Subjects -> ENGINEERING (Total: 2791 journals)
    - CHEMICAL ENGINEERING (248 journals)
    - CIVIL ENGINEERING (242 journals)
    - ELECTRICAL ENGINEERING (176 journals)
    - ENGINEERING (1402 journals)
    - ENGINEERING MECHANICS AND MATERIALS (452 journals)
    - HYDRAULIC ENGINEERING (56 journals)
    - INDUSTRIAL ENGINEERING (100 journals)
    - MECHANICAL ENGINEERING (115 journals)

ENGINEERING (1402 journals)                  1 2 3 4 5 6 7 8 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 2)
3D Research     Hybrid Journal   (Followers: 17)
AAPG Bulletin     Hybrid Journal   (Followers: 9)
Abstract and Applied Analysis     Open Access   (Followers: 1)
Aceh International Journal of Science and Technology     Open Access   (Followers: 3)
ACS Nano     Hybrid Journal   (Followers: 189)
Acta Geotechnica     Hybrid Journal   (Followers: 6)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 8)
Acta Nova     Open Access  
Acta Polytechnica : Journal of Advanced Engineering     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access   (Followers: 1)
Active and Passive Electronic Components     Open Access   (Followers: 5)
Additive Manufacturing Letters     Open Access   (Followers: 6)
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Energy and Sustainability Research     Open Access   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 10)
Advanced Engineering Research     Open Access  
Advanced Journal of Graduate Research     Open Access   (Followers: 1)
Advanced Quantum Technologies     Hybrid Journal   (Followers: 1)
Advanced Science     Open Access   (Followers: 11)
Advanced Science Focus     Free   (Followers: 5)
Advanced Science Letters     Full-text available via subscription   (Followers: 8)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 3)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 19)
Advanced Theory and Simulations     Hybrid Journal   (Followers: 2)
Advances in Applied Energy     Open Access   (Followers: 5)
Advances in Catalysis     Full-text available via subscription   (Followers: 7)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 19)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 27)
Advances in Natural Sciences : Nanoscience and Nanotechnology     Open Access   (Followers: 28)
Advances in Operations Research     Open Access   (Followers: 13)
Advances in OptoElectronics     Open Access   (Followers: 6)
Advances in Physics Theories and Applications     Open Access   (Followers: 12)
Advances in Polymer Science     Hybrid Journal   (Followers: 50)
Advances in Remote Sensing     Open Access   (Followers: 58)
Advances in Science and Research (ASR)     Open Access   (Followers: 8)
Aerobiologia     Hybrid Journal   (Followers: 2)
Aerospace Systems     Hybrid Journal   (Followers: 7)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 7)
AIChE Journal     Hybrid Journal   (Followers: 31)
Ain Shams Engineering Journal     Open Access   (Followers: 1)
Al-Nahrain Journal for Engineering Sciences     Open Access  
Al-Qadisiya Journal for Engineering Sciences     Open Access  
AL-Rafdain Engineering Journal     Open Access  
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 21)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 7)
American Journal of Engineering Education     Open Access   (Followers: 13)
American Journal of Environmental Engineering     Open Access   (Followers: 6)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
Annals of Civil and Environmental Engineering     Open Access   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Annals of Regional Science     Hybrid Journal   (Followers: 7)
Annals of Science     Hybrid Journal   (Followers: 9)
Annual Journal of Technical University of Varna     Open Access  
Antarctic Science     Hybrid Journal   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 1)
Applications in Energy and Combustion Science     Open Access   (Followers: 2)
Applications in Engineering Science     Open Access  
Applied Catalysis A: General     Hybrid Journal   (Followers: 7)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 9)
Applied Clay Science     Hybrid Journal   (Followers: 6)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 16)
Applied Energy     Partially Free   (Followers: 25)
Applied Engineering Letters     Open Access  
Applied Magnetic Resonance     Hybrid Journal   (Followers: 3)
Applied Nanoscience     Open Access   (Followers: 7)
Applied Network Science     Open Access   (Followers: 2)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 4)
Applied Physics Research     Open Access   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Arab Journal of Basic and Applied Sciences     Open Access  
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 1)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 10)
Arctic     Open Access  
Arid Zone Journal of Engineering, Technology and Environment     Open Access  
ArtefaCToS : Revista de estudios sobre la ciencia y la tecnología     Open Access  
Asian Journal of Applied Science and Engineering     Open Access  
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 5)
Assembly Automation     Hybrid Journal   (Followers: 2)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
AURUM : Mühendislik Sistemleri ve Mimarlık Dergisi = Aurum Journal of Engineering Systems and Architecture     Open Access   (Followers: 1)
