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检索条件"任意字段=3rd International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2021"

共 2040 条记录，以下是71-80 订阅

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Normative Modeling with Focal Loss and Adversarial Autoencoders for Alzheimer’s Disease Diagnosis and Biomarker Identification 3rd

Normative Modeling with Focal Loss and Adversarial Autoenc...

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3rd international Workshop on applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Zhao, Songlin Zhou, Rong Zhang, Yu Chen, Yong He, Lifang Department of Computer Science and Engineering Lehigh University BethlehemPA United States Department of Bioengineering Lehigh University BethlehemPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States

ISBN: (纸本)9783031820069

In this paper, we introduce a novel normative modeling approach that incorporates focal loss and adversarial autoencoders (FAAE) for Alzheimer’s Disease (AD) diagnosis and biomarker identification. Our method is an end-to-end approach that embeds an adversarial focal loss discriminator within the autoencoder structure, specifically designed to effectively target and capture more complex and challenging cases. We first use the enhanced autoencoder to create a normative model based on data from healthy control (HC) individuals. We then apply this model to estimate total and regional neuroanatomical deviation in AD patients. Through extensive experiments on the OASIS-3 and ADNI datasets, our approach significantly outperforms previous state-of-the-art methods. This advancement not only streamlines the detection process but also provides a greater insight into the biomarker potential for AD. Our code can be found at https://***/soz223/FAAE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Comparative Analysis of machine learning Models for Satellite Image Classification with Explainable AI Visualization 3

Comparative Analysis of Machine Learning Models for Satellit...

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3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Jennyfer Susan, M.B. Nishanthini, S. Avinashilingam Institute for Home Science and Higher Education for Women Centre for Machine Learning and Intelligence Coimbatore India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Satellite image classification is vital for applications like environmental monitoring, urban planning, and resource management. Manual methods are time-consuming and error-prone, highlighting the need for automated solutions. This study explores classifying satellite imagery with four distinct classes using sensor data and Google Map snapshots. Four machine learning models - Support Vector machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting machine (GBM), and Artificial Neural Network (ANN) - were implemented and assessed. The SVM model achieved the highest accuracy at 99%, followed by GBM with 98% accuracy, while KNN attained 94%. Although ANN had the lowest accuracy of 58%, it displayed fewer misclassifications. Explainable AI (XAI) tools like LIME and SHAP were used to interpret model decisions, highlighting how features influenced classifications. The results show machine learning's ability to automate satellite image classification with accuracy and actionable insights. © 2025 IEEE.

关键词： Decision Making Explainable AI machine learning Satellite Image Classification Supervised Classification

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Parallel Selective Meta-Heuristic Algorithms for Real-Time Non-Convex Optimization in Big data Analytics 3

Parallel Selective Meta-Heuristic Algorithms for Real-Time N...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Ghosal, Shreya Shankar, Amar Agarwal, Trapty Suchithra, R. Raj, Nimesh Hansda, Prity Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

With big data in modern analytics becoming more common, a lot of it is real-time optimization, which is an essential problem. This is because big data requires efficient and accurate optimization methods, leading to developing parallel selective viable meta-heuristic algorithms for nonconvex optimization in large analytics. They can be used to find optimal solutions rapidly by merging the complementary advantages of meta-heuristics and parallel computing. Algorithms do this using an optimization process, but not always for the whole data set used, saving computational performance. As information is updated in real-time, its approach will constantly be improved, resulting in a better and more accurate solution. A parallel implementation of these algorithms makes them effective for real-time optimization using large datasets. However, the performance of big data analytics may be significantly improved by implementing these algorithms because they have achieved satisfactory results in a number of applications, including machine learning, data mining, and pattern recognition. © 2025 IEEE.

关键词： Large datasets

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PR-GB-NET: Probabilistic Graph Bridging Network with data Resampling for Scene Graph Generation 3rd

PR-GB-NET: Probabilistic Graph Bridging Network with Data Re...

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3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Lu, Hongyu Liu, Guoliang Tian, Guohui Jiang, Jian Wang, Shanmei Shandong University Jinan China Shandong Massdatas Development Co. Ltd. Jinan China Inspur Intelligent Technology Co. Ltd. Jinan China

ISBN: (纸本)9789819624676

Scene graphs are structured and powerful representations of images that extract semantic information from images to enhance visual understanding and image reasoning. To improve the utilization of predicate prior knowledge in scene graphs, we use a probability mass function (PMF) for the predicate, given a subject-object pair during the initialization of the graph neural network. The PMFs are updated through a gating function during message passing of the graph neural network. To mitigate the effects of long-tailed distributions in datasets, we employ a data resampling strategy. During each training iteration, we try to balance the head, body and tail predicates as much as possible. Experiments show that the proposed methods achieve competitive performance compared to previous methods on dataset we created for home environment, demonstrating their effectiveness and generality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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Advancing Brain-Computer Interfaces: A Deep learning Approach for Enhanced Neural Signal Processing 3

