Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
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    - MATHEMATICS (714 journals)
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    - PROBABILITIES AND MATH STATISTICS (113 journals)

MATHEMATICS (714 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 4)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 14)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 5)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 48)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 2)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 4)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 5)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 6)
Advances in Catalysis     Full-text available via subscription   (Followers: 7)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 21)
Advances in Decision Sciences     Open Access   (Followers: 4)
Advances in Difference Equations     Open Access   (Followers: 4)
Advances in Fixed Point Theory     Open Access   (Followers: 1)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 20)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 6)
Advances in Materials Science     Open Access   (Followers: 24)
Advances in Mathematical Physics     Open Access   (Followers: 7)
Advances in Mathematics     Full-text available via subscription   (Followers: 21)
Advances in Numerical Analysis     Open Access   (Followers: 5)
Advances in Operations Research     Open Access   (Followers: 13)
Advances in Operator Theory     Hybrid Journal  
Advances in Pure Mathematics     Open Access   (Followers: 11)
Advances in Science and Research (ASR)     Open Access   (Followers: 9)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 10)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 8)
Afrika Matematika     Hybrid Journal   (Followers: 2)
Air, Soil & Water Research     Open Access   (Followers: 9)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 4)
AKSIOMATIK : Jurnal Penelitian Pendidikan dan Pembelajaran Matematika     Open Access  
Al-Jabar : Jurnal Pendidikan Matematika     Open Access  
Al-Qadisiyah Journal for Computer Science and Mathematics     Open Access   (Followers: 5)
AL-Rafidain Journal of Computer Sciences and Mathematics     Open Access   (Followers: 4)
Algebra and Logic     Hybrid Journal   (Followers: 10)
Algebra Colloquium     Hybrid Journal   (Followers: 3)
Algebra Universalis     Hybrid Journal   (Followers: 3)
Algorithmic Operations Research     Open Access   (Followers: 7)
Algorithms     Open Access   (Followers: 15)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Mathematical Analysis     Open Access   (Followers: 1)
American Journal of Mathematical and Management Sciences     Hybrid Journal  
American Journal of Mathematics     Full-text available via subscription   (Followers: 8)
American Journal of Operations Research     Open Access   (Followers: 6)
American Mathematical Monthly     Full-text available via subscription   (Followers: 5)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 12)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access  
Analysis and Applications     Hybrid Journal   (Followers: 2)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 7)
Anargya : Jurnal Ilmiah Pendidikan Matematika     Open Access  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 3)
Annales Universitatis Mariae Curie-Sklodowska, sectio A – Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 15)
Annals of Functional Analysis     Hybrid Journal   (Followers: 2)
Annals of Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 13)
Annals of PDE     Hybrid Journal   (Followers: 1)
Annals of Pure and Applied Logic     Open Access   (Followers: 5)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access   (Followers: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access   (Followers: 1)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access   (Followers: 2)
Annuaire du Collège de France     Open Access   (Followers: 6)
ANZIAM Journal     Open Access   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applications of Mathematics     Hybrid Journal   (Followers: 3)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 16)
Applied Mathematics     Open Access   (Followers: 6)
Applied Mathematics     Open Access   (Followers: 5)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 7)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal   (Followers: 1)
Applied Mathematics and Nonlinear Sciences     Open Access   (Followers: 2)
Applied Mathematics Letters     Full-text available via subscription   (Followers: 4)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 4)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 3)
Arabian Journal of Mathematics     Open Access   (Followers: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Armenian Journal of Mathematics     Open Access  
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites     Open Access   (Followers: 21)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 4)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Research Journal of Mathematics     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 5)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 4)
Axioms     Open Access   (Followers: 1)
Baltic International Yearbook of Cognition, Logic and Communication     Open Access   (Followers: 2)
Banach Journal of Mathematical Analysis     Hybrid Journal  
Basin Research     Hybrid Journal   (Followers: 6)
BIBECHANA     Open Access  
Biomath     Open Access  
BIT Numerical Mathematics     Hybrid Journal  
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription  
British Journal for the History of Mathematics     Hybrid Journal   (Followers: 4)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
British Journal of Mathematics & Computer Science     Full-text available via subscription   (Followers: 2)
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 3)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 3)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 3)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of Symbolic Logic     Full-text available via subscription   (Followers: 4)
Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics     Open Access   (Followers: 1)
Bulletin of the Australian Mathematical Society     Full-text available via subscription   (Followers: 2)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the Iranian Mathematical Society     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Cadernos do IME : Série Matemática     Open Access  
Calculus of Variations and Partial Differential Equations     Hybrid Journal   (Followers: 2)
Canadian Journal of Mathematics / Journal canadien de mathématiques     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 20)
Canadian Mathematical Bulletin     Hybrid Journal  
Carpathian Mathematical Publications     Open Access  
Catalysis in Industry     Hybrid Journal  
CAUCHY     Open Access   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals : X     Open Access   (Followers: 1)
ChemSusChem     Hybrid Journal   (Followers: 8)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Ciencia     Open Access  
CODEE Journal     Open Access  
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Hybrid Journal   (Followers: 3)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 5)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 21)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Combinatorics and Optimization     Open Access  
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 4)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 6)
Complex Analysis and its Synergies     Open Access   (Followers: 1)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Compositio Mathematica     Full-text available via subscription   (Followers: 2)
Comptes Rendus : Mathematique     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 4)
Computational and Mathematical Methods     Hybrid Journal  
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 5)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 13)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 10)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 11)
Confluentes Mathematici     Hybrid Journal  
Constructive Mathematical Analysis     Open Access   (Followers: 1)
Contributions to Discrete Mathematics     Open Access  
Contributions to Game Theory and Management     Open Access   (Followers: 1)
COSMOS     Hybrid Journal   (Followers: 1)
Cross Section     Full-text available via subscription   (Followers: 1)
Cryptography and Communications     Hybrid Journal   (Followers: 11)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Current Research in Biostatistics     Open Access   (Followers: 9)
Czechoslovak Mathematical Journal     Hybrid Journal  
Daya Matematis : Jurnal Inovasi Pendidikan Matematika     Open Access   (Followers: 1)
Demographic Research     Open Access   (Followers: 14)
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 38)
Desimal : Jurnal Matematika     Open Access  
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 4)
Differentsial'nye Uravneniya     Open Access  
Digital Experiences in Mathematics Education     Hybrid Journal   (Followers: 3)
Discrete Mathematics     Hybrid Journal   (Followers: 7)
Discrete Mathematics & Theoretical Computer Science     Open Access   (Followers: 1)
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 3)
Discussiones Mathematicae - General Algebra and Applications     Open Access  
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Diskretnaya Matematika     Full-text available via subscription  

