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

MATHEMATICS (714 journals)            First | 1 2 3 4     

Showing 601 - 538 of 538 Journals sorted alphabetically
Research in Nondestructive Evaluation     Hybrid Journal   (Followers: 7)
Research in Number Theory     Hybrid Journal   (Followers: 1)
Research in the Mathematical Sciences     Open Access  
Research Journal of Pure Algebra     Open Access   (Followers: 1)
Researches in Mathematics     Open Access  
Results in Control and Optimization     Open Access   (Followers: 5)
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: 13)
SIAM Journal on Computing     Hybrid Journal   (Followers: 12)
SIAM Journal on Control and Optimization     Hybrid Journal   (Followers: 21)
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: 6)
SIAM Journal on Matrix Analysis and Applications     Hybrid Journal   (Followers: 3)
SIAM Journal on Optimization     Hybrid Journal   (Followers: 15)
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: 8)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Stochastic Analysis and Applications     Hybrid Journal   (Followers: 3)
Stochastic Partial Differential Equations : Analysis and Computations     Hybrid Journal   (Followers: 2)
Stochastic Processes and their Applications     Hybrid Journal   (Followers: 6)
Stochastics and Dynamics     Hybrid Journal   (Followers: 2)
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   (Followers: 1)
The Ramanujan Journal     Hybrid Journal  
The VLDB Journal     Hybrid Journal   (Followers: 2)
Theoretical and Mathematical Physics     Hybrid Journal   (Followers: 8)
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: 1)
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: 3)
Water SA     Open Access   (Followers: 1)
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     

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Journal Cover
Soft Computing
Journal Prestige (SJR): 0.593
Citation Impact (citeScore): 2
Number of Followers: 8  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1433-7479 - ISSN (Online) 1433-7479
Published by Springer-Verlag Homepage  [2468 journals]
  • A PSO-optimized novel PID neural network model for temperature control of
           jacketed CSTR: design, simulation, and a comparative study

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      Abstract: Abstract This paper proposes a particle swarm optimization (PSO) tuned novel proportional integral derivative (PID) like neural network (PSO-PID-NN), to control the temperature of a nonlinear jacketed continuous stirred tank reactor (CSTR). The nonlinear continuous stirred tank reactor (CSTR) plant is one of the most popular reactors in the chemical industry. The proposed structure is elegant in design, having only three neurons in the hidden layer and a single output neuron. The three weights in the neural network's output layer represent the PID controller's proportional, integral, and derivative gains. The suggested approach uses the PSO method to optimize the output layer weights, which corresponds to the PID gains. Mean square error is used as an objective function to optimize the weights. The performance of the proposed PSO-PID-NN controller is tested by comparing the time domain specifications of the output response, against the conventional Zeigler Nichols tuned PID controller and the back propagation-based NN-PID controller (BP-NN-PID). The overshoot in the proposed controller is 23.13%, while it is 26.33% in BP-NN-PID, and 44.13% in Zeigler Nichols tuned PID controller. In addition, the rise time is 0.1283 s, while it is 0.2727 s in the BP-NN-PID controller and 0.2813 s in Zeigler Nichols tuned PID controller. The proposed controller is also tested for disturbance rejection, it was found to be more efficient in rejecting disturbance signals as compared to BP-NN-PID and ZN-tuned PID controllers.
      PubDate: 2024-03-01
       
  • Reliability-aware web service composition with cost minimization
           perspective: a multi-objective particle swarm optimization model in
           multi-cloud scenarios

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      Abstract: Abstract The multi-cloud environment (MCE) serves users on-demand by presenting miscellaneous online web services. Each web service which is delivered by every cloud provider has its own quality of features and also own pricing scheme. In the web service composition technology, the integration of the services required by the users is done with the aim of producing the efficient solutions with the desired quality. In some businesses, continuity of activities is very important and a business that fails a lot cannot be trusted by subscribers. In these businesses, it is necessary to maximize the reliability of the system along with minimizing the overall monetary costs. To this end, two new reliability and cost models are presented. All of the network equipment, communication, and elements affecting the total cost and reliability of the system are taken into consideration in the proposed models. Then, the web service composition issue is formulated to a multi-objective optimization problem. To solve this combinatorial problem in large search space of MCE, the multi-objective particle swarm optimization algorithm is suggested to maximize reliability while minimizing the cost of services and make Pareto optimal points. The results of the evaluations show that in different scenarios, the proposed solution proves the amount of 48%, 46%, and 12% averagely improvement over other comparative MOGWO, NSGA-II, and MOEA/D approaches in terms of service failure rate, service implementation cost in cloud providers, and the execution time respectively.
      PubDate: 2024-03-01
       
