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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

共 7533 条记录，以下是121-130 订阅

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Signal Processing and soft computing Techniques Driven Analysis of Chhattisgarhi Dialects Using MFCCs

Signal Processing and Soft Computing Techniques Driven Analy...

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Smart and Sustainable Developments in Electrical Engineering (SSDEE), IEEE international conference on

作者： Madhuri Gupta Megha Mishra Jaspal Bagga Department of Computer Science Shri Shankaracharya Technical Campus Rungta College of Engineering & Technology(Work) Bhilai India Department of Computer Science Shri Shankaracharya Technical Campus Bhilai India Department of Information Technology Shri Shankaracharya Technical Campus Bhilai India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Chhattisgarhi, spoken by over 12 million people in Chhattisgarh, is the most prominent, holding official language status and serving as a cultural and linguistic identifier. Baheliya and Chamari, though less documented, are vital to the region's linguistic diversity. This research investigates the acoustic and phonetic characteristics of three central Indian dialects—Chhattisgarhi, Baheliya, and Chamari—using advanced machine learning techniques and robust feature extraction methods. Leveraging Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, this study examines statistical parameters such as mean, median, and variance to distinguish the spectral properties of these dialects. Our contribution includes a detailed analysis of dialectal variations, revealing that Chamari exhibits the highest mean MFCC1 value (0.223), indicative of its higher spectral energy, while Chhattisgarhi shows the most negative mean MFCC2 (-0.322), reflecting unique spectral distinctions. Variance analysis highlights Baheliya and Chhattisgarhi as having greater spectral diversity, particularly in MFCC1 and MFCC2, showcasing their phonetic variability. These findings underline the acoustic uniqueness of each dialect, bridging gaps in existing research on underrepresented dialects. This work contributes to the preservation of linguistic diversity and provides a strong foundation for designing ASR systems tailored to regional dialects, addressing critical challenges in multilingual speech recognition.

关键词： Measurement Electrical engineering Visualization Machine learning Phonetics Signal processing Feature extraction Multilingual Mel frequency cepstral coefficient Sustainable development

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Proceedings of the 2022 11th international conference on computing and pattern recognition, ICCPR 2022

Proceedings of the 2022 11th International Conference on Com...

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11th international conference on computing and pattern recognition, ICCPR 2022

ISBN: (纸本)9781450397056

The proceedings contain 98 papers. The topics discussed include: multi-scale channel attention for Chinese scene text recognition;recognition of human walking motion using a wearable camera;an efficient lightweight spatio-temporal attention module for action recognition;rice disease recognition and feature visualization using a convolutional neural network;vehicle re-identification based on multi-scale attention feature fusion;improving pedestrian attribute recognition with dual adaptive fusion attention;coordinate attention-enabled ship object detection with electro-optical image;few-shot object detection via refining eigenspace;object detection algorithm based on coordinate attention and context feature enhancement;Swin transformer with multi-scale residual attention for semantic segmentation of remote sensing images;drivable area segmentation in unstructured roads for autonomous vehicles based on multi-sensor fusion;and a brief comparison of deep learning methods for semantic segmentation.

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Development of Neural Interface-Based Brain-Controlled Robotic Prosthetics for Enhanced Mobility in Amputees

