Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (714 journals)
    - MATHEMATICS (GENERAL) (45 journals)
    - NUMERICAL ANALYSIS (26 journals)
    - PROBABILITIES AND MATH STATISTICS (113 journals)

MATHEMATICS (714 journals)            First | 1 2 3 4     

Showing 601 - 538 of 538 Journals sorted alphabetically
Results in Mathematics     Hybrid Journal  
Results in Nonlinear Analysis     Open Access  
Review of Symbolic Logic     Full-text available via subscription   (Followers: 2)
Reviews in Mathematical Physics     Hybrid Journal   (Followers: 1)
Revista Baiana de Educação Matemática     Open Access  
Revista Bases de la Ciencia     Open Access  
Revista BoEM - Boletim online de Educação Matemática     Open Access  
Revista Colombiana de Matemáticas     Open Access   (Followers: 1)
Revista de Ciencias     Open Access  
Revista de Educación Matemática     Open Access  
Revista de la Escuela de Perfeccionamiento en Investigación Operativa     Open Access  
Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas     Partially Free  
Revista de Matemática : Teoría y Aplicaciones     Open Access   (Followers: 1)
Revista Digital: Matemática, Educación e Internet     Open Access  
Revista Electrónica de Conocimientos, Saberes y Prácticas     Open Access  
Revista Integración : Temas de Matemáticas     Open Access  
Revista Internacional de Sistemas     Open Access  
Revista Latinoamericana de Etnomatemática     Open Access  
Revista Latinoamericana de Investigación en Matemática Educativa     Open Access  
Revista Matemática Complutense     Hybrid Journal  
Revista REAMEC : Rede Amazônica de Educação em Ciências e Matemática     Open Access  
Revista SIGMA     Open Access  
Ricerche di Matematica     Hybrid Journal  
RMS : Research in Mathematics & Statistics     Open Access  
Royal Society Open Science     Open Access   (Followers: 7)
Russian Journal of Mathematical Physics     Full-text available via subscription  
Russian Mathematics     Hybrid Journal  
Sahand Communications in Mathematical Analysis     Open Access  
Sampling Theory, Signal Processing, and Data Analysis     Hybrid Journal  
São Paulo Journal of Mathematical Sciences     Hybrid Journal  
Science China Mathematics     Hybrid Journal   (Followers: 1)
Science Progress     Full-text available via subscription   (Followers: 1)
Sciences & Technologie A : sciences exactes     Open Access  
Selecta Mathematica     Hybrid Journal   (Followers: 1)
SeMA Journal     Hybrid Journal  
Semigroup Forum     Hybrid Journal   (Followers: 1)
Set-Valued and Variational Analysis     Hybrid Journal  
SIAM Journal on Applied Mathematics     Hybrid Journal   (Followers: 11)
SIAM Journal on Computing     Hybrid Journal   (Followers: 11)
SIAM Journal on Control and Optimization     Hybrid Journal   (Followers: 18)
SIAM Journal on Discrete Mathematics     Hybrid Journal   (Followers: 8)
SIAM Journal on Financial Mathematics     Hybrid Journal   (Followers: 3)
SIAM Journal on Mathematics of Data Science     Hybrid Journal   (Followers: 1)
SIAM Journal on Matrix Analysis and Applications     Hybrid Journal   (Followers: 3)
SIAM Journal on Optimization     Hybrid Journal   (Followers: 12)
Siberian Advances in Mathematics     Hybrid Journal  
Siberian Mathematical Journal     Hybrid Journal  
Sigmae     Open Access  
SILICON     Hybrid Journal  
SN Partial Differential Equations and Applications     Hybrid Journal  
Soft Computing     Hybrid Journal   (Followers: 7)
Statistics and Computing     Hybrid Journal   (Followers: 13)
Stochastic Analysis and Applications     Hybrid Journal   (Followers: 2)
Stochastic Partial Differential Equations : Analysis and Computations     Hybrid Journal   (Followers: 1)
Stochastic Processes and their Applications     Hybrid Journal   (Followers: 5)
Stochastics and Dynamics     Hybrid Journal  
Studia Scientiarum Mathematicarum Hungarica     Full-text available via subscription   (Followers: 1)
Studia Universitatis Babeș-Bolyai Informatica     Open Access  
Studies In Applied Mathematics     Hybrid Journal   (Followers: 1)
Studies in Mathematical Sciences     Open Access   (Followers: 1)
Superficies y vacio     Open Access  
Suska Journal of Mathematics Education     Open Access   (Followers: 1)
Swiss Journal of Geosciences     Hybrid Journal   (Followers: 1)
Synthesis Lectures on Algorithms and Software in Engineering     Full-text available via subscription   (Followers: 2)
Synthesis Lectures on Mathematics and Statistics     Full-text available via subscription   (Followers: 1)
Tamkang Journal of Mathematics     Open Access  
Tatra Mountains Mathematical Publications     Open Access  
Teaching Mathematics     Full-text available via subscription   (Followers: 10)
Teaching Mathematics and its Applications: An International Journal of the IMA     Hybrid Journal   (Followers: 4)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technometrics     Full-text available via subscription   (Followers: 8)
The Journal of Supercomputing     Hybrid Journal   (Followers: 1)
The Mathematica journal     Open Access  
The Mathematical Gazette     Full-text available via subscription   (Followers: 1)
The Mathematical Intelligencer     Hybrid Journal  
The Ramanujan Journal     Hybrid Journal  
The VLDB Journal     Hybrid Journal   (Followers: 2)
Theoretical and Mathematical Physics     Hybrid Journal   (Followers: 7)
Theory and Applications of Graphs     Open Access  
Topological Methods in Nonlinear Analysis     Full-text available via subscription  
Transactions of the London Mathematical Society     Open Access   (Followers: 1)
Transformation Groups     Hybrid Journal  
Turkish Journal of Mathematics     Open Access  
Ukrainian Mathematical Journal     Hybrid Journal  
Uniciencia     Open Access  
Uniform Distribution Theory     Open Access  
Unisda Journal of Mathematics and Computer Science     Open Access  
Unnes Journal of Mathematics     Open Access   (Followers: 2)
Unnes Journal of Mathematics Education     Open Access   (Followers: 2)
Unnes Journal of Mathematics Education Research     Open Access   (Followers: 1)
Ural Mathematical Journal     Open Access  
Vestnik Samarskogo Gosudarstvennogo Tekhnicheskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki     Open Access  
Vestnik St. Petersburg University: Mathematics     Hybrid Journal  
VFAST Transactions on Mathematics     Open Access   (Followers: 1)
Vietnam Journal of Mathematics     Hybrid Journal  
Vinculum     Full-text available via subscription  
Visnyk of V. N. Karazin Kharkiv National University. Ser. Mathematics, Applied Mathematics and Mechanics     Open Access   (Followers: 1)
Water SA     Open Access   (Followers: 2)
Water Waves     Hybrid Journal  
Zamm-Zeitschrift Fuer Angewandte Mathematik Und Mechanik     Hybrid Journal   (Followers: 1)
ZDM     Hybrid Journal   (Followers: 2)
Zeitschrift für angewandte Mathematik und Physik     Hybrid Journal   (Followers: 2)
Zeitschrift fur Energiewirtschaft     Hybrid Journal  
Zetetike     Open Access  

