Info
Applications of Robotics in Industry Using Advanced Mechanisms : Proceedings of International Conference on Robotics and Its Industrial Applications 2019 / / edited by Janmenjoy Nayak, Valentina E. Balas, Margarita N. Favorskaya, Bibhuti Bhusan Choudhury, S. Krishna Mohan Rao, Bighnaraj Naik
| Applications of Robotics in Industry Using Advanced Mechanisms : Proceedings of International Conference on Robotics and Its Industrial Applications 2019 / / edited by Janmenjoy Nayak, Valentina E. Balas, Margarita N. Favorskaya, Bibhuti Bhusan Choudhury, S. Krishna Mohan Rao, Bighnaraj Naik |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (417 pages) |
| Disciplina | 629.892 |
| Collana | Learning and Analytics in Intelligent Systems |
| Soggetto topico |
Computational intelligence
Automatic control Robotics Automation Computational Intelligence Control, Robotics, Automation |
| ISBN | 3-030-30271-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483501403321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Artificial Intelligence in Microbial Research : Bridging the Gap / / edited by Babita Pandey, Devendra Pandey, Aditya Khamparia, Venkatesh Dutta, Valentina E. Balas
| Artificial Intelligence in Microbial Research : Bridging the Gap / / edited by Babita Pandey, Devendra Pandey, Aditya Khamparia, Venkatesh Dutta, Valentina E. Balas |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XVI, 450 p. 104 illus., 94 illus. in color.) |
| Disciplina | 579.1788 |
| Collana | Microorganisms for Sustainability |
| Soggetto topico |
Microbial populations
Microbiology Cytology Microbial ecology Artificial intelligence Machine learning Artificial intelligence - Data processing Microbial Communities Cellular Microbiology Environmental Microbiology Artificial Intelligence Machine Learning Data Science |
| ISBN | 981-9634-48-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Thematic Analysis of Media Influence on the Adoption of AI Climate Prediction Models in Microbial Agriculture Practices: A Case Study of Uttar Pradesh Using Diffusion of Innovations Theory -- Chapter 2. Understanding Media Influence on the Adoption of AI Climate Prediction Models in Microbiological Agricultural Practices: A Study of Uttarakhand -- Chapter 3. Advancements in Precision Agriculture: Integrating Machine Learning Techniques for Crop Monitoring and Management -- Chapter 4. Advances in Agricultural Analytics Machine Learning Applications for Crop Monitoring and Management -- Chapter 5. AI Driven Strategies for Microbial Infection from Discovery to Therapeutic Design -- Chapter 6. Use of Artificial Intelligence for Monitoring Algal Blooms in Aquatic Ecosystem -- Chapter 7. Ai-Yolact Model for Automatic Severity Grading Of Microbial Based Anthracnose Infection in Camellia Leaves -- Chapter 8. An Explainable AI Based CNN model for Plant Disease Diagnosis -- Chapter 9. Artificial Intelligent Enable Intelligent Bio-Sensor for Microbial Analysisfor Lung Health -- Chapter 10. Biosensors Guided Ai Interventions in Personalized Medicines -- Chapter 11. Education and Training for Developing Responsible AI Solutions in Healthcare -- Chapter 12. Automation of Drug Discovery & Development -- Chapter 13. Genome Studies and Disease Diagnosis -- Chapter 14. Exploring Explainable Artificial Intelligence in Healthcare: Issues, Challenges and Opportunities -- Chapter 15. Investigating Integron as the Principal Factor of Antibiotic Resistance in the Human Gut: A Holistic Perspective -- Chapter 16. Hybrid Deep Learning for Predictive Modelling of Microbial Biostimulants in Precision Agriculture -- Chapter 17. Challenges and Opportunities In Integrating Generative Al With Wearable Devices -- Chapter 18. Medical Image Analysis and Morphology Using Artificial Intelligence -- Chapter 19. Simulation of Biological Structures Using Generative Artificial Intelligence -- Chapter 20. Neuromuscular Disease Classification: Leveraging Deep Learning Feature Extractors and Applications. |
