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Advanced Machining and Manufacturing Processes / / by Kaushik Kumar, Divya Zindani, J. Paulo Davim
| Advanced Machining and Manufacturing Processes / / by Kaushik Kumar, Divya Zindani, J. Paulo Davim |
| Autore | Kumar Kaushik |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (213 pages) : illustrations |
| Disciplina | 671.35 |
| Collana | Materials Forming, Machining and Tribology |
| Soggetto topico |
Manufactures
Machinery Electrochemistry Manufacturing, Machines, Tools, Processes Machinery and Machine Elements |
| ISBN | 3-319-76075-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Automated Conventional Machining Techniques -- Non Conventional Machining Techniques -- Virtual Manufacturing. |
| Record Nr. | UNINA-9910299946703321 |
Kumar Kaushik
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Hybrid Micro-Machining Processes / / by Sumit Bhowmik, Divya Zindani
| Hybrid Micro-Machining Processes / / by Sumit Bhowmik, Divya Zindani |
| Autore | Bhowmik Sumit |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (VII, 70 p. 12 illus., 5 illus. in color.) |
| Disciplina |
670
671.35 |
| Collana | SpringerBriefs in Applied Sciences and Technology |
| Soggetto topico |
Manufactures
Nanotechnology Manufacturing, Machines, Tools, Processes Nanotechnology and Microengineering |
| ISBN | 3-030-13039-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Overview of Hybrid Micromachining Processes -- Laser Assisted Micromachining -- Magnetic Field-Assisted Micro-EDM -- Electrorheological Fluid-Assisted Micro-USM -- Other Assisted Hybrid Micromachining Processes -- Combined Variant of Hybrid Micromachining Processes. |
| Record Nr. | UNINA-9910337632003321 |
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Bhowmik Sumit
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Industry 4.0 : Developments towards the Fourth Industrial Revolution / / by Kaushik Kumar, Divya Zindani, J. Paulo Davim
| Industry 4.0 : Developments towards the Fourth Industrial Revolution / / by Kaushik Kumar, Divya Zindani, J. Paulo Davim |
| Autore | Kumar Kaushik |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XII, 59 p. 2 illus., 1 illus. in color.) |
| Disciplina | 670 |
| Collana | Manufacturing and Surface Engineering |
| Soggetto topico |
Manufactures
Engineering economy Computer-aided engineering Electronic circuits Information technology Business—Data processing Production management Manufacturing, Machines, Tools, Processes Engineering Economics, Organization, Logistics, Marketing Computer-Aided Engineering (CAD, CAE) and Design Circuits and Systems IT in Business Operations Management |
| ISBN | 981-13-8165-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1: Intelligent Manufacturing -- Chapter 2: Process Planning in Era 4.0 -- Chapter 3: Requirements of Education and Qualification -- Chapter 4: Risk Management Implementation -- Chapter 5: Socio Economic Considerations -- Chapter 6: Sustainable Business Scenarios in 4.0 Era. |
| Record Nr. | UNINA-9910350297003321 |
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Kumar Kaushik
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| Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Mastering SolidWorks : Practical Examples / / by Kaushik Kumar, Divya Zindani, J. Paulo Davim
| Mastering SolidWorks : Practical Examples / / by Kaushik Kumar, Divya Zindani, J. Paulo Davim |
| Autore | Kumar Kaushik |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource (XIV, 316 p. 613 illus., 591 illus. in color.) |
| Disciplina |
620.00420285
620.00285 |
| Collana | Management and Industrial Engineering |
| Soggetto topico |
Applied mathematics
Engineering mathematics Software engineering Industrial engineering Production engineering Mathematical and Computational Engineering Software Engineering Industrial and Production Engineering |
| ISBN | 3-030-38901-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Introduction to Sketching -- Basic Sketch Relations and Dimensioning -- Part Modelling Using Features -- Sketch Entities -- CUT Features -- Thin Features -- Introduction to Sheet Metal -- Advanced features in Sheet metal -- Assembly -- Assembly Continued -- Basics of Drawing -- To Create A Flanged Coupling -- To Create A Foot Step Bearing. |
