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A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing

Energies 2023年第22期16卷 7660-7660页

作者： Hsu, Chao-Chung Jiang, Bi-Hai Lin, Chun-Cheng Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program in Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan

To enable highly automated manufacturing and net-zero carbon emissions, manufacturers have invested heavily in smart manufacturing. Sustainable and smart manufacturing involves improving the efficiency and environmental sustainability of various manufacturing operations such as resource allocation, data collecting and monitoring, and process control. Recently, a lot of artificial intelligence and optimization applications based on smart grid systems have improved the energy usage efficiency in various manufacturing operations. Therefore, this survey collects recent works on applications of artificial intelligence and optimization for smart grids in smart manufacturing and analyzes their features, requirements, and challenges. In addition, potential trends and further challenges for the integration of smart grids with renewable energies for smart manufacturing, applications of 5G and B5G (beyond 5G) technologies in the SG system, and next-generation smart manufacturing systems are discussed to provide references for further research. © 2023 by the authors.

关键词： Smart power grids

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A Novel Blockchain-Based Strategy for Energy Conservation in Cognitive Wireless Sensor Networks

A Novel Blockchain-Based Strategy for Energy Conservation in...

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Blockchain Technology and Information Security (ICBCTIS), International Conference on

作者： Khemraj Sharma Rohit Goyal Sukesh Kumar Bhagat Sheenu Agarwal Girish Singh Bisht Mohemmed Hussien Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Taiwan School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India Department of Computer Science & Engineering Uttaranchal University Dehradun India Department of Electronics and Communication Engineering Uttarakhand Technical University Dehradun India Department of Computer Science and Engineering Tula's Institute Dehradun India Dehradun India The Islamic university Najaf Iraq

ISBN: (数字)9798331528324

ISBN: (纸本)9798331528331

Internet of Things (IoT) industrial applications have increased in internet-connected devices. As a result, accessibility, scalability, connectivity, and adaptability have become major challenges. It is possible to create connections between loT devices over the wireless medium, but efficiently utilizing scarce spectrum is the biggest concern. The paper proposes a novel method for selecting sensing nodes with the goal of reducing energy usage and increasing sensing efficiency. In order to ensure that node trust values conform to each other without any ambiguity, cryptography encrypts all information that relates to a node in a central node (CN). Nodes participating in CSS are selected based on their performance. Based on a comparison of various algorithms using varying numbers of nodes, the proposed algorithm achieves a 10% sensing efficiency.

关键词： Wireless communication Wireless sensor networks Adaptation models Machine learning algorithms Heuristic algorithms Scalability Sensors Blockchains Internet of Things Reliability

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ali Toccacieli Manuela Petti MSc Program in Engineering in Computer Science Sapienza University of Rome Rome Italy dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them.

关键词： Measurement Blood platelets Network analyzers Cells (biology) Biomarkers Object recognition Gene expression

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A Hybrid Learning Approach for Automatic Data Labelling and Anomaly Detection in IoT Networks

A Hybrid Learning Approach for Automatic Data Labelling and ...

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Artificial Intelligence (ICAI), International Conference on

作者： Rimsha Kanwal Umara Noor Zahid Rashid Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul South Korea

Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to corrupted data or by any other uncertainty and ambiguity in data. The data can also be corrupted through a damage in device. These attacks or anomalies damage the working of the IoT networks. The anomalous data can be detected through detection techniques however most anomaly detection techniques depend upon labelled data but for IoT datasets, usually class labels are not available. Labeling is performed by a manual process which is time consuming and also costly. As data in IoT increases day by day so there is a need to label and classify data for future unseen data. In this paper a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset. The model contains two function. In the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous. In the second phase labelled dataset is used to train Random Forest model to detect anomalies in IoT networks. The results show that the proposed model is detecting anomalies in IoT networks with an accuracy 98%, precision 98 %, recall 98%, and F-meausre 0.98%.

