Copertina

Predictive Analysis of CFPB Consumer Complaints Using Machine Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Vaishnav, Dhwani, Neethinayagam, Manimozhi, Khaire, Akanksha, Woo, Jongwook
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Cyberattack Data Analysis in IoT Environments using Big Data
di: Patidar, Neelam, et al.
Pubblicazione: (2024)

FedCDC: A Collaborative Framework for Data Consumers in Federated Learning Market
di: Shi, Zhuan, et al.
Pubblicazione: (2025)

A Privacy-Preserving Machine Learning Framework for Edge Intelligence: An Empirical Analysis
di: Trieu, Quoc Lap, et al.
Pubblicazione: (2026)

Big Data-Driven Fraud Detection Using Machine Learning and Real-Time Stream Processing
di: Liu, Chen, et al.
Pubblicazione: (2025)

Metric Criticality Identification for Cloud Microservices
di: Singal, Akanksha, et al.
Pubblicazione: (2025)

Channel Prediction under Network Distribution Shift Using Continual Learning-based Loss Regularization
di: Mohsin, Muhammad Ahmed, et al.
Pubblicazione: (2025)

Building State Machine Replication Using Practical Network Synchrony
di: Wan, Yiliang, et al.
Pubblicazione: (2025)

Multi-Objective Optimization of Consumer Group Autoscaling in Message Broker Systems
di: Landau, Diogo, et al.
Pubblicazione: (2024)

A Privacy-Preserving Ecosystem for Developing Machine Learning Algorithms Using Patient Data: Insights from the TUM.ai Makeathon
di: Süwer, Simon, et al.
Pubblicazione: (2025)

SODA: Protecting Proprietary Information in On-Device Machine Learning Models
di: Atrey, Akanksha, et al.
Pubblicazione: (2023)

Optimizing the Variant Calling Pipeline Execution on Human Genomes Using GPU-Enabled Machines
di: Kumar, Ajay, et al.
Pubblicazione: (2025)

Benchmarking Machine Learning Applications on Heterogeneous Architecture using Reframe
di: Rae, Christopher, et al.
Pubblicazione: (2024)

Propius: A Platform for Collaborative Machine Learning across the Edge and the Cloud
di: Ding, Eric
Pubblicazione: (2025)

Rethinking Knowledge Distillation in Collaborative Machine Learning: Memory, Knowledge, and Their Interactions
di: Han, Pengchao, et al.
Pubblicazione: (2025)

AgentServe: Algorithm-System Co-Design for Efficient Agentic AI Serving on a Consumer-Grade GPU
di: Zhang, Yuning, et al.
Pubblicazione: (2026)

Generative Federated Learning for Smart Prediction and Recommendation Applications
di: Mukherjee, Anwesha, et al.
Pubblicazione: (2025)

Approximated Coded Computing: Towards Fast, Private and Secure Distributed Machine Learning
di: Qiu, Houming, et al.
Pubblicazione: (2024)

NetSenseML: Network-Adaptive Compression for Efficient Distributed Machine Learning
di: Wang, Yisu, et al.
Pubblicazione: (2025)

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
di: Wang, Hongyu, et al.
Pubblicazione: (2026)

LoHan: Low-Cost High-Performance Framework to Fine-Tune 100B Model on a Consumer GPU
di: Liao, Changyue, et al.
Pubblicazione: (2024)

EinDecomp: Decomposition of Declaratively-Specified Machine Learning and Numerical Computations for Parallel Execution
di: Bourgeois, Daniel, et al.
Pubblicazione: (2024)

Reducing the Impact of I/O Contention in Numerical Weather Prediction Workflows at Scale Using DAOS
di: Manubens, Nicolau, et al.
Pubblicazione: (2024)

Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction
di: Wang, Xiaoye, et al.
Pubblicazione: (2024)

COSMIC: Enabling Full-Stack Co-Design and Optimization of Distributed Machine Learning Systems
di: Raju, Aditi, et al.
Pubblicazione: (2025)

Federated Learning Using Coupled Tensor Train Decomposition
di: Zhang, Xiangtao, et al.
Pubblicazione: (2024)

Gradient Compression and Correlation Driven Federated Learning for Wireless Traffic Prediction
di: Zhang, Chuanting, et al.
Pubblicazione: (2025)

NineToothed: A Triton-Based High-Level Domain-Specific Language for Machine Learning
di: Huang, Jiacheng, et al.
Pubblicazione: (2025)

Distributed Hierarchical Machine Learning for Joint Resource Allocation and Slice Selection in In-Network Edge Systems
di: Rashid, Sulaiman Muhammad, et al.
Pubblicazione: (2025)

Ksurf: Attention Kalman Filter and Principal Component Analysis for Prediction under Highly Variable Cloud Workloads
di: Dang'ana, Michael, et al.
Pubblicazione: (2024)

Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning
di: Saba, Issa, et al.
Pubblicazione: (2024)

Machine Learning for Consistency Violation Faults Analysis
di: Giri, Kamal, et al.
Pubblicazione: (2025)

Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
di: Han, Xuefeng, et al.
Pubblicazione: (2025)

An Empirical Analysis of Secure Federated Learning for Autonomous Vehicle Applications
di: Mia, Md Jueal, et al.
Pubblicazione: (2025)

Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel
di: Song, Jaeyoung, et al.
Pubblicazione: (2024)

DART: A Solution for Decentralized Federated Learning Model Robustness Analysis
di: Feng, Chao, et al.
Pubblicazione: (2024)

OptimES: Optimizing Federated Learning Using Remote Embeddings for Graph Neural Networks
di: Naman, Pranjal, et al.
Pubblicazione: (2025)

Towards Designing an Energy Aware Data Replication Strategy for Cloud Systems Using Reinforcement Learning
di: Najjar, Amir, et al.
Pubblicazione: (2025)

Linearizability and State-Machine Replication: Is it a match?
di: Hauck, Franz J., et al.
Pubblicazione: (2024)

The Cost of Garbage Collection for State Machine Replication
di: Liang, Zhiying, et al.
Pubblicazione: (2024)

A Lightweight Approach for State Machine Replication
di: Cachin, Christian, et al.
Pubblicazione: (2025)