  • Design of a fuzzy trajectory tracking controller for a mobile manipulator
           system

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      Abstract: Abstract Mobile manipulator robots that combine mobile platforms and robotic arms have been attracting considerable attention in recent years. In this paper, the motion control problem of the mobile manipulator system is considered with the following proposed control strategy. Firstly, a decoupled dynamic model is created to increase operation safety and to reduce complexity such that the independent controllers can be designed for the mobile system and the manipulator system. Subsequently, an effective reference trajectory generator is proposed to guide the mobile manipulator systems to the proper position for the manipulation being able to grip the target. Thereafter, the fuzzy controllers are designed for the mobile system to eliminate the tracking error and for the manipulator system to accomplish the gripping mission. The stability of the mobile manipulator control system can be guaranteed by the Lyapunov theory. Finally, the numerical simulations are given to demonstrate the effectiveness of the proposed approach. From the simulation results, it can be seen that this paper as well as other compared methods have good control response in case of small controller gain, in which the convergence time are about 4–5 s. However, by increasing the controller gain to improve the control response, the other methods will make the system unstable or the controller output will produce a large amount of chattering. The proposed controller in this paper can not only decrease the convergence time, from 5 to 3 s, but also provide a smooth control response.
      PubDate: 2024-03-01
       
  • Dynamic link utilization empowered by reinforcement learning for adaptive
           storage allocation in MANET

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      Abstract: Abstract In modern wireless networks, mobile nodes often deal with the challenge of maintaining a sufficient number of data packets due to limited storage capacity within each cluster. It adversely impacts network performance by compromising data quality during transmissions. The ensuing delays, caused by data packets awaiting storage allocation, result in reduced throughput and increased end-to-end latency. To effectively address these issues, we present a Dynamic Link Utilization with Reinforcement Learning (DLU-RL) method, which is designed to optimize storage allocation for communication data packets, significantly enhancing network performance. Instead of static allocation, DLU-RL employs dynamic strategies guided by reinforcement learning algorithms. This innovative method not only tackles storage constraints but also proactively adapts to varying network conditions and traffic patterns. In our approach, we first perform a comprehensive analysis of storage capacities across all nodes, establishing a baseline for dynamic resource allocation. The DLU-RL framework then swiftly assigns storage space based on real-time demand and priority, optimizing storage utilization on the fly. As a result of implementing DLU-RL, substantial enhancements in throughput and concurrent minimization of end-to-end delays are achieved. This research not only contributes to efficient storage allocation techniques but also pioneers the integration of reinforcement learning for wireless communication network performance optimization. The proposed framework signifies a paradigm shift in storage management, offering adaptability, efficiency, and real-time optimization to tackle the evolving challenges of wireless communication.
      PubDate: 2024-03-01
       
  • Occlusion-robust workflow recognition with context-aware compositional
           ConvNet

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      Abstract: Abstract Workflow recognition relying on deep convolutional neural network has obtained promising performance. Though impressive results have been achieved on standard industrial workflow, the performance on heavily occluded workflow remains far from satisfactory. In this paper, we present an effective context-aware compositional ConvNet (CA-CompNet) for occluded workflow detection with the following contributions. First, we combine compositional model and original ConvNet together to build a unified deep architecture for occluded workflow detection, which has shown innate robustness to address the problem of object classification under occlusion. Second, in order to overcome the variable occlusion limitations, the bounding box annotations are utilized to segment the context from target workflow instance during training. Then, these segmentations are used to learn the proposed CA-CompNet, which enables the network to untangle the feature representation of workflow instance from the context. Third, a robust voting mechanism for candidate bounding box is introduced to improve the detection accuracy, which facilitates the model to precisely detect the bounding box of a specific workflow instance. Comprehensive experiments demonstrate that the proposed context-aware network can robustly detect workflow instance under occlusion in industrial environment, increasing the detection performance on MS COCO dataset by 4.6% (from 45.1 to 49.7%) in absolute performance compared to the advanced CenterNet.
      PubDate: 2024-03-01
       
