Subjects -> ENGINEERING (Total: 2688 journals)
    - CHEMICAL ENGINEERING (229 journals)
    - CIVIL ENGINEERING (237 journals)
    - ELECTRICAL ENGINEERING (176 journals)
    - ENGINEERING (1325 journals)
    - ENGINEERING MECHANICS AND MATERIALS (452 journals)
    - HYDRAULIC ENGINEERING (56 journals)
    - INDUSTRIAL ENGINEERING (98 journals)
    - MECHANICAL ENGINEERING (115 journals)

ENGINEERING (1325 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 3)
3D Research     Hybrid Journal   (Followers: 18)
AAPG Bulletin     Hybrid Journal   (Followers: 9)
Abstract and Applied Analysis     Open Access   (Followers: 1)
Aceh International Journal of Science and Technology     Open Access   (Followers: 6)
ACS Nano     Hybrid Journal   (Followers: 183)
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)
Adaptive Behavior     Hybrid Journal   (Followers: 8)
Additive Manufacturing Letters     Open Access   (Followers: 3)
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Energy and Sustainability Research     Open Access   (Followers: 5)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 10)
Advanced Engineering Research     Open Access   (Followers: 1)
Advanced Journal of Graduate Research     Open Access   (Followers: 1)
Advanced Quantum Technologies     Hybrid Journal   (Followers: 3)
Advanced Science     Open Access   (Followers: 12)
Advanced Science Focus     Free   (Followers: 6)
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 4)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 20)
Advanced Theory and Simulations     Hybrid Journal   (Followers: 2)
Advances in Applied Energy     Open Access   (Followers: 2)
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: 26)
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: 28)
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: 51)
Advances in Remote Sensing     Open Access   (Followers: 59)
Advances in Science and Research (ASR)     Open Access   (Followers: 8)
Aerobiologia     Hybrid Journal   (Followers: 2)
Aerospace Systems     Hybrid Journal   (Followers: 10)
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-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: 22)
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: 24)
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: 8)
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: 3)
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: 26)
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 Sciences     Open Access   (Followers: 3)
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   (Followers: 1)
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 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  
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 7)
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  
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 and Computational Biology     Open Access   (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: 5)
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: 50)
Carbon Resources Conversion     Open Access   (Followers: 2)
Carpathian Journal of Electronic and Computer Engineering     Open Access  
Case Studies in Thermal Engineering     Open Access   (Followers: 11)
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: 11)
CIRP Journal of Manufacturing Science and Technology     Hybrid Journal   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 23)
Clay Minerals     Hybrid Journal   (Followers: 8)
Cleaner and Circular Bioeconomy (CLCB)     Open Access   (Followers: 4)
Cleaner Energy Systems     Open Access   (Followers: 3)
Cleaner Engineering and Technology     Open Access   (Followers: 1)
Cleaner Environmental Systems     Open Access  
Cleaner Waste Systems     Open Access   (Followers: 3)
Coastal Engineering     Hybrid Journal   (Followers: 15)
Coastal Engineering Journal     Hybrid Journal   (Followers: 8)
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: 249)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 177)
Composites Part B : Engineering     Hybrid Journal   (Followers: 223)
Composites Part C : Open Access     Open Access   (Followers: 2)
Composites Science and Technology     Hybrid Journal   (Followers: 151)
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: 30)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 9)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 7)
Computers and Geotechnics     Hybrid Journal   (Followers: 12)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 31)
Conciencia Tecnologica     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: 3  
 
  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]
  • Petite term traffic flow prediction using deep learning for augmented flow
           of vehicles

    • Free pre-print version: Loading...

      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

    • Free pre-print version: Loading...

      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

    • Free pre-print version: Loading...

      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

    • Free pre-print version: Loading...

      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

    • Free pre-print version: Loading...