Australasian Journal of Engineering Education     Hybrid Journal   (Followers: 3)
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Hybrid Journal  
Autocracy : Jurnal Otomasi, Kendali, dan Aplikasi Industri     Open Access  
Automotive and Engine Technology     Hybrid Journal  
Automotive Experiences     Open Access  
Automotive Innovation     Hybrid Journal  
Avances en Ciencias e Ingenierías     Open Access  
Avances: Investigación en Ingeniería     Open Access  
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 2)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 6)
Batteries     Open Access   (Followers: 8)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access  
Beyond : Undergraduate Research Journal     Open Access  
Bhakti Persada : Jurnal Aplikasi IPTEKS     Open Access  
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access  
Bilge International Journal of Science and Technology Research     Open Access   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Hybrid Journal   (Followers: 11)
Biomedical Engineering     Hybrid Journal   (Followers: 11)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 3)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 4)
Biomedical Microdevices     Hybrid Journal   (Followers: 8)
Biomedical Science and Engineering     Open Access   (Followers: 4)
Biomicrofluidics     Open Access   (Followers: 7)
Biotechnology Progress     Hybrid Journal   (Followers: 42)
Black Sea Journal of Engineering and Science     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access  
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 12)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 15)
Cahiers Droit, Sciences & Technologies     Open Access   (Followers: 1)
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 28)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 51)
Carbon Resources Conversion     Open Access   (Followers: 2)
Carpathian Journal of Electronic and Computer Engineering     Open Access  
Case Studies in Thermal Engineering     Open Access   (Followers: 9)
Catalysis Communications     Hybrid Journal   (Followers: 7)
Catalysis Letters     Hybrid Journal   (Followers: 3)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 9)
Catalysis Science and Technology     Hybrid Journal   (Followers: 9)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 4)
Catalysis Today     Hybrid Journal   (Followers: 4)
CEAS Space Journal     Hybrid Journal   (Followers: 6)
Cell Reports Physical Science     Open Access  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 2)
CFD Letters     Open Access   (Followers: 7)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 1)
Chaos, Solitons & Fractals : X     Open Access   (Followers: 1)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Engineering     Open Access   (Followers: 1)
Chinese Journal of Population, Resources and Environment     Open Access  
Chinese Science Bulletin     Open Access  
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencia y Tecnología     Open Access  
Ciencias Holguin     Open Access   (Followers: 1)
CienciaUAT     Open Access  
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Hybrid Journal   (Followers: 10)
CIRP Journal of Manufacturing Science and Technology     Hybrid Journal   (Followers: 12)
City, Culture and Society     Hybrid Journal   (Followers: 23)
Clay Minerals     Hybrid Journal   (Followers: 7)
Cleaner Engineering and Technology     Open Access   (Followers: 4)
Cleaner Environmental Systems     Open Access   (Followers: 4)
Coastal Engineering     Hybrid Journal   (Followers: 16)
Coastal Engineering Journal     Hybrid Journal   (Followers: 7)
Coastal Engineering Proceedings : Proceedings of the International Conference on Coastal Engineering     Open Access   (Followers: 1)
Coastal Management     Hybrid Journal   (Followers: 29)
Coatings     Open Access   (Followers: 2)
Cogent Engineering     Open Access   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 2)
Color Research & Application     Hybrid Journal   (Followers: 1)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 18)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 21)
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering     Open Access  
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 27)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Composite Structures     Hybrid Journal   (Followers: 244)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 177)
Composites Part B : Engineering     Hybrid Journal   (Followers: 221)
Composites Part C : Open Access     Open Access   (Followers: 1)
Composites Science and Technology     Hybrid Journal   (Followers: 150)
Comptes Rendus : Mécanique     Open Access   (Followers: 2)
Computation     Open Access   (Followers: 1)
Computational Geosciences     Hybrid Journal   (Followers: 17)
Computational Optimization and Applications     Hybrid Journal   (Followers: 9)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 15)
Computers & Geosciences     Hybrid Journal   (Followers: 29)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 8)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 7)
Computers and Geotechnics     Hybrid Journal   (Followers: 11)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 31)
Conciencia Tecnologica     Open Access  
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 8)
Control Engineering Practice     Hybrid Journal   (Followers: 46)

