A hybridised neural network and optimisation algorithms for prediction and classification of neurological disorders

Kshirsagar, Pravin R.; Akojwar, Sudhir G.; Bajaj, Nidhi D.

doi:10.1504/ijbet.2018.095981

Cited by 40 publications

(16 citation statements)

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“…In [6,11,12] elaborate on the use of artificial intelligence for different classification and prediction problems and furthermore explain the use of hybrid artificial intelligence for feature extraction, classification, and prediction along with modeling with different algorithms and optimization techniques [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Spam detection methods made use of a variety of forms of functionality, including user-based and contentbased features and graphs, among others. The advantages and disadvantages of each extracted feature are addressed [10,11]. We use these characteristics to develop a classification method that distinguishes between false information and information that is true.…”

Section: Proposed System For Detecting Fake-accounts In Twitter Using Aimentioning

confidence: 99%

“…The obtained information must be preprocessed through multiple measures until entering every classifier to ensure the algorithm recognizes the data and creates the absolute best model. Formatting and data cleaning are one of the preprocessing activities [11]. Formatting is an essential method by which the data can be read acceptably for the classifier, for example, by translating the data type into a text file or a flat format.…”

Section: Data Preprocessingmentioning

confidence: 99%

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Machine Learning-Based Secure Data Acquisition for Fake Accounts Detection in Future Mobile Communication Networks

Kavin

Karki²,

Hemalatha³

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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Social media websites are becoming more prevalent on the Internet. Sites, such as Twitter, Facebook, and Instagram, spend significantly more of their time on users online. People in social media share thoughts, views, and facts and create new acquaintances. Social media sites supply users with a great deal of useful information. This enormous quantity of social media information invites hackers to abuse data. These hackers establish fraudulent profiles for actual people and distribute useless material. The material on spam might include commercials and harmful URLs that disrupt natural users. This spam content is a massive problem in social networks. Spam identification is a vital procedure on social media networking platforms. In this paper, we have proposed a spam detection artificial intelligence technique for Twitter social networks. In this approach, we employed a vector support machine, a neural artificial network, and a random forest technique to build a model. The results indicate that, compared with RF and ANN algorithms, the suggested support vector machine algorithm has the greatest precision, recall, and F-measure. The findings of this paper would be useful in monitoring and tracking social media shared photos for the identification of inappropriate content and forged images and to safeguard social media from digital threats and attacks.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed System For Detecting Fake-accounts In Twitter Using Aimentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

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Machine Learning-Based Secure Data Acquisition for Fake Accounts Detection in Future Mobile Communication Networks

Kavin

Karki²,

Hemalatha³

et al. 2022

Wireless Communications and Mobile Computing

Self Cite

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“…DDoS attack models are frequently classi ed into three groups: volume based assault, protocol-based assault and layer-based assault. [13][16] [17].…”

Section: Ddos Attacktypesmentioning

confidence: 99%

“…This rewall guarantees that the secure network can only be reached by trustworthy tra c from the overlay network nodes. While this mechanism guarantees that DDoS attacks are avoided, it is applicable to a private network and it is not suitable to a public server [14][16] [17].…”

Section: Ddos Preventative Assaultmentioning

confidence: 99%

Protection From Distributed Denial of Service Attack Using Fuzzy Logic

Hemalatha

Vani

et al. 2021

Preprint

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Distributed Denial of Service (DDoS) attacks represent an important challenge for public cloud as they invade the offender and completely delete Cloud service in order to serve the correct user and at the same time against the targets that cause system and service lack of access on infected devices. DDoS (Distributed Denial of Service) attacks are usually specific efforts to drain the resources of the victim or interrupt connections to networks by legitimate users. Traditional internet infrastructure is susceptible to DDoS assaults, and through leveraging their flaws to set up assault networks or Botnets, it offers an opening for an intruder to reach a wide number of infected machines. In order to identify and sustain improved detection accuracy, this work focuses on evaluating the different works and recommending a better solution to accommodate the cloud environment. A fuzzy logic for the detection and safety of DDOS attacks is proposed in this paper. The Fuzzy logic is used to dynamically select an algorithm from a collection of defined supervised learning that distinguish various DDoS variations and ultimately choose the relevant traffic algorithm.

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Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques

Hammoodi,

Obaid

2023

Intelligent Systems Reference Library

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A hybridised neural network and optimisation algorithms for prediction and classification of neurological disorders

Cited by 40 publications

References 0 publications

Machine Learning-Based Secure Data Acquisition for Fake Accounts Detection in Future Mobile Communication Networks

Machine Learning-Based Secure Data Acquisition for Fake Accounts Detection in Future Mobile Communication Networks

Protection From Distributed Denial of Service Attack Using Fuzzy Logic

Fake User Account Detection in Online Social Media Networks Using Machine Learning and Neural Network Techniques

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