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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Sagar Varma Samanthapudi Piyush Rohella Sheetal Temara Ketan Gupta S. Dhanasekaran Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Principal Product Manager Payments & Commerce GoDaddy Edmonds Washington USA Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Machine Learning (ML) gives the ability to automate economic regulatory responsibilities in terms of price savings and more green process control. Current advances in ML have enabled the improvement of models that may interpret complicated sets of facts and automate the technique of extracting the preferred facts from these records. The development of such models gives a price-effective and efficient opportunity to greater luxurious and time-consuming guide labor. Moreover, these fashions are capable of perceiving and correctly categorizing data to ensure information accuracy and compliance with regulatory policies. It allows the company to adhere to policies without human oversight, lowering each value and time constraints. It will be further prolonged to other factors of the enterprise by making use of unsupervised fashions and fashions advanced on switch learning, together with deep learning. Those models allow the automation of responsibilities inclusive of assessing the threat associated with investments or transactions, facilitating the development of quicker and more knowledgeable techniques. Ordinary, the usage of ML fashions for financial law automatization processes is precious for decreasing price and time constraints and making sure of compliance. Through leveraging the ability of ML, financial institutions can take advantage of extra internal manner efficiency and statistics accuracy with fewer risks.

关键词： Accuracy Regulators Biological system modeling Machine learning Switches Time factors Stakeholders

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Investigating the Role of AI in Personalized Consumer Banking Experiences

Investigating the Role of AI in Personalized Consumer Bankin...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Piyush Rohella Sheetal Temara Sagar Varma Samanthapudi Ketan Gupta K. Suganyadevi Principal Product Manager Payments & Commerce GoDaddy Edmonds Washington USA Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

This research examines the function of synthetic Intelligence in developing customized purchaser banking reviews. Artificial Intelligent -based total gear is increasingly being followed to offer consumers customized banking reports. Studies into the use of AI in this context will tell the layout of future banking products and services. The primary intention of this research is to understand the modern technological and personal adoption developments related to Artificial Intelligent and customized banking reviews. Particularly, this study will identify the modern use cases of Artificial Intelligent in personalized banking, look into clients' attitudes and motivations towards Artificial Intelligent -enabled banking experiences, and discover the ability possibilities and challenges related to utilizing Artificial Intelligent in growing individualized banking studies for purchasers. Additionally, the role of ethical issues in implementing and the use of Artificial Intelligent in personalized consumer banking experiences may be evaluated. This study is crucial for presenting a better understanding of the function of Artificial Intelligent in developing personalized and moral consumer banking experiences.

关键词： Ethics Reviews Shape Power control Layout Banking Fraud

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Echocardiogram Vector Embeddings Via R3D Transformer for the Improvement of Ejection Fraction Estimation Instruments

Echocardiogram Vector Embeddings Via R3D Transformer for the...

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IEEE Instrumentation and Measurement Technology Conference

作者： Somin Mindy Lee Daniel Chung Vasu Kaker Yongyi Zhao Irbaz Riaz Sudheesha Perera Prabhu Sasankan George Tang Po-Chih Kuo Jacques Kpodonu Brigitte Kazzi Leo Anthony Celi Dept. of Electrical & Computer Engineering University of Toronto Toronto Canada Sloan School of Management Massachusetts Institute of Technology Cambridge United States Dept. of Electrical Eng. & Comp. Sci. Massachusetts Institute of Technology Cambridge United States Dept. of Medicine Mayo Clinic Scottsdale United States Warren Alpert Medical School Brown University Providence United States Internal Medicine Beth Israel Deaconess Medical Center Boston United States College of Medicine University of California Irvine Irvine United States Dept. of Computer Science National Tsing Hua University Hsinchu City Taiwan Division of Cardiac Surgery Beth Israel Deaconess Medical Center Boston United States Dept. of Medicine Brigham and Women's Hospital Boston United States Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge United States

