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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 7177 条记录，以下是781-790 订阅

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Anomalous Behavior Detection in ATM using Different Methodologies: A Review 3

Anomalous Behavior Detection in ATM using Different Methodol...

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3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Thakre, Harsh Bhushanwar, Kush Patel, Anil Computer Engineering Department PIET Parul University Gujarat Vadodara India

ISBN: (纸本)9798331532420

The necessity for standardized anomaly detection systems in financial transactions is driven by the alarming increase in ATM fraud and criminal activities. Many techniques for detecting anomalous behavior in ATM systems are also summarized and evaluated in this review article. To better secure financial institutions and customers from fraudsters, it is crucial to examine the reasoning behind these systems, particularly in the areas that pertain to improving security. The overarching purpose of this paper is to present a clear and succinct explanation of the cutting-edge machine learning (ML) and deep learning (DL) algorithms that are presently the backbone of ATM anomaly pattern detection, including more contemporary methods like transfer learning. This review delves into the theoretical foundations of these strategies and compares different algorithms, along with the values of performance indicators that show their strengths and drawbacks. Our goal is to show, via an examination of current methods in actual settings, any flaws or problems that may hinder the development of ATM security in the future. Finally, this work can be used by researchers and those interested in bettering ATM systems and associated secure technologies in the future to study the subject and explore anomaly detection technologies as a means of both practical experience and future advancement. © 2024 IEEE.

关键词： Adversarial machine learning

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Smart Video Processing in the Cloud 3

Smart Video Processing in the Cloud

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3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Varun, Potla Raghu Priva Durga, Yenumula Jyothsna Praharshini, Kama Naga Lakhshmi Narayana, Nunna Charan Gangashetty, Suryakanth V Koneru Lakshmaiah Education Foundation Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350396157

This study capitalizes on cloud technology to revolutionize video processing, offering real-time object detection and labeling. Users can effortlessly upload videos via our user-friendly web portal, initiating cloud-based analysis for applications in security, surveillance, and home automation. Our system's design prioritizes privacy, ensuring sensitive data remains unexposed. With a commitment to accessibility and usability, the study fosters more interactive and intelligent video content. This initiative marks a significant stride toward unlocking the full potential of video in an increasingly digital world, empowering users to derive valuable insights from visual media while safeguarding their privacy and data © 2024 IEEE.

关键词： Sensitive data

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Enhanced data Mining Algorithms for Sybil Attack Prevention in Truth Discovery 24

Enhanced Data Mining Algorithms for Sybil Attack Prevention ...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Shao, Yanjing Zhao, Wei Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology China National Key Laboratory of Security Communication China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400718120

In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However, these systems are vulnerable to Sybil attacks, where attackers create multiple fake contributors to gain undeserved rewards. The presence of differential privacy noise complicates the detection of these fraudulent contributors, thereby reducing the accuracy of truth discovery. To address this challenge, we introduce an enhanced data mining algorithm, Truth Discovery against Sybil Attacks (TDSA). This algorithm is specifically designed to improve truth discovery by effectively mitigating Sybil attacks and reducing transmission errors in data mining contexts, utilizing machine learning clustering algorithms and lattice-based linear coding techniques. TDSA optimizes the mining process to better handle malicious data contributions and reduces errors associated with data aggregation. Experimental results on real-world datasets demonstrate that TDSA improves accuracy by approximately 20% compared to conventional methods lacking Sybil attack defenses, highlighting its efficacy in advanced data mining applications. © 2024 Copyright held by the owner/author(s).

关键词： data aggregation

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The Application of machine learning Techniques to the Diagnosis of Breast Cancer 3

The Application of Machine Learning Techniques to the Diagno...

引用

3rd international conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Prasad, Pratyush Manipal University Data Science and Engineering Jaipur India

ISBN: (纸本)9798350320749

Due to its high mortality rate and limited survival duration, early identification of breast cancer is vital. The primary application of machine learning models has been the prediction and early diagnosis of breast cancer. This article presents machine learning models and algorithms for automated breast cancer diagnosis using an exploratory approach. The technique makes use of eight distinct machine learning algorithms, including Gaussian Naive Bayes, Logistic Regression, Decision Tree, Random Forest, Support Vector Classifier, XG-Boost, and Deep learning Model. Their precision is used to evaluate the performance of algorithms and models. Additionally, probabilistic predictions have been made using activation functions like Relu. In terms of test accuracy on the Kaggle data set, Deep learning Model 2 and the Support Vector Classifier fared the best. © 2023 IEEE.

关键词： Decision trees

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A Novel Method to Authenticate Texture of Cotton and Silk Saree Using Image Processing & RCNN machine learning Techniques 3

A Novel Method to Authenticate Texture of Cotton and Silk Sa...

引用

3rd international conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Yogesh, B.S. Praveen, N. Patil, Annapurna P Nisarga, K. Dayananda Sagar College of Engineering Information Science & Engineering Bengaluru India Associate Software Engineer Iron Mountain Bengaluru India

ISBN: (纸本)9798350391244

The popularity of sarees, as Indian traditional garment, is spreading all over the world, and because of their variety and different type of materials used in making traditional garment need to automate and classification methods are required to identify best among the existing. This work offers a novel method for classifying and detecting saree types using computer vision techniques. The visual features of sarees are classified by using image processing with machine learning models as preprocessing to improve the classification model. With the use of region based CNNs texture feature will be extracted and identification of different types of material used (Silk/Cotton/Shiffon). The provided material and its category plays a vital role in differentiating Kanchivaram Silk Sarees. The model ensures stability across datasets and training conditions, as demonstrated by experimental findings that show its improved accuracy and speed over using traditional deep learning techniques. This research increases computer vision in apparel recognition and categorization with applications in e-commerce and fashion recommendation. © 2024 IEEE.

