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Handling Imbalanced Data for Credit Card Fraudulent Detection: A Machine Learning Approach 1st

Handling Imbalanced Data for Credit Card Fraudulent Detectio...

1st International Conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processing, AAIMB 2023

作者： Sujatha, E. Umarani, V. Rekha, K.S. Gopirajan, P.V. Manickavasagan, V. Sriperumbudur India Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031730672

Imbalanced data is crucial for contemporary machine learning models. Evaluating the effectiveness of machine learning classifiers under an unfair dataset is problematic. Several resampling techniques were applied to the unwarranted dataset and pre-processing stages to increase the accuracy of prognosis classifiers. This proposed work discusses the basics of resampling techniques such as Random under-sampling, Random oversampling, SMOTE (Synthetic Minority Over-sampling Technique), SMOTE-ENN (Synthetic minority over-sampling technique and Edited nearest neighbor), ADASYN (Adaptive Synthetic), and SMOTE-Tomek to handle this Weakness with unbalanced data. The proposed system uses an unstable credit card fraudulent dataset collected from Kaggle, which contains 21 columns and 28382 rows, of which 18497 negative samples and 4208 positive samples. The balanced dataset got by applying resampling methods SMOTE, SMOTE-ENN, ADASYN, and SMOTE-Tomek. Afterward, it uses machine learning techniques such as Logistic Regression (LR), Decision tree (DT), and Random Forest (RF) for classification. From this analysis, it selects the optimal scheme and classifier. The grey wolf optimization technique improves the classifier’s performance, which optimizes the samples after sampling. This hybrid method’s effectiveness was evaluated with standard evaluation parameters. The hybrid Random Forest technique achieved 92% accuracy from this analysis compared to hybrid models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Large Language Models in Psychiatry:Current Applications,Limitations,and Future Scope

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Big Data Mining and Analytics 2024年第4期7卷 1148-1168页

作者： Zhe Liu Yihang Bao Shuai Zeng Ruiyi Qian Miaohan Deng An Gu Jianye Li Weidi Wang Wenxiang Cai Wenhao Li Han Wang Dong Xu Guan Ning Lin Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicineand School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200030China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA School of Information Science and Technology Institute of Computational BiologyNortheast Normal UniversityChangchun 130024China

With the advancements in Artificial Intelligence(AI)technology,Large Language Models(LLMs)provide outstanding capabilities for natural language understanding and generation,enhancing various *** psychiatry,LLMs can empower healthcare by analyzing vast amounts of medical data to improve diagnostic accuracy,enhance therapeutic communication,and personalize patient care with their strength in understanding and generating human-like *** clinical AI,developing and utilizing robust and interpretable models has been a longstanding *** survey investigates the current psychiatric practice of LLMs,along with a series of corpus resources that could be used for training psychiatric *** discuss the limitations concerning LLM reproducibility,capabilities,usability,interpretability in clinical settings,and ethical ***,we propose potential future directions for research,clinical application,and education in psychiatric ***,we discuss the challenge of integrating LLMs into the evolving landscape of healthcare in real-world scenarios.

关键词： Artificial Intelligence(AI) Large Language Model(LLM) psychiatry medical application

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application 13

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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13th International Electrical engineering Congress, iEECON 2025

作者： Vanna-Iampikul, Pruek Boonnateephisit, Visutr Limsoontharakul, Somphop Moonrinta, Jednipat Dailey, Matthew Ekpanyapong, Mongkol Burapha University Department of Electrical Engineering Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Prince of Songkla University Dept. Computational Science Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand School of Engineering and Technology Asian Institute of Technology Dept. of Industrial Systems Engineering Pathumthani Thailand

ISBN: (纸本)9798331543952

LiDAR technology has rapidly advanced, offering highly accurate solutions for tree geometry estimation, which is particularly valuable in agriculture and forestry. Its ability to capture precise 2D and 3D data has revolutionized tree detection and measurement. In Eucalyptus plantations, continuous monitoring is critical to maximizing growth, enhancing paper production, and mitigating environmental damage. However, existing solutions face challenges in real-time monitoring due to factors such as parasitic interference, low-light conditions, and uneven terrain. In this work, we present a novel, end-to-end framework that enhances Eucalyptus tree measurement and monitoring. Our framework significantly improves data collection efficiency by allowing measurements at walking speed, reducing measurement distance by 50% compared to traditional manual approaches. This system integrates 2D-LiDAR with additional environmental sensors to extract key tree parameters, including circumference, position, and tree count. The collected data is stored in real-time and uploaded to a centralized server for further analysis. Experimental evaluations across 160 trees, under five distinct farm conditions, demonstrate that our system achieves 89% accuracy in tree diameter estimation and 98.5% accuracy in tree counting, significantly outperforming traditional methods. These results establish our framework as a highly effective solution for improving the accuracy and efficiency of tree monitoring in challenging environments. © 2025 IEEE.