  First | 1 2 3 4     

Similar Journals
Journal Cover
Soft Computing
Journal Prestige (SJR): 0.593
Citation Impact (citeScore): 2
Number of Followers: 7  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1433-7479 - ISSN (Online) 1433-7479
Published by Springer-Verlag Homepage  [2469 journals]
  • Modeling and optimization of multiobjective programming problems in
           neutrosophic hesitant fuzzy environment

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      Abstract: Abstract Multiobjective optimization techniques have much importance while solving real-life problems. There may get chances of neutral thoughts and hesitations in real-life problems. This paper has studied the multiobjective programming problems (MOPPs) under neutrosophic hesitant fuzzy uncertainty. The degrees of neutrality and hesitations in MOPPs were introduced, and simultaneously, we have developed the neutrosophic hesitant fuzzy multiobjective programming problems (NHFMOPPs) under a neutrosophic hesitant fuzzy environment. Besides, a new robust solution scheme, namely neutrosophic hesitant fuzzy Pareto optimal solution to the NHFMOPPs, is investigated, and two different optimization techniques are suggested to evaluate it. The validity and applicability of the proposed methods are unanimously implemented on different multiobjective real-life problems. Finally, conclusions, comparative study, and future research direction are addressed based on the presented work.
      PubDate: 2022-06-01
       
