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Handling Class Imbalance in Online Transaction Fraud Detection

computers, Materials & Continua 2022年第2期70卷 2861-2877页

作者： Kanika Jimmy Singla Ali Kashif Bashir Yunyoung Nam Najam UI Hasan Usman Tariq School of Computer Science and Engineering Lovely Professional UniversityPunjabIndia Department of Computing and Mathematics ManchesterMetropolitan UniversityUK&School of Electrical Engineering and Computer ScienceNational University of Science and TechnologyIslamabadPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Electrical and Computer Engineering Dhofar UniversitySalalahOman College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every ***,the fraud cases have also increased causing the loss of money to the ***,an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in ***,the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance *** this research work,an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e.,fraud transactions.A novel loss function named Weighted Hard-Reduced Focal Loss(WH-RFL)has been proposed which has achieved maximum fraud detection rate i.e.,True PositiveRate(TPR)at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate(TNR)in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional ***,Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.

关键词： Class imbalance deep learning fraud detection loss function thresholding

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FPGA Hardware Trojan Detection: Golden-Free Machine Learning Approach

FPGA Hardware Trojan Detection: Golden-Free Machine Learning...

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Ghimire, Ashutosh Amsaad, Fathi Hossain, Tanvir Hoque, Tamzidul Sherif, Ahmed Wright State University Department of Computer Science and Engineering United States University of Kansas Department of Electrical Engineering and Computer Science United States School of Computing Sciences and Computer Engineering The University of Southern Mississippi United States

ISBN: (纸本)9798350338782

Ensuring trust in the semiconductor IC supply chain necessitates the critical detection of Hardware Trojans, yet current methods relying on side-channel analysis often require the use of golden chips for verification. This research paper presents a novel approach to detect Hardware Trojans in the semiconductor IC supply chain, addressing the need for trust and eliminating the use of golden chips. By combining unsuper-vised machine learning and side-channel analysis, the proposed technique leverages unique features from on-chip ring-oscillator networks to identify anomalies through unsupervised clustering. Evaluation on FPGA chips with Trojan insertion demonstrated exceptional accuracy, surpassing alternative methods with a 99 % accuracy rate. The centroid-based clustering model exhibited superior performance with a slight edge in false positive rate and an fl score. This research contributes to enhancing trust in semiconductor IC supply chains by offering a fresh perspective on Hardware Trojan detection. © 2023 IEEE.

关键词： Hardware security

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication

Wireless Federated Learning over UAV-enabled Integrated Sens...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Shaon, Shaba Nguyen, Tien Mohjazi, Lina Kaushik, Aryan Nguyen, Dinh C. University of Alabama in Huntsville Department of Electrical and Computer Engineering United States Lac Hong University Department of Electrical and Electronics Engineering Viet Nam University of Glasgow James Watt School of Engineering United Kingdom Manchester Metropolitan University Department of Computing & Mathematics United Kingdom

ISBN: (纸本)9798331507428

This paper studies a new latency optimization problem in unmanned aerial vehicles (UAVs)-enabled federated learning (FL) with integrated sensing and communication. In this setup, distributed UAVs participate in model training using sensed data and collaborate with a base station (BS) serving as FL aggregator to build a global model. The objective is to minimize the FL system latency over UAV networks by jointly optimizing UAVs' trajectory and resource allocation of both UAVs and the BS. The formulated optimization problem is troublesome to solve due to its non-convexity. Hence, we develop a simple yet efficient iterative algorithm to find a high-quality approximate solution, by leveraging block coordinate descent and successive convex approximation techniques. Simulation results demonstrate the effectiveness of our proposed joint optimization strategy under practical parameter settings, saving the system latency up to 68.54% compared to benchmark schemes. © 2024 IEEE.

关键词： Resource allocation

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Adaptive Light Field Contrast Enhancement using Efficient Feature Extraction

Adaptive Light Field Contrast Enhancement using Efficient Fe...

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Guo, Xiangyan Zhao, Chun Cheng, Yongqiang Xu, Motong Yuan, Zhongyun Taiyuan University of Tachnology Department of Information and Computing Taiyuan China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350321524

Light field (LF) image can provide comprehensive image information and real visual experience. In this paper, we propose an adaptive enhancement method of LF sub-aperture images (SAIs) based on machine learning. The contrast of SAIs is enhanced with scene adaptive mapping curve. Firstly, the data set representing image features is extracted from dominance SAI. Then the multi-layer perceptron (MLP) is used to train the data set to obtain adaptive mapping curve parameters, thereby adjusting the image ulteriorly and enhance the contrast. Experimental results show that the predicted adaptive parameters using our network has high accuracy and is close to ground truth. Depending on comparing with other existing image contrast enhancement, the results demonstrate that the global image contrast is improved without over-enhancing the local region. The proposed method reduces the complexity of image processing and realizes real-time adaptive optical field enhancement. © 2023 IEEE.

