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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11-20 订阅

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3rd international TOOLympics Challenge, TOOLympics 2023 was part of the 26th European Joint conferences on Theory and Practice of Software, ETAPS 2023

3rd International TOOLympics Challenge, TOOLympics 2023 was ...

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3rd international TOOLympics Challenge, TOOLympics 2023 was part of the 26th European Joint conferences on Theory and Practice of Software, ETAPS 2023

The proceedings contain 6 papers. The special focus in this conference is on TOOLympics Challenge. The topics include: Behind the Scene of the Model Checking Contest, Analysis of Results from ...

ISBN: (纸本)9783031676949

The proceedings contain 6 papers. The special focus in this conference is on TOOLympics Challenge. The topics include: Behind the Scene of the Model Checking Contest, Analysis of Results from 2018 to 2023;Tools at the Frontiers of Quantitative Verification: QComp 2023 Competition Report;verifyThis 2023: An international Program Verification Competition;the VerifyThis Collaborative Long-Term Challenge Series.

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Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution for Complex Real-World Problems 3

Cloud-Enabled Bio-Inspired Optimization: A Scalable Solution...

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

作者： Shreya Sakthi, U.R. Sruthi, S. Shruti, S.G. Rajganesh, N. Department of Computer Science and Engineering Sri Venkateswara College of Engineering Sriperumbudur India

ISBN: (纸本)9798331527549

Bio-inspired optimization algorithms, encompassing swarm intelligence, neural networks, fuzzy logic, and evolutionary algorithms, are powerful tools for addressing complex real-world optimization problems. However, their effectiveness is often constrained by the computational demands of training and execution, which surpass the capabilities of traditional computing platforms—particularly when dealing with massive datasets or intricate optimization challenges. These limitations restrict the practical applicability of such algorithms. This work introduces a framework that leverages cloud computing as a scalable and efficient platform to overcome these computational barriers. By integrating bio-inspired optimization techniques with the elastic and high-performance resources of cloud environments, this research demonstrates significant improvements in both the scope and efficiency of these algorithms. Experimental results validate the proposed approach, showcasing enhanced performance, reduced execution time, and the ability to solve larger and more complex real-world problems, thereby unlocking the full potential of bio-inspired optimization in practical applications. © 2025 IEEE.

关键词： Cloud platforms

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An Efficient Approach to Data Clustering Using the K-Means Algorithm in Big Data Analytics 3

An Efficient Approach to Data Clustering Using the K-Means A...

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

作者： Vani, S. Sandhiya Ramya, P. Nagendar, Y. Prakash, P. Arun Chanti, Malladi Atheeswaran, Athiraja Rajalakshmi Engineering College Department Of Computer Science and Engineering Thandalam Chennai India Anna University University College of Engineering Department of Computer Science and Engineering Dindigul India Sr University Department of School of Computer Science and Artificial Intelligence Warangal Telangana Hyderabad India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Maisammaguda Telangana Hyderabad India Telangana Hyderabad India

ISBN: (纸本)9798331515911

The large-scale, high-dimensionality of datasets poses major obstacles to effective and precise data clustering in the field of big data analytics. This work focuses on a well-known technique for dividing huge datasets into informative clusters: the K-Means algorithm. This approach to data clustering is efficient. However, because of scalability and computational complexity problems, K-Means is not as effective when managing large amounts of data. In order to reduce processing cost and improve clustering accuracy, this work provides an improved version of the K-Means algorithm that blends dimensionality reduction approaches and efficient initialization methods. Principal Component Analysis (PCA) is used as a preprocessing step in the methodology to reduce the dimensionality of huge datasets. An optimized K-Means algorithm with improved centroid initialization is then applied. These changes lead to shorter iteration durations and higher-quality clusters. Comparing the results to standard K-Means, experiments on many big data benchmarks showed a 15% gain in cluster accuracy and a 25% reduction in execution time. To sum up, the incorporation of optimized initialization techniques and dimensionality reduction greatly improves the scalability and performance of K-Means in big data analytics, with useful implications for sectors that depend on effective data analysis. © 2025 IEEE.

