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Deep Learning Based Novel Cascaded Approach for Skin Lesion Analysis 7th

Deep Learning Based Novel Cascaded Approach for Skin Lesion ...

引用

7th international conference on Computer Vision and Image processing, CVIP 2022

作者： Innani, Shubham Dutande, Prasad Baheti, Bhakti Baid, Ujjwal Talbar, Sanjay Center of Excellence Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India

Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin can72;cer diagnosis and ensures effective treatment. the computer-aided diag72;... 详细信息

ISBN: (纸本)9783031314063

Patients diagnosed with skin cancer like melanoma are prone to a high mortality rate. Automatic lesion analysis is critical in skin can¬cer diagnosis and ensures effective treatment. the computer-aided diag¬nosis of such skin cancer in dermoscopic images can significantly reduce the clinicians’ workload and help improve diagnostic accuracy. Although researchers are working extensively to address this problem, early detec¬tion and accurate identification of skin lesions remain challenging. this research focuses on a two-step framework for skin lesion segmentation followed by classification for lesion analysis. We explored the effective¬ness of deep convolutional neural network (CNN) based architectures by designing an encoder-decoder architecture for skin lesion segmenta¬tion and CNN based classification network. the proposed approaches are evaluated quantitatively in terms of the Accuracy, mean Intersection over Union(mIoU) and Dice Similarity Coefficient. Our cascaded end-to-end deep learning-based approach is the first of its kind, where the classification accuracy of the lesion is significantly improved because of prior segmentation. the code is available at https://***/shubhaminnani/skin/lesion. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image enhancement

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Transfer Learning Models for MRI-Based Brain Tumor Detection

Transfer Learning Models for MRI-Based Brain Tumor Detection

引用

international conference on Advanced Technologies for Signal and Image processing (ATSIP)

作者： Moriba Bérété Amira Echtioui Lamia Sellami Ahmed Ben Hamida Advanced Technologies for Medicine and Signals Laboratory ‘ATMS’ National Engineering School of Sfax (ENIS) Sfax University Sfax Tunisia Department IS College of Computer Science King Khaled University ‘KKU’ Abha Kingdom of Saudi Arabia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

Human health, especially brain health, is paramount for overall well-being. Magnetic Resonance Imaging (MRI) serves as a vital diagnostic tool for assessing critical organs such as the brain. the abundance of data generated by MRI scans presents a significant opportunity for artificial intelligence (AI) to aid in health decision-making processes. In this paper, we focus on using AI techniques, particularly transfer learning, to classify brain tumors from MRI images. By leveraging transfer learning, we aim to enhance the performance of image processing algorithms in the classification of brain tumors. Our approach involves employing established architectures such as Xception, ResNet50, and DenseNet201, adapted through transfer learning for this specific task. Among the architectures tested, our results highlight the superiority of the ResNet50 model in binary classification of brain tumors using MRI scans. the proposed model achieves remarkable performance metrics, including an accuracy of $99 \%$, precision of $99.5 \%$, recall of $99.5 \%$, and F1 Score of $99 \%$. this research underscores the potential of transfer learning in advancing diagnostic capabilities for brain health assessment.

关键词： Measurement Accuracy Magnetic resonance imaging Image processing Transfer learning Brain modeling Artificial intelligence

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parallel High Utility Itemset Mining 35th

Parallel High Utility Itemset Mining

引用

35th international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Fan, Gaojuan Xiao, Huaiyuan Zhang, Chongsheng Almpanidis, George Fournier-Viger, Philippe Fujita, Hamido Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China Bank Zhengzhou Co Ltd Zhengzhou Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Iwate Prefectural Univ Fac Software & Informat Sci Takizawa Japan

ISBN: (纸本)9783031085307;9783031085291

Association rule mining is a popular data mining task for finding relationships between values from the itemsets that co-occur frequently in a transactional database. Association rule mining has many applications but the "support-confidence" framework it depends on is inadequate for many cases. In recent years, a generalised task called high utility itemset mining (HUIM) has gained much popularity;it aims at discovering itemsets that yield a high revenue as measured by a utility function. However, when facing large data volumes, the running time of state-of-the-art HUIM algorithms often grows exponentially. In this work, we investigate parallel HUIM algorithms (PHUIM) and adapt two state-of-the-art sequential HUIM algorithms for parallel processing based on the Apache Spark in-memory data processing platform. Extensive experiments on several benchmark and synthetic datasets show that the proposed methods improve considerably the efficiency of the baseline HUIM algorithms.

关键词： High-utility itemset Apache spark parallel d(2)HUP EFIM

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Towards Modularity: Integration Strategy of Radar into a LiDAR Based Robotics Mapping Platform 9th

Towards Modularity: Integration Strategy of Radar into a LiD...

