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E-Commerce Fraud Detection Based on machine Learning Techniques:Systematic Literature Review

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Big Data mining and Analytics 2024年第2期7卷 419-444页

作者： abed mutemi Fernando Bacao NOVA Information management School(NOVA ImS) Universidade Nova de LisboaCampus de CampolideLisboa 1070-312Portugal

The e-commerce industry’s rapid growth,accelerated by the COVID-19 pandemic,has led to an alarming increase in digital fraud and associated *** establish a healthy e-commerce ecosystem,robust cyber security and anti-fraud measures are ***,research on fraud detection systems has struggled to keep pace due to limited real-world *** in artificial intelligence,machine Learning(mL),and cloud computing have revitalized research and applications in this *** mL and data mining techniques are popular in fraud detection,specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking *** reviews provide broad overviews but fail to grasp the intricacies of mL algorithms in the e-commerce *** bridge this gap,our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and meta-Analysis(PRISmA)*** aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce *** the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated *** our investigation,we identify research opportunities and provide insights to industry stakeholders on key mL and data mining techniques for combating e-commerce *** paper examines the research on these techniques as published in the past *** the PRISmA approach,we conducted a content analysis of 101 publications,identifying research gaps,recent techniques,and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.

关键词： E-commerce machine Learning(mL) systematic review fraud detection organized retail fraud

movement Trend Alterations in the Periaqueductal Gray(PAG)-Employed Ratbot Navigation Are Correlated with Stimulation Parameters

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Journal of Bionic Engineering 2024年第2期21卷 866-876页

作者： Sina Khajei abed Khorasani mohammad Reza Afarinesh Vahid Sheibani Neuroscience Research Center Institute of NeuropharmacologyKerman University of Medical SciencesKerman***Iran Cognitive Neuroscience Research Center Institute of NeuropharmacologyKerman University of Medical SciencesKerman***Iran

In previous studies,Periaqueductal Gray(PAG)stimulation was used to stop ratbots from *** to the homology between the PAG and the intercollicular nucleus,which has been used for forward movement in birds,we investigated the possibility of PAG application to induce forward locomotion for the first *** a corridor maze,the traveled distances via PAG electrical stimulation were examined in nine wistar male rats during three sessions.A custom-designed stimulator was developed to apply the *** results showed reductions in responses to stimulation over ***,the traveled distances had negative slopes during the consecutive trials(in 8 out of the 9 rats),and the slope mean was significantly different from *** was a strong correlation between the stimulation parameters(electric Charge per Phase(CPP)and the Number of Pulses(NP))and the observed *** negative movement Slopes(mS)were highly correlated with the CPP and the NP,as the Pearson's linear correlation coefficients were−0.87 and−0.79,*** mS-CPP coefficients of determination(R-squared)were also between 0.76 and *** addition,the mS-NP coefficients of determination were between 0.63 and ***,it is concluded that the electrical stimulation parameters influence the behavioral outcomes ***,the PAG area may be considered a suitable candidate for forward locomotion control in the future if the area is harnessed effectively to prevent undesirable chaotic behaviors.

关键词： Periaqueductal Gray(PAG) Navigation Ratbot Animal-robot Forward locomotion movement slope

An efficient tool for Parkinson's disease detection and severity grading based on time-frequency and fuzzy features of cumulative gait signals through improved LSTm networks

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medicine in Novel Technology and Devices 2024年第2期22卷 38-50页

作者： Farhad abedinzadeh Torghabeh Yeganeh modaresnia Seyyed abed Hosseini Department of Biomedical Engineering Mashhad BranchIslamic Azad UniversityMashhadIran Department of Electrical Engineering Mashhad BranchIslamic Azad UniversityMashhadIran

Parkinson's disease(PD)is a widespread neurodegenerative condition that affects many individuals *** identification and monitoring of disease progression are crucial to effectively managing symptoms and preventing motor *** research proposes an automated PD diagnosis and severity-grading model based on time-frequency and fuzzy features using improved uni-directional and bi-directional long short-term memory networks with sensitive hyperparameters *** utilize vertical ground reaction force signals collected from Physionet's publicly available dataset recorded during regular and dual-task clinical trials of walking *** the cumulative signal of both feet was then utilized and segmented into 30-s windows without further ***,we extracted only four key time-frequency and fuzzy features from each segment,effectively capturing the signal's inherent *** optimization is employed in both detection and grading approaches to fine-tune the two critical hyperparameters:the initial learning rate and the number of hidden units in the *** detection phase yields an exceptional accuracy of 99.19%,surpassing state-of-the-art studies with the same *** the grading phase,classification based on the unified PD rating scale values achieves an accuracy of 92.28%.The proposed study delves into the potential of cumulative gait signals as a powerful diagnostic tool for PD,aiming to extract precise and intricate information by implementing straightforward and minimal processing *** method demonstrates significant effi-ciency in terms of complexity,cost,and energy consumption by utilizing a single-dimensional signal,eliminating the need for pre-processing steps,and limiting the features used for training.

