Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and videos.Interpreting human interaction using RGB images is one ...Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and videos.Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times.Numerous models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB images.This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features.To use human interaction image sequences as input data,we first perform a few denoising steps.Then,human-to-human analyses are employed to deliver more precise results.This phase follows feature engineering techniques,including diverse feature selection.Next,we used the graph mining method for feature optimization and AdaBoost for classification.We tested our proposed HVAA model on two benchmark datasets.The testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)dataset.The second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event detection.The proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.展开更多
To identify and establish beneficiation techniques for slime, a comprehensive research work was carried out on beneficiation plant 2 thickener underflow (Tailing). Iron ore slimes (-45 micron ~80.0%) generated at bene...To identify and establish beneficiation techniques for slime, a comprehensive research work was carried out on beneficiation plant 2 thickener underflow (Tailing). Iron ore slimes (-45 micron ~80.0%) generated at beneficiation plant 2 assaying 46.7% Fe, 10.82% SiO2, and 4.58% Al2O3 have been subjected to various beneficiation studies like cyclone, wet high intensity magnetic separator and flotation to recover the iron bearing minerals. The effects of different operating parameters have been studied to get a product suitable for pellet making. Detailed beneficiation studies indicate that it is possible to obtain a product containing 61.8% to 66.6% Fe, 1.80% to 3.35% SiO2, and 1.65% to 2.40% Al2O3 with 8.0% to 22.3% weight recovery by adopting de-sliming followed by magnetic separation. Within this product grade range the pellet grade fines (JSW norms-minimum 63.0% Fe) consist of 63.0% Fe, 2.97% SiO2 and 2.48% Al2O3 with 16.9% weight recovery and 56.9% Fe recovery at 0.5 Tesla magnetic field intensity, pulsation rate 230 rpm and 2 min residence time.展开更多
文摘Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and videos.Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times.Numerous models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB images.This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features.To use human interaction image sequences as input data,we first perform a few denoising steps.Then,human-to-human analyses are employed to deliver more precise results.This phase follows feature engineering techniques,including diverse feature selection.Next,we used the graph mining method for feature optimization and AdaBoost for classification.We tested our proposed HVAA model on two benchmark datasets.The testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)dataset.The second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event detection.The proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.
文摘To identify and establish beneficiation techniques for slime, a comprehensive research work was carried out on beneficiation plant 2 thickener underflow (Tailing). Iron ore slimes (-45 micron ~80.0%) generated at beneficiation plant 2 assaying 46.7% Fe, 10.82% SiO2, and 4.58% Al2O3 have been subjected to various beneficiation studies like cyclone, wet high intensity magnetic separator and flotation to recover the iron bearing minerals. The effects of different operating parameters have been studied to get a product suitable for pellet making. Detailed beneficiation studies indicate that it is possible to obtain a product containing 61.8% to 66.6% Fe, 1.80% to 3.35% SiO2, and 1.65% to 2.40% Al2O3 with 8.0% to 22.3% weight recovery by adopting de-sliming followed by magnetic separation. Within this product grade range the pellet grade fines (JSW norms-minimum 63.0% Fe) consist of 63.0% Fe, 2.97% SiO2 and 2.48% Al2O3 with 16.9% weight recovery and 56.9% Fe recovery at 0.5 Tesla magnetic field intensity, pulsation rate 230 rpm and 2 min residence time.