        1 2 3 4 5 6 7 8 | Last

Similar Journals
Journal Cover
Cognitive Computation
Journal Prestige (SJR): 0.908
Citation Impact (citeScore): 4
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1866-9964 - ISSN (Online) 1866-9956
Published by Springer-Verlag Homepage  [2469 journals]
  • A Novel Multiple Feature-Based Engine Knock Detection System using Sparse
           Bayesian Extreme Learning Machine

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      Abstract: Automotive engine knock is an abnormal combustion phenomenon that affects engine performance and lifetime expectancy, but it is difficult to detect. Collecting engine vibration signals from an engine cylinder block is an effective way to detect engine knock. This paper proposes an intelligent engine knock detection system based on engine vibration signals. First, filtered signals are obtained by utilizing variational mode decomposition (VMD), which decomposes the original time domain signals into a series of intrinsic mode functions (IMFs). Moreover, the values of the balancing parameter and the number of IMF modes are optimized using genetic algorithm (GA). IMFs with sample entropy higher than the mean are then selected as sensitive subcomponents for signal reconstruction and subsequently removed. A multiple feature learning approach that considers time domain statistical analysis (TDSA), multi-fractal detrended fluctuation analysis (MFDFA) and alpha stable distribution (ASD) simultaneously, is utilized to extract features from the denoised signals. Finally, the extracted features are trained by sparse Bayesian extreme learning machine (SBELM) to overcome the sensitivity of hyperparameters in conventional machine learning algorithms. A test rig is designed to collect the raw engine data. Compared with other technology combinations, the optimal scheme from signal processing to feature classification is obtained, and the classification accuracy of the proposed integrated engine knock detection method can achieve 98.27%. We successfully propose and test a universal intelligence solution for the detection task.
      PubDate: 2022-01-19
       
  • Observer and Command-Filter-Based Adaptive Neural Network Control
           Algorithms for Nonlinear Multi-agent Systems with Input Delay

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      Abstract: Over the last decades, many researchers have investigated the distributed adaptive consensus tacking control algorithm of multi-agent systems (MASs). Nevertheless, the existing works involving the command-filter-based adaptive consensus problem for nonlinear multi-agent systems subjected to the unmeasurable states are relatively few. Besides that, the immeasurable states and the input delay will bring few challenging in dealing with the consensus problem for MASs. (1) The radial basis function neural networks (RBF NNs) are utilized to approximate the unknown nonlinear functions and the NN-based observer is established to copy with the unmeasurable states. (2) The backstepping design method of distributed adaptive consensus control is put forward on basis of the command filtering method, which overcomes the complexity explosion problem and eliminates errors by introducing compensation signals. (3) The Pade approximation approach is served to remove the obstacle originating from the input delay. This paper addresses the observer and command-filter-based adaptive tracking control problem for nonlinear multi-agent systems with the unmeasurable states and input delay under the directed graph. The Lyapunov stability theory is utilized to prove that the proposed approach can ensure that all signals in the closed-loop system reach cooperatively semi-globally uniformly ultimately bounded (CSUUB). The simulation result is presented, and it further manifests that the effectiveness of this scheme.
      PubDate: 2022-01-18
       
  • Stability Analysis of Stochastic Delayed Differential Systems with
           State-Dependent-Delay Impulses: Application of Neural Networks

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      Abstract: The previously studied stochastic delayed nonlinear systems with constant delayed or time-varying delayed impulsive controller, while we study stochastic delayed nonlinear systems under state-dependent-delayed impulsive controller. The difficulty is how to determine the time of impulses occurrence. Employing the Halanay differential inequality, Itô’s formula, the average impulsive interval, impulsive control theory, comparison properties, several effective conditions ensuring stability of stochastic delayed nonlinear systems under state-dependent delayed impulsive controller are derived. This paper contributed to the stability analysis of delayed impulsive nonlinear systems with stochastic perturbation, which the impulsive involved delay is state dependent. We have developed exponential stability of delayed nonlinear systems with state-dependent-delay impulses and stochastic disturbance in this paper. In the future, more new methods should also be proposed. At the same time, we will consider the nonlinear systems with unbounded delays.
      PubDate: 2022-01-17
       