        1 2 3 4 | Last

Similar Journals
Journal Cover
Cognitive Computation
Journal Prestige (SJR): 0.908
Citation Impact (citeScore): 4
Number of Followers: 3  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1866-9964 - ISSN (Online) 1866-9956
Published by Springer-Verlag Homepage  [2468 journals]
  • Generative AI and Cognitive Computing-Driven Intrusion Detection System in
           Industrial CPS

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      Abstract: Abstract Industrial Cyber-Physical Systems (ICPSs) are becoming more and more networked and essential to modern infrastructure. This has led to an increase in the complexity of their dynamics and the challenges of protecting them from advanced cyber threats have escalated. Conventional intrusion detection systems (IDS) often struggle to interpret high-dimensional, sequential data efficiently and extract meaningful features. They are characterized by low accuracy and a high rate of false positives. In this article, we adopt the computational design science approach to design an IDS for ICPS, driven by Generative AI and cognitive computing. Initially, we designed a Long Short-Term Memory-based Sparse Variational Autoencoder (LSTM-SVAE) technique to extract relevant features from complex data patterns efficiently. Following this, a Bidirectional Recurrent Neural Network with Hierarchical Attention (BiRNN-HAID) is constructed. This stage focuses on proficiently identifying potential intrusions by processing data with enhanced focus and memory capabilities. Next, a Cognitive Enhancement for Contextual Intrusion Awareness (CE-CIA) is designed to refine the initial predictions by applying cognitive principles. This enhances the system’s reliability by effectively balancing sensitivity and specificity, thereby reducing false positives. The final stage, Interpretive Assurance through Activation Insights in Detection Models (IAA-IDM), involves the visualizations of mean activations of LSTM and GRU layers for providing in-depth insights into the decision-making process for cybersecurity analysts. Our framework undergoes rigorous testing on two publicly accessible industrial datasets, ToN-IoT and Edge-IIoTset, demonstrating its superiority over both baseline methods and recent state-of-the-art approaches.
      PubDate: 2024-06-10
       
  • A Novel Memristors Based Echo State Network Model Inspired by the
           Brain’s Uni-hemispheric Slow-Wave Sleep Characteristics

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      Abstract: Abstract Memristors serve as electronic components with the ability to store charge and demonstrate resistive states, which are similar to the synaptic connections between biological neurons. For this reason, they have been widely applied in the research and development of neuromorphic computing models to achieve functions similar to synaptic transmission. As early as the 1960s, some studies suggested that some animals could enter a sleep state in which one hemisphere of the brain is in an asleep state while the other remains active, controlling essential physiological functions like breathing, swimming, and avoiding danger. This phenomenon is known as uni-hemispheric slow-wave sleep (USWS). On the other hand, the echo state network (ESN) is a kind of neural network that has a simple but high-efficiency structure and has garnered significant attention lately. However, there is limited research on using ESN and MEM to simulate USWS. In this paper, we propose a new echo state network model based on memristors, which is used to simulate the brain of animals with USWS. In our model, two ESNs based on memristors are used as the left and right brains to switch between sleep and wakefulness. In addition, we use an elastic network to optimize the output, which combines the advantages of ridge regression and lasso regression. Finally, we evaluate the results using mean squared error (MSE). Our model demonstrates effective sequence data prediction and classification with high stability. Nevertheless, a drawback associated with this is the extended time required to accomplish the tasks. This paper proposes a new ESN model based on memristors, which simulates the USWS characteristics of the brain using the new intelligent component memristor. It addresses the architectural limitations of ESN and promotes the further development of memristor neural networks and brain-inspired models.
      PubDate: 2024-06-10
       
  • Effect of Leakage Delays on Bifurcation in Fractional-Order Bidirectional
           Associative Memory Neural Networks with Five Neurons and Discrete Delays

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      Abstract: Abstract As is well known that time delays are inevitable in practice due to the finite switching speed of amplifiers and information transmission between neurons. So the study on the Hopf bifurcation of delayed neural networks has aroused extensive attention in recent years. However, it’s worth mentioning that only the communication delays between neurons were generally considered in most existing relevant literatures. Actually, it has been proven that a kind of so-called leakage delays cannot be ignored because the self-decay process of a neuron’s action potential is not instantaneous in hardware implementation of neural networks. Though leakage delays have been taken into account in a few more recent works concerning the Hopf bifurcation of fractional-order bidirectional associative memory neural networks, the addressed neural networks were low-dimension or the involved time delays were single. In this paper, we propose a five-neuron fractional-order bidirectional associative memory neural network model, which includes leakage delays and discrete communication delays to meet the characteristics of real neural networks better. Then we use the stability theory of fractional differential equations and Hopf bifurcation theory to investigate its dynamic behavior of Hopf bifurcation. The Hopf bifurcation of the proposed model are studied by taking the involved two different leakage delays as the bifurcation parameter respectively, and two kinds of sufficient conditions for Hopf bifurcation are obtained. A numerical example as well as its simulation plots and phase portraits are given at last. Our results indicate that a Hopf bifurcation rises near the zero equilibrium point when the leakage delay reaches its critical value which is given by an explicit formula. Particularly, the results of numerical simulations show that the leakage delay would narrow the stability region of the proposed system and make the Hopf bifurcation occur earlier.
      PubDate: 2024-06-05
       