Advancing Brain-Computer Interfaces: A Deep Learning Approac...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Dharmendra, Kaku Riya Verma, Deepak Kumar Vats, Satvik Marwadi University Department of Computer Engineering Rajkot India Madan Mohan Malaviya University of Technology Department of Computer Science and Engineering U.P. Gorakhpur India

ISBN: (纸本)9798331542375

Brain-to-computer interfaces (BCIs) are a developing technology that has led to a straight link among the human brain and various systems. The complexity and lack of predictability of brain signals make the development of a trustworthy, accurate BCI system. In this study, we look into how advanced deep learning approaches like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Graph Isomorphism Networks (GIN), and Convolutional Neural Network (CNN) can be used to improve neural signal processing performance in BCI. LSTM and GRU are good at discovering the temporally related EEG sequences, while CNNs extract the spatial feature of raw EEG data very well. We model and analyze graph-structured data from functional brain networks using GIN, revealing insights into spatial dynamics and inter-channel connections. Both graph-based and spatiotemporal features are integrated as proposed in the framework, and a deep learning architecture is constructed for signal decoding and classification that allows for a higher signal decoding and classification accuracy. Our experimental results on publicly available EEG datasets demonstrate that this approach outperforms standalone deep learning models and traditional machine learning approaches. It designs its solutions to attain higher accuracy, robustness, and the ability to generalize across various BCI tasks, including cognitive state recognition and motor imagery classification. The result indicates that our model LSTM is 96.92%, CNN is 95.95%, GRU is 95.40%, and GIN is 88.68%. Finally, this study demonstrates how advanced deep learning techniques can address the current limitations of BCI systems, opening the door for more reliable and successful real-world applications. © 2025 IEEE.

关键词： Convolutional neural networks

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SMOTE-Based Sampling for Addressing Class Imbalance 3rd

SMOTE-Based Sampling for Addressing Class Imbalance

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3rd international conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Chaudhary, Shweta Parashar, Jyoti Malik, Nisar Ahmad Ali, Shalbbya Upreti, Kamal Vats, Prashant Department of Computer Science and Engineering JIMS Engineering Management Technical Campus Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Department of Information Technology Government Degree College Kulgam Jammu and Kashmir India Delhi NCR Ghaziabad India Department of CSE SCSE Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784639

Various real-world applications, including as text categorization, categorization of gender in facial recognition for medical evaluation, fraud detection, and satellites analysis of images for oil-spill monitoring, are frequently plagued by imbalanced data. The majority class is commonly the primary focus of machine learning algorithms, with the minority samples being ignored or classified in a secondary manner. Nevertheless, despite their rarity, these minority samples are very important. When it comes to classification tasks, the issue of class imbalance—where one class is underrepresented relative to another—presents a significant barrier. Specialized approaches including SMOTE, ADASYN, and cost-sensitive voting classifiers have been developed to address this problem. The minority class is oversampled in these methods, synthetic samples are created adaptively, and different prices are placed on misclassification mistakes in order to solve the issue of class imbalance. As a result, rigorous assessment utilizing pertinent metrics and cost considerations are required. The efficacy of these strategies, however, depends on dataset features and problem-specific factors. Class imbalance is still a hot topic for study, and there has been constant innovation in novel methods that are adapted to certain dataset characteristics and application fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Oil spills

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EEG-Driven machine learning Framework for Student Cognitive Load Classification

EEG-Driven Machine Learning Framework for Student Cognitive ...

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Smart Systems for applications in Electrical sciences (ICSSES), international conference on

作者： Varunkumar MS Siddharth Gupta Pramod TC Department of Artificial Intelligence and Data Science Siddaganga Institute of Technology Tumkur Karnataka India Department of Computer Science & Engineering Siddaganga Institute of Technology Tumkur Karnataka India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Understanding differences in learning abilities is essential for improving education. This paper presents a framework to differentiate between learners using EEG data. The framework identifies critical factors that influence learning experiences by applying data-driven approaches and statistical methods. It includes analyses such as band power, entropy calculation, and coherence measurement across EEG channels. The extracted features are used to train a classifier and to identify cognitive behavior patterns. The framework's accuracy is verified using cross-validation, classification reports, and confusion matrices. The experimental analysis shows major improvements in cognitive pattern understanding and identification. This framework provides educators with actionable insights to design personalized learning strategies. By enhancing the understanding of individual learning dynamics, the framework has the potential to optimize educational outcomes across diverse learner profiles.

关键词： Support vector machines Accuracy Education machine learning Coherence Feature extraction Brain modeling Cognitive load Electroencephalography Entropy

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Enhancing Cross-Domain data Analytics Through Multi-source Transfer learning 3rd

Enhancing Cross-Domain Data Analytics Through Multi-source T...

