Subjects -> ENGINEERING (Total: 2677 journals)
    - CHEMICAL ENGINEERING (235 journals)
    - CIVIL ENGINEERING (237 journals)
    - ELECTRICAL ENGINEERING (176 journals)
    - ENGINEERING (1308 journals)
    - ENGINEERING MECHANICS AND MATERIALS (452 journals)
    - HYDRAULIC ENGINEERING (56 journals)
    - INDUSTRIAL ENGINEERING (98 journals)
    - MECHANICAL ENGINEERING (115 journals)

ENGINEERING (1308 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted by number of followers
Composite Structures     Hybrid Journal   (Followers: 255)
IEEE Spectrum     Full-text available via subscription   (Followers: 227)
Composites Part B : Engineering     Hybrid Journal   (Followers: 227)
ACS Nano     Hybrid Journal   (Followers: 187)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 184)
Composites Science and Technology     Hybrid Journal   (Followers: 157)
IEEE Geoscience and Remote Sensing Letters     Hybrid Journal   (Followers: 156)
IEEE Instrumentation & Measurement Magazine     Hybrid Journal   (Followers: 149)
IEEE Communications Magazine     Full-text available via subscription   (Followers: 140)
IEEE Engineering Management Review     Full-text available via subscription   (Followers: 117)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 112)
IEEE Transactions on Control Systems Technology     Hybrid Journal   (Followers: 111)
IEEE Transactions on Instrumentation and Measurement     Hybrid Journal   (Followers: 110)
IEEE Transactions on Signal Processing     Hybrid Journal   (Followers: 92)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Industry Applications Magazine     Full-text available via subscription   (Followers: 82)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 79)
IEEE Transactions on Engineering Management     Hybrid Journal   (Followers: 75)
Engineering Failure Analysis     Hybrid Journal   (Followers: 68)
IEEE Microwave Magazine     Full-text available via subscription   (Followers: 63)
IEEE Signal Processing Letters     Hybrid Journal   (Followers: 60)
IEEE Transactions on Reliability     Hybrid Journal   (Followers: 54)
Experimental Techniques     Hybrid Journal   (Followers: 51)
IET Radar, Sonar & Navigation     Open Access   (Followers: 50)
IEEE Transactions on Microwave Theory and Techniques     Hybrid Journal   (Followers: 49)
Control Engineering Practice     Hybrid Journal   (Followers: 46)
IEEE Journal of Selected Topics in Signal Processing     Hybrid Journal   (Followers: 43)
IEEE Potentials     Full-text available via subscription   (Followers: 42)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
IEEE Journal on Selected Areas in Communications     Hybrid Journal   (Followers: 39)
International Journal for Numerical Methods in Engineering     Hybrid Journal   (Followers: 37)
Heat Transfer Engineering     Hybrid Journal   (Followers: 36)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 35)
Digital Signal Processing     Hybrid Journal   (Followers: 35)
IEEE Microwave and Wireless Components Letters     Hybrid Journal   (Followers: 35)
IEEE Transactions on Knowledge and Data Engineering     Hybrid Journal   (Followers: 32)
AIChE Journal     Hybrid Journal   (Followers: 31)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 31)
Flow, Turbulence and Combustion     Hybrid Journal   (Followers: 30)
Coastal Management     Hybrid Journal   (Followers: 30)
Computers & Geosciences     Hybrid Journal   (Followers: 30)
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 29)
Géotechnique     Hybrid Journal   (Followers: 28)
GPS Solutions     Hybrid Journal   (Followers: 28)
Fluid Dynamics     Hybrid Journal   (Followers: 27)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
IEEE Transactions on Power Delivery     Hybrid Journal   (Followers: 26)
Applied Energy     Partially Free   (Followers: 26)
IEEE Journal of Solid-State Circuits     Full-text available via subscription   (Followers: 24)
Engineering & Technology     Hybrid Journal   (Followers: 23)
Corrosion Science     Hybrid Journal   (Followers: 23)
Implementation Science     Open Access   (Followers: 22)
IET Image Processing     Open Access   (Followers: 22)
IEEE Transactions on Electronics Packaging Manufacturing     Hybrid Journal   (Followers: 21)
IET Signal Processing     Open Access   (Followers: 21)
Intermetallics     Hybrid Journal   (Followers: 21)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 21)
International Journal for Numerical Methods in Fluids     Hybrid Journal   (Followers: 20)
IEEE Transactions on Circuits and Systems II: Express Briefs     Hybrid Journal   (Followers: 20)
Engineering Optimization     Hybrid Journal   (Followers: 19)
International Communications in Heat and Mass Transfer     Hybrid Journal   (Followers: 19)
IET Circuits, Devices & Systems     Open Access   (Followers: 18)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 18)
International Journal of Adhesion and Adhesives     Hybrid Journal   (Followers: 18)
IEEE/ACM Transactions on Computational Biology and Bioinformatics     Hybrid Journal   (Followers: 18)
Integration     Hybrid Journal   (Followers: 18)
Experiments in Fluids     Hybrid Journal   (Followers: 17)
Engineering Geology     Hybrid Journal   (Followers: 17)
Computational Geosciences     Hybrid Journal   (Followers: 17)
IEEE Transactions on Intelligent Transportation Systems     Hybrid Journal   (Followers: 17)
IEEE Transactions on Energy Conversion     Hybrid Journal   (Followers: 16)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 16)
Coastal Engineering     Hybrid Journal   (Followers: 15)
European Journal of Mass Spectrometry     Hybrid Journal   (Followers: 15)
Electrophoresis     Hybrid Journal   (Followers: 15)
Energy Conversion and Management     Hybrid Journal   (Followers: 15)
IEEE Transactions on Magnetics     Hybrid Journal   (Followers: 14)
IEEE Journal of Biomedical and Health Informatics     Hybrid Journal   (Followers: 14)
IEEE Transactions on Automation Science and Engineering     Full-text available via subscription   (Followers: 13)
IEEE Transactions on Evolutionary Computation     Hybrid Journal   (Followers: 13)
Human Factors in Ergonomics & Manufacturing     Hybrid Journal   (Followers: 13)
Electromagnetics     Hybrid Journal   (Followers: 13)
Computers and Geotechnics     Hybrid Journal   (Followers: 13)
IEEE Transactions on Semiconductor Manufacturing     Hybrid Journal   (Followers: 12)
IET Renewable Power Generation     Open Access   (Followers: 12)
Heat Transfer - Asian Research     Hybrid Journal   (Followers: 11)
IEEE Journal of Oceanic Engineering     Hybrid Journal   (Followers: 11)
CIRP Annals - Manufacturing Technology     Hybrid Journal   (Followers: 11)
Biomedical Engineering     Hybrid Journal   (Followers: 11)