  • Optimization Based Deep Learning for COVID-19 Detection Using Respiratory
           Sound Signals

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      Abstract: Abstract The COVID-19 prediction process is more indispensable to handle the spread and death occurred rate because of COVID-19. However, early and precise prediction of COVID-19 is more difficult, because of different sizes and resolutions of input image. Thus, these challenges and problems experienced by traditional COVID-19 detection methods are considered as major motivation to develop SJHBO-based Deep Q Network. The classification issue of respiratory sound has perceived a great focus from the clinical scientists as well as the community of medical researcher in the previous year for the identification of COVID-19 disease. The major contribution of this research is to design an effectual COVID-19 detection model using devised SJHBO-based Deep Q Network. In this paper, the COVID-19 detection is carried out by the deep learning with optimization technique, namely Snake Jaya Honey Badger Optimization (SJHBO) algorithm-driven Deep Q Network. Here, the SJHBO algorithm is the incorporation of Jaya Honey Badger Optimization (JHBO) along with Snake optimization (SO). Here, the COVID-19 is detected by the Deep Q Network wherein the weights of Deep Q Network are tuned by the SJHBO algorithm. Moreover, JHBO is modelled by hybrids, which are the Jaya algorithm and Honey Badger Optimization (HBO) algorithm. Furthermore, the features, such as spectral contrast, Mel frequency cepstral coefficients (MFCC), empirical mode decomposition (EMD) algorithm, spectral flux, fast Fourier transform (FFT), spectral roll-off, spectral centroid, zero-crossing rate, root mean square energy, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude, are mined for enlightening the detection performance. The developed method realized the better performance based on the accuracy, sensitivity and specificity of 0.9511, 0.9506 and 0.9469. All test results are validated with the k-fold cross validation method in order to make an assessment of the generalizability of these results. Statistical analysis is performed to analyze the performance of the proposed method based on testing accuracy, sensitivity and specificity. Hence, this paper presents the newly devised SJHBO-based Deep Q-Net for COVID-19 detection. This research considers the audio samples as an input, which is acquired from the Coswara dataset. The SJHBO-based Deep Q network approach is developed for COVID-19 detection. The developed approach can be extended by including other hybrid optimization algorithms as well as other features that can be extracted for further improving the detection performance. The proposed COVID-19 detection method is useful in various applications, like medical and so on. Developed SJHBO-enabled Deep Q network for COVID-19 detection: An effective COVID-19 detection technique is introduced based on hybrid optimization–driven deep learning model. The Deep Q Network is used for detecting COVID-19, which classifies the feature vector as COVID-19 or non-COVID-19. Moreover, the Deep Q Network is trained by devised SJHBO approach, which is the incorporation of Jaya Honey Badger Optimization (JHBO) along with Snake optimization (SO).
      PubDate: 2024-06-01
       
  • A Review of Key Technologies for Emotion Analysis Using Multimodal
           Information

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      Abstract: Abstract Emotion analysis, an integral aspect of human–machine interactions, has witnessed significant advancements in recent years. With the rise of multimodal data sources such as speech, text, and images, there is a profound need for a comprehensive review of pivotal elements within this domain. Our paper delves deep into the realm of emotion analysis, examining multimodal data sources encompassing speech, text, images, and physiological signals. We provide a curated overview of relevant literature, academic forums, and competitions. Emphasis is laid on dissecting unimodal processing methods, including preprocessing, feature extraction, and tools across speech, text, images, and physiological signals. We further discuss the nuances of multimodal data fusion techniques, spotlighting early, late, model, and hybrid fusion strategies. Key findings indicate the essentiality of analyzing emotions across multiple modalities. Detailed discussions on emotion elicitation, expression, and representation models are presented. Moreover, we uncover challenges such as dataset creation, modality synchronization, model efficiency, limited data scenarios, cross-domain applicability, and the handling of missing modalities. Practical solutions and suggestions are provided to address these challenges. The realm of multimodal emotion analysis is vast, with numerous applications ranging from driver sentiment detection to medical evaluations. Our comprehensive review serves as a valuable resource for both scholars and industry professionals. It not only sheds light on the current state of research but also highlights potential directions for future innovations. The insights garnered from this paper are expected to pave the way for subsequent advancements in deep multimodal emotion analysis tailored for real-world deployments.
      PubDate: 2024-06-01
       
  • Enhanced Android Ransomware Detection Through Hybrid Simultaneous
           Swarm-Based Optimization

