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Credit card fraud detection: an evaluation of SMOTE resampling and machine learning model performance

International Journal of Business Intelligence and data Mining 2023年第1期23卷 1-13页

作者： Alshameri, Faleh Xia, Ran Department of Information Technology Data Science and Cybersecurity School of Business and Technology Marymount University United States

Credit card fraud has been a noted security issue that requires financial organisations to continuously improve their fraud detection system. In most cases, a credit transaction dataset is expected to have a significantly larger number of normal transactions than fraud transactions. Therefore, the accuracy of a fraud detection system depends on building a model that can adequately handle such an imbalanced dataset. The purpose of this paper is to explore one of the techniques of dataset rebalancing, the synthetic minority oversampling technique (SMOTE). To evaluate the effects of this technique on model training, we selected four basic classification algorithms, complement naïve Bayes (CNB), K-nearest neighbour (KNN), random forest and support vector machine (SVM). We then compared the performances of the four models trained on the rebalanced and original dataset using the area under precision-recall curve (AUPRC) plots. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Support vector machines

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Big data analytics for privacy through ND-homomorphic encryption

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Journal of Control and Decision 2023年第1期10卷 64-71页

作者： S.Saravanan N.Poornima Department of CSE SRM Institute of Science and TechnologyChennaiIndia Department of Data Science and Business Systems School of ComputingSRM Institute of Science and TechnologyKattankulathurIndia

Rapidly rising the quantity of Big data is an opportunity to flout the privacy of people. Whenhigh processing capacity and massive storage are required for Big data, distributed networkshave been used. There are several people involved in these activities, the system may contributeto privacy infringements frameworks have been developed for the preservation of privacy atvarious levels (e.g. information age, information the executives and information preparing) asfor the existing pattern of huge information. We plan to frame this paper as a literature surveyof these classifications, including the Privacy Processes in Big data and the presentation of theAssociate Challenges. Homomorphic encryption is particularised aimed at solitary single actionon the ciphered information. Homomorphic enciphering is restrained to an honest operation onthe encoded data. The reference to encryption project fulfils many accurate trading operationson coded numerical data;therefore, it protects the written in code-sensible information evenmore.

关键词： Security and confidentiality vast statistics data encryption nondeterministic fully homomorphic encryption

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Transforming the cybersecurity space of healthcare IoT devices using Deep Learning 4

Transforming the cybersecurity space of healthcare IoT devic...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Kumar, Priyan Malarvizhi Kavin, Balasubramanian Prabhu Jagathpally, Abhignya Shahwar, Tayyaba University of North Texas Department of Data Science Denton United States SRM Institute of Science and Technology Department of Data Science and Business Systems TamilNadu India

ISBN: (纸本)9798331518882

The proliferating growth of IoT devices has created a great revolution in the lives of mankind especially in the healthcare sector. On the other side, there is an inclining trend of cyberattacks on healthcare IoT devices as the clinical data is very sensitive and not many healthcare organizations impart stringent security protocols on Internet of Medical Things (MIoT) devices. The distinct property of IoT devices and their resource constrained nature limits the effectiveness of existing security solutions. To combat this issue, the present research work proposes a Deep Gated Recurrent Unit (D-GRU) as an AI based framework to predict the onset of attacks in MIoT devices in Critical Care Units (CCU). The proposed model explores the various network and communication parameters of the patient and environment monitoring devices employed in CCU to detect the attack. This framework is lightweighted and hence can be easily deployed in healthcare sectors, which would eventually transform the cybersecurity landscape of MIoT devices. The proposed model is used to predict the onset of cyberattacks in open source, synthetic IoT healthcare data generated through IoT Flock tool. The intricate performance analysis of the model on various classification metrics shows that the model is effect in detecting the attacks in MIoT. © 2025 IEEE.

关键词： Critical care units Cyber attacks Environment monitoring Gated Recurrent Units MIoT network traffic parameters Patient monitoring

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Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios

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Journal of Electronic science and technology 2025年第2期23卷 1-18页

作者： Zarina Kutpanova Mustafa Kadhim Xu Zheng Nurkhat Zhakiyev Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Department of Computational and Data Science Astana IT UniversityAstana010000Kazakhstan

Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network *** natural disasters,timely delivery of first aid supplies is *** UAVs face risks such as crashing into birds or unexpected *** systems with parachutes risk dispersing payloads away from target *** objective here is to use multiple UAVs to distribute payloads cooperatively to assigned *** civil defense department must balance coverage,accurate landing,and flight safety while considering battery power and *** Q-network(DQN)models are commonly used in multi-UAV path planning to effectively represent the surroundings and action *** strategies focused on advanced DQNs for UAV path planning in different configurations,but rarely addressed non-cooperative scenarios and disaster *** paper introduces a new DQN framework to tackle challenges in disaster *** considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return.A new DQN model is developed,which incorporates the battery life,safe flying distance between UAVs,and remaining delivery points to encode surrounding hazards into the state space and ***,a unique reward system is created to improve UAV action sequences for better delivery coverage and safe *** experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance.

