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Artificial Intelligence-based Healthcare Systems [[electronic resource] /] / edited by Manju, Sandeep Kumar, Sardar M. N. Islam
| Artificial Intelligence-based Healthcare Systems [[electronic resource] /] / edited by Manju, Sandeep Kumar, Sardar M. N. Islam |
| Autore | Manju |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (208 pages) |
| Disciplina | 362.1028563 |
| Altri autori (Persone) |
KumarSandeep
IslamSardar M. N |
| Collana | The Springer Series in Applied Machine Learning |
| Soggetto topico |
Artificial intelligence
Machine learning Internet of things Medical care Artificial Intelligence Machine Learning Internet of Things Health Care |
| ISBN | 3-031-41925-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Schedule and Routing In Home Healthcare System Using Clustering Analysis and Multi-Objective Optimization -- Obesity level prediction using Multinomial Logistic Regression -- Importance of Feature Selection methods in Machine Learning-based Obesity Prediction -- A Clinical Decision Support System Using Machine Learning To Forecast The Risk Of Chronic Pulmonary Disease And Anthracosis -- Smart Healthcare: A Breakthrough in the growth of technologies -- A Multidisciplinary Explanation of Healthcare AI Uses, Trends and Possibilities -- Optimum Utilization Of Bed Resources In Hospitals-A Stochastic Approach -- Early-Detection of Diabetic Retinopathy using Deep Learning -- Performance Analysis of Memory-Efficient Vision Transformers in Brain Tumor Segmentation -- Unlocking New Possibilities in Drug Discovery: A GAN-based Approach -- A Systematic Review on ECG and EMG Biomedical Signal using Deep Learning Approaches -- Smart AI bot for healthcare Assistance -- AI-Driven Hospital Readmission Predictor for Diabetic Patients -- Gleason Grading System for Prostate Cancer diagnosis. |
| Record Nr. | UNINA-9910755082503321 |
Manju
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| Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Cognitive Computing Models in Communication Systems
| Cognitive Computing Models in Communication Systems |
| Autore | Kumar Budati Anil |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (243 pages) |
| Altri autori (Persone) |
GoyalS. B
IslamSardar M. N |
| Collana | Smart and Sustainable Intelligent Systems Ser. |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-86560-3
1-119-86559-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgement -- 1 Design of a Low-Voltage LDO of CMOS Voltage Regulator for Wireless Communications -- 1.1 Introduction -- 1.2 LDO Controller Arrangement and Diagram Drawing -- 1.2.1 Design of the LDO Regulator -- 1.2.1.1 Design of the Fault Amplifier -- 1.2.1.2 Design of the MPT Phase -- 1.3 Conclusion -- References -- 2 Performance Analysis of Machine Learning and Deep Learning Algorithms for Smart Cities: The Present State and Future Directions -- 2.1 Introduction -- 2.2 Smart City: The Concept -- 2.3 Application Layer -- 2.3.1 Smart Homes and Buildings -- 2.3.1.1 Smart Surveillance -- 2.3.2 Smart Transportation and Driving -- 2.3.3 Smart Healthcare -- 2.3.4 Smart Parking -- 2.3.5 Smart Grid -- 2.3.6 Smart Farming -- 2.3.7 Sensing Layer -- 2.3.8 Communication Layer -- 2.3.9 Data Layer -- 2.3.10 Security Layer -- 2.4 Issues and Challenges in Smart Cities: An Overview -- 2.5 Machine Learning: An Overview -- 2.5.1 Supervised Learning -- 2.5.2 Support Vector Machines (SVMs) -- 2.5.3 Artificial Neural Networks -- 2.5.4 Random Forest -- 2.5.5 Naïve Bayes -- 2.6 Unsupervised Learning -- 2.7 Deep Learning: An Overview -- 2.7.1 Autoencoder -- 2.7.2 Convolution Neural Networks (CNNs) -- 2.7.3 Recurrent Neural Networks (RNNs) -- 2.8 Deep Learning vs Machine Learning -- 2.9 Smart Healthcare -- 2.9.1 Evolution Toward a Smart Healthcare Framework -- 2.9.2 Application of ML/DL in Smart Healthcare -- 2.10 Smart Transport System -- 2.10.1 Evolution Toward a Smart Transport System -- 2.10.2 Application of ML/DL in a Smart Transportation System -- 2.11 Smart Grids -- 2.11.1 Evolution Toward Smart Grids -- 2.11.2 Application of ML/DL in Smart Grids -- 2.12 Challenges and Future Directions -- 2.13 Conclusion -- References.
