Plant leaf recognition with shallow and deep learning: A comprehensive study

2020 ◽  
Vol 24 (6) ◽  
pp. 1311-1328
Author(s):  
Jozsef Suto
Keyword(s):  
Machine Learning ◽  
Plant Species ◽  
Research Area ◽  
Research Papers ◽  
Plant Leaf ◽  
Leaf Characteristics ◽  
The Past ◽  
The Earth ◽  
Test Sets ◽  
Comprehensive Study

Nowadays there are hundreds of thousands known plant species on the Earth and many are still unknown yet. The process of plant classification can be performed using different ways but the most popular approach is based on plant leaf characteristics. Most types of plants have unique leaf characteristics such as shape, color, and texture. Since machine learning and vision considerably developed in the past decade, automatic plant species (or leaf) recognition has become possible. Recently, the automated leaf classification is a standalone research area inside machine learning and several shallow and deep methods were proposed to recognize leaf types. From 2007 to present days several research papers have been published in this topic. In older studies the classifier was a shallow method while in current works many researchers applied deep networks for classification. During the overview of plant leaf classification literature, we found an interesting deficiency (lack of hyper-parameter search) and a key difference between studies (different test sets). This work gives an overall review about the efficiency of shallow and deep methods under different test conditions. It can be a basis to further research.

Soft matter research in India

2021 ◽  
Vol 34 (9) ◽  
pp. 090402
Author(s):  
Ranjini Bandyopadhyay ◽  
Jürgen Horbach
Keyword(s):  
Soft Matter ◽  
Soft Materials ◽  
Research Area ◽  
Experimental Investigations ◽  
Special Issue ◽  
Complex Flow ◽  
Research Papers ◽  
The Past ◽  
Biological Physics

Abstract Research on soft matter and biological physics has grown tremendously in India over the past decades. In this editorial, we summarize the twenty-three research papers that were contributed to the special issue on Soft matter research in India. The papers in this issue highlight recent exciting advances in this rapidly expanding research area and include theoretical studies and numerical simulations of soft and biological systems, the synthesis and characterization of novel, functional soft materials and experimental investigations of their complex flow behaviours.

Machine Learning and Convolution Neural Network Approaches to Plant Leaf Recognition

2021 ◽  
pp. 234-248
Author(s):  
Rajesh K. V. N. ◽  
Lalitha Bhaskari D.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Plant Species ◽  
Intelligent Systems ◽  
Human Life ◽  
Species Recognition ◽  
Convolution Neural Network ◽  
Plant Leaf ◽  
Network Approaches ◽  
Number Of Plant Species

Plants are very important for the existence of human life. The total number of plant species is nearing 400 thousand as of date. With such a huge number of plant species, there is a need for intelligent systems for plant species recognition. The leaf is one of the most important and prominent parts of a plant and is available throughout the year. Leaf plays a major role in the identification of plants. Plant leaf recognition (PLR) is the process of automatically recognizing the plant species based on the image of the plant leaf. Many researchers have worked in this area of PLR using image processing, feature extraction, machine learning, and convolution neural network techniques. As a part of this chapter, the authors review several such latest methods of PLR and present the work done by various authors in the past five years in this area. The authors propose a generalized architecture for PLR based on this study and describe the major steps in PLR in detail. The authors then present a brief summary of the work that they are doing in this area of PLR for Ayurvedic plants.

scholarly journals Weaker axially dipolar time-averaged paleomagnetic field based on multidomain-corrected paleointensities from Galapagos lavas

2015 ◽  
Vol 112 (49) ◽  
pp. 15036-15041 ◽  
Author(s):  
Huapei Wang ◽  
Dennis V. Kent ◽  
Pierre Rochette
Keyword(s):  
Field Strength ◽  
Mean Field ◽  
Axial Dipole ◽  
The Past ◽  
Galapagos Archipelago ◽  
The Earth ◽  
Geocentric Axial Dipole ◽  
Dynamo Process ◽  
Comprehensive Study

The geomagnetic field is predominantly dipolar today, and high-fidelity paleomagnetic mean directions from all over the globe strongly support the geocentric axial dipole (GAD) hypothesis for the past few million years. However, the bulk of paleointensity data fails to coincide with the axial dipole prediction of a factor-of-2 equator-to-pole increase in mean field strength, leaving the core dynamo process an enigma. Here, we obtain a multidomain-corrected Pliocene–Pleistocene average paleointensity of 21.6 ± 11.0 µT recorded by 27 lava flows from the Galapagos Archipelago near the Equator. Our new result in conjunction with a published comprehensive study of single-domain–behaved paleointensities from Antarctica (33.4 ± 13.9 µT) that also correspond to GAD directions suggests that the overall average paleomagnetic field over the past few million years has indeed been dominantly dipolar in intensity yet only ∼60% of the present-day field strength, with a long-term average virtual axial dipole magnetic moment of the Earth of only 4.9 ± 2.4 × 1022 A⋅m2.