  • Peak stress and peak strain evaluation of concrete columns confined with
           lateral ties under axial compression by artificial neural networks

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      Abstract: Abstract The peak stress and peak strain of concrete columns confined with lateral stirrups were important indicators for evaluating the load-bearing capacity and axial deformation of concrete columns under axial compression. However, it took much work to determine the peak stress and peak strain of concrete confined with lateral ties under axial compression due to the complicated arching actions of lateral ties and longitudinal reinforcements, as well as the complex interaction between concrete and lateral ties. In this paper, two typical artificial neural networks (ANN), including (BP networks and Elamn networks) were applied to evaluate the peak stress and peak strain of concrete columns confined with lateral ties based on a reliable database consisting of 196 test data sets for peak stress and 166 test data sets for peak strain collected from previous studies. Both proposed ANN models had high prediction performance in the training and testing process. Furthermore, By comparing with existing analytical models, the proposed BP networks had high reliability and applicability in predicting confined concrete’s peak stress. In contrast, the Elman network had high reliability and applicability in peak strain of concrete columns confined with lateral ties. Furthermore, based on the sensitivity analysis, the concrete strength and the properties of lateral ties obviously influence the peak stress of confined concrete. In contrast, the volumetric ratio of lateral ties significantly affected the peak strain of confined concrete.
      PubDate: 2024-03-01
       
  • A noise-immune and attention-based multi-modal framework for short-term
           traffic flow forecasting

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      Abstract: Abstract Accurately forecasting short-term traffic flow is essential for intelligent transportation systems. However, current methods often struggle to fully exploit implicit variation patterns and heterogeneous correlations in traffic flow data, and can be sensitive to non-Gaussian noise. In this paper, we propose a novel noise-immune and attention-based multi-modal model (NIAMNet) for short-term traffic flow forecasting. Inspired by the success of computer vision techniques, NIAMNet transforms one-dimensional traffic flow into images and embeds residual dual-attention blocks (RDB) to extract in-deep features. Besides, we introduce a dynamic noise-immune loss to address the impact of noise and outliers on model performance. Experimental results on four real-world benchmark datasets demonstrate the superiority of NIAMNet over existing methods, achieving the lowest MAPE (10.43, 9.79, 10.51, and 11.01) and RMSE (247.13, 192.36, 208.40, and 150.01). Additional ablation experiments are carried out to provide insight into the significance of each component. Our approach contributes to the development of more accurate and robust short-term traffic flow forecasting models.
      PubDate: 2024-03-01
       
  • Ensemble multi-attribute decision-making for material selection problems

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      Abstract: Abstract Material selection is influential in product design, manufacturing, and marketing. Appropriate material selection maximizes the performance of a product while minimizing its cost, whereas inappropriate material selection creates devastating results such as low performance, low quality, and high cost. Therefore, it is crucial how to choose the most suitable material. Unlike other studies, this study presents an ensemble multi-attribute decision-making approach for material selection. The approach involves four weighting methods—criteria importance through intercriteria correlation, Entropy, the method based on the removal effects of criteria, and statistical variance, five ranking methods—additive ratio assessment, combined compromise solution, multi-attributive border approximation area comparison, range of value, and the technique for order performance by similarity to the ideal solution, Spearman's correlation coefficients, and the Copeland method. Three different problems are considered to show the applicability of the proposed method and to reveal a comprehensive analysis. The results of each problem show valuable implications. The results of the ranking methods are sensitive to attribute weights. No ranking method alone can assure dependable selection for a given problem. Overall, the results reveal the importance of using multiple weighting and ranking methods and the superiority of the proposed integrated approach.
      PubDate: 2024-03-01
       
  • Grey wolf optimization algorithm-based PID controller for frequency
           stabilization of interconnected power generating system

