Impact of Deep Learning and
                  Machine Learning in Industry 4.0

Lilhore, Umesh Kumar; Simaiya, Sarita; Kaur, Amandeep; Prasad, Devendra; Khurana, Meenu; Verma, Deepak; Hassan, Afsan

doi:10.1201/9781003146711-12

Cited by 11 publications

(6 citation statements)

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“…, 2021). After selecting appropriate industry, a connection is introduced about scope, platform and team which give a detailed description of roles and duties, agenda, objective of adoption and layout of plan (Lilhore et al. , 2021).…”

Section: Discussion On Implementation Framework Of Industry 40mentioning

confidence: 99%

“…The identification of area is achieved based on present condition of maturity and find out where progress in maturity efforts is essential (Elsisi et al, 2021). After selecting appropriate industry, a connection is introduced about scope, platform and team which give a detailed description of roles and duties, agenda, objective of adoption and layout of plan (Lilhore et al, 2021). Further, we need to investigate and learn about mass production, processes in industry, their contents, material, wastage, human-machine collaboration, virtualization and available resources, etc.…”

Section: Blockchainmentioning

confidence: 99%

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Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment

Singh

Goyat

Panwar

2023

TQM

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PurposeAt the present time, Industry 4.0 has proven its effectiveness and significance in automation and data exchange within industries across different sectors worldwide. In the current literature, there is still a lack of research on adopting Industry 4.0 in the manufacturing setting in developing economies. The main purpose of the present study is to explore the fundamental pillars and framework for ease of adoption of Industry 4.0 in manufacturing environments, along with highlighting the benefits and challenges.Design/methodology/approachIn this study, a systematic literature review has been conducted through protocol, search, appraisal, synthesis, analysis, report (PSALSAR) model. In the literature, the articles are included within time span of 2008–2022, consisting keywords like Industry 4.0, blockchain, machine learning, artificial intelligence, Internet of Things, 3D printing, big data analytics, etc. Based on available literature, conceptual implementation framework of Industry 4.0 is proposed.FindingsThis study explored the key ingredients that play an essential role to bridge the gap and construct a strong relationship among physical and cyber world. The results reveals that the emerging technologies such as IoT, blockchain, artificial intelligence, augmented reality, 3D printing, big-data analytics, cloud-computing join hands to accomplish success in Industry 4.0 by reducing human interference for effective and efficient systems. In addition, the study also explored the possible benefits of emerging technologies with challenges faced by manufacturing setting during adaptation of Industry 4.0.Originality/valueAs per the authors' best knowledge, no research articles are found in literature which explore various emerging technologies in Industry 4.0 with its implementation framework in the manufacturing setting in developing economies. The main focus of the present study is to discover the literature review in defined area and find the research gap among current scenario and future trend for execution of Industry 4.0 in manufacturing environment.

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Section: Discussion On Implementation Framework Of Industry 40mentioning

confidence: 99%

Section: Blockchainmentioning

confidence: 99%

Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment

Singh

Goyat

Panwar

2023

TQM

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“…A TL algorithm takes a framework trained on a massive volume of data and applies its expertise to a relatively small database. We freeze the network’s initial CNN architecture for classification tasks and only train the final few layers that help to make a final prediction [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

Lilhore,

Manoharan,

Simaiya

et al. 2023

Sensors

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Industrial automation systems are undergoing a revolutionary change with the use of Internet-connected operating equipment and the adoption of cutting-edge advanced technology such as AI, IoT, cloud computing, and deep learning within business organizations. These innovative and additional solutions are facilitating Industry 4.0. However, the emergence of these technological advances and the quality solutions that they enable will also introduce unique security challenges whose consequence needs to be identified. This research presents a hybrid intrusion detection model (HIDM) that uses OCNN-LSTM and transfer learning (TL) for Industry 4.0. The proposed model utilizes an optimized CNN by using enhanced parameters of the CNN via the grey wolf optimizer (GWO) method, which fine-tunes the CNN parameters and helps to improve the model’s prediction accuracy. The transfer learning model helps to train the model, and it transfers the knowledge to the OCNN-LSTM model. The TL method enhances the training process, acquiring the necessary knowledge from the OCNN-LSTM model and utilizing it in each next cycle, which helps to improve detection accuracy. To measure the performance of the proposed model, we conducted a multi-class classification analysis on various online industrial IDS datasets, i.e., ToN-IoT and UNW-NB15. We have conducted two experiments for these two datasets, and various performance-measuring parameters, i.e., precision, F-measure, recall, accuracy, and detection rate, were calculated for the OCNN-LSTM model with and without TL and also for the CNN and LSTM models. For the ToN-IoT dataset, the OCNN-LSTM with TL model achieved a precision of 92.7%; for the UNW-NB15 dataset, the precision was 94.25%, which is higher than OCNN-LSTM without TL.

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“…UAVs play a vital role in precision agriculture for data collection and monitoring [219]- [224]. However, there is a research gap in understanding the security vulnerabilities associated with UAVs.…”

Section: Security Of Unmanned Aerial Vehicles (Uavs) In Agriculturementioning

confidence: 99%

A comprehensive examination of security and privacy in precision agriculture technologies

Pauline A. Ongadi

2024

GSC Adv. Res. Rev.

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Precision agriculture has revolutionized modern farming practices by integrating cutting-edge technologies such as sensors, drones, and data analytics to optimize crop management. While these advancements offer unprecedented benefits in terms of yield optimization and resource efficiency, they also raise significant concerns regarding the security and privacy of sensitive agricultural data. This research paper delves into the intricate landscape of precision agriculture security and privacy, aiming to identify potential threats, vulnerabilities, and the corresponding measures necessary for safeguarding farmers' data and ensuring the sustainability of precision farming practices. The study employs a multidisciplinary approach, combining insights from computer science, agriculture, and privacy studies to provide a comprehensive overview. Through an analysis of existing security frameworks and privacy regulations, this paper proposes recommendations for the development and implementation of robust security protocols and privacy-enhancing technologies tailored to the unique challenges of precision agriculture. The findings of this research contribute to the ongoing dialogue on responsible technology adoption in agriculture, emphasizing the importance of striking a balance between innovation and the protection of farmers' sensitive information in the era of precision farming.

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Impact of Deep Learning and Machine Learning in Industry 4.0

Cited by 11 publications

References 0 publications

Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment

Fundamental pillars for industry 4.0 development: implementation framework and challenges in manufacturing environment

HIDM: Hybrid Intrusion Detection Model for Industry 4.0 Networks Using an Optimized CNN-LSTM with Transfer Learning

A comprehensive examination of security and privacy in precision agriculture technologies

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