关键词： Photointerpretation

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Diverse Image Processing Techniques for machine learning Methods 3

Diverse Image Processing Techniques for Machine Learning Met...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Bhagya, A. Perumal, S. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350361186

machine learning is a trending topic in the area of computer vision, which makes the machine able to learn about it without being expressly programmed using various algorithms. When a model is designed to predict future-based on historical data using machine learning algorithms, data is vital because high quality data is needed to run the model efficiently and to predict highly accurate results. machine learning algorithms receive data in many forms, like audio (speech recognition), images (feature recognition), numeric data (like height and weight) etc. When it comes to computer vision, the machine learning algorithms must be able to understand the image data, and before the images are processed to the algorithms it should be pre-processed (cleaned and set to the desired format) using various image pre-processing techniques. This research paper presents various image processing techniques, literature reviews on various image processing techniques and image PSNR value after applying median filter, grayscale filter and box filters. © 2024 IEEE.

关键词： Median filters

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Prediction Of Autism Spectrum Disorder Using machine learning 3

Prediction Of Autism Spectrum Disorder Using Machine Learnin...

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3rd IEEE international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Arunprasath, T. Niranjana, M. Sharumathi, M. Buvanesh Pandian, V. Rajasekaran, M Pallikonda Ramaraj, Kottaimalai Kalasalingam Academy of Research and Education Department of Biomedical Engineering Krishnankoil621 216 India Kalasalingam Academy of Research and Education Department of Eee Krishnankoil621 216 India Kalasalingam Academy of Research and Education Department of Ece Krishnankoil621 216 India

ISBN: (纸本)9798350361186

This is to investigate the predictive potential of electroencephalogram (EEG) signals extracted from children diagnosed with autism spectrum disorder (ASD). The objective is to utilize machine learning techniques, specifically logistic regression, to discern patterns within EEG data that could aid in the early prediction of ASD. EEG signals collected from autism-affected children are analyzed and processed to extract relevant features. Subsequently, these features are used as inputs to train a logistic regression model. Thestudy evaluates the model's performance in distinguishing between ASD and typically developing individuals, aiming to provide a reliable predictive toolfor identifying ASD based on EEG signals. Results suggest promising prospects for leveraging EEG data and machine learning methods in the early detection and potential prediction of autism spectrum disorder in children. © 2024 IEEE.

关键词： Electroencephalography

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data Augmentation Method for Classification of Lymph Node Metastasis using Diffusion Model 3

Data Augmentation Method for Classification of Lymph Node Me...

引用

3rd international conference on Image Processing and Robotics, ICIPRoB 2024

作者： Suzuki, Kosuke Iwahori, Yuji Funahashi, Kenji Bhuyan, M.K. Ouchi, Akira Shimizu, Yasuhiro Nagoya Institute of Technology Department of Computer Science Aichi Japan Chubu University Department of Computer Science Aichi Japan Indian Institute of Technology Guwahati Dept. of Electronics and Electrical Eng. Guwahati India Aichi Cancer Center Hospital Aichi Japan

ISBN: (纸本)9798350374766

Automatic judgment of the presence or absence of cancer metastasis to lymph nodes is an important topic in cancer staging to reduce the burden of medical doctors to check a large number of CT images. Therefore, the machine learning approach becomes helpful and desired. However, using CT images for machine learning requires annotation by a special radiologist, and it is difficult to prepare a large dataset. In recent years, many approaches to the generation of images by machine learning have been proposed and applied to medical images. In this paper, we propose a method to generate images around a lymph node in an arbitrary metastatic state using a diffusion model, which is a generative model that is gaining attention on behalf of GAN. Experiments were also conducted to learn the classification of cancer metastasis using the generated images for data augmentation, showing that the generated images are effective in improving classification performance. © 2024 IEEE.

关键词： Medical imaging

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RECOGNITION OF SURFACE CORROSION MORPHOLOGY ON COASTAL engineering STRUCTURES USING machine VISION TECHNOLOGY 43

RECOGNITION OF SURFACE CORROSION MORPHOLOGY ON COASTAL ENGIN...

引用

ASME 43rd international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Yu, Qifeng Han, Yudong Shanghai Maritime Univ Coll Transport & Commun Shanghai Peoples R China

ISBN: (纸本)9780791887806

The harsh marine atmospheric conditions, including high temperatures, humidity, and salt spray, prevalent in the coastal areas of Hainan, pose a significant challenge to the durability of ground facilities and equipment, often resulting in corrosion and functional degradation. Hence, accurate monitoring of corrosion is paramount for maintaining coastal engineering structures. This study leveraged data from the Chinese National Center for Materials Corrosion and Protection Science to extract corrosion morphology features using image analysis techniques. Subsequently, a corrosion identification model was developed using machine learning algorithms. The model's accuracy was validated through various evaluation metrics. The research outcomes have practical applications in the maintenance and management of coastal engineering structures by providing automatic corrosion morphology recognition. This enables maintenance personnel to promptly undertake repair measures, thereby reducing maintenance costs and enhancing structural sustainability.

关键词： Corrosion morphology machine learning techniques image processing YOLO v5 automatic recognition

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A Comprehensive Survey on Breast Cancer Diagnostics: From Artificial Intelligence to Quantum machine learning 3

A Comprehensive Survey on Breast Cancer Diagnostics: From Ar...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Manideep Raya V S R S Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum machine learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.

关键词： Biopsy

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