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Multiscale Dynamic Attention Framework for Skin Lesion Segmentation and Classification

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International Journal of Imaging Systems and Technology 2025年第4期35卷

作者： G. Prince Devaraj V. Vanitha K. Vimala Praveen Kumar Gupta Department of Information Technology Francis Xavier Engineering College Tirunelveli India Sri Ramachandra Faculty of Engineering and Technology Sri Ramachandra Institute of Higher Education and Research Chennai Tamilnadu India Artificial Intelligence and Data Science Sri Krishna College of Technology Coimbatore Tamilnadu India School of Computer Science and Engineering Galgotias University Greater Noida India

This paper proposes a multitask deep learning framework for simultaneous skin lesion segmentation and classification, tailored for resource-constrained environments. The architecture integrates ShuffleNet as an encoder within a U-Net framework as a decoder to extract features. Using ShuffleNet's depthwise separable convolutions and channel shuffling, the model captures both low-level spatial details and high-level semantic features. To enhance feature representations, the proposed adaptive multiscale dynamic attention network (AMSDAN) is employed at two critical stages: first, to refine feature maps during segmentation by dynamically normalizing features across multiple scales; and second, to refine the segmented output for classification by highlighting discriminative patterns. The proposed work is trained and validated using HAM10000 and ISIC-2017 datasets. Experimental results demonstrate that the framework outperforms state-of-the-art techniques.

关键词： AMSDAN multitask learning ShuffleNet skin lesion classification skin lesion segmentation U-Net

ScalaBFS: A scalable BFS accelerator on HBM-enhanced FPGAs

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arXiv 2021年

作者： Li, Kexin Liu, Chenhao Shao, Zhiyuan Wang, Zeke Wu, Minkang Chen, Jiajie Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan30074 China Collaborative Innovation Center of Artificial Intelligence Zhejiang University China

High Bandwidth Memory (HBM) provides massive aggregated memory bandwidth by exposing multiple memory channels to the processing units. To achieve high performance, an accelerator built on top of an FPGA configured with HBM (i.e., FPGA-HBM platform) needs to scale its performance according to the available memory channels. In this paper, we propose an accelerator for BFS (Breadth-First Search) algorithm, named as ScalaBFS, that builds multiple processing elements to sufficiently exploit the high bandwidth of HBM to improve efficiency. We implement the prototype system of ScalaBFS and conduct BFS in both real-world and synthetic scale-free graphs on Xilinx Alveo U280 FPGA card real hardware. The experimental results show that ScalaBFS scales its performance almost linearly according to the available memory pseudo channels (PCs) from the HBM2 subsystem of U280. By fully using the 32 PCs and building 64 processing elements (PEs) on U280, ScalaBFS achieves a performance up to 19.7 GTEPS (Giga Traversed Edges Per Second). When conducting BFS in sparse real-world graphs, ScalaBFS achieves equivalent GTEPS to Gunrock running on the state-of-art Nvidia V100 GPU that features 64-PC HBM2 (twice memory bandwidth than U280). Copyright © 2021, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

QuantYOLO: A High-Throughput and Power-Efficient Object Detection Network for Resource and Power Constrained UAVs

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QuantYOLO: A High-Throughput and Power-Efficient Object Dete...

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Muhammad Gohar Javed Minahil Raza Muhammad Mohsin Ghaffar Christian Weis Norbert Wehn Muhammad Shahzad Faisal Shafait School of Electrical Engineering and Computer Science National University of Sciences and Technology Pakistan Microelectronic Systems Design Research Group Technische Universität Kaiserslautern Germany Deep Learning Laboratory National Center of Artificial Intelligence (NCAI) Pakistan Data Science in Earth Observation TU Munich Germany

Convolutional Neural Networks (CNNs) are producing state-of-the-art results in the object detection field. However, deep topologies of CNN are computationally intensive and typically require excessive resources (i.e. high-end GPUs), which hinder their deployment on resource and power constrained UAVs. In this work, we present a high-throughput and power efficient quantized object detection network, QuantYOLO, which is based on the Tiny-YOLOv2 topology. We conduct a detailed exploration of precision and filter pruning vs. accuracy, throughput and power consumption trade-off for the object detection task. As a result of these explorations, we select a network with binarized weights and 4-bit activations (except the output layer), which is 21.8× smaller than the Tiny-YOLOv2 achieving a mean Average Precision (mAP) of 51.5% on the PASCAL-VOC dataset. Finally, we present an FPGA based accelerator, which achieves 1.6× higher throughput (FPS) and is 3.1× more power efficient as compared to prior FPGA architectures.