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      Abstract: Abstract Ransomware is a significant security threat that poses a serious risk to the security of smartphones, and its impact on portable devices has been extensively discussed in a number of research papers. In recent times, this threat has witnessed a significant increase, causing substantial losses for both individuals and organizations. The emergence and widespread occurrence of diverse forms of ransomware present a significant impediment to the pursuit of reliable security measures that can effectively combat them. This constitutes a formidable challenge due to the dynamic nature of ransomware, which renders traditional security protocols inadequate, as they might have a high false alarm rate and exert significant processing demands on mobile devices that are restricted by limited battery life, CPU, and memory. This paper proposes a novel intelligent method for detecting ransomware that is based on a hybrid multi-solution binary JAYA algorithm with a single-solution simulated annealing (SA). The primary objective is to leverage the exploitation power of SA in supporting the exploration power of the binary JAYA algorithm. This approach results in a better balance between global and local search milestones. The empirical results of our research demonstrate the superiority of the proposed SMO-BJAYA-SA-SVM method over other algorithms based on the evaluation measures used. The proposed method achieved an accuracy rate of 98.7%, a precision of 98.6%, a recall of 98.7%, and an F1 score of 98.6%. Therefore, we believe that our approach is an effective method for detecting ransomware on portable devices. It has the potential to provide a more reliable and efficient solution to this growing security threat.
      PubDate: 2024-06-01
       
  • Intelligent Fisheries: Cognitive Solutions for Improving Aquaculture
           Commercial Efficiency Through Enhanced Biomass Estimation and Early
           Disease Detection

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      Abstract: Abstract With the burgeoning global demand for seafood, potential solutions like aquaculture are increasingly significant, provided they address issues like pollution and food security challenges in a sustainable manner. However, significant obstacles such as disease outbreaks and inaccurate biomass estimation underscore the need for optimized solutions. This paper proposes “Fish-Sense”, a deep learning-based pipeline inspired by the human visual system’s ability to recognize and classify objects, developed in conjunction with fish farms, aiming to enhance disease detection and biomass estimation in the aquaculture industry. Our automated framework is two-pronged: one module for biomass estimation using deep learning algorithms to segment fish, classify species, and estimate biomass; and another for disease symptom detection symptoms, employing deep learning algorithms to classify fish into healthy and unhealthy categories, and subsequently identifying symptoms and locations of bacterial infections if a fish is classified as unhealthy. To overcome data scarcity in this field, we have created four novel real-world datasets for fish segmentation, health classification, species classification, and fish part segmentation. Our biomass estimation algorithms demonstrated substantial accuracy across five species, and the health classification. These algorithms provide a foundation for the development of industrial software solutions to improve fish health monitoring in aquaculture farms. Our integrated pipeline facilitates the transition from research to real-world applications, potentially encouraging responsible aquaculture practices. Nevertheless, these advancements must be seen as part of a comprehensive strategy aimed at improving the aquaculture industry’s sustainability and efficiency, in line with the United Nations’ Sustainable Development Goals’ evolving interpretations. The code, trained models, and the data for this project can be obtained from the following GitHub repository: https://github.com/Vision-At-SEECS/Fish-Sense.
      PubDate: 2024-06-01
       
  • RA-Net: Region-Aware Attention Network for Skin Lesion Segmentation

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      Abstract: Abstract The precise segmentation of skin lesion in dermoscopic images is essential for the early detection of skin cancer. However, the irregular shapes of the lesions, the absence of sharp edges, the existence of artifacts like hair follicles, and marker color make this task difficult. Currently, fully connected networks (FCNs) and U-Nets are the most commonly used techniques for melanoma segmentation. However, as the depth of these neural network models increases, they become prone to various challenges. The most pertinent of these challenges are the vanishing gradient problem and the parameter redundancy problem. These can result in a decline in Jaccard index of the segmentation model. This study introduces a novel end-to-end trainable network designed for skin lesion segmentation. The proposed methodology consists of an encoder-decoder, a region-aware attention approach, and guided loss function. The trainable parameters are reduced using depth-wise separable convolution, and the attention features are refined using a guided loss, resulting in a high Jaccard index. We assessed the effectiveness of our proposed RA-Net on four frequently utilized benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, ISIC 2018, and PH2. The empirical results validate that our method achieves state-of-the-art performance, as indicated by a notably high Jaccard index.
      PubDate: 2024-06-01
       
  • Smart Data Driven Decision Trees Ensemble Methodology for Imbalanced Big
           Data

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      Abstract: Abstract Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack of data in the minority class due to the use of MapReduce paradigm have posed new challenges for tackling the imbalance between classes in Big Data scenarios. Ensembles have been shown to be able to successfully address imbalanced data problems. Smart Data refers to data of enough quality to achieve high-performance models. The combination of ensembles and Smart Data, achieved through Big Data preprocessing, should be a great synergy. In this paper, we propose a novel Smart Data driven Decision Trees Ensemble methodology for addressing the imbalanced classification problem in Big Data domains, namely SD_DeTE methodology. This methodology is based on the learning of different decision trees using distributed quality data for the ensemble process. This quality data is achieved by fusing random discretization, principal components analysis, and clustering-based random oversampling for obtaining different Smart Data versions of the original data. Experiments carried out in 21 binary adapted datasets have shown that our methodology outperforms random forest.
      PubDate: 2024-05-31
       