      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
       
  • Scene construction of nano particle system based on virtual technology

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      Authors: Han Yang, Chongzhong Jia, Jifeng Xie, Kun Wang, Xiaoling Hao
      Abstract: Concurrent Engineering, Ahead of Print.
      In view of the problems in traditional 3D scene simulation, such as the poor simulation effect and the inability to really feel the scene, this paper proposes the research of nano particle system scene construction based on virtual technology. By analyzing the advantages of virtual reality technology, the role of virtual reality in three-dimensional scene is determined; the method of three-dimensional geometry transformation is used to determine the scene building algorithm of virtual technology; the concept of nano particle system hierarchy is introduced to build nano particle subsystem with object-oriented concept. The functions of the system are mainly divided into system control module, user interaction module, scene management module, and nanoparticles management module. Based on the analysis of virtual technology and the construction of nano particle system, the construction of nano particle system scene based on virtual technology is realized. The experimental results show that: Based on the virtual technology, the nano particle system scene construction effect is better, and the scene construction time is less than 6 min, the work efficiency is higher, the scene is more realistic, and has a certain feasibility.
      Citation: Concurrent Engineering
      PubDate: 2021-07-23T10:20:06Z
      DOI: 10.1177/1063293X211031936
       
  • Applications of affordance and cognitive ergonomics in virtual design: A
           digital camera as an illustrative case

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      Authors: Mo Chen, Georges Fadel, Ivan Mata
      First page: 5
      Abstract: Concurrent Engineering, Ahead of Print.
      Affordance-based design (ABD) plays an important role in identifying interactions, especially effortless ones, between users and artifacts. Cognitive ergonomics extends our understanding of this effortless interaction. This study combines the two design methodologies together in order to reduce cognitive friction in using digital products. The design process of a compact digital camera is selected as a case study that includes the design of the physical shape for a camera and of its user interface. In designing a product shape, a design toolbox was developed that integrated a modified multi-objective genetic algorithm and the ABD, which was named as affordance-based interactive genetic algorithm. Using this toolbox and interactive user feedback, the camera design evolves toward a product that better satisfies the users. User interfaces (UIs) including linear and elliptic layouts were subsequently designed based on cognitive ergonomics. A predictive tool of UI, the Cog Tool, was used to evaluate the performance of skilled users on a given task by correlating the overall task completion time. Finally, this research has the potential to not only effectively address the shortcomings of the design of consumer electronics but also enrich the generation of design solutions during the preliminary design phase of such products.
      Citation: Concurrent Engineering
      PubDate: 2021-12-09T11:52:52Z
      DOI: 10.1177/1063293X211054132
       
  • Conducting product comparative analysis to outperform competitor’s
           product using Teardown JST Model

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      Authors: Cuiqing Jiang, Abdullah Alqadhi, Mahmood Almesbahi
      First page: 21
      Abstract: Concurrent Engineering, Ahead of Print.
      Due to the massive number of products being produced every year in every industry, firms have witnessed a tremendous growth in innovation of methods to create a sustainable competitive advantage. For the past decade and with the availability of online consumer reviews, companies and researchers have developed many approaches utilizing electronic Word-of-Mouth to improve and develop products and services to outperform competitors. The purpose of this study is to construct an effective method to perform a better product comparative analysis based on online consumer reviews. We propose a novel framework called Teardown Joint Sentiment-Topic analysis model consisting of a combination of text analytical approaches incorporated with a developed method of the traditional teardown analysis product comparative approach. The proposed approach is fully unsupervised model that employs Latent Dirichlet Allocation topic modeling to form topics which are classified according to their sentiments. Topics are then analyzed against competitive products and critical topics are identified using a developed teardown method. A case study analyzing online customer reviews of competing products in two domains (i.e., mobile phones and surveillance cameras) is conducted. The identified critical topics are further analyzed in view of products’ specifications perspective. We found that the detected aspects of the selected products are indeed critical, and hence, they need to be improved in order to gain a competitive advantage. The significant result of this study shows that the proposed method is effective in conducting products comparative analysis and provides valuable insights into utilizing the consumer reviews for product development.
      Citation: Concurrent Engineering
      PubDate: 2021-12-13T06:39:28Z
      DOI: 10.1177/1063293X211047290
       