  • PS-Net: Progressive Selection Network for Salient Object Detection

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      Abstract: Low-level features contain abundant details and high-level features have rich semantic information. Integrating multi-scale features in an appropriate way is significant for salient object detection. However, direct concatenation or addition taken by most methods ignores the distinctions of contribution among multi-scale features. Besides, most salient object detection models fail to dynamically adjust receptive fields to fit objects of various sizes. To tackle these problems, we propose a Progressive Selection Network (PS-Net). Specifically, PS-Net dynamically extracts high-level features and encourages high-level features to guide low-level features to suppress the background response of the original features. We proposed a salient model PS-Net that selects features progressively at multiply levels. First, we propose a Pyramid Feature Dynamic Extraction module to dynamically select appropriate receptive fields to extract high-level features by Feature Dynamic Extraction modules step by step. Besides, a Self-Interaction Attention module is designed to extract detailed information for low-level features. Finally, we design a Scale Aware Fusion module to fuse these multiple features for adequate exploitation of high-level features to refine low-level features gradually. Compared with 19 start-of-the-art methods on 6 public benchmark datasets, the proposed method achieves remarkable performance in both quantitative and qualitative evaluation. We performed a lot of ablation studies, and more discussions to demonstrate the effectiveness and superiority of our proposed method. In this paper, we propose a PS-Net for effective salient object detection. Extensive experiments on 6 datasets validate that the proposed model outperforms 19 state-of-the-art methods under different evaluation metrics.
      PubDate: 2022-01-16
       
  • Road Segmentation from High-Fidelity Remote Sensing Images using a Context
           Information Capture Network

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      Abstract: The automatic extraction of roads or buildings from remote sensing imagery plays a significant role in many urban applications. Recently, due to the impressive performance of deep learning, various road segmentation methods based on the fully convolutional network (FCN) have been proposed for optical remote sensing images. However, the existing FCN-based high-fidelity remote sensing image segmentation methods still have some limitations. As the repeated convolution and pooling operations employed in an FCN reduce the feature resolution and lose some detailed information, FCNs have a limited capacity to mine long-range dependencies among features. To address this issue, a context information capture network (CM-FCN) for road segmentation is proposed. To capture and aggregate multiscale contextual information, a dilated convolution module is designed. Furthermore, to boost the long-range dependencies of features for road detection, two attention modules employing the attention mechanism to adaptively combine local features with their global dependencies are designed. The context features extracted from the dilated convolution module are then fused into the attention modules to further improve the segmentation performance. The proposed model is evaluated on three challenging remote sensing image road segmentation datasets and one building segmentation dataset, including a dataset with our own manual labels. Comparisons demonstrate the effectiveness of our proposed method. We conclude that our proposed CM-FCN has the potential to automatically segment roads and buildings from high-resolution remote sensing images with an accuracy that renders it a useful tool for practical application scenarios.
      PubDate: 2022-01-15
       
  • LightAdam: Towards a Fast and Accurate Adaptive Momentum Online Algorithm

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      Abstract: Adaptive optimization algorithms enjoy fast convergence and have been widely exploited in pattern recognition and cognitively-inspired machine learning. These algorithms may however be of high computational cost and low generalization ability due to their projection steps. Such limitations make them difficult to be applied in big data analytics, which may typically be seen in cognitively inspired learning, e.g. deep learning tasks. In this paper, we propose a fast and accurate adaptive momentum online algorithm, called LightAdam, to alleviate the drawbacks of projection steps for the adaptive algorithms. The proposed algorithm substantially reduces computational cost for each iteration step by replacing high-order projection operators with one-dimensional linear searches. Moreover, we introduce a novel second-order momentum and engage dynamic learning rate bounds in the proposed algorithm, thereby obtaining a higher generalization ability than other adaptive algorithms. We theoretically analyze that our proposed algorithm has a guaranteed convergence bound, and prove that our proposed algorithm has better generalization capability as compared to Adam. We conduct extensive experiments on three public datasets for image pattern classification, and validate the computational benefit and accuracy performance of the proposed algorithm in comparison with other state-of-the-art adaptive optimization algorithms
      PubDate: 2022-01-11
       
  • Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from
           Chest X-ray Images

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      Abstract: Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.
      PubDate: 2022-01-11
       
  • A Fuzzy Collaborative Intelligence Approach to Group Decision-Making: a
           Case Study of Post-COVID-19 Restaurant Transformation

