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MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation

computers in Biology and Medicine 2024年 179卷 108847-108847页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Ejiyi, Makuachukwu Bennedith Ukwuoma, Chiagoziem Ejiyi, Thomas Ugochukwu Muoka, Gladys Wavinya Gyarteng, Emmanuel S.A. Bamisile, Olusola O. Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Chengdu China

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) – an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection. As the depth increases, our proposed spatial multi-head attention (SMA) is added to facilitate inter-layer communication and information exchange, enabling the network to effectively capture long-range dependencies and global context. MACCoM is also equipped with a convolutional cross-mixer we proposed to enhance the feature extraction capability of the model. By incorporating these modules, we effectively combine semantically similar features and reduce artifacts during the early stages of training. Experimental results on 4 biomedical datasets crafted from 3 datasets of varying modalities consistently demonstrate that MACCoM outperforms or matches state-of-the-art baselines in the segmentation tasks. With Breast Ultrasound Image (BUSI), MACCoM recorded 99.06 % Jaccard, 77.58 % Dice, and 93.92 % Accuracy, while recording 99.50 %, 98.44 %, and 99.29 % respectively for Jaccard, Dice, and Accuracy on the Chest X-ray (CXR) images used. The Jaccard, Dice, and Accuracy for the High-Resolution Fundus (HRF) images are 95.77 %, 74.35 %, and 95.95 % respectively. The findings here highlight MACCoM's effectiveness in improving segmentation performance and its valuable potential in image analysis. © 2024 Elsevier Ltd

关键词： Mixers (machinery)

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BurstBalancer: Do Less, Better Balance for Large-scale Data Center Traffic

BurstBalancer: Do Less, Better Balance for Large-scale Data ...

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International Conference on Network Protocols

作者： Zirui Liu Yikai Zhao Zhuochen Fan Tong Yang Xiaodong Li Ruwen Zhang Kaicheng Yang Zheng Zhong Yi Huang Cong Liu Jing Hu Gaogang Xie Bin Cui National Engineering Laboratory for Big Data Analysis Technology and Application School of Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen China Huawei Technologies CNIC CAS

ISBN: (数字)9781665482349

ISBN: (纸本)9781665482356

Layer-3 load balancing is a key topic in the networking field. It is well acknowledged that flowlet is the most promising solution because of its good trade-off between load balance and packet reordering. However, we find its one significant limitation: it makes the forwarding paths of flows unpredictable. To address this limitation, this paper presents BurstBalancer, a simple yet efficient load balancing system with a sketch, named BalanceSketch. Our design philosophy is doing less changes to keep the forwarding path of most flows fixed, which guides the design of BalanceSketch and balance operations. We have fully implemented BurstBalancer in a small-scale testbed built with Tofino switches, and conducted large-scale NS-2 simulations. Our results show that BurstBalancer achieves 5%∼35% smaller FCT than LetFlow in symmetric topology and up to 30× smaller FCT in asymmetric topology, while 58× fewer flows suffer from path changing. All related codes are open-sourced at Github 2 2 https://***/BurstBalancer/Burst-Balancer.

关键词： Data centers Philosophical considerations Protocols Codes Network topology Load management Topology

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A High-Performance Standalone Planar FAIMS System for Effective Detection of Chemical Warfare Agents

SSRN

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SSRN 2023年

作者： Hao, Jie Feng, Rong Li, Junhui Gao, Wenqing Yu, Jiancheng Tang, Keqi Institute of Mass Spectrometry Zhejiang Engineering Research Center of Advanced Mass spectrometry and Clinical Application Ningbo University Ningbo315211 China Zhenhai Institute of Mass Spectrometry Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China School of Material Science and Chemical Engineering Ningbo University Ningbo315211 China