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      Abstract: Abstract In the proposed research article, the grey wolf optimization (GWO) technique is utilized to optimize the proportional (P) integral (I) derivative (D) (PID) controller/regulator gain parameters in three-area grid-connected power networks. The interconnected power plant covers thermal plants, hydro plants, and nuclear power plants. The proposed controller is used as a secondary controller in the power system to perform load frequency control (LFC). Under unforeseen load conditions, the system frequency deviates from the norm. To control and stabilize this oscillation, the LFC system is used. During the investigation, a step load perturbation of one percent (SLP 1%) is applied for the analysis of the thermal power plant. The response of the suggested optimization technique-designed regulator performance is equated with the genetic algorithm (GA)-tuned, particle swarm optimization (PSO)-tuned, and ant colony optimization (ACO) technique-tuned PID regulator response. The performance response is evidence that the GWO-based PID regulator provides a regulated response with minimal time-domain specification parameters (settling time, peak shoots) over other tuning methods. The effectiveness and robustness of the improved response of the suggested technique-optimized controller are verified with various load values (1%, 2%, and 10% SLP) and nominal parameter (R, Tp, and Tij) variations (± 25% & ± 50%) from its nominal value.
      PubDate: 2024-03-01
       
  • An integrated study fusing systems biology and machine learning algorithms
           for genome-based discrimination of IPF and NSIP diseases: a new approach
           to the diagnostic challenge

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      Abstract: Abstract Idiopathic pulmonary fibrosis (IPF) and nonspecific interstitial pneumonia (NSIP) are the two types of idiopathic interstitial pneumonia that are most prevalent. IPF and NSIP, often known as chronic interstitial pneumonia, must be differentiated from other forms of idiopathic interstitial pneumonia. However, distinguishing IPF from NSIP on radiographic imaging is challenging. Our goal in this work is to propose a novel approach to this clinical diagnostic challenge by distinguishing IPF from NSIP and healthy individuals via a complete systems biology analysis of existing microarray datasets. The Gene Expression Omnibus (GEO) database was searched, and two microarray datasets were identified. These datasets included normal, IPF, and NSIP samples. A second dataset was retrieved to validate further the built prediction models trained on the first dataset. Following the completion of the stages for data preparation and normalization, the profiles of gene expression were analyzed to determine the differentially expressed genes (DEGs). After that, we constructed module analysis and identified possible biomarkers by leveraging the prioritized and statistically significant DEGs to construct protein–protein interaction networks. The DEGs with the most important priority were also utilized to determine the implicated Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene ontology (GO) enrichment analyses. Using the Kaplan–Meier approach, we performed three separate assessments of the gene biomarkers' effect on patients' chances of survival. In addition, the found genes were validated not just through several different categorization models, but also by analyzing the published experimental work on the target genes. A total of 32 distinct genes were found when comparing IPF to normal, NSIP to normal, and IPF to NSIP. This was accomplished by identifying seven (14 genes), six (7 genes), and eight (13 genes) modules, as well as three genes (i.e., C6, C5, STAT1). Results from GO analysis and the KEGG pathway evaluation showed evidence for biological processes, cellular components, and molecular activities. When considering the overall survival (OS), fast progression (FP), and post-progression survival (PPS) rates, the Kaplan–Meier analysis demonstrated that 27 out of 32, 16 out of 32, and 13 out of 32 genes were significant. Additionally, the identified biomarkers show high performance for the machine learning classification models. In addition, the scientific literature findings have validated each gene biomarker discovered for IPF, NSIP, and other lung-related conditions. The 32-mRNA signature shows promise as a gene set for IPF and NSIP and as a driver for treatments with the ability to predict and manage patients' survival rates accurately.
      PubDate: 2024-03-01
       
  • $$\delta $$ -ideals of p-algebras

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      Abstract: Abstract In p-algebras, the concepts of \(\delta \) -ideals and principal \(\delta \) -ideals are presented, and some of their respective properties are discussed. It is observed that the set \(I^{\delta }(L)\) of all \(\delta \) -ideals of a p-algebra L is a bounded lattice, and the class \(I_{p}^{\delta }(L)\) of all principal \(\delta \) -ideals forms a bounded sublattice of \(I^{\delta }(L)\) and a Boolean algebra on its own. A characterization of a \(\delta \) -ideal in terms of principal \(\delta \) -ideals, in p-algebras, is given. Also, the concept of comaximality of \(\delta \) -ideals is discussed in p-algebras. After that, a number of properties of the homomorphic image of \(\delta \) -ideals are considered.
      PubDate: 2024-03-01
       