IEEE Transactions on Professional Communication     Hybrid Journal   (Followers: 11)
IEEE Transactions on Education     Hybrid Journal   (Followers: 11)
IEEE Transactions on Nuclear Science     Hybrid Journal   (Followers: 10)
IEEE Transactions on Plasma Science     Hybrid Journal   (Followers: 10)
Proceedings of the Institution of Civil Engineers - Geotechnical Engineering     Hybrid Journal   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 10)
Computational Optimization and Applications     Hybrid Journal   (Followers: 9)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 9)
Fuel Cells Bulletin     Full-text available via subscription   (Followers: 9)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 9)
European Journal of Engineering Education     Hybrid Journal   (Followers: 9)
Annals of Science     Hybrid Journal   (Followers: 9)
Fuel Cells     Hybrid Journal   (Followers: 8)
Proceedings of the Institution of Civil Engineers - Bridge Engineering     Hybrid Journal   (Followers: 8)
IEEE Technology and Society Magazine     Full-text available via subscription   (Followers: 8)
IEEE Transactions on Advanced Packaging     Full-text available via subscription   (Followers: 8)
Adaptive Behavior     Hybrid Journal   (Followers: 8)
Biomedical Microdevices     Hybrid Journal   (Followers: 8)
Clay Minerals     Hybrid Journal   (Followers: 8)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 8)
Energy Engineering     Full-text available via subscription   (Followers: 8)
Geothermics     Hybrid Journal   (Followers: 7)
International Journal of Applied Ceramic Technology     Hybrid Journal   (Followers: 7)
Biomicrofluidics     Open Access   (Followers: 7)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
IEEE Journal of Selected Topics in Quantum Electronics     Hybrid Journal   (Followers: 7)
Advances in OptoElectronics     Open Access   (Followers: 7)
Fuel and Energy Abstracts     Full-text available via subscription   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
Basin Research     Hybrid Journal   (Followers: 7)
Discrete Optimization     Full-text available via subscription   (Followers: 7)
Designs, Codes and Cryptography     Hybrid Journal   (Followers: 7)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 7)
Applied Catalysis A: General     Hybrid Journal   (Followers: 7)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Fusion Engineering and Design     Hybrid Journal   (Followers: 6)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Applied Clay Science     Hybrid Journal   (Followers: 6)
Formal Methods in System Design     Hybrid Journal   (Followers: 6)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Composite Interfaces     Hybrid Journal   (Followers: 6)
IET Science, Measurement & Technology     Open Access   (Followers: 5)
International Journal of Adaptive Control and Signal Processing     Hybrid Journal   (Followers: 5)
IEEE Transactions on Applied Superconductivity     Hybrid Journal   (Followers: 5)
IEEE Transactions on Vehicular Technology     Hybrid Journal   (Followers: 5)
International Journal of Architectural Computing     Full-text available via subscription   (Followers: 5)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Proceedings of the Institution of Civil Engineers - Engineering Sustainability     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 5)
Focus on Powder Coatings     Full-text available via subscription   (Followers: 5)
Active and Passive Electronic Components     Open Access   (Followers: 5)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 4)
Grass and Forage Science     Hybrid Journal   (Followers: 4)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 4)
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Filtration & Separation     Full-text available via subscription   (Followers: 4)
Graphs and Combinatorics     Hybrid Journal   (Followers: 4)
Fluid Phase Equilibria     Hybrid Journal   (Followers: 4)
Current Applied Physics     Full-text available via subscription   (Followers: 4)
Catalysis Today     Hybrid Journal   (Followers: 4)
Concurrent Engineering     Hybrid Journal   (Followers: 4)
Proceedings of the Institution of Civil Engineers - Ground Improvement     Hybrid Journal   (Followers: 4)
Adsorption     Hybrid Journal   (Followers: 4)
Frontiers in Energy     Hybrid Journal   (Followers: 4)
Catalysis Letters     Hybrid Journal   (Followers: 3)
IET Optoelectronics     Open Access   (Followers: 3)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 3)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 3)
Engineering Computations     Hybrid Journal   (Followers: 3)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 3)
Focus on Pigments     Full-text available via subscription   (Followers: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Frontiers of Environmental Science & Engineering     Hybrid Journal   (Followers: 3)
Fuzzy Sets and Systems     Hybrid Journal   (Followers: 3)
Assembly Automation     Hybrid Journal   (Followers: 2)
International Journal of Abrasive Technology     Hybrid Journal   (Followers: 2)
Aerobiologia     Hybrid Journal   (Followers: 2)
IET Generation, Transmission & Distribution     Open Access   (Followers: 2)
Historical Records of Australian Science     Hybrid Journal   (Followers: 2)
Comptes Rendus : Mécanique     Open Access   (Followers: 2)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
IEEE Latin America Transactions     Full-text available via subscription   (Followers: 2)
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
ESAIM: Control Optimisation and Calculus of Variations     Open Access   (Followers: 2)
Focus on Surfactants     Full-text available via subscription   (Followers: 2)
Engineering Analysis with Boundary Elements     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 1)
Foundations of Science     Hybrid Journal   (Followers: 1)
Forschung     Hybrid Journal   (Followers: 1)
European Journal of Lipid Science and Technology     Hybrid Journal   (Followers: 1)
Antarctic Science     Hybrid Journal   (Followers: 1)
Épités - Épitészettudomány     Full-text available via subscription   (Followers: 1)
Dyes and Pigments     Hybrid Journal   (Followers: 1)
Bautechnik     Hybrid Journal   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Designed Monomers and Polymers     Open Access   (Followers: 1)
Color Research & Application     Hybrid Journal   (Followers: 1)
Abstract and Applied Analysis     Open Access   (Followers: 1)
Focus on Catalysts     Full-text available via subscription  
ESAIM: Proceedings     Open Access  
Environmetrics     Hybrid Journal  
COMBINATORICA     Hybrid Journal  
Chinese Science Bulletin     Open Access  
Calphad     Hybrid Journal  
Boundary Value Problems     Open Access  