A high-performance standalone planar field asymmetric waveform ion mobility spectrometry (p-FAIMS) system with a deconvolution algorithm (two-step particle swarm optimization algorithm, TSPSO) for overlapping peaks was developed to effectively detect chemical warfare agents (CWAs). Four CWA simulants were applied in this study to systemically evaluate the performance of the standalone p-FAIMS system. The experimental results showed that each CWA simulant in the mixture can be positively identified by carefully comparing the compensation voltage (CV) value of each peak in the FAIMS spectra for the mixture to the ones in the spectra acquired by using the same FAIMS system for the pure CWA simulant standards. The FAIMS spectrum of the CWA simulant mixture might consist of multiple overlapping peaks, which would be difficult to accurately determine the CV value for each CWA simulant peak. This problem has been effectively resolved in this study by deconvoluting the overlapping peaks via the TSPSO algorithm. As the effective peak deconvolution via TSPSO requires the degree of overlap between each FAIMS peak to be lower than a specific value, the flow rate of FAIMS carrier gas was degreased to further improve the resolution of the p-FAIMS system. After the accurate deconvolution, the resolution of original FAIMS spectrum can also be enhanced to achieve baseline separation by using TSPSO algorithm to narrow the peak width of each peak. The experimental results in this study proved that this system can achieve effective detection of the CWAs and also offered an effective new method for the high-resolution detection of other complex samples. © 2023, The Authors. All rights reserved.

关键词： Deconvolution

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A novel pixel-based deep neural network in posterior probability space for the detection of agriculture changes using remote sensing data

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Remote Sensing applications: Society and Environment 2025年 38卷

作者： Singh, Gurwinder Vyas, Narayan Dahiya, Neelam Singh, Sartajvir Bhati, Neha Sood, Vishakha Gupta, Dileep Kumar Department of Computer Application Chandigarh School of Business Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department Advanced Centre of Research & Innovation Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Application Vivekananda Global University Rajasthan Jaipur 303012 India Department of Computer Science Punjabi University Punjab Patiala 140307 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India University Institute of Engineering Chandigarh University Gharuan Punjab 140413 India Department of Research and Development Flexxited Karnataka Bengaluru 560061 India Department of Civil Engineering Indian Institute of Technology Ropar 140001 India Department of Electronics School of Engineering Galgotias University Greater Noida Gautam Buddh Nagar Uttar Pradesh 203201 India

Agricultural land classification is a crucial and demanding task, essential for managing resources and tracking changes in farming activities. Remote sensing (RS) is an excellent technology for monitoring agricultural land and detecting seasonal fluctuations globally. Deep learning models offer promising prospects for crop monitoring. While traditional machine learning methods struggle to capture temporal variations in agricultural land efficiently. This study addresses the challenge of accurately classifying land cover and detecting year-to-year changes using a deep learning (DL)-based approach. The novelty of this research is an integration using a pixel-based deep neural network (PDNN) classifier, which will advance the classification abilities for identifying land cover classes. By comparing images taken over time, the PDNN can help identify different land cover efficiently. The thematic images using the PDNN were derived, and change detection was carried out by adopting a posterior probability space (PPS)-based change detection. The application of the proposed model is demonstrated using the Landsat-9 dataset over Moga District, Punjab, India. Compared to the random forest (RF) and support vector machine (SVM), the PDNN model achieved great performance. While PDNN had an accuracy ranging from 90.6 % to 93.6 %, RF and SVM had lower accuracies, with RF ranging between 86.8 % and 92.2 % and SVM between 88 % and 92.4 %. The PDNN model also excelled in detecting changes in land cover, showing an accuracy between 87.4 % and 90 %, while RF achieved 82.9 %–86.2 % and SVM ranged from 79 % to 83.9 %. The proposed model was adept at capturing changes in agricultural land cover, such as year-to-year variations. The PDNN model demonstrated superior proficiency in capturing seasonal and year-to-year variations in agricultural land cover, effectively identifying subtle transitions in crop cycles. This highlights its potential for long-term agricultural monitoring and precisio

关键词： Agriculture Change detection Landsat-9 Pixel-based Pixel-based deep neural network (PDNN)

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A Surrogate-Assist Quasi-Affine Transformation Evolutionary for Multi-Objective Optimization of Empty Train Deployment on Heavy-Haul Railways

SSRN

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SSRN 2023年

作者： Du, Zhi-Gang Pan, Jeng-Shyang Xu, He-Ying Chu, Shu-Chuan Ni, Shao-Quan School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031 China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031 China

