Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes all the prerequisite methodologies in each chapter so that new researchers and practitioners will find it very useful. Chapters go from basic methodology to advanced methods, including detailed descriptions of proposed approaches and comprehensive critical discussions on experimental results and how they are applied to Biomedical Engineering, Electronic Health Records, and medical image processing.
- Examines a wide range of Deep Learning applications for Biomedical Engineering and Health Informatics, including Deep Learning for drug discovery, clinical decision support systems, disease diagnosis, prediction and monitoring
- Discusses Deep Learning applied to Electronic Health Records (EHR), including health data structures and management, deep patient similarity learning, natural language processing, and how to improve clinical decision-making
- Provides detailed coverage of Deep Learning for medical image processing, including optimizing medical big data, brain image analysis, brain tumor segmentation in MRI imaging, and the future of biomedical image analysis
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xiii | |
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1 Unified neural architecture for drug, disease, and clinical entity recognition |
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1 | (1) |
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2 | (5) |
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7 | (2) |
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1.4 Results and discussion |
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9 | (7) |
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16 | (1) |
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17 | (4) |
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2 Simulation on real time monitoring for user healthcare information |
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21 | (32) |
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21 | (1) |
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22 | (1) |
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2.3 Proposed model development |
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23 | (14) |
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2.4 Experimental observations |
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37 | (12) |
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49 | (1) |
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50 | (1) |
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50 | (3) |
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3 Multimodality medical image retrieval using convolutional neural network |
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53 | (44) |
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53 | (2) |
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3.2 Convolutional neural network |
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55 | (3) |
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58 | (9) |
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3.4 Medical image retrieval results and discussion |
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67 | (26) |
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3.5 Summary and conclusion |
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93 | (1) |
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93 | (2) |
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95 | (2) |
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4 A systematic approach for identification of tumor regions in the human brain through HARIS algorithm |
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97 | (22) |
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97 | (1) |
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4.2 The intent of this chapter |
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98 | (1) |
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4.3 Image enhancement and preprocessing |
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99 | (3) |
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4.4 Image preprocessing for skull removal through structural augmentation |
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102 | (1) |
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103 | (6) |
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4.6 Experimental analysis and results |
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109 | (6) |
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115 | (1) |
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115 | (1) |
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116 | (1) |
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117 | (2) |
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5 Development of a fuzzy decision support system to deal with uncertainties in working posture analysis using rapid upper limb assessment |
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119 | (22) |
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119 | (2) |
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121 | (1) |
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5.3 Uncertainties occur in analyzing the working posture using RULA |
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121 | (1) |
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122 | (9) |
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5.5 Analysis of postures of the female workers engaged in Sal leaf plate-making units: A case study |
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131 | (3) |
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5.6 Results and discussion |
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134 | (3) |
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137 | (1) |
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138 | (1) |
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138 | (3) |
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6 Short PCG classification based on deep learning |
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141 | (24) |
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Takhellambam Gautam Meitei |
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141 | (2) |
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6.2 Materials and methods |
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143 | (7) |
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6.3 Convolutional neural network |
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150 | (3) |
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6.4 CNN-based automatic prediction |
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153 | (4) |
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157 | (3) |
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160 | (1) |
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161 | (1) |
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162 | (3) |
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7 Development of a laboratory medical algorithm for simultaneous detection and counting of erythrocytes and leukocytes in digital images of a blood smear |
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165 | (22) |
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Ana Carolina Borges Monteiro |
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165 | (1) |
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7.2 Blood cells and blood count |
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166 | (3) |
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169 | (1) |
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170 | (1) |
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7.5 Digital image processing |
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171 | (2) |
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173 | (1) |
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174 | (1) |
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7.8 Materials and methods |
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175 | (2) |
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7.9 Results and discussion |
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177 | (6) |
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7.10 Future research directions |
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183 | (1) |
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183 | (1) |
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183 | (1) |
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184 | (3) |
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8 Deep learning techniques for optimizing medical big data |
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187 | (26) |
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8.1 Relationship between deep learning and big data |
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187 | (2) |
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8.2 Roles of deep learning and big data in medicine |
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189 | (2) |
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8.3 Medical big data promise and challenges |
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191 | (3) |
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8.4 Medical big data techniques and tools |
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194 | (5) |
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8.5 Existing optimization techniques for medical big data |
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199 | (4) |