关键词： Quantization (signal) Power demand Network topology Object detection computer architecture Throughput Topology

REST: Relational event-driven stock trend forecasting

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arXiv 2021年

作者： Xu, Wentao Liu, Weiqing Xu, Chang Bian, Jiang Yin, Jian Liu, Tie-Yan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Microsoft Research Asia Beijing China School of Artificial Intelligence Sun Yat-sen University Zhuhai China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Stock trend forecasting, aiming at predicting the stock future trends, is crucial for investors to seek maximized profits from the stock market. Many event-driven methods utilized the events extracted from news, social media, and discussion board to forecast the stock trend in recent years. However, existing event-driven methods have two main shortcomings: 1) overlooking the influence of event information differentiated by the stock-dependent properties;2) neglecting the effect of event information from other related stocks. In this paper, we propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks. The experimental studies on the real-world data demonstrate the efficiency of our REST framework. The results of investment simulation show that our framework can achieve a higher return of investment than baselines. © 2021, CC BY.

关键词： Forecasting

FusionTC: Encrypted App Traffic Classification Using Decision-Level Multimodal Fusion Learning of Flow Sequence

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Li, Shengbao Huang, Yuchuan Gao, Tianye Yang, Lanqi Chen, Yige Pan, Quanbo Zang, Tianning Chen, Fei Institute of Information Engineering Chinese Academy of Sciences Beijing100045 China School of Cyber Security University of Chinese Academy of Sciences Beijing101408 China National Computer Network Emergency Response Technical Team/Coordination Center of China Shandong Branch Jinan250004 China Ministry of Emergency Management Big Data Center Beijing100010 China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal University Anhui Wuhu240002 China College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325000 China National Computer Network Emergency Response Technical Team/Coordination Center of China Xinjiang Branch Urumqi830000 China

In recent years, an increasing number of mobile platforms and applications have adopted traffic encryption protocol technology to ensure privacy and security. Existing researches on encrypted traffic identification approaches often rely on a single-modal feature pattern (such as packet sequence and statistical features), which cannot fully represent the detail information of complex traffic features, and so their predictions are susceptible to anomalies. In order to improve the effect of classification on encrypted app traffic, we propose FusionTC, a novel app traffic classification framework based on feature fusion of flow sequence. FusionTC consists of two-level subclassifiers, which are used to perform decision-level fusion of multimodal features by an upgraded stacking method. The comprehensive capture and fusion of multimodal traffic details, coupled with the refined processing and segmentation of traffic, enables FusionTC to significantly promote classification accuracy and enhance robustness in challenging situations. Based on our self-built app traffic dataset, FusionTC improves the accuracy by at least 3.2% over the state-of-the-art approaches. © 2023 Shengbao Li et al.

关键词： Cryptography

Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

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engineering Applications of artificial intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

An Audio-Visual dataset and Deep Learning Frameworks for Crowded Scene Classification

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arXiv 2021年

作者： Pham, Lam Ngo, Dat Nguyen, Phu X. Hoang, Truong Schindler, Alexander Competence Unit Data Science & Artificial Intelligence Center for Digital Safety & Security Austria Institute of Technology Austria School of Computer Science and Electronic Engineering University of Essex United Kingdom Department of Computer Fundamentals FPT University Ho Chi Minh City700000 Viet Nam FPT Software Company Limited Viet Nam

This paper presents a task of audio-visual scene classification (SC) where input videos are classified into one of five real-life crowded scenes: 'Riot', 'Noise-Street', 'Firework-Event', 'Music-Event', and 'Sport-Atmosphere'. To this end, we firstly collect an audio-visual dataset (videos) of these five crowded contexts from Youtube (in-the-wild scenes). Then, a wide range of deep learning frameworks are proposed to deploy either audio or visual input data independently. Finally, results obtained from high-performed deep learning frameworks are fused to achieve the best accuracy score. Our experimental results indicate that audio and visual input factors independently contribute to the SC task's performance. Significantly, an ensemble of deep learning frameworks exploring either audio or visual input data can achieve the best accuracy of 95.7%. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos

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arXiv 2024年

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science University of Göttingen Campus Institute Data Science Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different model on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with the behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 competition track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024, CC BY-NC-ND.

关键词： Neurons

Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization

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Ablation-CAM: Visual Explanations for Deep Convolutional Net...

IEEE Workshop on Applications of computer Vision (WACV)

作者： Saurabh Desai Harish G. Ramaswamy Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI) Indian Institute of Technology Madras Department of Computer Science and Engineering Indian Institute of Technology Madras

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

In response to recent criticism of gradient-based visualization techniques, we propose a new methodology to generate visual explanations for deep Convolutional Neural Networks (CNN) - based models. Our approach - Ablation-based Class Activation Mapping (Ablation CAM) uses ablation analysis to determine the importance (weights) of individual feature map units w.r.t. class. Further, this is used to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Our objective and subjective evaluations show that this gradient-free approach works better than state-of-the-art Grad-CAM technique. Moreover, further experiments are carried out to show that Ablation-CAM is class discriminative as well as can be used to evaluate trust in a model.

关键词： Visualization Neurons Task analysis computer architecture data models data visualization Backpropagation