000			04196cam a2200565Ii 4500
003			OCoLC
005			20240806145607.0
008			220104s2021 sz o 101 0 eng d
020			_a9783030936204
020			_a3030936201
020			_z9783030936198
041			_aEnglish
049			_aMAIN
072		7	_aUNF _2bicssc
072		7	_aCOM021030 _2bisacsh
072		7	_aUNF _2thema
072		7	_aUYQE _2thema
082	0	4	_a006.312 _223 _bSRI
111	2		_aBDA (Conference) _n(9th : _d2021 : _cOnline)
245	1	0	_aBig data analytics : _b9th International Conference, BDA 2021, Virtual event, December 15-18, 2021, Proceedings / _cSatish Narayana Srirama, Jerry Chun-Wei Lin, Raj Bhatnagar, Sonali Agarwal, P. Krishna Reddy (eds.).
246	3		_aBDA 2021.
260			_aCham, Switzerland : _bSpringer, _c2021.
300			_a1 online resource (xiii, 355 pages) : _billustrations (some color).
490	1		_aLecture notes in computer science ; _v13147.
490	1		_aLNCS sublibrary, SL 3, Information systems and applications, incl. internet/web, and HCI.
500			_aIncludes author index.
505			_aIntro Preface Organization Contents Medical and Health Applications MAG-Net: Multi-task Attention Guided Network for Brain Tumor Segmentation and Classification 1 Introduction 2 Literature Review 3 Proposed Work 3.1 Encoder 3.2 Decoder 3.3 Classification 4 Experiment and Results 4.1 Dataset Setup 4.2 Training and Testing 4.3 Results 5 Conclusion References Smartphone Mammography for Breast Cancer Screening 1 Introduction 2 Related Work 3 System Description 4 Simulation 5 Results 6 Conclusion and the Future Work 4 The Solution to Contain Antibiotic Resistance 4.1 Diseasomics Knowledge Graph 4.2 Categorical Belief Knowledge Graph 4.3 Vector Embedding Through Node2Vec 4.4 Probabilistic Belief Knowledge Graph 4.5 De-escalation (Site-Specific and Patient-Specific Resistance) 4.6 The Right Automated Documentation 5 Conclusion References Tooth Detection from Panoramic Radiographs Using Deep Learning 1 Introduction 2 Related Works 3 Methodology 3.1 Data Collection 3.2 Data Annotation 3.3 Data Preprocessing 3.4 Object Detection Model 3.5 Performance Analysis 4 Experimental Results 4.1 Localization Loss 4.2 Total Loss 4.3 Learning Rate 4.4 Steps Per Epoch 5 Comparative Study 5.1 Comparison with Clinical Experts 5.2 Comparison with Other Works 6 Conclusion References Machine/Deep Learning Hate Speech Detection Using Static BERT Embeddings 1 Introduction 1.1 BERT 1.2 Attention in Neural Networks 2 Related Work 3 Proposed Methodology 3.1 Static BERT Embedding Matrix 4 Experiments 4.1 Choice of Dataset 4.2 Neural Network Architectures and Testing Environment 5 Results and Discussion 6 Conclusion References Fog Enabled Distributed Training Architecture for Federated Learning 1 Introduction 2 Related Work 3 Decentralized Federated Learning 3.1 Architecture 3.2 Online Training and Data Privacy 4 Evaluation and Results 4.1 Docker Based Fog Federation Framework 4.2 FMCW Radar Dataset for Federated Learning 4.3 Results and Analysis 5 Conclusions and Future Work References Modular ST-MRF Environment for Moving Target Detection and Tracking Under Adverse Local Conditions 1 Introduction 1.1 Data Collection and Pre-processing
650		0	_aBig data
650		0	_aDatabase management
650		0	_aData mining
650		0	_vCongresses.
650		0	_vCongresses.
650		0	_vCongresses.
700	1		_aSrirama, Satish Narayana,
700	1		_aLin, Jerry Chun-Wei,
700	1		_aBhatnagar, Raj,
700	1		_aAgarwal, Sonali,
700	1		_aReddy, P. Krishna
700	1		_d1978- _eeditor.
700	1		_eeditor.
700	1		_eeditor.
700	1		_eeditor.
700	1		_q(Polepalli Krishna), _eeditor.
830		0	_aLecture notes in computer science ; _v13147.
830		0	_aLNCS sublibrary. _nSL 3, _pInformation systems and applications, incl. Internet/Web, and HCI.
856	4	0	_uhttps://ezproxy.lib.gla.ac.uk/login?url=https://link.springer.com/10.1007/978-3-030-93620-4
856	4	0	_zConnect to e-book
907			_a.b38465188
942			_2ddc _cBOOKS
999			_c43327 _d43327