Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems

Shah, Faiza; Liu, Yumin; Anwar, Aamir; Shah, Yasir; Alroobaea, Roobaea; Hussain, Saddam; Ullah, Syed Sajid

doi:10.1155/2022/7149902

Cited by 15 publications

(8 citation statements)

References 54 publications

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“…(d) Power Source: The components of smart objects require power. The smart object is powered by batteries, and it is managed so that it uses as little energy as possible by putting it to sleep [21][22][23].…”

Section: Hardware Implementation: Smart Devicesmentioning

confidence: 99%

IoT Based Smart Framework Monitoring System for Power Station

Karn¹,

Manickam²,

Saravanan³

et al. 2023

Computers, Materials &Amp; Continua

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Power Station (PS) monitoring systems are becoming critical, ensuring electrical safety through early warning, and in the event of a PS fault, the power supply is quickly disconnected. Traditional technologies are based on relays and don't have a way to capture and store user data when there is a problem. The proposed framework is designed with the goal of providing smart environments for protecting electrical types of equipment. This paper proposes an Internet of Things (IoT)-based Smart Framework (SF) for monitoring the Power Devices (PD) which are being used in power substations. A Real-Time Monitoring (RTM) system is proposed, and it uses a state-of-the-art smart IoT-based System on Chip (SoC) sensors, a Hybrid Prediction Model (HPM), and it is being used in Big Data Processing (BDP). The Cloud Server (CS) processes the data and does the data analytics by comparing it with the historical data already stored in the CS. No-Structural Query Language Mongo Data Base (MDB) is used to store Sensor Data (SD) from the PSs. The proposed HPM combines the Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-algorithm for Outlier Detection (OD) and the Random Forest (RF) classification algorithm for removing the outlier SD and providing Fault Detection (FD) when the PD isn't working. The suggested work is assessed and tested under various fault circumstances that happened in PSs. The simulation outcome proves that the proposed model is effective in monitoring the smooth functioning of the PS. Also, the suggested HPM has a higher Fault Prediction (FP) accuracy. This means that faults can be found earlier, early warning signals can be sent, and the power supply can be turned off quickly to ensure electrical safety. A powerful RTM and event warning system can also be built into the system before faults happen.

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Section: Hardware Implementation: Smart Devicesmentioning

confidence: 99%

IoT Based Smart Framework Monitoring System for Power Station

Karn¹,

Manickam²,

Saravanan³

et al. 2023

Computers, Materials &Amp; Continua

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“…The gain ratio, Pearson correlation, and ANOVA are few of the techniques that are widely used. Feature selection helps us to reduce the input data size by removing redundant and irrelevant features and features with no impact on classification [53][54][55][56][57][58][59][60][61][62].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks

Muhammad

Arif

Usman

et al. 2022

Wireless Communications and Mobile Computing

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An intrusion detection system, often known as an IDS, is extremely important for preventing attacks on a network, violating network policies, and gaining unauthorized access to a network. The effectiveness of IDS is highly dependent on data preprocessing techniques and classification models used to enhance accuracy and reduce model training and testing time. For the purpose of anomaly identification, researchers have developed several machine learning and deep learning-based algorithms; nonetheless, accurate anomaly detection with low test and train times remains a challenge. Using a hybrid feature selection approach and a deep neural network- (DNN-) based classifier, the authors of this research suggest an enhanced intrusion detection system (IDS). In order to construct a subset of reduced and optimal features that may be used for classification, a hybrid feature selection model that consists of three methods, namely, chi square, ANOVA, and principal component analysis (PCA), is applied. These methods are referred to as “the big three.” On the NSL-KDD dataset, the suggested model receives training and is then evaluated. The proposed method was successful in achieving the following results: a reduction of input data by 40%, an average accuracy of 99.73%, a precision score of 99.75%, an F1 score of 99.72%, and an average training and testing time of 138% and 2.7 seconds, respectively. The findings of the experiments demonstrate that the proposed model is superior to the performance of the other comparison approaches.

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“…Developing a shell is a costly job, which requires technical expertise and modification to shells that become costly in time and budget. Many studies have been published on different shells and their usage in different environments [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Applying Real-Time Dynamic Scaffolding Techniques during Tutoring Sessions Using Intelligent Tutoring Systems

Anwar

Haq

Mian

et al. 2022

Mobile Information Systems

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An intelligent tutoring system (ITS) is a computer system or software application that is built to replicate human tutors by supporting the theory of “learning by doing.” Even though ITSs have been proven to be successful in academic studies, they still have not found large adoption by the industry due to the complexities of building such systems due to the high technical expertise and domain knowledge requirements. Attempts have been made to build authoring tools that can provide assistance in building tutoring systems; however, most of these tools are targeted toward authors that have considerable programming experience. This research proposes an authoring tool for ITS, which is targeted at novice authors with minimum technical/programming experience and provides real-time scaffolding to learner’s incomplete/incorrect answers using the best scaffolding techniques. Two evaluation techniques were applied for the evaluation of the performance of the proposed authoring tool, e.g., paired t-test analysis and postexperiment survey. The learning gains obtained from paired t-test contend a significant learning gain and improvement in the learning process with enhanced learning performance with multiple scaffolding techniques as compared to single scaffolding technique experience. The postexperiment survey has a notable result that shows the effectiveness of the tutor model that ensures a very user-friendly interface, deploying scaffolding techniques and adequate control of selecting and deploying scaffolding techniques and making the authoring process easy.

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Machine Learning: The Backbone of Intelligent Trade Credit-Based Systems

Cited by 15 publications

References 54 publications

IoT Based Smart Framework Monitoring System for Power Station

IoT Based Smart Framework Monitoring System for Power Station

A Deep Learning-Based Framework for Feature Extraction and Classification of Intrusion Detection in Networks

Applying Real-Time Dynamic Scaffolding Techniques during Tutoring Sessions Using Intelligent Tutoring Systems

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