关键词： Mapping

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Independent control of Spatial Wavefront of Beam Scanning Antenna with Transmissive Phase Gradient Metasurface

Independent control of Spatial Wavefront of Beam Scanning An...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Anand, Ravi Mezghani, Amine Sarkar, Anirban School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada

ISBN: (纸本)9798350364774

To control and shape the spatial wavefront of reconfigurable leaky-wave antenna (LWA) by integrating a single layered transmissive phase gradient metasurface (TPGMS) is *** amplitude and phase of transmission is controlled with single layer sub-wavelength design of TPGMS, where phase modulation is achieved through the geometrical manipulation of the unitcell *** proposed unique design of metasurface has advantage of transmission phase tunability and stability for polarization insensitive waves at different angle of *** numerical simulation of the proposed integrated model shows the full control of the scanning beam from 22° to 48° at 5GHz for sub-6GHz applications. © 2024 IEEE.

关键词： Scanning antennas

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Deep Learning-Based Approach for Arabic Visual Speech Recognition

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computers, Materials & Continua 2022年第4期71卷 85-108页

作者： Nadia H.Alsulami Amani T.Jamal Lamiaa A.Elrefaei Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt

Lip-reading technologies are rapidly progressing following the breakthrough of deep *** plays a vital role in its many applications,such as:human-machine communication practices or security *** this paper,we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning *** Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native *** primary purpose is to provide a high-performance model in terms of enhancing the preprocessing ***,we extract keyframes from our ***,we produce a Concatenated Frame Images(CFIs)that represent the utterance sequence in one single ***,the VGG-19 is employed for visual features extraction in our proposed *** have examined different keyframes:10,15,and 20 for comparing two types of approaches in the proposed model:(1)the VGG-19 base model and(2)VGG-19 base model with batch *** results show that the second approach achieves greater accuracy:94%for digit recognition,97%for phrase recognition,and 93%for digits and phrases recognition in the test ***,our proposed model is superior to models based on CFIs input.

关键词： Convolutional neural network deep learning lip reading transfer learning visual speech recognition

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Oblivion: Poisoning Federated Learning by Inducing Catastrophic Forgetting 42

Oblivion: Poisoning Federated Learning by Inducing Catastrop...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zhang, Chen Zhou, Boyang He, Zhiqiang Liu, Zeyuan Chen, Yanjiao Xu, Wenyuan Li, Baochun The Hang Seng University of Hong Kong Department of Computing Hong Kong Zhejiang University College of Electrical Engineering Hangzhou China University of Toronto Department of Electrical and Computer Engineering Toronto Canada

ISBN: (纸本)9798350334142

Federated learning is exposed to model poisoning attacks as compromised clients may submit malicious model updates to pollute the global model. To defend against such attacks, robust aggregation rules are designed for the centralized server to winnow out outlier updates, and to significantly reduce the effectiveness of existing poisoning attacks. In this paper, we develop an advanced model poisoning attack against defensive aggregation rules. In particular, we exploit the catastrophic forgetting phenomenon during the process of continual learning to destroy the memory of the global model. Our proposed framework, called Oblivion, features two special components. The first component prioritizes the weights that have the most influence on the model accuracy for poisoning, which induces a more significant degradation on the global model than equally perturbing all weights. The second component smooths malicious model updates based on the number of selected compromised clients in the current round, adjusting the degree of poisoning to suit the dynamics of each training round. We implement a fully-functional prototype of Oblivion in PLATO, a real-world scalable federated learning framework. Our extensive experiments over three datasets demonstrate that Oblivion can boost the attack performance of model poisoning attacks against unknown defensive aggregation rules. © 2023 IEEE.

关键词： Learning systems

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Online Event-Triggered Switching for Frequency Control in Power Grids With Variable Inertia

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IEEE Transactions on Power Systems 2024年第4期40卷 3347-3360页

作者： Feng, Jie Cui, Wenqi Cortes, Jorge Shi, Yuanyuan University of California San Diego Department of Electrical and Computer Engineering San DiegoCA92093 United States California Institute of Technology Department of Computing and Mathematical Sciences PasadenaCA91125 United States University of California San Diego Department of Mechanical and Aerospace Engineering San DiegoCA92093 United States

The increasing integration of renewable energy resources into power grids has led to time-varying system inertia and consequent degradation in frequency dynamics. A promising solution to alleviate performance degradation is using power electronics interfaced energy resources, such as renewable generators and battery energy storage for primary frequency control, by adjusting their power output set-points in response to frequency deviations. However, designing a frequency controller under time-varying inertia is challenging. Specifically, the stability or optimality of controllers designed for time-invariant systems can be compromised once applied to a time-varying system. We model the frequency dynamics under time-varying inertia as a nonlinear switching system, where the frequency dynamics under each mode are described by the nonlinear swing equations and different modes represent different inertia levels. We identify a key controller structure, named Neural Proportional-Integral (Neural-PI) controller, that guarantees exponential input-to-state stability for each mode. To further improve performance, we present an online event-triggered switching algorithm to select the most suitable controller from a set of Neural-PI controllers, each optimized for specific inertia levels. Simulations on the IEEE 39-bus system validate the effectiveness of the proposed online switching control method with stability guarantees and optimized performance for frequency control under time-varying inertia. © 1969-2012 IEEE.

关键词： Renewable energy

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