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      Abstract: In a fuzzy group decision-making task, when decision makers lack consensus, existing methods either ignore this fact or force a decision maker to modify his/her judgment. However, these actions may be unreasonable. In this study, a fuzzy collaborative intelligence approach that seeks the consensus among experts in a novel way is proposed. Fuzzy collaborative intelligence is the application of biologically inspired fuzzy logic to a group task. The proposed methodology is based on the fact that a decision maker must make a choice even if he/she is uncertain. As a result, the decision maker’s fuzzy judgment matrix may not be able to represent his/her judgment. To solve such a problem, the fuzzy judgment matrix of each decision maker is decomposed into several fuzzy judgment submatrices. From the fuzzy judgment submatrices of all decision makers, a consensus can be easily identified. The proposed fuzzy collaborative intelligence approach and several existing methods have been applied to the case of the post-COVID-19 transformation of a Japanese restaurant in Taiwan. Because such transformation was beyond the expectation of the Japanese restaurant, the employees lacked consensus if existing methods were applied to identify their consensus. The proposed methodology solved this problem. The optimal transformation plan involved increasing the distance between tables, erecting screens between tables, and improving air circulation. In a fuzzy group decision-making task, an acceptable decision cannot be made without the consensus among decision makers. Ignoring this or forcing decision makers to modify their preferences is unreasonable. Identifying the consensus among experts from another point of view is a viable treatment.
      PubDate: 2022-01-10
       
  • Automatic Assessment of Motor Impairments in Autism Spectrum Disorders: A
           Systematic Review

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      Abstract: Autism spectrum disorder (ASD) is mainly described as a disorder of communication and socialization. However, motor abnormalities are also common in ASD. New technologies may offer quantitative and automatic metrics to measure movement difficulties. We sought to identify computational methods to automatize the assessment of motor impairments in ASD. We systematically searched for the terms ’autism’, ’movement’, ’automatic’, ’computational’ and ’engineering’ in IEEE (Institute of Electrical and Electronics Engineers), Medline and Scopus databases and reviewed the literature from inception to 2018. We included all articles discussing: (1) automatic assessment/new technologies, (2) motor behaviours and (3) children with ASD. We excluded studies that included patient’s or parent’s reported outcomes as online questionnaires that focused on computational models of movement, but also eye tracking, facial emotion or sleep. In total, we found 53 relevant articles that explored static and kinetic equilibrium, like posture, walking, fine motor skills, motor synchrony and movements during social interaction that can be impaired in individuals with autism. Several devices were used to capture relevant motor information such as cameras, 3D cameras, motion capture systems, accelerometers. Interestingly, since 2012, the number of studies increased dramatically as technologies became less invasive, more precise and more affordable. Open-source software has enabled the extraction of relevant data. In a few cases, these technologies have been implemented in serious games, like “Pictogram Room”, to measure the motor status and the progress of children with ASD. Movement computing opens new perspectives for patient assessment in ASD research, enabling precise characterizations in experimental and at-home settings, and a better understanding of the role of sensorimotor disturbances in the development of social cognition and ASD. These methods would likely enable researchers and clinicians to better distinguish ASD from other motors disorders while facilitating an improved monitoring of children’s progress in more ecological settings (i.e. at home or school).
      PubDate: 2022-01-10
       
  • An Attention-Driven Multi-label Image Classification with Semantic
           Embedding and Graph Convolutional Networks

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      Abstract: Multi-label image classification is a fundamental and vital task in computer vision. The latest methods are mostly based on deep learning and exhibit excellent performance in understanding images. However, in previous studies, only capture the image content information has been captured using convolutional neural networks (CNNs), and the semantic structure information and implicit dependencies between labels and image regions have been ignored. Therefore, it is necessary to develop more effective methods for integrating semantic information and visual features in multi-label image classification. In this study, we propose a novel framework for multi-label image classification, named FLNet, which simultaneously takes advantage of the visual features and semantic structure. Specifically, to enhance the association between semantic annotations and image regions, we first integrate the attention mechanism with a CNN to focus on the target regions while ignoring other useless surrounding information and then employ graph convolutional network (GCN) to capture the structure information between multiple labels. Based on our architecture, we also introduce the lateral connections to repeatedly inject the label system into the CNN backbone during the GCN learning process to improve performance and, consequently, learn interdependent classifiers for each image label. We apply our method to multi-label image classification. The experiments on two public multi-label benchmark datasets, namely, MS-COCO and PASCAL visual object classes challenge (VOC 2007), demonstrate that our approach outperforms other existing state-of-the-art methods. Our method learns specific target regions and enhances the association between labels and image regions by using semantic information and attention mechanism. Thus, we combine the advantages of both visual and semantic information to further improve the image classification performance. Finally, the correctness and effectiveness of the proposed method are proven by visualizing the classifier results.
      PubDate: 2022-01-09
       
