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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sarathamani, T. Kavitha, K. Thirumoorthi, C. Vagini, K Jayanthi Manikandaprabhu, P. Sumathi, P. Kolkata India Mohan Babu University School of Computing Department of Ai and Ml Sree Sainath Nagar Tirupati India Psg College of Arts and Science Department of Computer Science Tamilnadu Coimbatore India Kpr College of Arts Science and Research Department of Computer science with Data Analytics Tamilnadu Coimbatore India Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Karnataka Mysuru India Dr. G R Damodaran College of Science Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350368413

Women all across the world are affected by the potentially fatal condition known as breast cancer. According to clinical experts, early cancer detection helps to save lives. Several machine learning algorithms have been developed to classify cancers in order to diagnose breast cancer, and medical image processing has been found to be a beneficial area for increasing classification accuracy. One of the most crucial signs for detecting breast cancer early is micro classifications. Machine learning methods have been employed in numerous studies to address this problem. Various classification algorithms are used to find the presence of breast cancer whether it is Benign or Malignant. For earlier cancer detection and diagnosis, an image enhancement approach is being developed. A machine learning technique is employed in this work to attempt to categorize breast cancer. The purpose of the present research was to increase the accuracy of machine learning methods for disease diagnosis using ML techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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CAT-Net: A Co-Adaptive Transfer Learning Network for BCI-Assisted Neurorehabilitation

CAT-Net: A Co-Adaptive Transfer Learning Network for BCI-Ass...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Shuailei Ding, Yi Jiang, Muyun Tang, Ning Chew, Effie Ang, Kai Keng Guan, Cuntai College of Computing and Data Science Nanyang Technological University Singapore National University Hospital Singapore Singapore National University Hospital National University of Singapore Singapore Agency for Science Technology and Research Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Brain-computer interfaces (BCIs) hold great potential for motor recovery in post-stroke patients. However, the motor imagery decoding accuracy is limited by the nonstationarity of EEG signals across subjects and sessions. We propose CAT-Net: a Co-Adaptive Transfer learning network to simultaneously address the inter-subject variability and intersession nonstationarity in EEG data. The proposed method selects a relevant subset of data from all the available subjects' data to train an initial model, followed by subject-specific transfer learning from the initial model to the target subject to establish a pretrain model. Subsequently, online adaptive training is then applied to incrementally train the pretrain model using the data from previous sessions for the target subject. This proposed network using this unique co-adaptive training method is then evaluated on both upper and lower-limb neurorehabilitation EEG datasets comprising 358 sessions from 33 stroke patients. The results showed significant accuracy improvements, achieving averaged accuracies of 70.6% and 72.3% on the respective datasets, surpassing the state-of-the-art baselines. © 2025 IEEE.

关键词： Electroencephalography Neurorehabilitation Transfer Learning

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Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks

Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimize...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Aly, Salah A. Bakhiet, Ali Balat, Mazen Badya University Faculty of Computing & Data Science Giza Egypt Fayoum University Faculty of Science Computer Science Dept. Fayoum Egypt Computer Science Dept. October 6th City Giza Egypt E-Japanese University of Science & Tech. Cs & It Dept. Alexandria Egypt

ISBN: (纸本)9798350367560

Lymphoma diagnosis, particularly distinguishing between subtypes, is critical for effective treatment but remains challenging due to the subtle morphological differences in histopathological images. This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Dense Neural Network (DNN) for classification, optimized using the Harris Hawks Optimization (HHO) algorithm. The model was trained on a dataset of 15,000 biopsy images, spanning three lymphoma subtypes: Chronic Lymphocytic Leukemia (CLL), Follicular Lymphoma (FL), and Mantle Cell Lymphoma (MCL). Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability. Comprehensive evaluation using precision, recall, F1-score, and ROC-AUC underscores the model's robustness and potential for clinical adoption. This framework offers a scalable solution for improving diagnostic accuracy and efficiency in oncology. © 2024 IEEE.

关键词： Lung cancer

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Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation 41

Finite Volume Features, Global Geometry Representations, and...

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41st International Conference on Machine Learning, ICML 2024

作者： Jessica, Loh Sher En Arafat, Naheed Anjum Lim, Wei Xian Chan, Wai Lee Kong, Adams Wai Kin Rolls-Royce@NTU Corporate Lab Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore School of Mechanical & Aerospace Engineering Nanyang Technological University Singapore

Computational fluid dynamics (CFD) simulation is an irreplaceable modelling step in many engineering designs, but it is often computationally expensive. Some graph neural network (GNN)based CFD methods have been proposed. However, the current methods inherit the weakness of traditional numerical simulators, as well as ignore the cell characteristics in the mesh used in the finite volume method, a common method in practical CFD applications. Specifically, the input nodes in these GNN methods have very limited information about any object immersed in the simulation domain and its surrounding environment. Also, the cell characteristics of the mesh such as cell volume, face surface area, and face centroid are not included in the message-passing operations in the GNN methods. To address these weaknesses, this work proposes two novel geometric representations: Shortest Vector (SV) and Directional Integrated Distance (DID). Extracted from the mesh, the SV and DID provide global geometry perspective to each input node, thus removing the need to collect this information through message-passing. This work also introduces the use of Finite Volume Features (FVF) in the graph convolutions as node and edge attributes, enabling its message-passing operations to adjust to different nodes. Finally, this work extends the use of residual training to improve flow field prediction for a GNN scenario with immersed object, when low resolution data is available. Experimental results on two datasets with five different state-of-the-art GNN methods for CFD indicate that SV, DID, FVF and residual training can effectively reduce the predictive error of current GNN-based methods by as much as 41%. Our codes and datasets are available at https://***/toggled/FvFGeo. Copyright 2024 by the author(s)