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

The estimation of ejection fraction (EF) is critical to intensive care. Doctors commonly use estimated EF to create plans for ICU patients, and a low EF can indicate ventricular systolic dysfunction, which increases the risk of adverse events including heart failure. Recently, interest has grown in deep learning (DL) instruments that can measure cardiac activity to estimate EF automatically. In particular, the vector embeddings learned by DL-based EF estimation models provide valuable mappings of echocardiograms within latent space, which can be utilized to understand the error patterns of DL-based EF estimators and thus improve these instruments. In this work, we provide those embeddings. To this end we repurpose an R3D transformer, a state-of-the-art deep learning model for Video Action Recognition, to classify whether patients have ventricular dysfunction or not (ejection fraction below or above 50%) using echocardiogram data. Our R3D model achieves a test AUC of 0.916 and a test accuracy of 87.5%, approaching the performance of previous comparable studies with a fraction of the training time. Most importantly, the vector embeddings learned by this model will enable future analysis of the errors of DL-based EF estimators, democratizing the improvement of these instruments. The quality of these embeddings, furthermore, is evidenced by the strong results of the model that learned them.

关键词： Heart Deep learning Ultrasonic imaging Accuracy Ultrasonic variables measurement Instruments Estimation

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LinearSankoff: Linear-time Simultaneous Folding and Alignment of RNA Homologs

arXiv

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arXiv 2023年

作者： Li, Sizhen Dai, Ning Zhang, He Malik, Apoorv Mathews, David H. Huang, Liang School of Electrical Engineering & Computer Science Oregon State University CorvallisOR97330 United States Baidu Research USA SunnyvaleCA94089 United States Dept. of Biochemistry & Biophysics University of Rochester Medical Center RochesterNY14642 United States Center for RNA Biology University of Rochester Medical Center RochesterNY14642 United States Dept. of Biostatistics & Computational Biology University of Rochester Medical Center RochesterNY14642 United States

The classical Sankoff algorithm for the simultaneous folding and alignment of homologous RNA sequences is highly influential, but it suffers from two major limitations in efficiency and modeling power. First, it takes O(n6) for two sequences where n is the average sequence length. Most implementations and variations reduce the runtime to O(n3) by restricting the alignment search space, but this is still too slow for long sequences such as full-length viral genomes. On the other hand, the Sankoff algorithm and all its existing implementations use a rather simplistic alignment model, which can result in poor alignment accuracy. To address these problems, we propose LinearSankoff, which seamlessly integrates the original Sankoff algorithm with a powerful Hidden Markov Model-based alignment module. This extension substantially improves alignment quality, which in turn benefits secondary structure prediction quality, confirmed over a diverse set of RNA families. LinearSankoff also applies beam search heuristics and the A* algorithm to achieve that runtime scales linearly with sequence length. LinearSankoff is the first linear-time algorithm for simultaneous folding and alignment, and the first such algorithm to scale to coronavirus genomes (n 30, 000nt). It only takes 10 minutes for a pair of SARS-CoV-2 and SARS-related genomes, and outperforms previous work at identifying crucial conserved structures between the two genomes. Copyright © 2023, The Authors. All rights reserved.

关键词： RNA

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A Dual Self-supervised Deep Trajectory Clustering Method

A Dual Self-supervised Deep Trajectory Clustering Method

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IEEE Symposium on Computers and Communications (ISCC)

作者： Junjun Si Yang Xiang Jin Yang Li Li Bo Tu Xiangqun Chen Rongqing Zhang School of Software & Microelectronics Peking University Beijing China Research & Development Dept. Hezhixin (Shandong) Big Data Technology Co. Ltd. Jinan China School of Information Management Beijing Information Science & Technology University Beijing China School of Software Engineering Tongji University Shanghai China

Trajectory clustering is a cornerstone task in trajectory mining. Sparse and noisy trajectories like Call Detail Records (CDR) have become popular with the rapid development of mobile applications. However, existing trajectory clustering methods' performance is limited on these trajectories. Therefore, we propose a dual Self-supervised Deep Trajectory Clustering (SDTC) method, to optimize trajectory representation and clustering jointly. First, we leverage the BERT model to learn spatial-temporal mobility patterns and incorporate them into the embeddings of location IDs. Second, we fine-tune the BERT model to learn cluster-friendly representations of trajectories by designing a dual self-supervised cluster layer, which improves the intra-cluster similarities and inter-cluster dissimilarities. Third, we conduct extensive experiments with two real-world datasets. Results show that SDTC improves the clustering accuracy by 12.1% (on a noisy and sparse dataset) and 3.8% (on a very sparse dataset) compared with SOTA deep clustering methods.