  • Knowledge capitalization in mechatronic collaborative design

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      Authors: Mouna Fradi, Raoudha Gaha, Faïda Mhenni, Abdelfattah Mlika, Jean-Yves Choley
      First page: 32
      Abstract: Concurrent Engineering, Ahead of Print.
      In mechatronic collaborative design, there is a synergic integration of several expert domains, where heterogeneous knowledge needs to be shared. To address this challenge, ontology-based approaches are proposed as a solution to overtake this heterogeneity. However, dynamic exchange between design teams is overlooked. Consequently, parametric-based approaches are developed to use constraints and parameters consistently during collaborative design. The most valuable knowledge that needs to be capitalized, which we call crucial knowledge, is identified with informal solutions. Thus, a formal identification and extraction is required. In this paper, we propose a new methodology to formalize the interconnection between stakeholders and facilitate the extraction and capitalization of crucial knowledge during the collaboration, based on the mathematical theory ‘Category Theory’ (CT). Firstly, we present an overview of most used methods for crucial knowledge identification in the context of collaborative design as well as a brief review of CT basic concepts. Secondly, we propose a methodology to formally extract crucial knowledge based on some fundamental concepts of category theory. Finally, a case study is considered to validate the proposed methodology.
      Citation: Concurrent Engineering
      PubDate: 2021-12-09T11:34:45Z
      DOI: 10.1177/1063293X211050438
       
  • A discrete manufacturing SCOS framework based on functional interval
           parameters and fuzzy QoS attributes using moving window FPA

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      Authors: Jie Gao, Xianguo Yan, Hong Guo
      First page: 46
      Abstract: Concurrent Engineering, Ahead of Print.
      Manufacturing service composition and optimal selection (SCOS) is a key technology that improves resource utilization and reduces the cost in discrete manufacturing. However, the lack of evaluation of the service composition function and the unconformity of the actual composition vague characteristics, resulting in the incomplete evaluation of the service composition. Additionally, various optimization and selection algorithms have defects of premature convergence and low efficiency. At the same time, the fitness value distribution of the service composition has a non-linear characteristic. In this article, a framework called discrete manufacturing SCOS (DMSCOS) is proposed to overcome these issues. DMSCOS uses the functional interval parameter and fuzzy QoS attribute aware evaluation model (FIPFQA) to achieve composition evaluation and introduces a moving window flower pollination algorithm (MWFPA) to achieve optimization and selection for the non-linear characteristic population. Experiments show that DMSCOS has good performance for optimization and selection. The FIPFQA has a good effect on service composition evaluation. Furthermore, compared with two other extended algorithms, the proposed MWFPA performs better when addressing the optimal and selection problem.
      Citation: Concurrent Engineering
      PubDate: 2021-08-06T09:03:54Z
      DOI: 10.1177/1063293X211032343
       
  • Connector-link-part-based disassembly sequence planning

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      Authors: Hwai-En Tseng, Chien-Cheng Chang, Shih-Chen Lee, Cih-Chi Chen
      First page: 67
      Abstract: Concurrent Engineering, Ahead of Print.
      Under the trend of concurrent engineering, the correspondence between functions and physical structures in product design is gaining importance. Between the functions and parts, connectors are the basic unit for engineers to consider. Moreover, the relationship between connector-liaison-part will help accomplish the integration of information. Such efforts will help the development of the Knowledge Intensive CAD (KICAD) system. Therefore, we proposed a Connector-liaison-part-based disassembly sequence planning (DSP) in this study. First, the authors construct a release diagram through an interference relationship to express the priority of disassembly between parts. The release diagram will allow designers to review the rationality of product disassembly planning. Then, the cost calculation method and disassembly time matrix are established. Last, the greedy algorithm is used to find an appropriate disassembly sequence and seek suggestions for design improvement. Through the reference information, the function and corresponding modules are improved, from which the disassembly value of a product can be reviewed from a functional perspective. In this study, a fixed support holder is used as an example to validate the proposed method. The discussion of the connector-liaison-part will help the integration of the DSP and the functional connector approach.
      Citation: Concurrent Engineering
      PubDate: 2021-12-08T03:50:03Z
      DOI: 10.1177/1063293X211050930
       