        1 2 3 4 5 6 7 | Last

Similar Journals
Journal Cover
Concurrent Engineering
Journal Prestige (SJR): 0.642
Citation Impact (citeScore): 2
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1063-293X - ISSN (Online) 1531-2003
Published by Sage Publications Homepage  [1174 journals]
  • Robust product line pricing under the multinomial logit choice model

    • Free pre-print version: Loading...

      Authors: Wei Qi, Xinggang Luo, Xuwang Liu, Zhong-Liang Zhang
      Abstract: Concurrent Engineering, Ahead of Print.
      Incorporating consumer choice behavior into a product line design optimization model enhances the understanding of consumer choices and improves the opportunities to increase profit. Most product line optimization problems assume that parameters are precisely known in consumer choice model. However, the decision maker does not precisely know the model parameters because of insufficient sample data, measurement problems, and other factors. We investigate the problem of establishing robust product line pricing under a multinomial logit model to account for the uncertainty of the valuation parameter. First, we present a nominal product line model to maximize profit. We then establish a robust product line model to maximize the worst-case expected profit, where the valuation parameter lies in an uncertainty set. We consider both single and multiple products development and derive the optimal prices’ closed-form expressions. Through numerical experiments, we illustrate the benefit of robust product line pricing to address parameter uncertainty. We demonstrate that the difference between the expected nominal profit and the worst-case profit increases with the increase of the interval of the uncertainty set, and the robust profit relative to the worst-case nominal profit improves. The robust product line design can ensure steadier, even higher profit.
      Citation: Concurrent Engineering
      PubDate: 2022-06-16T07:39:24Z
      DOI: 10.1177/1063293X221102205
       
  • Machine Learning and Automation in Concurrent Engineering

    • Free pre-print version: Loading...