| Record Nr. | UNINA-9911007364703321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Big Data Processing Using Spark in Cloud [[electronic resource] /] / edited by Mamta Mittal, Valentina E. Balas, Lalit Mohan Goyal, Raghvendra Kumar
| Big Data Processing Using Spark in Cloud [[electronic resource] /] / edited by Mamta Mittal, Valentina E. Balas, Lalit Mohan Goyal, Raghvendra Kumar |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XIII, 264 p. 89 illus., 62 illus. in color.) |
| Disciplina | 005.7 |
| Collana | Studies in Big Data |
| Soggetto topico |
Big data
Computer security Big Data Systems and Data Security Big Data/Analytics |
| ISBN | 981-13-0550-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Concepts of Big Data and Apache Spark -- Big Data Analysis in Cloud and Machine Learning -- Security Issues and Challenges related to Big Data -- Big Data Security Solutions in Cloud -- Data Science and Analytics -- Big Data Technologies -- Data Analysis with Casandra and Spark -- Spin up the Spark Cluster -- Learn Scala -- IO for Spark -- Processing with Spark -- Spark Data Frames and Spark SQL -- Machine Learning and Advanced Analytics -- Parallel Programming with Spark -- Distributed Graph Processing with Spark -- Real Time Processing with Spark -- Spark in Real World -- Case Studies. . |
| Record Nr. | UNINA-9910739483403321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Bio-inspired Neurocomputing / / edited by Akash Kumar Bhoi, Pradeep Kumar Mallick, Chuan-Ming Liu, Valentina E. Balas
| Bio-inspired Neurocomputing / / edited by Akash Kumar Bhoi, Pradeep Kumar Mallick, Chuan-Ming Liu, Valentina E. Balas |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
| Descrizione fisica | 1 online resource (427 pages) : illustrations |
| Disciplina | 006.38 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Image processing - Digital techniques Computer vision Neurosciences Machine learning Neural networks (Computer science) Computational Intelligence Computer Imaging, Vision, Pattern Recognition and Graphics Neuroscience Machine Learning Mathematical Models of Cognitive Processes and Neural Networks |
| ISBN | 981-15-5495-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Performance Measurement of various Hybridized kernels for Noise Normalization -- A precise analysis of Deep Learning for Medical Image Processing -- Artificial Intelligence for Internet of Things -- A Brief Review on Brain Tumour Detection -- Deep Learning Techniques for Electronic Health -- A Review on Psychological Brainwaves Behavior. |
| Record Nr. | UNINA-9910483945103321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Communication and Networks : Proceedings of GUCON 2019 / / edited by Lakhmi C. Jain, George A. Tsihrintzis, Valentina E. Balas, Dilip Kumar Sharma
| Data Communication and Networks : Proceedings of GUCON 2019 / / edited by Lakhmi C. Jain, George A. Tsihrintzis, Valentina E. Balas, Dilip Kumar Sharma |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (347 pages) : illustrations |
| Disciplina | 004.6 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Telecommunication
Computer networks - Security measures Data mining Big data Engineering - Data processing Communications Engineering, Networks Mobile and Network Security Data Mining and Knowledge Discovery Big Data Data Engineering |
| ISBN | 981-15-0132-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910366605303321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Data Mining and Information Security : Proceedings of ICDMIS 2024, Volume 4 / / edited by Soumi Dutta, Abhishek Bhattacharya, Valentina E. Balas, Mohammad Kamrul Hasan