  • Assessing the interactions amongst index tracking model formulations and
           genetic algorithm approaches with different rebalancing strategies

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      Abstract: Abstract The index tracking problem consists of constructing a portfolio that has the closest performance of a given index as possible, with fewer assets in its composition. When considering a large number of assets, including all of them on the solution may incur high transaction fees, harming the accumulated returns in the long term. The constraint that limits the portfolio size imposes a combinatorial nature on the problem and the computation of the optimal solution becomes infeasible as the universe of assets grows. To get around this issue, pure and hybrid metaheuristics have been proposed in the literature to achieve good solutions in practical time. Different from pure metaheuristics, hybrid metaheuristics do not need any constraint handling or solution repairing approaches since they often use general-purpose solvers to adjust portfolio weights. This work presents a comparison between pure and hybrid genetic algorithms, which is one of the most popular heuristics applied in the portfolio selection field. We considered linear and nonlinear index tracking models in the experiments, where the GA that obtained the best performance in a single period optimization strategy, was selected to backtest in a dynamic index tracking approach. The results showed that hybrid GAs can compute good or even better solutions than the CPLEX solver and pure GAs, in a shorter time.
      PubDate: 2024-03-01
       
  • Prediction of service time for home delivery services using machine
           learning

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      Abstract: Abstract With the rise of ready-to-assemble furniture, driven by international giants like IKEA, assembly services were increasingly offered by the same retailers. When planning orders with assembly services, the estimation of the service time leads to additional difficulties compared to standard delivery planning. Assembling large wardrobes or kitchens can take hours or even days while assembling a chair can be done in a few minutes. Combined with the usually vast amounts of offered products, a lot of knowledge is required to plan efficient and exact delivery routes. This paper shows how an artificial neural network (ANN) can be used to accurately predict the service time of a delivery based on factors such as the goods to be delivered or the personnel providing the service. The data used include not only deliveries with assembly of furniture, but also deliveries of goods without assembly and delivery of goods requiring electrical installation. The goal is to create a solution that can predict the time needed based on criteria such the type of furniture, the weight of the goods, and the experiences of the service technicians. The findings show that ANNs can be applied to this scenario and outperform more classical approaches, such as multiple linear regression or support vector machines. Still existing problems are largely due to the provided data, e.g., a large difference between the number of short and longer duration orders, which made it harder to accurately predict orders with longer duration.
      PubDate: 2024-03-01
       
  • Stochastic operating room scheduling: a new model for solving problem and
           an approach for determining the factors that affect operation time
           variations

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      Abstract: Abstract The most common situation encountered in the operating room scheduling is the uncertainty of operation times. This situation may cause the operations to be delayed or canceled. In this study, stochastic operating room scheduling is discussed under the uncertainty of operation times. In real life, variability may vary depending on many factors from operation to operation/patient to patient. These factors are the surgeon's experience, the difficulty of the operation, the patient's weight, age, disease history, etc. In this study, separate coefficients of variability were determined for each operation, taking into account the variability factors. Operations are scheduled, taking into account the operation-specific coefficients of variation. To evaluate the variability factors, analytical network process method was used considering the interaction between them. The level of uncertainty/coefficient of variation of each operation was determined with the PROMETHEE method. Finally, the logical modeling power of constraint programming is used to solve the operating room scheduling problem. In the proposed constraint programming model, the flexibility of the goal programming method was utilized. For the modeling of uncertainties, a chance-constrained approach was used. The case study demonstrates that the proposed approach is a novel and outstanding technique, and the proposed CP model is efficient in solving the problem. As a result of the study, the uncertainty in the operation time of each patient was calculated as the variability according to the factor weights, and the tables were reconstructed according to this situation. The performance and effectiveness of the new schedules obtained under variability are shown.
      PubDate: 2024-03-01
       
  • Design of a novel panoptic segmentation using multi-scale pooling model
           for tooth segmentation

