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检索条件"机构=Computer Engineering and Computer Science"

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The Development of Polycystic Ovary Syndrome Risk Evaluation System using Advanced Machine Learning Technique 7

The Development of Polycystic Ovary Syndrome Risk Evaluation...

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Velvizhi, R. Arya Kiruthiknivas, R. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

A complex endocrine disorder that affects fertile women, polycystic ovary syndrome, also known as PCOS, is characterized by an extensive spectrum of symptoms. This work extends previous efforts in PCOS detection and tracking with the goal of utilizing state-of-the-art machine learning techniques to overcome existing constraints. The goal of the project is to improve medical outcomes by utilizing predictive analytics to analyze big patient datasets that include genetic and clinical data. In this work, robust prediction models are developed by applying machine learning techniques, including Random Forest, decision trees, and support vector machines. These models aim to accurately identify individuals who are susceptible to PCOS and forecast the course of the disease in those who have already been diagnosed. Crucially, an extensive performance assessment is provided, contrasting the suggested models with current ones to emphasize their advantages and shortcomings. In-depth dataset investigation, rigorous model training, and comprehensive assessment metrics are conducted as part of the experiments. The outcomes highlight the usefulness of the models and indicate how they could assist medical practitioners in making defensible decisions. Predictive analytics integrated into electronic health records (EHRs) allows for ongoing PCOS patient monitoring, enabling timely interventions, individualized treatment plans, and better patient results. This research advances the area by improving the accuracy of PCOS diagnosis and therapy and by addressing the shortcomings of the previous literature. The suggested machine learning methods improve patient satisfaction, health outcomes, and system productivity, in addition to enhancing identification accuracy. This revised strategy guarantees a more impactful and targeted portrayal of the research activity, in line with the input that was given. © 2024 IEEE.

关键词： Decision trees

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BiMoeFormer: A Novel Transformer-Based Framework for 3D Human Motion Prediction with Bi-Level Routing Attention and Mixture of Experts 5

BiMoeFormer: A Novel Transformer-Based Framework for 3D Huma...

引用

5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Zhang, Wei Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350385557

We introduce a novel BiMoeFormer based on the Transformer architecture for 3D human motion prediction. Previous approaches primarily focus on the relationships between body joints in human poses, while neglecting their independencies. Our method addresses this by incorporating a Bi-Level Routing Attention (BRA) mechanism, which emphasizes balancing the correlation and independence among these joints. More specifically, BRA selects the top k most relevant joints for each joint, enhancing the overall modeling capability. Moreover, to accurately capture the diversity and complexity of human motion, we employ a Mixture of Experts (MoE) architecture to enhance the expressiveness of the Transformer. This allows us to control the model complexity by sharing the MoE across blocks. Comprehensive evaluations on the Human3.6M, AMASS, and 3DPW datasets demonstrate that our method consistently outperforms state-of-the-art baselines. © 2024 IEEE.

关键词： Motion estimation

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An Enhanced Vision-Language Pre-Training Approach for Scene Text Detection 5

An Enhanced Vision-Language Pre-Training Approach for Scene ...

引用

5th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2024

作者： Gu, Tao Zhang, Chongyang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331518677

Recent advancements in Vision-Language Pre-training (VLP) techniques have greatly improved performance in Scene Text Detection tasks by leveraging the rich visual and textual content in scene text images. We propose an innovative integration of contrastive learning with Masked Language Modeling (MLM) and Masked Image Modeling (MIM), inspired by the Masked Autoencoder (MAE) approach. This synthesis enhances self-supervised learning by combining discriminative and generative learning. Notably, we introduce masked image modeling for text detection, enabling effective representation learning in text image regions. To distinguish our approach from traditional BERT's MLM, we develop a custom tokenizer tailored for scene text detection. Our pre-trained model aligns visual and textual information, resulting in improved performance of existing scene text detectors. Extensive experiments on datasets such as ICDAR2015 show that our method significantly outperforms previous pre-training approaches. © 2024 IEEE.

关键词： Contrastive Learning

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A Systematic Review on Skin Cancer Classification and Novel Approaches in Deep Learning 3rd

A Systematic Review on Skin Cancer Classification and Novel ...

