A Deep Swarm-Optimized Model for Leveraging Industrial Data Analytics in Cognitive Manufacturing

To compete in the current data-driven economy, it is essential that industrial manufacturers leverage real-time tangible information assets and embrace big data technologies. Data classification is one of the most proverbial analytical techniques within the cognitively capable manufacturing industries for finding the patterns in the structured and unstructured data at the plant, enterprise, and industry levels. This article presents a cognition-driven analytics model, CNN-WSADT, for the real-time data classification using three soft computing techniques, namely, deep learning [convolution neural network (CNN)], machine learning [decision tree (DT)], and swarm intelligence [wolf search algorithm (WSA)]. The proposed deep swarm-optimized classifier is a feature-boosted DT, which learns features using a deep convolution net and an optimal feature set built using a metaheuristic WSA. The performance of CNN-WSADT is studied on two benchmark datasets and the experimental results depict that the proposed cognition model outperforms the other considered algorithms in terms of the classification accuracy.

[1]  Francesco Orabona,et al.  The ABACOC Algorithm: A Novel Approach for Nonparametric Classification of Data Streams , 2015, 2015 IEEE International Conference on Data Mining.

[2]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[3]  Akshi Kumar,et al.  Deep Learning Based Sentiment Classification on User-Generated Big Data , 2020 .

[4]  Simon Fong,et al.  Wolf search algorithm with ephemeral memory , 2012, Seventh International Conference on Digital Information Management (ICDIM 2012).

[5]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[6]  Albert Y. Zomaya,et al.  Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data , 2020, Inf. Process. Manag..

[7]  Zhanna Reznikova,et al.  Animal Intelligence: From Individual to Social Cognition , 2007 .

[8]  Boris Ryabko,et al.  The use of ideas of Information Theory for studying "language" and intelligence in ants , 2009, Entropy.

[9]  Tao Chen,et al.  Combining Convolutional Neural Network and Support Vector Machine for Sentiment Classification , 2015, SMP.

[10]  Feng Zhou,et al.  Affective and cognitive design for mass personalization: status and prospect , 2012, Journal of Intelligent Manufacturing.

[11]  Ajith Abraham,et al.  Industry 4.0: A bibliometric analysis and detailed overview , 2019, Eng. Appl. Artif. Intell..

[12]  Ray Y. Zhong,et al.  User-experience Based Product Development for Mass Personalization: A Case Study , 2017 .

[13]  Burairah Hussin,et al.  Opinion Mining of Movie Review using Hybrid Method of Support Vector Machine and Particle Swarm Optimization , 2013 .

[14]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[15]  R. Sarpong,et al.  Bio-inspired synthesis of xishacorenes A, B, and C, and a new congener from fuscol† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c9sc02572c , 2019, Chemical science.

[16]  Akshi Kumar,et al.  Sentiment Analysis Using Cuckoo Search for Optimized Feature Selection on Kaggle Tweets , 2019, Int. J. Inf. Retr. Res..

[17]  Akshi Kumar,et al.  Firefly Algorithm for Feature Selection in Sentiment Analysis , 2017 .

[18]  Zhihan Lv,et al.  Big Data Analysis Based Network Behavior Insight of Cellular Networks for Industry 4.0 Applications , 2020, IEEE Transactions on Industrial Informatics.

[19]  Marek Obitko,et al.  Understanding Data Heterogeneity in the Context of Cyber-Physical Systems Integration , 2017, IEEE Transactions on Industrial Informatics.

[20]  Hui-Huang Hsu,et al.  Hybrid feature selection by combining filters and wrappers , 2011, Expert Syst. Appl..

[21]  Ali Kashif Bashir,et al.  A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things , 2021, IEEE Transactions on Industrial Informatics.

[22]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Deepak Kumar Gupta,et al.  PSO-ASent: Feature Selection Using Particle Swarm Optimization for Aspect Based Sentiment Analysis , 2015, NLDB.

[24]  Akshi Kumar,et al.  Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on twitter , 2019, Multimedia Tools and Applications.

[25]  Sherali Zeadally,et al.  Millimeter-Wave Communication for Internet of Vehicles: Status, Challenges, and Perspectives , 2020, IEEE Internet of Things Journal.

[26]  Shahid Mumtaz,et al.  Survey on the Internet of Vehicles: Network Architectures and Applications , 2020, IEEE Communications Standards Magazine.

[27]  P. Alam.  ‘K’ , 2021, Composites Engineering.

[28]  Gui Xiaolin,et al.  Deep Convolution Neural Networks for Twitter Sentiment Analysis , 2018, IEEE Access.

[29]  Akshi Kumar,et al.  Systematic literature review of sentiment analysis on Twitter using soft computing techniques , 2019, Concurr. Comput. Pract. Exp..

[30]  Bandana Mahapatra,et al.  Into the World of Underwater Swarm Robotics: Architecture, Communication, Applications and Challenges , 2020 .

[31]  Rose Qingyang Hu,et al.  Subcarrier Index Modulation for Future Wireless Networks: Principles, Applications, and Challenges , 2020, IEEE Wireless Communications.

[32]  Akshi Kumar,et al.  Paradigm shifts: from pre-web information systems to recent web-based contextual information retrieval , 2010, Webology.

[33]  W. Marsden.  I and J , 2012 .

[34]  Lei Cao,et al.  Scalable distance-based outlier detection over high-volume data streams , 2014, 2014 IEEE 30th International Conference on Data Engineering.