  • Improved transfer learning of CNN through fine-tuning and classifier
           ensemble for scene classification

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      Abstract: Abstract In high-resolution remote sensing imageries, the scene classification is one of the challenging problems due to the similarity of image structure and available datasets are all small. Performing training with small datasets on new convolutional neural network (CNN) is inclined to overfitting, and attainability is poor. To overcome this, we go for a stream of transfer learning, fine-tuning strategy. Here, we consider AlexNet, VGG 19, and VGG 16 pre-trained CNNs. First, design a network by replacing the classifier stage layers with revised ones through transfer learning. Second, apply fine-tuning from right to left and perform retraining on the classifier stage and part of the feature extraction stage (last convolutional block). Third, form a classifier ensemble by using the majority voting learner strategy to explore better classification results. The datasets called UCM and SIRI-WHU were used and compared with the state-of-the-art methods. Finally, to check the usefulness of our proposed methods, form sub-datasets from AID and WHU-RS19 datasets with likely labeled class names. To assess the performance of the proposed classifiers compute overall accuracy using confusion matrix and F1-score. The results of the proposed methods improve the accuracy from 93.57 to 99.04% for UCM and 91.34 to 99.16% for SIRI-WHU.
      PubDate: 2022-06-01
       
  • A joint method for Chinese word segmentation and part-of-speech labeling
           based on deep neural network

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      Abstract: Abstract Aiming at the sequential tasks of Chinese word segmentation and part-of-speech labeling, this paper proposes a parallel model for word segmentation and part-of-speech labeling that combines BERT model, bidirectional long-short memory model, and conditional random field model, Markov family model (MFM) or Tree Probability (TLP). In part-of-speech labeling combined with MFM or TLP, the part-of-speech of the current word is not only related to the part-of-speech of the previous word, but also related to the current word itself. The use of the joint method helps to use part-of-speech information to achieve word segmentation, and organically combining the two is beneficial to eliminate ambiguity and improve the accuracy of part-of-speech labeling or word segmentation tasks. Experimental data shows that the joint model for part-of-speech labeling and Chinese word segmentation proposed in this paper can significantly enhances the precision of Chinese word segmentation and the accuracy of part-of-speech labeling.
      PubDate: 2022-06-01
       
  • An intelligent tool for early drop-out prediction of distance learning
           students

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      Abstract: Abstract Early identification of vulnerable students who are prone to drop-out is critical for devising effective educational retention strategies. Based on the Activity Theory, we undertake this challenge by considering students’ online activities as a useful predictor of their academic performance. Specifically, six artificial intelligence and related prediction models in individual and ensemble structures for tackling classification and multi-objective optimization tasks pertaining to early prediction of students’ performance are presented. A real database comprising online learning activities of 2544 students over 2 years in 84 science, engineering, and technology courses from an open distance education institution is used for evaluation. Comparing with other studies in the literature, the huge numbers of students and courses involved in this study pose a great challenge, due to increase in complexity of the problem and data dimensionality. The empirical results reveal statistically significant improvements of the ensemble-based models as compared with individual models in prediction of students’ performance. Implications of the results are analyzed and discussed from the Activity Theory perspective.
      PubDate: 2022-06-01
       
  • A review of enhancing online learning using graph-based data mining
           techniques

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      Abstract: Abstract In recent years, graph-based data mining (GDM) is the most accepted research due to numerous applications in a broad selection of software bug localization, computational biology, practical field, computer networking, and keyword searching. Moreover, graph data are subject to suspicions because of incompleteness and vagueness of data. Graph data mining of uncertain graphs is the most challenging and semantically different from correct data mining. The main problem of the GDM is mining uncertain graph data and subgraph pattern frequency. This paper discussed different techniques related to GDM, complexities, and the different size of the graph, and also investigated the dataset used for GDM, techniques of GDM like clustering analysis, and anomaly detection. To improve the performance of the online learning system, GDM is introduced. Additionally, the study algorithm is used for GDM, dataset, advantages, and disadvantages. In the end, future directions to enrich online learning based on the results of GDM are discussed. Performance metrics of different techniques such as accuracy, precision, recall, F-measure, and runtime are observed. Finally, conclude the survey with a discussion and overall performance of graph-based data mining.
      PubDate: 2022-06-01
       