引用

3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Narmada, Vunnam Vardhan, K. Asish Department of Computer Science Engineering Mallareddy University Telangana Hyderabad India

ISBN: (纸本)9789819783540

In the current global context, people face the risk of losing their lives to various fatal diseases. One significant concern is the potential impact of cellular network radiation and mobile phone usage on health, particularly in relation to cancer. Cancer is characterized by abnormal and uncontrolled tissue growth within the body, with the possibility of spreading to other parts beyond its point of origin. Skin cancer stands out as one of the most perilous illnesses globally. Accurate identification of skin lesions in the early stages can significantly assist clinical judgment by offering precise disease diagnoses, potentially enhancing the likelihood of a cure before cancer spreads. Nevertheless, the unbalanced and finite availability of skin disease images utilized for training presents difficulties in automating skin disease classification. Additionally, ensuring the model’s adaptability across different domains and its robustness are crucial hurdles. Recent advancements have witnessed the widespread application of deep learning-based methods in skin cancer classification to address these challenges and achieve promising outcomes. Despite these efforts, comprehensive reviews encompassing the forefront issues in skin cancer classification remain scarce. This paper aims to fill this gap by providing a thorough overview of the latest algorithms for skin cancer classification based on deep learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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DeepRetina: A Deep Learning Approach for Diabetic Retinopathy Detection and Stage Classification

DeepRetina: A Deep Learning Approach for Diabetic Retinopath...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Nithishvikraman, H. Sudharsan, G. Anand Joseph Daniel, D.I. Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Using color fundus imaging to identify diabetic retinopathy (DR) is a tough process that requires skilled doctors to comprehend the existence and significance of certain small characteristics. This effort is further complicated by a complex categorization system. This research aims to use convolution neural network (CNN) to consistently diagnose diabetic retinopathy and grade patients into five groups or stages. An automatically generated diagnostic may be provided by a data-enhanced CNN architecture network that can identify the complex components involved in the class task, as well as exudates, hemorrhages, and micro-aneurysms in the retina, without requiring human input. This work trained CNN using data that was made accessible to the public. When compared to other algorithms on the same dataset, it demonstrated an astounding performance. It was used to the global dataset and obtained a great accuracy of 97% on the validation image. © 2024 IEEE.

关键词： Complex networks

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Classifying Human Emotions through EEG data with Machine Learning

Classifying Human Emotions through EEG data with Machine Lea...

引用

2024 International Conference Automatics and Informatics, ICAI 2024

作者： Kaur, Gaganjot Gupta, Meenu Kumar, Rakesh Chandigarh University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350353907

The Psychological and neurobiological phenomenon is called an emotion. Nowadays, using emotional engineering for the prediction of human emotions is one of the major attempts of Machine Learning (ML). However, the capability of human beings to present different expressions clashes with the emotional state of mind. Therefore, by observing physical appearance it is challenging to be able to identify a human's real emotional state. In this study, the objective is to examine the usage of machine learning techniques for an efficient emotion prediction that relies on Electroencephalography (EEG) signals. Using the Kaggle dataset that was collected through a Muse headband, five distinct ML methods are applied to classify emotions. To differentiate between different classification techniques performance metrics such as precision, recall, F1 score, support, and accuracy are used, Random Forest turned out to be the best method due to its high accuracy. Three emotions are predicted based on the results of performance metrics. This study is beneficial for mental health monitoring, neurofeedback therapy, stress management, human-computer interaction, etc. This study lays the groundwork for exploration into this field and helps in the development of more sophisticated control systems in the future. © 2024 IEEE.

关键词： Emotion Recognition

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From Pixels to Prognosis: A Deep Dive into Lung Cancer Subtype Classification using Transfer learning 2

From Pixels to Prognosis: A Deep Dive into Lung Cancer Subty...

