{"doi":"10.1109/icaccs.2019.8728327","title":"Comparative Analysis on variants of Neural Networks: An Experimental Study","abstract":null,"journal":"2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)","year":2019,"id":663274,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1731653,"name":"T. V. Madhusudhana Rao","orcid":null,"position":1,"is_corresponding":false},{"id":1731654,"name":"Ch. Kannam Naidu","orcid":null,"position":2,"is_corresponding":false},{"id":1731652,"name":"S. Vani","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Comparative Analysis on variants of Neural Networks: An Experimental Study","abstract":"Neural Networks, with their remarkable capacity to get significance from convoluted information can be utilized to remove patterns that are too composite to be in any way seen by humans. A prepared neural network can be thought of as a specialist in the classification of data which is given to analyze. There are different kinds of Neural Networks like Artificial Neural Network (ANN), Feedforward Neural Network, Recurrent Neural Network(RNN), Recursive Recurrent Neural Network (RRNN), Convolutional Neural Network(CNN), Modular Neural Network (MNN), Restricted Boltzmann Machine (RBM) etc. In this paper, we have discussed the performance of ANN, CNN, RNN, and RBM where CNN has outplayed the remaining with accuracy of 97.81%.","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W2950022897","authors":[],"funders":[],"total_grants":0,"fwci":0.183,"citation_percentile":0.47152522,"influential_citations":0,"citation_trend":[{"year":2021,"count":1},{"year":2022,"count":1},{"year":2025,"count":1}],"oa_status":"closed","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/8722826/8728281/08728327.pdf?arnumber=8728327","host_type":"publisher"},{"url":"https://doi.org/10.1109/icaccs.2019.8728327","host_type":""}],"fields_of_study":["Currency Recognition and Detection","Neural Networks and Applications","Advanced Neural Network Applications"],"mesh_terms":[],"keywords":["Recurrent neural network","Artificial neural network","Probabilistic neural network","Time delay neural network","Computer science","Feedforward neural network","Artificial intelligence","Types of artificial neural networks","Convolutional neural network","Nervous system network models","Physical neural network","Restricted Boltzmann machine","Stochastic neural network","Boltzmann machine","Deep learning","Machine learning"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-12T20:51:18.657373Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}