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Deep Learning for Targeted Treatments : Transformation in Healthcare
| Deep Learning for Targeted Treatments : Transformation in Healthcare |
| Autore | Malviya Rishabha |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (458 pages) |
| Altri autori (Persone) |
GhineaGheorghita
DhanarajRajesh Kumar BalusamyBalamurugan SundramSonali |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-85798-8
1-119-85797-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgement -- 1 Deep Learning and Site-Specific Drug Delivery: The Future and Intelligent Decision Support for Pharmaceutical Manufacturing Science -- 1.1 Introduction -- 1.2 Drug Discovery, Screening and Repurposing -- 1.3 DL and Pharmaceutical Formulation Strategy -- 1.3.1 DL in Dose and Formulation Prediction -- 1.3.2 DL in Dissolution and Release Studies -- 1.3.3 DL in the Manufacturing Process -- 1.4 Deep Learning Models for Nanoparticle-Based Drug Delivery -- 1.4.1 Nanoparticles With High Drug Delivery Capacities Using Perturbation Theory -- 1.4.2 Artificial Intelligence and Drug Delivery Algorithms -- 1.4.3 Nanoinformatics -- 1.5 Model Prediction for Site-Specific Drug Delivery -- 1.5.1 Prediction of Mode and a Site-Specific Action -- 1.5.2 Precision Medicine -- 1.6 Future Scope and Challenges -- 1.7 Conclusion -- References -- 2 Role of Deep Learning, Blockchain and Internet of Things in Patient Care -- 2.1 Introduction -- 2.2 IoT and WBAN in Healthcare Systems -- 2.2.1 IoT in Healthcare -- 2.2.2 WBAN -- 2.2.2.1 Key Features of Medical Networks in the Wireless Body Area -- 2.2.2.2 Data Transmission & -- Storage Health -- 2.2.2.3 Privacy and Security Concerns in Big Data -- 2.3 Blockchain Technology in Healthcare -- 2.3.1 Importance of Blockchain -- 2.3.2 Role of Blockchain in Healthcare -- 2.3.3 Benefits of Blockchain in Healthcare Applications -- 2.3.4 Elements of Blockchain -- 2.3.5 Situation Awareness and Healthcare Decision Support with Combined Machine Learning and Semantic Modeling -- 2.3.6 Mobile Health and Remote Monitoring -- 2.3.7 Different Mobile Health Application with Description of Usage in Area of Application -- 2.3.8 Patient-Centered Blockchain Mode -- 2.3.9 Electronic Medical Record -- 2.3.9.1 The Most Significant Barriers to Adoption Are.
2.3.9.2 Concern Regarding Negative Unintended Consequences of Technology -- 2.4 Deep Learning in Healthcare -- 2.4.1 Deep Learning Models -- 2.4.1.1 Recurrent Neural Networks (RNN) -- 2.4.1.2 Convolutional Neural Networks (CNN) -- 2.4.1.3 Deep Belief Network (DBN) -- 2.4.1.4 Contrasts Between Models -- 2.4.1.5 Use of Deep Learning in Healthcare -- 2.5 Conclusion -- 2.6 Acknowledgments -- References -- 3 Deep Learning on Site-Specific Drug Delivery System -- 3.1 Introduction -- 3.2 Deep Learning -- 3.2.1 Types of Algorithms Used in Deep Learning -- 3.2.1.1 Convolutional Neural Networks (CNNs) -- 3.2.1.2 Long Short-Term Memory Networks (LSTMs) -- 3.2.1.3 Recurrent Neural Networks -- 3.2.1.4 Generative Adversarial Networks (GANs) -- 3.2.1.5 Radial Basis Function Networks -- 3.2.1.6 Multilayer Perceptron -- 3.2.1.7 Self-Organizing Maps -- 3.2.1.8 Deep Belief Networks -- 3.3 Machine Learning and Deep Learning Comparison -- 3.4 Applications of Deep Learning in Drug Delivery System -- 3.5 Conclusion -- References -- 4 Deep Learning Advancements in Target Delivery -- 4.1 Introduction: Deep Learning and Targeted Drug Delivery -- 4.2 Different Models/Approaches of Deep Learning and Targeting Drug -- 4.3 QSAR Model -- 4.3.1 Model of Deep Long-Term Short-Term Memory -- 4.3.2 RNN Model -- 4.3.3 CNN Model -- 4.4 Deep Learning Process Applications in Pharmaceutical -- 4.5 Techniques for Predicting Pharmacotherapy -- 4.6 Approach to Diagnosis -- 4.7 Application -- 4.7.1 Deep Learning in Drug Discovery -- 4.7.2 Medical Imaging and Deep Learning Process -- 4.7.3 Deep Learning in Diagnostic and Screening -- 4.7.4 Clinical Trials Using Deep Learning Models -- 4.7.5 Learning for Personalized Medicine -- 4.8 Conclusion -- Acknowledgment -- References -- 5 Deep Learning and Precision Medicine: Lessons to Learn for the Preeminent Treatment for Malignant Tumors. 