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DFU_MultiNet: A deep neural network approach for detecting diabetic foot ulcers through multi-scale feature fusion using the DFU dataset

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Intelligence-Based Medicine 2023年 8卷 100128-100128页

作者： Biswas, Shuvo Mostafiz, Rafid Paul, Bikash Kumar Mohi Uddin, Khandaker Mohammad Rahman, Md Masudur Shariful, F.N.U. Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Bangladesh Institute of Information Technology Noakhali Science and Technology University Bangladesh Department of Software Engineering Daffodil International University Bangladesh Department of Computer Science and Engineering Dhaka International University Bangladesh Department of Information and Communications Engineering Noakhali Science and Technology University Bangladesh School of Computing University of North Florida Jacksonville FL United States

Diabetic foot ulcer (DFU) is a common problem among people with diabetes that can result in amputation of the affected limb. Modern DFU treatment and diagnosis methods are expensive and time-consuming. Today, the development of the computer-aided diagnosis (CAD) method makes it possible for pathologists to diagnose DFU more swiftly and accurately. This has led to a rise in interest in deep learning (DL) approaches based on CAD. In this study, we introduce a novel framework called “DFU_MultiNet,” which focuses on the transfer learning approach to classify healthy and ulcer skin images using publicly available repositories. The proposed framework is developed to offer an efficient and robust method for DFU classification that determines the distinction between healthy and ulcerated skin. The proposed approach extracts features from foot samples using three well-known pre-trained CNN models: VGG19, DenseNet201, and NasNetMobile. Finally, these extracted results are merged through a summing layer to create a powerful hybrid network. Through obtaining impressive accuracy (99.06 %), precision (100.00 %), recall (98.18 %), specificity (100.00 %), F1-score (99.08 %), and AUC (99.09 %) the proposed “DFU_MultiNet” framework holds great potential as a diagnostic tool in healthcare and clinical settings. © 2023 The Author(s)

关键词： computer aided diagnosis (CAD) DenseNet201 Diabetic foot ulcer (DFU) Feature fusion NasNetMobile VGG19

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Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation

arXiv

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arXiv 2022年

作者： Wang, Ziming Huo, Xiaoliang Chen, Zhenghao Zhang, Jing Sheng, Lu Xu, Dong School of Software Beihang University China School of Electrical and Information Engineering The University of Sydney Australia Department of Computer Science The University of Hong Kong Hong Kong

Point cloud registration aims at estimating the geometric transformation between two point cloud scans, in which point-wise correspondence estimation is the key to its success. In addition to previous methods that seek correspondences by hand-crafted or learnt geometric features, recent point cloud registration methods have tried to apply RGB-D data to achieve more accurate correspondence. However, it is not trivial to effectively fuse the geometric and visual information from these two distinctive modalities, especially for the registration problem. In this work, we propose a new Geometry-Aware Visual Feature Extractor (GAVE) that employs multi-scale local linear transformation to progressively fuse these two modalities, where the geometric features from the depth data act as the geometry-dependent convolution kernels to transform the visual features from the RGB data. The resultant visual-geometric features are in canonical feature spaces with alleviated visual dissimilarity caused by geometric changes, by which more reliable correspondence can be achieved. The proposed GAVE module can be readily plugged into recent RGB-D point cloud registration framework. Extensive experiments on 3D Match and ScanNet demonstrate that our method outperforms the state-of-the-art point cloud registration methods even without correspondence or pose supervision. The code is available at https://***/514DNA/LLT. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear transformations

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Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

arXiv

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arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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A Review of Algorithms’s Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms’s Complexities on Different Valued S...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Asad Majeed Mahnoor Iftikhar Raja Hashim Ali Usama Arshad Muhammad Zeeshan Shabbir Ali Zeeshan Ijaz Nisar Ali Ali Aftab Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan Faculty of Electronic Systems Engineering University of Regina Regina Canada

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant.

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Approximation Algorithm for Connected Submodular Function Maximization Problems

Approximation Algorithm for Connected Submodular Function Ma...

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International Conference on Distributed Computing Systems

作者： Wenzheng Xu He Xue Jing Li Weifa Liang Zichuan Xu Pan Zhou Xiaohua Jia Sajal K. Das College of Computer Science Sichuan University Chengdu P. R. China Sichuan Fire Science and Technology Research Institute of MEM Chengdu P. R. China Department of Computing The Hong Kong Polytechnic University Hong Kong P. R. China Department of Computer Science City University of Hong Kong Hong Kong P. R. China School of Software Dalian University of Technology Dalian P. R. China School of Cyber Science and Engineering Huazhong University of Science & Technology P. R. China Department of Computer Science Missouri University of Science and Technology Rolla USA

