Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers

Abstract— Detection of fake review and reviewers is currently a challenging problem in cyber space. It is challenging primarily due to the dynamic nature of the methodology used to fake the review. There are several aspects to be considered when analyzing reviews to classify them effective into genuine and fake. Sentiment analysis, opinion mining and intend mining are fields of research that try to accomplish the goal through Natural Language Processing of the text content of the review. In this paper, an approach that uses the review ratings evaluated along a timeline is presented. An Amazon dataset comprising of ratings indicated for a wide range of products was used for the analysis presented here. The analysis of the ratings was carried out for an electronic product over a period of six years. The computed average rating helps to identify linear classifiers that define solution boundaries within the dataspace. This enables a product specific classification of review ratings and suitable recommendations can also be generated automatically. The paper explains a methodology to evaluate the average product ratings over time and presents the research outcomes using a novel classification tool. The proposed approach helps to determine the optimal point to distinguish between fake and genuine ratings for each product. Index Terms: Fake reviews, Fake Ratings, Product Ratings, Online Shopping, Amazon Dataset.

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Uso de fungicidas en el manejo postcosecha de limón persa (Citrus Latifolia)

Revista Biológico Agropecuaria Tuxpan ◽

10.47808/revistabioagro.v6i2.202 ◽

2018 ◽

Vol 6 (2) ◽

pp. 107-111

Author(s):

María del Rosario Dávila Lezama ◽

Néstor Manuel Lorenzo Flores ◽

Teresita Ramírez Hernández ◽

María Alva Ángel Lara ◽

Carlos Jesús Real Garrido

Keyword(s):

Botrytis Cinerea ◽

Geotrichum Candidum ◽

Penicillium Digitatum ◽

Rhizopus Stolonifer ◽

Penicillium Italicum ◽

Citrus Latifolia ◽

Penicillium Spp

Estudios realizados, han identificado que los hongos responsables que limitan la vida de anaquel de los cítricos son principalmente: Penicillium digitatum (55-80%); Penicillium italicum (2-30%); Alternaria citri y A. alternata (8-15%); Botrytis cinerea (8-20%): Colletotrichum gloesporioides (2.5-6%); Geotrichum candidum (2-3%); Rhizopus stolonifer y R. oryzae (1-3%); Phytophtora citrophtora (2%) (Salvador et al., 2007). El objetivo del experimento Evaluar la efectividad de dos fingicidas para el control de enfermedades provocadas por hongos en limón persa (Citrus latifolia) en postcosecha. El Proyecto se realizó en Cuajilote, Cuitláhuac, Ver. Trasladando las muestras al laboratorio general número 4 de la Facultad de Ciencias Biológicas y Agropecuarias, región Orizaba-Córdoba, de la Universidad Veracruzana. Los tratamientos donde se aplicaron los fungicidas Bankit Gold® (Azoxystrobin + Fludioxonil) y Magnate Sulphate® (Imazalil) en limón persa (Citrus latifolia) en el proceso de postcosecha, no tuvieron presencia de patógenos que provocan daños en el fruto por lo cual los fungicidas cumplieron con su objetivo, sin embargo, el tratamiento 1 (testigo absoluto) tuvo presencia del patógeno Penicillium spp. en su evaluación a los 30 DDA, esto, basándonos en los resultados de los análisis microbiológicos de limón persa (Citrus latifolia), la contaminación por Penicillium spp. probablemente fue en el almacenamiento del limón persa (Citrus latifolia). Respecto a los resultados de las propiedades fisicoquímicas están dentro los parámetros de calidad.

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Explaining Predictions of Non-Linear Classifiers in NLP

10.18653/v1/w16-1601 ◽

2016 ◽

Cited By ~ 16

Author(s):

Leila Arras ◽

Franziska Horn ◽

Grégoire Montavon ◽

Klaus-Robert Müller ◽

Wojciech Samek

Keyword(s):

Linear Classifiers ◽

Non Linear

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Classification of Cognitive Level of Patients with Leukoaraiosis on the Basis of Linear and Non-Linear Functional Connectivity

Frontiers in Neurology ◽

10.3389/fneur.2017.00002 ◽

2017 ◽

Vol 8 ◽

Cited By ~ 3

Author(s):

Ranran Li ◽

Youzhi Lai ◽

Yumei Zhang ◽

Li Yao ◽

Xia Wu

Keyword(s):

Functional Connectivity ◽

Linear Functional ◽

Cognitive Level ◽

Non Linear

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Decay Detection in Citrus Fruits Using Hyperspectral Computer Vision

Intelligent Data Analysis for Real-Life Applications ◽

10.4018/978-1-4666-1806-0.ch006 ◽

2012 ◽

pp. 104-123

Author(s):

Juan Gómez-Sanchis ◽

Emilio Soria-Olivas ◽

Delia Lorente-Garrido ◽

José M. Martínez-Martínez ◽

Pablo Escandell-Montero ◽

...

Keyword(s):

Computer Vision ◽

Agricultural Sector ◽

Vision System ◽

Penicillium Digitatum ◽

Machine Learning Techniques ◽

Economic Losses ◽

Citrus Fruits ◽

Penicillium Italicum ◽

Citrus Industry ◽

Learning Techniques

The citrus industry is nowadays an important part of the Spanish agricultural sector. One of the main problems present in the citrus industry is decay caused by Penicillium digitatum and Penicillium italicum fungi. Early detection of decay produced by fungi in citrus is especially important for the citrus industry of distribution. This chapter presents a hyperspectral computer vision system and a set of machine learning techniques in order to detect decay caused by Penicillium digitatum and Penicillium italicum fungi that produce more economic losses to the sector. More specifically, the authors employ a hyperspectral system and artificial neural networks. Nowadays, inspection and removal of damaged citrus is done manually by workers using dangerous ultraviolet light. The proposed system constitutes a feasible and implementable solution for the citrus industry; this has been proven by the fact that several machinery enterprises have shown their interest in the implementation and patent of the system.

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