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Industry 4.0 – Applications, challenges and opportunities in industries and academia: A review

Materials Today: Proceedings 2023年 79卷 389-394页

作者： Rana, Bharti Rathore, Sanjay S. Department of Computer Science University of Delhi Delhi India Skill Faculty of Engineering and Technology Shri Vishwakarma Skill University Dudhola Palwal Haryana India

Industrial revolutions have always influenced the other sectors since the first industrial revolution. "Industry 4.0 (I4.0)", the fourth industrial revolution, in particular, has impacted the ecosystem of the industries where the focus is on developing cyber-physical systems (CPS). The ultimate goal of I4.0 is building "smart" machines that can predict and take intelligent/smart decision(s) using enablers of I4.0 like data analysis, communication, and internet-of-things. I4.0 has applications in many sectors like healthcare, agriculture, wood, food, and education. It has boosted not only these sectors but also new concepts like Smart Material, Agriculture 4.0, Health 4.0, Smart Operator 4.0, and many more have evolved in the literature. Adopting I4.0 also highlighted creating a new educational paradigm that focuses on developing the Smart Workforce, Smart Operators, Smart Material and Smart Project Managers and boosted the concept of Education 4.0. The objective of the present work is to review the research works of I4.0, its applications, and challenges in the above-listed prominent sectors. The focus of the work is to report (i) the current state of various technologies of I4.0 in the industries, (ii) the present status of I4.0 in the academia ecosystem, and (iii) key identified challenges in the adoption of I4.0. It was observed that technologies, mainly Artificial Intelligence, IoT, machine learning, CPS, and robotics, are popularly used. However, the popularity of technologies varies according to the sector or application area. Also, an attempt was made to report the various opportunities for the researchers and how Academia can support the demands of I4.0. © 2023

关键词： Industry 4.0

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Daily Life Conversation in Bangla Sign Language Using Machine Learning Technique 6

Daily Life Conversation in Bangla Sign Language Using Machin...

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6th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Shakil, Mirza Md Amin, Al Anam, Tahmid Hussein, Molla Rashied Alam, Tahira Chowdhury, Jamim Hossain University of Asia Pacific Department of Computer Science and Engineering Dhaka Bangladesh University of Skill Enrichment & Technology Department of Computer Science and Engineering Narayanganj Bangladesh

ISBN: (纸本)9798350396133

Sign language is a way of communicating and interacting for access to information. Currently, deaf and mute people rely heavily on sign language to communicate. It enables the challenged person to interact with a hearing-enabled person to express themselves and participate in all facets of life. In these circumstances, research is needed in the area of sign language recognition as limited work has been conducted. Also, as it varies from region to region and in the context of BangIa sign language, related research is more mandated. The dataset in this study consisted of 9,000 video data. Alongside this, Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) were used to establish a two-way communication system. LSTM is used for long-term dependencies and sequence predictions. To gather video data and extract characteristics from the video frames, MediaPipe and OpenCV were employed. Fine-tuning techniques, such as the number of hidden layers, the density of layers, the number of neurons within each layer, the learning rate, the optimizer, and other parameters were used to determine the optimal accuracy. The results showed that 96% of the categories were accurately represented by scikit-Iearn. This result offers a reference for employing deep learning structures for the detection of BangIa sign language and the development of dependable assistive technology tailored to the necessity of the hearing-impaired community. © 2024 IEEE.

关键词： Gesture recognition

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Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

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computers, Materials & Continua 2023年第1期74卷 881-895页

作者： Nitin Sharma Mohd Anul Haq Pawan Kumar Dahiya B.R.Marwah Reema Lalit Nitin Mittal Ismail Keshta Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Electronics&Communication Engineering DCRUST Murthal131039India Department of Transportation Engineering IITKanpur208016Uttar PradeshIndia Department of Computer Science Applications PIETSmalkha132102India Skill Faculty of Science and Technology Shri Vishwakarma Skill UniversityPalwal121102India Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile *** is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight *** the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency *** Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic *** study is based on a comprehensive and detailed literature evaluation in the field of *** major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car *** research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for *** properties of characters are employed to recognise the Indian licence plate in this *** licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were *** comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

关键词： Intelligent transportation system automatic license plate recognition system neural network random forest convolutional neural network support vector machine

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Modeling and Predictive Analytics of Breast Cancer Using Ensemble Learning Techniques:An Explainable Artificial Intelligence Approach

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computers, Materials & Continua 2024年第12期81卷 4033-4048页

