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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Cyber Security Fraud Detection Using machine learning Approach

Cyber Security Fraud Detection Using Machine Learning Approa...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Ramakrishnan Raman Mohit Tiwari Dharam Buddhi Snehal Trivedi Shivaji Bothe R. Ponnusamy Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Paschim Vihar Delhi Uttaranchal Institute of Technology Uttaranchal University Department of Mech Parul University Department of IT & Business Analytics Sri Balaji University Pune (SBUP) Department of Computer Science & Engineering Chennai Institute of Technology

It is notoriously harder to identify fraudsters for its fluid nature in terms of recognizable trends. Latest technological breakthroughs were used by scammers. Individuals overcome the protection, incurring millions of dollars of lost revenue. data mining approaches could be employed to crunch numbers and discover out-of-the-ordinary behaviors, allowing it to be pursued to its cause, in this case a fraudulent payment, activities. In this study, we really would like to evaluate and contrast various popular ml algorithms, namely k-nearest peer (KNN), randomized forest (RF), and support vector (SVM), along with popular deep neural networks, including auto - encoder, Classifiers, Multiple solutions, and multi - layer perceptron (DBN). The European Union (EU), Canada, and Netherlands information will be used. The measures utilized for evaluations are the Region That under Fitted Model (AUC), the Matthew R Squared (MCC), and the cost of failing.

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Fraud Detection Using machine learning: An Evaluation of Logistic Regression and Random Forest

Fraud Detection Using Machine Learning: An Evaluation of Log...

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Smart City Challenges & Outcomes for Urban Transformation (SCOUT)

作者： Xijie Zhang Computing Software & Technologies Hong Kong Baptist University Hong Kong China

ISBN: (数字)9798331528638

ISBN: (纸本)9798331528645

With the increasing importance of digital security in the current world of finance, it is a must to find ways to implement artificial intelligence techniques to detect financial fraud behaviours. This paper explores the possibility of implementing machine learning algorithms to detect credit card fraud among bank transactions. Whether algorithms could correctly classify transactions into fraud and non-fraud categories was experimented. dataset containing the results of Principle Component Analysis (PCA)-preprocessed real-world transaction information was extracted from Kaggle. Then, the dataset is separated into a training set and test set. As fraud transactions only account for less than 1/500 of the whole dataset, Synthetic Minority Oversampling Technique was implemented to the training set to mitigate the imbalance generated by the huge difference in quantity. However, it was not applied to the test set, so that a reality situation could be simulated. After that, Logistic Regression and Random Forest models were used for model training. Finally, trained models were applied to the test set for model testing. Their performance was evaluated by Confusion Matrix, Area under Precision-Recall Curve and the classification report. Outcomes showed that Random Forest model surpassed Logistic Regression in terms of minimizing fraud cases that were incorrectly identified as but performed slightly worse in detecting true fraud cases. Nevertheless, both algorithms showed satisfactory results in classifying fraud among all transactions. Thus, it is proved that machine learning techniques can be implemented in detecting credit card fraud.

关键词： Training Logistic regression machine learning algorithms Finance Credit cards Fraud Classification algorithms data mining Random forests Testing

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Time-frequency Domain pattern Analysis of Tai Chi 12 GONG FA Based on Skeleton Key Points Detection

Time-frequency Domain Pattern Analysis of Tai Chi 12 GONG FA...

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international conference on Neural Networks, Information and Communication Engineering

作者： Pang, Yiqun Wang, Qiurui Zhang, Changnian Capital Univ Phys Educ & Sports 11 North 3rd Ring West Rd Beijing 100191 Peoples R China

ISBN: (数字)9781510647381

ISBN: (纸本)9781510647381;9781510647374

The purpose of this study is to develop an intelligent automatic scoring system of Tai Chi 12 GONG FA based on machine learning to assist the teaching, training and promotion of 12 GONG FA. On the basis of obtaining the action videos of the practitioners with different exercise levels, the two-dimensional human skeleton key point recognition technology was used to extract the different behavior patterns of fine and bad actions by analyzing the complex periodic changes of the spatial distance of different key points in the time domain;And the artificial neural network was used to reduce the dimension of features predict whether the actions conform to the norms. The system only uses monocular RGB video as input, and by the proposed algorithm of recognizing fine action and bad action, 12 kinds of action can be well identified. The intelligent scoring system based on key point detection achieves similar results with the experts' score.