      Authors: K Vijayakumar
      Abstract: Concurrent Engineering, Ahead of Print.

      Citation: Concurrent Engineering
      PubDate: 2022-06-15T06:15:40Z
      DOI: 10.1177/1063293X221108831
       
  • Modeling and solving the two-sided U-type assembly line balance based on a
           heuristic algorithm of a multi-priority rule

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      Authors: Yu-ling Jiao, Xue Deng, Lin Li, Xin-ran Liu, Nan Cao
      Abstract: Concurrent Engineering, Ahead of Print.
      In order to improve the efficiency of assembly line and optimize the layout, this paper presents a collaborative optimization model for a two-sided U-type assembly line and a novel design with p-l partition layout is proposed to minimize number of workstations without increasing the length of the assembly line. Considering the task orientation and time sequencing in cross-workstation, the mathematical model of two-sided U-type assembly line balancing problem is derived. A multi-level priority rule heuristic algorithm is developed to drive the optimization process. The multi-level priority rule heuristic algorithm, modified particle swarm optimization algorithm, and the bi-objective integer programming method are applied to 20 classic examples, respectively. The calculation results suggest that the optimal results of the proposed method account for 90%, which verifies the rationality of the collaborative optimization model and algorithm, and provides a useful reference for the modeling and solution of the two-sided U-type assembly line balancing problems.
      Citation: Concurrent Engineering
      PubDate: 2022-06-07T02:45:19Z
      DOI: 10.1177/1063293X221104527
       
  • Detection of Pneumonia from Chest X-Ray images using Machine Learning

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      Authors: SureshKumar M, Varalakshmi Perumal, Gowtham Yuvaraj, Sakthi Jaya Sundar Rajasekar
      Abstract: Concurrent Engineering, Ahead of Print.
      The survival percentage of lung patients can be improved if pneumonia is detected early. Images of the chest X-ray (CXR) are the most common way of identifying and diagnosing pneumonia. A competent radiologist faces a difficult problem in detecting pneumonia from CXR images. Many people are at danger of contracting pneumonia, especially in developing countries where billions of people live in energy poverty and rely on polluting energy sources. Though there are effective tools in existence to prevent, diagnose and treat pneumonia, pneumonia-related deaths are prevalent in most of the countries. But only a small amount of health budgets is allocated to eradicate pneumonia. If the diagnosis of the disease is made in more reliable and cost effective way, tackling the disease won’t be a herculean task. Machine learning algorithms paved a great way to easily identify, diagnose and predict the disease with minimal amount of time. This paper represents the identification of pneumonia from chest X-Ray by implementing traditional machine learning algorithms with ensemble using optimal number of image features with the help of correlation co-efficient. Also deep learning approach has been implemented. The proposed method traditional machine learning approach and deep learning approach achieved accuracy rates of 93.57% and 93.59% and time required for pneumonia detection is 157,452 s (approx.) and 240,253 s (approx.) respectively.
      Citation: Concurrent Engineering
      PubDate: 2022-06-06T02:23:52Z
      DOI: 10.1177/1063293X221106501
       
  • Federation payment tree: An improved payment channel for scaling and
           

    • Free pre-print version: Loading...

      Authors: P Shamili, B Muruganantham
      Abstract: Concurrent Engineering, Ahead of Print.
      Federation Payment Tree, a new Off-chain with zero-knowledge hash time lock commitment setup is proposed in this paper. The security of blockchain is based on consensus protocols that delay when number of concurrent transactions processed in given throughput framework. The scalability of blockchain is the ability to perform support increasing workload transaction. The FP-Tree provides zero knowledge hash lock commitment connect with off-chain protocols by using the payment channel, which enables execution of off-chain protocol that allows interaction between the parties without involving the consensus protocol. It allows to make payment across an authorization path of payment channel. Such a payment tree requires two commitment scheme is [math] and [math], each party lock fund for a time period. The main challenges we faced in this paper is that the computational power, storage and cryptography. Furthermore, we discussed many attacks on off-chain payment channel that allows a malicious adversary to make fund lose. The FP-Tree supports multi-parti computation (MPC) merging transactions into single hash value in payment tree. We enable the parties to generate single hash value by consumes both less than [math] and space less than [math] time combine element over length of single hash. The results were discussed in this paper and efficiency of FP-Tree is well suited for the blockchain technology. We achieved the accuracy of 60.2% in federated payment tree when compared with the proof of work and proof of authority.
      Citation: Concurrent Engineering
      PubDate: 2022-05-26T02:26:50Z
      DOI: 10.1177/1063293X221101358
       
  • Petite term traffic flow prediction using deep learning for augmented flow
           of vehicles