5.1 Introduction -- 5.2 Role of DL in Gene Identification, Unique Genomic Analysis, and Precise Cancer Diagnosis -- 5.2.1 Gene Identification and Genome Data -- 5.2.2 Image Diagnosis -- 5.2.3 Radiomics, Radiogenomics, and Digital Biopsy -- 5.2.4 Medical Image Analysis in Mammography -- 5.2.5 Magnetic Resonance Imaging -- 5.2.6 CT Imaging -- 5.3 DL in Next-Generation Sequencing, Biomarkers, and Clinical Validation -- 5.3.1 Next-Generation Sequencing -- 5.3.2 Biomarkers and Clinical Validation -- 5.4 DL and Translational Oncology -- 5.4.1 Prediction -- 5.4.2 Segmentation -- 5.4.3 Knowledge Graphs and Cancer Drug Repurposing -- 5.4.4 Automated Treatment Planning -- 5.4.5 Clinical Benefits -- 5.5 DL in Clinical Trials-A Necessary Paradigm Shift -- 5.6 Challenges and Limitations -- 5.7 Conclusion -- References -- 6 Personalized Therapy Using Deep Learning Advances -- 6.1 Introduction -- 6.2 Deep Learning -- 6.2.1 Convolutional Neural Networks -- 6.2.2 Autoencoders -- 6.2.3 Deep Belief Network (DBN) -- 6.2.4 Deep Reinforcement Learning -- 6.2.5 Generative Adversarial Network -- 6.2.6 Long Short-Term Memory Networks -- References -- 7 Tele-Health Monitoring Using Artificial Intelligence Deep Learning Framework -- 7.1 Introduction -- 7.2 Artificial Intelligence -- 7.2.1 Types of Artificial Intelligence -- 7.2.1.1 Machine Intelligence -- 7.2.1.2 Types of Machine Intelligence -- 7.2.2 Applications of Artificial Intelligence -- 7.2.2.1 Role in Healthcare Diagnostics -- 7.2.2.2 AI in Telehealth -- 7.2.2.3 Role in Structural Health Monitoring -- 7.2.2.4 Role in Remote Medicare Management -- 7.2.2.5 Predictive Analysis Using Big Data -- 7.2.2.6 AI's Role in Virtual Monitoring of Patients -- 7.2.2.7 Functions of Devices -- 7.2.2.8 Clinical Outcomes Through Remote Patient Monitoring -- 7.2.2.9 Clinical Decision Support. 7.2.3 Utilization of Artificial Intelligence in Telemedicine -- 7.2.3.1 Artificial Intelligence-Assisted Telemedicine -- 7.2.3.2 Telehealth and New Care Models -- 7.2.3.3 Strategy of Telecare Domain -- 7.2.3.4 Role of AI-Assisted Telemedicine in Various Domains -- 7.3 AI-Enabled Telehealth: Social and Ethical Considerations -- 7.4 Conclusion -- References -- 8 Deep Learning Framework for Cancer Diagnosis and Treatment -- 8.1 Deep Learning: An Emerging Field for Cancer Management -- 8.2 Deep Learning Framework in Diagnosis and Treatment of Cancer -- 8.3 Applications of Deep Learning in Cancer Diagnosis -- 8.3.1 Medical Imaging Through Artificial Intelligence -- 8.3.2 Biomarkers Identification in the Diagnosis of Cancer Through Deep Learning -- 8.3.3 Digital Pathology Through Deep Learning -- 8.3.4 Application of Artificial Intelligence in Surgery -- 8.3.5 Histopathological Images Using Deep Learning -- 8.3.6 MRI and Ultrasound Images Through Deep Learning -- 8.4 Clinical Applications of Deep Learning in the Management of Cancer -- 8.5 Ethical Considerations in Deep Learning-Based Robotic Therapy -- 8.6 Conclusion -- Acknowledgments -- References -- 9 Applications of Deep Learning in Radiation Therapy -- 9.1 Introduction -- 9.2 History of Radiotherapy -- 9.3 Principal of Radiotherapy -- 9.4 Deep Learning -- 9.5 Radiation Therapy Techniques -- 9.5.1 External Beam Radiation Therapy -- 9.5.2 Three-Dimensional Conformal Radiation Therapy (3D-CRT) -- 9.5.3 Intensity Modulated Radiation Therapy (IMRT) -- 9.5.4 Image-Guided Radiation Therapy (IGRT) -- 9.5.5 Intraoperative Radiation Therapy (IORT) -- 9.5.6 Brachytherapy -- 9.5.7 Stereotactic Radiosurgery (SRS) -- 9.6 Different Role of Deep Learning with Corresponding Role of Medical Physicist -- 9.6.1 Deep Learning in Patient Assessment -- 9.6.1.1 Radiotherapy Results Prediction. 