| Data Mining and Information Security : Proceedings of ICDMIS 2024, Volume 4 / / edited by Soumi Dutta, Abhishek Bhattacharya, Valentina E. Balas, Mohammad Kamrul Hasan |
| Edizione | [1st ed. 2025.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
| Descrizione fisica | 1 online resource (XVIII, 629 p. 302 illus., 260 illus. in color.) |
| Disciplina | 006.3 |
| Collana | Lecture Notes in Networks and Systems |
| Soggetto topico |
Computational intelligence
Artificial intelligence Data mining Data protection Computational Intelligence Artificial Intelligence Data Mining and Knowledge Discovery Data and Information Security |
| ISBN | 981-9660-63-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Exploring AI Classifiers to Identify Gestational Diabetes Mellitus during pregnancy -- Indian Sign Language Translator -- Desktop-based virtual assistant using python based on natural language processing -- Leveraging Machine Learning for Robust IoT Security: A Focus on Anomaly Detection Systems -- Analyzing the Effectiveness of Machine Learning Algorithms in Intrusion Detection. |
| Record Nr. | UNINA-9911021976603321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Digital Twin Technologies for Healthcare 4. 0
| Digital Twin Technologies for Healthcare 4. 0 |
| Autore | Dhanaraj Rajesh Kumar |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Stevenage : , : Institution of Engineering & Technology, , 2023 |
| Descrizione fisica | 1 online resource (217 pages) |
| Disciplina | 610.285 |
| Altri autori (Persone) |
MurugesanSanthiya
BalusamyBalamurugan BalasValentina E |
| Collana | Healthcare Technologies Series |
| Soggetto topico |
Digital twins (Computer simulation)
Medical telematics Artificial intelligence - Medical applications |
| ISBN |
1-83724-477-4
1-5231-5539-6 1-83953-580-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Title -- Copyright -- Contents -- About the editors -- 1 Introduction: digital twin technology in healthcare -- 1.1 Introduction -- 1.2 Digital twin - background study -- 1.3 Research on digital twin technologies -- 1.4 Digital twin sectors in healthcare -- 1.4.1 Digital patient -- 1.4.2 Pharmaceutical industry -- 1.4.3 Hospital -- 1.4.4 Wearable technologies -- 1.5 Challenges and issues in implementation -- 1.5.1 Trust -- 1.5.2 Security and privacy -- 1.5.3 Standardization -- 1.5.4 Diversity and multisource -- References -- 2 Convergence of Digital Twin, AI, IOT, and machine learning techniques for medical diagnostics -- 2.1 Introduction -- 2.2 DT technology -- 2.2.1 Steps in DT creation -- 2.2.2 DT types and functions -- 2.3 DT and its supporting technologies - AI, Cloud computing, DL, Big Data analytics, ML, and IoT -- 2.4 DT integration with other technologies for medical diagnosis and health management -- 2.5 DT technology and its application -- 2.5.1 DT application in manufacturing industry -- 2.5.2 Applications of DT in automotive & -- aerospace -- 2.5.3 Medicine diagnosis and device development -- 2.5.4 Wind twin technology -- 2.6 Conclusion -- References -- 3 Application of digital twin technology in model-based systems engineering -- 3.1 Evolution of DTT -- 3.2 Basic concepts of DTT -- 3.3 DTT implementation in power system -- 3.3.1 Characteristics of DTT in power systems -- 3.4 Power system network modeling using DTT -- 3.4.1 Model-based approach -- 3.4.2 Data-driven approach -- 3.4.3 Combination of both -- 3.5 Integration of power system with DTT -- 3.6 Future scope of DTT in power systems -- 3.7 Conclusion -- References -- 4 Digital twins in e-health: adoption of technology and challenges in the management of clinical systems -- 4.1 Introduction -- 4.2 Digital twin -- 4.3 Evolution of healthcare services.