关键词： martial arts Tai Chi artificial intelligence pose estimation

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Graph-Theoretic Measures for Supporting Diagnosis and Identification of Biomarkers for Borderline Personality Disorder

Graph-Theoretic Measures for Supporting Diagnosis and Identi...

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data, Decision and Systems (ICDDS), IEEE international conference on

ISBN: (数字)9798350363890

ISBN: (纸本)9798350363906

Borderline Personality Disorder (BPD) is a mental health disorder which renders an individual a victim of severe mood swings, impulsive behaviour, self-destructive behaviour and a distorted self-image. The diagnosis of BPD is usually conducted based on subjective assessments. However with the growing developments in neuroimaging, computational techniques can be developed leading to faster and more accurate diagnosis. In our study, we utilize neuroimaging techniques, specifically functional Magnetic Resonance Imaging (fMRI), to explore how different regions of the brain communicate. We employ graph theoretical methods to model this intricate neural data with the aim of objective diagnosis of the disorder and identifying neurological markers that could improve the diagnostic process for BPD. To achieve this, we make use of publicly available, annotated task-fMRI datasets designed to differentiate between individuals with BPD and healthy controls (HC). Through rigorous statistical analysis, correlation studies, and the application of graph-based metrics, we aim to not only identify whether an individual is suffering from the disorder or not, but also uncover specific brain regions that exhibit distinct activity patterns in individuals with BPD compared to HC. This research seeks to deepen our comprehension of BPD and potentially enhance diagnostic procedures by acting as an aid to health professionals.

关键词： Neuroimaging Measurement Statistical analysis Mood Mental health machine learning Functional magnetic resonance imaging Graph theory data models Object recognition

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A review of machine learning approaches in plant leaf disease detection and classification 3

A review of machine learning approaches in plant leaf diseas...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Applalanaidu, Majji V. Kumaravelan, G. Pondicherry University Department of Computer Science Puducherry India

ISBN: (纸本)9780738111834

To identify the recent advancements in the development of plant disease detection and classification system based on machine learning (ML) and Deep learning (DL) models. In this study, we have collected more than 45 papers published during the year 2017-2020 from the peer-reviewed journals of different databases such as Scopus and Web of Science analogous to the keywords such as plant disease identification, recognition, and classification using ML and DL algorithms. An organized way of analysis of various plant disease classification models has been shown in well-formed tables. In this paper, we have conducted a systematic literature study on the applications of the state-of-the-art ML and DL algorithms such as Support Vector machine (SVM), Neural Network (NN), K-Nearest Neighbor (KNN), Naïve Bayes (NB), other few popular ML algorithms and AlexNet, GoogLeNet, VGGNet, and other few popular DL algorithms respectively for plant disease categorization. Each stated algorithm is characterized through the corresponding processing methods such as image segmentation, feature extraction, along with the standardized experimental-setup metrics such as total number of training/testing dataset employed, number of diseases under considerations, type of classifier utilized, and the percentage of classification accuracy. This work will be a beneficial resource for researchers to recognize any particular type of plant diseases through data-driven approaches. The development of mobile-based applications using the studied ML/DL approaches will surely increase agricultural productivity. © 2021 IEEE.