  • Dense Tissue Pattern Characterization Using Deep Neural Network

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      Abstract: Breast tumors are from the common infections among women around the world. Classifying the various types of breast tumors contribute to treating breast tumors more efficiently. However, this classification task is often hindered by dense tissue patterns captured in mammograms. The present study has been proposed a dense tissue pattern characterization framework using deep neural network. A total of 322 mammograms belonging to the mini-MIAS dataset and 4880 mammograms from DDSM dataset have been taken, and an ROI of fixed size 224 × 224 pixels from each mammogram has been extracted. In this work, tedious experimentation has been executed using different combinations of training and testing sets using different activation function with AlexNet, ResNet-18 model. Data augmentation has been used to create a similar type of virtual image for proper training of the DL model. After that, the testing set is applied on the trained model to validate the proposed model. During experiments, four different activation functions ‘sigmoid’, ‘tanh’, ‘ReLu’, and ‘leakyReLu’ are used, and the outcome for each function has been reported. It has been found that activation function ‘ReLu’ perform always outstanding with respect to others. For each experiment, classification accuracy and kappa coefficient have been computed. The obtained accuracy and kappa value for MIAS dataset using ResNet-18 model is 91.3% and 0.803, respectively. For DDSM dataset, the accuracy of 92.3% and kappa coefficient value of 0.846 are achieved. After the combination of both dataset images, the achieved accuracy is 91.9%, and kappa coefficient value is 0.839 using ResNet-18 model. Finally, it has been concluded that the ResNet-18 model and ReLu activation function yield outstanding performance for the task.
      PubDate: 2022-01-08
       
  • Embeddings Evaluation Using a Novel Measure of Semantic Similarity

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      Abstract: Lexical taxonomies and distributional representations are largely used to support a wide range of NLP applications, including semantic similarity measurements. Recently, several scholars have proposed new approaches to combine those resources into unified representation preserving distributional and knowledge-based lexical features. In this paper, we propose and implement TaxoVec, a novel approach to selecting word embeddings based on their ability to preserve taxonomic similarity. In TaxoVec, we first compute the pairwise semantic similarity between taxonomic words through a new measure we previously developed, the Hierarchical Semantic Similarity (HSS), which we show outperforms previous measures on several benchmark tasks. Then, we train several embedding models on a text corpus and select the best model, that is, the model that maximizes the correlation between the HSS and the cosine similarity of the pair of words that are in both the taxonomy and the corpus. To evaluate TaxoVec, we repeat the embedding selection process using three other semantic similarity benchmark measures. We use the vectors of the four selected embeddings as machine learning model features to perform several NLP tasks. The performances of those tasks constitute an extrinsic evaluation of the criteria for the selection of the best embedding (i.e. the adopted semantic similarity measure). Experimental results show that (i) HSS outperforms state-of-the-art measures for measuring semantic similarity in taxonomy on a benchmark intrinsic evaluation and (ii) the embedding selected through TaxoVec achieves a clear victory against embeddings selected by the competing measures on benchmark NLP tasks. We implemented the HSS, together with other benchmark measures of semantic similarity, as a full-fledged Python package called TaxoSS, whose documentation is available at https://pypi.org/project/TaxoSS.
      PubDate: 2022-01-08
       
  • Bifurcation Study for Fractional-Order Three-Layer Neural Networks
           Involving Four Time Delays

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      Abstract: During the past several decades, many scholars deal with the stability behavior and Hopf bifurcation phenomenon of fractional-order delayed neural networks. However, the literature involving the stability issue and Hopf bifurcation behavior of fractional-order neural networks with multiple time delays is relatively scarce. This article is principally concerned with the stability problem and of Hopf bifurcation behavior of fractional-order three-layer neural networks involving multiple time delays. Variable substitution, Laplace transform, bifurcation principle of fractional-order dynamical system and computer simulation skill are employed. The delay-independent stability criterion and the sufficient condition of onset of Hopf bifurcation of three-layer neural networks are set up. It shows that if the sum of both different delays passes a key value, then the system loses its stability and the Hopf bifurcation phenomenon will take place. The study manifests that delay plays a most momentous part in stabilizing system and controlling bifurcation behavior for the fractional-order delayed three-layer neural networks. The researchful results of this article are an important theoretical cornerstone in controlling and adjusting neural networks. The obtained conclusions are completely novel and complement the earlier research results.
      PubDate: 2022-01-07
       
  • Three-Way Decision Models Based on Multi-granulation Rough Intuitionistic
           Hesitant Fuzzy Sets

