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Emerging Smart computing and Informatics (ESCI), Conference on

作者： Vaasudev Sharma Riyansh Dagar S. Sharanya Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

Proactive maintenance for the optimized lifecycle of equipment is now effortless via condition monitoring and fault diagnosis. Recognition and avoidance of a state of failure help to achieve solidity and reliability. Artificial Intelligence and Machine Learning (ML) forecast such situations and aid in the prevention of economic and safety emergencies. This work reviews such ML algorithms and ML models that help diagnose faults in Turbofan engines via measurement of the Remaining Useful Life parameter. This proposed an ensemble machine-learning model optimized by Extreme Gradient Boosting. The approach is then evaluated via different metrics and their performance is measured to current innovative methods. The system is useful for all processes that require high reliability quality and efficiency and reduces unplanned downtime, maintenance costs and asset quality.

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Interactive E-Commerce: Addressing Cart Abandonment through Gamified User Engagement

Interactive E-Commerce: Addressing Cart Abandonment through ...

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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： Mayank Kumar Sohanda Mihir Gupta A. V. Kalpana Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

This paper addresses the pervasive issue of cart abandonment in online shopping, a phenomenon that significantly impacts e-commerce revenue streams. Through an investigation into the underlying causes, we identify the lack of social engagement as a critical factor contributing to this behavior. Our primary objective is to bridge the gap between item selection and purchase completion by enhancing the user experience and boosting conversion rates. To tackle this challenge, we propose a novel approach by integrating gamification strategies into the online shopping experience. Our methods include implementing real-time user activity indicators, such as visual representations of active shoppers and interactive leaderboards, to foster a sense of community and competition among users. Additionally, we introduce social shopping features, such as cart sharing and real-time feedback systems, to promote user collaboration and interaction. Preliminary data analysis reveals a significant increase in conversion rates following the implementation of these gamification strategies. Users are more inclined to complete purchases within a community-centric and competitive environment. Moreover, the introduction of social elements has enriched the overall shopping experience, as evidenced by user feedback and heightened engagement on the platform. In conclusion, our study demonstrates the effectiveness of integrating gamification elements into online shopping platforms to combat cart abandonment. By creating a more engaging and communal shopping environment, users are not only motivated to complete purchases, but also to enjoy a more fulfilling shopping experience. Our findings highlight the potential of gamification as a powerful tool for enhancing e-commerce platforms and offer valuable insights for future advancements in online retail strategies.

关键词： Visualization Technological innovation Sockets User centered design Collaboration Transforms Real-time systems

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DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

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Fundamental Research 2024年第4期4卷 752-760页

作者： Jiajie Peng Zhijie Bao Jingyi Lia Ruijiang Han Yuxian Wang Lu Han Jinghao Peng Tao Wang Jianye Hao Zhongyu Wei Xuequn Shang AI for Science Interdisciplinary Research Center Schoolof Computer ScienceNorthwestern Polyechnical UniversityXi'an 129China Key Laboratoryof Bg Data Sorage and Management Northwestern Polytechnical UniversityMinisty of Industy and Information TechnologyX'an 129Chin College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of Data Science Fudan UniversityShanghai 200433China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518000China

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant *** widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple *** address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype *** demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.

关键词： Disease risk prediction Deep learning Polygenic risk score Common disease risk Disease prevention

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Income Tax Fraud Deduction Using Machine Learning 4

Income Tax Fraud Deduction Using Machine Learning

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Sudhakaran, Pradeep Kolamudi, Prudhvi Preetha, M. Pragash, K. Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Srm Institute of Science and Technology Career Development Centre Potheri Tamil Nadu Kattankulathur603203 India Sri Manakula Vinayagar Engineering College Department of Artificial Intelligence and Data Science Madagadipet Puducherry605107 India