作者： Avi Deb Raha Fatema Jannat Dihan Mrityunjoy Gain Saydul Akbar Murad Apurba Adhikary Md.Bipul Hossain Md.Mehedi Hassan Taher Al-Shehari Nasser A.Alsadhan Mohammed Kadrie Anupam Kumar Bairagi Computer Science and Engineering Discipline Khulna UniversityKhulna9208Bangladesh Information and Communication Engineering Noakhali Science and Technology UniversityNoakhali3814Bangladesh School of Computing Sciences and Computer Engineering University of Southern MississippiHattiesburgMS 39401USA Computer Skills Department of Self-Development SkillCommon First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia

Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer *** disease arises when cells in the breast undergo unregulated proliferation,resulting in the formation of a tumor that has the capacity to invade surrounding *** is not confined to a specific gender;both men and women can be diagnosed with breast cancer,although it is more frequently observed in *** detection is pivotal in mitigating its mortality *** key to curbing its mortality lies in early ***,it is crucial to explain the black-box machine learning algorithms in this field to gain the trust of medical professionals and *** this study,we experimented with various machine learning models to predict breast cancer using the Wisconsin Breast Cancer Dataset(WBCD)*** applied Random Forest,XGBoost,Support Vector Machine(SVM),Multi-Layer Perceptron(MLP),and Gradient Boost classifiers,with the Random Forest model outperforming the others.A comparison analysis between the two methods was done after performing hyperparameter tuning on each *** analysis showed that the random forest performs better and yields the highest result with 99.46%*** performance evaluation,two Explainable Artificial Intelligence(XAI)methods,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-Agnostic Explanations(LIME),have been utilized to explain the random forest machine learning model.

关键词： Breast cancer prediction machine learning models explainable artificial intelligence random forest hyperparameter tuning

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Doctor's Specialty Recommendation Using Deep Learning Trained Adaptive Al-Biruni Earth Radius Optimization 1

Doctor's Specialty Recommendation Using Deep Learning Traine...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Jims, Anupama Philip, Felix M Computer Science and Engineering Department India Cs & It Department India

ISBN: (纸本)9798350330915

In recent years, healthcare development has been a prominent element of medical world. As a result, the healthcare system is critical for recognizing larger quantities of patient data for disease diagnosis. Furthermore, the health intelligent systems are important for the decision-making of healthcare platforms and the selection of specialist doctor for the disease symptoms are needed to cure the disease on the earlier stage. Therefore, this research developed the hybrid optimized deep learning (DL) technique for doctor's specialty recommendation system. The Min-max normalization-based data preprocessing is the beginning stage of this process. The Kumar-Hassebrook similarity is used to choose the required features in feature selection. In data augmentation, the oversampling technique is utilized to increase the size and quality of the data. Furthermore, the deep Kronecker Network (DKN) provided the best outcomes for doctor's specialist recommendation. The training of DKN is done via the proposed adaptive Al-Biruni Earth Radius optimization (adaptive BER). Moreover, the performance of the model is analyzed usingDoctor's Specialty Recommendation dataset and the proposed method offers the good performance with respect to accuracy, sensitivity and specificity with the superior values 0.907, 0.905 and 0.907, respectively. It improves the online consultation experience and offline treatment convenience of patients. © 2023 IEEE.

关键词： Al-Biruni Earth Radius optimization Deep Kronecker Network Deep Learning Doctor's Specialty Recommendation Medical data

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Automatic Violence Detection Technique Using Mobinet 6

Automatic Violence Detection Technique Using Mobinet

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6th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2024

作者： Karim, Md Abdul Islam Razin, Md. Jahidul Shopon, Md Alam, Tahira Chowdhury, Jamim Hossain Arman, Shajedul Hasan University of Asia Pacific Department of Computer Science and Engineering Dhaka Bangladesh University of Calgary Department of Computer Science Calgary Canada University of Skill Enrichment & Technology Department of Computer Science and Engineering Dhaka Bangladesh Panace Live Dhaka Bangladesh

ISBN: (纸本)9798350396133

Deep learning holds great significance in machine learning since it effectively addresses a wide range of problems. The ability to identify violence from various video surveillance systems is one such crucial real-world application. Many crimes are occurring in numerous public spaces as a result of inadequate security. Many methods have been proposed to solve this specific problem, however they have drawbacks. Additionally, they are ineffective since they are dependent on certain conditions. Therefore, we presented an efficient automatic violence detection method for video datasets. We created a video dataset consisting of 1000 videos, half of which featured violent content and the other half did non-violent content. We used a Deep Neural Network technique called MobilNet for detecting violence from videos. Additionally, we employed a variety of deep learning and machine learning strategies to increase the precision. With the training data, the classification model showed an accuracy of 97.50% while with the test data, the accuracy was 95.60%. Performance evaluation results demonstrated that the suggested method identified violent content in videos successfully. The method for video violence detection performed better than many other methods already in use. © 2024 IEEE.

