A Comparative Analysis of Particle Swarm Optimization and Support Vector Machines for Devnagri Character Recognition: An Android Application

Kakde, Prashant M.; Gulhane, S. M.

doi:10.1016/j.procs.2016.03.044

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…We can effectively reduce packet delay and packet loss rate, avoid network congestion to some extent, and ensure that the multimedia receiver can receive as many data packets as possible within the effective time by controlling the transmission of streaming media through feedback control and some optimization control methods [11], to ensure smooth video and audio playback and the desired picture and sound effect [12]. How to improve network service quality with limited network resources while ensuring user QoE (quality of experience) has become an urgent problem for ISPs (Internet service providers) to solve [13]. At the same time, the current Internet architecture and best-effort service mode are insufficient for streaming media transmission [14].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Recognition of Sports Video in Physical Coaching Process Using Particle Swarm Optimization Algorithm

Wu-jun

2022

Mobile Information Systems

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The proportion of network video traffic in the network is increasing as Internet and streaming media technology continues to advance. The current network environment is becoming more complicated as new application services emerge. ISPs are grappling with how to effectively allocate limited network bandwidth resources while maintaining network service quality (Internet service provider). Currently, in the process of physical coaching, trainers use the video replay and analysis management method with less difficulty to explain the essentials of movements to athletes, but it is not intuitive enough to identify and classify network video services and can guide Internet service providers (ISPs) to optimise network resource management and rationally configure traffic engineering, in order to ensure the quality of service (QOS) of video services. Finally, using the shot as the unit, find similar video clips in the video database based on the video examples submitted by the user, and return retrieval results based on similarity. This paper uses streaming video as the research object and examines the transmission control of sports video using the particle swarm optimization algorithm in detail, based on a comprehensive introduction to the analysis and recognition of sports video in the process of physical coaching.

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Section: Introductionmentioning

confidence: 99%

Analysis and Recognition of Sports Video in Physical Coaching Process Using Particle Swarm Optimization Algorithm

Wu-jun

2022

Mobile Information Systems

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“…These features make recognition of handwritten Hindi characters, a complex problem. Lots of efforts have been done for handwritten Hindi character recognition till date using various pattern recognition techniques such as artificial neural networks [4][5][6][7][8], fuzzy logic [9], support vector machines [10] [11], evolutionary techniques [12] etc. Due to these sincere and continuous efforts in the field, research in handwritten Hindi character recognition is getting mature significantly.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Extraction Approach for Handwritten Character Classification Using Feed-forward Neural Network Techniques

Jain¹,

Singh²,

Sharma³

2018

IJCA

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Automatic character recognition is one of the most discovered and crucial application areas of pattern recognition field. Due to the increasing demand of machine processing of handwritten characters, this area of pattern recognition is gaining a lot of attention of researchers. Despite of a great deal of efforts done so far in this direction, still a lot is required to get done. In this work, we are focusing our attention on handwritten Hindi characters. Automatic recognition of Handwritten Hindi characters is complex due to the cursive nature and high level of similarity (such as presence of header line, vertical bar, etc.) in the structure of characters. Hybrid feature extraction approach is followed to extract meaningful features from the collected handwritten characters data. A comparative study of performance analysis of selected neural networks models is performed. Results indicate that the Radial basis function network model perform with 99.09% recognition accuracy.

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Hybrid Model with Word2vector in Information Retrieval Ranking

Pandey

Mathur

Joshi

2021

Data Analytics and Management

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A Comparative Analysis of Particle Swarm Optimization and Support Vector Machines for Devnagri Character Recognition: An Android Application

Cited by 4 publications

References 4 publications

Analysis and Recognition of Sports Video in Physical Coaching Process Using Particle Swarm Optimization Algorithm

Analysis and Recognition of Sports Video in Physical Coaching Process Using Particle Swarm Optimization Algorithm

Hybrid Feature Extraction Approach for Handwritten Character Classification Using Feed-forward Neural Network Techniques

Hybrid Model with Word2vector in Information Retrieval Ranking

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