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

共 3218 条记录，以下是1-10 订阅

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Explainable Artificial intelligence based ML Models for Heart Disease Prediction 3

Explainable Artificial Intelligence based ML Models for Hear...

引用

3rd international conference on computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Kommineni, Sivaram Muddana, Sanvitha Senapati, Rajiv SRM University AP Computer Science & Engineering Andhra Pradesh India

ISBN: (纸本)9798350361391

Heart disease prediction is important in healthcare because it enables timely identification and intervention of actual condition of the patient. However, the task of accurately predicting disease remains a challenging task. In this paper, we have proposed a framework for heart disease prediction using explainable artificial intelligence (XAI) based Machine Learning (ML) models such as Decision Tree (DT), Random Forest (RF), k-nearest neighbors (KNN), AdaBoost, Logistic Regression (LR), Naive Bayes (NB), and Neural Network (NN). The efficiency of those models were evaluated using MCC, accuracy, precision, recall, and AUC. Finally, it is observed that, DT emerges as the most effective model offering interpretability. This study underscores the importance of transparent models in healthcare and advocates in order to incorporate XAI to enhance interpretability and medical decision-making. © 2024 IEEE.

关键词： Prediction models

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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...

引用

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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Advancing Road Safety through Driver Drowsiness Detection Using Deep Learning Model 3

Advancing Road Safety through Driver Drowsiness Detection Us...

引用

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

作者： Pandey, Sanjeev Lekha, J. Kadyan, Sweety University Lavasa School of Science Maharashtra Pune India

ISBN: (纸本)9798350372120

Driver drowsiness poses a significant threat to public safety, contributing to numerous road accidents and fatalities annually. Drowsy drivers exhibit characteristic changes in facial expressions and behaviors, including eye closure, head nodding, and yawning. These indicators can be detected through various techniques, including image processing, computer vision, and machine learning. This research investigates a promising approach: utilizing a ResNet-101 deep convolutional neural network (CNN) for driver drowsiness detection based on eye, head, and mouth states. The model was trained on a vast dataset of 2.2 million images, covering diverse driving conditions. Despite achieving a 69% accuracy, suggesting real-world potential, computational limitations restricted training to only a quarter of the data. This necessitates further research with larger datasets and increased resources to enhance accuracy and robustness. © 2024 IEEE.

关键词： computer vision

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Image Clustering Approach based on Artificial Bee Colony and enhanced K-means algorithm 3

Image Clustering Approach based on Artificial Bee Colony and...

引用

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

作者： Haouari, Oussama El Khrissi, Lahbib Mountasser, Imadeddine Akkad, Nabil El ENSA of Fez LSATE Department of Computer Science Fez Morocco Faculty of Science Dhar El Mahraz LSATE Department of Computer Science Fez Morocco

ISBN: (纸本)9798331522056

Clustering plays a pivotal role in image segmentation by grouping pixels based on similarity, enabling efficient analysis and interpretation. Traditional methods like K-means, despite their popularity, are hindered by their sensitivity to initial centroid placement, often converging to suboptimal solutions. This paper presents a novel hybrid approach that integrates the Artificial Bee Colony (ABC) algorithm with K-means to address these limitations. The proposed method leverages the global optimization capabilities of ABC to enhance centroid initialization, thereby improving clustering accuracy and robustness. The method was evaluated on benchmark image datasets, including Lenna, House, Baboon, and Elephants, using widely adopted metrics such as IoU, Precision, F1 Score, and Dice Coefficient. Results demonstrate consistent improvements across all datasets, with significant gains in accuracy compared to K-means, Fuzzy C-Means, and Mean Shift. Despite higher computational overhead, the proposed method exhibits superior segmentation quality, particularly in complex images with intricate structures. Future work aims to optimize the computational efficiency of the approach and explore its application in text clustering and time-series data analysis. © 2024 IEEE.

关键词： Image segmentation

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'Optimizing Algorithmic Efficiency: Exploring Advanced Data Structures for High-Performance Computing' 3

'Optimizing Algorithmic Efficiency: Exploring Advanced Data ...

引用

3rd international conference on computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Rao, Lade Gunakar Nitya, Erupaka Devi Kusumba, Jyoshna Rajchandar, K. Narasimha Murthy, S. SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Telangana Hyderabad India Government Degree College Computer Science And Applications Telangana Warangal India Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350361391

This work explores advanced data structures as a means of optimizing algorithmic efficiency in high-performance computing. The search for faster and more scalable algorithms becomes essential as computing demands rise further. This paper explores state-of-the-art data structures intended to improve overall performance, memory usage, and computational speed. We hope to offer insights into the applicability and impact of various advanced data structures on high-performance computing tasks through a thorough review and analysis of them, including but not limited to self-balancing trees, hash tables, and advanced graph structures. The results of this study not only advance our theoretical knowledge of these data structures but also provide developers and researchers with useful advice on how to utilize algorithmic efficiency in quickly fully. It is important to remember that algorithmic urban planning is an interdisciplinary field that encompasses data science, computer science, urban planning, and many other fields. The goal of integrating algorithms and smart technologies is to build more efficient, sustainable, and liveable urban environments.[8]. © 2024 IEEE.

