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End-to-End Transformer Architecture with Novel Ensemble Learning Method Integrating CT Scans and Clinical Narratives for Brain Stroke Diagnosis 41st

End-to-End Transformer Architecture with Novel Ensemble Lea...

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41st computer Graphics international Conference, CGI 2024

作者： Abdul Wahid, Junaid Ayoub, Muhammad Xu, Mingliang Jiang, Xiaoheng Lei, Shi Li, Lifeng Hussain, Shabir Khawaja, Ashfaque Gao, Yufei School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Electronic Information and Electrical Engineering Shanghai Jiaotong University Shanghai200240 China School of Computer Science and Engineering Central South University Hunan Changsha336017 China School of Cyber Space and Security Zhengzhou University Zhengzhou450002 China Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate School Shenzhen100084 China

ISBN: (纸本)9783031820236

This study is an extension of our previously published manuscript [2]. Building upon the foundations laid in our earlier work, this research further refines and extends the transformer-based approach to enhance its diagnostic capabilities. The focus remains on the integration of multi-modal information, combining both textual clinical narratives and imaging data from multi slice CT scans, to provide a more comprehensive and accurate diagnosis of brain strokes. In addition, we introduce a new ensemble learning approach based on logic gates. This innovative method combines information from clinical narratives and CT scans to enhance the diagnostic capabilities of our framework, marking a significant evolution in our approach to advancing precision in brain stroke detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computerized tomography

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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Chiral exceptional point enhanced active tuning and non reciprocity in micro-resonators

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光：科学与应用（英文版） 2025年第2期14卷 401-410页

作者： Hwaseob Lee Lorry Chang Ali Kecebas Dun Mao Yahui Xiao Tiantian Li Andrea Alù Sahin K.Özdemir Tingyi Gu Department of Electrical and Computer Engineering University of DelawareNewarkDelaware 19716USA Department of Engineering Science and Mechanics Pennsylvania State UniversityUniversity ParkPA 16802USA Photonics Initiative Advanced Science Research CenterCity University of New YorkNew YorkNY 10031USA Physics Program Graduate CenterCity University of New YorkNew YorkNY 10016USA Department of Engineering Science and Mechanics Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Computer Engineering Saint Louis UniversitySaint LouisMO 63103USA

Exceptional points(EPs)have been extensively explored in mechanical,acoustic,plasmonic,and photonic ***,little is known about the role of EPs in tailoring the dynamic tunability of optical devices.A specific type of EPs known as chiral EPs has recently attracted much attention for controlling the flow of light and for building sensors with better responsivity.A recently demonstrated route to chiral EPs via lithographically defined symmetric Mie scatterers on the rim of resonators has not only provided the much-needed mechanical stability for studying chiral EPs,but also helped reduce losses originating from nanofabrication imperfections,facilitating the in-situ study of chiral EPs and their contribution to the dynamics and tunability of ***,we use asymmetric Mie scatterers to break the rotational symmetry of a microresonator,to demonstrate deterministic thermal tuning across a chiral EP,and to demonstrate EP-mediated chiral optical nonlinear response and efficient electro-optic *** results indicate asymmetric electro-optic modulation with up to 17 dB contrast at GHz and CMOS-compatible voltage *** wafer-scale nano-manufacturing of chiral electro-optic modulators and the chiral EP-tailored tunning may facilitate new micro-resonator functionalities in quantum information processing,electromagnetic wave control,and optical interconnects.

关键词： active point micro chiral enhanced exceptional reciprocity resonators tuning

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Email Spam Detection Using Logistic Regression and Explainable AI

Email Spam Detection Using Logistic Regression and Explainab...

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international Conference on electrical, computer and Communication engineering (ECCE)

作者： Sumit Kanti Sarker Ratul Bhattacharjee Md Abu Sufian Md Solaiman Ahamed Md Abu Talha Faiza Tasnim K.M. Nazmul Islam Sharia Tasnim Adrita Department of Computer Science & Engineering (CSE) Faculty of Science & Technology (FST) American International University Bangladesh Dhaka Bangladesh Bachelor of Science in Information Technology (BSIT) Cybersecurity Program Concentrations Washington University of Science and Technology (WUST) VA USA Department of Electrical and Electronics Engineering (EEE) Faculty of Engineering (FE) American International University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering (CSE) Faculty of Science & Engineering Northern University Bangladesh-(NUB) Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Email spam detection is crucial for ensuring a positive user experience and maintaining communication security. This study presents a novel spam detection approach leveraging Logistic Regression, optimized through hyperparameter tuning and enhanced with Explainable Artificial Intelligence. The proposed method is evaluated on a large-scale dataset of emails collected from a real-world spam detection system. Term Frequency-Inverse Document Frequency is utilized for feature extraction, converting email text into numerical representations. XAI introduces interpretability by highlighting critical features such as "pill," "PHP," and "Businessweek," which indicate spam classification patterns. Metrics such as precision, recall, and the confusion matrix are employed to assess the model’s performance, ensuring a balanced evaluation. Hyperparameter optimization using Grid Search CV achieves an optimal regularization parameter (C = 100) and maximum iterations (maxiter=100), resulting in an impressive F1 score of 98.76%, accuracy of 98.82%, precision of 98.62%, and recall of 98.90%.