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      Authors: J Indumathi, V Kaliraj
      Abstract: Concurrent Engineering, Ahead of Print.
      An Intelligent Transport System (ITS) model that is contingent on the compulsion and expertise of the Traffic Prediction System in the contemporary urban context is proposed in this paper. Deep Learning (DL) is computationally becoming comfortable to train and set as many hyperparameters automatically as possible. The researchers and practitioners crave to set as many hyperparameters inevitably as possible in the DL. To be a great enabler, ITS has to find suitable solutions to issues like—alert on live time traffic information to interested parties along with facility to retrieve on demand the long-term statistical data, reduce the middling waiting time for commuters, offer protected, consistent, value-added services, control with vitality the signal timing based on the traffic flow etc., All these limitations call for instant attention. Among all the listed issues the problems like the sharp nonlinearities due to changeovers amid free flow, breakdown, retrieval and congestion. The contributions in this paper are as follows: (i) Adopt an ascendable approach to kindle the scarce information formed; (ii) Exploit the attention mechanism to exterminate the disadvantages of Long Short-Term Memory (LSTM) methods for traffic prediction; (iii) Suggest a new fusion smoothing model; (iv) Investigating, developing, and utilizing the Bayesian contextual bandits; (v) Recommend a Linear model based on LSTM, in combo with Bayesian contextual bandits. The travel speed prediction is done by LSTM. The results authenticate that the proposed model can adeptly achieve the goal of developing a system. The proposed model is definitely the best solution to overcome the issues.
      Citation: Concurrent Engineering
      PubDate: 2022-05-20T05:58:43Z
      DOI: 10.1177/1063293X221094345
       
  • Detection and classification of epilepsy using hybrid convolutional neural
           network

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      Authors: A Sabarivani, R Ramadevi
      Abstract: Concurrent Engineering, Ahead of Print.
      In recent years, more than 50 million people have been affected by the epilepsy, neurological disorder diseases. To monitor the situation of the epilepsy patient requires experienced and skilled person. In order to overcome these issues, autonomous detection of electroencephalogram (EEG) signal by deep learning model has evolved. Convolutional neural network (CNN) is one of the sub-category of neural network and widely used in the various field such as weather forecasting, signal processing and medical applications. In this article, the University of California Irvine (UCI) respiratory EEG signals are used to analyse the proposed hybrid CNN and results are compared to the pre-trained GoogleNet Network. EEG signals are initially converted into three different forms such as scalogram, spectrogram and time domain images and classification of images are carried out by the pre-trained GoogleNet network results in an accuracy of 85%. Then time domain images are combined with spectrogram and scalogram EEG signal separately and detection has been carried out by the CNN. It is found that the CNN network yields an accuracy of 92% which was higher than the pre-trained GoogleNet. To enhance the classification accuracy further, scalogram, spectrogram and time domain images are combined as single input images and applied to the CNN network and it results with the accuracy of 98%. The performance metrics such as Sensitivity, Specificity, F1 Score, Precision and misclassification rate of GoogleNet and proposed hybrid CNN networks are evaluated. It is observed from the result that proposed CNN results less than 10% misclassification rate, whereas for GoogleNet it was more than 20%. Similarly, the precision value of GoogleNet and proposed CNN networks are 82% and 93%, respectively.
      Citation: Concurrent Engineering
      PubDate: 2022-05-20T05:24:01Z
      DOI: 10.1177/1063293X221089089
       
  • Fusion-based advanced encryption algorithm for enhancing the security of
           Big Data in Cloud

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      Authors: A Vidhya, P Mohan Kumar
      Abstract: Concurrent Engineering, Ahead of Print.
      Every organization in this digital age is expected to exponentially increase its digital data due to generations from machines. The advanced computations of Big Data are now showing various opportunities for the researchers who work on security enhancements to ensure the efficient accessibility of the data stores. Our research work aims to derive a Fusion-based Advanced Encryption Algorithm (FAEA) for a cost-optimized satisfiable security model toward the usage of Big Data in the cloud. The FAEA method is evaluated for its performance toward efficiency, scalability, and security and proved to be 98% ahead of the existing methods of Security Hadoop Distributed File System Sec (HDFS) and Map Reduce Encryption Scheme (MRE). On the other hand, this work aims to address the problems of usage of Big Data in the cloud toward the sole solution, cost-effective solutioning, and proof of ownership. The outcome analysis of FAEA revolves around addressing these three major problems. This research work would be much helpful for the IT industries to manage Big Data in Cloud with security aspects for the decade.
      Citation: Concurrent Engineering
      PubDate: 2022-05-20T05:10:41Z
      DOI: 10.1177/1063293X221089086
       
  • A secured biomedical image processing scheme to detect pneumonia disease
           using dynamic learning principles