  • Parameter estimation of different solar cells using a novel swarm
           intelligence technique

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      Abstract: Abstract Since the demand for a clean source of energy has increased, it led to the consequent rise in the importance of solar energy. Thus, the concept of solar cell modelling has drawn the attention of various researchers across the world. A useful and accurate mathematical model for such cells is therefore necessary. In the literature, the three-diode model is suggested as a more reliable approach to satisfy the behaviour of photovoltaic (PV) cells. This paper proposes a new swarm intelligent technique, namely the chaotic chicken swarm optimization, which is originated from the parent chicken swarm algorithm, for parameter assessment of the three-diode PV model, as the three-diode PV model incorporate the grain boundaries and leakage current. Ten different popular chaotic maps have been considered for the study to identify the parameters from the manufacturer datasheet. The chicken swarm technique yields great optimization results both in terms of accuracy and robustness. Further, the use of chaotic maps improves the diversification feature of this metaheuristic technique for which it is preferred. The performance of the proposed approach proved better when compared with some of the popular techniques available in the literature in terms of convergence accuracy and speed. The accuracy of the proposed technique is also verified by extracting I–V and P–V curves. Moreover, several nonparametric statistical tests are also performed to validate the significance of the outcomes obtained by the proposed method.
      PubDate: 2022-06-01
       
  • The potential of integrated hybrid data processing techniques for
           successive-station streamflow prediction

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      Abstract: Abstract Streamflow is one of the most important issues in river engineering due to its impact on planning and operation of the water resources system. In this study, the capability of newly integrated hybrid prediction models based on artificial intelligence and data processing methods was assessed for monthly river streamflow modeling. In this regard, three successive hydrometric stations of Housatonic River were selected and based on the previous time steps of streamflow values during the period of 1941–2018 several models were developed. During the modeling process, two states based on stations own data (state 1) and upstream stations data (state 2) were considered. For data preprocessing, first temporal features of the streamflow series were decomposed via wavelet transform (WT). Then, the obtained subseries were further broken down into intrinsic mode functions using ensemble empirical mode decomposition (EEMD) to obtain features with higher stationary properties. Finally, the most efficient subseries were selected and used for artificial intelligence approaches [i.e., feed forward neural network (FFNN), kernel extreme learning machine (KELM), and support vector machine (SVM)] as inputs. Also, data post-proceeding was done using simple linear averaging (SLAM) and nonlinear neural ensemble (NNEM) methods. Based on the results, the pre- and post- processing data were more accurate as compared to single artificial intelligence method. The integrated pre-post-processing models improved the models efficiency by approximately 45%. It was observed that via the integrated approaches, the upstream stations data could be applied successfully for streamflow modeling when the stations own data were not available.
      PubDate: 2022-06-01
       
  • Neutrosophic pre-I-open set in neutrosophic ideal bitopological space

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      Abstract: Abstract In this article, we introduce the notion of neutrosophic ideal bitopological space (NIBTS) by generalizing the idea of neutrosophic ideal topological space (NITS). Besides, we procure the notion of local function on neutrosophic bitopological space (NBTS) and studied its different properties. Further, we procure the concept of neutrosophic pre-I-open set (NPIOS), neutrosophic semi-I-open set (NSIOS), neutrosophic b-I-open set (N-b-IOS), neutrosophic α-I-open set (N-α-IOS) via NIBTSs. By defining NPIOS, NSIOS, N-b-IOS, N-α-IOS, we establish several interesting results on NIBTS in the form of theorem, proposition, lemma, etc.
      PubDate: 2022-06-01
       
  • Optimized selection of axial pile bearing capacity predictive methods
           based on multi-criteria decision-making (MCDM) models and database
           approach

