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10th International Conference on systems and Informatics, ICSAI 2024

作者： Sun, Chenggong Liu, Xiwei Tongji University Department of Computer Science and Technology Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China

ISBN: (纸本)9798331510138

In this paper, we explore and establish several necessary and sufficient conditions that are instrumental in achieving consensus within second-order discrete-time multi-agent systems (SDMAS). Different with previous research, this study addresses the more intricate scenario where the velocity coupling matrix is distinct from the position coupling matrix. We introduce a novel eigenvector condition tailored to this challenge. Furthermore, in response to the burgeoning interest in multiweighted networks, this study is extended to SDMAS with multiple weights. We employ a technique, that rearranges the order of variables, to effectively simplify the analysis of multiweighted networks by transforming them into single-weighted equivalents. The efficacy of our method is substantiated through three numerical examples, which illustrate the practical value in elucidating consensus dynamics within SDMAS featuring diverse coupling matrices. © 2024 IEEE.

关键词： Multi agent systems

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Transfer Learning Based Vascular Stenosis Detection

Transfer Learning Based Vascular Stenosis Detection

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2022 International Conference on Emerging Trends in Smart Technologies, ICETST 2022

作者： Noreen, Aqsa Jawaid, Moazzam Narejo, Sanam Memon, Madeha Kumar, Kunal Mehran University of Engineering & Technology Department of Computer System Engineering Jamshoro Pakistan

ISBN: (数字)9781665459358

ISBN: (纸本)9781665459358

One of the leading causes of mortality and morbidity in the global population is coronary artery disease. Clinical data analysis and healthcare professionals have a crucial problem in predicting and diagnosing the disease in order to save lives and prevent people from developing it. Massive volumes of data are gathered by the healthcare sector, some of which contains diagnostic information for cardiac disease and is useful for decision-making. Deep neural networks can also be used to examine and identify cardiac problems. The most common kind of heart disease, atherosclerosis or stenosis, is occurred when coronary arteries are narrowed abnormally. Since blood vessels are also known as the coronary arteries that provide the heart with oxygen, blood, and nutrients, they can get narrowed, which can result in chest pain (angina), pain, and difficulty breathing. As a result, it may result in discomfort, nausea, dizziness, and even a heart attack[1]. This project intends to acquire Cross-Sectional Images from 3-Dimendional CT (computed tomography) using CPR (curve planar reformation) and to train Transfer Learning Model over customized CT data for stenosis detection and quantification. The model we have used is VGG (Visual Geometric Group) model, which refers to a convolutional neural network model proposed by A. Zisserman and K. Simonyan of the University of Oxford. VGG16 is pre-trained model which is trained on ImageNet dataset. The model's training and testing accuracy is 89.08% and 88.52%, respectively. The efficiency of model is measured by its accuracy, recall, precision, and f-score. © 2022 IEEE.

关键词： Heart

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GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network

GHVC-Net: Hypervolume Contribution Approximation Based on Gr...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Wu, Guotong Nan, Yang Shang, Ke Ishibuchi, Hisao Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665410205

This paper proposes a framework called GHVC-Net that uses the graph neural network (GNN) model to approximate each solution's hypervolume contribution (HVC). GHVC-Net is permutation invariant and can handle solution sets of arbitrary size, similar to the properties of GNN. Compared to HVC-Net (i.e., a machine learning model for HVC approximation), GHVC-Net achieves better accuracy with less training time. GHVC-Net is also compared with traditional approximation methods, such as line-based and point-based methods, to demonstrate its ability to identify the solution with the smallest (largest) HVC. © 2024 IEEE.

关键词： Neural network models

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Congestion Control Mechanisms and Techniques in computer Network: A Review

Congestion Control Mechanisms and Techniques in Computer Net...