4.4 Elderly medical services and demands -- 4.5 Cloud computing -- 4.6 Cloud computing DT in healthcare -- 4.6.1 Use cases -- 4.7 Digital healthcare modeling process -- 4.8 Cloud-based healthcare facility platform -- 4.9 Applications of DT technology -- 4.9.1 Cardiovascular application -- 4.9.2 Cadaver high temperature -- 4.9.3 Diabetes meters -- 4.9.4 Stress monitoring -- 4.10 Benefits of DT technology -- 4.10.1 Remote monitoring -- 4.10.2 Group cooperation -- 4.10.3 Analytical maintenance -- 4.10.4 Transparency -- 4.10.5 Future prediction -- 4.10.6 Information -- 4.10.7 Big data analytics and processing -- 4.10.8 Cost effectiveness -- 4.11 DT challenges in healthcare -- 4.11.1 Cost effectiveness -- 4.11.2 Data collection -- 4.11.3 Data protection -- 4.11.4 Team collaboration -- 4.11.5 Monitoring -- 4.11.6 Software maintenance and assurance -- 4.11.7 Regulatory complications -- 4.11.8 Security and privacy-related issues -- 4.11.9 Targets of attackers -- 4.12 Conclusion -- References -- 5 Digital twin and big data in healthcare systems -- 5.1 Introduction -- 5.1.1 Working of DT technology -- 5.2 Need for DT and big data in healthcare -- 5.3 DT and big data benefits for healthcare -- 5.3.1 Monitoring of patients -- 5.3.2 Individualized medical care -- 5.3.3 Patient individuality and freedom -- 5.4 Applications of DT in healthcare -- 5.4.1 Diagnosis and decision support -- 5.4.2 Patient monitoring -- 5.4.3 Drug and medical device development -- 5.4.4 Personalized medicine -- 5.4.5 Medical imaging and wearables -- 5.5 Enabling technologies for DT and data analytics in healthcare -- 5.5.1 Technologies for DT in healthcare -- 5.5.2 Technologies for data analytics in healthcare -- 5.6 Research challenges of DT and big data in healthcare -- 5.6.1 Problem complexities and challenges -- 5.6.2 Research challenges for DT in healthcare. 5.6.3 Useful information -- 5.7 Future research directions -- 5.8 Conclusion -- References -- 6 Digital twin data visualization techniques -- 6.1 Introduction - twin digital -- 6.2 Invention of DT -- 6.2.1 Function of DT technology -- 6.2.2 What problems has it solved? -- 6.3 DT types -- 6.3.1 Parts twinning -- 6.3.2 Product twinning -- 6.3.3 System twinning -- 6.3.4 Process twinning -- 6.4 When to use -- 6.5 Design DT -- 6.5.1 Digital data -- 6.5.2 Models -- 6.5.3 Linking -- 6.5.4 Examples -- 6.5.5 How has it impacted the industry? -- 6.5.6 DT usage -- 6.6 DT technology's characteristics -- 6.6.1 Connectivity -- 6.6.2 Homogenization -- 6.6.3 Reprogrammable -- 6.6.4 Digital traces -- 6.6.5 Modularity -- 6.7 Twin data to data -- 6.7.1 Requirements for obtaining complete data -- 6.7.2 Requirements on knowledge mining -- 6.7.3 Data fusion in real time -- 6.7.4 Data interaction in real time -- 6.7.5 Optimization in phases -- 6.7.6 On-demand data usage -- 6.7.7 Data composed of DTs -- 6.8 Data principles for DTs -- 6.8.1 Principle of complementary -- 6.8.2 The principle of standardization -- 6.8.3 The principle of timeliness -- 6.8.4 The association principle -- 6.8.5 Fusion principle -- 6.8.6 Information growth principle -- 6.8.7 The principle of servitization -- 6.9 DTD methodology -- 6.9.1 Information gathering for the DT -- 6.9.2 Data storage of DTs -- 6.9.3 DT data interaction -- 6.9.4 Association of DT data -- 6.9.5 Fusion of data from DTs -- 6.9.6 Data evolution in the DT -- 6.9.7 Data servitization for the DT -- 6.9.8 DT data's key enabler technologies -- 6.9.9 Advantages of DT -- 6.9.10 Disadvantages of DT -- 6.10 Conclusion -- References -- 7 Healthcare cyberspace: medical cyber physical system in digital twin -- 7.1 Introduction -- 7.2 Cyber physical systems -- 7.3 Digital twin -- 7.4 DT in healthcare -- 7.4.1 Patient monitoring using DT. 7.4.2 Operational efficiency in hospital using DT -- 7.4.3 Medical equipment and DT -- 