ISBN: (纸本)9798350364729

Specifically, the program is based on boosting methods in the context of the used machine learning, to reveal potential cases of evasion of income tax. The procedure consists of obtaining and using datasets of income tax for the learning and training purpose. The first and crucial goal is to enhance the accuracy of predicting fraud cases through learning boosting algorithms and graphical representation of the most significant factors leading to fraud detection. Depending on accuracy and sensitivity as well as specificity and precision for that matter, the said assessment will be conducted. The first goal entails a mechanization of the process of identifying foraging activities, eliminating possible errors, and improving efficiency of and security in income tax assessments with particular emphasis in distinguishing between probable real and potentially fraudulent tax returns [10]. The boosting techniques alongside other futuristic ways of identifying the suspicious transactions can help the tax authorities to improve the risk management processes of their organization. In other words, this approach of fraud detection makes it possible to proceed to more efficient work in terms of both accuracy and the reliability of certain results. Also, the channels of tax avoidance can be closed by concentrating efforts on cases of high risk as the authorities are more likely to lose a lot of revenues. Machine learning technologies can easily address these requirements, giving the necessary flexibility and precision to prevent and identify fraudulent activities and strategies. These strategies are critical in protecting the Tax system a longer term game which can effectively counter financial fraudsters. The goal of the project are to gather information on the corporate taxation;to apply boosting algorithms for fraudulent activity detection, and to evaluate the efficiency of boosting algorithms. The main focus is primarily aimed at the increased application of automatic extraction

关键词： Risk management

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Targeted View-Invariant Adversarial Perturbations for 3D Object Recognition

arXiv

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arXiv 2024年

作者： Green, Christian Ergezer, Mehmet Zeybey, Abdurrahman School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

Adversarial attacks pose significant challenges in 3D object recognition, especially in scenarios involving multi-view analysis where objects can be observed from varying angles. This paper introduces View-Invariant Adversarial Perturbations (VIAP), a novel method for crafting robust adversarial examples that remain effective across multiple viewpoints. Unlike traditional methods, VIAP enables targeted attacks capable of manipulating recognition systems to classify objects as specific, pre-determined labels, all while using a single universal perturbation. Leveraging a dataset of 1,210 images across 121 diverse rendered 3D objects, we demonstrate the effectiveness of VIAP in both targeted and untargeted settings. Our untargeted perturbations successfully generate a singular adversarial noise robust to 3D transformations, while targeted attacks achieve exceptional results, with top-1 accuracies exceeding 95% across various epsilon values. These findings highlight VIAP’s potential for real-world applications, such as testing the robustness of 3D recognition systems. The proposed method sets a new benchmark for view-invariant adversarial robustness, advancing the field of adversarial machine learning for 3D object recognition. © 2024, CC BY.

关键词： Adversarial machine learning

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RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer 39

RNTrajRec: Road Network Enhanced Trajectory Recovery with Sp...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Chen, Yuqi Zhang, Hanyuan Sun, Weiwei Zheng, Baihua Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Shanghai Institute of Intelligent Electronics & Systems China Singapore Management University School of Computing and Information Systems Singapore

ISBN: (纸本)9798350322279

GPS trajectories are the essential foundations for many trajectory-based applications. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of road network and only use grid information or raw GPS points as input. Therefore, the encoder model is not able to capture rich spatial information of the GPS points along the trajectory, making the prediction less accurate and less spatial consistent. In this paper, we propose a road network enhanced transformer-based framework, namely RNTrajRec, for trajectory recovery. RNTrajRec first uses a graph model, namely GridGNN, to learn the embedding features of each road segment. It next develops a spatial-temporal transformer model, namely GPSFormer, to learn rich spatial and temporal features along with a Sub-Graph Generation module to capture the spatial features for each GPS point in the trajectory. It finally forwards the outputs of encoder model to a multi-task decoder model to recover the missing GPS points. Extensive experiments based on three large-scale real-life trajectory datasets confirm the effectiveness of our approach. © 2023 IEEE.

关键词： Recovery

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Polycystic Ovary Syndrome Prediction through CNN based Image Analysis: A Deep Learning Based Approach

Polycystic Ovary Syndrome Prediction through CNN based Image...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Yazhini Karthik Sruthi R Sujithra M Department of Computing - Data Science Coimbatore Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women during their reproductive years. It is characterized by irregular menstrual cycles, excessive hair growth, and the presence of cysts in the ovaries. The diagnosis of PCOS typically involves a combination of clinical evaluations and laboratory tests. The paper proposes a novel approach using Convolutional Neural Networks (CNNs) for predicting PCOS through the analysis of ultrasound images. The dataset comprises images obtained from ovarian ultrasound scans of both PCOS-positive and PCOS-negative individuals. These images undergo preprocessing and augmentation techniques such as rescaling, shearing, zooming, flipping, and rotation to enhance the diversity of the training data. Multiple CNN-based models with varying architectures are constructed, altering parameters such as the number of convolutional and pooling layers, activation functions, and other hyperparameters. The models are trained and validated using a split of the dataset into training and validation sets, with metrics like loss and accuracy monitored during training to assess model performance. Upon completion of training, the models are tested using unseen data to evaluate their predictive capabilities. The results demonstrate that deep learning models trained on ultrasound images can accurately predict the presence or absence of PCOS. Such models hold promise in aiding healthcare professionals with early diagnosis and management of PCOS, ultimately improving patient care and outcomes.