关键词： Deep Q-network First aid delivery Multi-UAV path planning Reinforcement learning Unmanned aerial vehicle(UAV)

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StealthFace:Transfer Learning-Based Ensemble Model for Disguised Face Recognition using Skin-Segmented Images

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2025年第4期52卷 1244-1260页

作者： Padmashree, G. Kotegar, Karunakar A Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Disguised face identification is challenging since people cover their identities by wearing masks, hats, sunglasses, or other disguises. These disguises dramatically modify face features, making identifying individuals a difficult task. In this study, we introduce StealthFace, an ensemble model that utilizes transfer learning and skin-segmented images to improve the recognition of disguised faces. To improve the accuracy and resilience of disguised face recognition, StealthFace leverages the power of deep learning algorithms, transfer learning, and ensemble methods. The framework employs pre-trained convolutional neural network models, such as ResNet50 and DenseNet121 for feature extraction and to learn discriminative features that are important in identifying individuals despite the disguises. The ensemble approach combines models’ predictions, utilizing their collective expertise and capturing multiple perspectives on disguised faces. Our ensemble model outperforms other state-of-the-art methods with an overall accuracy of 99.24% and contributes to enhancing security measures and advancing the development of face recognition systems. © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Contrastive Learning

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Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

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Computers, Materials & Continua 2024年第1期78卷 249-263页

作者： Hyun Kwon Sanghyun Lee Department of Artificial Intelligence and Data Science Korea Military AcademySeoulKorea Graduate School of Information Security Korea Advanced Institute of Science and TechnologyDaejeonKorea

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech *** military applications,deep neural networks can detect equipment and recognize *** military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural *** have been no previous studies on the detection of real rifle numbers using real rifle image *** this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are *** proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer *** the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 *** detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical *** used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental *** was used as the machine learning *** results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle *** proposed method is effective in detecting rifle numbers.

关键词： Machine learning deep neural network rifle number recognition detection

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Understanding the Aftermath of COVID-19: A data-Driven Exploration of Health Effects 3

Understanding the Aftermath of COVID-19: A Data-Driven Explo...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Selvaraj, Arikumar K. Naveen Akash, P. Venkata Swamy, B. Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

The COVID-19 pandemic has ravaged public health in a manner that is unprecedented: millions of infections and fatalities worldwide. Now that the immediate crisis seems to recede, there is growing concern over long-term health consequences faced by survivors, commonly referred to as "long COVID." This study aims to systematically explore the multifaceted health effects experienced by individuals who have recovered from COVID-19, focusing on physical, mental, and cognitive health issues that have emerged in the aftermath of the infection. Employing a data-driven approach, the collected extensive survey data from a diverse cohort of COVID-19 survivors to assess their post-recovery health status. The survey included questions targeting various health dimensions, such as fatigue, pain, anxiety, depression, and cognitive function. To analyze the data, utilized several machine learning algorithms to identify significant health complications and to determine the interrelationships among various health factors. © 2025 IEEE.

关键词： COVID-19

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P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

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Journal of Computer science & technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

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H2O Guard: Predicting Water Potability with Machine Learning and Blockchain Integration 3

H2O Guard: Predicting Water Potability with Machine Learning...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Nair, Rahul Aalim, Ansab Manhaas, Ishaan Rastogi, Kshitij Kalpana, A.V. Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798331509675

Access to clean drinking water is critical to public health and well - being. Water compatibility refers to the safety of drinking water for human consumption and includes various physical, chemical and biological parameters. Contaminated water can cause serious health problems, particularly among vulnerable populations due to water-borne diseases. Traditionally, water quality assessment relies on time - consuming laboratory tests that can delay responses to contamination. The H2OGuard project presents a novel approach: It uses deep learning and machine learning algorithms to predict water sustainability based on key environmental factors such as pH, turbidity and chemical contaminants. With these advanced methods, we aim to improve the efficiency and accuracy of water quality testing. In addition, the integration of blockchain technology is intended to ensure secure and immutable storage of water quality data and promote integrity and transparency. This paper discusses the methods of the H2OGuard project, evaluates the performance of various predictive models, and highlights the critical importance of blockchain in securing data. With this innovative framework, we aim to enable more efficient and timely assessment of drinking water safety, ultimately contributing to improved public health outcomes. © 2025 IEEE.

关键词： Electronic health record

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CSNet:A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting

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植物表型组学（英文） 2024年第4期6卷 995-1009页

作者： Yaoxi Li Xingcai Wu Qi Wang Zhixun Pei Kejun Zhao Panfeng Chen Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China National Key Laboratory of Green Pesticide Key Laboratory of Green Pesticide and Agricultural BioengineeringMinistry of EducationGuiyang 550025China

Wheat is the most widely grown crop in the world,and its yield is closely related to global food *** number of ears is important for wheat breeding and yield ***,automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain ***,all existing methods require position-level annotation for training,implying that a large amount of labor is required for annotation,limiting the application and development of deep learning technology in the agricultural *** address this problem,we propose a count-supervised multiscale perceptive wheat counting network(CSNet,count-supervised network),which aims to achieve accurate counting of wheat ears using quantity *** particular,in the absence of location information,CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear *** conduct comparative experiments on a publicly available global wheat head detection dataset,showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error(MAE)and root mean square error(RMSE).This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs,demonstrating its great potential for agricultural counting *** code is available at .

关键词： network counting csnet mlp-mixer multiscale supervised wheat

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