In recent years, surrogate-assisted evolutionary algorithms have become increasingly popular for solving expensive and time-consuming single-objective and multi-objective problems. However, in multi-objective optimization, the Pareto non-dominated solution set space can quickly become computationally intractable, making it challenging to select which samples to evaluate using the expensive fitness function. In this paper, we propose a novel surrogate-assist QUasi-Affine TRansformation Evolutionary algorithm (SA-QUATRE/MO) for solving multi-objective optimization problems. The SA-QUATRE/MO algorithm uses a radial basis neural network as a surrogate model to improve the speed of the algorithm operation by replacing the expensive fitness evaluation with the surrogate model. To ensure the excellence and diversity of the selected samples while keeping the archived sample space fixed, we propose a technique called Vector Space Sampling, which samples objective points in the current set of non-dominated solutions by dividing several sub-vector spaces. Additionally, we propose an uncertain sample infilling strategy to select samples for real fitness evaluation using a designed uncertainty function. We compare the SA-QUATRE/MO algorithm with three state-of-the-art algorithms for multi-objective problems in the DTLZ, MaF, and ZDT test function suites. Finally, we apply the SA-QUATRE/MO algorithm to solve the optimization problem of the empty train deployment at the loading end of a heavy-haul railway. We build a model based on the S12 section of a certain railway and optimize it using the SA-QUATRE/MO algorithm, demonstrating the effectiveness and practicality of our proposed approach. © 2023, The Authors. All rights reserved.

关键词： Vector spaces

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Empirical Analysis of Prediction of Crime and Forecasting With IGWO-SVM Approach

Empirical Analysis of Prediction of Crime and Forecasting Wi...

引用

International Conference on Edge Computing and applications (ICECAA)

作者： R Vasuki Neha Ganvir Rohita Sharma K Senthil Kumar SVHN Krishna Kumari Kamlesh Singh Department of Computer Application Mannar Thirumalai Naicker College Madurai Tamilnadu India KJ College of Engineering and Management Pune Maharashtra India Fine Arts Lovely Professional University Punjab India Department of and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of CSE (Artificial Intelligence and Machine Learning) Vardhaman College of Engineering Telangana India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

In order to understand the nuances of the crime data from recent years, in-depth analysis is necessary. Involved parties in law enforcement will benefit from this procedure in terms of making arrests and guiding efforts to reduce crime. Strategically or tactically, it benefits from having a better understanding of where, when, and how crimes will occur in the future. However, given the rising incidence of crime in the modern world, developing an effective method for predicting future criminal behavior is a difficult issue. As a result, it is crucial to develop a strategy for predicting criminal activity in order to prevent further crimes and lower crime rates. Researchers are currently attempting to predict criminal behavior based on a variety of factors. SVM, CNN and ANN are just few of the prediction approaches that can be used to assess a model's accuracy. Although their studies have yielded encouraging findings, there are still major blind spots that preclude an accurate prediction of crime hubs. There have been many earlier publications of academic papers on this subject. So, the results are compiled and conducted a comprehensive assessment of all of them in this report. Proposed goal is to catalog the various forms the crime prediction method has taken and the ways in which it might be improved in the future. Preprocessing, feature extraction, and model training are the three main components of the proposed method. Preprocessing includes activities including data cleansing, integration, transformation, reduction, and discretization. It uses principal component analysis to glean features. The model is trained with IGWO-SVM. When compared to two existing models, the proposed model performs exceptionally well.

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An Analysis of Mobile Security Protocols in Wireless Sensor Networks

An Analysis of Mobile Security Protocols in Wireless Sensor ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sheril. S Priyanka D. More Rahul Mishra Neeraj Das Ramachandran T Zahid Ahmed Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India

This examines examines the cutting-edge state of security protocols for wi-fi sensor networks within the mobile communications area. We examine the performance of present protection protocols for wi-fi sensor networks that specialize in their effectiveness, strength-efficiency, and scalability. We then endorse an advanced protection protocol which can higher cope with the security challenges of wi-fi sensor networks. Our new protection protocol incorporates cryptographic algorithms tailored for the unique necessities of wi-fi sensor networks, as well as key trade mechanisms that offer more desirable scalability and energy-performance. We evaluate our proposed security protocol, and examine its tradeoffs in evaluation with existing security protocols. Our consequences display that the proposed protocol is extra comfortable, gives higher scalability, and achieves more electricity-efficiency. We also check out the impact of different routing protocols on the security of wireless sensor networks in cellular communications and analyze how they may be tormented by exclusive security protocols. Our findings advise that a hybrid of two current routing protocols provides suitable security for wireless sensor networks.