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      Abstract: Abstract Utilizing individual annotation of panoramic radiographs, a comprehensive deep learning multi-scale spatial pooling (ms-SP)-based panoptic segmentation technique is tested for its effectiveness in segmenting teeth autonomously. On a panoramic radiograph, each tooth was meticulously tagged by an oral radiologist to accurately depict its real structure. From the initial data points, we used the augmentation strategy to create training samples to reduce over-fitting. With the proposed multi-scale spatial pooling (ms-SP), a completely deep learning approach was used to locate and identify the dental traits. Performance was evaluated using the F1 score, and visual analysis. The suggested method resulted in a mean IoU of 87% and an F1 score of 98.9%, accuracy of 98.5%, recall of 93%, precision of 94.5%, dice score of 94.5% and PFOM is 80.5%. The segmentation technique was evaluated visually, and the results were very similar to the actual data. The technique produced effective results for automating the segmentation of teeth on panoramic dental photos. The suggested technique may be advantageous for the first stages of forensic identification and diagnostic automation, which both involve similar segmentation tasks.
      PubDate: 2024-03-01
       
  • A non-linear generalization of optimization problems subjected to
           continuous max-t-norm fuzzy relational inequalities

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      Abstract: Abstract Recently, the latticized linear programming problems subjected to max–min and max-product fuzzy relational inequalities (FRI) have been studied extensively and have been utilized in many interesting applications. In this paper, we introduce a new generalization of the latticized optimization problems whose objective is a non-linear function defined by an arbitrary continuous s-norm (t-conorm), and whose constraints are formed as an FRI defined by an arbitrary continuous t-norm. Firstly, the feasible region of the problem is completely characterized and two necessary and sufficient conditions are proposed to determine the feasibility of the problem. Also, a general method is proposed for finding the exact optimal solutions of the non-linear model. Then, to accelerate the general method, five simplification techniques are provided that reduce the work of computing an optimal solution. Additionally, a polynomial-time method is presented for solving general latticized linear optimization problems subjected to the continuous FRI. Moreover, an application of the proposed non-linear model is described where the objective function and the FRI are defined by the well-known Lukasiewicz s-norm and product t-norm, respectively. Finally, a numerical example is provided to illustrate the proposed algorithm.
      PubDate: 2024-03-01
       
  • Seasonal prediction of solar irradiance with modified fuzzy Q-learning

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      Abstract: Abstract Renewable energy plays an important role in the power mix of India being sustainable and environmental source of energy. In this study, modified fuzzy Q-learning (MFQL)-based solar radiation forecasting has been proposed to forecast 30-min-ahead solar irradiance. Application of MFQL is novel in this field, as it uses reinforcement learning and model-free environment. Raw data have been collected for four Indian cities in the state of Rajasthan, i.e. Jodhpur, Ajmer, Jaipur and Kota via the data portal of National Institute of Wind Energy and Wind Resource (NIWE). Empirical mode decomposition (EMD) has been used as the data pre-processing technique, and relevant features are extracted from Pearson’s correlation coefficient. The results obtained from the MFQL forecaster are promising with forecasting accuracy of 92.38% for winter, 93.73% for summer, 91.54% for monsoon and 92.05% for autumn season for the city of Ajmer, and similar results have been obtained for other cities as well. MFQL lends itself as an effective tool for forecasting of seasonal solar irradiance. Proposed prediction model can be effectively utilized for solar irradiance forecasting and for optimal generation of power from incident solar radiation.
      PubDate: 2024-03-01
       
  • Predictive and simultaneous weighting of criteria and alternatives (PSWCA)
           in multi-criteria decision making based on past data

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      Abstract: Abstract The emergence of complex technologies and economic competition in recent decades has led to the increasing importance of decisions about the future of organizations. Most of the former methods require a combination with the criterion weighting methods, which increases the complexity of the calculations. On the other hand, the importance of a criterion changes over time. Therefore, the provided weights must be proportional to the changes expected in the future. In this way, making future decisions based on past knowledge may not guarantee the best choice, but it can guide decision makers in the right direction. In this study, an innovative technique for simultaneously ranking options, and weighting criteria based on historical data is presented. In this method, it is possible that the reference weight is affected by other weighting methods and more accurate weights are assigned to the criteria. In addition, all the records are considered to evaluate the alternatives in relation to the criteria. After calculating the deviation and the starting point changes, a nonlinear mathematical model determines the coefficients of the reference weight (that is, the weight with the smallest difference from the values of the coefficients) and the final score of the options. Finally, the efficiency of the proposed method (PSWCA) is obtained on four real case studies, and the results are compared with other methods.
      PubDate: 2024-03-01
       