关键词： Deforestation

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Influence of Unbalance on Tyre Pressure Monitoring System Based on C-GRU Approach 7

Influence of Unbalance on Tyre Pressure Monitoring System Ba...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Sivashankar, G. Prasad, Shailandra Kumar Saravanan, A. Ghamande, Manasi Vyankatesh Aravind, A.R. Sharma, Manish SRMIST Department of Computational Intelligence KTR Campus India RVS College of Engineering and Technology Mechanical Engineering Department Jamshedpur India Kongu Engineering College Department of Mechanical Engineering Erode India Vishwakarma Institute of Information Technology E and AS Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350340600

When the air pressure in either tire falls below a predetermined level, the present Tyre Pressure Monitoring System alerts the driver. The annual death toll is estimated at 650 now because of this avoidable signal. Constant monitoring of performance indicators proves the project's value. Therefore, our proposed system demonstrated a method to compile tyre factors and offer them to the driver in a single location. Preprocessing, feature selection, and model training are the first three steps of the proposed method. Radial acceleration is utilized in preprocessing to take into consideration the noise that occurs during actual driving conditions. The proposed system employ information gain and entropy reduction to make feature selections. Following feature selection using information gain, C-GRU is used to train the models. The proposed method outperforms both GRU and CNN, the two most popular alternatives. © 2023 IEEE.

关键词： Feature extraction

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A Multi-phase Deep Learning Approach for Predicting HCC Response to TACE using Complete Computed Tomography

A Multi-phase Deep Learning Approach for Predicting HCC Resp...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Chang, Shun Cheng Liu, Chien Fu Misztal, Marta Hsieh, Yi Hsien Lin, Che Taiwan School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Electrical Engineering Ntu Taiwan Center for Computational and Systems Biology Center for Biotechnology Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350383386

In this research study, we compare the predictive performance of two advanced deep learning-based models in order to provide a solution to TACE (Transarterial Chemoembolization) response prediction in HCC (Hepatocellular Carcinoma) patients. Using entire abdominal CT scans enabled a broader perspective available for the model, eliminating the need for segmentation during the preprocessing. Making use of both single-phase and multi-phase CT imaging, we have used DenseNet121 and have obtained an accuracy of 80% for the multi-phase *** Relevance: The ability to predict the effectiveness of TACE treatment prior to its administration makes it possible to provide a better decision-making aid for physicians and patients. © 2023 IEEE.

关键词： computerized tomography

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Attention Based Generative Adversarial Networks to Augment Image Data for Rapid Training

Attention Based Generative Adversarial Networks to Augment I...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Vijayakumar Adaickalam Sadagopan Selvarasu Gowthul Alam M A Suresh Kumar S Annamalai Sangeetha N School of Computer Science and Engineering and Information Presidency University Bangalore Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai School of Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Department of AI & ML City Engineering College Bangalore

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

In this paper, we develop an Attention based Generative Adversarial Networks (AGAN) to augment image data for the purpose of robust training for efficient processing of the hyper spectral imaging. The AGAN model enables generation of images from the sample images that helps in training the classifier and in this study a fundamental classifier namely a convolutional neural network is used. A robust training is conducted to test the accuracy of detecting the instances effectively using the dataset. The simulation shows that the proposed AGAN-CNN attains improved accuracy after robust training than the existing methods.

关键词： Training Accuracy Training data Imaging Production computer architecture Generative adversarial networks Grid computing Convolutional neural networks Hyperspectral imaging

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NORPPA: NOvel Ringed Seal Re-Identification by Pelage Pattern Aggregation

NORPPA: NOvel Ringed Seal Re-Identification by Pelage Patter...

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IEEE Winter Applications and computer Vision Workshops (WACVW)

作者： Ekaterina Nepovinnykh Tuomas Eerola Heikki Kälviäinen Ilia Chelak Department of Computational Engineering School of Engineering Sciences Computer Vision and Pattern Recognition Laboratory (CVPRL) Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Computer Science Faculty of Science University of Helsinki Helsinki Finland

We propose a method for Saimaa ringed seal (Pusa hispida saimensis) re-identification. Access to large image volumes through camera trapping and crowdsourcing provides novel possibilities for animal conservation and monitoring and calls for automatic methods for analysis, in particular, when re-identifying individual animals from the images. The proposed method NOvel Ringed seal re-identification by Pelage Pattern Aggregation (NORPPA) utilizes the permanent and unique pelage pattern of Saimaa ringed seals and content-based image retrieval techniques. First, the query image is preprocessed, and each seal instance is segmented. Next, the seal's pelage pattern is extracted using a U-net encoder-decoder based method. Then, CNN-based affine invariant features are embedded and aggregated into Fisher Vectors. Finally, the cosine distance between the Fisher Vectors is used to find the best match from a database of known individuals. We perform extensive experiments of various modifications of the method on challenging Saimaa ringed seals re-identification dataset. The proposed method is shown to produce the best re-identification accuracy on our dataset in comparisons with alternative approaches.