关键词： Adversarial machine learning

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Intrinsic Entropy: A Novel Adaptive Method for Measuring the Instantaneous Complexity of Time Series

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IEEE Signal Processing Letters 2023年 30卷 160-164页

作者： Huang, Po-Hsun Hsiao, Tzu-Chien Hsinchu300 Taiwan Nycu Department of Computer Science College of Cs and Institute of Biomedical Engineering College of Electrical and Computer Engineering Hsinchu300 Taiwan

The determination of appropriate parameters and an appropriate window size in most entropy-based measurements of time-series complexity is a challenging problem. Inappropriate settings can lead to the loss of intrinsic information within a time series. Therefore, two parameter-free methods, namely the intrinsic entropy (IE) and ensemble IE (eIE) methods, are proposed in this paper. The eIE method requires two parameters, which can be easily determined through an orthogonality test. The proposed approaches can measure instantaneous complexity;thus, they do not require a predetermined window size. White noise and three other varieties of colored noise were used to test the stability of the proposed methods, and five types of synthetic signals and logistic maps were applied for measuring instantaneous complexity and regularity. The results revealed that the IE and eIE methods exhibit satisfactory stability. Both methods provide point-by-point entropy measures for time series. The eIE method is useful for measuring the complexity of frequency and amplitude modulation. Furthermore, the periodicity of time series can be detected using the two proposed methods. © 1994-2012 IEEE.

关键词： Empirical mode decomposition

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A Deep Learning Approach to Industrial Corrosion Detection

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computers, Materials & Continua 2024年第11期81卷 2587-2605页

作者： Mehwash Farooqui Atta Rahman Latifa Alsuliman Zainab Alsaif Fatimah Albaik Cadi Alshammari Razan Sharaf Sunday Olatunji Sara Waslallah Althubaiti Hina Gull Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information Systems(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The proposed study focuses on the critical issue of corrosion,which leads to significant economic losses and safety risks worldwide.A key area of emphasis is the accuracy of corrosion detection *** recent studies have made progress,a common challenge is the low accuracy of existing detection *** models often struggle to reliably identify corrosion tendencies,which are crucial for minimizing industrial risks and optimizing resource *** proposed study introduces an innovative approach that significantly improves the accuracy of corrosion detection using a convolutional neural network(CNN),as well as two pretrained models,namely YOLOv8 and *** leveraging advanced technologies and methodologies,we have achieved high accuracies in identifying and managing the hazards associated with corrosion across various industrial *** advancement not only supports the overarching goals of enhancing safety and efficiency,but also sets a new benchmark for future research in the *** results demonstrate a significant improvement in the ability to detect and mitigate corrosion-related concerns,providing a more accurate and comprehensive solution for industries facing these *** CNN and EfficientNetB0 exhibited 100%accuracy,precision,recall,and F1-score,followed by YOLOv8 with respective metrics of 95%,100%,90%,and 94.74%.Our approach outperformed state-of-the-art with similar datasets and methodologies.

关键词： Deep learning YOLOv8 EfficientNetB0 CNN corrosion detection Industry 4.0 sustainability

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Industry 4.0 – Applications, challenges and opportunities in industries and academia: A review

Industry 4.0 – Applications, challenges and opportunities i...

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关键词： Industry 4.0

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Efficient practical Byzantine Consensus-based reputation method for IoT based electronic waste tracking and tracing system using blockchain

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Multimedia Tools and Applications 2024年第37期83卷 84639-84672页

作者： Sharma, Lokesh Gupta, Ravi Kant Lamba, Chattar Singh Kumar, Ajay Lathar, Pankaj Department of IT Manipal University Jaipur Rajasthan303007 India Department of Mechanical Engineering Manipal University Jaipur Rajasthan303007 India Department of Computer Science & amp Engineering Manipal University Jaipur Rajasthan303007 India Department of Mechatronics Engineering Manipal University Jaipur Rajasthan303007 India Department of Computer Science and Applications Delhi Skill and Entrepreneurship University Dwarka New Delhi Delhi110077 India

The utilization of smart contracts enables the integration of blockchain technology to provide incentives for the gathering of electronic trash. Traditional blockchain-based Internet of Things (IoT)-E-Waste Tracking systems have challenges such as data privacy issues, significant delays, and low transaction per second (TPS) rates. In order to tackle these difficulties, the proposed approach utilizes a blockchain-based technology called IoT-enabled Efficient Practical Byzantine Consensus-based Reputation (EPBCR). This approach effectively monitors the post-production business processes of electronic devices by using hybrid consensus and reputation optimization algorithms. As a result, it enhances transparency, traceability, and privacy. The system efficiently regulates and oversees electronic gadgets throughout its supply chain by employing smart contracts on an unchangeable distributed ledger. The methodology is supported by analyzing the gas usage of Smart contracts on the Ethereum network. This results in the creation of, efficient, and high-throughput system that assists participants in conducting economic transactions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Smart contract

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