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AI-Driven Dermatology: CNN-RF Hybrid Model for Skin Lesion's Disease Classification 15

AI-Driven Dermatology: CNN-RF Hybrid Model for Skin Lesion's...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Vats, Satvik Mehta, Shiva Computer Science and Engineering Graphic Era Hill University Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

the spectrum of skin diseases, based on their nature, poses significant barriers to health that affect individuals internationally. thus, basic procedures such as accurate and early diagnosis are paramount to successful treatment. this paper presents a hybrid model called Convolutional Neural Network and Random Forest (CNNRF) for accurately diagnosing five different types of skin diseases: Melanoma, Benign nevus, Basal cell carcinomas 5CC, Dermatofibroma, vascular lesions. Resizing, normalization techniques, and data augmentation were applied to preprocess the dataset, including annotated photos of skin lesions. the CNN-RF method is based on Convolutional Neural Networks (CNNs) feature extraction and classification provided by Random Forests (RFs). Based on the results of the experiments, it was established that the proposed CNN-RF model achieved greater classification accuracy of 94% compared to the CNN model with 9 1% and the RF model with 8 8%. Moreover, for the same experiment, the CNN-RF model established an accuracy of 93 per cent, recall of 94 per cent, and F 1 score of 94 per cent. the model's performance was demonstrated and analyzed using a confusion matrix and classification reports. the accuracy and recall rates were very high for this type of skin lesion, specifically up to 95% accuracy and 96% recall for Dermatofibroma and Vascular Lesions. Nonetheless, this research asserts the reliability of the proposed CNN-RF model as a feasible instrument for diagnosing skin diseases automatically, as well as with superior precision and stability. thus, the findings hold significant implications for clinical practice alerts, as the model can assist dermatologists in providing timely identification to improve the quality of treatment for their clients. Future work will focus on expanding the dataset and exploring other types of compound models to increase the accuracy of the diagnosis. this work contributes to the advancement of automated diagnostic-making systems

关键词： 3D Image Scans Convolutional Neural Networks (CNNs) Data Augmentation Decision Support Systems (DSSs) Machine Learning in Healthcare Neurological Damage Assessment Skin Lesion's Disease (PD)

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QID2: An Image-Conditioned Diffusion Model for Q-Space Up-Sampling of DWI Data 15th

QID2: An Image-Conditioned Diffusion Model for Q-Space Up-S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2024

作者： Chen, Zijian Wang, Jueqi Venkataraman, Archana Department of Electrical and Computer Engineering Boston University Boston United States

ISBN: (纸本)9783031869198

We propose an image-conditioned diffusion model to estimate high angular resolution diffusion weighted imaging (DWI) from a low angular resolution acquisition. Our model, which we call QID2, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction. We leverage a U-Net architecture with cross-attention to preserve the positional information of the reference images, further guiding the target image generation. We train and evaluate QID2 on single-shell DWI samples curated from the Human Connectome Project (HCP) dataset. Specifically, we sub-sample the HCP gradient directions to produce low angular resolution DWI data and train QID2 to reconstruct the missing high angular resolution samples. We compare QID2 with two state-of-the-art GAN models. Our results demonstrate that QID2 not only achieves higher-quality generated images, but it consistently outperforms state-of-the-art baseline methods in downstream tensor estimation across multiple metrics and in generalizing to downsampling scenario during testing. Taken together, this study highlights the potential of diffusion models, and QID2 in particular, for q-space up-sampling, thus offering a promising toolkit for clinical and research applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep Learning Diffusion Models Diffusion Weighted Imaging Q-Space Up-sampling Tensor Reconstruction

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Snapipeline: Accelerating Snapshot Startup for FaaS Containers 15

Snapipeline: Accelerating Snapshot Startup for FaaS Containe...