关键词： Data accuracy

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Adaptive Weather Routing for Advanced Ship Navigation: A Comprehensive Review 3

Adaptive Weather Routing for Advanced Ship Navigation: A Com...

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

作者： Venkatesan, Iyyappan Dharanish, M.B. Muthumari Lakshmi, S. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routing technique, which uses the predictive model and the current weather conditions for efficient navigation. The review mainly emphasizes the basic principles and computational simulation for sustainable navigation in advancing the route defining algorithm. Discusses about the conventional route defining algorithms and dynamic route defining algorithm by adjusting for wind, waves, and currents. A comparison analysis shows how well different algorithms work in various weather conditions, and case studies show how they are used in the actual world, while new trends are being discussed, major problems also concern the need for accurate climate predictions, high-speed data processing, and an efficient response plan. The Advancements in artificial intelligence and composite path forecasting also address issues related to major needs, such as accurate weather forecasts, high-speed data processing, and effective reaction tactics. Through this insights of this review provides on future directions for adaptive weather routing research and development that the sustainable growth of intelligent naval navigation is supported. © 2025 IEEE.

关键词： Weather forecasting

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Advances in AI for Women’s Healthcare: A Review of Deep Learning and NLP Approaches 3

Advances in AI for Women’s Healthcare: A Review of Deep Lea...

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

作者： Choudhary, Jyoti Thakur, Madhuri School of Computer Science and Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798331527549

Women’s health faces critical challenges, particularly in maternal and reproductive healthcare, where complications significantly contribute to global mortality rates. Despite advancements in medical science, inequities in healthcare access and diagnosis persist, especially in underserved regions. Innovative approaches leveraging artificial intelligence (AI) technologies, such as deep learning (DL) and natural language processing (NLP), are transforming healthcare by enabling early diagnosis, personalized treatment, and scalable preventive care. This paper evaluates the transformative role of these AI-driven methods in addressing critical women’s health issues, including cancer detection, maternal health, and reproductive disorders, with a focus on advancements from 2018 to 2023. These years represent a period of rapid AI evolution and its integration into healthcare systems. The present study captures important challenges like restricted diversity of datasets, issues concerning transparency in models, and obstacles in actual deployment. It collates findings to give a global perspective of AI development in women's health. It is also prepared with practical future action steps for its research to lead AI-influenced innovations shaping the future of women's healthcare. © 2025 IEEE.

关键词： Deep learning

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Artificial intelligence as Automated Technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

引用

3rd international conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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A Survey of Advanced Algorithms and Experimental Approaches for Kidney Abnormality Detection in Ultrasound Imaging 3

A Survey of Advanced Algorithms and Experimental Approaches ...

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

作者： Chitra, N. Thulasi Gogula, Sreenivasulu Satapathy, Sukla Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad500075 India Telangana Hyderabad501218 India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Machine Learning Telangana Hyderabad500075 India

ISBN: (纸本)9798331509675

Kidney abnormalities, such as cysts, tumors, and stones, are critical health concerns requiring early and precise detection. This study reviews advanced algorithms and experimental approaches for kidney abnormality detection in ultrasound imaging, emphasizing deep learning (DL) methodologies and explainable AI (XAI) techniques. Contributions include exploring CNN-based segmentation frameworks like SDFNet and U-Net, multi-scale feature fusion, and transfer learning to enhance diagnostic accuracy. Data augmentation and adversarial training are employed to address dataset bias, while Grad-CAM and CAManim provide interpretability, fostering clinician trust. Results demonstrate significant improvements in diagnostic performance, with sensitivity and specificity exceeding 95% and Dice coefficients surpassing 95% on diverse clinical datasets. The findings highlight the transformative potential of DL to automate kidney abnormality detection, reducing dependency on operator expertise while improving accuracy and efficiency. Challenges such as dataset scarcity and computational complexity persist, but future research on multimodal imaging, CNN optimization, and emerging technologies like quantum machine learning presents promising opportunities. This study serves as a guide for advancing diagnostic tools, paving the way for next-generation solutions in clinical applications. © 2025 IEEE.