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      Abstract: Abstract As prevalent substructure systems in geotechnical engineering, piles are susceptible to underlying uncertainties and complex interactions to surrounding soils, associated with their geometry, pattern, and the project cost influences. The spatial variability of surrounding soils and diversity of knowledge in pile engineering highlight selecting an appropriate predictive method(s) for design. Various methods, including static analyses, in situ-based methods, static pile load testing (SPLT), dynamic methods, and numerical analyses, have been developed to predict the axial pile bearing capacity. The cone penetration test (CPT) is one of the in situ tests that provides continuous and reliable records with depth. Due to the similarities between CPT and pile, different CPT-based methods have been advanced. However, these methods result in a wide range of predictions. In this regard, several statistical, probabilistic, and reliability-based criteria are used to assess the accuracy, precision, and error embedded in each method. Therefore, multi-criteria decision-making (MCDM) models are introduced and implemented to distinguish superseding methods. Accordingly, a database of 60 driven piles with adjacent CPT records has been compiled. About twelve static analyses and in situ-based methods were implemented to predict the pile axial bearing capacity for the investigated database. Moreover, aggregative methods, such as Copeland or Borda count, are employed to evaluate and revisit these various decision-making models and designate more critical approaches for the current database. Regarding the collected database and among twelve considered methods, the Meyerhof (CPT-based) and the UniCone (CPTu-based) methods have outperformed the others considering the studied criteria and decision models.
      PubDate: 2022-06-01
       
  • Performance enhancement of meta-heuristics through random mutation and
           simulated annealing-based selection for concurrent topology and sizing
           optimization of truss structures

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      Abstract: Abstract The present investigation includes the performance enhancement concept of discrete meta-heuristics (MHs) for truss design with concurrent size and topology optimization. The five basic MHs, viz. teaching–learning-based optimization (TLBO), whale optimization algorithm (WOA), dragonfly algorithm (DA), heat transfer search (HTS), and ant lion optimization (ALO) algorithm, are investigated. Often these MHs found incompetent in answering complex problems like concurrent topology and sizing optimization of truss structures due to their poor convergence rate, local optima trap, and higher computation time. Also, the balance between diversification and intensification is very significant for MHs efficiency. A mutation is a strong strategy for balancing the diversification and intensification of MHs and can assist in guiding the population towards the global optimum. Moreover, population diversity can be improved by incorporating the selection of simulated annealing that can reduce the chances of local optima tarp. Therefore, an effective search technique based on a random mutation search along with simulated annealing-based selection is developed. Here, five modified MHs, i.e. the modified DA (MDA), modified ALO (MALO), modified WOA (MWOA), modified HTS (MHTS), and modified TLBO (MTLBO) algorithms, using random mutation search phase and SA-based selection are proposed. The developed techniques are implemented on three standard test problems where dynamic and static constraints and multiple load cases are imposed. The comparative performance of the proposed algorithms and their original algorithms is carried out. An empirical evaluation was done using Friedman rank, and the respective algorithms ranks are assigned. The findings reveal that the new technique results in significant performance enhancement of the various MHs by synchronizing the diversification and intensification of search.
      PubDate: 2022-06-01
       
  • An effective spatiotemporal deep learning framework model for short-term
           passenger flow prediction

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      Abstract: Abstract The accurate prediction of short-term passenger flow is of high importance to efficiently manage the passenger flow of metro systems and adjust timetable accordingly. However, the existing methods of passenger flow prediction cannot achieve adequate accurate results due to its complex nonlinear spatiotemporal characteristics. To improve the accuracy of short-term passenger flow prediction, this paper proposes a deep learning model based on a spatiotemporal framework. Firstly, the graph convolutional network, which incorporates prior domain knowledge (such as travel time and origin–destination demand), is used to extract spatial features of passenger flow. Secondly, the attention mechanism is integrated into the gated recurrent unit to extract the time correlation of passenger flow. Finally, external factors are introduced to capture their impact on passenger flow as well. A case study of the Beijing Subway system is illustrated to verify the performance of the proposed model. The results show that compared with the existing models, the proposed model achieves the highest prediction accuracy and strong robustness. Furthermore, we demonstrate that the adjacency matrix based on travel time outperforms the one based on OD demand, especially during evening peak hours. In addition, it is also verified that the attention mechanism and external factors can improve the prediction performance of the proposed model.
      PubDate: 2022-06-01
       
  • Providing a genetic algorithm-based method to optimize the fuzzy logic
           controller for the inverted pendulum

