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检索条件"任意字段=3rd International Conference on Computer Science and Computational Intelligence, ICCSCI 2018"

共 3235 条记录，以下是21-30 订阅

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Hybrid architecture CNN-BLSTM for automatic speech recognition 3

Hybrid architecture CNN-BLSTM for automatic speech recogniti...

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3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Dar, Muzaffar Ahmad Pushparaj, Jagalingam Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Computational Intelligence Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

With several deep learning approaches, the domain of automatic speech recognition (ASR) has seen notable advancements in recent times. The domains of intelligent human- computer interaction and machine translation greatly benefited from accurate speech recognition. The present study introduces a hybrid architecture amalgamating a convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) for speech recognition. For aligning speech input sequences with corresponding text output sequences, it uses the connectionist temporal classification (CTC) technique. The experiments were done on the LJ speech dataset, and the results showed with an increased number of training samples, the performance of the speech recognition algorithm tended to be increased but the time taken for training gradually increased over time. Moreover, a trained speech recognition algorithm exhibits a longer training time when the recognition accuracy is lower. In this study, we implemented a hybrid deep learning model CNN-BLSTM, in conjunction with the CTC loss function, which attains a word error rate (WER) of 36.97% on the testing dataset. © 2024 IEEE.

关键词： Speech recognition

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Parallelization of the Smith-Waterman Algorithm to Accelerate DNA Sequence Alignment 3

Parallelization of the Smith-Waterman Algorithm to Accelerat...

引用

3rd international conference on Artificial intelligence For Internet of Things, AIIoT 2024

作者： Dhar, Akanksha Chauhan, Ishita Azad, Hiteshwar Kumar Bamzai, Anusha Yadav, Takshu Vellore Institute of Technology Vellore Computer Science and Engineering India

ISBN: (纸本)9798350372120

The genome is a rapidly evolving topic that constantly offers researchers with new biological and computational application challenges. One of the biggest challenges is biological sequence comparison, which involves identifying similarities or identical segments between a query sequence and a reference sequence. Global and local alignments are two common types of sequence alignments. Global alignment identifies the optimal alignment across entire sequences, while local alignment focuses on matching specific segments rather than the entire length. Local alignment algorithms offer faster performance compared to global alignment since they only align specific portions of sequences. In this study, the Smith-Waterman method would be executed serially and in parallel for comparison and analysis. The results reveal that parallel execution has a significant advantage over serial execution, with a maximum improvement of 78.5 \% above serial performance. Furthermore, increasing the matrix size enhances parallel execution performance over serial processing. © 2024 IEEE.

关键词： DNA sequences

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A Survey of 3D Gaussian Splatting: Optimization Techniques, Applications, and AI-Driven Advancements 3

A Survey of 3D Gaussian Splatting: Optimization Techniques, ...

引用

3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Santosh Reddy, P. Abhiram, H. Archish, K.S. B.N.M. Institute of Technology Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331515911

3D Gaussian Splatting (3DGS) has emerged as a transformative technique for real-time, high-quality 3D rendering. This paper surveys the advancements in 3DGS, focusing on optimization strategies, applications across diverse domains, and AI-driven innovations that enhance its utility. Optimization techniques, such as Mini-Splatting and sparsification frameworks, address challenges like computational overhead and memory inefficiency while maintaining visual fidelity. AI has further expanded the capabilities of 3DGS through frameworks like SceneTeller, ART3D and GaussianEditor, enabling text-to-3D generation, object-level manipulation, and artistic scene creation with multi-view consistency. Applications of 3DGS range from autonomous driving and robotics to virtual and augmented reality, highlighting its adaptability and efficiency. This survey underscores the importance of continued research to refine 3DGS's scalability and applicability, solidifying its role at the intersection of computer graphics and artificial intelligence. © 2025 IEEE.

关键词： Augmented reality

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Automated Fish Species Classification using Convolutional Neural Networks 3

Automated Fish Species Classification using Convolutional Ne...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yamsani, Nagendar Nasra, Parul Gupta, Sheifali School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

Appropriate classification of species in fish is important for biodiversity conservation, management of fisheries, and ecological research. This paper introduces a novel deep learning method for the automatic classification of aquatic species using CNNs. Testing the proposed model on a large-scale and diverse fish dataset resulted in impressive classification accuracy, reaching 92%. The architecture is made to balance the computational cost and predictability. It uses more complicated techniques, such as data augmentation, batch normalization, and dropout, in order to generalize. The study demonstrated evaluation of the model on multiple categories of the fish species and showed that it is precise and reliable in classification. The results show a potential for using CNN-based models for fully automating fish classification tasks, providing a scalable tool with a high degree of reliability in monitoring marine and freshwater ecosystems. Future work includes incorporating real-time detection capabilities into the model and adapting the same design to classify species in video streams. © 2025 IEEE.