引用

9th international conference on Modelling and Simulation for Autonomous Systems

作者： Murphy, Kevin Charter, Dylan Pasley, Dylan Lopez-Toledo, Israel Soylemezoglu, Ahmet Construct Engn Res Lab Champaign IL 61801 USA Univ Illinois Champaign IL 61801 USA

ISBN: (纸本)9783031312670;9783031312687

Removing Combat Engineers, first responders, and disaster relief workers from dangerous situations remains a major motivation for researching semi-autonomous mapping platforms. the successful adoption of such systems requires the implementation of a series of complex sensors collecting, processing, and storing terabytes of data, in near real-time. Operations in uncertain and austere environments has great potential for sensor and computational hardware unavailability and damage;robustness to these difficulties requires careful planning and dedicated practices. While simulation allows for rapid creation and testing of such complicated robotic systems and algorithms, development in simulation and on hardware introduces additional complications. this research explores these challenges and proposes a conventional workflow method for modular development of hardware and software in parallel. the Large-group parallel Simulation-Hardware Development (LaPaSHD) workflow arose from years of US Army Corps of Engineers (USACE) Engineer Research and Development Center's (ERDC) research with Unmanned Ground Vehicles (UGV). LaPaSHD, system requirements and considerations, best practices, and the challenges motivating this workflow are introduced through a case study: incorporating radar systems into an existing LiDAR based simulated platform.

关键词： ROS Sensor Modularity Scalability Modularity Workflow parallel Development UGV

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P-UNet: parallel Attention Based UNet for Crack Detection 7

P-UNet: Parallel Attention Based UNet for Crack Detection

引用

7th international conference on Signal and Image processing, ICSIP 2022

作者： Zhang, Xiaohu Huang, Haifeng Sun Yat-sen University Guangzhou China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Crack Detection plays a vital role in the field of social public property protection. Recently, Attention based UNet model has achieved the state-of-art results via surppressing the interference of light, shadow and other factors. Since these models could automatically select crack related features and suppress the interference. However, carefully analyzing the structure of these Attention based UNet models, we find that these models use serial structure, mainly focusing on selecting spatial features in different layers, without considering selecting features from different feature channels. thus, in our proposed model, attention mechanism is used in two dimensions, spatial dimension and channel dimension. thus more complementary crack features are obtained by fusing these two features, which can effectively suppress interference and improve the detection accuracy. Based in the idea, a parallel Attention Block is designed and a crack detection model based on UNet with parallel Attention Block (P-UNet) is proposed by us. Compared with the state-of-the-art crack detection method, our proposed P-UNet demonstrates competitive segmentation performance. Experimental results show that our P-UNet model achieves accuracy of 92%, 80%, 98% on Cracktree200, CRACK500 and CFD dataset respectively, which is 2%, 3% and 1% higher than that of the traditional attention model. © 2022 IEEE.

关键词： Image segmentation

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Skywalker: Efficient Alias-method-based Graph Sampling and Random Walk on GPUs 30

Skywalker: Efficient Alias-method-based Graph Sampling and R...

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30th international conference on parallel architectures and Compilation Techniques (PACT)

作者： Wang, Pengyu Li, Chao Wang, Jing Wang, Taolei Zhang, Lu Leng, Jingwen Chen, Quan Guo, Minyi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Qi Zhi Inst Shanghai Peoples R China

ISBN: (纸本)9781665442787

Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt computing-intensive algorithms on large-scale graphs. Existing system frameworks for these tasks are not only spatially and temporally inefficient, but many also lead to biased results. this paper presents Skywalker, a high-throughput, quality-preserving random walk and sampling framework based on GPUs. Skywalker makes three key contributions: first, it takes the first step to realize efficient biased sampling with the alias method on a GPU. Second, it introduces well-crafted load-balancing techniques to effectively utilize the massive parallelism of GPUs. third, it accelerates alias table construction and reduce the GPU memory requirement with efficient memory management scheme. We show that Skywalker greatly outperforms the state-of-the-art CPU-based and GPU-based baselines, in a wide spectrum of workload scenarios.

关键词： Graph sampling random walk graphics processing unit

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A Hybrid Multi-threaded parallel Iterated Greedy Algorithm for Distributed Flowshop Group Scheduling Problems with Preventive Maintenance

A Hybrid Multi-Threaded Parallel Iterated Greedy Algorithm f...

引用

Advanced algorithms and Control Engineering (ICAACE), international conference on

作者： Xiaobin Sun Hongyan Sang Yasheng Zhao Wanzhong Wu School of computing LiaoCheng University Liaocheng China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

the distributed flowshop group scheduling abstracted from the production process of printed circuit boards is a recent active research topic. the distributed flow shop group scheduling problem is based on the assumption that processing machines can run continuously. However, in actual production, factories not only pursue productivity, but also pay attention to the reliability and stability of the production process. In this paper, we study a distributed flowshop group scheduling problem with preventive maintenance (DFGSP/PM) to minimize the makespan. Based on the characteristics of DFGSP/PM, we proposed a hybrid multi-threaded parallel iterated greedy algorithm (HMPIG). the greedy NEH algorithm based on the LPT rule is used in the initialization phase of the algorithm to generate the initial solution. Different destruction and reconstruction rules for groups and jobs are designed to update the solution in the destruction and reconstruction phase. In the local search phase, a hybrid multithreaded parallel search strategy is introduced to improve the search efficiency of the IG algorithm so that the optimal insertion position of the group in the sequence can be searched more quickly. the numerous experimental results show that HMPIG has better solution quality as well as solution stability.

