Erratum: New preprocessing technique for digital facsimile transmission

1980 ◽  
Vol 16 (16) ◽  
pp. 644
Author(s):  
M.G.B. Ismail ◽  
R.J. Clarke
Keyword(s):  
Preprocessing Technique ◽  
Digital Facsimile

New preprocessing technique for digital facsimile transmission

1980 ◽  
Vol 16 (10) ◽  
pp. 355 ◽  
Author(s):  
M.G.B. Ismail ◽  
R.J. Clarke
Keyword(s):  
Preprocessing Technique ◽  
Digital Facsimile

Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring

2021 ◽  
Vol 70 ◽  
pp. 1-11
Author(s):  
Bingchang Hou ◽  
Dong Wang ◽  
Yi Wang ◽  
Tongtong Yan ◽  
Zhike Peng ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Signal Preprocessing ◽  
Machine Health Monitoring ◽  
Machine Health ◽  
Preprocessing Technique

Exploring the Vis-NIR wavelength importance in SOC models under field and lab conditions in a long-term field experiment

2021 ◽  
Author(s):  
Javier Reyes ◽  
Mareike Ließ
Keyword(s):  
Field Experiment ◽  
Spectral Response ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Field Conditions ◽  
Partial Least Square Regression ◽  
Preprocessing Technique ◽  
Term Field

<p>Soil organic carbon (SOC) is of particular interest in the study of agricultural systems as an indicator of soil quality and soil fertility. In the use of Vis-NIR spectroscopy for SOC detection, the interpretation of the spectral response with regards to the importance of individual wavelengths is challenging due to the soil’s composition of multiple organic and minerals compounds. Under field conditions, additional aspects affect the spectral data compared to lab conditions. This study compared the spectral wavelength importance in partial least square regression (PLSR) models for SOC between field and lab conditions. Surface soil samples were obtained from a long-term field experiment (LTE) with high SOC variability located in the state of Saxony-Anhalt, Germany. Data sets of Vis-NIR spectra were acquired in the lab and field using two spectrometers, respectively. Four different preprocessing methods were applied before building the models. Wavelength importance was observed using variable importance in projection. Differences in wavelength importance were observed depending on the measurement device, measurement condition, and preprocessing technique, although pattern matches were identifiable, especially in the NIR range. It is these pattern matches that aid model interpretation to effectively determine SOC under field conditions.</p>

scholarly journals Dual Shot Face Detecting using Deep Learning

2020 ◽  
Vol 8 (6) ◽  
pp. 5669-5672
Keyword(s):  
Deep Learning ◽  
High Dimensionality ◽  
Absolute Accuracy ◽  
Robust Method ◽  
Learning Technique ◽  
Benchmark Datasets ◽  
Dual Images ◽  
Preprocessing Technique ◽  
Day By Day ◽  
Feature Extraction Technique

In the paper, we have used a deep learning technique to identify dual faces i.e. nothing but detecting dual shot faces. As the data is emerging day by day with high dimensionality, recognizing dual faces is a major problem. So wasting time on identifying images is like fiddling around. In order to save time and get absolute accuracy we have implemented a fast preprocessing technique named as Convolutional Neural Network (CNN) along with feature extraction technique which is used to knob the relevant features to detect and identify images/faces. By performing this robust method, our intention is to detect dual images in an efficient way. This technique results in decreased feature cardinality and preserves unique efficiency of the data. The experiment is performed on extensive well liked face detecting benchmark datasets, Wider Face and FDDB. CNN with FE demonstrates the results with superiority and the accuracy was in-depth analyzed by CNN classifier.

scholarly journals Rule Based Morphological Variation Removable Stemming Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 1809-1814
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Morphological Variation ◽  
Morphological Changes ◽  
The Internet ◽  
Rule Based ◽  
The People ◽  
Stop Word ◽  
Preprocessing Technique ◽  
Better Than

Sentiment analysis is a technique to analyze the people opinion, attitude, sentiment and emotion towards any particular object. Sentiment analysis has the following steps to predict the opinion of a review sentences. The steps are preprocessing, feature selection, classification and sentiment prediction. Preprocessing is the main important step and it consists of many techniques. They are Stop word Removal, punctuation removal, conversion of numbers to number names. Stemming is another important preprocessing technique which is used to transform the words in text into their grammatical root form and is mainly used to improve the retrieval of the information from the internet. It is applied mainly to get strengthen the retrieval of the information. Many morphological languages have immense amount of morphological deviation in the words. It triggered vast challenges. Many algorithms exist with different techniques and has several drawbacks. The aim of this paper is to propose a rule based stemmer that is a truncating stemmer. The new stemming mechanism in this paper has brought about many morphological changes. The new rule based morphological variation removable stemming algorithm is better than the existing other algorithms such as New Porter, Paice/Lovins and Lancaster stemming algorithm

scholarly journals Respiratory Diseases Detection using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 2698-2701
Author(s):  
Shubham Shitole
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Lung Diseases ◽  
Respiratory Diseases ◽  
Diagnostic System ◽  
Extraction Process ◽  
Image Feature ◽  
Feature Selection Technique ◽  
Preprocessing Technique ◽  
Lengthy Process

Prediction of the Respiratory diseases in the earlier stage can be very useful specially to improve the survival rate of that patient. CT scan images are used to detect various lung diseases .These CT scan reports are sent to pathologists for further process. Pathologists analyze CT scan report and predict the infected tissues which are the main cause of the particular disease. This is lengthy process and to avoid this steps and increase the accuracy of the prediction Machine learning plays an important role . The system proposes to build "Predictive Diagnostic System" of infectious lung by using the concept of image processing in conjunction with machine learning. Proposed system will detect the disease from CT scan images and use preprocessing technique that will remove the noise and disturbance in image. Feature extraction process is applied to extract the useful features of underlying image, and feature selection technique will further optimize the top ranking features. CNN algorithm is then applied to classify the images for detection of Respiratory disease. After detection of disease, report will be generated and submitted to patient.

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