Smart Diagnostic Expert System for Defect in Forging Process by Using Machine Learning Process

Mewada, Shivlal; Saroliya, Anil; Chandramouli, N.; Kumar, T. Rajasanthosh; Lakshmi, M.; Mary, S. Suma Christal; Jayakumar, Mani

doi:10.1155/2022/2567194

Cited by 57 publications

(6 citation statements)

References 24 publications

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“…The proposed model outperforms traditional machine learning approaches. In [19], the article introduces the CS-OCAN model for fraud detection, combining autoencoders and Complementary GANs. This approach maximizes inter-class distances and minimizes intra-class variances, improving detection accuracy compared to existing one-class classification models.…”

Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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An in-depth familiarity with ML and DL models for fraud detection is essential due to the growing frequency and complexity of fraudulent activity across many domains. Despite the abundance of research on the subject, empirical analyses of these models, especially in their real-time implementations, are typically lacking. This study fills that need by meticulously reviewing and analysing ML and DL models developed for fraud detection. We draw attention to the shortcomings of existing approaches, which are crucial in the ever-changing field of fraud detection and include problems with recall, scalability, complexity, precision, and accuracy. By evaluating various ML and DL models using these measures, our evaluation method is based on a rigorous empirical approach. Provided that insights into the practical consequences as well as flexibility of each model in real-time circumstances, they thoroughly assess their performance. There are many ways in which this work will be useful; for example, it will help professionals choose the best models for their fraud detection needs, improve academic knowledge of these models in practice, and open the door to more studies that will concentrate on developing better fraud detection tools. This comprehensive research gives academics and companies a foundation for better, more effective and more scalable fraud detection systems in this period of essential digital security.

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Section: Detailed Study Of Fraud Detection Methodsmentioning

confidence: 99%

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

L. K. Vishwamitra

2024

jes

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“…The study's findings will allow for targeted interventions and better public awareness of potential dangers. The data collection was comprised of actual incidents of cybercrime that happened in Elaz province between 2018 and 2022 [22]. It was difficult to get hands-on real information and wash it up so that it could be analysed using ML techniques.…”

Section: Methodsmentioning

confidence: 99%

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

Singh,

Shivaputra

et al. 2024

EAI Endorsed Trans IoT

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Crimes committed online rank among the most critical global concerns. Daily, they cause country and citizen economies to suffer massive financial losses. With the proliferation of cyber-attacks, cybercrime has also been on the rise. To effectively combat cybercrime, it is essential to identify its perpetrators and understand their methods. Identifying and preventing cyber-attacks are difficult tasks. To combat these concerns, however, new research has produced safety models and forecast tools grounded on artificial intelligence. Numerous methods for predicting criminal behaviour are available in the literature. While they may not be perfect, they may help in cybercrime and cyber-attack tactic prediction. To find out whether an attack happened and, if so, who was responsible, one way to look at this problem is by using real-world data. There is data about the crime, the perpetrator's demographics, the amount of property damaged, and the entry points for the assault. Potentially, by submitting applications to forensics teams, victims of cyber-attacks may get information. This study uses ML methods to analyse cyber-crime consuming two patterns and to forecast how the specified characteristics will furnish to the detection of the cyber-attack methodology and perpetrator. Based on the comparison of eight distinct machine-learning methods, one can say that their accuracy was quite comparable. The Support Vector Machine (SVM) Linear outperformed all other cyber-attack tactics in terms of accuracy. The initial model gave us a decent notion of the assaults that the victims would face. The most successful technique for detecting malevolent actors was logistic regression, according to the success rate. To anticipate who the perpetrator and victim would be, the second model compared their traits. A person’s chances of being a victim of a cyber-attack decrease as their income and level of education rise. The proposed idea is expected to be used by departments dealing with cybercrime. Cyber-attack identification will also be made easier, and the fight against them will be more efficient.

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“…Similar to other economic interests, educational service prices depend on demand and cost increases. The statistics for the City of Chennai's Recreation, Education, and Sports Groups from 2011 to 2019 are shown in Figure 1, according to the Chennai Central Bureau of Data [13][14][15]. Leisure and Sports Group usually has somewhat consistent inflation rates.…”

Section: Introductionmentioning

confidence: 99%

GRU-RNN Model to Analyze and Predict the Inflation by Consumer Price Index

Jitendrasinh Jamadar

2024

jes

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The general population need data on the expected rate of inflation for educational expenses to plan for and manage family budgets. When formulating educational policy, this data is also useful for the government. Chennai serves as one of Indonesia's educational hubs, yet there is a lack of publicly accessible data on the city's inflation rate for education expenses. Furthermore, there is a lack of prior studies on forecasting that specifically used the Education Expenditure Group's Consumer Price Index (CPI) information to determine inflation rates in education prices utilizing the Deep Learning approach. The goal of this study is to utilize the Deep Learning Method to create a model that can predict how much tuition will rise in Chennai. The Education Expenditure Group in Chennai relied on Consumer Price Index (CPI) information for its study. An improvement in the Long and Short-Term Memory (LSTM) and Recurrent Neural Network (RNN), approach is used for predicting purposes. A method with four hidden nodes and a single hidden layer produced the best results, with RMSE=8.38 and MAPE=2.8766%.

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Smart Diagnostic Expert System for Defect in Forging Process by Using Machine Learning Process

Cited by 57 publications

References 24 publications

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis

Analyse and Predict the Detection of the Cyber - Attack Process by Using a Machine-Learning Approach

GRU-RNN Model to Analyze and Predict the Inflation by Consumer Price Index

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