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15th Annual ACM symposium on Cloud computing, SoCC 2024

作者： Lan, Yuqiao Peng, Xiaohui Wang, Yifan Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400712869

Due to the frequent starts and stops of numerous services in FaaS (Function as a Service), reducing cold start overhead is a core issue in improving the performance of container-based FaaS services. Snapshot and restore-based mechanisms effectively reduce the cold start time of containers by transforming container initialization overhead into restoration overhead. Consequently, this mechanism has become a research hotspot in accelerating the cold start of FaaS containers. Researchers introduce snapshot compression and decompress the snapshots to reduce the storage cost before starting instances. However, existing works have the following shortcomings: (1) File-mapped memory pages are not processed during snapshot compression, resulting in a significant amount of redundant data in memory;(2) the serial execution of snapshot decompression and instance restoration leads to high instance startup latency. To address these shortcomings, we propose the Snapipeline mechanism, which implements the following optimizations: (1) In the snapshot compression phase, it extends deduplication to file-mapped memory;(2) In the restoration phase, it leverages the hot and cold distinction in FaaS application memory to prioritize the memory pages restoration, pipelining snapshot decompression, memory pages restoration, and function execution. this mechanism hides the expensive snapshot decompression latency behind the instance restoration and function execution latency and removes the snapshot decompression from the critical path of instance startup. Evaluation on real-world FaaS application datasets shows that Snapipeline reduces memory usage by up to 28% and decreases end-to-end latency by an average of 53%, compared to the baseline. © 2024 Owner/Author.

关键词： Restoration

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BKIDset - A New Intrusion Detection Dataset To Mitigate the Class Imbalance Problem 15

BKIDset - A New Intrusion Detection Dataset To Mitigate The ...

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15th International Conference on Advanced computing and Applications (ACOMP)

作者： Nguyen the Anh Le Huy Hoang Vu Dinh Minh Tran Hoang Hai Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam

ISBN: (纸本)9781665406390

Applying machine learning techniques to Intrusion Detection System (IDS) is one efficient method to detect existing and new types of network attacks with minimal human supervision and great accuracy. the classification performance of such systems depend heavily on the dataset they are trained on. Most currently deployed datasets suffer from a high imbalance between normal network traffic and anomaly network traffic, which limits the system's ability to detect minority classes with high accuracy. In this paper, we propose a new model for generating and collecting anomaly network traffic, which is used in conjunction with CICIDS2017 [1] dataset to create a new dataset namely BKIDset, which mitigates the class imbalance problem encountered in CICIDS2017 dataset and introduces additional types of denial-of-service attacks. the paper also proposes different machine learning models trained on BKIDset, that achieve a high accuracy score of 99%.

关键词： Machine learning algorithms Intrusion detection Telecommunication traffic Machine learning computer architecture Network architecture Denial-of-service attack

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Poster: Conceptual Design for FPGA Based Artifical Intelligence Model for HIL Applications 28

Poster: Conceptual Design for FPGA Based Artifical Intellige...

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28th IEEE symposium on computers and Communications, ISCC 2023

作者： Kordi, Farshideh Barnard, Christian Fortier, Paul Miled, Amine Université Laval Departement of Electrical and Computer Engineering QuébecQC Canada

ISBN: (纸本)9798350300482

Hardware-in-the-Loop (HIL) simulators play a critical role in the automotive industry by providing extensive testing and validation capabilities for electronic control units (ECUs). One of the main challenges faced by HIL simulators involves the task of constructing a virtual environment that accurately replicate the behavior of the actual system. Artificial intelligence (AI) algorithms can be useful in generating precise virtual environments for HIL simulations of complex systems. Moreover, minimal latency is essential for establishing a reliable virtual environment. FPGA (Field Programmable Gate Array) can effectively reduce latency in HIL simulations by providing high-performance computing resources. this paper aims to address these challenge by introducing a machine learning-driven HIL simulator implemented on FPGA. the proposed architecture employs FPGA technology to enhance the computational speed of a temporal convolutional neural network (TCN). © 2023 IEEE.

