scholarly journals A grapevine leaves dataset for early detection and classification of esca disease in vineyards through machine learning

Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106809
Author(s):  
M. Alessandrini ◽  
R. Calero Fuentes Rivera ◽  
L. Falaschetti ◽  
D. Pau ◽  
V. Tomaselli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Esca Disease ◽  
Grapevine Leaves

scholarly journals Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1302 ◽  
Author(s):  
Reem Ibrahim Hasan ◽  
Suhaila Mohd Yusuf ◽  
Laith Alzubaidi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Early Detection ◽  
Feature Fusion ◽  
State Of The Art ◽  
Vital Role ◽  
Considerable Improvement ◽  
Plant Diseases ◽  
Recent Emergence

Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

Identification and Classification of Cyber Threats Through SSH Honeypot Systems

2020 ◽  
pp. 105-129
Author(s):  
José María Jorquera Valero ◽  
Manuel Gil Pérez ◽  
Alberto Huertas Celdrán ◽  
Gregorio Martínez Pérez
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Security Systems ◽  
Detection Techniques ◽  
Detection Systems ◽  
Cyber Threats ◽  
Detection Capabilities

As the number and sophistication of cyber threats increases year after year, security systems such as antivirus, firewalls, or Intrusion Detection Systems based on misuse detection techniques are improved in detection capabilities. However, these traditional systems are usually limited to detect potential threats, since they are inadequate to spot zero-day attacks or mutations in behaviour. Authors propose using honeypot systems as a further security layer able to provide an intelligence holistic level in detecting unknown threats, or well-known attacks with new behaviour patterns. Since brute-force attacks are increasing in recent years, authors opted for an SSH medium-interaction honeypot to acquire a log set from attacker's interactions. The proposed system is able to acquire behaviour patterns of each attacker and link them with future sessions for early detection. Authors also generate a feature set to feed Machine Learning algorithms with the main goal of identifying and classifying attacker's sessions, and thus be able to learn malicious intentions in executing cyber threats.

scholarly journals Machine learning for epilepsy detection and forecast review: new challenges and perspectives

2018 ◽  
pp. 98-101
Author(s):  
V. Gaidar
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Clinical Utility ◽  
Epileptic Seizures ◽  
Seizure Prediction ◽  
Pattern Recognition Techniques ◽  
Machine Learning Methods ◽  
Prediction Techniques ◽  
The Mathematical Model

The comparative analysis of machine learning methods has performed to solve the problem of early detection and prediction of epileptic seizures using electroencephalographic signals. Recent studies has shown that it is possible to predict seizures in prior of its physical appearance. Our goal is to present and analyse different approaches of seizure prediction techniques, particulary in machine learning and deep learning. Seizure prediction has made important advances over the last decade, nevertheless it is still a problem to provide steady algorithm of seizure early detection. Also, within individual patients exhibit distinctive dynamics, is it cruicial to find algorithms providing greater clinical utility. This article focuses of the problem of features development from electroencephalography signals in order to provide the accurate pattern recognition techniques for detection and classification of epilepsy seizures in advance. The mathematical model of the algorithms is constructed and quantitative data presented for estimating the methods efficiency.

scholarly journals Classification of Control and Neurodegenerative Disease Subjects Using Tree Based Classifiers

2020 ◽  
pp. 63-73
Author(s):  
Syed Ahsin Ali Shah ◽  
Nazneen Habib ◽  
Wajid Aziz ◽  
Ehsan Ullah Khan ◽  
Malik Sajjad Ahmed Nadeem
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Classification Accuracy ◽  
Neural Control ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Pharmacological Interventions ◽  
Non Invasive

Background: The medical researchers are developing different non-invasive methods for early detection of Neurodegenerative Diseases (NDDs) when pharmacological interventions are still possible to further prevent the disease progression. The NDDs are associated with the degradation in the complex gait dynamics and motor activity. The classification of gait data using machine learning techniques can assist the physicians for early diagnosis of the neural disorder when clinical manifestation of the diseases is not yet apparent. Aims: The present study was undertaken to classify the control and NDD subjects using decision trees based classifiers (Random Forest (RF), J48 and REPTree). Methodology: The data used in the study comprises of 16 control, 20 Huntington’s Disease (HD), 15 Parkinson’s Disease (PD), and 13 Amyotrophic Lateral Sclerosis (ALS) subjects, which were taken from publicly available database from Physionet. The age range of control subjects was 20-74, HD subjects was 36-70, PD subjects was 44-80, and ALS subjects was 29-71. There were 13 attributes associated with the data. Important features/attributes of the data were selected using correlation feature selection - subset evaluation (cfs) method. Three tree based machine learning algorithms (RF, J48 and REPTree) were used to classify the control and NDD subjects. The performance of classifiers were evaluated using Precision, Recall, F-Measure, MAE and RMSE. Results: In order to evaluate the performance of tree based classifiers, two different settings of data i.e. complete features and selected features were used. In classifying control vs HD subjects, RF provides the robust separation with classification accuracy of 84.79% using complete features and 83.94% using selected features. While in classifying control vs PD subjects, and control vs ALS subjects, RF also provides the best separation with classification accuracy of 86.51% and 94.95% respectively using complete features and 85.19% and 93.64% respectively using selected features. Conclusion: The variability analysis of physiological signals provides a valuable non-invasive tool for quantifying the system of dynamics of healthy subjects and to examine the alternations in the controlling mechanism of these systems with aging and disease. It is concluded that selected features encode adequate information about neural control of the gait. Moreover, the selected features along with tree based machine learning algorithms can play a vital for early detection of NDDs, when pharmacological interventions are still possible.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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