Enhancement in manufacturing systems using Grey-Fuzzy and LK-SVM approach

Latchoumi, T. P.; Kalusuraman, G.; Banu, J. Faritha; Yookesh, T. L.; Ezhilarasi, T. P.; Balamurugan, K.

doi:10.1109/icissgt52025.2021.00026

Cited by 24 publications

(6 citation statements)

References 17 publications

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“…The selection of 2 principal components in [17] is rather heuristic and is dependent on measurements of an eigenvalue. We tried to explain the important question about how many principal components are required to model pulmonary movement in a statistical model in this paper.…”

Section: Methodsmentioning

confidence: 99%

Biomedical image restoration using machine learning GPU acceleration approach for precision improvement

Hazela

Aarti

Khandelwal

et al. 2022

ijhs

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Focusing the image in a single x-ray projection, an algorithm proposed for actual time volumetric image rebuilt and 3-dimensional location of the lump. Using the Principal Component Analysis (PCA) initialize the parameterized Deformation Vector Fields (DVF) of pulmonary movement. By adjusting the PCA coefficients, applied the DVF applied to optimize the reference image so that, the simulated projection of the rebuilt image matches the observed projection. The digital phantom & patient information was used to evaluate the technique. The phantom has an average relative image rebuilt error of 7.5 percent and a 3-dimensional location of the lump inaccuracy of 0.9 mm, correspondingly. The patient's location of the lump inaccuracy is less than 2 mm. On a GPU NVIDIA C1060, recreating a single volumetric image from every projection takes about 0.2 & 0.3 secs for patient and phantom. From a single image, clinical relevance could lead to reliable 3-dimensional lump tracking.

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Section: Methodsmentioning

confidence: 99%

Biomedical image restoration using machine learning GPU acceleration approach for precision improvement

Hazela

Aarti

Khandelwal

et al. 2022

ijhs

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“…Hinton developed a ground-breaking field of computer vision called Deep Learning, which had been influenced by the structure of the human mind [7]. In-depth learning methods could outperform predictive modeling models by including complex data structure sets at multiple levels.…”

Section: Literature Surveymentioning

confidence: 99%

Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm

Sivasankaran,

Dhanaraj

2024

RIA

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Deep learning was a valuable and effective modeling object classification technique. Furthermore, since huge datasets for clinical images were not always accessible, there are not many accurate algorithms used in diagnostic imaging diagnostics. The researchers used data from Lung Image Database Consortium (LIDC) dataset to explore the feasibility of utilizing deep learning models to detect lung-cancer. Abnormalities on every Computed Tomography (CT) slice are classified by doctor brands. Humans got 174412 examples with 52 images already and related reality data after dimension reduction and rotation. Deep Belief Networks (DBN) Stacked Denotion Autoencoders (SDAE) and Convolutional Neural Networks (CNN) were three Deep Learning techniques that have been designed and developed. The researchers created a method with 28 feature representations and just an Support Vector Machine (SVM) classifier to monitor the effectiveness of deep learning models with those of traditional Computer-Assisted Diagnosis (CADx) systems. CNN, DBNs and SDAE have levels of accuracy of 0.7976, 0.8119 and 0.7929, correspondingly; their proposed conventional CADx has an efficiency of 0.7940 that is rather lesser than DBNs and CNN. Researchers and discovered that a incorrectly labeled nodules utilizing DBNs were 4 percentage bigger than standard CADx, which could be due to the downstream sample selection losing some lesions showing the significance.

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“…Throughout an actuator phase, there is substantial persistence. According to either the OHMA was stretching and shrinking, they might have 2 distinct widths for constant stress [16]. A predictive framework can manage uniformity, however, it's more challenging to describe stationarity with persistence.…”

Section: Figure 1 Ankle Robot -Setupmentioning

confidence: 99%

Fuzzy-based optimization and linear feedback fluid actuators for soft handheld robots

Kulshrestha

Mahandiran²,

Kurikyala

et al. 2022

ijhs

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Because these were elastic & inexpensive, Hydraulic Muscle Activator (HMA) has a lot of promise in portable and responsive rehabilitative equipment. The transducers' changing and continuous response, on the other hand, create modeling and management problems that are hard to grasp. Our study offers a novel portable ankle rehabilitative robotic, that globes only of its type, which is powered with Optimized HMAs (OHMA) run tandem. The goal of this paper is to provide an adaptive regulator that will help OHMA-driven gadgets conquer their problems. To properly anticipate that performance for OHMA, fuzzy feedback regulate operator is developed. To find the best combination input settings for imprecise controllers, a Genetic Algorithm (GA) Based is used. The propoed OHMA-driven gait training robots have been evaluated, and the iteration controllers are effective in tracing the complicated connection between length, velocity, and tension of the OHMA with great precision. Whenever provided several intended trajectories, empirical findings demonstrate that the microcontroller can follow them quite well.

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Enhancement in manufacturing systems using Grey-Fuzzy and LK-SVM approach

Cited by 24 publications

References 17 publications

Biomedical image restoration using machine learning GPU acceleration approach for precision improvement

Biomedical image restoration using machine learning GPU acceleration approach for precision improvement

Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm

Fuzzy-based optimization and linear feedback fluid actuators for soft handheld robots

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