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2022 International Conference on Data Science and Intelligent Computing, ICDSIC 2022

作者： Altameemi, Ahmed Malik Nassar, Khulood A. College of Computer Science and Information Technology University of Basrah Department of Computer Information System Basrah Iraq

ISBN: (纸本)9798350334883

One of the most important and most pioneering topics in the field of networks is congestion and how to control it or avoid its occurrence because this topic has a huge impact on the network and the quality of service (QoS). Congestion reduces the efficiency of the network and stops the service in many cases. Therefore, there is an urgent need to develop techniques and mechanisms to avoid congestion or reduce its impact to the extent possible to maintain the permanence of data flow in the network. Congestion is avoided at two levels, node and link, by using different techniques, including closed loop or open loop. This paper reviews an explanation of congestion, how it occurs, and its impact on the network, and then shows the mechanisms through which congestion eliminated can be bed or avoided before it occurs. It also reviews a set of algorithms previously used by researchers in various types of networks and compares the performance between them. © 2022 IEEE.

关键词： Quality of service

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Clustering Mechanism in Particle Swarm Optimization Algorithm for Data Aggregation 13

Clustering Mechanism in Particle Swarm Optimization Algorith...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Sharmin, Sharmin Ahmedy, Ismail Noor, Rafidah Md Ismail, Habibah Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Univesrsiti Teknologi MARA Computer Science Unit Centre of Foundation Studies Selangor Dengkil43800 Malaysia

ISBN: (纸本)9798350347319

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes with distinct resources working together to achieve a common objective efficiently. A critical issue is extending the network lifetime of WSNs, especially for data transfer, as sensor nodes depend on their internal batteries to carry out essential functions and communicate with one another. Efficient energy use per sensor is essential for ensuring the overall energy efficiency of the network. Clustering has been the most energy-efficient approach but lacking a method for selecting the most effective cluster head results in decreased data aggregation efficiency, leading to the increased power consumption of sensor nodes. The project aims to enhance energy efficiency and extend network service life by using Particle Swarm optimization (PSO) for clustering in WSNs. PSO is used to select the cluster head based on member node distance from the base station and remaining energy. The proposed PSO technique was tested against Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), Zonal Stable Election Protocol Z-SEP, and Extended Stable Election Protocol (EZ-SEP) and found to be more effective in reducing energy usage and prolonging the network's lifespan. The recommended technique can extend the service life of WSNs and reduce energy consumption, ultimately leading to more cost-effective and eco-friendly solutions for various applications such as environmental monitoring, surveillance, and healthcare. © 2023 IEEE.

关键词： Energy utilization

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Semantic Segmentation and YOLO Detector over Aerial Vehicle Images

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computers, Materials & Continua 2024年第8期80卷 3315-3332页

作者： Asifa Mehmood Qureshi Abdul Haleem Butt Abdulwahab Alazeb Naif Al Mudawi Mohammad Alonazi Nouf Abdullah Almujally Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Cognitive Systems Lab University of BremenBremen28359Germany

Intelligent vehicle tracking and detection are crucial tasks in the realm of highway ***,vehicles come in a range of sizes,which is challenging to detect,affecting the traffic monitoring system’s overall *** learning is considered to be an efficient method for object detection in vision-based *** this paper,we proposed a vision-based vehicle detection and tracking system based on a You Look Only Once version 5(YOLOv5)detector combined with a segmentation *** model consists of six *** the first step,all the extracted traffic sequence images are subjected to pre-processing to remove noise and enhance the contrast level of the *** pre-processed images are segmented by labelling each pixel to extract the uniform regions to aid the detection phase.A single-stage detector YOLOv5 is used to detect and locate vehicles in *** detection was exposed to Speeded Up Robust Feature(SURF)feature extraction to track multiple *** on this,a unique number is assigned to each vehicle to easily locate them in the succeeding image frames by extracting them using the feature-matching ***,we implemented a Kalman filter to track multiple *** the end,the vehicle path is estimated by using the centroid points of the rectangular bounding box predicted by the tracking *** experimental results and comparison reveal that our proposed vehicle detection and tracking system outperformed other state-of-the-art *** proposed implemented system provided 94.1%detection precision for Roundabout and 96.1%detection precision for Vehicle Aerial Imaging from Drone(VAID)datasets,respectively.

