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data mining based Hybrid Latent Representation Induced Ensemble Model Towards Fraud Prediction

Data Mining based Hybrid Latent Representation Induced Ensem...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： J. Anita Smiles T. Kamalakannan Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

The increasing growth of e-commerce and mobile payments has contributed to a rise in the effects of financial payment services fraud over the last few years. "At 2025, the losses in fraud around the world could amount to about $44 billion, according to McKinsey." Every year, trillions of dollars are lost from fraudulent card transactions. An efficient fraud detection system based on advanced machine learning techniques have gained excellent value in all Financial Institutions to reduce these losses. The proposed algorithm SLREnsemble (Stochastic Latent Representation Ensemble) is a hybrid model that uses deep neural network Autoencoders to obtain the latent representation of genuine and fraudulent transactions and these representations are given as an input to the balanced ensemble model. The technique is tested against other existing methods, i.e., Isolation Forest, Random Forest, and XGBoost algorithms, by various performance metrics such as accuracy, precision, and recall. The results prove that the SLREnsemble model is highly efficient in Fraud detection and performs well with minimal training data and yields good precision, recall and f1-score with high computational speed values.

关键词： data models conferences Correlation Support vector machines Predictive models machine learning algorithms Forestry

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Key Technologies for Dynamic Early Warning of BIM Power Monitoring data in Bridge Operation and Maintenance

Key Technologies for Dynamic Early Warning of BIM Power Moni...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Yu Peng Tian Wang Chongqing College of Electronic Engineering Chongqing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

Bridge operation and maintenance is an important solution to how to ensure the safe operation of bridges and extend their service life. In this paper, a dynamic early warning technology of BIM (Building Information Modeling) power monitoring data was proposed to supervise the safety of bridges. Through the layout of sensors and monitoring equipment, the power data of the bridge was obtained in real-time. The quality control and data correction of the collected power data were carried out, and data cleaning, data correlation and other technologies were used to process the power data and extract useful characteristic information. An early warning model was also constructed, and machine learning, data mining and other methods were used to analyze and model power data to realize dynamic early warning of bridge power systems. After experiments and analysis, it was concluded that the stability of the method in this paper was maintained at 89%-98%. Through the dynamic early warning of BIM power monitoring data, real-time monitoring and fault diagnosis of the bridge power system can be realized and abnormal conditions and potential problems can be found in a timely manner.

关键词： Bridges Power system dynamics Power system stability data models Stability analysis Real-time systems Maintenance Safety data mining Monitoring

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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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Post-COVID Cardiovascular Risk Assessment Using an Efficient machine learning Model

Post-COVID Cardiovascular Risk Assessment Using an Efficient...

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Advancement in Technology (ICONAT), international conference for

作者： D Srinuvasa Rao Desidi Narsimha Reddy Lova Naga Babu Ramisetti M S Radha Manga Mani Harikrishna Pathipati Siva Kumar Pathuri Mohana Prasad Mendu Dept of CSE GMR Institute of Technology Rajam AP India Soniks Consulting LLC 101 E park blvd Plano TX USA Information Technology 10333 Harwin Dr. #375e Houston TX Department of Information Technology SRKR Engineering College Bhimavaram AP India Information Technology ITG Technologies10998 S Wilcrest Dr Houston TX USA Dept of CSE Koneru Lakshmaih Education Foundation Vaddeswaram AP India Dept of CSE Malineni Suseelamma Womens’s Engineering College PrakasamDst Singarayakonda AP India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Individuals who were exposed to COVID-19 in different parts of their bodies, including heart attacks, faced major consequences. The frequency of cardiovascular diseases is increasing in the healthcare industry. This illness most commonly affects people aged 20 to 48. In supervised learning, data mining mainly depends on categorization algorithms. Using various combinations of calculations and algorithms, the information obtained from hospital data analysis is used for early-stage prediction of post-Covid cardiovascular disease. machine learning is one of the strange inventions that have been widely used in a variety of fields, including the application of medical services to disease prediction. We examined the accuracy of machine learning algorithms that can be used in this study to predict heart disease analyzes and predict overall risk. One of the most current data mining methods is classification. The classification method’s primary goal is to keep the data in the correct class. In this work, classification methods such as Logistic Regression Classifier, Naive-Bayes, and Ensemble Cardiovascular Prediction Classification Algorithm were used (ECVDPCA) and the dataset is taken from Kaggle repository. The goal of this study is to find the most exact classification approach and to improve accuracy in predicting Post-Covid Cardio-Vascular illness. Python was used to get the ideal solution in this case to predict the accuracy of the classifier in which the proposed classifier gave the best accuracy when compared with the other two classifiers i.e., 95

