Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning

Abidi, Wajhe Ul Husnian; Daoud, Mohammad Sh.; Ihnaini, Baha; Khan, Muhammad Adnan; Alyas, Tahir; Fatima, Areej; Ahmad, Munir

doi:10.1109/access.2021.3098628

Cited by 13 publications

(4 citation statements)

References 11 publications

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“…Sensitivity and specificity are used to display the results. KNN [ 27 , 28 ] is the least impressive model that can be used to classify the disease. It produced 59% and 91% specificity results.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach

Alyas

Hamid

Alissa

et al. 2022

BioMed Research International

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There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classification and machine learning play a significant role in the timely detection of thyroid diseases. This timely classification will indeed affect the timely treatment of the patients. Automatic and precise thyroid nodule detection in ultrasound pictures is critical for reducing effort and radiologists’ mistake rate. Medical images have evolved into one of the most valuable and consistent data sources for machine learning generation. In this paper, various machine learning algorithms like decision tree, random forest algorithm, KNN, and artificial neural networks on the dataset create a comparative analysis to better predict the disease based on parameters established from the dataset. Also, the dataset has been manipulated for accurate prediction for the classification. The classification was performed on both the sampled and unsampled datasets for better comparison of the dataset. After dataset manipulation, we obtained the highest accuracy for the random forest algorithm, equal to 94.8% accuracy and 91% specificity.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach

Alyas

Hamid

Alissa

et al. 2022

BioMed Research International

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“…The ability of a NN to learn from experience is referred to as learning. Similar to real neurons, artificial neural networks (ANN) [ 7 , 8 ] have been constructed with strategies to familiarise themselves with a set of specified inputs. In this context, there are two types of learning: supervised [ 9 ] and unsupervised [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Training a Feedforward Neural Network Using Hybrid Gravitational Search Algorithm with Dynamic Multiswarm Particle Swarm Optimization

Nagra

Alyas

Hamid

et al. 2022

BioMed Research International

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One of the most well-known methods for solving real-world and complex optimization problems is the gravitational search algorithm (GSA). The gravitational search technique suffers from a sluggish convergence rate and weak local search capabilities while solving complicated optimization problems. A unique hybrid population-based strategy is designed to tackle the problem by combining dynamic multiswarm particle swarm optimization with gravitational search algorithm (GSADMSPSO). In this manuscript, GSADMSPSO is used as novel training techniques for Feedforward Neural Networks (FNNs) in order to test the algorithm’s efficiency in decreasing the issues of local minima trapping and existing evolutionary learning methods’ poor convergence rate. A novel method GSADMSPSO distributes the primary population of masses into smaller subswarms, according to the proposed algorithm, and also stabilizes them by offering a new neighborhood plan. At this time, each agent (particle) increases its position and velocity by using the suggested algorithm’s global search capability. The fundamental concept is to combine GSA’s ability with DMSPSO’s to improve the performance of a given algorithm’s exploration and exploitation. The suggested algorithm’s performance on a range of well-known benchmark test functions, GSA, and its variations is compared. The results of the experiments suggest that the proposed method outperforms the other variants in terms of convergence speed and avoiding local minima; FNNs are being trained.

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“…The prediction of each algorithm was used as an input to the fuzzy fusion model and the decision on whether there is a fraud or not is taken. Experimental results of the methodology indicated a high accuracy of 99.63% in shill biding detection, outperforming other state-of-the-art techniques [ 70 ]. Card fraud detection is also of crucial importance nowadays, as more and more users are using credit cards for their e-commerce transactions.…”

Section: Resultsmentioning

confidence: 99%

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

Daskalakis¹,

Remoundou²,

Πεππές³

et al. 2022

Sensors

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The extreme rise of the Internet of Things and the increasing access of people to web applications have led to the expanding use of diverse e-commerce solutions, which was even more obvious during the COVID-19 pandemic. Large amounts of heterogeneous data from multiple sources reside in e-commerce environments and are often characterized by data source inaccuracy and unreliability. In this regard, various fusion techniques can play a crucial role in addressing such challenges and are extensively used in numerous e-commerce applications. This paper’s goal is to conduct an academic literature review of prominent fusion-based solutions that can assist in tackling the everyday challenges the e-commerce environments face as well as in their needs to make more accurate and better business decisions. For categorizing the solutions, a novel 4-fold categorization approach is introduced including product-related, economy-related, business-related, and consumer-related solutions, followed by relevant subcategorizations, based on the wide variety of challenges faced by e-commerce. Results from the 65 fusion-related solutions included in the paper show a great variety of different fusion applications, focusing on the fusion of already existing models and algorithms as well as the existence of a large number of different machine learning techniques focusing on the same e-commerce-related challenge.

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Real-Time Shill Bidding Fraud Detection Empowered With Fussed Machine Learning

Cited by 13 publications

References 11 publications

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach

Training a Feedforward Neural Network Using Hybrid Gravitational Search Algorithm with Dynamic Multiswarm Particle Swarm Optimization

Applications of Fusion Techniques in E-Commerce Environments: A Literature Review

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