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Research of Association Rules Mining based on Fuzzy Alarm Extraction 2020

Research of Association Rules Mining based on Fuzzy Alarm Ex...

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4th International Conference on Digital Signal Processing, ICDSP 2020

作者： Tongyan, Li Dong, Lei Zhiguang, Qin Chengdu University of Information Technology Dept. of Communication Engineering China Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China China

ISBN: (纸本)9781450376877

In communication alarm correlation analysis, original alarm data often have problems about information redundancy, data incompleteness, time synchronization and "sharp boundary". This paper proposed a novel alarm extraction algorithm in order to resolve above problems. We first fuzzy the original alarms to avoid "sharp boundary"during the alarm preprocessing process, and then we proposed a novel double constraint based sliding time window method to improve the efficiency on extracting data. Performance tests of the algorithm indicate that this method has higher extracting efficiency than traditional methods, which is suitable for alarm data in telecommunication networks. © 2020 ACM.

关键词： Efficiency

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Enhancing Fraud Detection in Online E-Commerce Transactions through Deep Learning Auto encoder Model

Enhancing Fraud Detection in Online E-Commerce Transactions ...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： K. Raghuveer Taviti Naidu Gongada Divya Nimma Mohd Aarif Keerthi Samudrala B Kiran Bala School of Management V.R. Siddhartha Engineering College (Deemed to be University) Kanuru A.P India Dept of Operations GITAM School of Business GITAM (Deemed to be) University Visakhapatnam India PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of Commerce Aligarh Muslim University Aligarh Uttar Pradesh India Department of CSE Marri laxman Reddy Institute Of Technology and Management India Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Fraud detection in internet transactions is crucial for maintaining security in digital transactions. Traditional methods often rely on rule-based systems or supervised learning algorithms, which struggle to capture complex patterns in fraudulent behaviour and require extensive manual feature engineering. In this study, a novel approach using deep learning auto encoder models is proposed to enhance fraud detection accuracy. Unlike conventional methods, this approach does not necessitate fraudulent data for training, as the auto encoder learns to reconstruct legitimate transactions and identify anomalies based on reconstruction error. The trained model is integrated into the market for real-time detection. Results demonstrate significant improvements in fraud detection accuracy compared to traditional methods, with reduced reliance on manual feature engineering and labelled fraudulent data. This approach offers a scalable and effective solution for detecting fraudulent activities in online e-commerce transactions, thereby safeguarding users and businesses from financial losses and maintaining trust in digital transactions. The autoencoder model expands detection accuracy by 2% over RF and 1% over SVM, with an AUCROC increase of 1%.

关键词： Deep learning Training Support vector machines Adaptation models Accuracy Manuals Real-time systems Fraud Electronic commerce Business

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PREFACE: TECHNICAL COMMISSION II

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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information sciences 2022年第2期5卷 7-7页

作者： Yilmaz, Alper Wegner, Jan Dirk Remondino, Fabio Fuse, Takashi Toschi, Isabella Dept. Of Civil Environmental And Geodetic Engineering The Ohio State University ColumbusOH United States EcoVision Lab Photogrammetry And Remote Sensing Eth Zurich Zurich8093 Switzerland Institute For Computational Science University Of Zurich Zurich8057 Switzerland Trento Italy Dept. Of Civil Engineering University Of Tokyo Hongo 7-3-1 Bunkyo-ku Tokyo113-8656 Japan