关键词： COVID-19 Technological innovation Accuracy machine learning algorithms Supervised learning Stacking Predictive models Prediction algorithms Classification algorithms data mining

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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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A New SAFR Tool for Face recognition Using EGV-LBP-CMI-LDA Wrapped with Secured DWT Based Steganography

A New SAFR Tool for Face Recognition Using EGV-LBP-CMI-LDA W...

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IEEE international Advance Computing conference

作者： Shami Jhodge Girija Chiddarwar Gitanjali Shinde Department of MCA Sinhgad College of Management Department of Computer Engineering Sinhgad College of Engineering Department of Computer Engineering SKN College of Engineering

ISBN: (纸本)9781467345279

Face recognition technique nowadays is emerging as the most significant and challenging aspects in terms of security for identification of images in various fields viz. banking, police records, biometric etc. other than an individual's thumb and documented identification proofs. Till date for efficient net banking to be initiated, one has to provide the appropriate user name and password for purpose of authentication. This project introduces a vehicle to take a step forward in easy and more reliable authentication of an individual by providing Face Image along with User Name and Password to the system. In this an individual's face is identified by biometric authentication support with which, only a person whose account is, can access it. However while transferring this sensitive data of user image, from client machine to bank server it has to be protected from hackers and intruders from manhandling it, hence it is transferred using covert communication called Wavelet Decomposition based steganography. As face images are affected by different expressions, poses, occlusions, illuminations and aging over a period of time and it differs from the same person than those from different ones is the main difficult task in face recognition. Whenever image information is jointly co-ordinated in three aspects viz. image space, scale and orientation domains they carry much higher clues than seen in each domain individually. In the proposed method combination of Local Binary pattern (LBP) and Gabor features are used to increase the face recognition performance significantly to compare individual's face presentations. Hence face recognition and representation of Gabor faces are done using E-GV-LBP and CMI-LDA based feature recognition method. Gabor faces uses space, scale and orientation to support accurate face recognition, making net banking easier, authentic, reliable and user friendly.

关键词： DWT (Discrete Wavelet Transformation) E-GV-LBP (Effective Gabor Volume Linear Binary pattern) CMI (Conditional Mutual Information)

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Research on Evaluation Index and Index System of Athletes' Physical Fitness Training Based on data mining

Research on Evaluation Index and Index System of Athletes' P...

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2019 3rd international conference on Economics,Management Engineering and Education Technology(ICEMEET 2019)

作者： Li Li Hunan Vocational College of Modern

With the development of modern sports, the requirement for the comprehensive quality of athletes is getting higher and higher. According to the characteristics of sports, the level of athletes and the tasks of different training stages, the training proportion of the two is reasonably arranged, and the physical training and technical and tactical training are effectively combined. Therefore, in the process of training and training athletes, countries strive to standardize, scientific and innovative. On the basis of defining the concept of physical fitness training, this paper expounds the main composition of athletes' physical fitness level. data mining is the process of extracting valuable knowledge from a large amount of data by using machine learning method. The athletes' sports training assisted decision support system constructed by data mining technology realizes the organic integration of different aspects of athletes, applies scientific training theory and advanced training methods to sports training management, and studies the athlete's special physical ability from the perspective of evaluation, to conduct a deep theoretical analysis of the athlete's special physical fitness characteristics, and build an athlete's special physical fitness evaluation index system.