9.6.1.2 Respiratory Signal Prediction -- 9.6.2 Simulation Computed Tomography -- 9.6.3 Targets and Organs-at-Risk Segmentation -- 9.6.4 Treatment Planning -- 9.6.4.1 Beam Angle Optimization -- 9.6.4.2 Dose Prediction -- 9.6.5 Other Role of Deep Learning in Corresponds with Medical Physicists -- 9.7 Conclusion -- References -- 10 Application of Deep Learning in Radiation Therapy -- 10.1 Introduction -- 10.2 Radiotherapy -- 10.3 Principle of Deep Learning and Machine Learning -- 10.3.1 Deep Neural Networks (DNN) -- 10.3.2 Convolutional Neural Network -- 10.4 Role of AI and Deep Learning in Radiation Therapy -- 10.5 Platforms for Deep Learning and Tools for Radiotherapy -- 10.6 Radiation Therapy Implementation in Deep Learning -- 10.6.1 Deep Learning and Imaging Techniques -- 10.6.2 Image Segmentation -- 10.6.3 Lesion Segmentation -- 10.6.4 Computer-Aided Diagnosis -- 10.6.5 Computer-Aided Detection -- 10.6.6 Quality Assurance -- 10.6.7 Treatment Planning -- 10.6.8 Treatment Delivery -- 10.6.9 Response to Treatment -- 10.7 Prediction of Outcomes -- 10.7.1 Toxicity -- 10.7.2 Survival and the Ability to Respond -- 10.8 Deep Learning in Conjunction With Radiomoic -- 10.9 Planning for Treatment -- 10.9.1 Optimization of Beam Angle -- 10.9.2 Prediction of Dose -- 10.10 Deep Learning's Challenges and Future Potential -- 10.11 Conclusion -- References -- 11 Deep Learning Framework for Cancer -- 11.1 Introduction -- 11.2 Brief History of Deep Learning -- 11.3 Types of Deep Learning Methods -- 11.4 Applications of Deep Learning -- 11.4.1 Toxicity Detection for Different Chemical Structures -- 11.4.2 Mitosis Detection -- 11.4.3 Radiology or Medical Imaging -- 11.4.4 Hallucination -- 11.4.5 Next-Generation Sequencing (NGS) -- 11.4.6 Drug Discovery -- 11.4.7 Sequence or Video Generation -- 11.4.8 Other Applications -- 11.5 Cancer -- 11.5.1 Factors. 11.5.1.1 Heredity. |
| Record Nr. | UNINA-9910595599103321 |
Malviya Rishabha
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing [[electronic resource] ] : ICCIC 2022, 27–28 December, Hyderabad, India; Volume 1 / / edited by Amit Kumar, Gheorghita Ghinea, Suresh Merugu
| Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing [[electronic resource] ] : ICCIC 2022, 27–28 December, Hyderabad, India; Volume 1 / / edited by Amit Kumar, Gheorghita Ghinea, Suresh Merugu |
| Autore | Kumar Amit |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (755 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
GhineaGheorghita
MeruguSuresh |
| Collana | Cognitive Science and Technology |
| Soggetto topico |
Computational intelligence
Machine learning Artificial intelligence Data mining Internet of things Computational Intelligence Machine Learning Artificial Intelligence Data Mining and Knowledge Discovery Internet of Things |
| ISBN | 981-9927-42-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Making Cell- Free Massive MIMO using MRC technique -- VIP Development of SPI Controller for Open-Power Processor Based Fabless SOC -- Cell-Free Massive MIMO versus Small Cells -- High Precision Navigation using Particle Swarm Optimization based KF -- Recent Advancements for Detection and Prediction of Breast Cancer using Deep Learning A Review. |
| Record Nr. | UNISA-996550550803316 |
|
Kumar Amit
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing : ICCIC 2022, 27–28 December, Hyderabad, India; Volume 1 / / edited by Amit Kumar, Gheorghita Ghinea, Suresh Merugu
| Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing : ICCIC 2022, 27–28 December, Hyderabad, India; Volume 1 / / edited by Amit Kumar, Gheorghita Ghinea, Suresh Merugu |
| Autore | Kumar Amit |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
| Descrizione fisica | 1 online resource (755 pages) |
| Disciplina | 006.3 |
| Altri autori (Persone) |
GhineaGheorghita
MeruguSuresh |
| Collana | Cognitive Science and Technology |
| Soggetto topico |
Computational intelligence
Machine learning Artificial intelligence Data mining Internet of things Computational Intelligence Machine Learning Artificial Intelligence Data Mining and Knowledge Discovery Internet of Things |
| ISBN | 981-9927-42-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Making Cell- Free Massive MIMO using MRC technique -- VIP Development of SPI Controller for Open-Power Processor Based Fabless SOC -- Cell-Free Massive MIMO versus Small Cells -- High Precision Navigation using Particle Swarm Optimization based KF -- Recent Advancements for Detection and Prediction of Breast Cancer using Deep Learning A Review. |
| Record Nr. | UNINA-9910746963203321 |
|
Kumar Amit
|
||
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing . Volume 2 : ICCIC 2022, 27-28 December, Hyderabad, India / / Amit Kumar, Gheorghita Ghinea, and Suresh Merugu, editors
| Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing . Volume 2 : ICCIC 2022, 27-28 December, Hyderabad, India / / Amit Kumar, Gheorghita Ghinea, and Suresh Merugu, editors |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore Pte Ltd, , [2023] |
| Descrizione fisica | 1 online resource (0 pages) |
| Disciplina | 006.3 |
| Collana | Cognitive Science and Technology Series |
| Soggetto topico |
Computational intelligence
Soft computing |
| ISBN | 981-9927-46-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- Automatic Multiple-Choice Question and Answer (MCQA) Generation Using Deep Learning Model -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Deep Learning Model for MCQA -- 4 Experimental Results -- 5 Conclusion -- References -- Deep Learning and IoT-Based Driver Helmet Detection and Bike Ignition -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Algorithm -- 3.2 Working -- 4 Implementation -- 5 Conclusion and Future Scope -- References -- Cloud-Based Saline Monitoring System -- 1 Introduction -- 2 Problem Statement -- 3 Literature Survey -- 4 Background Work -- 5 Motivation -- 6 Scope of Work -- 7 Methodology -- 8 Implementation -- 9 Cloud Interface -- 10 Results -- 11 Conclusion and Future Scope -- References -- A Study on Different Neural Network Methods of Leaf Image Processing for Disease Identification -- 1 Introduction -- 2 Literature Survey -- 3 CNN Based Methods -- 4 Conclusion -- References -- CNN Depending Spectrum Sensing for Effective Data Transmission in Wireless Communication -- 1 Introduction -- 2 Dataset Details -- 3 Description of Dataset -- 4 CNN-Based Spectrum Sensing Model -- 5 Details of Training Procedure -- 6 Conclusions -- References -- Fake News Detection and Analysis Using Online Machine Learning Techniques -- 1 Introduction -- 2 Review of Related Work -- 3 Types of Feature Extraction -- 3.1 Count Vectorizer -- 3.2 Hashing Vectorizer -- 3.3 TFIDF Vectorizer -- 4 Machine Learning Algorithms -- 4.1 Passive Aggressive Classifier -- 4.2 Multinomial NB Algorithm -- 5 Methodology and Workflow of Fake News Detection -- 6 Results and Analysis -- 6.1 Passive Aggressive Algorithm -- 6.2 Multinomial NB Algorithm -- 7 Conclusions -- References -- Controlling Electrical Bulb Through Mobile Based on Light Intensity -- 1 Introduction -- 1.1 NodeMCU -- 1.2 Relay Module -- 1.3 LDR Sensor.
