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SOCA-DOM:A Mobile System-on-Chip Array System for Analyzing Big Data on the Move

Journal of computer science & Technology 2022年第6期37卷 1271-1289页

作者： Le-Le Li Jiang-Yi Liu Jian-Ping Fan Xue-Hai Qian Kai Hwang Yeh-Ching Chung Zhi-Bin Yu Center for Heterogeneous and Intelligent Computing Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Science and Engineering The Chinese University of Hong KongShenzhenShenzhen 518172China Center for High Performance Computing Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Shenzhen 518055China Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos Angeles CA 90089-0001U.S.A. Department of Computer Science University of Southern CaliforniaLos AngelesCA 90089-0001U.S.A. School of Data and Science The Chinese University of Hong KongShenzhenShenzhen 518172China

Recently,analyzing big data on the move is *** requires that the hardware resource should be low volume,low power,light in weight,high-performance,and highly scalable whereas the management software should be flexible and consume little hardware *** meet these requirements,we present a system named SOCA-DOM that encompasses a mobile system-on-chip array architecture and a two-tier“software-defined”resource manager named ***,we design an Ethernet communication board to support an array of mobile ***,we propose a two-tier software architecture for Chameleon to make it ***,we devise data,configuration,and control planes for Chameleon to make it“software-defined”and in turn consume hardware resources on ***,we design an accurate synthetic metric that represents the computational power of a computing *** employ 12 Apache Spark benchmarks to evaluate ***,SOCA-DOM consumes up to 9.4x less CPU resources and 13.5x less memory than Mesos which is an existing resource *** addition,we show that a 16-node SOCA-DOM consumes up to 4x less energy than two standard Xeon *** on the results,we conclude that an array architecture with fine-grained hardware resources and a software-defined resource manager works well for analyzing big data on the move.

关键词： edge computing mobile architecture resource management big data analytics software-defined systems

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Automated Estimation of Left Myocardial Strain from Cine CTA: A Comparison with Cine MRI

Automated Estimation of Left Myocardial Strain from Cine CTA...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Sun, An-Yu Hsu, Li-Yueh Chang, Lin-Ching Bandettini, W Patricia Chen, Marcus Y. National Institutes of Health National Heart Lung and Blood Institute BethesdaMD United States Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States National Institutes of Health Clinical Center BethesdaMD United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States

ISBN: (纸本)9798350362480

Cardiovascular disease, the leading cause of death in the U.S., affects both the heart and blood vessels. Contrast-enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for assessing heart and coronary artery morphology, aiding in the diagnosis of cardiovascular disease. Cine CTA, which captures multiple 3D heart images throughout the cardiac cycle, is particularly useful for evaluating nonischemic cardiomyopathy, a common cause of heart failure unrelated to coronary artery disease. Key biomarkers, such as left ventricular ejection fraction, which measures the amount of blood pumped from the heart's lower chambers per contraction, and myocardial strain, which evaluates the deformation of the heart muscle during contraction and relaxation, are vital for assessing heart function. We developed an automated artificial intelligence (AI) framework to segment various cardiac structures across all image volumes representing different phases of the cardiac cycle. The framework also automatically extracts three short-axis slices to calculate the myocardial strain in the left ventricle. The results are compared with strain measurements obtained from cardiovascular magnetic resonance (CMR) cine imaging. Our study shows both radial and circumferential strains from cine CTA are comparable to those from cine CMR. The proposed AI framework facilitates a comprehensive evaluation of myocardial function throughout the cine CTA, supporting improved diagnostic accuracy. © 2024 IEEE.