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      Authors: Venkata Samy Raja Nanammal, Venu Gopalakrishnan Jayagopalan
      Abstract: Concurrent Engineering, Ahead of Print.
      Now-a-days, the medical industry is growing a lot with the adaptation of latest technologies as well as the logical evaluation and security norms provides a robust platform to enhance the effectiveness of the industry at a drastic level. In this paper, a digital bio-medical image processing based Pneumonia disease identification system is introduced with enhanced security features. Due to improving the efficiency of the application, a well-known watermarking based security constraint is included to provide the protection to the respective hospital environment and patients as well. To avoid these issues, some sort of security aspects need to be followed so that this paper included watermarking based security to provide a rich level of protection to the images going to be tested. The main intention of this paper is to introduce a novel security enabled digital image processing scheme to identify the Pneumonic disease in earlier stages with respect to the proper classification principles. In this paper, a novel deep learning algorithm is introduced called enhanced Dynamic Learning Neural Network in which it is a hybrid algorithm with the combinations of conventional DLNN algorithm and the Support Vector Classification algorithm. This proposed approach effectively identifies the Pneumonia disease in earlier stages but the security inspection on the testing stage is so important to analyze the disease. The respective testing image is properly watermarked with the logo of the corresponding hospital; the image is processed otherwise the proposed approach skips the image to process. These kinds of security features emphasize the medical industry and boost up the levels more as well as the patients can get an appropriate error free care with the help of such technology. A proper Chest X-Ray based Kaggle dataset is considered to process the system as well as which contains 5856 Chest X-Ray images under two different categories such as Pneumonia and Normal. With respect to processing these images and identifying the Pneumonia disease effectively as well as the proposed watermarking enabled security features provide a good impact in the medical field protection system. The resulting section provides the proper proof to the effectiveness of the proposed approach and its prediction efficiency.
      Citation: Concurrent Engineering
      PubDate: 2022-05-09T06:22:35Z
      DOI: 10.1177/1063293X221097447
       
  • Optimal feature reduction for biometric authentication using intelligent
           computing techniques

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      Authors: N Umasankari, B Muthukumar
      Abstract: Concurrent Engineering, Ahead of Print.
      The Intelligent Computing area such as Automatic Biometric authentication is an emerging and high priority research work where the researchers invent several biometric applications which result in the revolutionary development in the recent era. In this approach, a novel algorithm is known as Modified AntLion Optimization (MALO) with Multi Kernel Support Vector Machine (MKSVM) was used to classify and recognize the fingerprint, and retina image efficiently. In the early stage of this research, the pre-processing of the biometric images was done for contrast enhancement and it was implemented by histogram equalization technique. Next, features were extracted by Gray Level Co-occurrence Matrix (GLCM), minutiae, Gray Level Run Length Matrix (GLRLM), and Autocorrelation methods. Then the features extracted were reduced by Probabilistic Principal Component Analysis (PPCA) method. Then the feature selection method was employed and the optimal features were attained by applying the Modified AntLion Optimization (MALO) technique. Finally, the machine learning classification technique was executed for categorizing biometric recognition. Here, the machine learning classification technique named Multi Kernel Support Vector Machine (MKSVM) has been used. The performance of the proposed algorithm was analyzed in terms of accuracy, sensitivity, and specificity. Results indicate that the Multi Kernel Support Vector Machine (MKSVM) yields the best accuracy of 91.60% and 90.30% for fingerprint and retina image recognition respectively, yields the sensitivity of 84.70% and 89.41% for fingerprint and retina image recognition, respectively, yields the specificity of 91.30% and 92.70% for fingerprint and retina image recognition respectively.
      Citation: Concurrent Engineering
      PubDate: 2022-04-24T03:31:37Z
      DOI: 10.1177/1063293X221081543
       
  • The role of industry 4.0 technologies in overcoming pandemic challenges
           for the manufacturing sector

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      Authors: Parham Dadash Pour, Mohammad A Nazzal, Basil M Darras
      Abstract: Concurrent Engineering, Ahead of Print.
      Industry 4.0 aims to revolutionize the manufacturing sector to achieve sustainable and efficient production. The novel coronavirus pandemic has brought many challenges in different industries globally. Shortage in supply of raw material, changes in product demand, and factories closures due to general lockdown are all examples of such challenges. The adaption of Industry 4.0 technologies can address these challenges and prevent their recurrence in case of another pandemic outbreak in future. A prominent advantage of Industry 4.0 technologies is their capability of building resilient and flexible systems that are responsive to exceptional circumstances such as unpredictable market demand, supply chain interruptions, and manpower shortage which can be crucial at times of pandemics. This work focuses on discussing how different Industry 4.0 technologies such as Cyber Physical Systems, Additive Manufacturing, and Internet of Things can help the manufacturing sector overcome pandemics challenges. The role of Industry 4.0 technologies in raw material provenance identification and counterfeit prevention, collaboration and business continuity, agility and decentralization of manufacturing, crisis simulation, elimination of single point of failure risk, and other factors is discussed. Moreover, a self-assessment readiness model has been developed to help manufacturing firms determine their readiness level for implementing different Industry 4.0 technologies.
      Citation: Concurrent Engineering
      PubDate: 2022-04-23T07:22:19Z
      DOI: 10.1177/1063293X221082681
       