关键词： Ultrasonic imaging

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Human Activity Recognition with EfficientNetB3: A Model-Driven Approach to Complex Activity Classification 3

Human Activity Recognition with EfficientNetB3: A Model-Driv...

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

作者： Kaur, Jashanpreet Rani, Shalli Singh, Gurpreet Archana Reddy, R. Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

Human Activity Recognition (HAR) is an application that encompasses surveillance to health care monitoring. The paper propounds deep learning (DL) in the classification of 15 different activities including calling, cycling, dancing, and running etc. through the use of EfficientNetB3 for the convolutional neural network. The dataset comprises more than 12,000 images with one specific category per image. In the experiment, different forms of data augmentation were used to avoid overfitting and enhance generalization. EfficientNetB3 has excellent balance between accuracy and computational efficiency in handling intricate complex patterns with fewer parameters and thus adopted for this experiment. The model was trained from scratch for 50 epochs, resulting in an accuracy of 90.10%, which was higher than that of traditional and hybrid methods of HAR. From a comparative analysis, the proposed model outperforms all competing approaches, such as YOLOv5 with LDA and SVM, as well as hierarchical classification models. The results show that DL based models work very well in discrimination of complex human activities. In an attempt to further make the system more versatile, future work will concentrate on optimization of the model for real-time HAR, fusion with sensor data, and extension to larger datasets with more classes of activities. © 2025 IEEE.

关键词： Convolutional neural networks

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Generative Replay and Multi-steps Knowledge Distillation in Class-Incremental Learning 24

Generative Replay and Multi-steps Knowledge Distillation in ...

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

作者： Meng, Yicheng Ping, Jinming Shi, Jingye Zhi, Ruicong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400707308

Data-free generative modeling (GM) methods have gained much attention because pseudo data avoids security and privacy issues, and no amount limitation for storing real data in Class-Incremental Learning (CIL). However, GM-based methods have three drawbacks: un stable quality of generated image, semantic sifting, and domain bias for knowledge and distillation. To tackle with these problems, we propose an effective two-stage training method based on generative replay distillation, namely GA-RCT in this paper. It mainly consists of three modules. The Generative Adaptive Replay (GAR) module in stage one obtainsa well-trained generator for every task, then freezes generator for generating stable images, and trains feature extractor with mixture of real data and generated data. The Global Average Class (GAC) module is designed to reserve class prototypes to reduce semantic drift in the training stage. And the Generative Auxiliary Teaching (GAT) module in stage two performs generative distillation in dual-teacher to student way, for solving domain bias. Experiments on four datasets have shown that GA-RCT performs better than other generative replay methods. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Stock Price Prediction Using Gradient Boosting Machine with Technical Indicators 3

Stock Price Prediction Using Gradient Boosting Machine with ...

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

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Jayanthi, G. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India Saveetha Nagar Tamil Nadu Chennai India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

This research endeavors to design a predictive model to forecast stock prices using a Gradient Boosting Machine (GBM) regressor, specifically emphasizing the adjusted closing price to mitigate the autocorrelation issues intrinsic to time series data. The investigation encompasses a 12-month duration of historical stock data, integrating two technical indicators, namely Exponential Moving Averages (EMA-5 and EMA-10), to augment predictive precision. Gradient Boosting, recognized for its proficiency in managing intricate and non-linear data patterns, was employed to forecast forthcoming stock prices. Critical performance indicators, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2), played a role in evaluating model accuracy, attaining an R2 score of 0.99, thereby signifying an almost perfect correlation between actual and predicted prices. Beyond numerical assessments, visual examinations through scatter plots, residual plots, and prediction error distribution further elucidated the model's efficiency. The findings reveal that the amalgamation of Gradient Boosting with Technical Indicators such as EMA-5 and EMA-10, coupled with an emphasis on the adjusted closing price, proves exceptionally effective for stock price forecasting. This methodology achieved an accuracy rate nearing 99%, illustrating its substantial promise for stock market prediction and its proficiency in alleviating autocorrelation challenges within time series datasets. © 2025 IEEE.

关键词： Autocorrelation

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