Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT

<div>The influence of <i>c</i>,<svg height="9.39034pt" id="M33" style="vertical-align:-3.42943pt" version="1.1" viewbox="-0.0498162 -5.96091 7.52435 9.39034" width="7.52435pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M546 430L539 434C529 434 505 438 495 440C473 444 450 448 430 448C352 448 265 412 213 366C145 306 96 203 96 103C96 22 135 -12 160 -12C190 -12 238 14 262 32C310 68 368 120 411 184H413C403 117 396 75 384 21C353 -118 325 -158 291 -184C270 -200 241 -205 208 -205C133 -205 90 -164 74 -110C70 -98 58 -100 49 -107C34 -119 23 -140 23 -155C23 -190 74 -261 166 -261C219 -261 280 -233 314 -208C383 -157 446 -79 470 81C491 223 529 388 546 430ZM456 386C452 357 433 283 420 252C402 216 366 174 325 129C288 88 239 56 212 56C192 56 182 77 182 120C182 165 199 242 226 292C256 348 281 377 311 389C327 395 353 402 375 402C408 402 436 394 456 386Z"></path></g></svg> parameters on the prediction accuracy of ABC-SVM.</div>

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Figure 6

Figure 6: Data Fusion Approach for Managing Clinical Data in an Industrial Environment using IoT 