  • Sports highlight recognition and event detection using rule inference
           system

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      Authors: Kanimozhi Soundararajan, Mala T
      Abstract: Concurrent Engineering, Ahead of Print.
      Computer vision in sport is a very interesting application. People spend a lot of time watching sports videos because this is one of the best field of entertainment. Sports video broadcasts generally take a lot of time, ranging from two to four hours. However, the interesting part happens for just a few minutes. Detecting the highlighted event in a sport will be useful for people who like to watch only the prominent events section instead of watching the whole video broadcast. Event detection will give precise details about the action that occurred for a particular time, but the detection of highlighted events is more complex. This is due to the fact that a sports video contains collections of events. Among them, segregation of the required event is a time-consuming process but it requires more knowledge about the sport as well as processing time. Hence, a novel work is proposed focused on identifying the location of the functional object using agglomerative clustering and annotating the event highlights automatically by means of the rule inference mechanism. The SHRED (Sports Highlight Recognition and Event Detection) system achieves an overall accuracy of about 97.38% relative to other state-of-art methods in event class annotation.
      Citation: Concurrent Engineering
      PubDate: 2022-04-15T08:36:10Z
      DOI: 10.1177/1063293X221088353
       
  • Computer-aided diagnosis for breast cancer detection and classification
           using optimal region growing segmentation with MobileNet model

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      Authors: J Dafni Rose, K VijayaKumar, Laxman Singh, Sudhir Kumar Sharma
      Abstract: Concurrent Engineering, Ahead of Print.
      Globally, breast cancer is considered a major reason for women’s morality. Earlier and accurate identification of breast cancer is essential to increase survival rates. Therefore, computer-aided diagnosis (CAD) models are developed to help radiologists in the detection of mammographic lesions. Presently, machine-learning (ML) and deep-learning (DL) models are widely employed in the disease diagnostic process. In this view, this paper designs a novel CAD using optimal region growing segmentation with a MobileNet (CAD-ORGSMN) model for breast cancer identification and classification. The proposed CAD-ORGSMN model involves different stages of operations, namely, pre-processing, segmentation, feature extraction, and classification. Primarily, the proposed model uses a Weiner filtering (WF)–based pre-processing technique to remove the existence of noise in the mammogram images. The CAD-ORGSMN model involves a glowworm swarm optimization (GSO)–based region growing technique for image segmentation where the initial seed points and threshold values are optimally created by the GSO algorithm. Besides, a MobileNet-based feature extractor is used in which the hyperparameters of the MobileNet model are optimally selected using a swallow swarm optimization (SSO) algorithm. Lastly, variational autoencoder is applied as a classifier to determine the class labels for the input mammogram images. The utilization of the GSO algorithm for the region growing technique and the SSO algorithm for hyperparameter optimization helps to considerably improve the breast cancer detection performance of the CAD-ORGSMN model. The performance validation of the CAD-ORGSMN model takes place against the Mini-MIAS database, and the obtained results highlighted the promising performance of the CAD-ORGSMN model over the recent state-of-the-art methods in terms of different measures.
      Citation: Concurrent Engineering
      PubDate: 2022-04-14T05:18:17Z
      DOI: 10.1177/1063293X221080518
       
  • Phased array ultrasonic test signal enhancement and classification using
           Empirical Wavelet Transform and Deep Convolution Neural Network

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      Authors: Jayasudha JC, Lalithakumari S
      Abstract: Concurrent Engineering, Ahead of Print.
      In the recent past, Non-Destructive Testing (NDT) has become the most popular technique due to its efficiency and accuracy without destroying the object and maintaining its original structure and gathering while examining external and internal welding defects. Generally, the NDT environment is harmful which is distinguished by huge volatile fields of electromagnetic, elevated radiation emission instability, and elevated heat. Therefore, a suitable NDT approach could be recognized and practiced. In this paper, a novel algorithm is proposed based on a Phased array ultrasonic test (PAUT) for NDT to attain the proper test attributes. In the proposed methodology, the carbon steel welding section is synthetically produced with various defects and tested using the PAUT method. The signals which are acquired from the PAUT device are having noise. The Adaptive Least Mean Square (ALMS) filter is proposed to filter PAUT signal to eliminate random noise and Gaussian noise. The ALMS filter is the combination of low pass filter (LPF), high pass filter (HPF), and bandpass filter (BPF). The time-domain PAUT signal is converted into a frequency-domain signal to extract more features by applying the Empirical Wavelet Transform (EWT) algorithm. In the frequency domain signal, first order and second order features extraction techniques are applied to extract various features for further classification. The Deep Learning methodology is proposed for the classification of PAUT signals. Based on the PAUT signal features, the Deep Convolution Neural Network (DCNN) is applied for further classification. The DCNN will classify the welding signal as to whether it is defective or non-defective. The Confusion Matrix (CM) is used for the estimation of measurement of performance of classification as calculating accuracy, sensitivity, and specificity. The experiments prove that the proposed methodology for PAUT testing for welding defect classification is obtained more accurately and efficiently across existing methodologies by providing numerical and graphical results.
      Citation: Concurrent Engineering
      PubDate: 2022-02-22T09:32:10Z
      DOI: 10.1177/1063293X211073714
       