关键词： Semantic segmentation YOLOv5 vehicle detection and tracking Kalman filter SURF

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Incorporating Lasso Regression to Physics-Informed Neural Network for Inverse PDE Problem

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computer Modeling in Engineering & Sciences 2024年第10期141卷 385-399页

作者： Meng Ma Liu Fu Xu Guo Zhi Zhai National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong UniversityXi’an710049China School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an710049China Department of Electrical and Computer Engineering University of Massachusetts LowellLowellMA 01854USA

Partial Differential Equation(PDE)is among the most fundamental tools employed to model dynamic *** PDE modeling methods are typically derived from established knowledge and known phenomena,which are time-consuming and ***,discovering governing PDEs from collected actual data via Physics Informed Neural Networks(PINNs)provides a more efficient way to analyze fresh dynamic systems and establish *** study proposes Sequentially Threshold Least Squares-Lasso(STLasso),a module constructed by incorporating Lasso regression into the Sequentially Threshold Least Squares(STLS)algorithm,which can complete sparse regression of PDE coefficients with the constraints of l0 *** further introduces PINN-STLasso,a physics informed neural network combined with Lasso sparse regression,able to find underlying PDEs from data with reduced data requirements and better *** addition,this research conducts experiments on canonical inverse PDE problems and compares the results to several recent *** results demonstrated that the proposed PINN-STLasso outperforms other methods,achieving lower error rates even with less data.

关键词： Physics-informed neural network inverse partial differential equation Lasso regression scientific machine learning

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Change Management Implementation on E-Government system Using PROSCI ADKAR with Control Standard of COBIT2019 and ITIL v4

Change Management Implementation on E-Government System Usin...

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2024 International Conference on computer Engineering, Network and Intelligent Multimedia, CENIM 2024

作者： Ciptaningtyas, Henning Titi Hari Ginardi, Raden Venantius Aliski, Ariel Daffansyah Caronongan, Diosdado Santoso, Marlina Institut Teknologi Sepuluh Nopember Department of Information Technology Surabaya Indonesia College of Computer Studies University of Luzon Dagupan Philippines STMIK Kharisma Department of Information System Makasar Indonesia

ISBN: (纸本)9798350368802

This research aims to implement Change Management in an Electronic-Based Government system (e-government) using the ADKAR methodology based on the COBIT2019 and ITIL v4 framework, with a focus on improving the maturity measurement of the Surabaya City Government's SPBE implementation on Change Management indicators. This research applies to the ADKAR method for all stages of implementing change, while COBIT2019 and ITIL v4 are used in each stage to ensure alignment with corporate goals. This research produces outputs in the form of a Change Management Guidebook document, Standard Operating Procedures (SOPs), and work instructions (worksheets) that the Surabaya City Government will use. The results of this implementation can help the Surabaya City Government to improve its SPBE Change Management indicator's maturity level from the current level of 2 (measurable) to 3 (standardized) out of 5. This research also shows that not all processes or best practices in COBIT2019 and ITIL v4 can be fully used due to cultural differences in regional government organizations. © 2024 IEEE.

关键词： e-government

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Empowering Deaf Children with Sinhala Sign Language, Emotion Detection, and Sound Recognition 5

Empowering Deaf Children with Sinhala Sign Language, Emotion...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Fernando, W.H.K. Perera, P.L.D.N. Fernando, M.I.N. Gunasekara, P.T.E.W. Fernando, Harinda De Zoysa, Rivoni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study describes a broad endeavor to use cutting-edge technologies to empower deaf primary school students in Sri Lanka. Three key elements make up the study: a sound recognition and classification system, an Android software for translating gestures in Sinhala Sign Language (SSL), and a mobile app for emotion recognition and text-tospeech in Sinhala. By promptly alerting caregivers to potential dangers, the sound recognition technology secures the security of deaf children at home, providing an invaluable layer of security. The Android software bridges the communication gap and improves family interactions by translating SSL gestures captured by the device's camera into textual representations. This promotes successful communication between deaf children and their parents. Convolutional Neural Networks (CNNs) are used in the mobile application to detect and understand emotions based on facial expressions. This enables non-verbal youngsters to communicate successfully through Sinhala textto-speech output with adaptive tone modulation. This research aims to build a caring and inclusive atmosphere, enabling increased independence and enhanced communication skills in deaf children by combining these advances. The suggested solutions could significantly improve the lives of deaf kids, providing them with opportunities for better learning, social interaction, and emotional health. © 2023 IEEE.

关键词： Emotion Recognition

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