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8.6 Analyzing big data in precision medicine |
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203 | (3) |
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206 | (1) |
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206 | (5) |
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211 | (2) |
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9 Simulation of biomedical signals and images using Monte Carlo methods for training of deep learning networks |
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213 | (24) |
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9.1 Introduction to simulation for biomedical signals and images |
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213 | (2) |
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9.2 Simulation of biological images and signals |
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215 | (4) |
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9.3 Classification of optical coherence tomography images in heart tissues |
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219 | (14) |
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233 | (1) |
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234 | (3) |
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10 Deep learning-based histopathological image analysis for automated detection and staging of melanoma |
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237 | (30) |
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237 | (2) |
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239 | (1) |
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240 | (13) |
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10.4 Cell proliferation index calculation |
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253 | (10) |
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263 | (1) |
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263 | (4) |
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11 Potential proposal to improve data transmission in healthcare systems |
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267 | (18) |
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Ana Carolina Borges Monteiro |
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267 | (2) |
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11.2 Telecommunications channels |
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269 | (1) |
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11.3 Scientific grounding |
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270 | (2) |
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11.4 Proposal and objectives |
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272 | (1) |
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273 | (1) |
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274 | (1) |
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11.7 Signal validation by DQPSK modulation |
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275 | (1) |
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276 | (3) |
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279 | (2) |
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281 | (1) |
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281 | (2) |
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283 | (2) |
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12 Transferable approach for cardiac disease classification using deep learning |
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285 | (20) |
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285 | (2) |
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287 | (2) |
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289 | (4) |
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12.4 Network architecture |
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293 | (2) |
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12.5 Experimental results |
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295 | (7) |
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302 | (1) |
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302 | (3) |
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13 Automated neuroscience decision support framework |
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305 | (22) |
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305 | (1) |
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13.2 Psychophysiological measures |
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306 | (1) |
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13.3 Neurological data preprocessing |
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307 | (4) |
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311 | (2) |
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13.5 Neuroscience decision support framework |
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313 | (2) |
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13.6 System design and methodology |
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315 | (3) |
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318 | (3) |
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321 | (1) |
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322 | (1) |
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323 | (4) |
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14 Diabetes prediction using artificial neural network |
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327 | (14) |
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327 | (1) |
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328 | (2) |
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14.3 Designing and developing the ANN-based model |
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330 | (4) |
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334 | (1) |
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335 | (1) |
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336 | (1) |
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14.7 Comparative analysis |
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336 | (2) |
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338 | (1) |
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338 | (1) |
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339 | (2) |
| Index |
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341 | |
Dr. Basant Agarwal works as an Assistant Professor at the Indian Institute of Information Technology Kota (IIIT-Kota), India, which is an Institute of National Importance. He holds a Ph.D. and M.Tech. from the Department of Computer Science and Engineering, Malaviya National Institute of Technology Jaipur, India. He has more than 9 years of experience in research and teaching. He has worked as a Postdoc Research Fellow at the Norwegian University of Science and Technology (NTNU), Norway, under the prestigious ERCIM (European Research Consortium for Informatics and Mathematics) fellowship in 2016. He has also worked as a Research Scientist at Temasek Laboratories, National University of Singapore (NUS), Singapore. His research interest include Artificial Intelligence, Cyber physical systems, Text mining, Natural Language Processing, Machine learning, Deep learning, Intelligent Systems, Expert Systems and related areas. Valentina Emilia Balas is currently a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, Aurel Vlaicu University of Arad, Romania. She holds a PhD cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, and an expert evaluator for national and international projects and PhD theses. Lakhmi C. Jain, BE(Hons), ME, PhD, Fellow (IE Australia) is with the Faculty of Education, Science, Technology & Mathematics at the University of Canberra, Australia and the University of Technology Sydney, Australia. He is a Fellow of the Institution of Engineers Australia.
Professor Jain founded the KES International for providing a professional community the opportunities for publications, knowledge exchange, cooperation and teaming. Involving around 5,000 researchers drawn from universities and companies world-wide, KES facilitates international cooperation and generate synergy in teaching and research. KES regularly provides networking opportunities for professional community through one of the largest conferences of its kind in the area of KES. www.kesinternational.org
His interests focus on the artificial intelligence paradigms and their applications in complex systems, security, e-education, e-healthcare, unmanned air vehicles and intelligent agents. Working at Amity University, Jaipur, Rajasthan as Associate Professor in Amity Institute of Information Technology. Worked with Jaipur National University, Jaipur, Rajasthan as Assistant Professor in Department of Computer Science and Engineering. Worked with Stani Memorial College of Engineering and Technology, Phagi (Jaipur) as a Lecturer in the department of IT. Worked with Sri Balaji College of Engineering and Technology, Jaipur as a Lecturer in the department of IT. Worked with Mahrishi Computer & Management College, Sadulpur, Churu as a Lecturer in the Computer department. Currently teaching at CCT, University of Rajasthan. Worked as Associate Professor(Computer Science) in Department of Computer Science, Apaji Institute, Banasthali University. Worked as Sr. Assistant Professor and Assistant Professor(CS) in Department of Computer Science, Apaji Institute, Banasthali University. Worked as Programmer in Department of Computer Science, Apaji Institute, Banasthali University.
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