  • Efficient Deep Learning Approach for Diagnosis of
           

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      Abstract: Abstract Attention-deficit/hyperactivity disorder (ADHD) is a behavioral disorder in children that can persist into adulthood if not treated. Early diagnosis of this condition is crucial for effective treatment. The database includes 61 children with attention-deficit/hyperactivity disorder and 60 healthy children as a control group. To diagnose children with ADHD, features were first extracted from EEG signals. Next, a convolutional neural network model was trained, and a new residual network was introduced. The two proposed models were evaluated using tenfold cross-validation on the test data. The average accuracy and F1 score were 92.52% and 93.6%, respectively, for the convolutional model and 96.8% and 97.1% for the ResNet model on the epoch data, respectively. On the other hand, accuracy for subject-based prediction was 96.5% for the convolution model and 98.6% for the modified ResNet model. Accuracy, precision, recall, and F1 score for the proposed ResNet model are better than the convolution model proposed in previous studies and better than the proposed model in the literature. This work presents a paradigm shift in the cognitive-inspired domain by introducing a novel ResNet model for ADHD diagnosis. The model’s exceptional accuracy, exceeding conventional methods, showcases its potential as a biologically inspired tool. This opens avenues for exploring the neurological underpinnings of ADHD because the model can be used for the manifold learning of EEG signals. Analyzing the proposed network can lead to a deeper understanding of EEG, bridging the gap between artificial intelligence and cognitive neuroscience. The paper’s innovative approach has far-reaching implications, offering a concrete application of cognitive principles to improve mental health diagnostics in children. It is important to note that the data were augmented and the classification model is based on a single experiment containing a very small number of children but the results, and accuracy of classification, are based on classifying augmented data samples that compose the EEG signals of this small number of individuals. It is prudent to undertake a comprehensive investigation into the efficacy of these models across a broad cohort of subjects.
      PubDate: 2024-05-31
       
  • Evaluating Explainable Machine Learning Models for Clinicians

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      Abstract: Abstract Gaining clinicians’ trust will unleash the full potential of artificial intelligence (AI) in medicine, and explaining AI decisions is seen as the way to build trustworthy systems. However, explainable artificial intelligence (XAI) methods in medicine often lack a proper evaluation. In this paper, we present our evaluation methodology for XAI methods using forward simulatability. We define the Forward Simulatability Score (FSS) and analyze its limitations in the context of clinical predictors. Then, we applied FSS to our XAI approach defined over an ML-RO, a machine learning clinical predictor based on random optimization over a multiple kernel support vector machine (SVM) algorithm. To Compare FSS values before and after the explanation phase, we test our evaluation methodology for XAI methods on three clinical datasets, namely breast cancer, VTE, and migraine. The ML-RO system is a good model on which to test our XAI evaluation strategy based on the FSS. Indeed, ML-RO outperforms two other base models—a decision tree (DT) and a plain SVM—in the three datasets and gives the possibility of defining different XAI models: TOPK, MIGF, and F4G. The FSS evaluation score suggests that the explanation method F4G for the ML-RO is the most effective in two datasets out of the three tested, and it shows the limits of the learned model for one dataset. Our study aims to introduce a standard practice for evaluating XAI methods in medicine. By establishing a rigorous evaluation framework, we seek to provide healthcare professionals with reliable tools for assessing the performance of XAI methods to enhance the adoption of AI systems in clinical practice.
      PubDate: 2024-05-31
       
  • Counterfactual Explanations in the Big Picture: An Approach for Process
           Prediction-Driven Job-Shop Scheduling Optimization

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      Abstract: Abstract In this study, we propose a pioneering framework for generating multi-objective counterfactual explanations in job-shop scheduling contexts, combining predictive process monitoring with advanced mathematical optimization techniques. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization, our approach enhances the generation of counterfactual explanations that illuminate potential enhancements at both the operational and systemic levels. Validated with real-world data, our methodology underscores the superiority of NSGA-II in crafting pertinent and actionable counterfactual explanations, surpassing traditional methods in both efficiency and practical relevance. This work advances the domains of explainable artificial intelligence (XAI), predictive process monitoring, and combinatorial optimization, providing an effective tool for improving automated scheduling systems’ clarity, and decision-making capabilities.
      PubDate: 2024-05-30
       
  • Detection of Cardiovascular Diseases Using Data Mining Approaches:
           Application of an Ensemble-Based Model

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      Abstract: Abstract Cardiovascular diseases are the leading contributor of mortality worldwide. Accurate cardiovascular disease prediction is crucial, and the application of machine learning and data mining techniques could facilitate decision-making and improve predictive capabilities. This study aimed to present a model for accurate prediction of cardiovascular diseases and identifying key contributing factors with the greatest impact. The Cleveland dataset besides the locally collected dataset, called the Noor dataset, was used in this study. Accordingly, various data mining techniques besides four ensemble learning-based models were implemented on both datasets. Moreover, a novel model for combining individual classifiers in ensemble learning, wherein weights were assigned to each classifier (using a genetic algorithm), was developed. The predictive strength of each feature was also investigated to ensure the generalizability of the outcomes. The ultimate ensemble-based model achieved a precision rate of 88.05% and 90.12% on the Cleveland and Noor datasets, respectively, demonstrating its reliability and suitability for future research in predicting the likelihood of cardiovascular diseases. Not only the proposed model introduces an innovative approach for specifying cardiovascular diseases by unraveling the intricate relationships between various biological variables but also facilitates early detection of cardiovascular diseases.
      PubDate: 2024-05-30
       
  • Vision-Enabled Large Language and Deep Learning Models for Image-Based
           Emotion Recognition