| Record Nr. | UNINA-9910377825403321 |
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Kumar Kaushik
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Materials and Manufacturing Processes / / by Kaushik Kumar, Hridayjit Kalita, Divya Zindani, J. Paulo Davim
| Materials and Manufacturing Processes / / by Kaushik Kumar, Hridayjit Kalita, Divya Zindani, J. Paulo Davim |
| Autore | Kumar Kaushik |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (113 pages) |
| Disciplina |
670
670.42 |
| Collana | Materials Forming, Machining and Tribology |
| Soggetto topico |
Manufactures
Metals Materials Machines, Tools, Processes Metals and Alloys Materials Engineering |
| ISBN | 3-030-21066-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Materials -- Mechanical Behaviour of materials -- Casting -- Forming -- Welding -- Machining. |
| Record Nr. | UNINA-9910337614103321 |
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Kumar Kaushik
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Medical Analytics for Clinical and Healthcare Applications
| Medical Analytics for Clinical and Healthcare Applications |
| Autore | Kalita Kanak |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2025 |
| Descrizione fisica | 1 online resource (283 pages) |
| Disciplina | 610.285 |
| Altri autori (Persone) |
ZindaniDivya
GaneshNarayanan GaoXiao-Zhi |
| Collana | Machine Learning in Biomedical Science and Healthcare Informatics Series |
| Soggetto topico | Medical informatics |
| ISBN |
1-394-30148-0
1-394-30147-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: Foundations of Medical Analytics -- Chapter 1 Exploring Trends in Depression and Anxiety Using Machine and Deep Learning Models -- 1.1 Introduction -- 1.2 Exploratory Data Analysis -- 1.2.1 Exploratory Data Analysis (EDA) -- 1.2.2 Interactive Visualizations with Plotly -- 1.2.3 Data Cleaning and Transformation -- 1.2.4 Chart Creation with Matplotlib and Seaborn -- 1.2.5 Documentation and Report Writing -- 1.3 Problem Statement and Motivation -- 1.4 Literature Survey -- 1.5 Data Visualization -- 1.5.1 Integration of Machine Learning-Powered Insights -- 1.5.2 Dynamic Time-Series Visualizations with Plotly -- 1.5.3 Fusion of Art and Data with Seaborn Styling -- 1.5.4 Ethical Considerations in Visual Storytelling -- 1.5.5 Seamless Collaboration Through Git Version Control -- 1.6 Overview of Dataset -- 1.6.1 Dataset Acquisition Links -- 1.6.2 Household Pulse Survey -- 1.7 Methodology -- 1.7.1 Data Selection and Attributes -- 1.7.2 Data Pre-Processing -- 1.8 Modules -- 1.8.1 Linear Regression -- 1.8.2 K-Means Clustering -- 1.8.3 Random Forest -- 1.8.4 Simple Convolutional Neural Network (CNN) -- 1.8.5 MobileNetV2 -- 1.8.6 Basic Visualizations -- 1.9 Results and Discussion -- 1.10 Conclusion -- References -- Part 2: Disease Detection and Diagnosis -- Chapter 2 An Innovative Framework for the Detection and Classification of Breast Cancer Disease Using Logistic Regression Compared with Back Propagation Neural Network -- 2.1 Introduction -- 2.2 Materials and Methods -- 2.2.1 Back Propagation Neural Network (BPNN) -- 2.2.2 Logistic Regression (LR) -- 2.2.3 Statistical Analysis -- 2.3 Results -- 2.4 Discussion -- 2.5 Conclusion -- References -- Chapter 3 An Approach to Conduct the Diabetes Prediction Using AdaBoost Algorithm Compared with Decision Tree Classifier Algorithm.
3.1 Introduction -- 3.2 Materials and Methods -- 3.2.1 AdaBoost Algorithm -- 3.2.1.1 Procedure for AdaBoost Algorithm -- 3.2.1.2 Decision Tree -- 3.2.1.3 Statistical Analysis -- 3.3 Results and Discussion -- 3.4 Conclusion -- References -- Chapter 4 Efficient Net V2-Based Pneumonia Detection: A Comparative Study with Transfer Learning Models -- 4.1 Introduction -- 4.2 Related Works -- 4.3 Materials and Methods -- 4.3.1 Dataset Details -- 4.3.2 CLAHE -- 4.3.3 Data Augmentation -- 4.3.4 Transfer Learning Models -- 4.3.4.1 VGG-16 -- 4.3.4.2 Xception -- 4.3.4.3 EfficientNetV2 -- 4.4 Results and Discussion -- 4.4.1 Ensemble Model -- 4.5 Conclusion and Future Work -- References -- Chapter 5 A Histogram Equalized Median Filtered