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      Abstract: Abstract A classic system in dynamics and control is the inverted pendulum, which is known as a topic in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, to find the optimal fuzzy rule-based system, different approaches have been developed using a genetic algorithm. The proposed method’s purpose is to set fuzzy rules and their membership function and the learning process length based on the use of a genetic algorithm. The proposed method’s results show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. The use of a fuzzy system in a dynamic inverted pendulum environment has better results than definite systems; in addition, the optimization of the control parameters increases this model quality even beyond the simple case.
      PubDate: 2022-06-01
       
  • Derivations of equality algebras

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      Abstract: Abstract In this paper, we introduced the concept of derivation on equality algebra \(E\) by using the notions of inner and outer derivations. Then, we investigated some properties of (inner, outer) derivation and we introduced some suitable conditions that they help us to define a derivation on \(E\) . We introduced kernel and fixed point sets of derivation on \(E\) and prove that under which condition they are filters of \(E\) . Finally, we prove that the equivalence relations on \((E,\rightsquigarrow ,1)\) coincide with the equivalence relations on \(E\) with derivation \( d \) .
      PubDate: 2022-06-01
       
  • Automatic identification of drug sensitivity of cancer cell with novel
           regression-based ensemble convolution neural network model

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      Abstract: Abstract Identifying responses/sensitivity of drugs to explore personalized therapy related to drugs has been a significant issue in pharmacogenomic-related studies. Cancer is an acute disease involving heterogeneous behavior of similar tumor types of human’s data related to drug therapy. Some large-scale pharmacogenomic studies related to cancer cell lines have been emerging pharma clinical systems to predicting advanced genetic sub relations like sensitivity for influence therapeutic approach. Advanced machine learning approaches related to pharmacogenomic data studies have evaluated the ability to identify drug/compound sensitivity. Recently, drug sensitivity prediction has been critical in drug identification and design because some of the pharmacogenomic online data contribute as open access. These databases are used to identify drug sensitivity by developing computational methodologies. However, all these methodologies are not sufficient to predict the sensitivity of drugs because of multi-features. So that in this paper, we propose a Novel Regression-based Ensemble Convolution Neural Network Model (NRECNNM) to identify the sensitivity of drugs based on multiple pharma omics data & addresses heterogeneity in the selection of features to sub pharmacogenomic parameters. This approach also evaluates the number of components with the regression calculation method. Ensemble convolution network describes the significance of distance metrics relates to neighborhood dependencies related to drug therapy relations. The proposed approach uses Cancer cell line Encyclopedia (CCLE) data sets, and experimental results examine the performance in predicting sensitivity can enhance with conventional approaches.
      PubDate: 2022-06-01
       
  • Powerful enhanced Jaya algorithm for efficiently optimizing numerical and
           engineering problems

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      Abstract: Abstract Over the last decade, the size and complexity of real-world problems have grown dramatically, necessitating more effective tools. Nature-inspired metaheuristic algorithms have proven to be a promising tool for solving such problems due to their performance in a variety of fields. JAYA algorithm is a novel population-based algorithm which could have been able to present reliable results. This is because it does not need any parameters to be set other than the population size and the maximum number of iteration. Despite its positive feedbacks, this algorithm should be modified to witness more efficiency. This paper aims to amend the original version of Jaya to present a high-efficiency version named Powerful Enhanced Jaya (PEJAYA). In other words, the methodology of updating position in Jaya is modified to enhance the convergence and search capabilities. This approach is assessed according to solve 20 well-known benchmark functions, feature selection, and statistical tests. The output results of proposed optimization algorithm are then evaluated by comparing it with other recent algorithms including crow search algorithm (CSA), standard version of JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO) and sine–cosine algorithm (SCA). Solving a real-world problem is another way of checking the efficiency of this approach with other published works. Prompt escape from local minima, superior convergence, and stability demonstrate that the suggested approach is a very powerful instrument that may be employed in a variety of optimization situations.
      PubDate: 2022-06-01
       
  • Consideration of a robust watermarking algorithm for color image using
           improved QR decomposition