关键词： learning algorithms

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An Autonomous Emotion recognition Strategy Employing Deep learning for Self-learning

An Autonomous Emotion Recognition Strategy Employing Deep Le...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Kamred Udham Singh Ankit Kumar Gaurav Kumar Teekam Singh Santosh Kumar Satya Prakash Yadav School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University Mathura UP India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Department of Management Jaipuria Institute of Management Jaipur India Department of Computer Science and Engineering GLBITM Greater Noida UP India

Because of their individuality and diversity, facial expressions are crucial to deciphering human communication and humour. Predicting a person's facial expression accurately not only helps you understand their emotional state but also gives you greater control over how you engage with them. Since personalised interactions are shown to enhance development and rapport., this ability to autonomously recognise human facial emotions has tremendous promise in fields like finance and education. In this study., we provide a new method for identifying human emotions based on Convolutional Neural Networks (CNNs). This method departs from conventional approaches by harnessing the potential of modern machine learning methods that make use of face datasets. When implemented in the TensorFlow framework., this method might significantly alter the way grammatical constructions are understood. Convolutional Neural Networks are at the core of our technique since they are a complicated design that can easily deal with complex picture data. We overcome the limits of previous methods by using CNNs to extract complex characteristics from face photos, which we access by abandoning traditional paradigms. To ensure the efficacy of our approach., we ran trials using the Facial Expression recognition Challenge (FERC-2013) dataset., a collection of 32 thousand different human faces. Surprisingly., our method achieved classification rates over 65%., demonstrating its efficiency in deciphering human emotions across a wide range of photos. Our approach is built on the incorporation of the AlexNet Architecture., a powerful algorithm that acts as the process”s conductor. One interesting feature is that it can be trained in batches., which speeds up the learning process and requires less resources. This effectiveness not only aids in faster learning., but also makes the method more widely applicable. Our work essentially advances facial emotion identification into the cutting edge of machine learni

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Pre Cardiac Failure Detection using different supervised machine learning Methods with CNN

Pre Cardiac Failure Detection using different supervised Mac...

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international Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Amit Gupta Meeravali Shaikh G. Jithender Reddy Arun Singh Chouhan Department of Cyber Security School of Engineering Malla Reddy University Telangana Department of CSE School of Engineering Malla Reddy University Telangana GITAM University Visakhapatnam

For aspect, a convolution Neural Network (CNN) for cardiac arrest would help establish a better output. Whilst a cardiac arrest occurs, the limited interventions available to shop-affected person’s lives are related to badly affected outcomes. Therefore, the best way to enhance affected person results and lower related healthcare prices might be to prevent cardiac arrest. This remark highlights the significance of prediction models that continuously perceive excessive-risk individuals and assist healthcare providers in providing centered care to the appropriate patient at the appropriate time for the early identification of cardiac arrest patients who may need immediate medical attention. Moreover, the significant aspect lies in the proper symmetry formation or can, say, the better scenario to compute or make a clear recognition of the image having a clear resolution of the image. Starting with a small model to determine things as simple as CNN (Convolution Neural Network). In this proposed paper, we are used in seven different supervised machine learning method for simulation of ECG data set results in different time interval with different duration and amplitude. In the CNN with a partially connected convolution layer, the image has a single neuron connected to a small layer region. The CNN (convolution Neural Network) image classification takes the input, processes it and classification is done on the dimensions organized in three dimensions width, height, and depth.

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Exploring State-of-the-Art Nearest Neighbor (NN) Search Techniques 21

Exploring State-of-the-Art Nearest Neighbor (NN) Search Tech...

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3rd ACM India Joint international conference on data Science and Management of data (CODS-COMAD)

作者： Nagarkar, Parth Bhattacharya, Arnab Jafari, Omid New Mexico State Univ Las Cruces NM 88003 USA Indian Inst Technol Kanpur Uttar Pradesh India