Development of Neural Interface-Based Brain-Controlled Robot...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Manisha Veer Pradnya Borkar Rohan Phatak Kirti H. Wanjale Shirish Inamdar Saurabh Bhattacharya Department of Pharmaceutical Chemistry Krishna Institute of Pharmacy Krishna Institute of Pharmacy Krishna Vishwa Vidyapeeth "Deemed to be University" Maharashtra India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Pharmacogonsy Krishna Institute of Pharmacy Krishna Institute of Pharmacy Krishna Vishwa Vidyapeeth "Deemed to be University" Karad Maharashtra India Vishwakarma Institute of Technology Pune Department of Pharmacy Practice Krishna Institute of Pharmacy Krishna Institute of Pharmacy Krishna Vishwa Vidyapeeth "Deemed to be University" Karad Maharashtra India School of Computer Science & Engg. Galgotias University Greater Noida UP India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Brain-controlled robotic devices made possible by neural interfaces are a hopeful new way to help amputees recover, as they give them more freedom and a better quality of life. Traditional prosthetics don't always offer real-time, natural control, which makes them frustrating to use and limits their abilities. This essay talks about how important it is to have prostheses that can work with the user's brain so that replacing limbs feels more normal and works better. Our goal is to create and test a neural interface technology that lets robotic replacement limbs be controlled directly from the brain. One of the ways used is creating a machine learning system to understand neural signals obtained through noninvasive electroencephalography (EEG), and these signals are then combined with a cutting-edge robotic limb. We used complex signal processing and pattern recognition techniques to figure out what the user was trying to do and make the prosthetic arm move precisely. Our system can correctly figure out different moving intents with little to no delay, according to the results. Preliminary tests with amputees showed that the new prostheses made it much easier and faster to do different jobs compared to standard ones. Participants said they were happier and more comfortable, which shows that this technology could change how prosthetics are controlled. This study lays the groundwork for future research on brain-controlled artificial systems that are scalable and easy for anyone to use. These systems could greatly assist amputees all over the world.

关键词： Training Accuracy Machine learning Signal processing Electroencephalography Real-time systems Delays Robots Standards Prosthetics

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8th international conference on computing and pattern recognition, ICCPR 2019

8th International Conference on Computing and Pattern Recogn...

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8th international conference on computing and pattern recognition, ICCPR 2019

ISBN: (纸本)9781450376570

The proceedings contain 82 papers. The topics discussed include: a reparameterized construction in L2-norm weight decay for rectifier neural networks;velocity-driven particle swarm optimization;neural network hyperparameter tuning based on improved genetic algorithm;the impact of online word-of-mouth on movie box office based on fine-grained sentiment analysis;understanding the structure of streaming documents based on neural network;optimization of recurrent neural networks on natural language processing;few-shot knowledge reasoning method based on attention mechanism;data repair method based on timeliness and conditional function dependency rules;and data acquisition system of weather sounding air pressure sensor.

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Big Data Clustering Algorithms: Improving Efficiency and Scalability for Massive Data Mining and pattern recognition

Big Data Clustering Algorithms: Improving Efficiency and Sca...

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Pervasive Computational Technologies (ICPCT), international conference on

作者： Shanmugam Muthu G Ramesh Kalyan V. Samuthira Pandi Shobana D Lakshmi Priya J Jaishika S Shipt Inc Birmingham USA Department of Computer Science and Engineering SNS College of Technology Coimbatore Tamilnadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai Department of ECE Saveetha Engineering College Chennai Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The emergence of big data has completely changed the way data analysis is done. As a result, there is a pressing need for effective clustering algorithms that can handle large datasets from different fields. Efficiency, scalability, and finding meaningful patterns in enormous data are some of the important difficulties that this research tackles in relation to big data clustering. We thoroughly examine and evaluate a range of clustering algorithms, from more conventional ones like hierarchical and K-Means clustering to more cutting-edge ones like density-based spatial clustering (DBSCAN) and model-based clustering. To further improve the performance and scalability of clustering algorithms, we investigate new methods that make use of distributed computing frameworks like Apache Spark and MapReduce. We offer a thorough assessment of their efficacy in terms of clustering quality, computational efficiency, and resource use by using and evaluating these methods on large-scale datasets. While conventional clustering algorithms may fail miserably when faced with extremely big data sets, our research shows that newer methods that make use of optimization techniques and distributed processing greatly outperform their predecessors. The effect of feature selection and dimensionality reduction techniques on improving clustering results is also shown. By shedding light on how to choose and implement clustering algorithms for effective data mining and pattern identification, this study adds to the continuing endeavors in the domain of big data analytics. Progress in areas like customer segmentation, anomaly detection, and social network analysis can be achieved with the help of adaptive algorithms that can handle large datasets while keeping clustering results accurate and dependable, according to the study.