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High-Accuracy Animal Species Classification Using Transfer Learning with ResNet50 on Wildlife Camera Trap Images for Enhanced Conservation Efforts

High-Accuracy Animal Species Classification Using Transfer L...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Divya Nimma Varalakshmi Dandu Ankur Singh Bisht Vuda Sreenivasa Rao P. Santosh Kumar S Muthuperumal PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Department of School of Management Presidency University Bangalore Karnataka India Department of CSE Graphic Era Hill University Bhimtal Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India Dept of ECE Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Mathematics K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Accurately identifying animal species from wildlife camera trap images is essential for conservation and biodiversity monitoring. However, manual processing is severely constrained by the massive volume of data collected by camera traps. Using transfer learning and the ResNet50 model, a high-accura cy animal species classification framework was offered as an approach to this challenge. Utilization of the iWildCam2020_256 dataset—a varied collection of wildlife photos taken in a range of ecosystems optimize ResNet50, a pre-trained neural network to improve its performance on this particular job. With the use of transfer learning approaches, this approach aims to maximize the model's feature extraction capabilities and make it capable of accurately differentiating between a large number of animal species. Compared with traditional techniques, the suggested framework significantly improved classification accuracy when trained and verified on the iWildCam2020_256 dataset. the application of data augmentation techniques and network hyperparameter tuning to reduce overfitting and enhance generalization in various contexts. The outcomes show how well the model can classify species in difficult situations, like changing lighting and partial occlusions. By offering a reliable instrument for automated species identification, our study advances the area of animal conservation by enabling more effective and scalable biodiversity assessments. With a 97% accuracy score, the suggested framework performs remarkably well and shows consistent species recognition across a wide range of animal images. It is essential to include the suggested framework into conservation initiatives, which would help with the precise and timely monitoring of wildlife populations—a crucial component in maintaining biodiversity.

关键词： Technological innovation Accuracy Biological system modeling Wildlife Transfer learning Cameras Biodiversity Monitoring Tuning Residual neural networks

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Usage of Computer Aided Methods for Detection and Evaluation of Breast Cancer in Mammograms

Usage of Computer Aided Methods for Detection and Evaluation...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： M. Nagabhushana Rao L. Venkateswara Reddy D. Ganesh Lakkireddy. Udanth Reddy R Madana Mohana CSE Marri Laxman reddy institute of Technology & Management Hyderabad Department of CSE Joginapally B. R. Engineering College Yenkepally RR Dist Hyderabad Telangana India CSE Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupati Andhra Pradesh India Dept. of ECE VIT-AP University Amaravath Artificial Intelligence & Data Science Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Breast cancer accounts for an extremely high annual death rate. Many tools have been created to facilitate earlier diagnosis of breast cancer, which is the most common cancer among females. According to the research, cancer of the breast is also regarded as the second most deadly form of the disease. It is the most prominent kind of the disease and the top reason for death among females worldwide. Any progress made in the early detection and identification of cancer is crucial to maintaining good health. Therefore, it is essential to improve both the therapeutic aspect and the survival standard of patients by utilizing high precision in cancer prognosis. A reliable, effective, and speedy reaction from medical professionals is provided by an automatic disease identification system, which also reduces the danger of death. Recently, breast cancer screening methods that utilise deep learning have shown promising results, allowing for earlier identification and thereby enhancing patients’ chances of survival thanks to the introduction of artificial intelligence (AI). Deep learning reduces the need for manual intervention while extracting features, in comparison to traditional machine learning methods. We provide a brief overview of deep learning techniques, data availability, and the many breast cancer screening options, such as mammography, thermography, ultrasound, and MRI. Using demographic, laboratory, & mammographic data, this investigation sought to forecast breast cancer using various Deep learning algorithms. Ultimately, we apply artificial intelligence to breast cancer clinical trials and compare the proposed approach to current algorithms.

关键词： Deep learning Ultrasonic imaging Manuals Feature extraction Breast cancer Mammography Reliability Prognostics and health management Standards Medical diagnostic imaging

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