关键词： Invertebrates

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A Comprehensive Survey on Enhancing Digital Twin Security Systems with Explainable AI Techniques 3

A Comprehensive Survey on Enhancing Digital Twin Security Sy...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Lalithadevi, B. Krishnaveni, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

Digital Twin (DT) technology has emerged as a cornerstone in modern engineering, enabling real-time digital replication of physical assets for monitoring, diagnostics, and optimization. However, as DTs become increasingly integrated into critical infrastructure, their security has become a paramount concern. This paper presents a comprehensive survey on the integration of Explainable Artificial intelligence (XAI) into Digital Twin security, highlighting the potential of XAI in enhancing the transparency, interpretability, and trustworthiness of AI - driven security mechanisms in DTs. We explore various XAI techniques, including model- agnostic approaches, interpretable neural networks, and rule-based systems, detailing their potential applications in bolstering the security of Digital Twins. Furthermore, we identify several key challenges in implementing XAI for DT security, such as the need for standardized metrics for interpretability, the trade-offs between model complexity and explainability, and the potential for adversarial attacks on explainable models. Through our analysis, we highlight the critical research gaps, including the security of Digital Twin (DT) systems and explores the integration of Explainable AI (XAI) to enhance transparency, interpretability, and trustworthiness. Outcomes include detailed XAI applications for DT security, identification of limitations, and a roadmap for future research. We discuss various XAI techniques, their applications, and the current challenges in implementing XAI for DT security. Our survey identifies key research gaps and offers a roadmap for future research in this emerging field. © 2025 IEEE.

关键词： Artificial intelligence

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3rd Workshop on computational Mathematics Modeling in Cancer Analysis, CMMCA 2024 was held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

3rd Workshop on Computational Mathematics Modeling in Cancer...

引用

3rd Workshop on computational Mathematics Modeling in Cancer Analysis, CMMCA 2024 was held in conjunction with the 27th international conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031733598

The proceedings contain 12 papers. The special focus in this conference is on computational Mathematics Modeling in Cancer Analysis. The topics include: Follicular Lymphoma Grading Based on 3D-DDcGAN and Bayesian CNN Using PET-CT Images;Multi-channel Multi-model Fusion Module (MMFM) Based Circulating Abnormal Cells (CACs) Detection for Lung Cancer Early Diagnosis with Fluorescence in Situ Hybridization (FISH) Images;domain Game: Disentangle Anatomical Feature for Single Domain Generalized Segmentation;Attention-Fusion Model for Multi-omics (AMMO) Data Integration in Lung Adenocarcinoma;PD-L1 Expression Prediction Using Scalable Multi Instance Transformer;improving Single-Source Domain Generalization via Anatomy-Guided Texture Augmentation for Cervical Tumor Segmentation;PANDA: Pneumonitis Anomaly Detection Using Attention U-Net;estimating the Average Treatment Effect Using Weighting Methods in Lung Cancer Immunotherapy;Beyond Conventional Parametric Modeling: Data-Driven Framework for Estimation and Prediction of Time Activity Curves in Dynamic PET Imaging;assessment of Radiomics Feature Repeatability and Reproducibility and Their Generalizability Across Image Modalities by Perturbation in Nasopharyngeal Carcinoma Patients.

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An Evolutionary Algorithm for the Travelling Sales Man problem 3

An Evolutionary Algorithm for the Travelling Sales Man probl...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Waseem, Shaik Mohammad Jeshwanth, N.L. Lingesh, A. Jayanth, Matta Bhargavi, C.H. Dhanwanth, Batini Dept. of Computer Science and Engineering Chennai India Dept. of Artificial Intelligence and Data Science Chennai India Sai Spurthi Institute of Technology Dept. of Computer Science and Engineering Gangaram Sathuaplly India Jönköping University Dept. of Ai Engineering Jönköping Sweden

ISBN: (纸本)9798331509675

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques. © 2025 IEEE.