关键词： Hardware-in-the-loop simulation

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Study of the Magnetic Behaviour of Oleic-Acid Coated Co Ferrite Nanoparticles: A Multiscale Modeling Approach Using high-performance computing 15th

Study of the Magnetic Behaviour of Oleic-Acid Coated Co F...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Vasilakaki, Marianna Ntallis, Nikolaos Trohidou, Kalliopi N. Institute of Nanoscience and Nanotechnology NCSR ‘Demokritos’ Aghia Paraskevi Attiki153 10 Greece

ISBN: (纸本)9783031857027

A multi-scale modeling approach is employed for the study of the effect of oleic-acid (OA) coverage on the magnetic behaviour of Co ferrite nanoparticles (CFNs), using high performance computing (HPC). Our study is performed in three different length scales: first we perform electronic structure calculations using parallel implementation of the density functional theory (DFT) method to study the magnetic properties of ultra-small OA coated CFNs. Next taking input from the DFT data, we calculate the magnetic characteristics of larger in size OA coated CFNs at an atomic scale performing Monte Carlo simulations. Finally, a mesoscopic modeling approach for interacting assemblies of nanoparticles is employed to reduce further computation time and sources for the study of the magnetic behaviour of CFNs covered with different percentage of oleic-acid, at finite temperature. the results demonstrate that the DFT magnetic moment and magnetic anisotropy of the nanoparticle decrease with the increase of the percentage of the surfactant. However, in the assembly of CFNs the interplay between the exchange and dipolar inter-particle interactions results in the increase of the magnetic anisotropy and the decrease of the saturation magnetization as the percentage of OA coverage increases, in agreement with experimental findings. the proposed multi-scale computational approach, implemented in HPC environment, illustrates its ability to handle numerical calculations on complex magnetic interactions of multiple structural components. It can overcome computational limitations to predict optimum parameters for hybrid organic/inorganic nanomaterials for various applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Saturation magnetization

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An Analysis of Neural architecture Search and Hyper Parameter Optimization Methods 8th

An Analysis of Neural Architecture Search and Hyper Paramete...

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8th Latin American high performance computing Conference (CARLA)

作者： Puentes G, David E. Barrios H, Carlos J. Navaux, Philippe O. A. CAGE Univ Ind Santander SC3UIS Bucaramanga Colombia Univ Fed Rio Grande do Sul Informat Inst Porto Alegre RS Brazil

ISBN: (纸本)9783031042096;9783031042089

the growing interest for Deep Learning has leading the develop of methods for Neural architecture Search (NAS) and Hyperparameter Optimization (HPO). Generally, architectures are designed in an iterative and expensive process of trial and error, besides being restricted to designer creativity and its specific knowledge about the topic. this paper presents an analysis and discussion about the frequent algorithms used to automate neural networks' design and tuning of its hyperparameters Similarly, Floating Point Operations per Second (FLOPS), training time, and network complexity are complementary objectives to accuracy in classification, conceiving the performance as the sum of precision and computational consumption. this study seeks to pinpoint possible future directions to reduce human intervention in constructing machine learning models and understanding Artificial Neural Networks.

关键词： Hyperparameter optimization Deep learning Artificial neural network computing performance Automated machine learning

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KSRR: Knowledge Centric Semantically Driven Recipe Recommendation Framework Encompassing Collective Intelligence 15th

KSRR: Knowledge Centric Semantically Driven Recipe Recommend...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Shah, Haiya Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9783031889912