关键词： computerized tomography

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Performance Evaluation of Multi-Contrast Dixon MRI and CT for Abdominal Fat and Muscle Segmentation Using a UNet CNN

Performance Evaluation of Multi-Contrast Dixon MRI and CT fo...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Heller, Andrew Liu, Andrew Chang, Lin-Ching Cohen, Gregg Jones, Elizabeth C. Hsu, Li-Yueh National Institutes of Health Clinical Center BethesdaMD United States Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States

ISBN: (纸本)9798350362480

We evaluate the performance of a deep learning framework for segmenting abdominal fat and muscle using multi-contrast Dixon magnetic resonance (MR) and computed tomography (CT) images. We aim to compare MR image segmentation by testing Dixon fat-only, water-only, and combination of both types of images and comparing the results with CT images. Nineteen subjects underwent abdominal CT and Dixon MR imaging on the same day. For each participant, three pairs of matched axial images from both CT and MR were selected at the intervertebral levels of L2-L3, L3-L4, and L4-L5 for analysis. References labels were generated through semi-automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and muscle areas. They were then used to train and evaluate a U-Net based Convolutional Neural Network (CNN) framework with a 3-fold cross-validation to compare the segmentation performance across CT, Dixon fat-only and water-only MR images. Combining the fat-only and water-only MR image inputs produced superior results in all labels. Our study demonstrates that CNN-based segmentation performance for abdominal fat and muscle improves with the inclusion of additional input channels, such as combining Dixon fat-only and water-only MR images. While CT results represent the gold standard in abdominal image segmentation, increasing the number of input image channels used in MR segmentation can approach, and even match, the results of CT. © 2024 IEEE.

关键词： Image segmentation

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Anchorage Accurately Assembles Anchor-Flanked Synthetic Long Reads 24

Anchorage Accurately Assembles Anchor-Flanked Synthetic Long...

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24th International Workshop on Algorithms in Bioinformatics, WABI 2024

作者： Zang, Xiaofei Carl Li, Xiang Metcalfe, Kyle Ben-Yehezkel, Tuval Kelley, Ryan Shao, Mingfu Huck Institutes of the Life Sciences The Pennsylvania State University University ParkPA United States Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States Element Biosciences San DiegoCA United States

ISBN: (纸本)9783959773409

Modern sequencing technologies allow for the addition of short-sequence tags, known as anchors, to both ends of a captured molecule. Anchors are useful in assembling the full-length sequence of a captured molecule as they can be used to accurately determine the endpoints. One representative of such anchor-enabled technology is LoopSeq Solo, a synthetic long read (SLR) sequencing protocol. LoopSeq Solo also achieves ultra-high sequencing depth and high purity of short reads covering the entire captured molecule. Despite the availability of many assembly methods, constructing full-length sequence from these anchor-enabled, ultra-high coverage sequencing data remains challenging due to the complexity of the underlying assembly graphs and the lack of specific algorithms leveraging anchors. We present Anchorage, a novel assembler that performs anchor-guided assembly for ultrahigh-depth sequencing data. Anchorage starts with a kmer-based approach for precise estimation of molecule lengths. It then formulates the assembly problem as finding an optimal path that connects the two nodes determined by anchors in the underlying compact de Bruijn graph. The optimality is defined as maximizing the weight of the smallest node while matching the estimated sequence length. Anchorage uses a modified dynamic programming algorithm to efficiently find the optimal path. Through both simulations and real data, we show that Anchorage outperforms existing assembly methods, particularly in the presence of sequencing artifacts. Anchorage fills the gap in assembling anchor-enabled data. We anticipate its broad use as anchor-enabled sequencing technologies become prevalent. Anchorage is freely available at https://***/Shao-Group/anchorage;the scripts and documents that can reproduce all experiments in this manuscript are available at https://***/Shao-Group/anchorage-test. © Xiaofei Carl Zang, Xiang Li, Kyle Metcalfe, Tuval Ben-Yehezkel, Ryan Kelley, and Mingfu Shao

关键词： anchor-guided assembly de Bruijn graph Genome assembly LoopSeq synthetic long reads

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Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction

Enhancing Prognostication in Colorectal Cancer with Integrat...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Velmurugan, R. Suresh, K. Thilagaraj, T. Sharma, Meenakshi Saranya, D. Bangalore India Department of Computer Science Bangalore India Moodlakatte Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Kundapura India Global Group of Institutes Punjab Amritsar India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India

