Disease Detection System (DDS) Using Machine Learning Technique

2020 ◽  
pp. 107-132
Author(s):  
Sumana De ◽  
Baisakhi Chakraborty
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Disease Detection ◽  
Machine Learning Technique ◽  
Learning Technique

Heart Disease Detection using Machine Learning Technique

2021 ◽  
Author(s):  
Likitha KN ◽  
Nethravathi R ◽  
Nithyashree K ◽  
Ritika Kumari ◽  
Sridhar N ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Detection ◽  
Machine Learning Technique ◽  
Learning Technique

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

2021 ◽  
Vol 35 (6) ◽  
pp. 477-482
Author(s):  
Daneshwari Ashok Noola ◽  
Dayananda Rangapura Basavaraju
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Classification Systems ◽  
Selection Model ◽  
Plant Phenotyping ◽  
Disease Detection ◽  
Machine Learning Technique ◽  
Leaf Disease ◽  
Learning Technique ◽  
Pertinent Feature

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.

A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks

2021 ◽  
pp. 209-230
Author(s):  
Keshav Sinha
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Supervised Machine Learning ◽  
The Internet ◽  
Machine Learning Technique ◽  
Network Intrusion ◽  
Learning Technique ◽  
Enormous Amount ◽  
Static Checking

During this time, COVID-19 has affected the lifestyles of many individuals; in the meantime, an enormous amount of users are connected with the internet. This will also increase the chance of network intrusion due to congestion and overloading of the server. So, to cope with this problem, the authors proposed an automated intrusion detection system (IDS) which helps in monitoring the traffic and service request. The model is used to identify the illegal access and counterparts with static checking capabilities of the firewall. The classical KDDCup 99 dataset is used for training and testing purposes.

scholarly journals Intrusion Detection System using Machine Learning

2020 ◽  
pp. 61-71
Author(s):  
Jayesh Zala ◽  
Aditya Panchal ◽  
Advait Thakkar ◽  
Bhagirath Prajapati ◽  
Priyanka Puvar
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Technique ◽  
Software Application ◽  
Malicious Activity ◽  
The Past ◽  
Learning Technique

Intrusion Detection System (IDS) is a tool, or software application, that monitors network or system activity and detects malicious activity occurring. The protected evolution of the network must incorporate new threats and related approaches to avoid these threats. The key role of the IDS is to secure resources against the attacks. Several approaches, methods and algorithms of the intrusion detection help to detect a plethora of attacks. The main objective of this paper is to provide a complete system to detect intruding attacks using the Machine Learning technique which identifies the unknown attacks using the past information gained from the known attacks. The paper explains preprocessing techniques, model comparisons for training as well as testing, and evaluation technique.

scholarly journals Flow Based Intrusion Detection System for Software Defined Networking using Hybrid Machine Learning Technique

2019 ◽  
Vol 9 (2S2) ◽  
pp. 1026-1033
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Software Defined Networking ◽  
Machine Learning Technique ◽  
Security Issues ◽  
Learning Technique ◽  
Flow Information ◽  
Hybrid Machine

Software Defined Networking and OpenFlow protocol have been recently emerged as dynamic and promising framework for future networks. Even though, programmable features and logically centralized controller leads to large number of security issues. To address the security problems, we have to impose Intrusion Detection System module to continuously keep track of the network traffic and to detect the malicious activities in the SDN environment. In this paper, we have implemented flow-based IDS with the help of hybrid machine learning technique. By collecting the flow information from the controller, we classify the traffic, extract the essential features and classify the attack using machine learning based classifier module. For classifier, we have developed hybrid machine learning model with the help of Modified K-Means and C4.5 algorithm. Our proposed work is compared with single machine learning classifier and our experimental results show that, proposed work can classify the normal and attack instances with accuracy of 97.66%.

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