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      Abstract: Abstract The significant advancements in the capabilities, reasoning, and efficiency of artificial intelligence (AI)-based tools and systems are evident. Some noteworthy examples of such tools include generative AI-based large language models (LLMs) such as generative pretrained transformer 3.5 (GPT 3.5), generative pretrained transformer 4 (GPT-4), and Bard. LLMs are versatile and effective for various tasks such as composing poetry, writing codes, generating essays, and solving puzzles. Thus far, LLMs can only effectively process text-based input. However, recent advancements have enabled them to handle multimodal inputs, such as text, images, and audio, making them highly general-purpose tools. LLMs have achieved decent performance in pattern recognition tasks (such as classification), therefore, there is a curiosity about whether general-purpose LLMs can perform comparable or even superior to specialized deep learning models (DLMs) trained specifically for a given task. In this study, we compared the performances of fine-tuned DLMs with those of general-purpose LLMs for image-based emotion recognition. We trained DLMs, namely, a convolutional neural network (CNN) (two CNN models were used: \(CNN_1\) and \(CNN_2\) ), ResNet50, and VGG-16 models, using an image dataset for emotion recognition, and then tested their performance on another dataset. Subsequently, we subjected the same testing dataset to two vision-enabled LLMs (LLaVa and GPT-4). The \(CNN_2\) was found to be the superior model with an accuracy of 62% while VGG16 produced the lowest accuracy with 31%. In the category of LLMs, GPT-4 performed the best, with an accuracy of 55.81%. LLava LLM had a higher accuracy than \(CNN_1\) and VGG16 models. The other performance metrics such as precision, recall, and F1-score followed similar trends. However, GPT-4 performed the best with small datasets. The poor results observed in LLMs can be attributed to their general-purpose nature, which, despite extensive pretraining, may not fully capture the features required for specific tasks like emotion recognition in images as effectively as models fine-tuned for those tasks. The LLMs did not surpass specialized models but achieved comparable performance, making them a viable option for specific tasks without additional training. In addition, LLMs can be considered a good alternative when the available dataset is small.
      PubDate: 2024-05-27
       
  • Generative Adversarial Network-Assisted Framework for Power Management

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      Abstract: Abstract The rise in power consumption (PC) is caused by several factors such as the growing global population, urbanization, technological advances, economic development, and growth of businesses and commercial sectors. In these days, intermittent renewable energy sources (RESs) are widely utilized in electric grids to meet the need for power. Data-driven techniques are essential to assuring the steady operation of the electric grid and accurate power consumption and generation forecasting. Conversely, the available datasets for time series electric power forecasting in the energy industry are not as large as those for other domains such as in computer vision. Thus, a deep learning (DL) framework for predicting PC in residential and commercial buildings as well as the power generation (PG) from RESs is introduced. The raw power data obtained from buildings and RESs-based power plants are conceded by the purging process where absent values are filled in and noise and outliers are eliminated. Next, the proposed generative adversarial network (GAN) uses a portion of the cleaned data to generate synthetic parallel data, which is combined with the actual data to make a hybrid dataset. Subsequently, the stacked gated recurrent unit (GRU) model, which is optimized for power forecasting, is trained using the hybrid dataset. Six existent power data are used to train and test sixteen linear and nonlinear models for energy forecasting. The best-performing network is selected as the proposed method for forecasting tasks. For Korea Yeongam solar power (KYSP), individual household electric power consumption (IHEPC), and advanced institute of convergence technology (AICT) datasets, the proposed model obtains mean absolute error (MAE) values of 0.0716, 0.0819, and 0.0877, respectively. Similarly, its MAE values are 0.1215, 0.5093, and 0.5751, for Australia Alice Springs solar power (AASSP), Korea south east wind power (KSEWP), and, Korea south east solar power (KSESP) datasets, respectively.
      PubDate: 2024-05-27
       
  • Quasi-projective Synchronization Control of Delayed Stochastic
           Quaternion-Valued Fuzzy Cellular Neural Networks with Mismatched
           Parameters

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      Abstract: Abstract This paper deals with the quasi-projective synchronization problem of delayed stochastic quaternion fuzzy cellular neural networks with mismatch parameters. Although the parameter mismatch of the drive-response system increases the computational complexity of the article, it is of practical significance to consider the existence of deviations between the two systems. The method of this article is to design an appropriate controller and construct Lyapunov functional and stochastic analysis theory based on the Itô formula in the quaternion domain. We adopt the non-decomposable method of quaternion FCNN, which preserves the original data and reduces computational effort. We obtain sufficient conditions for quasi-projective synchronization of the considered random quaternion numerical FCNNs with mismatched parameters. Additionally, we estimate the error bounds of quasi-projective synchronization and then carry out a numerical example to verify their validity. Our results are novel even if the considered neural networks degenerate into real-valued or complex-valued neural networks. This article provides a good research idea for studying the quasi-projective synchronization problem of random quaternion numerical FCNN with time delay and has obtained good results. The method in this article can also be used to study the quasi-projective synchronization of a Clifford-valued neural network.
      PubDate: 2024-05-27
       
  • Investigating the Influence of Scene Video on EEG-Based Evaluation of
           Interior Sound in Passenger Cars

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      Abstract: Abstract The evaluation of automobile sound quality is an important research topic in the interior sound design of passenger car, and the accurate and effective evaluation methods are required for the determination of the acoustic targets in automobile development. However, there are some deficiencies in the existing evaluation studies of automobile sound quality. (1) Most of subjective evaluations only considered the auditory perception, which is easy to be achieved but does not fully reflect the impacts of sound on participants; (2) similarly, most of the existing subjective evaluations only considered the inherent properties of sounds, such as physical and psychoacoustic parameters, which make it difficult to reflect the complex relationship between the sound and the subjective perception of the evaluators; (3) the construction of evaluation models only from physical and psychoacoustic perspectives does not provide a comprehensive analysis of the real subjective emotions of the participants. Therefore, to alleviate the above flaws, the auditory and visual perceptions are combined to explore the inference of scene video on the evaluation of sound quality, and the EEG signal is introduced as a physiological acoustic index to evaluate the sound quality; simultaneously, an Elman neural network model is constructed to predict the powerful sound quality combined with the proposed indexes of physical acoustics, psychoacoustics, and physiological acoustics. The results show that evaluation results of sound quality combined with scene videos better reflect the subjective perceptions of participants. The proposed objective evaluation indexes of physical, psychoacoustic, and physiological acoustic contribute to mapping the subjective results of the powerful sound quality, and the constructed Elman model outperforms the traditional back propagation (BP) and support vector machine (SVM) models. The analysis method proposed in this paper can be better applied in the field of automotive sound design, providing a clear guideline for the evaluation and optimization of automotive sound quality in the future.
      PubDate: 2024-05-25
       
  • Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE)—A Novel DL
           Model for Image Multi-resolution

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      Abstract: Abstract In this paper, we design and evaluate the performance of the Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE) model, a deep learning (DL)-based architecture specifically designed for achieving multi-resolution super-resolved images from low-resolution (LR) inputs at various scaling factors. For this purpose, we expand on the recently introduced Twinned Residual Auto-Encoders (TRAE) paradigm for single-image super-resolution (SISR) to extend it to the multi-resolution (MR) domain. The main contributions of this work include (i) the architecture of the MR-TRAE model, which utilizes cascaded trainable up-sampling modules for progressively increasing the spatial resolution of low-resolution (LR) input images at multiple scaling factors; (ii) a novel loss function designed for the joint and semi-blind training of all MR-TRAE model components; and (iii) a comprehensive analysis of the MR-TRAE trade-off between model complexity and performance. Furthermore, we thoroughly explore the connections between the MR-TRAE architecture and broader cognitive paradigms, including knowledge distillation, the teacher-student learning model, and hierarchical cognition. Performance evaluations of the MR-TRAE benchmarked against state-of-the-art models (such as U-Net, generative adversarial network (GAN)-based, and single-resolution baselines) were conducted using publicly available datasets. These datasets consist of LR computer tomography (CT) scans from patients with COVID-19. Our tests, which explored multi-resolutions at scaling factors \(\times (2,4,8)\) , showed a significant finding: the MR-TRAE model can reduce training times by up to \(60\%\) compared to those of the baselines, without a noticeable impact on achieved performance.
      PubDate: 2024-05-21
       
  • Neuromorphic Cognitive Learning Systems: The Future of Artificial
           Intelligence'

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      Abstract: Abstract In this position paper, I outline the caveats of the current artificial intelligence (AI) field driven by deep learning (DL) and large data volumes. Although AI/DL has demonstrated huge potential and attracted huge investments globally, it encounters big problems – it not only need to collect huge datasets and spend enormous time and resources to be trained on them, but also the trained system cannot deal effectively with any never encountered before (novel) data. From a human perspective, any current AI/DL system is completely unintelligent. It is only able to represent information but have no awareness of what this information means. I propose as an alternative the Neuromorphic Cognitive Learning Systems (NCLS), intimate imitations of animal and human brains, able to address the AI/DL limitations and achieve true artificial general intelligence. Similar to human and animal brains NCLS are unparalleled in their ability to rapidly, and on their own, adapt and learn from changing and unexpected environmental contingencies with very limited resources. I describe how NCLS driven AI inspired by human/animal brains can pave the way to new computing technologies with the potential to revolutionize the industry, economy and society. It is my strong belief that NCLS investigations will have major impact to real-time autonomous systems to achieve human-like intelligence capabilities.
      PubDate: 2024-05-19
       
  • Generative Model-Driven Synthetic Training Image Generation: An Approach
           to Cognition in Railway Defect Detection

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      Abstract: Abstract Recent advancements in cognitive computing, through the integration of artificial intelligence (AI) techniques, have facilitated the development of intelligent cognitive systems (ICS). This benefits railway defect detection by enabling ICS to emulate human-like analysis of defect patterns in image data. Although visual defect classification based on convolutional neural networks (CNN) has achieved decent performance, the scarcity of large datasets for railway defect detection remains a challenge. This scarcity stems from the infrequent nature of accidents that result in defective railway parts. Existing research efforts have addressed the challenge of data scarcity by exploring rule-based and generative data augmentation approaches. Among these approaches, variational autoencoder (VAE) models can generate realistic data without the need for extensive baseline datasets for noise modeling. This study proposes a VAE-based synthetic image generation technique for training railway defect classifiers. Our approach introduces a modified regularization strategy that combines weight decay with reconstruction loss. Using this method, we created a synthetic dataset for the Canadian Pacific Railway (CPR), consisting of 50 real samples across five classes. Remarkably, our method generated 500 synthetic samples, achieving a minimal reconstruction loss of 0.021. A visual transformer (ViT) model, fine-tuned using this synthetic CPR dataset, achieved high accuracy rates (98–99%) in classifying the five railway defect classes. This research presents an approach that addresses the data scarcity issue in railway defect detection, indicating a path toward enhancing the development of ICS in this field.
      PubDate: 2024-05-17
       