关键词： Dimensionality reduction Machine learning algorithms Accuracy Social networking (online) Scalability Heuristic algorithms Clustering algorithms Adaptive algorithms Big Data Computational efficiency

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international conference on Artificial Intelligence and pattern recognition, AIPR 2014, Held at the 3rd World Congress on computing and Information Technology, WCIT

International Conference on Artificial Intelligence and Patt...

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international conference on Artificial Intelligence and pattern recognition, AIPR 2014

The proceedings contain 25 papers. The topics discussed include: cloud and mobile security: challenges and future research directions;DLP-technologies: new directions and trends;using fuzzy logic to evaluate trust in e-commerce;gamification of teaching and learning activity: prospect and challenges of mobile game-based learning;ComboSplit: combining various splitting criteria for building a single decision tree;text classification using computational model of the cerebral cortex;restricted Boltzmann machines for modeling businesses;variables selection for multiclass SVM using the multiclass radius margin bound;on the enumeration of frequent patterns in sequences;predicting movie incomes using search engine query data;best-parameterized sigmoid ELM for benign and malignant breast cancer detection;inference engine for classification of expert systems using keyword extraction technique;comparison of classifiers for retinal pathology images using surf and bag-of-words model;content based video quality control for wide-area video surveillance systems;line detection by centre and width estimation;and interactive versus passive 2D face spoofing detection.

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A Bone Fracture Detection Using CNN Model 4th

A Bone Fracture Detection Using CNN Model

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4th international conference on Intelligent Systems and pattern recognition, ISPR 2024

作者： Nadia, Afsana Nuruzzaman, Md. Nomani, Khan Md. Shibli Billal, Md. Masum East West University Dhaka Bangladesh Hajee Mohammad Danesh Science and Technology University Dinajpur Bangladesh American International University-Bangladesh Dhaka Bangladesh University of Science and Technology of China Hefei China

ISBN: (纸本)9783031821554

Accurate bone fracture detection can be critical in the medical diagnosis process. This paper proposes a bone fracture detection approach based on deep learning using Convolutional Neural Networks (CNNs) with X-ray images. We have explored three models: one predefined model (MNASNet1) and two custom models (Proposed Model 1 and Proposed Model 2). Model performance is evaluated using metrics such as F1-Score, precision, and accuracy. Results show that the proposed models achieved outstanding performance compared to the MNASNet1 model in most of the metrics. Specifically, Proposed Model 1 achieves the highest overall F1-score, recall, and precision (1.00) and also has the highest validation accuracy (99.89%). Our objective is to develop a custom CNN architecture that demonstrates the potential of our proposed models to enhance fracture detection accuracy and develop a web application to detect bone fractures faster to facilitate more precise clinical assessment and treatment decisions. Doctors can emphasize the benefits and explore collaboration opportunities in a relatable way to integrate our web application into medical practices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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ICCPR 2023 - Proceedings of the 2023 12th international conference on computing and pattern recognition

ICCPR 2023 - Proceedings of the 2023 12th International Conf...

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12th international conference on computing and pattern recognition, ICCPR 2023

ISBN: (纸本)9798400707988

The proceedings contain 85 papers. The topics discussed include: entity attribute alignment method based on attribute value distribution;solving the lunar lander problem with multiple uncertainties using a deep Q-learning based short-term memory agent;sentiment analysis of Weibo text using a deep combination model;an ontology-enhanced knowledge graph embedding method;Chinese event extraction algorithm of multi-information semantic enhancements;comparative analysis of classification methods for diagnosing myasthenia gravis based on lumbar electromyography;rare association rule mining based on reinforcement learning;research on identification and prediction of financial fraud of listed companies based on machine learning;using process enhancement to predict organizational citizenship behavior via the role of sustainable training practices;self-supervised contrastive few-shot learning for motor imagery brain-computer interfaces;and a wearable brain-computer interface system for fatigue detection in driving.

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Intelligent computing in Signal Processing and pattern recognition : international conference On Intelligent computing, ICIC 2006, Kunming, China, August 16-19, 2006 1

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丛书名： Lecture Notes in Control and Information Sciences

2006年

作者： Huang De-Shuang. Li Kang. Irwin G. W.

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th international conference on Bioinformatics and Intelligent computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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