Simaiya et al., 2022 - Google Patents

An improved deep neural network-based predictive model for traffic accident's severity prediction

Simaiya et al., 2022

Document ID
5248030899870479820
Author
Simaiya S
Lilhore U
Pandey H
Trivedi N
Anand A
Sandhu J
Publication year
Publication venue
Ambient Communications and Computer Systems: Proceedings of RACCCS 2021

External Links

Snippet

In recent decades, a vehicle crash has become a worldwide issue and labeled it the twelfth prominent reason for death worldwide. Artificial intelligence-based techniques, ie, machine learning and deep learning play a vital role in various aspects of modern society and …
Continue reading at link.springer.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587—Details of specialised database models
    • G06F17/30595—Relational databases
    • G06F17/30598—Clustering or classification
    • G06F17/30601—Clustering or classification including cluster or class visualization or browsing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
    • G06F17/30994—Browsing or visualization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis

Similar Documents

Publication Publication Date Title
Chand et al. Road traffic accidents: An overview of data sources, analysis techniques and contributing factors
Das et al. Supervised association rules mining on pedestrian crashes in urban areas: identifying patterns for appropriate countermeasures
Roccetti et al. An alternative approach to dimension reduction for pareto distributed data: a case study
Jha et al. A performance analysis of prediction techniques for impacting vehicles in hit-and-run road accidents
Wang et al. Learning performance prediction via convolutional GRU and explainable neural networks in e-learning environments
Jianfeng et al. A classification and recognition model for the severity of road traffic accident
Peng et al. Examining Bayesian network modeling in identification of dangerous driving behavior
Feng et al. Association rule mining for road traffic accident analysis: A case study from UK
Sahu et al. Analysis of road accidents prediction and interpretation using KNN classification model
Simaiya et al. An improved deep neural network-based predictive model for traffic accident’s severity prediction
Mase et al. A review of intelligent systems for driving risk assessment
Cheng et al. Crash severity prediction and interpretation for road determinants based on a hybrid method
Zhao et al. SafeTraffic Copilot: adapting large language models for trustworthy traffic safety assessments and decision interventions
Haghshenas et al. Review of applications of ML approaches in driver behavior analysis using qualitative and quantitative analysis
Bakiş et al. Prediction of traffic accidents trend with learning methods: a case study for Batman, Turkey
Ahmed et al. Real-time sideswipe conflict risk prediction on the expressway
Woo et al. Artificial intelligence and transportations on road safety: A bibliometric review
Xiong et al. Multi-level prediction framework of driving risk based on the matter-element extension model
Xu et al. On-road driving risk assessment of regular passenger buses based on multi-source data integration
Huang et al. Investigating the interrelationships among factors associated with automated vehicle crashes using additive Bayesian network
Mostafa et al. A machine learning-based data fusion model for online traffic violations analysis
Zhu et al. Modeling the traffic accident probability of Urban roads based on an improved Bayesian network
Hao et al. Knowledge graph–based safety risk evaluation method for hazardous behaviors of road transport vehicles
Bi et al. Accident Severity Detection Using Machine Learning A Review
Saranyadevi et al. Road risk assessment using fuzzy Context-free Grammar based Association Rule Miner