 
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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)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 4)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 4)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 14)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 5)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 48)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 2)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 4)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 5)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 10)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 6)
Advances in Catalysis     Full-text available via subscription   (Followers: 7)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 21)
Advances in Decision Sciences     Open Access   (Followers: 4)
Advances in Difference Equations     Open Access   (Followers: 4)
Advances in Fixed Point Theory     Open Access   (Followers: 1)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 20)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 6)
Advances in Materials Science     Open Access   (Followers: 24)
Advances in Mathematical Physics     Open Access   (Followers: 7)
Advances in Mathematics     Full-text available via subscription   (Followers: 21)
Advances in Numerical Analysis     Open Access   (Followers: 5)
Advances in Operations Research     Open Access   (Followers: 13)
Advances in Operator Theory     Hybrid Journal  
Advances in Pure Mathematics     Open Access   (Followers: 11)
Advances in Science and Research (ASR)     Open Access   (Followers: 9)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 10)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 8)
Afrika Matematika     Hybrid Journal   (Followers: 2)
Air, Soil & Water Research     Open Access   (Followers: 9)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 4)
AKSIOMATIK : Jurnal Penelitian Pendidikan dan Pembelajaran Matematika     Open Access  
Al-Jabar : Jurnal Pendidikan Matematika     Open Access  
Al-Qadisiyah Journal for Computer Science and Mathematics     Open Access   (Followers: 5)
AL-Rafidain Journal of Computer Sciences and Mathematics     Open Access   (Followers: 4)
Algebra and Logic     Hybrid Journal   (Followers: 10)
Algebra Colloquium     Hybrid Journal   (Followers: 3)
Algebra Universalis     Hybrid Journal   (Followers: 3)
Algorithmic Operations Research     Open Access   (Followers: 7)
Algorithms     Open Access   (Followers: 15)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Mathematical Analysis     Open Access   (Followers: 1)
American Journal of Mathematical and Management Sciences     Hybrid Journal  
American Journal of Mathematics     Full-text available via subscription   (Followers: 8)
American Journal of Operations Research     Open Access   (Followers: 6)
American Mathematical Monthly     Full-text available via subscription   (Followers: 5)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 12)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access  
Analysis and Applications     Hybrid Journal   (Followers: 2)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 7)
Anargya : Jurnal Ilmiah Pendidikan Matematika     Open Access  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 3)
Annales Universitatis Mariae Curie-Sklodowska, sectio A – Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 15)
Annals of Functional Analysis     Hybrid Journal   (Followers: 2)
Annals of Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 13)
Annals of PDE     Hybrid Journal   (Followers: 1)
Annals of Pure and Applied Logic     Open Access   (Followers: 5)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access   (Followers: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access   (Followers: 1)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access   (Followers: 2)
Annuaire du Collège de France     Open Access   (Followers: 6)
ANZIAM Journal     Open Access   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applications of Mathematics     Hybrid Journal   (Followers: 3)
Applied Categorical Structures     Hybrid Journal   (Followers: 5)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 16)
Applied Mathematics     Open Access   (Followers: 6)
Applied Mathematics     Open Access   (Followers: 5)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 7)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal   (Followers: 1)
Applied Mathematics and Nonlinear Sciences     Open Access   (Followers: 2)
Applied Mathematics Letters     Full-text available via subscription   (Followers: 4)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 4)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 3)
Arabian Journal of Mathematics     Open Access   (Followers: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Armenian Journal of Mathematics     Open Access  
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites     Open Access   (Followers: 21)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 4)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Research Journal of Mathematics     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 5)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 4)
Axioms     Open Access   (Followers: 1)
Baltic International Yearbook of Cognition, Logic and Communication     Open Access   (Followers: 2)
Banach Journal of Mathematical Analysis     Hybrid Journal  
Basin Research     Hybrid Journal   (Followers: 6)
BIBECHANA     Open Access  
Biomath     Open Access  
BIT Numerical Mathematics     Hybrid Journal  
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription  
British Journal for the History of Mathematics     Hybrid Journal   (Followers: 4)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 19)
British Journal of Mathematics & Computer Science     Full-text available via subscription   (Followers: 2)
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 3)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 3)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 3)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of Symbolic Logic     Full-text available via subscription   (Followers: 4)
Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics     Open Access   (Followers: 1)
Bulletin of the Australian Mathematical Society     Full-text available via subscription   (Followers: 2)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the Iranian Mathematical Society     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Cadernos do IME : Série Matemática     Open Access  
Calculus of Variations and Partial Differential Equations     Hybrid Journal   (Followers: 2)
Canadian Journal of Mathematics / Journal canadien de mathématiques     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 20)
Canadian Mathematical Bulletin     Hybrid Journal  
Carpathian Mathematical Publications     Open Access  
Catalysis in Industry     Hybrid Journal  
CAUCHY     Open Access   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals : X     Open Access   (Followers: 1)
ChemSusChem     Hybrid Journal   (Followers: 8)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Ciencia     Open Access  
CODEE Journal     Open Access  
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Hybrid Journal   (Followers: 3)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 5)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 21)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Combinatorics and Optimization     Open Access  
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 4)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 6)
Complex Analysis and its Synergies     Open Access   (Followers: 1)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Compositio Mathematica     Full-text available via subscription   (Followers: 2)
Comptes Rendus : Mathematique     Open Access  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 4)
Computational and Mathematical Methods     Hybrid Journal  
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 5)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 13)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 10)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 11)
Confluentes Mathematici     Hybrid Journal  
Constructive Mathematical Analysis     Open Access   (Followers: 1)
Contributions to Discrete Mathematics     Open Access  
Contributions to Game Theory and Management     Open Access   (Followers: 1)
COSMOS     Hybrid Journal   (Followers: 1)
Cross Section     Full-text available via subscription   (Followers: 1)
Cryptography and Communications     Hybrid Journal   (Followers: 11)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Current Research in Biostatistics     Open Access   (Followers: 9)
Czechoslovak Mathematical Journal     Hybrid Journal  
Daya Matematis : Jurnal Inovasi Pendidikan Matematika     Open Access   (Followers: 1)
Demographic Research     Open Access   (Followers: 14)
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 38)
Desimal : Jurnal Matematika     Open Access  
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 4)
Differentsial'nye Uravneniya     Open Access  
Digital Experiences in Mathematics Education     Hybrid Journal   (Followers: 3)
Discrete Mathematics     Hybrid Journal   (Followers: 7)
Discrete Mathematics & Theoretical Computer Science     Open Access   (Followers: 1)
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 3)
Discussiones Mathematicae - General Algebra and Applications     Open Access  
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Diskretnaya Matematika     Full-text available via subscription  

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