scholarly journals LITERATURE SURVEY ON MACHINE LEARNING BASED TECHNIQUES IN MEDICAL DATAANALYSIS

2019 ◽  
pp. 1-4
Author(s):  
Lavanya Vemulapalli
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Intelligent Algorithms ◽  
The Past ◽  
Disease Prognosis ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Comprehensive Survey

Machine Learning plays a significant role among the areas of Artificial Intelligence (AI). During recent years, Machine Learning (ML) has been attracting many researchers, and it has been successfully applied in many fields such as medical, education, forecasting etc., Right now, the diagnosis of diseases is mostly from expert's decision. Diagnosis is a major task in clinical science as it is crucial in determining if a patient is having the disease or not. This in turn decides the suitable path of treatment for disease diagnosis. Applying machine learning techniques for disease diagnosis using intelligent algorithms has been a hot research area of computer science. This paper throws a light on the comprehensive survey on the machine learning applications in the medical disease prognosis during the past decades

scholarly journals A Survey of Network Traffic Classification

2020 ◽  
Vol 32 (6) ◽  
pp. 137-154
Author(s):  
Aleksandr Igorevich Getman ◽  
Maria Kirillovna Ikonnikova
Keyword(s):  
Machine Learning ◽  
Network Traffic ◽  
Learning Algorithms ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Traffic Classification ◽  
Research Papers ◽  
Advantages And Disadvantages ◽  
Network Traffic Classification ◽  
Insight Into

This survey is dedicated to the task of network traffic classification, particularly to the use of machine learning algorithms in this task. The survey begins with the description of the task, its variations and possible uses in real-world problems. It then proceeds to the description of the methods used historically to solve this task, their limitations and evolution of traffic making machine learning the main way to solve the problem. Then the most popular machine learning algorithms used in this task are described, with the examples of research papers, providing the insight into their advantages and disadvantages in relation to this field. The task of feature selection is discussed, followed by the more global problem of acquiring the suitable dataset to use in the research; some examples of such popular datasets and their descriptions are provided. The paper concludes with the outline of the current problems in this research area to be solved.

The Origin of the Moon and its Relationship to the Origin of the Solar System

1962 ◽  
Vol 14 ◽  
pp. 133-148 ◽  
Author(s):  
Harold C. Urey
Keyword(s):  
Solar System ◽  
The Moon ◽  
The Past ◽  
The Earth ◽  
Short Period ◽  
Origin Of The Moon

During the last 10 years, the writer has presented evidence indicating that the Moon was captured by the Earth and that the large collisions with its surface occurred within a surprisingly short period of time. These observations have been a continuous preoccupation during the past years and some explanation that seemed physically possible and reasonably probable has been sought.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

A Review on Fast Dissolving Tablet

2017 ◽  
Vol 8 (03) ◽  
Author(s):  
Sagar T. Malsane ◽  
Smita S. Aher ◽  
R. B. Saudagar
Keyword(s):  
Drug Delivery ◽  
Patient Compliance ◽  
Research Area ◽  
Oral Route ◽  
Dosage Forms ◽  
The Novel ◽  
The Past ◽  
Orally Disintegrating Tablets ◽  
Novel Drug ◽  
Experience Difficulty

Oral route is presently the gold standard in the pharmaceutical industry where it is regarded as the safest, most economical and most convenient method of drug delivery resulting in highest patient compliance. Over the past three decades, orally disintegrating tablets (FDTs) have gained considerable attention due to patient compliance. Usually, elderly people experience difficulty in swallowing the conventional dosage forms like tablets, capsules, solutions and suspensions because of tremors of extremities and dysphagia. In some cases such as motion sickness, sudden episodes of allergic attack or coughing, and an unavailability of water, swallowing conventional tablets may be difficult. One such problem can be solved in the novel drug delivery system by formulating “Fast dissolving tablets” (FDTs) which disintegrates or dissolves rapidly without water within few seconds in the mouth due to the action of superdisintegrant or maximizing pore structure in the formulation. The review describes the various formulation aspects, superdisintegrants employed and technologies developed for FDTs, along with various excipients, evaluation tests, marketed formulation and drugs used in this research area.

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