SIFT-EfficientNet Based on Deep Learning Approach for Lung Disease Detection -- 5.1 Introduction -- 5.2 Related Works -- 5.3 Materials and Methods -- 5.3.1 Dataset -- 5.3.2 Methodology: HMS-E -- 5.3.3 Pre-Processing -- 5.3.4 Histogram Equalization -- 5.3.5 Median Filter -- 5.3.6 Feature Extraction -- 5.3.6.1 Detection of Scale-Space Extrema -- 5.3.6.2 Localization of Key Points -- 5.3.6.3 Generation of Key Point Descriptors -- 5.3.7 Deep Learning Model -- 5.3.7.1 EfficientNetB0 -- 5.4 Performance Measure -- 5.5 Results and Discussion -- 5.6 Conclusion and Future Work -- References -- Part 3: Predictive Analytics in Healthcare -- Chapter 6 Comparing the Efficiency of ResNet-50 and Convolutional Neural Networks for Facial Mask Detection -- 6.1 Introduction -- 6.2 Materials and Methods -- 6.3 ResNet-50 Architecture -- 6.4 Convolutional Neural Networks (CNN) -- 6.5 Statistical Analysis -- 6.6 Results and Discussion -- 6.7 Conclusion -- References -- Chapter 7 Enhancing Accuracy in Predicting Knee Osteoarthritis Progression Using Kellgren-Lawrence Grade Compared with Deep Convolutional Neural Network -- 7.1 Introduction. 7.2 Materials and Methods -- 7.2.1 Kellgren-Lawrence Grade -- 7.2.2 Deep Convolutional Neural Network -- 7.2.3 Statistical Analysis -- 7.3 Results and Discussion -- 7.3.1 Accuracy and Loss Analysis -- 7.3.2 Group Statistical Analysis -- 7.3.3 Independent Sample T-Test Results -- 7.3.4 Performance Comparison -- 7.3.5 Discussion -- 7.4 Conclusion -- References -- Chapter 8 A Comparative Analysis of Support Vector Machine over K-Neighbors Classifier for Predicting Hospital Mortality with Improved Accuracy -- 8.1 Introduction -- 8.1.1 Integration of Present-On-Admission Indicators -- 8.1.2 Challenges in Implementing Machine Learning Models -- 8.1.3 Predicting in-Hospital Mortality in ICU Patients -- 8.1.4 Machine Learning Algorithms in Mortality Prediction -- 8.1.5 Ethical and Operational Considerations -- 8.2 Materials and Methods -- 8.2.1 Software and Hardware Configuration -- 8.2.2 SVM Algorithm -- 8.2.3 K-Nearest Neighbor Algorithm -- 8.2.4 Statistical Analysis -- 8.3 Results and Discussion -- 8.3.1 Accuracy and Loss Analysis -- 8.3.2 Independent Sample T-Test Analysis -- 8.3.3 Comparison of SVM and KNN -- 8.3.4 Real-World Application and Implications -- 8.3.5 Limitations and Future Scope -- 8.4 Conclusion -- References -- Chapter 9 Asthma Prediction Using Vowel Inspiration: A Machine Learning Approach -- 9.1 Introduction -- 9.2 Literature Survey -- 9.3 Motivation and Background -- 9.4 Proposed Method -- 9.4.1 Vowel Inspiration -- 9.4.2 Voice Activity Detection -- 9.4.3 Inspiration Filtering -- 9.4.4 Feature Extraction -- 9.4.4.1 Pause Frequency -- 9.4.4.2 Average Phonation Time -- 9.4.4.3 Vocalization-to-Inhalation Ratio -- 9.4.4.4 Inspiration Sound Energy -- 9.4.5 Classification -- 9.4.6 Praat Parameters -- 9.4.6.1 Jitter -- 9.4.6.2 NHR -- 9.4.6.3 HNR -- 9.5 Discussion -- 9.5.1 Classification for First Method -- 9.5.1.1 Logistic Regression. 9.5.1.2 Naïve Bayes -- 9.5.1.3 K-Nearest Neighbor -- 9.5.1.4 Support Vector Machine -- 9.5.1.5 Decision Tree -- 9.5.1.6 Random Forest -- 9.5.1.7 Logistic Regression with Random Forest -- 9.5.2 Classification for Second Method -- 9.5.2.1 K-Nearest Neighbor with Classification Report -- 9.5.2.2 Logistic Regression with Discussion on Classification Report -- 9.5.2.3 Decision Tree with Discussion on Classification Report -- 9.5.2.4 Naïve Bayes with Discussion on Classification Report -- 9.6 Results -- 9.7 Conclusion -- References -- Part 4: Medical Data Analysis and Security -- Chapter 10 Improvement of Accuracy in Prevention of Medical Images from Security Threats Using Novel Lasso Regression in Comparison with K-Means Classifier -- 10.1 Introduction -- 10.2 Materials and Methods -- 10.2.1 Dataset Variables -- 10.2.2 Machine Learning Algorithms: Novel Lasso Regression and K-Means Classifiers -- 10.2.2.1 Novel Lasso Regression -- 10.2.2.2 K-Means Classifier -- 10.2.3 Statistical Analysis -- 10.3 Result -- 10.4 Discussion -- 10.5 Conclusion -- References -- Chapter 11 Renal Cancer Detection from Histopathological Images Using Deep Learning -- 11.1 Introduction -- 11.1.1 Motivation -- 11.2 Materials