2 Proposed Work -- 2.1 System Design -- 3 Implementation -- 3.1 Interfacing LDR with NodeMCU -- 3.2 Interfacing Relay and NodeMCU -- 3.3 Setting up Mobile Application -- 4 Result -- 5 Conclusion -- References -- Secure Node Energetic Routing Method for Cracked Paths Identification in WSN -- 1 Introduction -- 2 Related Works -- 3 Proposed Scheme -- 3.1 Mobile Nodes Travel in Various Direction -- 3.2 Node Energetic Routing (NER) Method -- 3.3 Algorithm to Select Master Node -- 4 Performance Analysis -- 5 Conclusion -- References -- Telangana Air Pollution Stations Classification Using HACA -- 1 Introduction -- 1.1 Effects of PM 10 -- 1.2 Effects of NOx -- 2 Materials and Methods -- 2.1 Data Collection -- 2.2 Hierarchical Agglomerative Cluster Analysis (HACA) -- 3 Results and Discussions -- 4 Conclusions -- References -- Evaluation of Gastric Cancer Using Explainable AI Techniques -- 1 Introduction -- 1.1 Key Objectives of the Proposed Work -- 2 Explainable AI -- 3 Materials and Methods -- 3.1 Dataset -- 3.2 Methodology -- 4 Results and Discussion -- 4.1 Experimental Setup -- 4.2 Experimental Analysis -- 5 Conclusion -- References -- QR-Code Crime-Specific Framework with Crime-Specific Intelligence -- 1 Introduction -- 1.1 QR-Code Crime Categories -- 1.2 Digital Forensic Investigation Framework -- 2 Investigating QR-Code Crime Case -- 2.1 QR-Code Attack Scenarios -- 2.2 QR-Code Financial Fraud Scenario -- 2.3 Literature -- 2.4 The Need for a DFI Framework for QR-Code Crime Investigation -- 3 The Proposed Framework -- 4 The Crime-Specific Intelligence -- 5 Methodology -- 6 Results and Discussion -- 6.1 Observations and Findings -- 7 Conclusion -- References -- N-Gram based Convolutional Neural Network Approach for Authorship Identification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dataset Preparation -- 3.2 Creating N-grams. 3.3 Model Building -- 4 Experimental Studies -- 4.1 Novel Dataset by Different Authors and Genres -- 4.2 Canadian Authors-English Novels Dataset -- 4.3 PAN Dataset-Stamatatos06-Authorship-Attribution-Dataset-C10 -- 5 Conclusion -- References -- A Proficient Digital Signature Scheme Using Lightweight Cryptography -- 1 Introduction -- 2 Related Work -- 2.1 ElGamal Digital Signature -- 3 Proposed Methodology -- 3.1 Shortened Complex Digital Signature (SCDSA) -- 3.2 Parameters of SCDSA -- 4 Conclusion -- References -- Delightful Drips: A Smart Irrigation System -- 1 Introduction -- 2 Methodology -- 2.1 Message Queue Telemetry Transport -- 2.2 Master-Slave Architecture -- 2.3 Adafruit.io -- 2.4 Blynk -- 3 Components Used -- 3.1 Raspberry Pi -- 3.2 ESP8266-NodeMCU -- 3.3 MH-Series Soil Moisture Sensor -- 3.4 HW-383 Dual Channel 5v Relay -- 3.5 Solenoid Valve -- 4 Circuit Diagrams -- 5 Implementation -- 6 System Architecture -- 7 Results -- 8 Conclusion -- References -- Diagnosis of Autism Spectrum Disorder Using Context-Based Pooling and Cluster-Graph Convolution Networks -- 1 Introduction -- 2 Related Work -- 3 The Autism Brain Imaging Data Exchange Dataset -- 4 Methodology -- 4.1 Building Graph Model from the Pre-processed Data -- 4.2 Graph Convolution Networks and Context-Based Pooling -- 4.3 Population Graph -- 4.4 Classification -- 5 Results -- 6 Conclusion -- References -- Comparison of Effective Machine Learning Technique for Air Quality Forecast -- 1 Introduction -- 2 Background Work -- 3 Related Work -- 4 Proposed Work -- 4.1 Data Size Data Preprocessing -- 4.2 Data Visualization -- 4.3 Model Testing -- 5 Conclusion and Future Scope -- References -- Prediction of Toxic Gases Tolerance Level and Analysis of Impact on Human Respiratory System Using Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Proposed Method. 