Random decision forest based sustainable green machining using Citrullus lanatus extract as bio-cutting fluid

2021 ◽  
Vol 68 ◽  
pp. 1814-1823
Author(s):  
R. Sankaranarayanan ◽  
N. Rajesh Jesudoss Hynes ◽  
J. Senthil Kumar ◽  
J. Angela Jennifa Sujana
Keyword(s):  
Citrullus Lanatus ◽  
Cutting Fluid ◽  
Green Machining ◽  
Random Decision Forest ◽  
Decision Forest

scholarly journals Lifelong Machine Learning and root cause analysis for large-scale cancer patient data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Gautam Pal ◽  
Xianbin Hong ◽  
Zhuo Wang ◽  
Hongyi Wu ◽  
Gangmin Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lifelong Learning ◽  
Root Cause Analysis ◽  
Cause Analysis ◽  
Training Time ◽  
Root Cause ◽  
Random Decision Forest ◽  
Batch Data ◽  
Over Time ◽  
Decision Forest

Abstract Introduction This paper presents a lifelong learning framework which constantly adapts with changing data patterns over time through incremental learning approach. In many big data systems, iterative re-training high dimensional data from scratch is computationally infeasible since constant data stream ingestion on top of a historical data pool increases the training time exponentially. Therefore, the need arises on how to retain past learning and fast update the model incrementally based on the new data. Also, the current machine learning approaches do the model prediction without providing a comprehensive root cause analysis. To resolve these limitations, our framework lays foundations on an ensemble process between stream data with historical batch data for an incremental lifelong learning (LML) model. Case description A cancer patient’s pathological tests like blood, DNA, urine or tissue analysis provide a unique signature based on the DNA combinations. Our analysis allows personalized and targeted medications and achieves a therapeutic response. Model is evaluated through data from The National Cancer Institute’s Genomic Data Commons unified data repository. The aim is to prescribe personalized medicine based on the thousands of genotype and phenotype parameters for each patient. Discussion and evaluation The model uses a dimension reduction method to reduce training time at an online sliding window setting. We identify the Gleason score as a determining factor for cancer possibility and substantiate our claim through Lilliefors and Kolmogorov–Smirnov test. We present clustering and Random Decision Forest results. The model’s prediction accuracy is compared with standard machine learning algorithms for numeric and categorical fields. Conclusion We propose an ensemble framework of stream and batch data for incremental lifelong learning. The framework successively applies first streaming clustering technique and then Random Decision Forest Regressor/Classifier to isolate anomalous patient data and provides reasoning through root cause analysis by feature correlations with an aim to improve the overall survival rate. While the stream clustering technique creates groups of patient profiles, RDF further drills down into each group for comparison and reasoning for useful actionable insights. The proposed MALA architecture retains the past learned knowledge and transfer to future learning and iteratively becomes more knowledgeable over time.

scholarly journals OPTIMUM PERFORMANCE OF GREEN MACHINING ON THIN WALLED TI6AL4V USING RSM AND ANN IN TERMS OF CUTTING FORCE AND SURFACE ROUGHNESS

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
Muhammad Yanis ◽  
Amrifan Saladin Mohruni ◽  
Safian Sharif ◽  
Irsyadi Yani
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Fluid ◽  
Depth Of Cut ◽  
Ann Model ◽  
Green Machining ◽  
Radial Depth ◽  
Thin Walled

Thin walled titanium alloys are mostly applied in the aerospace industry owing to their favorable characteristic such as high strength-to-weight ratio. Besides vibration, the friction at the cutting zone in milling of thin-walled Ti6Al4V will create inconsistencies in the cutting force and increase the surface roughness. Previous researchers reported the use of vegetable oils in machining metal as an effort towards green machining in reducing the undesirable cutting friction. Machining experiments were conducted under Minimum Quantity Lubrication (MQL) using coconut oil as cutting fluid, which has better oxidative stability than other vegetable oil. Uncoated carbide tools were used in this milling experiment. The influence of cutting speed, feed and depth of cut on cutting force and surface roughness were modeled using response surface methodology (RSM) and artificial neural network (ANN). Experimental machining results indicated that ANN model prediction was more accurate compared to the RSM model. The maximum cutting force and surface roughness values recorded are 14.89 N, and 0.161 µm under machining conditions of 125 m/min cutting speed, 0.04 mm/tooth feed, 0.25 mm radial depth of cut (DOC) and 5 mm axial DOC. 

A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm

2019 ◽  
Vol 15 (2) ◽  
pp. 170-183
Author(s):  
Sunil Babu Melingi ◽  
V. Vijayalakshmi
Keyword(s):  
Ischemic Stroke ◽  
Magnetic Resonance ◽  
Acute Ischemic Stroke ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Mr Images ◽  
Random Decision Forest ◽  
Number Of Leaves ◽  
Gravitational Search ◽  
Decision Forest

Background: The sub-acute ischemic stroke is the most basic illnesses reason for death on the planet. We evaluate the impact of segmentation technique during the time of breaking down the capacities of the cerebrum. </P><P> Objective: The main objective of this paper is to segment the ischemic stroke lesions in Magnetic Resonance (MR) images in the presence of other pathologies like neurological disorder, encephalopathy, brain damage, Multiple sclerosis (MS). Methods: In this paper, we utilize a hybrid way to deal with segment the ischemic stroke from alternate pathologies in magnetic resonance (MR) images utilizing Random Decision Forest (RDF) and Gravitational Search Algorithm (GSA). The RDF approach is an effective machine learning approach. Results: The RDF strategy joins two parameters; they are; the number of trees in the forest and the number of leaves per tree; it runs quickly and proficiently when dealing with vast data. The GSA algorithm is utilized to optimize the RDF data for choosing the best number of trees and the number of leaves per tree in the forest. Conclusion: This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method.

scholarly journals Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest

2018 ◽  
Vol 10 (12) ◽  
pp. 758-767 ◽  
Author(s):  
Zhixiong Zhang ◽  
Lili Chen ◽  
Brock Humphries ◽  
Riley Brien ◽  
Max S. Wicha ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Computer Vision ◽  
Artificial Neural Network ◽  
Cell Migration ◽  
Cancer Cell ◽  
Machine Learning Algorithms ◽  
Cancer Cell Migration ◽  
Random Decision Forest ◽  
Decision Forest

Cell migratory direction and speed are predicted based on morphological features using computer vision and machine learning algorithms.

A Novel Learned Model for Boundary Detection

2014 ◽  
Vol 998-999 ◽  
pp. 802-805
Author(s):  
Ming Zheng Shao ◽  
Jian Feng Qi ◽  
Ya Chen
Keyword(s):  
Machine Learning ◽  
Boundary Detection ◽  
Machine Learning Techniques ◽  
Single Image ◽  
Human Segmentation ◽  
Learning Techniques ◽  
Image Patches ◽  
Random Decision Forest ◽  
Traditional Approaches ◽  
Decision Forest

Boundary detection is a critical, well-studied computer vision problem. Clearly it would be nice to have algorithms which know where one object stops and another starts. Traditional approaches look for intensity discontinuities in an image, however we believe that detecting boundary from a single image is fundamentally difficult, whereas machine learning techniques have a promising prospect on boundary detection. A novel learning model is proposed in this paper. Random decision forest classifier trained by human segmentation images will applied to determine the mapping from image patches to boundary probabilities. The results on BSDS500 dataset show that our model ties the performance of other competing approaches, but being magnitude faster than others.

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