关键词： Greedy algorithms Productivity Job shop scheduling Printed circuits Search problems Production facilities Circuit stability

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Towards Automatic Block Size Tuning for Image processing algorithms on CUDA

Towards Automatic Block Size Tuning for Image Processing Alg...

引用

17th international conference on Software Technologies (ICSOFT)

作者： Guerfi, Imene Kriaa, Lobna Saidane, Leila Azouz Univ Manouba Natl Sch Comp Sci ENSI CRISTAL Lab RAMSIS Pole Manouba Tunisia

ISBN: (纸本)9789897585883

With the growing amount of data, computational power has became highly required in all fields. To satisfy these requirements, the use of GPUs seems to be the appropriate solution. But one of their major setbacks is their varying architectures making writing efficient parallel code very challenging, due to the necessity to master the GPU's low-level design. CUDA offers more flexibility for the programmer to exploit the GPU's power with ease. However, tuning the launch parameters of its kernels such as block size remains a daunting task. this parameter requires a deep understanding of the architecture and the execution model to be well-tuned. Particularly, in the Viola-Jones algorithm, the block size is an important factor that improves the execution time, but this optimization aspect is not well explored. this paper aims to offer the first steps toward automatically tuning the block size for any input without having a deep knowledge of the hardware architecture, which ensures the automatic portability of the performance over different GPUs architectures. the main idea is to define techniques on how to get the optimum block size to achieve the best performance. We pointed out the impact of using static block size for all input sizes on the overall performance. In light of the findings, we presented two dynamic approaches to select the best block size suitable to the input size. the first one is based on an empirical search;this approach provides the optimal performance;however, it is tough for the programmer, and its deployment is time-consuming. In order to overcome this issue, we proposed a second approach, which is a model that automatically selects a block size. Experimental results show that this model can improve the execution time by up to 2.5x over the static approach.

关键词： GPU Computing parallel Programming Program Optimization Auto-tuning and Face Detection

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Neural Network Architecture for Scheduling Tank Trucks Loading at Petroleum Products Storages

Neural Network Architecture for Scheduling Tank Trucks Loadi...

引用

international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Yuriy V. Ignatyev Gennady I. Afanasyev Department of Information Processing and Management Systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

this study presents a decomposition of the scheduling problem for the process of petroleum product dispatch from petroleum storage facilities using road transport into major functional blocks. the proposed decomposition is aimed at developing a specialized modular architecture of an artificial neural network to address the scheduling problem for petroleum product dispatch via road transport, as well as for solving scheduling problems of similar classes. It is demonstrated that the neural network task of assigning work groups to executor groups can be reduced to a sequence-to-sequence transformation problem utilizing attention mechanisms. Neural architectures implementing the identified functional blocks were selected, along with appropriate methods for training the model. the resulting modular components enable separate, block-wise training of target models for constructing suboptimal schedules.

关键词： Training Schedules Power engineering Scheduling algorithms Roads Loading Scheduling Numerical models Petroleum Load modeling

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DRRA-based Reconfigurable Architecture for Mixed-Radix FFT

DRRA-based Reconfigurable Architecture for Mixed-Radix FFT

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international conference on VLSI Design

作者： Reeshita Kallapu Dimitrios Stathis Srinivas Boppu Ahmed Hemani School of Electrical Sciences Indian Institute of Technology Bhubaneswar (IITBBS) India Royal Institute of Technology (KTH) Sweden

Fast-Fourier Transform is an important algorithm which is used in digital signal processing and communication applications. Furthermore, mixed-radix FFT provides flexibility and increases the speed of FFT computation. For real-time processing, efficient hardware implementation using reconfigurable architectures is preferred which can offer higher performance and flexibility. In this paper, we propose an architecture for the implementation of the FFT that is derived from the Dynamically Reconfigurable Resource Array and has multiple parallel processing cells while also providing the flexibility to select the radix for each stage of the FFT. the twiddle factor generator proposed in this architecture minimizes the memory requirements and simplifies the hardware. Using the proposed architecture, various length FFTs were mapped onto either single cell or multiple cells in parallel. It is observed that the proposed architecture improves the performance by 2x times when compared to the existing FFT architectures.

关键词： Memory management Signal processing algorithms Computer architecture Transforms Switches Very large scale integration Hardware

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