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A Reference Vector-Assisted Many-Objective Optimization Algorithm with Adaptive Niche Dominance Relation

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Intelligent Automation & Soft Computing 2024年第2期39卷 189-211页

作者： Fangzhen Ge Yating Wu Debao Chen Longfeng Shen School of Computer Science and Technology Huaibei Normal UniversityHuaibei340604China School of Physic and Electronic Information Huaibei Normal UniversityHuaibei340604China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior(ICACB) Huaibei Normal UniversityHuaibei340604China Anhui Province Key Laboratory of Intelligent Computing and Applications Huaibei Normal UniversityHuaibei340604China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei230000China

It is still a huge challenge for traditional Pareto-dominatedmany-objective optimization algorithms to solve manyobjective optimization problems because these algorithms hardly maintain the balance between convergence and diversity and can only find a group of solutions focused on a small area on the Pareto front,resulting in poor performance of those *** this reason,we propose a reference vector-assisted algorithmwith an adaptive niche dominance relation,for short *** new dominance relation forms a niche based on the angle between candidate *** comparing these solutions,the solutionwith the best convergence is found to be the non-dominated solution to improve the selection *** reproduction,a mutation strategy of k-bit crossover and hybrid mutation is used to generate high-quality *** 23 test problems with up to 15-objective,we compared the proposed algorithm with five state-of-the-art *** experimental results verified that the proposed algorithm is competitive.

关键词： Many-objective optimization evolutionary algorithm Pareto dominance reference vector adaptive niche

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RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning

arXiv

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arXiv 2024年

作者： Gan, Yuhang Xuan, Wenjie Chen, Hang Liu, Juhua Du, Bo School of Computer Science Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Institute of Artificial Intelligence Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization of features in the intermediate stages, thus resulting in sub-optimal results. To this end, we propose a novel framework, named RFL-CDNet, that utilizes richer feature learning to boost change detection performance. Specifically, we first introduce deep multiple supervision to enhance intermediate representations, thus unleashing the potential of backbone feature extractor at each stage. Furthermore, we design the Coarse-To-Fine Guiding (C2FG) module and the Learnable Fusion (LF) module to further improve feature learning and obtain more discriminative feature representations. The C2FG module aims to seamlessly integrate the side prediction from previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions. Experiments on several benchmark datasets show that our proposed RFL-CDNet achieves state-of-the-art performance on WHU cultivated land dataset and CDD dataset, and the second best performance on WHU building dataset. The source code and models are publicly available at https://***/Hhaizee/RFL-CDNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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A Comprehensive Evaluation of Face Mask Identification System using Modified Deep Learning Methodology

A Comprehensive Evaluation of Face Mask Identification Syste...

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International Conference on Communications and Signal Processing

作者： Bijee Lakshman R. Vanitha R. Vasuki Satyajit S. Uparkar S. Arun R. Thiagarajan PG Department of Data Science Women’s Christian College College Road Chennai India Department of Cyber Security Jerusalem College of Engineering Chennai India Department of CSE School of Engineering and Technology Dhanalakshmi srinivasan University Trichy India Department of Computer Application Shri Ramdeobaba College of Engineering and Management Nagpur India Department of ECE Jerusalem College of Engineering Chennai India Department of IT Prathyusha Engineering College Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Recent years have seen an explosion in the prevalence, infectiousness, and potential harm posed to humanity by the corona virus family of viruses. When one person breathes out the virus, it lands on surfaces and other people breathe them in, causing the sickness to spread from person to person. As a result, safeguarding ourselves and those close to us has assumed paramount importance. There are measures we may do to protect ourselves, including as keeping our distance from others, wearing a mask, washing our hands often (every two hours), and using hand sanitizer. Masks worn by the general public have recently grown ubiquitous across the globe. The situation in India is particularly severe because of the country’s dense population packed into a relatively limited landmass. Using a cross-validation strategy involving the conventional learning model known as Convolutional Neural Network (CNN), this paper presents a method called Modified Learning based Mask Identification Rule (MLMIR) for detecting whether or not a face mask is worn in offices or other places with a high volume of employees. The model’s performance using live video streaming was astonishing once it was trained with a dataset made from real-world data. Using a number of additional parameters and a large number of people placed at different heights and angles inside the picture, we further evaluate the model’s accuracy.

关键词： Deep learning Solid modeling Humanities Face recognition Sociology Streaming media Signal processing

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