关键词： Training Deep learning Ultrasonic imaging Neural networks Training data Medical services Predictive models Convolutional neural networks Shearing Monitoring

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Bilateral Adaptation for Human-Object Interaction Detection with Occlusion-Robustness

Bilateral Adaptation for Human-Object Interaction Detection ...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Guangzhi Wang Yangyang Guo Ziwei Xu Mohan Kankanhalli Institute of Data Science National University of Singapore School of Computing National University of Singapore

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Human-Object Interaction (HOI) Detection constitutes an important aspect of human-centric scene understanding, which requires precise object detection and interaction recognition. Despite increasing advancement in detection, recognizing subtle and intricate interactions remains challenging. Recent methods have endeavored to leverage the rich semantic representation from pretrained CLIP, yet fail to efficiently capture finer-grained spatial features that are highly informative for interaction discrimination. In this work, instead of solely using representations from CLIP, we fill the gap by proposing a spatial adapter that efficiently utilizes the multi-scale spatial information in the pretrained detector. This leads to a bilateral adaptation that mutually produces complementary features. To further improve interaction recognition under occlusion, which is common in crowded scenarios, we propose an Occluded Part Extrapolation module that guides the model to recover the spatial details from manually occluded feature maps. Moreover, we design a Conditional Contextual Mining module that further mines informative contextual clues from the spatial features via a tailored cross-attention mechanism. Extensive experiments on V-COCO and HICO-DET benchmarks demonstrate that our method significantly outperforms prior art on both standard and zero-shot settings, resulting in new state-of-the-art performance. Additional ablation studies further validate the effectiveness of each component in our method.

关键词： Extrapolation Computer vision Adaptation models Art Semantics Object detection Detectors

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Predictive Stress Management for Working Women in Educational Institutions using Federated Learning and Wearable Technology

Predictive Stress Management for Working Women in Educationa...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： D. Sudha Devi M. Sujithra P. Velvadivu V. V. Kavya S. Jhunandhini Department of Computing Data Science Coimbatore Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The proposed methodology uses Artificial Neural Networks (ANN) to predict stress levels of women working in educational institutions by analyzing physiological and demographic data collected through wearable technology. TensorFlow Federated framework is used here to ensure the data privacy by enabling secure and decentralized training of the proposed model. The approach integrates various steps, including data preprocessing, feature selection, and model training with federated learning. Based on predicted stress levels, the platform provides personalized recommendations to manage stress and improve mental well-being, while keeping user data private. The process involves collecting data from surveys and wearable devices, optimizing the model through techniques like hyperparameter tuning and regularization, and deploying the system via a web interface for real-time stress management.

关键词： Training Surveys data privacy Federated learning Human factors Artificial neural networks data models Wearable devices Tuning Synthetic data

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Ethereum Phishing Detection Using Hyperbolic Neural Networks and Temporal Information 22

Ethereum Phishing Detection Using Hyperbolic Neural Networks...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xu, Rong Zhang, Jun Zhu, Xiaoyue Song, Zhaoxiong Yu, Zhi Inner Mongolia University College of Computer Science Hohhot China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331509712

In recent years, the frequent occurrence of phishing scams on Ethereum has posed serious threats to transaction security and the financial safety of users. This paper proposes an Ethereum phishing scam detection method based on Hyperbolic Neural Networks (HGNNs) and temporal information. The method maps the Ethereum transaction network to hyperbolic space for structural feature extraction, effectively capturing hierarchical structures and complex relationships within the graph, thereby improving the accuracy of phishing scam detection. The model includes a structural feature extraction module and a temporal feature extraction module. It uses HGNN and self-attention mechanism to extract the structural features of the transaction graph, and uses a multi-head attention mechanism to capture the dynamic evolution pattern of the graph. Experimental validation on real Ethereum datasets demonstrates that the proposed model outperforms benchmark models, showcasing its effectiveness. © 2024 IEEE.

关键词： Phishing

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