Corn Leaf Disease Detection with Pertinent Feature Selection Model Using Machine Learning Technique with Efficient Spot Tagging Model

Crop diseases constitute a substantial threat to food safety but, due to the lack of a critical basis, their rapid identification in many parts of the world is challenging. The development of accurate techniques in the field of image categorization based on leaves produced excellent results. Plant phenotyping for plant growth monitoring is an important aspect of plant characterization. Early detection of leaf diseases is crucial for efficient crop output in agriculture. Pests and diseases cause crop harm or destruction of a section of the plant, leading to lower food productivity. In addition, in a number of less-developed countries, awareness of pesticide management and control, as well as diseases, is limited. Some of the main reasons for decreasing food production are toxic diseases, poor disease control and extreme climate changes. The quality of farm crops may be influenced by bacterial spot, late blight, septoria and curved yellow leaf diseases. Because of automatic leaf disease classification systems, action is easy after leaf disease signs are detected. Applying image processing and machine learning methodologies, this research offers an efficient Spot Tagging Leaf Disease Detection with Pertinent Feature Selection Model using Machine Learning Technique (SPLDPFS-MLT). Different diseases deplete chlorophyll in leaves generating dark patches on the surface of the leaf. Machine learning algorithms can be used to identify image pre-processing, image segmentation, feature extraction and classification. Compared with traditional models, the proposed model shows that the model performance is better than those existing.

Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping

Finding outliers in functional infinite-dimensional vector spaces is widely present in the industry for data that may originate from physical measurements or numerical simulations. An automatic and unsupervised process of outlier identification can help ensure the quality of a dataset (trimming), validate the results of industrial simulation codes, or detect specific phenomena or anomalies. This paper focuses on data originating from expensive simulation codes to take into account the realistic case where only a limited quantity of information about the studied process is available. A detection methodology based on different features, such as h-mode depth or the dynamic time warping, is proposed to evaluate the outlyingness both in the magnitude and shape senses. Theoretical examples are used to identify pertinent feature combinations and showcase the quality of the detection method with respect to state-of-the-art methodologies of detection. Finally, we show the practical interest of the method in an industrial context thanks to a nuclear thermal-hydraulic use case and how it can serve as a tool to perform sensitivity analysis on functional data.

Blueberry bud freeze damage detection using optical sensors: Identification of spectral features through hyperspectral imagery

BACKGROUND: Highbush blueberry (Vaccinium corymbosum), the species primarily grown in the state of Washington, U.S., is relatively cold hardy. However, low temperatures in winter and early spring can still cause freeze damage to the buds. OBJECTIVE: This study intended to explore hyperspectral imaging (HSI) for detecting freeze induced bud damage. Blueberry buds (c.v. Duke) were collected over two seasons and tested in the laboratory to detect damage at four typical phenological stages. METHODS: The HSI data was acquired via line scan HSI system with spectral wavelength ranging from 517 to 1729 nm for buds grouped into either normal or injured mortalities. The successive projection algorithm was employed for pertinent feature wavelength selection. Analysis of variance and linear regression were then applied for evaluating sensitivity of feature wavelengths. RESULTS: Overall, five salient wavelengths (706, 723, 872, 1384, and 1591 nm) were selected to detect bud freeze injury. A quadratic discriminant analysis method-based analysis verified reliability of these five wavelengths in bud damage detection with overall accuracy in the ranges of 64 to 82%for the test datasets of each stage in two seasons. CONCLUSIONS: This study indicated potential of optical sensing to identify the injured buds using five salient wavelengths.

An Efficient Big Data Analytic for Forecasts the User Behavior Bus Rapid Transit System

The present paper proposes in road based mass transit system, this stage might be a solution to consider by provides quality of service. This text propose a path of predict stage for this sort of transport system. This system estimates time by acknowledging its historical behavior, diagrammatic by historical profiles, and more additionally those present conduct recorded on the overall public transport vehicle that the prediction is will be made. The model employments those k-medoids bunch algorithmic system on get historical travel chance profiles. A pertinent feature of the model may be that it doesn't necessity later period knowledge from elective vehicles. To this reason, the planned model may be also used on intercity transport contexts in which service coming up with is administrated per timetables. The fast pace of developments in computer science (AI) is providing new opportunities to boost the performance of various industries and businesses, together with the transport sector. The innovations introduced by AI embody extremely advanced procedure ways that mimic the means the human brain works.