  • Analyzing threat flow over network using ensemble-based dense network
           model

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      Abstract: Abstract Cyberattacks may occur in any device with an Internet connection. The majority of businesses either advise preventative measures or creating gadgets with integrated cyber threat protection mechanisms. However, the availability of tools and methods needs to go beyond standard preventative measures which make the process more difficult to identify cyber threats. One important tool for combating these intrusions is an intrusion detection system based on deep learning. To analyze intrusion detection systems, this study suggests random forest-based ensemble methods. Using random forest, tests were carried out in the first phase. In the subsequent stage, random forest is utilized due to their recent notable advancements in supervised learning performance. Deep learning methods like long short-term memory (LSTM) and autoencoder (AE) networks are used in the experiment. The work is optimized using Harris hawks optimization (HHO). For experimental purposes, the Kaggle dataset is utilized. Using this dataset, the results demonstrate that IDS have greatly improved, surpassing the state of the art. The applicability model in IDS is strengthened by this enhancement.
      PubDate: 2024-02-03
       
  • Lifetime maximization of wireless sensor networks while ensuring intruder
           detection

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      Abstract: Abstract Wireless sensor networks (WSN) have a wide variety of application areas and one of these areas is border crossing security. Unauthorized crossing of border areas, unauthorized arms and drug trafficking can be avoided at a lower cost and easier than conventional methods by monitoring the borders with the help of a WSN. In this study, we offer a mathematical model that guarantees the detection of possible intruders by scheduling the activities of the sensors whatever the route the intruder follows throughout the border zone or whatever the time the intruder enters to the route. To achieve the highest possible WSN management efficiency, we integrate coverage, routing, data routing, and sensor scheduling WSN design issues into the mathematical model. We first demonstrate the effectiveness of scheduling the sensors by the help of the offered mathematical model by comparing it against a random activity schedule of the sensors with respect to network lifetime and intruder detection ratio performance measures. We also develop a Lagrangean heuristic strategy to solve realistic sized instances of the proposed problem. We produce several random border zone instances with varying sizes and test the proposed solution strategy to illustrate the effectiveness of the offered solution strategy by comparing its performance against the performance of a commercial mixed-integer linear programming (MILP) solver.
      PubDate: 2024-02-01
       
 
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  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
Research in Nondestructive Evaluation     Hybrid Journal   (Followers: 7)
Research in Number Theory     Hybrid Journal   (Followers: 1)
Research in the Mathematical Sciences     Open Access  
Research Journal of Pure Algebra     Open Access   (Followers: 1)
Researches in Mathematics     Open Access  
Results in Control and Optimization     Open Access   (Followers: 5)
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: 13)
SIAM Journal on Computing     Hybrid Journal   (Followers: 12)
SIAM Journal on Control and Optimization     Hybrid Journal   (Followers: 21)
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: 6)
SIAM Journal on Matrix Analysis and Applications     Hybrid Journal   (Followers: 3)
SIAM Journal on Optimization     Hybrid Journal   (Followers: 15)
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: 8)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Stochastic Analysis and Applications     Hybrid Journal   (Followers: 3)
Stochastic Partial Differential Equations : Analysis and Computations     Hybrid Journal   (Followers: 2)
Stochastic Processes and their Applications     Hybrid Journal   (Followers: 6)
Stochastics and Dynamics     Hybrid Journal   (Followers: 2)
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   (Followers: 1)
The Ramanujan Journal     Hybrid Journal  
The VLDB Journal     Hybrid Journal   (Followers: 2)
Theoretical and Mathematical Physics     Hybrid Journal   (Followers: 8)
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: 1)
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: 3)
Water SA     Open Access   (Followers: 1)
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  

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Heriot-Watt University
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Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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