关键词： Traveling salesman problem

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Sentiment Analysis of Movie Reviews Using Deep Learning 24

Sentiment Analysis of Movie Reviews Using Deep Learning

引用

3rd international conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Liu, Minghao Faculty of Computer Science and Technology Qilu University of Technology Shandong Jinan China

ISBN: (纸本)9798400710834

The rapid proliferation of mobile devices has driven the integration of artificial intelligence (AI) technologies, making the efficient operation of complex deep learning models within resource-limited environments a critical challenge in AI. This study addresses this challenge by focusing on mobile applications and proposing a streamlined sentiment analysis model specifically tailored for English movie reviews. We begin by exploring the diverse applications of sentiment analysis in user feedback and public opinion monitoring, underscoring its growing significance. Subsequently, we provide an overview of the evolving landscape of related technologies. In our experiment, we utilize the bag-of-words model for data preprocessing, which we combine with a convolutional neural network (CNN) and dropout regularization to construct a lightweight model architecture. Through iterative parameter optimization, we successfully achieve a delicate balance between model performance and complexity. We trained the model on a dataset consisting of 20,000 samples and achieved an accuracy of 81.76% on a test set of 5,000 samples. Compared to traditional, more complex sentiment analysis models, our proposed approach significantly reduces computational demands, demonstrating its potential for practical deployment on mobile devices. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Machine Learning based Intelligent Diagnosis of Orthodontics: A comprehensive review 3

Machine Learning based Intelligent Diagnosis of Orthodontics...

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3rd IEEE international conference on computational intelligence and Knowledge Economy, ICCIKE 2023

作者： Iwin Thanakumar Joseph, S. Sarveshwaran, Velliangiri Vijayakumar, K. Raj, S Benson Edwin Elngar, Ahmed A Devadurai, Anand Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Guntur India SRM Institute of Science and Technology Kattankulathur Campus Department of Computer Science and Engineering Chennai India Higher Colleges of Technology Department of Computer Information Sciences Fujairah United Arab Emirates Beni-Suef University Faculty of Computers and Artificial Intelligence Beni-Suef City Egypt King Khalid University Department of Computer Science Gregar Abha Saudi Arabia

ISBN: (纸本)9798350338263

An effective prediction of orthodontics treatment outcome is highly essential for further scheme of clinical treatment for the patient with the simplified cost. An appropriate utilization of information technology simplifies the treatment for teeth irregularities and misaligned jaws in an effective manner. Lot of research works carried out in the area of dentistry to come out with an optimistic result. With the advent of artificial intelligence and machine learning methodologies in recent years, lot of clinical problems and difficulties can be efficiently managed and overcome by orthodontists. This research article provides the complete insight about the impact of intelligent technologies in the field of orthodontics. It provides a complete overview of futuristic applications in the field of orthodontic clinical diagnosis, planning of treatment for various cases and the correct prediction of outcome of clinical treatment which regularly faced by an orthodontist in the health care sector. © 2023 IEEE.

关键词： Dentistry

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Review of 3D Scene Reconstruction: From Traditional Methods to Advanced Deep Learning Models 3

Review of 3D Scene Reconstruction: From Traditional Methods ...

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3rd international conference on Embedded Systems and Artificial intelligence, ESAI 2024

作者： Belkaid, Maryem Alaoui, El Arbi Abdellaoui Merras, Mostafa Berrajaa, Achraf Akkad, Nabil El EST Moulay Ismail University IMAGE Laboratory Meknes Morocco ENS Moulay Ismail University Department of Science Meknes Morocco University Mohamed First Faculty of Sciences Department of Computer Science Oujda Morocco ENSA Sidi Mohamed Ben Abdellah University Department of Science Fez Morocco

ISBN: (纸本)9798331522056

3D scene reconstruction represents a pivotal domain within computer vision, involving a diverse array of techniques ranging from classical geometry-driven approaches to modern deep learning-based models. This review article offers an extensive summary of cutting-edge methods for 3D reconstruction, including techniques such as Structure-from-Motion (SfM) and Multi-View Stereo (MVS), as well as cutting-edge deep learning techniques including CNNs, GANs,Variational Autoencoders (VAEs) and Neural Radiance Fields (NeRF). We analyze the strengths and limitations of each approach, particularly in terms of accuracy, efficiency, generalization, and their ability to handle complex scenes. Furthermore, we delve into the key challenges faced in 3D scene reconstruction, including the trade-offs between computational efficiency and model accuracy, the generalization to diverse environments, and the integration of multi-modal data sources. Special attention is given to NeRF, a breakthrough in the field, discussing its current capabilities and potential areas for improvement in future research. This review aims to serve as a resource for researchers and practitioners by summarizing the current landscape of 3D reconstruction technologies and identifying promising directions for future exploration. © 2024 IEEE.

关键词： Generative adversarial networks

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