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3rd international conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Li, Jiarong Wang, Jingge Wang, Jingyang Xu, Chihan Wang, Hao Zhang, Chaobo Liang, Xiaojun Ding, Wenbo Gui, Weihua Pengcheng Laboratory Shenzhen 518055 China Tsinghua-Berkeley Shenzhen Institute Shenzhen International Graduate School Tsinghua University Shenzhen 518055 China RISC-V International Open Source Laboratory Shenzhen 518055 China Advanced Technology Research Institute Beijing Institute of Technology Beijing 100081 China School of Future Technology South China University of Technology Guangzhou 511442 China School of Automation Central South University Changsha 410083 China

ISBN: (纸本)9789819645053

The rapid evolution of machine learning and computer vision has given rise to numerous new tasks, primarily driven by deep convolutional neural networks’ ability to create complex mappings between feature space X and label space Y. These tasks often necessitate large-scale, human-labeled datasets for supervised training of the network. However, manual labeling is both costly and time-consuming. To overcome this hurdle, it is imperative to develop algorithms that can leverage existing labeled datasets to extract knowledge about the target domain. While most prior research has focused on single-source transfer learning, our study delves into multi-source transfer across various domains and tasks. The paper presents an improved cross-domain data analytics model that supports multi-source transfer learning by training a classifier to re-weight different sources and adjusting the rich and intricate information among sources to boost target learning. Experiments involving two source domains transferring to a single target domain showcase the effectiveness of our methodology. The model’s potential applications in image recognition include object recognition, medical imaging diagnosis, and so on. Future research could explore the scalability of the model to accommodate larger datasets and further refine the transfer learning process for more efficient knowledge extraction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional Neural Network Deep learning Image Classification Multi-source Transfer learning

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FraudGuard: Advancing Credit Card Fraud Detection with machine learning Benchmarks

FraudGuard: Advancing Credit Card Fraud Detection with Machi...

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Artificial Intelligence and data Engineering (AIDE), international conference on

作者： Avisha V Shetty Carol Jeswin Dcunha Deepthi Devishree Bangera D. K. Sreekantha Department of Computer Science Engg. (3rd Year BTech) NITTE (Deemed to be University) NMAM Institute of Technology Nitte India Department of Computer Science Engg. NITTE (Deemed to be University) NMAM Institute of Technology Nitte India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This research presents a comprehensive approach to credit card fraud detection, combining machine learning algorithms with user-centric applications. Analysing trends and fraudulent activities across various parameters, the study enhances fraud prevention strategies for stakeholders. Employing algorithms like XGBoost, Decision Tree, and Random Forest, and using SMOTE to balance the dataset, XGBoost achieved the highest accuracy. Practical tools developed include a real-time fraud detection app using Streamlit and a Tkinter-based desktop GUI, both leveraging the trained XGBoost model for instant predictions.

关键词： machine learning algorithms Accuracy data preprocessing Predictive models Credit cards data models Real-time systems Fraud Decision trees Random forests

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Automated X-Ray Image Classification for COVID-19 Detection Using DenseNet-121 and SVM 3

Automated X-Ray Image Classification for COVID-19 Detection ...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Ameta, Gaurav Kumar Khekare, Ganesh Phulre, Ajay Kumar Jayachitra, S. Singh, Vijendra Pratap Vijayan, Vinodh P. Parul Institute of Technology Parul University Department of Computer Science and Engineering Gujarat Vadodara India School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University Sehore India Psna College of Engineering and Technology Department of Ece Dindigul India Mahatma Gandhi Kashi Vidyapith Department of Computer Science & Applications Uttar Pradesh Varanasi India Mangalam College of Engineering Kerala Kottayam India

ISBN: (纸本)9798331542375

This paper provides an algorithm to detect COVID-19 from the chest X-Ray images using Two-class classification with DenseNet-121 and Support Vector machine. For effective identification of patterns suggestive of COVID-19 in the provided X-ray images, we leverage the DenseNet-121 model due to its feature extracting prowess, complemented by the SVM classifier to accurately classify the images between the COVID-19 to non-COVID-19 positive cases. The method delivers a high accuracy, which is more than 93% for accuracy, precision, recall, and F1-score, which makes it efficient in the COVID-19 quick screening. The Improved DenseNet-121 + SVM was compared with other deep learning models like ResNet50, VGG16, and confirmed that the proposed model has better efficiency in classifying COVID-19 cases. The performance of the model is checked with a five fold cross validation to ensure consistency and reliability on different sets of data not only in terms of performance with the IMDB data but also with the test data. The working paper is relevant to the current research practices in an effort to construct machine diagnostic systems that can be used by practicing healthcare professionals for identifying COVID-19 in the early stages, thus enhancing outcomes in patients' treatment. © 2025 IEEE.

关键词： COVID-19

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