ISBN: (纸本)9781450388177

Finding nearest neighbors (NN) is a fundamental operation in many diverse domains such as databases, machine learning, data mining, information retrieval, multimedia retrieval, etc. Due to the data deluge and the application of nearest neighbor queries in many applications where fast performance is necessary, efficient index structures are required to speed up finding nearest neighbors. Different application domains have different data characteristics and, therefore, require different types of indexing techniques. While the internal indexing and searching mechanism is generally hidden from the top-level application, it is beneficial for a data scientist to understand these fundamental operations and choose a correct indexing technique to improve the performance of the overall end-to-end workflow. Choosing the correct searching mechanism to solve a nearest neighbor query can be a daunting task, however. A wrong choice can potentially lead to low accuracy, slower execution time, or in the worst case, both. The objective of this tutorial is to present the audience with the knowledge to choose the correct index structure for specific applications. We present the state-of-the-art Nearest Neighbor (NN) indexing techniques for different data characteristics. We also present the effect, in terms of time and accuracy, of choosing the wrong index structure for different application needs. We conclude the tutorial with a discussion on the future challenges in the Nearest Neighbor search domain.

关键词： Nearest Neighbor Search Similarity Search database Indexing High-dimensional Spaces data-independent Hashing Product Quantization Proximity-based ANN Hierarchical Methods

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Frequency Band Power-based EEG Channel Selection for Emotion recognition

Frequency Band Power-based EEG Channel Selection for Emotion...

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Advancement in Technology (ICONAT), international conference for

作者： Priyam Kar Jupitara Hazarika Electronics and Instrumentation Engineering National Institute of Technology Silchar Silchar India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Effective management of human emotion is of vital importance in maintaining general health and well-being. Consequently, with the growing frequency of human-machine interaction in everyday life, the presence of a computer system capable of adapting to human emotions could be of immense importance. Furthermore, an emotion-adaptive Brain-Computer Interface (BCI) system has the capacity to not only improve the healthcare system but also boost the overall quality of life for individuals. Currently, most emotion-adaptive Brain-Computer Interface (BCI) systems primarily rely on Electroencephalogram (EEG) components due to their non-invasive nature and greater temporal resolution. However, most of the recent efforts in the area of EEG-based emotion detection are primarily focused on improving the effectiveness of the classifier for data collected from a substantially large number of EEG channels. Nevertheless, choosing a smaller set of EEG channels that are pertinent to different emotional states can result in a more resilient and less computationally demanding emotion-adaptive BCI system. Thus, in this work, we have introduced a channel selection technique based on EEG band power to classify positive and negative emotions. To assess the effectiveness of the proposed technique, we used a publicly available dataset and conducted a comparison with the approach that does not include channel selection and also with state-of-the-art literature. The results showed that the suggested method outperformed the state-of-the-art approaches.

关键词： Deep learning Human computer interaction Emotion recognition Correlation Neural networks Medical services Electroencephalography Brain-computer interfaces Complexity theory Character recognition

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A Robust and Efficient SLAM System in Dynamic Environment Based on Deep Features 3rd

A Robust and Efficient SLAM System in Dynamic Environment Ba...

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3rd international conference on Artificial Intelligence, 2022

作者： Wang, Bin Wang, Shaoming Ma, Lin Qin, Danyang School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electronics Engineering Heilongjiang University Harbin China

ISBN: (纸本)9789811694226

In the field of mobile robots, positioning and mapping is one of the most basic problems. A robust and efficient Synchronous Localization and Mapping (SLAM) system is essential for autonomous movement of robots. However, due to the complexity and time-varying nature of the real environment, the positioning and mapping effects will be greatly reduced due to scene changes. At the same time, because of its importance in pattern recognition, deep learning has a relatively mature theoretical foundation and practical framework for feature extraction. In this paper, we propose a visual SLAM system based on deep features in dynamic scenes, which combines mature convolutional neural networks (CNNs) HF-Net into an existing SLAM system. First, use HF-Net to detect the input image, and give local descriptors and global descriptors of the image. Then, these descriptors are used by different modules of the SLAM system. Because the features are not obtained by hand, they are very robust to scene changes. In loop closure detection, a distributed bag-of-words (DBoW) is used to form a vocabulary table, and local and global features are all considered at the same time, so the performance is more reliable. The results show that the entire system has lower trajectory error and higher accuracy on the evaluation data set. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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