ISBN: (纸本)9798331509828

Machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector Machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM;as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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⁠Climate Change Prediction in Sustainable Healthcare Systems for Biodiverse Ecosystem Based on Satellite Data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 283-293页

作者： Jadhav, Makarand Mohan Agarwal, Pankaj Umadevi, B. Khatibi, Ali Akhila, N. Sandeep, Kasturi Sai Banerjee, Sudipta Department of Electronics and Telecommunication NBN Sinhgad Technical Institutes Campus Pune 411041 India School of Engineering and Technology K.R. Mangalam University Gurgaon India Department of Mathematics JSS Academy of Technical Education Karnataka Bengaluru 560060 India Management and Science University Selangor Shah Alam Malaysia Department of Computer Science & amp Engineering Aditya University Surampalem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist.Vaddeswaram Andhra Pradesh 522302 India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) (SIU) Pune India

Every area of human life is being impacted by climate change more and more. According to recent studies, there is a strong association between temperature and human health. It is predicted that by 2100, fluctuations in temperature will cause 73 deaths for every 100,000 people worldwide. Algorithms for machine learning (ML) have made significant progress, leading to innovations in other fields of study and, most recently, being proposed as a tool to support climate analysis. In order to mitigate these risks, a proximity environmental feature-based tree health assessment (PTA) methodology is suggested, which offers recommendations for early warning systems for possible poor tree health. Tree health is determined and assessed in PTA development using proximity environmental characteristics (PEFs). This research proposes novel technique in climate change-based health data analysis using satellite data analysis for biodiverse ecosystem in sustainable modelling. Here, the input is collected as regional climate change data and analysed for feature selection with extraction. This data has selection carried out using kernel adversarial Gaussian linear regression model;the selected features show the abnormality in health issues. Then, the selected features have been extracted using convolutional U-net extreme attention neural network. The extracted features show healthcare data analysis based on climate changes. The simulation analysis is carried out for various climate change-based healthcare analysis dataset in terms of detection accuracy, normalised error, precision, F-measure, and recall. The suggested method produced 96% detection accuracy, 56% normalised error, 90% precision, 95% recall, and 92% F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biodiverse ecosystem Climate change Health data analysis Machine learning Satellite data analysis

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A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4861-4875页

作者： Yang, Shangshang Tian, Ye He, Cheng Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR Department of Computer Science University of Surrey Guildford U.K

It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded as a promising alternative for training NNs in recent years. However, EAs suffer from the curse of dimensionality and are inefficient in training deep NNs (DNNs). By inheriting the advantages of both the gradient-based approaches and EAs, this article proposes a gradient-guided evolutionary approach to train DNNs. The proposed approach suggests a novel genetic operator to optimize the weights in the search space, where the search direction is determined by the gradient of weights. Moreover, the network sparsity is considered in the proposed approach, which highly reduces the network complexity and alleviates overfitting. Experimental results on single-layer NNs, deep-layer NNs, recurrent NNs, and convolutional NNs (CNNs) demonstrate the effectiveness of the proposed approach. In short, this work not only introduces a novel approach for training DNNs but also enhances the performance of EAs in solving large-scale optimization problems.

关键词： Artificial neural networks Training Optimization Search problems Scalability Complexity theory Genetic algorithms Deep neural networks (DNNs) evolutionary algorithm (EA) genetic operator gradient descent sparsity

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Comparison of Machine Learning Techniques Based on Feature Reduction to Detect Diabetic Retinopathy 9

Comparison of Machine Learning Techniques Based on Feature R...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Singh, Devendra Joshi, Deepika Dobhal, Dinesh C. Pant, Janmejay Pant, Himanshu Graphic Era Hill University Computer Science and Engineering Department Nainital India Amrapali Group of Institutes Faculty of Technology and Computer Application Haldwani India Graphic Era Deemed to Be University Computer Application Department Uttarakhad Dehradun India Graphic Era Hill University School of Computing Department Nainital India