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Modelling e-waste management data in smart cities 2

Modelling e-waste management data in smart cities

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2nd Sustainable Cities Latin America Conference, SCLA 2021

作者： De Novais, Marcos Pereira De Almeida Xavier, Victor Xavier, Lucia Helena Hwang, Junseok Seoul National University Technology Management Economics and Policy Program Siheung-si Korea Republic of University of Rio de Janeiro Program in Computer Science and Systems Engineering Rio de Janeiro Brazil Centre for Mineral Technology Ministry of Science Technology and Inovation Rio de Janeiro Brazil

ISBN: (纸本)9781665437684

The smart cities concept is strongly related to energy efficiency and low carbon patterns, what that refers to meeting the sustainability criteria established by regulatory mechanisms. The Smart City Index (SCI) data can reach a higher quality of living by observing aspects as mobility, health, safety, transparency, smart management, and many others, like the waste management. Thus, this set of indicators was used to evaluate the e-waste management as an emergent challenge in urban cities, providing both, the generation of waste from the intense consumption of electronic devices, as well meeting the questions arising from urbanization from the integration of digital solutions. This study proposes a model of analysis using simple linear regression and k-mean clustering as correlation methods. The findings suggested correlation between the SCI ranking, the E-waste generated, and the economic situation of the entire set of smart cities. Nevertheless, it was found weak correlation for the best rated cities. The results provided the perception that it is possible to identify correlation from the Smart City Index and aspects from the e-waste management system, enabling the discussion over the importance of next smart city indexes and given a new perspective to the e-waste management. © 2021 IEEE.

关键词： Environmental management

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Comparison between technical parameters recommended by regulatory agencies and relevant institutions for ventilatory therapy equipment used in patients with COVID-19

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Research on Biomedical engineering 2021年第2期37卷 375-388页

作者： de Souza, Daniel Baldoino Frondana, Fernanda Vieira Silva, Fábia Diniz Centre for Innovation and Technology Assessment in Health Postgraduate Program in Electrical and Biomedical Engineering Faculty of Electrical Engineering Federal University of Uberlândia Uberlândia Brazil Health Science Center Postgraduate Program in Health Management and Innovation Federal University of Rio Grande do Norte Natal Brazil Pneumology Division Heart Institute at Clinical Hospital Faculty of Medicine University of São Paulo São Paulo Brazil

Background: Patients contaminated with the new coronavirus, SARS-CoV-2, and who develop the severe form of the disease require ventilatory support to maintain adequate gas exchange. The objective of this systematic review was to establish reference parameters of ventilator therapy used in the various levels of severity of adult patients affected by COVID-19 to assist health professionals and hospital managers who will purchase this equipment. Methods: It used four databases: Medline, Cochrane Central, Scielo, and Pubmed. Restrictions applied as to the language, restricted to English, and to the patient’s profile, only adults affected by SARS-CoV-2. The review process was carried out by three independent reviewers using the PRISMA-P method. Results: Nineteen articles were included, in addition to the recommendations of the Brazilian Association of Intensive Care Medicine (Brazil), Federal Commission for Protection against Health Risks (Mexico), Medicines and Healthcare Products Regulatory Agency (UK), Pan American Health Organization, and World Health Organization. The types of ventilatory therapies found in the articles eligible for this study were continuous positive airway pressure (CPAP), non-invasive ventilation (NIV), and invasive mechanical ventilation (IMV). For each ventilatory therapy, the parameters presented in the article and the recommendations of regulatory agencies and relevant institutions were compared and analyzed. Conclusion: It was possible to suggest a relationship between the respiratory support strategy adopted by the clinical team and the clinical conditions of the patient infected with SARS-CoV-2. The included studies used ventilation modes and parameters within the specifications and recommendations of relevant regulatory agencies and institutions. However, some minor differences were observed in the instruction presented among the agencies more related to the recommended ventilation modes. Further studies are needed. © 2021, Sociedade Bras

关键词： Coronavirus

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Ravindra Pandey Aruna Sri Rongali S. Gopinath Nilesh Anute Forda Nama Sachin Pund Department - Management Hult International Business School Doctoral Information Technology/Information Technology Emphasis/Information Technology University of The Cumberlands US Department of Electronics And Communication Engineering Karpagam Institute of Technology Coimbatore Management ASM's Institute of Business Management and Research Pune Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets.

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IoT human needs inside compact house

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Journal of Open Innovation: Technology, Market, and Complexity 2023年第1期9卷 1-9页

作者： Azis, Baskoro Ong, Ardvin Kester S. Prasetyo, Yogi Tri Persada, Satria Fadil Young, Michael N. Sari, Yanuarita Kusuma Permata Nadlifatin, Reny Interior Design Department School of Design Bina Nusantara University Jakarta 11480 Indonesia School of Industrial Engineering and Engineering Management Mapua University Manila 658 Muralla St. Intramuros Manila 1002 Philippines Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Communication Science Department Faculty of Digital Communication and Hotel & Tourism Bina Nusantara University Jakarta 11480 Indonesia Department of Information Systems Institut Teknologi Sepuluh November Kampus ITS Sukolilo Surabaya 60111 Indonesia

