scholarly journals Optimization of Fruit Disease Detection Process: Using Gaussian Filtering Along With Enhanced SVM

2018 ◽  
Vol 7 (2) ◽  
pp. 18-20
Author(s):  
Hardeep Singh ◽  
Sandeep Sharma
Keyword(s):  
Early Stage ◽  
Average Error ◽  
Disease Detection ◽  
Selection Mechanism ◽  
Processing Stage ◽  
Naked Eye ◽  
Feature Extraction And Selection ◽  
Weighted Kernel ◽  
Fruit Disease ◽  
A New Technique

Fruit disease detection becomes critical since economic and related issues are influenced through the healthy and non-healthy fruits. Technology has advanced and is used to primarily detect and abnormality which is not visible through the naked eye. This paper proposes a new technique of fruit disease detection at early stage for which Gaussian smoothening is used at pre-processing stage along with weighted kernel function within SVM for achieving higher classification accuracy. Feature extraction and selection mechanism uses rank based mechanism that allocates ranks on the basis of predictive significance. The result is obtained in terms of prediction accuracy and mean or average error. Result is optimized by the factor of 10%.

scholarly journals Fruit Disease Detection Using Convolution Neural Network Approach

2018 ◽  
Vol 7 (2) ◽  
pp. 62-65
Author(s):  
Shivani . ◽  
Sharanjit Singh
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Texture Features ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Disease Information ◽  
Fruit Disease ◽  
Farming Industry

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.

On the Nature of Gender Categorization

2012 ◽  
Vol 43 (1) ◽  
pp. 14-27 ◽  
Author(s):  
Silvia Tomelleri ◽  
Luigi Castelli
Keyword(s):  
Early Stage ◽  
Processing Stage ◽  
Brain Potentials ◽  
Ongoing Process ◽  
Gender Categorization

In the present paper, relying on event-related brain potentials (ERPs), we investigated the automatic nature of gender categorization focusing on different stages of the ongoing process. In particular, we explored the degree to which gender categorization occurs automatically by manipulating the semantic vs. nonsemantic processing goals requested by the task (Study 1) and the complexity of the task itself (Study 2). Results of Study 1 highlighted the automatic nature of categorization at an early (N170) and on a later processing stage (P300). Findings of Study 2 showed that at an early stage categorization was automatically driven by the ease of extraction of category-based knowledge from faces while, at a later stage, categorization was more influenced by situational constrains.

Naked-eye colorimetric detection of HCV RNA mediated by a 5′ UTR-targeted antisense oligonucleotide and plasmonic gold nanoparticles

The Analyst ◽  
2021 ◽  
Author(s):  
Almas Shamaila Mohammed ◽  
Aniket Balapure ◽  
Mahammad Nanne Khaja ◽  
Ramakrishnan Ganesan ◽  
Jayati Ray Dutta
Keyword(s):  
Gold Nanoparticles ◽  
Antisense Oligonucleotide ◽  
Early Stage ◽  
Colorimetric Detection ◽  
Sensitive Detection ◽  
Clinical Samples ◽  
Hcv Rna ◽  
Naked Eye

An Au NP based facile strategy for the rapid, early-stage, and sensitive detection of HCV RNA in clinical samples which avoids thiol tagging to the antisense oligonucleotide and expensive infrastructure is presented.

AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images

2021 ◽  
Author(s):  
L. Sathish Kumar ◽  
S. Hariharasitaraman ◽  
Kanagaraj Narayanasamy ◽  
K. Thinakaran ◽  
J. Mahalakshmi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Disease Detection ◽  
Brain Images ◽  
Mri Brain ◽  
Mri Brain Images

scholarly journals Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model

2021 ◽  
Author(s):  
Hepzibah Elizabeth David ◽  
K. Ramalakshmi ◽  
R. Venkatesan ◽  
G. Hemalatha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Stage ◽  
Computational Effort ◽  
Tomato Leaf ◽  
Disease Detection ◽  
Tomato Production ◽  
Classification Image ◽  
Leaf Disease ◽  
Learning Techniques

Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.

Clinical Proteomics: From Biomarker Discovery and Cell Signaling Profiles to Individualized Personal Therapy

2005 ◽  
Vol 25 (1-2) ◽  
pp. 107-125 ◽  
Author(s):  
Katherine R. Calvo ◽  
Lance A. Liotta ◽  
Emanuel F. Petricoin
Keyword(s):  
Information Flow ◽  
Biomarker Discovery ◽  
Ex Vivo ◽  
Early Stage ◽  
Clinical Proteomics ◽  
Disease Detection ◽  
Early Disease ◽  
Early Stage Disease ◽  
Early Disease Detection ◽  
Highly Sensitive

The discovery of new highly sensitive and specific biomarkers for early disease detection and risk stratification coupled with the development of personalized “designer” therapies holds the key to future treatment of complex diseases such as cancer. Mounting evidence confirms that the low molecular weight (LMW) range of the circulatory proteome contains a rich source of information that may be able to detect early stage disease and stratify risk. Current mass spectrometry (MS) platforms can generate a rapid and high resolution portrait of the LMW proteome. Emerging novel nanotechnology strategies to amplify and harvest these LMW biomarkers in vivo or ex vivo will greatly enhance our ability to discover and characterize molecules for early disease detection, subclassification and prognostic capability of current proteomics modalities. Ultimately genetic mutations giving rise to disease are played out and manifested on a protein level, involving derangements in protein function and information flow within diseased cells and the interconnected tissue microenvironment. Newly developed highly sensitive, specific and linearly dynamic reverse phase protein microarray systems are now able to generate circuit maps of information flow through phosphoprotein networks of pure populations of microdissected tumor cells obtained from patient biopsies. We postulate that this type of enabling technology will provide the foundation for the development of individualized combinatorial therapies of molecular inhibitors to target tumor-specific deranged pathways regulating key biologic processes including proliferation, differentiation, apoptosis, immunity and metastasis. Hence future therapies will be tailored to the specific deranged molecular circuitry of an individual patient's disease. The successful transition of these groundbreaking proteomic technologies from research tools to integrated clinical diagnostic platforms will require ongoing continued development, and optimization with rigorous standardization development and quality control procedures.

Application of Image Processing in Fruit and Vegetable Analysis: A Review

2015 ◽  
Vol 24 (4) ◽  
pp. 405-424 ◽  
Author(s):  
Shiv Ram Dubey ◽  
Anand Singh Jalal
Keyword(s):  
Image Processing ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Disease Classification ◽  
Disease Detection ◽  
Fruit And Vegetable ◽  
Image Processing Techniques ◽  
Agricultural Operations ◽  
Fruit Disease ◽  
Processing Techniques

AbstractImages are an important source of data and information in the agricultural sciences. The use of image-processing techniques has outstanding implications for the analysis of agricultural operations. Fruit and vegetable classification is one of the major applications that can be utilized in supermarkets to automatically detect the kinds of fruits or vegetables purchased by customers and to determine the appropriate price for the produce. Training on-site is the underlying prerequisite for this type of arrangement, which is generally caused by the users having little or no expert knowledge. We explored various methods used in addressing fruit and vegetable classification and in recognizing fruit disease problems. We surveyed image-processing approaches used for fruit disease detection, segmentation and classification. We also compared the performance of state-of-the-art methods under two scenarios, i.e., fruit and vegetable classification and fruit disease classification. The methods surveyed in this paper are able to distinguish among different kinds of fruits and their diseases that are very alike in color and texture.

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