7.4.4 DT in device development -- 7.5 Applications of DT in healthcare -- 7.5.1 Patient monitoring using DT -- 7.5.2 Medical wearables -- 7.5.3 Medical tests and procedures -- 7.5.4 Medical device optimization -- 7.5.5 Drug development -- 7.5.6 Regulatory services -- 7.6 DT framework in healthcare -- 7.6.1 Prediction phase -- 7.6.2 Monitoring phase -- 7.6.3 Comparison phase -- 7.7 Cyber resilience in healthcare DT -- 7.8 Cyber physical system and DT -- 7.8.1 Mapping in CPS and DTs -- 7.8.2 Unit level -- 7.8.3 System level -- 7.8.4 SoS level -- 7.9 Advantages of DT -- 7.10 Summary -- References -- 8 Cloud security-enabled digital twin in e-healthcare -- 8.1 Introduction -- 8.2 E-healthcare and cloud security-enabled digital twin -- 8.2.1 ICT facilities -- 8.2.2 Cloud security-enabled digital twin -- 8.3 Cloud healthcare service platform with digital twin -- 8.3.1 Wearable technologies -- 8.3.2 Pharmaceutical industry -- 8.3.3 Digital patients -- 8.3.4 Hospital -- 8.4 Security and privacy requirements for cloud security-enabled digital twin in e-healthcare -- 8.4.1 Security requirements for cloud security-enabled digital twin in e-healthcare -- 8.4.2 Privacy requirements for cloud security-enabled digital twin in e-healthcare -- 8.5 Challenges in cloud-based digital twin in e-healthcare -- 8.6 Conclusion -- References -- 9 Digital twin in prognostics and health management system -- 9.1 Introduction -- 9.2 Pile of DT -- 9.2.1 Digital mirror (physical infrastructure) -- 9.2.2 Digital data flow -- 9.2.3 Digital virtual thread -- 9.3 A complete DT model -- 9.4 Phases of DT development -- 9.4.1 Developing a simulation -- 9.4.2 Fusion of data -- 9.4.3 Interaction -- 9.4.4 Service -- 9.5 DT applications in healthcare -- 9.5.1 Healthcare system. 9.5.2 Recovery of the patient -- 9.5.3 Precision medicine -- 9.5.4 Research in pharmaceutical development -- 9.5.5 Drug administration -- 9.5.6 Disease treating ways -- 9.6 Challenges in DT implementation -- 9.6.1 Infrastructure for information technology -- 9.6.2 Data utilization -- 9.6.3 Consistent modeling -- 9.6.4 Modeling of domains -- 9.7 Role of DT in healthcare -- 9.7.1 Medicine that is tailored to the individual -- 9.7.2 Development of virtual organs -- 9.7.3 Medicine based on genomic data -- 9.7.4 Healthcare apps -- 9.7.5 Surgery scheduling -- 9.7.6 Increasing the effectiveness of healthcare organizations -- 9.7.7 Improving the experience of caregivers -- 9.7.8 Increasing productivity -- 9.7.9 Critical treatment window shrinking -- 9.7.10 Healthcare delivery system based on value -- 9.7.11 Rapid hospital erection -- 9.7.12 Streamlining interactions in call center -- 9.7.13 Development of pharmaceuticals and medical devices -- 9.7.14 Detecting the dangers in drugs -- 9.7.15 Simulating the new production lines -- 9.7.16 Improving the device availability -- 9.7.17 Post-sales surveillance -- 9.7.18 Human variability simulation -- 9.7.19 A lab's DT -- 9.7.20 Improving drug distribution -- 9.8 Benefits -- References -- 10 Deep learning in Covid-19 detection and diagnosis using CXR images: challenges and perspectives -- 10.1 Introduction -- 10.1.1 CNN -- 10.1.2 ANN -- 10.1.3 RNN -- 10.1.4 LSTM -- 10.1.5 GRU -- 10.1.6 Deep autoencoders -- 10.1.7 Deep Boltzmann's machine -- 10.2 Related work -- 10.2.1 Detection/localization -- 10.2.2 Segmentation -- 10.2.3 Registration -- 10.2.4 Classification -- 10.2.5 Application -- 10.3 Proposed model -- 10.3.1 Image pre-processing -- 10.3.2 Data augmentation -- 10.3.3 CNN with transfer learning -- 10.3.4 ChestXRay20 dataset -- 10.4 Experiments and result discussion -- Case 1: Covid-19 vs. healthy. Case 2: Covid-19 vs. pneumonia. |
| Record Nr. | UNINA-9911006696603321 |
Dhanaraj Rajesh Kumar