引用

2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Ravindra, Chaya Nalband, Abdul Haq Kumar, Govind Basheer, Salman Ravindra, Manjunath School of Computer Science Engineering REVA University Bengaluru India

ISBN: (纸本)9798350383652

Current diagnostic procedures, such as imaging tests and biopsies, are time intensive and prone to human error. As a result, we employed deep learning to uncover patterns and identify lung cancer from histology pictures. We hypothesised that combining two separate methods (transfer learning and data augmentation) would increase the efficacy of lung cancer detection. The study's dataset consisted of histology images for Lung Cancer and was quite unbalanced, with very few photos containing tumours. We hypothesised that transfer learning, which employs existing knowledge for classification and detection, would be effective on such an imbalanced dataset. To boost our learning, we employed data augmentation to incorporate changes into the dataset. This can be accomplished by implementing Sequential Keras Model Layers. We then tested the efficacy of various convolutional neural network models on this job. We achieved an accuracy of 87.50% utilising the transfer learning model MobileNetV2 as the basis model and numerous geometric changes, beating the cutting-edge convolutional neural network-based technique. We then analysed the results and published them on a website that people can access at any time and from any location. © 2024 IEEE.

关键词： Lung cancer

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High Degree Dependency Study of Ensemble Models in Credit Card Fraud Analysis 2

High Degree Dependency Study of Ensemble Models in Credit Ca...

引用

2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Rani, Shalini Tiwari, Mayank Tyagi, Shiva Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Credit card fraud detection is an increasingly critical issue due to the growth of digital transactions and the sophistication of fraudulent activities. This study proposes a hybrid framework combining Graph Neural Networks (GNNs), ensemble methods (e.g., XGBoost, Random Forest), and polynomial feature engineering to address key challenges, including class imbalance, non-linear data relationships, and real-time applicability. The system achieved a detection accuracy of 96.7%, precision of 96.9%, recall of 96.4%, and a false-negative rate of 3.6% on benchmark datasets. These improvements are attributed to the integration of advanced sampling techniques, polynomial feature transformations, and the hybrid architecture. The findings demonstrate the effectiveness of the proposed approach in enhancing fraud detection systems and provide a scalable solution for real-world applications. © 2025 IEEE.

关键词： Graph neural networks

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Attention-Guided Energy-Based Model for Out-of-Distribution Data Detection 27th

Attention-Guided Energy-Based Model for Out-of-Distribution ...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Cao, Zongjing Li, Yan Shin, Byeong-Seok Department of Computer Science and Engineering Inha University Incheon Korea Republic of

ISBN: (纸本)9783031783944

Detecting out-of-distribution (OOD) data is crucial for the safe and reliable deployment of deep learning models in open-world scenarios. While energy-based models (EBMs) have shown promising potential in OOD detection through the use of an energy function to capture the underlying probability distribution of data, previous approaches have primarily utilized logits or class probabilities from the fully connected layer to compute energy scores. However, logits are inherently class-specific and focus mainly on the relationship between the input and known classes, potentially ignoring the rich information embedded in raw feature representations that are essential for identifying OOD samples. This study introduces a novel approach that utilizes patterns within the feature space to calculate energy scores instead of relying on logits or class probabilities. We propose a spatial attention score to generate class-specific features for each category, which are then used to compute the energy score. Furthermore, we develop a new energy function that transforms these features into energy scores, significantly improving the OOD detection performance of EBMs. In experiments conducted on a Cifar-10 pre-trained ResNet-50, our feature-based energy score method reduced the average false positive rate at a true positive rate of 95% by 5.33% compared to the logits-based approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy-based model Out-of-distribution detection Uncertainty quantification.

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Building Secure and Decentralized Survey Methodology: A Comprehensive Solution with FIDO2 Authentication and Fluree 14

Building Secure and Decentralized Survey Methodology: A Comp...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Balachandher, Jawahar Dongre, Chandana Bhattacharyya, Somanshu Hunashikatti, Rohit Sarasvathi, V. Department of Computer Science and Engineering PES University Karnataka Bangalore India

ISBN: (纸本)9798331531935

Traditional survey systems struggle with data immutability, privacy protection, and reliable storage. These limitations compromise transparency, security, and data integrity. This paper proposes a decentralized and secure survey platform to address these challenges. Fast Identity Online 2 (FIDO2) passwordless authentication strengthens user verification, backed by public key cryptography. Fluree, a permissioned blockchain based graph database, ensures decentralized storage and minimizes data tampering. Smart codes are designed based on user requirements, while integrated statistical analyses enhance data insights. This approach of integrating these technologies prioritizes transparency, security, and integrity in the data collection process while maintaining a seamless user experience. By fostering trust among stakeholders, it enables reliable, data driven decision-making. © 2025 IEEE.

关键词： Public key cryptography

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