关键词： Advanced Data Structures Algorithmic Efficiency Computing (keywords) High-Performance Computing Optimizing

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Transforming dentistry using artificial intelligence based innovations for advanced diagnostics and sustainable healthcare 3

Transforming dentistry using artificial intelligence based i...

引用

3rd international conference on computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Chhabra, Deepak Kumar Dhingra, Ashwani Kaushik, Ashish Dhingra, Sunita Kumar Sahdev, Ravinder Dhingra, Sunil Maharshi Dayanand University Department of Mechanical Engineering UIET Rohtak India Deenbandhu Chhotu Ram University of Science and Technology Department of Mechanical Engineering Sonipat India Maharshi Dayanand University Department of Computer Science and Engineering UIET Rohtak India University Institute of Engineering and Technology Kurukshetra University Department of Mechanical Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350361391

Modernizing and enhancing primary dental health care is imperative to address the growing demands of patients suffering from oral diseases to achieve the sustainable development goal of ensuring healthy lives and promoting well-being. Artificial intelligence (AI) is one such emerging technology that can radically transform dental practices by providing AI-driven imaging solutions, advanced diagnostic capabilities, and accurate treatment planning. This study explores the current state of artificial intelligence in dentistry using bibliometric analysis, along with an in-depth discussion on widely used artificial intelligence techniques for minimally invasive dentistry. Furthermore, several applications of AI in dentistry, along with benefits and current challenges, are also described. The study found that AI-powered tools are demonstrably enhancing diagnostic accuracy, streamlining workflows, and offering personalized treatment options. © 2024 IEEE.

关键词： Sustainable development goals

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computer Modeling of Optimization in Seismoacoustic Tomographic Diagnostics of Velocity Inhomogeneities 3

Computer Modeling of Optimization in Seismoacoustic Tomograp...

引用

3rd IEEE international conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Gervas, Nikolai V. Gervas, Daria V. Zerkal, Sergey M. Novosibirsk State Technical University Dept. of Computer Science Novosibirsk Russia

ISBN: (纸本)9798331516321

This paper presents a computational technology for kinematic diagnostics of the velocity distribution of a probing signal in seismoacoustic media under conditions of linearization of the inverse kinematic problem. The problem formulation, solution construction, and a computational algorithm based on the computed tomography method are provided. The use of computed tomography is facilitated by organizing a tomographic observation system (arrangement of signal sources and receivers along a circle). Linearization is performed near the linear depth function (Z variable in the X Y Z space), which is highlighted in the target velocity function and assumed to be known. Special attention is paid to organizing this component, as it significantly affects the accuracy and reliability of the solution obtained. The results of computer experiments that demonstrate the solution to the problem are presented. In addition, the prospects for further research aimed at advancing the proposed approach using artificial intelligence technologies, particularly those related to heuristic search, have been outlined. © 2024 IEEE.

关键词： Inverse problems

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Remo Vision: A computer Vision Web Application 3

Remo Vision: A Computer Vision Web Application

引用

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

作者： Silat, Safa Mishra, Ved P. Sadath, Lipsa Amity University Dubai Computer Science and Engineering Dubai United Arab Emirates

ISBN: (纸本)9798350338263

With computer vision providing several functions which are used in many fields, it is essential to have accessibility to all available functions in one place and by all. The aim of this project is to develop a web application as a platform implementing various features such as real-Time object detection, removal of background text and reduction of noise in images. This involves using the Flask framework to run python scripts to create a web application. This web application can then also be easily integrated into other systems as well as easily customisable on different devices with better flexibility and scalability of the application. Most of the features focused on are based on removal, hence the name RemoVision. © 2023 IEEE.

关键词： computer vision

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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...

引用

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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Swarm intelligence Approach for Parametric Optimization of Railway Simply Supported Beam Bridges with a Single Dynamic Load and Vibration Mode 3

Swarm Intelligence Approach for Parametric Optimization of R...

引用

3rd international conference on computer Technologies, ICCTech 2024

作者： Sanchez-Haro, Javier Galvez, Akemi Capellan, Guillermo Iglesias, Andres University of Cantabria Department of Structural Engineering Santander Spain University of Cantabria Dept. of Applied Mathematics & Computational Sciences Santander Spain Arenas y Asociados Ingenieria de Diseño S.L. Santander Spain

ISBN: (纸本)9798350395419

Analyzing the structural response of bridges to dynamic loads is a crucial concern in bridge engineering, with the primary goal to ensure the bridge safety and stability under varying conditions. The application of artificial intelligence techniques to address this challenge represents a promising yet relatively unexplored area of research in the existing literature. In this context, this paper introduces a swarm intelligence approach known as the bat algorithm for the optimal determination of the geometric and mechanical parameters in railway simply supported beam bridges. This work focuses on the assumption of a single dynamic load and one vibration mode. Our results, validated through Sofistik, a renowned software package for civil engineering, demonstrate that our approach performs well as it yields satisfactory engineering solutions. This opens the door for further swarm intelligence-based developments in this field. © 2024 IEEE.

关键词： Swarm intelligence

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