  • Balancing of parallel U-shaped assembly lines with a heuristic algorithm
           based on bidirectional priority values

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      Authors: Yuling Jiao, Xue Deng, Mingjuan Li, Xiaocui Xing, Binjie Xu
      First page: 80
      Abstract: Concurrent Engineering, Ahead of Print.
      Aiming at improving assembly line efficiency and flexibility, a balancing method of parallel U-shaped assembly line system is proposed. Based on the improved product priority diagram, the bidirectional priority value formula is obtained. Then, assembly lines are partitioned into z-q partitions and workstations are defined. After that, the mathematical model of the parallel U-shaped assembly line balancing problem is established. A heuristic algorithm based on bidirectional priority values is used to solve explanatory examples and test examples. It can be seen from the results and the effect indicators of the assembly line balancing problem that the heuristic algorithm is suitable for large balancing problems. The proposed method has higher calculation accuracy and shorter calculation time. The balancing effect of the parallel U-shaped assembly line is better than that of single U-shaped assembly line, which verifies the superiority of the parallel U-type assembly line and effectiveness of the proposed method. It provides a theoretical and practical reference for parallel U-type assembly line balancing problem.
      Citation: Concurrent Engineering
      PubDate: 2021-12-22T09:29:50Z
      DOI: 10.1177/1063293X211065506
       
  • Automated glaucoma detection from fundus images using wavelet-based
           denoising and machine learning

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      Authors: Sibghatullah I. Khan, Shruti Bhargava Choubey, Abhishek Choubey, Abhishek Bhatt, Pandya Vyomal Naishadhkumar, Mohammed Mahaboob Basha
      First page: 103
      Abstract: Concurrent Engineering, Ahead of Print.
      Glaucoma is a domineering and irretrievable neurodegenerative eye disease produced by the optical nerve head owed to extended intra-ocular stress inside the eye. Recognition of glaucoma is an essential job for ophthalmologists. In this paper, we propose a methodology to classify fundus images into normal and glaucoma categories. The proposed approach makes use of image denoising of digital fundus images by utilizing a non-Gaussian bivariate probability distribution function to model the statistics of wavelet coefficients of glaucoma images. The traditional image features were extracted followed by the popular feature selection algorithm. The selected features are then fed to the least square support vector machine classifier employing various kernel functions. The comparison result shows that the proposed approach offers maximum classification accuracy of nearly 91.22% over the existing best approaches.
      Citation: Concurrent Engineering
      PubDate: 2021-07-09T09:40:43Z
      DOI: 10.1177/1063293X211026620
       
  • Deep learning based fusion model for COVID-19 diagnosis and classification
           using computed tomography images

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      Authors: RT Subhalakshmi, S Appavu alias Balamurugan, S Sasikala
      First page: 116
      Abstract: Concurrent Engineering, Ahead of Print.
      Recently, the COVID-19 pandemic becomes increased in a drastic way, with the availability of a limited quantity of rapid testing kits. Therefore, automated COVID-19 diagnosis models are essential to identify the existence of disease from radiological images. Earlier studies have focused on the development of Artificial Intelligence (AI) techniques using X-ray images on COVID-19 diagnosis. This paper aims to develop a Deep Learning Based MultiModal Fusion technique called DLMMF for COVID-19 diagnosis and classification from Computed Tomography (CT) images. The proposed DLMMF model operates on three main processes namely Weiner Filtering (WF) based pre-processing, feature extraction and classification. The proposed model incorporates the fusion of deep features using VGG16 and Inception v4 models. Finally, Gaussian Naïve Bayes (GNB) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the DLMMF model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity of 96.53%, specificity of 95.81%, accuracy of 96.81% and F-score of 96.73%.
      Citation: Concurrent Engineering
      PubDate: 2021-06-09T10:31:35Z
      DOI: 10.1177/1063293X211021435
       
 
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