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Machine Learning-based Information Security Model for Botnet Detection
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Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet detection for information security. For effectual recognition of botnets, the proposed model involves data pre-processing at the initial stage. Besides, the model is utilized for the identification and classification of botnets that exist in the network. In order to optimally adjust the SVM parameters, the DFA is utilized and consequently resulting in enhanced outcomes. The presented model has the ability in accomplishing improved botnet detection performance. A wide-ranging experimental analysis is performed and the results are inspected under several aspects. The experimental results indicated the efficiency of our model over existing methods.

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Publication Date
Wed May 01 2024
Journal Name
Scientific Visualization
Shadow Detection and Elimination for Robot and Machine Vision Applications
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Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit

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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
Concepts of statistical learning and classification in machine learning: An overview
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Statistical learning theory serves as the foundational bedrock of Machine learning (ML), which in turn represents the backbone of artificial intelligence, ushering in innovative solutions for real-world challenges. Its origins can be linked to the point where statistics and the field of computing meet, evolving into a distinct scientific discipline. Machine learning can be distinguished by its fundamental branches, encompassing supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Within this tapestry, supervised learning takes center stage, divided in two fundamental forms: classification and regression. Regression is tailored for continuous outcomes, while classification specializes in c

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Publication Date
Fri Jan 01 2021
Journal Name
Artificial Intelligence For Covid-19
An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
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Publication Date
Sat Jan 01 2022
Journal Name
Turkish Journal Of Physiotherapy And Rehabilitation
classification coco dataset using machine learning algorithms
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In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

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Publication Date
Mon Jan 01 2024
Journal Name
Bio Web Of Conferences
An overview of machine learning classification techniques
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Machine learning (ML) is a key component within the broader field of artificial intelligence (AI) that employs statistical methods to empower computers with the ability to learn and make decisions autonomously, without the need for explicit programming. It is founded on the concept that computers can acquire knowledge from data, identify patterns, and draw conclusions with minimal human intervention. The main categories of ML include supervised learning, unsupervised learning, semisupervised learning, and reinforcement learning. Supervised learning involves training models using labelled datasets and comprises two primary forms: classification and regression. Regression is used for continuous output, while classification is employed

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Detecting Textual Propaganda Using Machine Learning Techniques
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Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota

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Publication Date
Mon Oct 30 2023
Journal Name
Traitement Du Signal
A Comprehensive Review on Machine Learning Approaches for Enhancing Human Speech Recognition
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Publication Date
Wed Jun 01 2022
Journal Name
Baghdad Science Journal
Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning
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       In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific

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Publication Date
Thu Sep 30 2021
Journal Name
Iraqi Journal Of Science
Elderly Healthcare System for Chronic Ailments using Machine Learning Techniques – a Review
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     World statistics declare that aging has direct correlations with more and more health problems with comorbid conditions. As healthcare communities evolve with a massive amount of data at a faster pace, it is essential to predict, assist, and prevent diseases at the right time, especially for elders. Similarly, many researchers have discussed that elders suffer extensively due to chronic health conditions.  This work was performed to review literature studies on prediction systems for various chronic illnesses of elderly people. Most of the reviewed papers proposed machine learning prediction models combined with, or without, other related intelligence techniques for chronic disease detection of elderly patie

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Publication Date
Thu Oct 29 2020
Journal Name
Complexity
Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting
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The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

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