In the era of Web 3.0, there is a pressing need for a knowledge-centric model to recommend recipes, as the current structure of the worldwide Web lacks specialized recommendation frameworks for recipe recommendations, even though standard search engines offer some level of recipe recommendation. this paper aims to address this gap by proposing a dedicated recommendation channel. the model utilizes user historical activity on food apps as one of the criteria for personalization, leveraging the SimRank algorithm and Grey Wolf optimization to generate a comprehensive termset. Additionally, the model incorporates semantic similarity-based reasoning, employing techniques such as SimRank, Morisita overlap index, and normalized information distance. the Grey Wolf optimization framework further refines the solution set. the topic network comprises recipe ebooks, food blogs, and crawl labels from food blogs, with the inclusion of scientific ebooks to enrich the knowledge base. A restriction classifier is employed to control the features of the data center, ensuring computational feasibility and reducing complexity. Furthermore, a lightweight decision tree classifier demonstrates strong classification performance, characterized by high precision, a lower false discovery rate (FDR), and notable expressive percentage values. Extensive evaluations reveal that the proposed framework outperforms baseline models, establishing it as the leading model for recipe recommendations. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Search engines

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Delay Optimization for Consensus Communication in Blockchain-Based End-Edge-Cloud Network 15th

Delay Optimization for Consensus Communication in Blockchain...

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15th International symposium on Advanced Parallel Processing Technologies, APPT 2023

作者： Ma, Shengcheng Wang, Shuai Tsai, Wei-Tek Zhang, Yaowei School of Computer Science and Engineering Beihang University No. 37 Xueyuan Road Beijing China Digital Society and Blockchain Laboratory Beihang University No. 37 Xueyuan Road Beijing100191 China China Mobile Information Security Management and Operation Center Beijing China

ISBN: (纸本)9789819978717

With the rapid development of smart IoT technology, various innovative mobile applications improve many aspects of our daily life. End-edge-cloud collaboration provides data transmission in connecting heterogeneous IoT devices and machines with improvements in high quality of service and capacity. However, the end-edge cloud architecture still remains some challenges including the risks of data privacy and tolerance transmission delay. Blockchain is a promising solution to enable data processing in a secure and efficient way. In this paper, blockchain is considered as an infrastructure of the end-edge-cloud network and the time cost of the PBFT consensus is analyzed from the perspective of the leader’s position. Considering the concurrent processing of tasks in cellular networks, multi-intelligent deep reinforcement learning is used to train the assignment strategy of the edge server. the numerical results show that the proposed method can achieve better performance improvement in terms of the time consumption of data processing. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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high-performance Asynchronous CNN Accelerator with Early Termination 15

High-Performance Asynchronous CNN Accelerator with Early Ter...

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15th IEEE International symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022

作者： Loo, Tan Rong Teo, T. Hui Tiruye, Mulat Ayinet Wey, I-Chyn Singapore University of Technology and Design Engineering Product Development Singapore Singapore University of Technology and Design Engineering Product Development Science Mathematics and Technology Singapore Artificial Intelligence Research Center School of Electrical and Computer Engineering College of Engineering Graduate Institute of Electrical Engineering Chang Gung University Chang Gung University Taiwan

ISBN: (纸本)9781665464994

Convolutional Neural Network (CNN), especially very deep networks, are highly computation intensive, resulting in long delays and high-power consumption. the dynamically varying environmental conditions in real world inference can result in highly complex problems, and hence the need for these inefficient deep networks to guarantee satisfactory accuracy all the time. Several studies employ approximation techniques to execute partial computation of the network, in attempt to reduce the amount of computation were unnecessary. However, such approaches are still highly sequential in nature, since they still need to run the whole network. this paper proposes an early termination architecture on an already-trained CNN to allow for testing the partial results midway through the network, reducing computations by terminating the main network when it is sufficient as the inference results. the first proposal is implemented in synchronous circuit, however, due to its nature all memory elements are required to capture even when no new data is generated. the second proposal employs the use of asynchronous circuit to significantly reducing power consumption and further sped up the architecture since an operation need not wait the slowest critical path in the circuit. the proposed circuits were designed on FPGA platform. the results of the asynchronous circuit show a nearly 20% increment in speed with about 12% reduction in power consumption in comparison with a synchronous circuit. © 2022 IEEE.

关键词： Electric power utilization

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