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| Stevenage : , : Institution of Engineering & Technology, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Engineering Research Methodology : A Practical Insight for Researchers / / by Dipankar Deb, Rajeeb Dey, Valentina E. Balas
| Engineering Research Methodology : A Practical Insight for Researchers / / by Dipankar Deb, Rajeeb Dey, Valentina E. Balas |
| Autore | Deb Dipankar |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (xii, 105 pages) |
| Disciplina | 620.0072 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Engineering
Study skills Education—Research Job Careers in Science and Engineering Research Skills Research Methods in Education Writing Skills |
| ISBN | 981-13-2947-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Literature Review and Technical Reading -- Attributions and Citations: Giving credit wherever due -- Building Intellectual Property Rights -- Ethics in Engineering Research -- Technical Writing and Publishing -- Contributions, Arguments and Dealing with Criticisms -- Research Management, Planning and Collaboration -- Communicating Research Work: Presentation Skills -- Bibliometrics and Research Quality. |
| Record Nr. | UNINA-9910350307203321 |
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Deb Dipankar
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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A Handbook of Internet of Things in Biomedical and Cyber Physical System / / edited by Valentina E. Balas, Vijender Kumar Solanki, Raghvendra Kumar, Md. Atiqur Rahman Ahad
| A Handbook of Internet of Things in Biomedical and Cyber Physical System / / edited by Valentina E. Balas, Vijender Kumar Solanki, Raghvendra Kumar, Md. Atiqur Rahman Ahad |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (331 pages) |
| Disciplina | 004.678 |
| Collana | Intelligent Systems Reference Library |
| Soggetto topico |
Computer engineering
Internet of things Embedded computer systems Computational intelligence Biomedical engineering Cyber-physical systems, IoT Computational Intelligence Biomedical Engineering/Biotechnology Biomedical Engineering and Bioengineering |
| ISBN | 3-030-23983-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910366605503321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Innovations in Infrastructure [[electronic resource] ] : Proceedings of ICIIF 2018 / / edited by Dipankar Deb, Valentina E. Balas, Rajeeb Dey
| Innovations in Infrastructure [[electronic resource] ] : Proceedings of ICIIF 2018 / / edited by Dipankar Deb, Valentina E. Balas, Rajeeb Dey |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (621 pages) |
| Disciplina | 690 |
| Collana | Advances in Intelligent Systems and Computing |
| Soggetto topico |
Telecommunication
Renewable energy sources Management Production of electric energy or Refuse and refuse disposal Communications Engineering, Networks Control and Systems Theory Renewable and Green Energy Innovation/Technology Management Power Electronics, Electrical Machines and Networks Waste Management/Waste Technology |
| ISBN | 981-13-1966-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Innovation in Telecommunication Infrastructure -- Innovation in Control Engineering -- Innovation in Power system Infrastructure -- Innovation in Smart Infrastructure -- Innovation in Waste Management -- Sustainable Solutions for Infrastructure Development -- Innovation in Thermal Engineering -- Innovation in Manufacturing -- Innovation in Industrial Infrastructure -- Innovation in Renewable Energy -- Innovation in Hybrid Vehicles -- New Optimization Techniques -- Innovation in Mechatronics -- Innovation in Machine Learning -- Innovation in Indigenous and Rural technologies. |
| Record Nr. | UNINA-9910350312103321 |
| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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