5 Sources and Distribution of Toxic Gases -- 5.1 Hydrogen Sulfide -- 5.2 Chlorine -- 5.3 Carbon Di-oxide -- 6 Working Model of the System -- 6.1 Gas Collection -- 6.2 Sensor Chamber -- 6.3 Data Register -- 6.4 Machine Learning Model -- 7 KNN Algorithm -- 8 Results -- 9 Conclusion -- References -- Efficient Online Circulation of Blood in Geo-Blood Management: BEST Using Support Vector Machine -- 1 Introduction -- 2 Web Application for Blood Donor Search -- 2.1 Admin Module -- 2.2 User Module -- 2.3 Advantages of BEST -- 3 Donor Selection Using Support Vector Machine -- 4 Results and Discussion -- 5 Conclusion -- References -- Battery Management System Implementation in Electric Vehicle with Varying Loads Using MATLAB -- 1 Introduction -- 2 Development of Battery Management System -- 3 DC Machine Powered by Lithium-Ion Battery -- 4 DC Machine Under Different Load Conditions -- 5 Conclusion -- References -- Smart Assistant for Challenged People Using Embedded Systems -- 1 Introduction -- 2 Literature Survey and Objective -- 2.1 Literature Survey -- 2.2 Objectives -- 3 Proposed Technique -- 3.1 Software Dumping -- 4 Results and Discussions -- 5 Conclusion -- References -- Novel Approach to Identify and Eliminate Deadlocks in the 'C' Program -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Methodology -- 4 Conclusion -- References -- Novel Approach to Abstract Object Features from Java Program -- 1 Introduction -- 2 Literature Survey -- 3 Methodology -- 4 Results and Discussion -- 5 Conclusion -- References -- Detecting Logging of Forest Trees Using Sound Event Detection -- 1 Introduction -- 2 Literature Review -- 3 Methodology -- 3.1 Dataset -- 3.2 Feature Extraction -- 3.3 Model Implementation -- 4 Results and Analysis -- 5 Conclusion -- References -- Education Technologies Based on Artificial Intelligence -- 1 Introduction. 2 Effective AI in Education -- 3 Screen Time -- 4 Current Trends of AI in the Education Sector -- 5 In Future -- 6 Conclusion -- References -- A Comprehensive Review on the Subject of Securing Web Application Data in Cloud Environments -- 1 Introduction -- 2 The Use of Cloud Computing in a Variety of Different Domains -- 3 The Requirement for Security in the Setting of Cloud Computing -- 4 Analysis of Cloud Security: A Detailed Review -- 4.1 Abbreviations -- 5 Results and Analysis -- 5.1 The Most Important Factors to Consider in Relation to This Research -- 6 Conclusion -- References -- Domain Adaptation of Pretrained Models for Telugu Wh-Questions -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Work -- 4 Data -- 5 Results -- 6 Conclusion -- References -- A Preprocessing and Segmentation Approach for Accurate Identification of Diseases in Potato Plant -- 1 Introduction -- 2 Literature Review -- 3 Proposed Methodology -- 3.1 Data Collection and Resizing the Image -- 3.2 Grayscale Conversion and Applying Gaussian Blur Technique -- 3.3 Segmentation -- 3.4 K-Means Clustering -- 4 Experimental Results -- 5 Conclusion -- References -- Implementation of Intelligent Advanced Extensible Interface for Dual-Port RAM-A Hardware Software Co-design Approach -- 1 Introduction -- 2 Implementation Details -- 3 Results -- 4 Conclusions -- References -- A Systematic Review on Compatibility Requirements for Communication in Hetero-Multi Blockchain Systems -- 1 Introduction -- 1.1 Consensus Mechanisms of HMBC Systems -- 1.2 Types of Consensus Mechanisms -- 1.3 Consensus Mechanisms Properties and Limitations -- 2 Smart Contracts [16] -- 2.1 Necessities of Smart Contracts [17] -- 2.2 Role of Smart Contract Locator (SCL) -- 2.3 Smart Contract Issues -- 3 Existing Solutions of HMBC Communications -- 4 Conclusion -- References. Deep Learning-Based Emotion-Aware Music Recommendation System. |
| Altri titoli varianti | Proceedings of the Second International Conference on Cognitive and Intelligent Computing |
| Record Nr. | UNINA-9910746961203321 |
| Singapore : , : Springer Nature Singapore Pte Ltd, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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