scholarly journals Simple Screening Method of Maize Disease using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 464-467 ◽  
Keyword(s):  
Machine Learning ◽  
Screening Method ◽  
Dark Spot ◽  
Field Screening ◽  
Plant Leaf ◽  
Fuzzy C Means ◽  
Efficient Machine ◽  
The Common ◽  
Common Plant ◽  
Simple Screening

Plant leaf diseases are significant issue in agriculture field. Some of the common plant leaf diseases are powdery mildew, dark spot and rust. They are a noteworthy wellspring of an immense number of dollar worth of setbacks to farmers on a yearly premise. Plant breeders frequently need to screen countless number of plant leaves to find the stage of diseases of their crops to perform an early treatments. Therefore, a robust method for field screening is needed in order to spare the farmers and the environment as well. Inappropriate used of treatments such as impulsive pesticides can imperil the environment. Hence, this paper present a simple and efficient machine learning method which is Fuzzy C-Means algorithm to screen leaf disease severity in maize. Fuzzy C-Means is a new algorithm and very efficient to be used in object detection. Therefore, it is applicable to detect disease spot in plant leaf and measure the diseases severity. This field screening method help the farmer to identify the progression of the diseases in their crops quicker and easier than the other field screening techniques.

scholarly journals A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

2018 ◽  
Vol 6 ◽  
Author(s):  
Carlos Fernando Odir Rodrigues Melo ◽  
Luiz Claudio Navarro ◽  
Diogo Noin de Oliveira ◽  
Tatiane Melina Guerreiro ◽  
Estela de Oliveira Lima ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Random Forest ◽  
Zika Virus ◽  
Screening Method ◽  
Metabolomic Data ◽  
Simple Screening

Optimal Fuzzy Cluster Partitioning by Crow Search Meta-Heuristic for Biomedical Data Analysis

2021 ◽  
Vol 12 (2) ◽  
pp. 49-66
Author(s):  
Janmenjoy Nayak ◽  
Bighnaraj Naik ◽  
Pandit Byomakesha Dash ◽  
Danilo Pelusi
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Data Processing ◽  
Biomedical Informatics ◽  
Learning Model ◽  
Biomedical Data ◽  
Fuzzy C Means ◽  
Biomedical Data Analysis ◽  
Machine Learning Model ◽  
Efficient Machine

Biomedical data is often more unstructured in nature, and biomedical data processing task is becoming more complex day by day. Thus, biomedical informatics requires competent data analysis and data mining techniques for designing decision support system's framework to solve clinical and heathcare-related issues. Due to increasingly large and complex data sets and demand of biomedical informatics research, researchers are attracted towards automated machine learning models. This paper is proposed to design an efficient machine learning model based on fuzzy c-means with meta-heuristic optimizations for biomedical data analysis and clustering. The main contributions of this paper are 1) projecting an efficient machine learning model based on fuzzy c-means and meta-heuristic optimization for biomedical data classification, 2) employing benchmark validation techniques and critical hypothesises testing, and 3) providing a background for biomedical data processing with a view of data processing and mining.

The Diagnosis of COVID-19 through X-ray Images via Transfer Learning and Fine-Tuned Dense Layer on Pipeline

Mekatronika ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 19-24
Author(s):  
Amiir Haamzah Mohamed Ismail ◽  
Mohd Azraai Mohd Razman ◽  
Ismail Mohd Khairuddin ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Screening Method ◽  
Learning Technologies ◽  
Fine Tuning ◽  
Dense Layer ◽  
X Ray ◽  
Chest X Ray ◽  
The Common ◽  
Fully Connected

X-ray is used in medical treatment as a method to diagnose the human body internally from diseases. Nevertheless, the development in machine learning technologies for pattern recognition have allowed machine learning of diagnosing diseases from chest X-ray images. One such diseases that are able to be detected by using X-ray is the COVID-19 coronavirus. This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Next, hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield the highest classification accuracy model. InceptionV3 which is one of the common neural network is used for feature extraction from chest X-ray images. Subsequently, the loss and accuracy graphs are used to find the pipeline which performs the best in classification task. The findings in this research will open new possibilities in screening method for COVID-19.

scholarly journals Human Induction in Machine Learning

2021 ◽  
Vol 54 (3) ◽  
pp. 1-18
Author(s):  
Petr Spelda ◽  
Vit Stritecky
Keyword(s):  
Machine Learning ◽  
A Posteriori ◽  
Experimental Paradigm ◽  
Risk Minimisation ◽  
Inductive Risk ◽  
Acceptable Accuracy ◽  
Space And Time ◽  
The Common ◽  
Testing Set

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, then an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The article asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach “elsewhere” in space and time or deploy ML models in non-benign environments. The article argues that the only viable version of the contract can be based on optimality (instead of on reliability, which cannot be justified without circularity) and aligns this position with Schurz's optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (“elsewhere” and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full.

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.

scholarly journals Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1217
Author(s):  
Nicolò Bellin ◽  
Erica Racchetti ◽  
Catia Maurone ◽  
Marco Bartoli ◽  
Valeria Rossi
Keyword(s):  
Machine Learning ◽  
Fuzzy Sets ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Unsupervised Machine Learning ◽  
Fuzzy C Means ◽  
Air Temperatures ◽  
Physico Chemical ◽  
Shallow Ponds

Machine Learning (ML) is an increasingly accessible discipline in computer science that develops dynamic algorithms capable of data-driven decisions and whose use in ecology is growing. Fuzzy sets are suitable descriptors of ecological communities as compared to other standard algorithms and allow the description of decisions that include elements of uncertainty and vagueness. However, fuzzy sets are scarcely applied in ecology. In this work, an unsupervised machine learning algorithm, fuzzy c-means and association rules mining were applied to assess the factors influencing the assemblage composition and distribution patterns of 12 zooplankton taxa in 24 shallow ponds in northern Italy. The fuzzy c-means algorithm was implemented to classify the ponds in terms of taxa they support, and to identify the influence of chemical and physical environmental features on the assemblage patterns. Data retrieved during 2014 and 2015 were compared, taking into account that 2014 late spring and summer air temperatures were much lower than historical records, whereas 2015 mean monthly air temperatures were much warmer than historical averages. In both years, fuzzy c-means show a strong clustering of ponds in two groups, contrasting sites characterized by different physico-chemical and biological features. Climatic anomalies, affecting the temperature regime, together with the main water supply to shallow ponds (e.g., surface runoff vs. groundwater) represent disturbance factors producing large interannual differences in the chemistry, biology and short-term dynamic of small aquatic ecosystems. Unsupervised machine learning algorithms and fuzzy sets may help in catching such apparently erratic differences.

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