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      Abstract: Abstract In order to protect the digital image copyright, it is necessary to design a robust watermarking algorithm. To achieve this purpose, a novel color image watermarking scheme based on an improved QR decomposition is proposed in this paper. The proposed method gives a new algorithm to find elements of Q and R matrices instead of using the Gram–Schmidt algorithm for QR factorization. First, the R matrix is performed by solving a set of linear equations where diagonal elements of R are checked and modified if they are zero or negative. After that, the Q matrix is computed based on the R matrix. In addition, a novel formula is proposed to improve the extracting time where the first element R(1, 1) of the R matrix is found instead of computing QR decomposition as the previous proposals. Experimental results show that the proposed method outperforms other considered methods in this paper in terms of the quality of the watermarked images. Furthermore, the execution time is significantly improved, and the extracted watermark is more robust against almost tested attacks.
      PubDate: 2022-06-01
       
  • Optimal siting and sizing of hybrid PV and wind energy distribution
           network

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      Abstract: Abstract To mitigate the impact of HES variations on power generation reliability and quality, this paper proposes an appropriate placement and sizing of the battery energy storage system (BESS) in distribution networks with hybrid energy sources (HES) of distribution network operators (DNO). The daily cost incurred by the distribution network as a result of voltage deviation, power losses, and peak demands is taken into consideration while determining the ideal location and size of the BESS. IEEE 33-bus distribution network is used to test and evaluate the simulation findings of the BESS installation in the field. For this optimization problem, the genetic algorithm (GA) and particle swarm optimization (PSO) are used to solve it, and the results achieved from these two techniques are compared. When comparing the results of the BESS installation in the distribution network to the results of the case without BESS installation, the voltage variation, power losses, and peak demands are all lower than in the case with BESS installation.
      PubDate: 2022-06-01
       
  • A data-driven stochastic decision support system to investment portfolio
           problem under uncertainty

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      Abstract: Abstract In this paper, considering an investment portfolio problem with stochastic returns, we present a novel approach to design a Stochastic Rule-Based Decision Support System (SRBDSS). The SRBDSS helps investors to infer reliable and near optimal investment weights without solving the conventional optimization model directly. The introduced rule-based inference system establishes a relationship between patterns of stock returns, optimal weights and objective function. Also, an implication method is proposed to infer the investment ratio/weights for a given realization of the portfolio returns. In the presented model, optimum knowledge, derived from a stochastic portfolio problem, is used to construct SRBDSS, instead of using a few number of experts’ opinion. A case study is presented to illustrate the model employing real data adopted from Tehran Stock Exchange. Provided results reveal that not only is the presented approach easy to use, but it also provides acceptable results without directly solving the investment portfolio problem.
      PubDate: 2022-06-01
       
  • A unified framework of deep unfolding for compressed color imaging

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      Abstract: Abstract Traditional iterative-based reconstruction algorithms for compressed color imaging often suffer from long reconstruction time and low reconstruction accuracy at extreme low-rate subsampling. This paper proposes a model-driven deep learning framework for compressed color imaging. In the training step, extract the image blocks at the same position of the R, G, and B channel images as the ground truth, and then, singular value decomposition is performed on the measurement matrix to obtain the optimized measurement matrix and low-dimensional measurements; afterward, the ground-truth and optimized measurements are utilized to construct a large amount of training data pairs to train an ‘end-to-end’ deep unfolding model for compressed color imaging. In the test step, the single pretrained model is used to reconstruct high-quality images from optimized low-dimensional compressed measurements for each channel and synthesize a color image. Numerical experiments demonstrate that our proposed unified framework can achieve high accuracy and real-time reconstruction for the color image at extremely low subsampling rate.
      PubDate: 2022-06-01
       
  • A generalized collision algorithm for geometric graphics

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      Abstract: Abstract The two-dimensional graphical nesting problem is widespread in industrial production and is an NP-complete problem. The core technology of various nesting methods is the graphical collision algorithm. In this paper, a general algorithm for geometric graphics is proposed. According to the geometrical characteristics of the packed parts, the idea of divide and conquer is adopted, and the corresponding collision strategies are designed, respectively. Two-point bidirectional collision calculation, aligned bidirectional collision and slipping calculation are proposed to determine the collision relationship between graphics. The interpolation strategy is used to reduce the computation of NFP (No-Fit-Polygon). The precise interpolation between graphics is achieved by first marking and positioning, and then sliding interpolation in both directions, which improves the interpolation efficiency. Finally, the results of the comparison test by several cases of different types show that the algorithm is effective, stable, reliable and adaptable.
      PubDate: 2022-06-01
       
 
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