and Methods -- 11.2.1 Dataset Used -- 11.2.2 Models Used -- 11.3 Results and Discussions -- 11.3.1 High Classification Accuracy -- 11.3.2 Strong Performance on Classification Metrics -- 11.3.3 Fuhrman Grade Classification -- 11.4 Conclusion and Future Work -- References -- Chapter 12 A Novel Method to Predicting Tumor in Fallopian Tube Using DenseNet Over Linear Regression with Enhanced Efficiency -- 12.1 Introduction -- 12.2 Materials and Methods -- 12.2.1 DenseNet Algorithm -- 12.2.1.1 Pseudocode for DenseNet -- 12.2.2 Linear Regression -- 12.2.2.1 Pseudocode for Linear Regression -- 12.2.3 Statistical Analysis -- 12.3 Results and Discussion. 12.3.1 Accuracy and Loss Comparison -- 12.3.2 Statistical Analysis Results -- 12.3.3 Group Statistical Analysis -- 12.3.4 Discussion -- 12.3.5 DenseNet's Performance Advantages -- 12.3.6 Linear Regression's Limitations -- 12.3.7 Significance of Statistical Analysis -- 12.3.8 Limitations and Future Work -- 12.4 Conclusion -- References -- Chapter 13 Protected Medical Images Against Security Threats Using Lasso Regression and K-Means Algorithms -- 13.1 Introduction -- 13.2 Materials and Methods -- 13.3 K-Means Classifier -- 13.4 Procedure for K-Means Classifier -- 13.5 Lasso Regression -- 13.6 Procedure for Lasso Regression -- 13.7 Statistical Analysis -- 13.8 Results -- 13.9 Discussion -- 13.10 Conclusion -- References -- Part 5: Emerging Trends and Technologies -- Chapter 14 Predicting the Factors Influencing Alcoholic Consumption of Teenagers Using an Optimized Random Forest Classifier in Comparison with Logistic Regression -- 14.1 Introduction -- 14.2 Materials and Methods -- 14.3 Random Forest Classifier -- 14.4 Algorithm for Random Forest Classifier -- 14.5 Logistic Regression Classifier -- 14.6 Algorithm for Logistic Regression Classifier -- 14.7 Results -- 14.8 Discussion -- 14.9 Conclusion -- References -- Chapter 15 Harnessing Food Waste Potential: Advancing Protein Sequence Motif Analysis with Novel Cluster Sequence Analyzer Machine Learning Model -- 15.1 Introduction -- 15.1.1 Motif Regions -- 15.2 Suffix Tree -- 15.3 Clustering Algorithms in PPI -- 15.4 Classification Agorithms in PPI -- 15.5 CSA and PPI Interaction Results -- 15.6 Conclusion -- Bibliography -- Chapter 16 "Hi-Tech People, Digitized HR- Are We Missing the Humane Link?"-Use of People Analytics as an Effective HRM Tool in a Selected Healthcare Sector -- 16.1 Introduction -- 16.2 Research Background -- 16.3 Literature Review -- 16.4 Research Gaps -- 16.5 Research Methodology. 16.6 Objectives. |
| Record Nr. | UNINA-9911022471703321 |
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Kalita Kanak
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| Newark : , : John Wiley & Sons, Incorporated, , 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Micro and Nano Machining of Engineering Materials : Recent Developments / / edited by Kaushik Kumar, Divya Zindani, Nisha Kumari, J. Paulo Davim
| Micro and Nano Machining of Engineering Materials : Recent Developments / / edited by Kaushik Kumar, Divya Zindani, Nisha Kumari, J. Paulo Davim |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (155 pages) |
| Disciplina | 671.35 |
| Collana | Materials Forming, Machining and Tribology |
| Soggetto topico |
Nanotechnology
Manufactures Nanotechnology and Microengineering Manufacturing, Machines, Tools, Processes |
| ISBN | 3-319-99900-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Nano-machining and Micro- machining using conventional machining techniques -- Nano-machining and Micro- machining using non conventional machining techniques -- Materials in micro machining and nano machining -- Tools and processes for micro machining and nano machining -- Management of micro machining and nano machining processes -- Micro machining and nano machining enterprises -- Healthcare technologies and Environmental systems -- Advanced micro/ nanomachining processes -- Design of Micro and Nano sized products such as Microfluidic systems, Micro pumps, Micro engines etc -- Nanofinishing processes. |
| Record Nr. | UNINA-9910337657203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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