Indonesia is entering Industry 4.0, but is still not using supportive technology, especially in residential areas. A compact house is one of the dwellings built on limited modern land, providing flexible use of space according to the activities carried out by residents. The current version of this type of residence, namely smart homes, is still far from what is expected. There is a need for the modern use of IoT in every room, which can adapt to the activities of residents to make it easier for residents to move. A grounded theory and descriptive approach was used to find the continuity of modern Industry 4.0 and IoT technology in smart homes with the compact house concept to find the novelty of design standards. The findings are in the form of lighting that can be adjusted via IoT to reduce the crime rates in abandoned homes or in homes that are not active because the residents are traveling. © 2023 The Authors

关键词： Activity Compact house IoT Light Smart home

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An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels

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Computers in Biology and Medicine 2025年 189卷 109974-109974页

作者： Mohammed, Areej Alshraideh, Hussam Abu-Helalah, Munir Shamayleh, Abdulrahim Department of Industrial Engineering Engineering Systems Management Program American University of Sharjah SharjahP.O. Box 26666 United Arab Emirates Department of Industrial Engineering American University of Sharjah SharjahP.O. Box 26666 United Arab Emirates Industrial Engineering Department Jordan University of Science and Technology Irbid Jordan Department of Family and Community Medicine School of Medicine University of Jordan Public Health Institute Amman Jordan

Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for biomarker prediction. These models offer the potential to enhance disease diagnosis through data-driven approaches relying on non-invasive techniques. However, no studies have explored the application of fully non-invasive methods for predicting thyroid-stimulating hormone (TSH) levels. Consequently, this study introduces a novel, fully non-invasive framework for predicting TSH levels by developing an innovative hybrid machine learning model that balances performance, complexity, and interpretability. Seven ML models were evaluated, and the best-performing models were integrated into a hybrid approach to balance performance, complexity, and interpretability. A dataset of 6190 instances from Jordan was used for model development. Four-dimensional non-invasive factors, including demographics, symptoms, family history, and newly engineered symptom scores, were incorporated into the model. The hybrid model achieved an R2 of 94.2 % and RMSE of 0.015, demonstrating superior predictive performance. Model interpretability was ensured using LIME and SHAP explainers, confirming aggregated symptom scores' critical role in enhancing prediction accuracy. A robust feature selection technique was implemented, reducing model complexity and enhancing performance. Among the top ten features for predicting TSH levels were hypothyroidism and hyperthyroidism symptom scores, family history, cold intolerance, itchy-dry skin, sweating, hand tremors, and palpitations. The model can be employed to develop cost-effective diagnostic tools for thyroid disorders. It also offers a robust framework that can be generalized to predict other biomarkers and applied in diverse contexts. © 2025 Elsevier Ltd

关键词： Prediction models

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Organizational Culture's Significance in Technology and Innovation management

Organizational Culture's Significance in Technology and Inno...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Somanchi Hari Krishna Ulfah Fajarini Arun Kant Painoli C. Visvesvaran K. N. Jahnavi Melanie Lourens Department of Business Management Vignana Bharathi Institute of Technology India Faculty of Education Social Science Study Program Syarif Hidayatullah State Islamic University Jakarta Indonesia School of Management Studies Baddi University of Emerging Sciences and Technology Baddi Himachal Pradesh India Sri Krishna College of Engineering and Technology Coimbatore India SIndhi Institute of Management Bangalore Faculty of Management Sciences Durban University of Technology South Africa

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Several studies have examined the connection between innovation, technology, and organizational culture in recent years. management of technology and innovation is crucial to an organization's expansion and success. The development of an innovative organizational culture comes before technology and innovation management. This study's primary goal was to assess how organizational culture affects innovation in the Indian service sector. The study made use of a paradigm for understanding organisational culture created by Cameron and Quinn, which looks at culture as a collection of conflicting values from four angles. A descriptive survey research design was used in the investigation. Employees of service industries made up the study's population. More than twenty IT organizations participated in the survey, and 120 out of 180 respondents returned completed questionnaires. This indicated a response rate of 66%.The collected data was analyzed using inferential data analysis techniques and descriptive statistics. Tables and figures were created after analysis with IBM SPSS software. The results demonstrated that the strongest associations with innovation and technology were found in companies with flexible, collaborative, and employee-friendly cultures. Lastly, the study suggests areas for additional research, such as employing a new organizational culture model for the study or qualitative techniques for data collecting and analysis to reveal deeper cultural features.

关键词： Surveys Industries Technological innovation Technology management Quantum computing Sociology Innovation management

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