关键词： Parkinson's disease grading Cumulative gait signal Vertical ground reaction force fuzzy feature Bayesian optimization Long short-term memory

Tensile Strain Capacity Prediction of Engineered Cementitious Composites (ECC) Using Soft Computing Techniques

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Computer modeling in Engineering & Sciences 2024年第3期138卷 2925-2954页

作者： Rabar H.Faraj Hemn Unis Ahmed Hardi Saadullah Fathullah Alan Saeed Abdulrahman Farid abed Civil Engineering Department University of HalabjaHalabjaIraq University of Sulaimani College of EngineeringCivil Engineering DepartmentSulaimaniIraq Civil Engineering Department Komar University of Science and TechnologySulaimaniIraq Department of Engineering Kurdistan Institution for Strategic Studies and Scientific ResearchesSulaimaniIraq Department of Civil Engineering American University of SharjahSharjah26666UAE

Plain concrete is strong in compression but brittle in tension,having a low tensile strain capacity that can significantly degrade the long-term performance of concrete structures,even when steel reinforcing is *** order to address these challenges,short polymer fibers are randomly dispersed in a cement-based matrix to forma highly ductile engineered cementitious composite(ECC).Thismaterial exhibits high ductility under tensile forces,with its tensile strain being several hundred times greater than conventional *** concrete is inherently weak in tension,the tensile strain capacity(TSC)has become one of the most extensively researched *** a result,developing a model to predict the TSC of the ECC and to optimize the mixture proportions becomes ***,the effort required for laboratory trial batches to determine the TSC is *** achieve the research objectives,five distinct models,artificial neural network(ANN),nonlinear model(NLR),linear relationship model(LR),multi-logistic model(mLR),and m5P-tree model(m5P),are investigated and employed to predict the TSCof ECCmixtures containing fly *** from115 mixtures are gathered and analyzed to develop a new *** input variables include mixture proportions,fiber length and diameter,and the time required for curing the various *** model’s effectiveness is evaluated and verified based on statistical parameters such as R2,mean absolute error(mAE),scatter index(SI),root mean squared error(RmSE),and objective function(OBJ)***,the ANN model outperforms the others in predicting the TSC of the ECC,with RmSE,mAE,OBJ,SI,and R2 values of 0.42%,0.3%,0.33%,0.135%,and 0.98,respectively.

关键词： Engineered cementitious composites fly ash curing time tensile strain capacity modeling

An Investigation into Forced Convection of a Nanofluid Flowing in a Rectangular microchannel under the Influence of a magnetic Field

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Fluid Dynamics & materials Processing 2024年第2期20卷 311-323页

作者： muataz S.Alhassan Ameer A.Alameri Andrés Alexis Ramírez-Coronel I.B.Sapaev Azher m.abed David-Juan Ramos-Huallpartupa Rahman S.Zabibah Division of Advanced Nanomaterial Technologies Scientific Research CenterAl-Ayen UniversityThi-QarIraq Department of Chemistry College of ScienceUniversity of BabylonBabylonIraq Research Group in Educational Statistics National University of EducatiosnAzoguesEcuador Department Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization EngineersNational Research UniversityTashkentUzbekistan Akfa University TashkentUzbekistan New Uzbekistan University TashkentUzbekistan Department of Airconditioning and Refrigeration Engineering Al-Mustaqbal University CollegeHilla51001Iraq Academic Department of Agro-Industrial Engineering and Technology Universidad Nacional JoséMaría ArguedasApurimacPerú The Islamic University NajafIraq

In line with recent studies,where it has been shown that nanoﬂuids containing graphene have a stronger capacity to boost the heat transfer coefﬁcient with respect to ordinary nanoﬂuids,experiments have been conducted using water with cobalt ferrite/graphene *** particular,a circular channel made of copper subjected to a constant heatﬂux has been *** nanoparticles are sensitive to the presence of a magneticﬁeld,different conditions have been examined,allowing both the strength and the frequency of such aﬁeld to span relatively wide ranges and assuming different concentrations of *** to theﬁndings,the addition of nanoparticles to theﬂuid causes its rotation speed to increase by a factor of two,whereas ultraviolet radiation plays a negligible *** amount of time required to attain the maximum rotation speed of the nanoﬂuid and the Nusselt number have been measured under both constant and alternating magneticﬁelds for a ferroﬂuid with a concentration of 0.5%and atﬂow Reynolds number of 550 and 1750.

