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

共 3188 条记录，以下是1-10 订阅

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Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation 3

Transfer Learning based Analysis of Chest X-rays for Accurat...

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

作者： Perla, Someswari Veledendi, Sudhakar Jaiswal, Pooja Muppidi, Satish Mandala, Jyothi Maram, Balajee Gmr Institute of Technology Department of Cse - Ai&ml Andhra Pradesh Rajam India Sr University School of Computer Science and Artificial Intelligence Warangal India Galgotias University India Gmr Institute of Technology Departnent of Computer Science and Engineering Rajam India University Department of Computer Science and Engineering Banglore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798331515911

This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. © 2025 IEEE.

关键词： Lung cancer

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Deep Learning-Driven computational intelligence for Leaf Disease Identification for Smart Agriculture 3

Deep Learning-Driven Computational Intelligence for Leaf Dis...

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

作者： Phani Prasad, J. Jadala, Vijaya Chandra Department of CS&AI School of Computer Science and Artificial Intelligence SR University Warangal506371 India

ISBN: (纸本)9798331527549

In the context of smart farming, Artificial intelligence (AI) plays a crucial role in enhancing agricultural practices, from soil management to crop yield optimization. By leveraging deep learning and image processing techniques, AI can address various challenges across different agricultural sectors, particularly in detecting and mitigating plant diseases. This paper focuses on the use of these technologies for leaf disease detection to minimize crop loss. Deep learning and image processing are key components in identifying, classifying, and monitoring plant diseases. The study reviews various types of leaf diseases and examines the effectiveness of existing algorithms for detection. To improve performance, an algorithm based on ensemble learning methods is proposed. Experimental investigation is conducted on tomato leaf diseases, followed by a comparative analysis of the results. The proposed AI-driven system enables early disease detection, facilitating timely monitoring and intervention, ultimately reducing crop loss and improving overall crop health through proactive management and precautionary measures. © 2025 IEEE.

关键词： Contrastive Learning

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Architectural Framework for Multi-Modal Video Generation via Fine-Tuned Stable Diffusion Models 24

Architectural Framework for Multi-Modal Video Generation via...

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3rd international conference on Artificial intelligence and Intelligent Information Processing, AIIIP 2024

作者： Wei, Shuai Nan Liu, Yuan Yuan Yang, Yu Tianjin College University of Science and Technology Beijing Tianjin China

ISBN: (纸本)9798400707308

In recent years, large language models (LLMs) like ChatGPT have driven advancements in artificial intelligence (AI) across various domains, pushing the boundaries of multi-modal applications. The emergence of Stable Diffusion XL in April 2023 brought image generation into the mainstream. The generation of videos from textual input poses a significant computational challenge within computer science. Nonetheless, recent advancements in text-to-video artificial intelligence (AI) technologies have showcased notable progress within this domain. [1] Following the rapid development of LLMs, OpenAI’s release of the Sora promotional video in February 2024 marked a significant milestone in video generation. In this paper, we explore a novel approach to generating coherent videos by fine-tuning Stable Diffusion, enabling the production of temporally consistent video frames. Our architecture integrates LLM-based text expansion, multi-frame generation using Stable Diffusion, and final video synthesis. Experimental results demonstrate that our method significantly improves the temporal coherence of generated videos. © 2024 Copyright held by the owner/author(s).

关键词： Coherent Frame Generation

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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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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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Artificialization of intelligence and Embodied Cognition 3rd

Artificialization of Intelligence and Embodied Cognition

引用

3rd international conference on Human and Artificial Rationalities, HAR 2024

作者： Uzan, Pierre Catholic University of Paris Paris France CHArt Laboratory Paris France

ISBN: (纸本)9783031845949

The project of artificializing human intelligence currently faces the difficulty of simulating generalist intelligence. "Generalist" intelligence, whose essential properties are autonomy, versatility and fluidity, requires almost instantaneous access to an immense quantity of relevant information about the situations encountered, information that can range from simple factual data to understanding their emotional and social context. The difficulty in creating an intelligent machine in this general sense finds its origin in the computational paradigm of cognition, which assimilates the cognitive subject to a sophisticated calculator endowed with cognitive abilities specified by algorithms, and whose task would be to represent, using symbols or global states, a pre-existing world with equally well predefined properties. I will show that this limitation could be overcome by rebasing the project of artificialization of intelligence on the embodied approach to cognition explored by Francisco Varela, an approach which takes into account the essential role of the body and its insertion in the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ambient intelligence

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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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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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An Evolutionary Algorithm for the Travelling Sales Man problem 3

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

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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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Automated Fish Species Classification using Convolutional Neural Networks 3

Automated Fish Species Classification using Convolutional Ne...

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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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