IDSFS: A Signature Based Intrusion Detection System with High Pertinent Feature Selection Method

Securing a network from the attackers is a challenging task at present as many users involve in variety of computer networks. To protect any individual host in a network or the entire network, some security system must be implemented. In this case, the Intrusion Detection System (IDS) is essential to protect the network from the intruders. The IDS have to deal with a lot of network packets with different characteristics. A signature-based IDS is a potential tool to understand former attacks and to define suitable method to conquest it in variety of applications. This research article elucidates the objective of IDS with a mechanism which combines the network and host-based IDS. The benchmark dataset for DARPA is considered to generate the IDS mechanism. In this paper, a frame work IDSFS – a signature-based IDS with high pertinent feature selection method is framed. This frame work consists of earlier proposed Feature Selection method (HPFSM), Artificial Neural Network for classification of nodes or packets in the network, then the signatures or attack rules are configured by implementing Association Rule mining algorithm and finally the rules are restructured using a pattern matching algorithm-Aho-Corasick to ease the rule checking. The metrics like number of features, classification accuracy, False Positive Rate (FPR), Precision, Number of rules, Running Time and Memory consumption are checked and proved the proposed frame work’s efficiency.

A New Approach for Coronary Artery Diseases Diagnosis Based on Genetic Algorithm

Feature Selection (FS) has become the motivation of much research on decision support systems areas for which datasets with large number of features are analyzed. This paper presents a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapper Bayes Naïve (BN). Initially, thirteen attributes were involved in predicting CAD. In GA–BN algorithm, GA produces in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final result set of attribute holds the most pertinent feature model that increases the accuracy. The accuracy results showed that the algorithm produces 85.50% classification accuracy in the diagnosis of CAD. Therefore, the strength of the Algorithm is then compared with other machine learning algorithms such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Then, the GA wrapper BN Algorithm is similarly compared with other FS algorithms. The Obtained results have shown very favorable outcomes for the diagnosis of CAD.

Hermite collocation method for fractional order differential equations

This paper focuses on the approximate solutions of the higher order fractional differential equations with multi terms by the help of Hermite Collocation method (HCM). This new method is an adaptation of Taylor's collocation method in terms of truncated Hermite Series. With this method, the differential equation is transformed into an algebraic equation and the unknowns of the equation are the coefficients of the Hermite series solution of the problem. This method appears as a useful tool for solving fractional differential equations with variable coefficients. To show the pertinent feature of the proposed method, we test the accuracy of the method with some illustrative examples and check the error bounds for numerical calculations.

Radical right-wing parties in Europe

In this paper I discuss, critically, the literature on populism and the extent to which it applies to the contemporary radical right-wing parties in Europe. These parties are often – and increasingly – referred to as populist parties. I argue that it is misleading to label these parties ‘populist parties’, since populism is not the most pertinent feature of this party family. These parties are mainly defined by ethnic nationalism, and not a populist ideology. In their discourse they are primarily preoccupied with questions pertaining to national identity and national security – and their ‘negative’ doubles immigration, multiculturalism, Islamist threat – and they consistently pit ‘the people’ mainly against elites that they view as responsible for a cultural and political threat against their idealized image of their nation state. The ethnic nationalism of European radical right-wing parties is more important for their discourse and tends to influence the populist elements.

A New Approach for Coronary Artery Diseases Diagnosis Based on Genetic Algorithm

Feature Selection (FS) has become the motivation of much research on decision support systems areas for which datasets with large number of features are analyzed. This paper presents a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapper Bayes Naïve (BN). Initially, thirteen attributes were involved in predicting CAD. In GA–BN algorithm, GA produces in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final result set of attribute holds the most pertinent feature model that increases the accuracy. The accuracy results showed that the algorithm produces 85.50% classification accuracy in the diagnosis of CAD. Therefore, the strength of the Algorithm is then compared with other machine learning algorithms such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Then, the GA wrapper BN Algorithm is similarly compared with other FS algorithms. The Obtained results have shown very favorable outcomes for the diagnosis of CAD.