关键词： Nano fluid rectengular tube magnetic field

Aminated Cyclopropylmethylphosphonates as Potent Prostate Cancer Inhibitors

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Journal of Biosciences and medicines 2024年第7期12卷 239-244页

作者： abed Al Aziz Al Quntar Ibrahim Abasy Department of Chemistry and Chemical Technology Faculty of Science and Technology Al-Quds University Jerusalem Palestine Department of microbiology and Immunology Al-Quds University Jerusalem Palestine

Inspired by the anti-pancreatic promising results of our novel aminated cyclopropylmethylphosphonate compounds, an in vitro anti-prostate cancer activity exploration of these compounds was carried out on human prostate cancer cell line PC-3, and showed potent inhibiting activity at low micromolar concentrations (with an IC50 of approximately 45 μm).

关键词： Prostate Cancer Cancer Cyclopropylphophonates Aminophosphonates Cyclopropanes Phosphonates Alkynylphosphonates Anti-Cancer Prostate

Performance Evaluation ofmulti-Agent Reinforcement Learning Algorithms

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Intelligent Automation & Soft Computing 2024年第2期39卷 337-352页

作者： Abdulghani m.Abdulghani mokhles m.Abdulghani wilbur L.walters Khalid H.abed Department of Electrical&Computer Engineering and Computer Science Jackson State UniversityJacksonMS 39217USA

multi-Agent Reinforcement Learning(mARL)has proven to be successful in cooperative *** is used to investigate how autonomous agents with the same interests can connect and act in one *** cooperation scenarios are explored in recreational cooperative augmented reality environments,as well as realworld scenarios in *** this paper,we explore the realm of mARL and its potential applications in cooperative *** focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory withminimal *** accomplish this,we utilize the StarCraftmulti-Agent Challenge(SmAC)environment and train four mARL algorithms:Q-learning with mixtures of Experts(QmIX),Value-DecompositionNetwork(VDN),multi-agent Proximal PolicyOptimizer(mAPPO),andmulti-Agent Actor Attention Critic(mAA2C).These algorithms allow multiple agents to cooperate in a specific scenario to achieve the targeted *** results show that the QmIX algorithm outperforms the other three algorithms in the attacking scenario,while the VDN algorithm achieves the best results in the defending ***,the VDNalgorithmreaches the highest value of battle wonmean and the lowest value of dead *** research demonstrates the potential formARL algorithms to be used in real-world applications,such as controllingmultiple robots to provide helpful services or coordinating teams of agents to accomplish tasks that would be impossible for a human to *** SmAC environment provides a unique opportunity to test and evaluate mARL algorithms in a challenging and dynamic environment,and our results show that these algorithms can be used to achieve victory with minimal damage.

关键词： Reinforcement learning RL multi-agent mARL SmAC VDN QmIX mAPPO

Food intolerance and the few-foods(or oligoantigenic)diet in children with attention-deficit hyperactivity disorder

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Food Science and Human wellness 2024年第4期13卷 1729-1738页

作者： Klaus w.Lange Andreas Reissmann Yukiko Nakamura Katharina m.Lange Faculty of Human Sciences University of RegensburgRegensburg 93040Germany Department of Psychology University of BathBath BA27AYUnited Kingdom

The hypothesis that some children with attention-deficit hyperactivity disorder(ADHD)may show sensitivity or allergic reactions to various food items has led to the development of the the few-foods(or oligoantigenic)*** rationale of the diet is to eliminate certain foods from the diet in order to exclude potential allergens contained either naturally in food or in artificial ingredients with allergenic *** oligoantigenic diet attempts to identify individual foods to which a person might be ***,ADHD symptoms are monitored while multiple foods are excluded from the ***,if symptoms remit,foods are re-introduced,while observing the individual for the return of *** advantage of the oligoantigenic diet is that it can be tailored to the individual.A growing body of evidence suggests that behavioral symptoms of subgroups of children with ADHD may benefit from the elimination of certain *** effect sizes of an oligoantigenic diet regarding improvement of ADHD symptoms have been found to be medium to *** evidence suggests that the investigation of the role of food hypersensitivities in ADHD is a promising avenue worthy of further *** large-scale,randomized controlled studies including assessment of long-term outcome are therefore warranted.

关键词： Attention-deficit hyperactivity disorder Food intolerance Few-foods diet Etiology Treatment

A deep learning fusion model for accurate classification of brain tumours in magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef mohammed Dilovan Asaad Zebari mazin abed mohammed Diyar Qader Zeebaree Haydar Abdulameer marhoon Karrar Hameed Abdulkareem Seifedine Kadry wattana Viriyasitavat Jan Nedoma Radek martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. while having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of magnetic Resonance Imaging (mRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from mRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created mRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model mRI images multi‐classification