scholarly journals Diabetic Retinopathy Detection System

2021 ◽  
Vol 9 (9) ◽  
pp. 1498-1502
Author(s):  
Manisha Gangesh
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Web Application ◽  
Detection System ◽  
Retinal Images ◽  
Learning Approaches ◽  
Keywords Diabetic Retinopathy ◽  
Machine Learning Model ◽  
User Friendly

Abstract: Diabetic Retinopathy is a diabetes problem that affects the eye. Injury to the blood vessels of the light sensitive tissue inside the rear of the eye (retina) is that the most reason for diabetic retinopathy. To begin with, Diabetic Retinopathy may have no symptoms or just cause minor vision problems. It has the potential to lead to blindness. Machine learning approaches can be used for the early detection of Diabetic Retinopathy. This paper proposes an automated Diabetic Retinopathy detection system that can detect the presence of Diabetic Retinopathy from retinal images. This work uses ResNet50 for the detection and classification of Diabetic Retinopathy. ResNet50 is a type of neural network used as a backbone for many computer-vision tasks. This paper proposes a machine learning model which is developed using ResNet50, then the model will be deployed as a user-friendly web application where the user can upload the retinal images as input to the system then system will detect the presence of Diabetic Retinopathy and classifies it into the stage or class which the particular image belongs to. Keywords: Diabetic Retinopathy, ResNet50, Proliferative diabetic retinopathy, non-proliferative diabetic retinopathy.

scholarly journals A Novel PCA-Firefly Based XGBoost Classification Model for Intrusion Detection in Networks Using GPU

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 219 ◽  
Author(s):  
Sweta Bhattacharya ◽  
Siva Rama Krishnan S ◽  
Praveen Kumar Reddy Maddikunta ◽  
Rajesh Kaluri ◽  
Saurabh Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Comprehensive Evaluation ◽  
Detection System ◽  
Human Life ◽  
Principal Component ◽  
Cyber Attacks ◽  
Classification Model ◽  
Learning Approaches ◽  
Machine Learning Model

The enormous popularity of the internet across all spheres of human life has introduced various risks of malicious attacks in the network. The activities performed over the network could be effortlessly proliferated, which has led to the emergence of intrusion detection systems. The patterns of the attacks are also dynamic, which necessitates efficient classification and prediction of cyber attacks. In this paper we propose a hybrid principal component analysis (PCA)-firefly based machine learning model to classify intrusion detection system (IDS) datasets. The dataset used in the study is collected from Kaggle. The model first performs One-Hot encoding for the transformation of the IDS datasets. The hybrid PCA-firefly algorithm is then used for dimensionality reduction. The XGBoost algorithm is implemented on the reduced dataset for classification. A comprehensive evaluation of the model is conducted with the state of the art machine learning approaches to justify the superiority of our proposed approach. The experimental results confirm the fact that the proposed model performs better than the existing machine learning models.

scholarly journals Attack and Anomaly Detection in IoT Networks Using Supervised Machine Learning Approaches

2021 ◽  
Vol 35 (1) ◽  
pp. 11-21
Author(s):  
Himani Tyagi ◽  
Rajendra Kumar
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Detection System ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Testing Time ◽  
Learning Approaches ◽  
Reduction Techniques ◽  
Share Data

IoT is characterized by communication between things (devices) that constantly share data, analyze, and make decisions while connected to the internet. This interconnected architecture is attracting cyber criminals to expose the IoT system to failure. Therefore, it becomes imperative to develop a system that can accurately and automatically detect anomalies and attacks occurring in IoT networks. Therefore, in this paper, an Intrsuion Detection System (IDS) based on extracted novel feature set synthesizing BoT-IoT dataset is developed that can swiftly, accurately and automatically differentiate benign and malicious traffic. Instead of using available feature reduction techniques like PCA that can change the core meaning of variables, a unique feature set consisting of only seven lightweight features is developed that is also IoT specific and attack traffic independent. Also, the results shown in the study demonstrates the effectiveness of fabricated seven features in detecting four wide variety of attacks namely DDoS, DoS, Reconnaissance, and Information Theft. Furthermore, this study also proves the applicability and efficiency of supervised machine learning algorithms (KNN, LR, SVM, MLP, DT, RF) in IoT security. The performance of the proposed system is validated using performance Metrics like accuracy, precision, recall, F-Score and ROC. Though the accuracy of Decision Tree (99.9%) and Randon Forest (99.9%) Classifiers are same but other metrics like training and testing time shows Random Forest comparatively better.