3 Application of Machine Learning Algorithms and Models in 3D Printing -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Methods and Materials -- 3.4 Results and Discussion -- 3.5 Conclusion -- References -- 4 A Novel Model for Optimal Reliable Routing Path Prediction in MANET -- 4.1 Introduction -- 4.2 Analytical Hierarchical Process Technique -- 4.3 Mathematical Models and Protocols -- 4.3.1 Rough Sets -- 4.3.1.1 Pawlak Rough Set Theory Definitions -- 4.3.2 Fuzzy TOPSIS -- 4.4 Routing Protocols -- 4.4.1 Classification of Routing Paths -- 4.5 RTF-AHP Model -- 4.5.1 Rough TOPSIS Fuzzy Set Analytical Hierarchical Process Algorithm -- 4.6 Models for Optimal Routing Performance -- 4.6.1 Genetic Algorithm Technique -- 4.6.2 Ant Colony Optimization Technique -- 4.6.3 RTF-AHP Model Architecture Flow -- 4.7 Results and Discussion -- 4.8 Conclusion -- References -- 5 IoT-Based Smart Traffic Light Control -- 5.1 Introduction -- 5.2 Scope of the Proposed Work -- 5.3 Proposed System Implementation -- 5.4 Testing and Results -- 5.5 Test Results -- 5.6 Conclusions -- References -- 6 Differential Query Execution on Privacy Preserving Data Distributed Over Hybrid Cloud -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Proposed Solution -- 6.3.1 Data Transformation -- 6.3.2 Data Distribution -- 6.3.3 Query Execution -- 6.4 Novelty in the Proposed Solution -- 6.5 Results -- 6.6 Conclusion -- References -- 7 Design of CMOS Base Band Analog -- 7.1 Introduction -- 7.2 Proposed Technique of the BBA Chain for Reducing Energy Consumption -- 7.3 Channel Preference Filter -- 7.4 Programmable Amplifier Gain -- 7.5 Executed Outcomes -- 7.6 Conclusion -- References -- 8 Review on Detection of Neuromuscular Disorders Using Electromyography -- 8.1 Introduction -- 8.2 Materials -- 8.3 Methods -- 8.4 Conclusion -- References. 9 Design of Complementary Metal- Oxide Semiconductor Ring Modulator by Built-In Thermal Tuning -- 9.1 Introduction -- 9.2 Device Structure -- 9.3 DC Performance -- 9.4 Small-Signal Radiofrequency Assessments -- 9.5 Data Modulation Operation (High Speed) -- 9.6 Conclusions and Acknowledgments -- References -- 10 Low-Power CMOS VCO Used in RF Transmitter -- 10.1 Introduction -- 10.2 Transmitter Architecture -- 10.3 Voltage-Controlled Ring Oscillator Design -- 10.4 CMOS Combiner -- 10.5 Conclusion -- References -- 11 A Novel Low-Power FrequencyModulated Continuous Wave Radar Based on Low-Noise Mixer -- 11.1 Introduction -- 11.2 FMCW Principle -- 11.3 Results -- 11.4 Conclusion -- References -- 12 A Highly Integrated CMOS RF Tx Used for IEEE 802.15.4 -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Simulation Results and Discussion -- 12.4 Conclusion -- References -- 13 A Novel Feedforward Offset Cancellation Limiting Amplifier in Radio Frequencies -- 13.1 Introduction -- 13.2 Hardware Design -- 13.2.1 Limiting Amplifier -- 13.2.2 Offset Extractor -- 13.2.3 Architecture and Gain -- 13.2.4 Quadrature Detector -- 13.2.5 Sensitivity -- 13.3 Experimental Results -- 13.4 Conclusion -- References -- 14 A Secured Node Authentication and Access Control Model for IoT Smart Home Using Double-Hashed Unique Labeled Key-Based Validation -- 14.1 Introduction -- 14.2 Challenges in IoT Security and Privacy -- 14.2.1 Heterogeneous Communication and Devices -- 14.2.2 Physical Equipment Integration -- 14.2.3 Resource Handling Limitations -- 14.2.4 Wide Scale -- 14.2.5 Database -- 14.3 Background -- 14.4 Proposed Model -- 14.4.1 Communication Flow -- 14.4.1.1 IoT Node and Registration Authority -- 14.4.1.2 User and Local Authorization Authority -- 14.5 Results -- 14.6 Conclusion -- 14.7 Claims -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910623988303321 |
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Kumar Budati Anil
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Empirical finance : modelling and analysis of emerging financial and stock markets / Sardar M. N. Islam, Sethapong Watanapalachaikul
| Empirical finance : modelling and analysis of emerging financial and stock markets / Sardar M. N. Islam, Sethapong Watanapalachaikul |
| Autore | ISLAM, Sardar M. N. |
| Pubbl/distr/stampa | Heidelberg : Physica-Verlag, c2005 |
| Descrizione fisica | XIV, 200 p. : tab. ; 24 cm |
| Disciplina | 332.015118(Economia finanziaria. Modelli matematici) |
| Altri autori (Persone) | WATANAPALACHAIKUL, Sethapong |
| Collana | Contribution to economics. - New York |
| Soggetto topico | Mercati finanziari - Modelli econometrici |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-990005516210203316 |
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ISLAM, Sardar M. N.
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| Heidelberg : Physica-Verlag, c2005 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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