
JOURNAL OF BUSINESS RESEARCH
(2016)
Article
Computer Science, Artificial Intelligence
Sergio Moro et al.
DECISION SUPPORT SYSTEMS
(2014)
Article
Engineering, Industrial
Md. Mazharul Haque et al.
Article
Behavioral Sciences
Tsung-Hsun Hsieh et al.
BEHAVIOURAL BRAIN RESEARCH
(2011)
Article
Computer Science, Theory & Methods
Saratha Sathasivam et al.
Article
Computer Science, Artificial Intelligence
I-Cheng Yeh et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2009)
Article
Computer Science, Artificial Intelligence
VS Verykios et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2004)