ISBN: (纸本)9798350397376

Diabetic retinopathy is considered as an eye disorder condition that can affect persons who are diagnosed positive against diabetes. This condition causes damage to the retina's blood vessels. Diabetic retinopathy detection at an early stage may help patients protect their vision. This disease is often asymptomatic in the early stages but has a significant impact in the latter stages. The timely identification and intervention of diabetic retinopathy (DR) can greatly mitigate the potential for visual impairment. It takes time, effort, and money for ophthalmologists to manually diagnose DR retina fundus images. So there is a need for a screening system that can detect the disease in its early stages. There are various machine learning-based approaches available to detect diabetic retinopathy. In our proposed work first, we have to select the optimal features then we perform the comparative analysis with the help of various machine learning algorithms. We have used Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machine (SVM), Naïve Bayes, and AdaBoost. In our experiment, we observed that Logistic Regression achieved the highest accuracy of 74.2 with optimal features. © 2023 IEEE.

关键词： Adaptive boosting

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Design of Wastewater Treatment Apparatus Based on Photocatalytic Materials 8th

Design of Wastewater Treatment Apparatus Based on Photocatal...

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8th International Conference on Advances in Energy Resources and Environment engineering, ICAESEE 2022

作者： Peng, ShiJie Li, LinSen Wang, WenZhuo Ni, YueQi Zhang, ManLin Wang, JingWen Ke, CuiLing He, Kun School of Mechanical and Electrical Engineering Wuhan Institute of Technology Wuhan China School of Optical Information and Energy Engineering Wuhan Institute of Technology Wuhan China Polytechnic Institute of Zhejiang University Zhejiang Hangzhou China School of Computer Science & Engineering Wuhan Institute of Technology Wuhan China School of Chemical Engineering & Pharmacy Wuhan Institute of Technology Wuhan China Hefei Institutes of Physical Science Institute of Plasma Physics Chinese Academy of Sciences Hefei China Science Island Branch of Graduate School University of Science and Technology of China Hefei China

ISBN: (纸本)9783031425622

With the development of technology and the improvement of people’s living standards, the requirements for wastewater treatment are becoming higher and higher. In view of the current problems of environmental pollutant reduction and energy utilization, we propose to combine the semiconductor photocatalytic reaction with activated carbon membrane modules and RO membrane modules to generate strong oxidizing radicals through sunlight excitation of semiconductor photocatalytic materials at room temperature in order to achieve the treatment of harmful and refractory organic pollutants when photocatalytic materials are excited by sunlight in ordinary temperature, solving the problems of traditional wastewater treatment such as high energy consumption, incomplete treatment and easy generation of secondary pollution. The wastewater treatment apparatus designed in this paper and related technology can not only be used for the pretreatment or advanced treatment of refractory organic wastewater in pharmaceutical, breeding, and medical industries, but also for the purification of micro-polluted water sources. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Wastewater treatment

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Tomato Maturity Detection in Complex Orchard Environment

Tomato Maturity Detection in Complex Orchard Environment

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Xiaoming Li Tingting Qian Guo Li Wenyi Ren College of Computer Science and Engineering Guangxi Normal University Guilin China Agricultural Information Institutes of Science and Technology Shanghai Academy of Agriculture Sciences Shanghai China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

This paper presents a model for detecting tomato maturity called EFC-YOLO11. It utilizes the C3k2-EMSDCEEM, C3k2-Faster, and C-BiFPN modules to enhance feature extraction, multi-scale feature fusion, and detection accuracy. The model also demonstrates strong adaptability to different conditions and good generalization ability. In experiments using the PF500 dataset, it shows a 2.5% improvement in mAP50 and a 3.7% improvement in mAP50-95. On the Chunxiang variety dataset, it improves mAP50 by 2.1 % and mAP50-95 by 2.4%. Additionally, in the purple passion fruit detection task, the model increases recall by 5.2%. The overall mAP50 reaches 86%.

关键词： YOLO Adaptation models Accuracy Computational modeling Lighting Greenhouses Glass Feature extraction Reliability engineering Consumer electronics

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