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      Abstract: In practice, people may hesitate to evaluate uncertain things. As an extension of fuzzy sets, intuitionistic hesitant fuzzy sets use multiple membership and non-membership degrees to express uncertain evaluations. Multi-granulation rough set theory is utilized to deal with information in an intuitionistic hesitant fuzzy decision information system, and three-way decision models are established to make decisions. First, rough intuitionistic hesitant fuzzy sets and four multi-granulation rough intuitionistic hesitant fuzzy set models are proposed, and their properties are discussed. Second, we define the combination formula for the upper and lower approximations of multi-granulation rough intuitionistic hesitant fuzzy sets, and present a new intuitionistic hesitant fuzzy cross-entropy. Then, the conditional probabilities under four cases are calculated by the TOPSIS approach. Third, the thresholds in intuitionistic hesitant fuzzy decision-theoretic rough sets are calculated, and corresponding three-way decision rules are given. Finally, four kinds of three-way decision models based on the proposed multi-granulation rough intuitionistic hesitant fuzzy sets are constructed. Furthermore, the decision rule extraction algorithm is designed. The example proved that the four kinds of three-way decision models can evaluate objects with different attitudes and provide decision-making solutions, which demonstrates the feasibility and effectiveness of the proposed algorithm.
      PubDate: 2022-01-06
       
  • Short-term Airline Passenger Flow Prediction Based on the Attention
           Mechanism and Gated Recurrent Unit Model

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      Abstract: The pressure of civil aviation traffic is increasing with the prosperity of economy. The accurate forecast of civil aviation flow not only effectively improves the operation efficiency of airlines, but also brings considerable profits to airlines. However, the existing aviation flow forecasting methods generally have the problem of poor forecasting accuracy. Inaccurate traffic prediction models not only fail to bring benefits, but also waste resources of airlines to a certain extent. Therefore, a high-precision forecast of aviation flow is necessary. On the basis of attention mechanism, a high-precision aviation flow model is constructed. First, the deep belief network is used to reduce the dimension of the data. Then, the Gated Recurrent Unit model is used to extract the time series features of the reduced dimension data. Finally, the attention mechanism is used to preserve the key features to achieve high-precision prediction. By analyzing historical data, the model which we proposed can accurately perceive the evolution process of civil aviation traffic and realize the high-precision prediction of short-term passenger flow. Experimental results show that the prediction accuracy of the model in this paper is significantly higher than other existing models, and the application of this model will bring considerable benefits to airlines.
      PubDate: 2022-01-06
       
  • Frontal Intrinsic Connectivity Networks Support Contradiction
           Identification During Inductive and Deductive Reasoning

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      Abstract: Deductive and inductive reasoning are fundamental logical processes critical to the solution of common practical problems in daily life. We used functional magnetic resonance imaging (fMRI) to investigate the brain networks involved in Contradictory, Deductive, and Inductive judgments. The experimental paradigm was based on categorical propositions of the Aristotelian Square of Opposition (ASoO). In a full factorial design, identical sentences were combined into premise–conclusion pairs. Each sentence started with ‘every’ or ‘some’. The order of the two propositions in the pair created two types of logical operators (every→some: deductive, or some→every: inductive). The descriptive attributes of the category could be Contradictory or non-Contradictory. Imaging data was analyzed using Group Independent component analysis of fMRI Toolbox (GIFT). Connectivity of nodes within four intrinsic connectivity networks (ICNs) was sensitive to attribute manipulation (Contradiction): the anterior default mode network (aDMN), and the language and cerebellum networks were more involved in Contradictory than non-Contradictory statements, while the anterior salience network (aSN) showed the opposite pattern. Five networks were associated with logical operator manipulation. Stronger positive associations with Inductive than Deductive reasoning were observed in the dorsal and ventral parts of the aDMN, aSN, and orbitofrontal networks (OFN). A stronger negative association with deductive than inductive reasoning was observed in the executive control (ExCN) and dorsal attention (DAN) networks. Differences in the fractional amplitude of low‐frequency fluctuation of the BOLD signal in aDMN, ExCN, and OFN explained 67% of the variance of the behavioural cost of inductive relative to deductive reasoning. The results suggest that different ICNs support logical reasoning and conflict identification. Finally, the magnitude of the differences was positively correlated with behavioural cost.
      PubDate: 2022-01-06
       
  • Detection of Autism Spectrum Disorder using fMRI Functional Connectivity
           with Feature Selection and Deep Learning