关键词： data mining Athletes Physical Training Evaluation Index

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A Hybrid Approach for Intrusion Detection System using data Minining and Artificial Neural Network

A Hybrid Approach for Intrusion Detection System using Data ...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： Anjali Pathania Vivek Ghai Chandigarh University Mohali Candigarh India

ISBN: (纸本)9781665438124

This paper is going to present a hybrid model for IDS using ANN and data mining approaches. it is important to deploy a hybrid network using ANN based and data mining based IDS systems that can enable the system in making decisions on intrusion in a mobile environment. MANET is an infrastructure-less ad-hoc network. It is a collection of mobile nodes that are connected in an arbitrary and dynamic manner. ANN (Artificial Neural Network) is a type of machine learning scientific and statistical model that is used by computer systems in order to perform a task without involvement of explicit intrusion, rather than relying on interference and patterns instead. As there is node mobility that can impose different security issues, MANETs are found more susceptible in security provision. In order to resolve this security issue, some authentication and encryption techniques can be proposed for the first-line defensein order to mitigate the security risks. However, complete eradication of these types of risks is next to impossible. For theSecond line defense, the necessity of an Intrusion Detection System (IDS) is essential in this case. IDS can be referred as a method tool or resource that can help to detect access and warn for any activity of unapproved or unauthorized network activity. The current research has been done to prevent the network from the intruders. The number of attackers has been increased in the network because of more usage of technology and wireless network. data is not allowed by the attackers to transmit successfully from source node to destination node. These intruders want to steal data and let to the unsuccessful transmission. In this research we are deploying 48 to 66 nodes in a defined communication area of 1000*1000 height and width. While starting the transmission source node and destination node is defined. After that while using IDS and AODV protocol route is created between destination and source node. If network decreases its performance will

关键词： Intrusion detection Artificial neural networks data models Classification algorithms Encryption Security data mining

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Soft Computing Signal Processing for Health Monitoring of Tie-Bar of Rotor Head Structure

Soft Computing Signal Processing for Health Monitoring of Ti...

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Intelligent Sensors, Sensor Networks and Information Processing conference (ISSNIP)

作者： P. J. Escamilla-Ambrosio N. Lieven Department of AeroSpace Engineering University of Bristol Bristol UK

The need for robust health monitoring and prognostics of structural components in remote or difficult-to-access locations, e.g. helicopter rotor-head structure, is driving the advancement of wireless intelligent sensor devices (WISD). Damage detection techniques, combined with advanced signal processing, are the core components of a structural health monitoring (SHM) system. In this context, feature extraction is an essential component of a SHM system that converts raw sensor data into useful information about the structure health condition. The level of signal processing that can be performed in a WISD depends on the capability of the processing element in terms of speed, memory and energy consumption. But the real bottleneck for energy efficiency is the fact that communications dominate the WISD energy consumption. Therefore, running intelligent local data interrogation algorithms on-board the WISD is a mechanism through which considerable battery power can be preserved. In that sense, in this paper a soft histogram feature extraction algorithm is developed to extract damage-sensitive information from measured response data of tie-bar component of the main rotor hub of a Lynx helicopter. In addition, a method for pattern recognition and critical degradation detection of tie-bar is proposed based on the extracted features and a combination of statistical process control and fuzzy sets theory. Results show the applicability of the proposed approaches.

关键词： Signal processing Remote monitoring Feature extraction Signal processing algorithms Helicopters Intelligent sensors Energy consumption data mining Robustness Wireless sensor networks

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Extracting Clinical entities and their assertions from Chinese Electronic Medical Records Based on machine learning

Extracting Clinical entities and their assertions from Chine...

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2016 3rd international conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Jianhong Wang Yousong Peng Bin Liu Zhiqiang Wu Lizong Deng Taijiao Jiang College of Computer Science and Electronic Engineering Hunan University Center of System Medicine Institute of Basic Medical SciencesChinese Academy of Medical Sciences & Peking Union Medical College

With the rapid growth of electronic medical records（EMRs） in China, large amounts of clinical data have been accumulated. However, limited work for extracting information from EMRs in Chinese has been conducted. In this work, using manually annotated dataset of EMRs in Chinese, we investigated the clinical Named Entities recognition（NER） based on Conditional Random Field（CRF） and further built a Support Vector machine（SVM） classifier to determine their assertion status and evaluate the contributions of different features for assertion classification. For Chinese clinical NER, our CRF-based classifier achieved the best F-measure of 89.07%, while the SVM-based assertion classifier achieved a maximum F-measure of 94.10%. Our work suggests that machine learning methods are helpful in NER and assertion determination for Chinese medical clinical records.

关键词： Chinese Electronic Medical Records Information extraction Named Entity recognition assertion classification machine learning

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