  • Identifying modular candidates in engineer-to-order companies

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      Authors: Carsten Keinicke Fjord Christensen, Niels Henrik Mortensen
      Abstract: Concurrent Engineering, Ahead of Print.
      The purpose of this paper is to address a gap of missing modularization methods for engineer-to-order (ETO) companies. The research project was initiated by clarifying the challenges facing ETO companies, based on these challenges synthesis of existing methods was done to conceptualize a method. This article presents the modular candidate identification (MCI) method aimed at identifying modular candidates in ETO companies. The method analyzes five dimensions, namely, market segments, customer requirements, product architectures, cost and lead time to find modular candidates. The method was applied in a Danish ETO company and shown to be successful in identifying two modular candidates. Both were recognized by management and redesigned in modular product development projects.
      Citation: Concurrent Engineering
      PubDate: 2022-02-05T02:13:22Z
      DOI: 10.1177/1063293X211072192
       
  • A cost-effective computer vision-based vehicle detection system

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      Authors: Altaf Alam, Zainul Abdin Jaffery, Himanshu Sharma
      Abstract: Concurrent Engineering, Ahead of Print.
      Vehicle detection plays an important role in the development of an autonomous driving system. Fast processing and accurate detection are two major aspects of generating the autonomous vehicle detection system. This paper proposes a novel computer vision-based cost-effective vehicle detection system. Here, a Gentle Adaptive Boosting algorithm is trained with Haar-like features to generate the hypothesis of vehicles. Haar-like feature generates hypotheses very fast but may detect false vehicle candidates. The support vector machine algorithm is trained with the histogram of oriented gradient features to filter out the generated false hypothesis. The histogram of oriented gradients descriptor utilizes the shape and outlines of the vehicles, hence detects vehicles more accurately. Haar-Likes features and histogram of oriented gradients features are organized to accomplish the aspects of autonomous driving. The performance of the proposed vehicle detector is evaluated for day time and night time captured images and compared with three different existing vehicle detectors. The average precision of the proposed system for day time captured image is 0.97 and for night time captured image is 0.94. The proposed system requires 15 times less training time as compared to the existing technique for the same number of image data and on the same CPU.
      Citation: Concurrent Engineering
      PubDate: 2022-02-02T04:53:21Z
      DOI: 10.1177/1063293X211069193
       
  • Editorial

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      Authors: DR. K. Vijayakumar
      First page: 3
      Abstract: Concurrent Engineering, Ahead of Print.

      Citation: Concurrent Engineering
      PubDate: 2022-03-08T04:57:34Z
      DOI: 10.1177/1063293X221085830
       
  • Embedded mobile computational framework for multidimensional diabetic
           retinopathy extraction and detection technique using recursive neural
           network approach for unstructured tomography datasets

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      Authors: K.T. Ilayarajaa, E. Logashanmugam
      First page: 93
      Abstract: Concurrent Engineering, Ahead of Print.
      Diabetic Retinopathy (DR) is considered to be the leading cause for preventive blindness in humans, the DR is sighted with a diabetic stage of progression and hence the patient is required to undergo regular health checkups on DR formation and detection. In this paper, the objective is to extract and detect the patterns of DR with respect to the propagation stages using Recursive Neural Network (RNN). In this work, we have developed and validated a novel Inter-Correlated Attribute Coordination (ICAC) Technique for attribute based feature mapping and feature inter-dependent cluster generation. The ICAC technique generates a series of standard dataset attributes [math] for process alignment towards the generation of feature set (f). The proposed technique has validated the categorization of DR into grade 1 and grade 0 patients for an unambiguous decision making. The technique’s trained datasets provide a self-learning RNN for multidimensional tomography dataset processing. The ICAC technique has developed a detection rate of 97.3% for the 276 feature set clusters.
      Citation: Concurrent Engineering
      PubDate: 2022-01-31T04:38:28Z
      DOI: 10.1177/1063293X211071044
       
  • Special issue on “intelligent computing and communication in
           concurrent engineering”

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      Authors: K Vijayakumar, V Rajinikanth
      First page: 128
      Abstract: Concurrent Engineering, Ahead of Print.

      Citation: Concurrent Engineering
      PubDate: 2022-03-07T02:40:03Z
      DOI: 10.1177/1063293X221082331
       
 
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