关键词： Mesh generation

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Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection

arXiv

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arXiv 2024年

作者： Fang, Xiang Easwaran, Arvind Genest, Blaise Interdisciplinary Graduate Programme ERI@N College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE Singapore College of Computing and Data Science Nanyang Technological University Singapore Singapore

Out-of-distribution (OOD) detection targets to detect and reject test samples with semantic shifts, to prevent models trained on in-distribution (ID) dataset from producing unreliable predictions. Existing works only extract the appearance features on image datasets, and cannot handle dynamic multimedia scenarios with much motion information. Therefore, we target a more realistic and challenging OOD detection task: OOD action detection (ODAD). Given an untrimmed video, ODAD first classifies the ID actions and recognizes the OOD actions, and then localizes ID and OOD actions. To this end, in this paper, we propose a novel Uncertainty-Guided Appearance-Motion Association Network (UAAN), which explores both appearance features and motion contexts to reason spatial-temporal inter-object interaction for ODAD. Firstly, we design separate appearance and motion branches to extract corresponding appearance-oriented and motion-aspect object representations. In each branch, we construct a spatial-temporal graph to reason appearance-guided and motion-driven inter-object interaction. Then, we design an appearance-motion attention module to fuse the appearance and motion features for final action detection. Experimental results on two challenging datasets show that UAAN beats state-of-the-art methods by a significant margin, illustrating its effectiveness. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion tracking

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Generated and Pseudo Content guided Prototype Refinement for Few-shot Point Cloud Segmentation 38

Generated and Pseudo Content guided Prototype Refinement for...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wei, Lili Lang, Congyan Chen, Ziyi Wang, Tao Li, Yidong Liu, Jun School of Computer Science & Technology Beijing Jiaotong University China Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education China School of Computing and Communications Lancaster University United Kingdom

Few-shot 3D point cloud semantic segmentation aims to segment query point clouds with only a few annotated support point clouds. Existing prototype-based methods learn prototypes from the 3D support set to guide the segmentation of query point clouds. However, they encounter the challenge of low prototype quality due to constrained semantic information in the 3D support set and class information bias between support and query sets. To address these issues, in this paper, we propose a novel framework called Generated and Pseudo Content guided Prototype Refinement (GPCPR), which explicitly leverages LLM-generated content and reliable query context to enhance prototype quality. GPCPR achieves prototype refinement through two core components: LLM-driven Generated Content-guided Prototype Refinement (GCPR) and Pseudo Query Context-guided Prototype Refinement (PCPR). Specifically, GCPR integrates diverse and differentiated class descriptions generated by large language models to enrich prototypes with comprehensive semantic knowledge. PCPR further aggregates reliable class-specific pseudo-query context to mitigate class information bias and generate more suitable query-specific prototypes. Furthermore, we introduce a dual-distillation regularization term, enabling knowledge transfer between early-stage entities (prototypes or pseudo predictions) and their deeper counterparts to enhance refinement. Extensive experiments demonstrate the superiority of our method, surpassing the state-of-the-art methods by up to 12.10% and 13.75% mIoU on S3DIS and ScanNet, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

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Neural computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

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Alignment and matching tests for high-dimensional tensor signals by tensor contraction

arXiv

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arXiv 2024年

作者： Liu, Ruihan Wang, Zhenggang Yao, Jianfeng School of Computing and Data Science The University of Hong Kong Hong Kong School of Data Science The Chinese University of Hong Kong Shenzhen China

We consider two hypothesis testing problems for low-rank and high-dimensional tensor signals, namely the tensor signal alignment and tensor signal matching problems. These problems are challenging due to the high dimension of tensors and lack of meaningful test statistics. By exploiting a recent tensor contraction method, we propose and validate relevant test statistics using eigenvalues of a data matrix resulting from the tensor contraction. The matrix has a long range dependence among its entries, which makes the analysis of the matrix challenging, involved and distinct from standard random matrix theory. Our approach provides a novel framework for addressing hypothesis testing problems in the context of high-dimensional tensor *** Codes Primary 62H15, secondary 60B20, 62H10 Copyright © 2024, The Authors. All rights reserved.

关键词： Matrix algebra

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Empowering Oral Healthcare: CNN-Assisted Diagnosis for Improved Patient Outcomes 2

Empowering Oral Healthcare: CNN-Assisted Diagnosis for Impro...

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Garrepalli, Vaishnavi Gupta, Vishwanath Abhishek, S. Anjali, T. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Amritapuri India Verizon Data Services India Pvt Ltd Hyderabad India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350381665

In today's world, health is very important, and among the many aspects of well-being, oral wellness is one of the crucial components. Diagnostic investigation in everyday clinical procedures primarily consists of reviewing periapical radiography, which visualizes dental anatomy such as teeth, bone, and soft tissues within the oral cavity. Nonetheless, subjective errors may arise during human interpretation of dental radiographs which also take a lot of time. Most of these dental X-rays have an important role in diagnosing various conditions like Implant, Fillings, Cavity, and Impacted teeth under accurate assessment of classification of dental radiographs. These imaging techniques can help dentists detect anomalies within the mouth through this diagnosis process. To improve the accuracy of diagnosis and reduce the costs of clinical operations, this study presents a novel approach that automates the detection of problematic teeth and other dental diseases using these x-ray images using CNN (Convolutional Neural Networks) architecture VGG19 for making the right diagnosis choices. © 2024 IEEE.

关键词： Image classification

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Accumulative Time Based Ranking Method to Reputation Evaluation in Information Networks

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Journal of Computer science & Technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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