scholarly journals Effective Intrusion Detection System by using LOS Classifier

2019 ◽  
Vol 9 (2) ◽  
pp. 2961-2966
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Social Networking ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Approaches ◽  
Area Unit ◽  
Hybrid Development

With winning advances like catch of Things, Cloud Computing and Social Networking, mammoth proportions of framework traffic associated information area unit made Intrusion Detection System for sort out security suggests the strategy to look at partner unapproved access on framework traffic. For Intrusion Detection System we are going to call attention to with respect to Machine Learning Approaches. it's accomplice rising field of enrolling which can explicitly act with a decent arrangement of less human affiliation. System gains from the data intentionally affirmation and makes perfect objectives. all through this paper we keep an eye on zone unit going to separated styles of Machine Learning pulls in near and had done relative examination in it. inside the last we keep an eye on territory unit going to foreseen the idea of hybrid development, that might be a blend of host principally and framework based for the most part Intrusion Detection System.

scholarly journals IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6743
Author(s):  
Vasiliki Kelli ◽  
Vasileios Argyriou ◽  
Thomas Lagkas ◽  
George Fragulis ◽  
Elisavet Grigoriou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Network Flow ◽  
Detection System ◽  
Human Life ◽  
Industrial Sector ◽  
Machine Learning Techniques ◽  
Sensitive Information ◽  
Learning Approaches ◽  
Monitoring And Control

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.

Network Intrusion Detection and Prevention Systems for Attacks in IoT Systems

2019 ◽  
pp. 128-141 ◽  
Author(s):  
Vetrivelan Pandu ◽  
Jagannath Mohan ◽  
T. S. Pradeep Kumar
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Detection System ◽  
Security Threats ◽  
Network Intrusion ◽  
Security Personnel ◽  
Prevention System ◽  
Based Instruction ◽  
Vast Network ◽  
Intrusion Detection And Prevention

Internet of things (IoT) has transformed greatly the improved way of business through machine-to-machine (M2M) communications. This vast network and its associated technologies have opened the doors to an increasing number of security threats which are dangerous to IoT and 5G wireless networks. The first part of this chapter presents instruction detection system (IDS) which detect the various attacks in 6LoWPAN layer. An IDS is to detect and analyze both inbound and outbound network traffic for abnormal activities. An IPS complements an IDS configuration by proactively inspecting a system's incoming traffic to weed out malicious requests. A typical IPS configuration uses web application firewalls and traffic filtering solutions to secure applications. An IPS prevents attacks by dropping malicious packets, blocking offending IPs and alerting security personnel to potential threats. Machine learning (ML)-based instruction detection and prevention system (IDPS) is proposed and implemented in Contiki simulation environment.

scholarly journals Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning Approaches

2020 ◽  
Vol 28 (4) ◽  
pp. 532-551
Author(s):  
Blake Miller ◽  
Fridolin Linder ◽  
Walter R. Mebane
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Random Sampling ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
Simulation Studies ◽  
Text Data ◽  
Passive Learning ◽  
Machine Learning Model ◽  
The Cost

Supervised machine learning methods are increasingly employed in political science. Such models require costly manual labeling of documents. In this paper, we introduce active learning, a framework in which data to be labeled by human coders are not chosen at random but rather targeted in such a way that the required amount of data to train a machine learning model can be minimized. We study the benefits of active learning using text data examples. We perform simulation studies that illustrate conditions where active learning can reduce the cost of labeling text data. We perform these simulations on three corpora that vary in size, document length, and domain. We find that in cases where the document class of interest is not balanced, researchers can label a fraction of the documents one would need using random sampling (or “passive” learning) to achieve equally performing classifiers. We further investigate how varying levels of intercoder reliability affect the active learning procedures and find that even with low reliability, active learning performs more efficiently than does random sampling.

scholarly journals Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Abu Sayed Chowdhury ◽  
Sarah M. Reehl ◽  
Kylene Kehn-Hall ◽  
Barney Bishop ◽  
Bobbie-Jo M. Webb-Robertson
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Web Application ◽  
Amino Acid Sequences ◽  
Peptide Sequence ◽  
Antiviral Therapies ◽  
Link Type ◽  
Machine Learning Model ◽  
Antiviral Peptides ◽  
Peptide Prediction

Abstract The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR.

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