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      Abstract: Autism spectrum disorder (ASD) is notoriously difficult to diagnose despite having a high prevalence. Existing studies have shifted toward using neuroimaging data to enhance the clinical applicability and the effectiveness of the diagnostic results. However, the time and financial resources required to scan neuroimages restrict the scale of the datasets and further weaken the generalization ability of the statistical results. Furthermore, multi-site datasets collected by multiple worldwide institutions make it difficult to apply machine learning methods due to their heterogeneity. We propose a deep learning approach combined with the F-score feature selection method for ASD diagnosis using a functional magnetic resonance imaging (fMRI) dataset. The proposed method is evaluated on the worldwide fMRI dataset, known as ABIDE (Autism Brain Imaging Data Exchange). The fMRI functional connectivity features selected using our method can achieve an average accuracy of 64.53% on intra-site datasets and an accuracy of 70.9% on the whole ABIDE dataset. Moreover, based on the selected features, the network topology analysis showed a significant decrease in the path length and the cluster coefficient in ASD, indicating a loss of small-world architecture to a random network. The altered brain network may provide insight into the underlying pathology of ASD, and the functional connectivity features selected by our method may serve as biomarkers.
      PubDate: 2022-01-05
       
  • Multi-branch Bounding Box Regression for Object Detection

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      Abstract: Localization and classification are two important components in the task of visual object detection. In recent years, object detectors have increasingly focused on creating various localization branches. Bounding box regression is vital for two-stage detectors. Therefore, we propose a multi-branch bounding box regression method called Multi-Branch R-CNN for robust object localization. Multi-Branch R-CNN is composed of the fully connected head and the fully convolutional head. The fully convolutional head focuses on the utilization of spatial semantics. It is complementary to the fully connected head that prefers local features. The features extracted from the two localization branches are fused, then flow to the next stage for classification and regression. The two branches cooperate to predict more precise localization, which significantly improves the performance of the detector. Extensive experiments were conducted on public PASCAL VOC and MS COCO benchmarks. On the COCO dataset, our Multi-Branch R-CNN with ResNet-101 backbone achieved state-of-the-art single model results by obtaining an mAP of 43.2. Extensive comparative experiments prove the effectiveness of the proposed method.
      PubDate: 2022-01-05
       
  • Performance Evaluation of Human Resources Based on Linguistic Neutrosophic
           Maclaurin Symmetric Mean Operators

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      Abstract: For corporates, the performance evaluation of human resources is always a significant strategic activity based on cognitive information. This study aims to develop a novel decision-making method with the computation of cognitive information to make assessments of human resources. First, the cognitive information is described by means of linguistic neutrosophic numbers (LNNs) to capture aspects of indeterminacy and fuzziness. Then, as the Maclaurin symmetric mean (MSM) operators can reflect the interrelations among multiple inputs, several extended MSM operators are proposed to aggregate cognitive information in the linguistic neutrosophic environments. Meanwhile, some important properties of these operators are justified. Thereafter, a linguistic neutrosophic decision-making method based on MSM operators is introduced to address qualitative evaluation problems during cognitive processes. Finally, the validity of our method is revealed by presenting a case study of selecting the best employee in a company. Moreover, the advantages of the proposed method are highlighted by the discussion of the effect of the parameter existing in aggregation operators and the comparison with other methods. The results show that the proposed method is feasible and the study can provide guidelines for the performance evaluation and management of human resources. The utilization of LNNs enriches the expression of cognitive information. Furthermore, the proposed method can be regarded as a potential choice for disposing of cognitive computation.
      PubDate: 2022-01-04
       
  • A Multitask Framework to Detect Depression, Sentiment and Multi-label
           Emotion from Suicide Notes

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      Abstract: The significant rise in suicides is a major cause of concern in public health domain. Depression plays a major role in increasing suicide ideation among the individuals. Although most of the suicides can be avoided with prompt intercession and early diagnosis, it has been a serious challenge to detect the at-risk individuals. Our current work focuses on learning three closely related tasks, viz. depression detection, sentiment citation, and to investigate their impact in analysing the mental state of the victims. We extend the existing standard emotion annotated corpus of suicide notes in English, CEASE, with additional 2539 sentences collected from 120 new notes. We annotate the consolidated corpus with appropriate depression labels and multi-label emotion classes. We further leverage weak supervision to annotate the corpus with sentiment labels. We propose a deep multitask framework that features a knowledge module that uses SenticNet’s IsaCore and AffectiveSpace vector-spaces to infuse external knowledge specific features into the learning process. The system models emotion recognition (the primary task), depression detection and sentiment classification (the secondary tasks) simultaneously. Experiments show that our proposed multitask system obtains the highest cross-validation MR of 56.47 %. Evaluation results show that all our multitask models perform better than their single-task variants indicating that the secondary tasks (depression detection and sentiment classification) improve the performance of the primary task (emotion recognition) when all tasks are learned jointly.
      PubDate: 2022-01-01
       
 
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