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget $K$ , the problem is to find a subset $S$ with $K$ nodes from a graph $G$ so that a given submodular function $f (S)$ on $S$ is maximized while the induced subgraph $G[S]$ by the nodes in $S$ is connected, where the submodular function $f$ can be used to model many practical application problems, such as the number of users within different service areas of the deployed UAVs in $S$ , the sum of data rates of users served by the UAVs, the number of covered PoIs by placed sensors, etc. We then propose a novel $\frac{1-1/e}{2h+2}$ -approximation algorithm for the problem, improving the best approximation ratio $\frac{1-1/e}{2h+3}$ for the problem so far, through estimating a novel upper bound on the problem and designing a smart graph decomposition technique, where $e$ is the base of the natural logarithm, $h$ is a parameter depends on the problem and its typical value is 2. In addition. when $h= 2$ , the algorithm approximation ratio is at least $\frac{1-1/e}{5}$ and may be as large as 1 in some special cases when $K$ ≤21, and is no less than $\frac{1-1/e}{6}$ when $K$ ≥ 22, compared with the current best approximation ratio $\frac{1-1/e}{7}(= \frac{1-1/e}{2h+3})$ for the problem. We finally evaluate the algorithm performance in the application of deploying a UAV network. Experimental results demonstrate the number of users within the service area of the deployed UAV network by the proposed algorithm is up to 7.5% larger than those by existing algorithms, and its empirical approximation ratio is between 0.7 and 0.99, which is close to the theoretical maximum value one.

关键词： Upper bound Approximation algorithms Autonomous aerial vehicles Data models Distributed computing Intelligent sensors Monitoring

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Global Reliable Diagnosis of Networks Based on Self-Comparative Diagnosis Model and G-Good-Neighbor Property

SSRN

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SSRN 2024年

作者： Wang, Mujiangshan Xu, Shuhao Jiang, Jincheng Xiang, Dong Hsieh, Sun-Yuan Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Software Tsinghua University Beijing100084 China Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Guangdong Academy of Agricultural Sciences China

In the research field of system-level diagnosis, people used to focus on studying different diagnosabilities with restrictions. By studying this, reliability measured by these diagnosabilities of various of networks is obtained. However, for the decentralized parallel computing systems, there are always upper bonds for diagnosabilities, which means there exists an undiagnosable subnetwork cut off by a faulty vertex cut if the subnetwork is large enough. In this paper, we set up a 2-stage plan for the global reliable diagnosis of networks combining Self-Comparative diagnosis (SC) model and the g-good-neighbor property, which means every vertex in the networks is diagnosed with high accuracy. Six algorithms are proposed for the realization of the plan and diagnosis model for g≥2. In the end, we code a simulator for the plan and test how well it works and the codes are provided via Github link for readers' check. © 2024, The Authors. All rights reserved.

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Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue

arXiv

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arXiv 2023年

作者： Sun, Geng He, Long Sun, Zemin Wu, Qingqing Liang, Shuang Li, Jiahui Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Information Science and Technology Northeast Normal University Changchun130024 China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads. Copyright © 2023, The Authors. All rights reserved.

关键词： Fog computing

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Towards Seamless Connectivity: Implementing 6G Communication Technologies In Next-Generation Networks

Towards Seamless Connectivity: Implementing 6G Communication...

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Advancement in Technology (ICONAT), International Conference for

作者： K. Kalaiselvi R. Sankar S Supriya G Kaushik Hemanth Swamy M Devika Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamilnadu India Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India Senior Software Engineer Motorola Solutions Department of AI& DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can’t meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, AI-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues.

关键词： 6G mobile communication 5G mobile communication Wireless networks Complex networks Market research Communications technology Internet of Things Optimization Next generation networking Edge computing

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Min-max Submodular Ranking for Multiple Agents

arXiv

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arXiv 2022年

作者： Chen, Qingyun Im, Sungjin Moseley, Benjamin Xu, Chenyang Zhang, Ruilong Electrical Engineering and Computer Science University of California at Merced United States Tepper School of Business Carnegie Mellon University United States Software Engineering Institute East China Normal University China College of Computer Science Zhejiang University China Department of Computer Science City University of Hong Kong Hong Kong

In the submodular ranking (SR) problem, the input consists of a set of submodular functions defined on a ground set of elements. The goal is to order elements for all the functions to have value above a certain threshold as soon on average as possible, assuming we choose one element per time. The problem is flexible enough to capture various applications in machine learning, including decision trees. This paper considers the min-max version of SR where multiple instances share the ground set. With the view of each instance being associated with an agent, the min-max problem is to order the common elements to minimize the maximum objective of all agents—thus, finding a fair solution for all agents. We give approximation algorithms for this problem and demonstrate their effectiveness in the application of finding a decision tree for multiple agents. © 2022, CC BY-NC-ND.

关键词： Decision trees

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UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper

arXiv

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arXiv 2023年

作者： Zhang, Chuang Sun, Geng Wu, Qingqing Li, Jiahui Liang, Shuang Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network. In this work, we aim to utilize the UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of collusive eavesdropping attacks in time-domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for simultaneously maximizing the achievable sum rate of associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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