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Blood Cancer Detection and Classification using Auto Encoder and Regularized Extreme Learning Machine 8

Blood Cancer Detection and Classification using Auto Encoder...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Kanimozhi, N. Nayak, Swikruti Keshav Kumar, K. Manjramkar, Vinda Kumar, Ranjeet Suganthi, D. Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chennai Tamilnadu Kattankulathur India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India Department of Humanities and Mathematics Shaikpet Telangana Hyderabad India Zoology Maharashtra Thane India University College of Engineering & Technology Vinoba Bhave University Department of Electronics & Communication Jharkhand Hazaribagh India Saveetha Institute of Medical and Technical Sciences Saveetha College of Liberal Arts and Sciences Department of Computer Science Thandalam Chennai India

ISBN: (纸本)9798350396638

Leukaemia is considered as the most common type of blood cancer. The length of time one requires to recover from this disease is determined by how quickly it is recognized and treated. Leukaemia develops when the bone marrow generates an abnormally large amount of white blood cells. Since images of human blood samples are studied under the microscope, haematologists employ techniques such as microscopic colour imaging, image segmentation, grouping, and classification to diagnose patients as fast as possible. Microscopy may identify blood cancer in visible and immature white blood cells in a variety of methods. Early and accurate diagnosis of leukaemia is critical for clinicians to properly treat patients. After the input image is provided, the gaussian filter is used to perform image enhancement, and the Wiener filter is used in image Deblurring, Adaptive K-Means clustering technique, Colour feature, Texture feature, GLCM and Hausdroff are also used, and lastly the Model is trained using AE and RELM. The proposed system outperforms conventional models such as RELM and ELM, which generates an accuracy of 98.45%. © 2023 IEEE.

关键词： Image enhancement

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An Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern

An Efficient Prevention and Challenges in Wireless Sensor Ne...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Sumathy G K. Geetha Maheshwari A AR Arunarani Prithi Samuel Department of Computational Intelligence SRMIST Chennai India Department of Computer Science and Engineering Periyar Maniammai Institute of science and Technology Thanjavur

To have the good quality design of applications for Wireless Sensor Network (WSN), it's is much important for considering the challenges. WSN emerged with some common criteria with wireless network; they have the technical challenges, which is different from the traditional WSNs. The algorithm and protocol, which was proposed for traditional WSN, is not comparably good for the modern WSN. WSN has multiple type of sensors depending on the wide range of parameters. The deployment of nodes in WSN is in a random manner or the nodes will the manually planted in the desired location. The deployment of node in the areas where manual attending is difficult, there are chances of energy deficiency due to regular discharge of battery or by the short circuit. For minimizing these data loss issues, the affected nodes should be detected. The deployment of Sensor Node (SN) makes congestion in network by continuous transmissions by many SN. The other could be by the length of the link physically. Like, two nearby nodes cannot communicate with each other due to the physical interference (whereas distance nodes can be connected). The SN could have larger number of the sensor nodes which has the ability of sense, processing the data and commutation abilities. This paper describes the simulation tool for the implementation. Finally, the performance of Efficient Prevention and Challenges in Wireless Sensor Networks for Energy and Security Concern is provided. From the experimental results, security management system is estimated.

关键词： Wireless sensor networks Protocols Wireless networks Security management Manuals Interference Fault location

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Symmetric matrix completion with ReLU sampling 24

Symmetric matrix completion with ReLU sampling

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Proceedings of the 41st International Conference on Machine Learning

作者： Huikang Liu Peng Wang Longxiu Huang Qing Qu Laura Balzano Antai College of Economics and Management Shanghai Jiao Tong University Department of Electrical Engineering and Computer Science University of Michigan Department of Computational Mathematics Science and Engineering & Department of Mathematics Michigan State University

We study the problem of symmetric positive semidefinite low-rank matrix completion (MC) with deterministic entry-dependent sampling. In particular, we consider rectified linear unit (ReLU) sampling, where only positive entries are observed, as well as a generalization to threshold-based sampling. We first empirically demonstrate that the landscape of this MC problem is not globally benign: Gradient descent (GD) with random initialization will generally converge to stationary points that are not globally optimal. Nevertheless, we prove that when the matrix factor with a small rank satisfies mild assumptions, the nonconvex objective function is geodesically strongly convex on the quotient manifold in a neighborhood of a planted low-rank matrix. Moreover, we show that our assumptions are satisfied by a matrix factor with i.i.d. Gaussian entries. Finally, we develop a tailor-designed initialization for GD to solve our studied formulation, which empirically always achieves convergence to the global minima. We also conduct extensive experiments and compare MC methods, investigating convergence and completion performance with respect to initialization, noise level, dimension, and rank.

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