关键词： Logistic regression Explainable AI Filtering Unsolicited e-mail Computational modeling Predictive models Transformers Frequency conversion Feature extraction Tuning

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

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IEEE Transactions on Mobile Computing 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In Nanyang Technological University College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Singapore University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.

关键词： Resource allocation

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CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion Detection

arXiv

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arXiv 2025年

作者： Jahin, Md Abrar Soudeep, Shahriar Mridha, M.F. Kabir, Raihan Islam, Md Rashedul Watanobe, Yutaka Physics and Biology Unit Okinawa Institute of Science and Technology Graduate University Okinawa904-0412 Japan Department of Computer Science American International University-Bangladesh Dhaka1229 Bangladesh Department of Electrical & Computer Engineering University of Patras Rion-Patras Patras26504 Greece Department of Computer Science and Engineering University of Asia Pacific Dhaka1205 Bangladesh Department of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu Japan

Cybersecurity threats are growing, making network intrusion detection essential. Traditional machine learning models remain effective in resource-limited environments due to their efficiency, requiring fewer parameters and less computational time. However, handling short and highly imbalanced datasets remains challenging. In this study, we propose the fusion of Contrastive Attentive Graph Network and Graph Attention Network (CAGN-GAT Fusion), and benchmark it against 15 other models, including both Graph Neural Networks (GNNs) and traditional ML models. Our evaluation is conducted on four benchmark datasets (KDD-CUP-1999, NSL-KDD, UNSW-NB15, and CICIDS2017) using a short and proportionally imbalanced dataset with a constant size of 5000 samples to ensure fairness in comparison. Results show that CAGN-GAT Fusion demonstrates stable and competitive accuracy, recall, and F1-score, even though it does not achieve the highest performance in every dataset. Our analysis also highlights the impact of adaptive graph construction techniques, including small changes in connections (edge perturbation) and selective hiding of features (feature masking), improving detection performance. The findings confirm that GNNs, particularly CAGN-GAT Fusion, are robust and computationally efficient, making them well-suited for resource-constrained environments. Future work will explore GraphSAGE layers and multiview graph construction techniques to further enhance adaptability and detection accuracy. © 2025, CC BY.

关键词： Network intrusion

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Norm Augmented Graph AutoEncoders for Link Prediction

Norm Augmented Graph AutoEncoders for Link Prediction

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international Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunhui Liu Huaisong Zhang Xinyi Gao Liuye Guo Zhen Tao Tieke He State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Link Prediction (LP) is a crucial problem in graph-structured data. Graph Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders (GAEs) being a notable representation. However, our empirical findings reveal that GAEs’ LP performance suffers heavily from the long-tailed node degree distribution, i.e., low-degree nodes tend to exhibit inferior LP performance compared to high-degree nodes. What causes this degree-related bias, and how can it be mitigated? In this study, we demonstrate that the norm of node embeddings learned by GAEs exhibits variation among nodes with different degrees, underscoring its central significance in influencing the final performance of LP. Specifically, embeddings with larger norms tend to guide the decoder towards predicting higher scores for positive links and lower scores for negative links, thereby contributing to superior performance. This observation motivates us to improve GAEs’ LP performance on low-degree nodes by increasing their embedding norms, which can be implemented simply yet effectively by introducing additional self-loops into the training objective for low-degree nodes. This norm augmentation strategy can be seamlessly integrated into existing GAE methods with light computational cost. Extensive experiments on various datasets and GAE methods show the superior performance of norm-augmented GAEs.

关键词： Training Bridges Heavily-tailed distribution Autoencoders Signal processing Graph neural networks Acoustics Decoding Computational efficiency Speech processing

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Norm Augmented Graph AutoEncoders for Link Prediction

arXiv

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arXiv 2025年

作者： Liu, Yunhui Zhang, Huaisong Gao, Xinyi Guo, Liuye Tao, Zhen He, Tieke State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia

关键词： Graph neural networks

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Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Du, Hongyang Wang, Jiacheng Niyato, Dusit Kim, Dong In College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

关键词： Mobile edge computing

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