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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Hossain, Mohammad Alamgir Shahi, Fazal Imam Husain, Sadia Suliman, Abdelrazig Kamal, Mohammad Shahid Ahmad, Yasir Talukdar, Mohammad Mazedul Huq Martin, R. John Jazan University College of Engineering & Computer Science Department of computer Science Jazan Saudi Arabia Jazan University ELearning Center Jazan Saudi Arabia University of Bisha Faculty of Computers and Information Technology Department of Information Technology Saudi Arabia Jazan University College of Arts and Humanities Department of Foreign Languages Jazan Saudi Arabia

ISBN: (纸本)9798331513894

The automated segmentation of lungs within chest CT scans remains crucial for many medical applications that require disease diagnosis and treatment design. The presented deep incremental learning (DIL) system addresses fundamental limitations that arise during lung segmentation processes. The exceptional segmentation outcomes result from the neural network structure which includes masked RCNN+UNet for tissue segmentation and GAN+VARMA for classification. Medical literature identifies lung tissue structures as difficult to study because they present irregular forms and complex structures. The task of obtaining precise segmentation proves very challenging. The application of masked RCNN+UNet enables shape control and improves segmentation precision of the lung system. The DIL function helps maintain improved features within segmented images. The applied framework enables improved performance through DSC and MAE and JI metrics evaluation. Experimental evidence shows that the new framework achieves superior performance compared to current top-ranking methods for lung segmentation. The proposed framework exhibits excellent precision and detection efficiency with a DSC value of 0.96 and JI value of 0.93 and a low MAE © 2025 IEEE.

关键词： Medical applications

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Shuffling for Semantic Secrecy

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IEEE Transactions on information Forensics and Security 2025年 20卷 5240-5255页

作者： Chen, Fupei Xiang, Liyao Sun, Haoxiang Cheng, Hei Victor Shen, Kaiming Shanghai Jiao Tong University John Hopcroft Center for Computer Science Shanghai200240 China Shanghai Jiao Tong University School of Information and Electronic Engineering Shanghai200240 China Aarhus University Electrical and Computer Engineering Department Aarhus8000 Denmark The Chinese University of Hon g Kong School of Science and Engineering Shenzhen518172 China

Deep learning draws heavily on the latest progress in semantic communications. The present paper aims to examine the security aspect of this cutting-edge technique from a novel shuffling perspective. Our goal is to improve upon the conventional secure coding scheme to strike a desirable tradeoff between transmission rate and leakage rate. To be more specific, for a wiretap channel, we seek to maximize the transmission rate while minimizing the semantic error probability under the given leakage rate constraint. Toward this end, we devise a novel semantic security communication system wherein the random shuffling pattern plays the role of the shared secret key. Intuitively, the permutation of feature sequences via shuffling would distort the semantic essence of the target data to a sufficient extent so that eavesdroppers cannot access it anymore. The proposed random shuffling method also exhibits its flexibility in working for the existing semantic communication system as a plugin. Simulations demonstrate the significant advantage of the proposed method over the benchmark in boosting secure transmission, especially when channels are prone to strong noise and unpredictable fading. © 2005-2012 IEEE.

关键词： Fading channels

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Fixed-point quantum continuous search algorithm with optimal query complexity

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science China(Physics,Mechanics & Astronomy) 2025年第6期68卷 80-91页

作者： Shan Jin Yuhan Huang Shaojun Wu Guanyu Zhou Chang-Ling Zou Luyan Sun Xiaoting Wang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610051China Key Laboratory of Quantum Information CASUniversity of Science and Technology of ChinaHefei 230022China Center for Quantum Information Institute for Interdisciplinary Information SciencesTsinghua UniversityBeijing 100084China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China Hefei National Laboratory Hefei 230088China

Continuous search problems(CSPs),which involve finding solutions within a continuous domain,frequently arise in fields such as optimization,physics,and *** discrete search problems,CSPs require navigating an uncountably infinite space,presenting unique computational *** this work,we propose a fixed-point quantum search algorithm that leverages continuous variables to address these challenges,achieving a quadratic *** by the discrete search results,we manage to establish a lower bound on the query complexity of arbitrary quantum search for CSPs,demonstrating the optimality of our *** addition,we demonstrate how to design the internal structure of the quantum search oracle for specific ***,we develop a general framework to apply this algorithm to a range of problem types,including optimization and eigenvalue problems involving continuous variables.

关键词： quantum search quantum algorithm quantum computation optimization spectral problems

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A Study on the Use of High Dynamic Range Imaging for Gaussian Splatting Methods: Are 8 bits Enough?

A Study on the Use of High Dynamic Range Imaging for Gaussia...

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2024 Eurographics Italian Chapter Conference on Smart Tools and Applications in Graphics, STAG 2024

作者： Piras, Valentina Bonatti, Amedeo F. De Maria, Carmelo Cignoni, Paolo Banterle, Francesco Computer Science Department University of Pisa Italy ISTI-CNR Italy Department of Information Engineering Research Center E. Piaggio The University of Pisa Pisa Italy

ISBN: (纸本)9783038682653

The recent rise of Neural Radiance Fields (NeRFs)-like methods has revolutionized high-fidelity scene reconstruction, with 3D Gaussian Splatting (3DGS) standing out for its ability to generate photorealistic images while maintaining fast, efficient rendering. 3DGS delivers high-fidelity representations of complex scenes at any scale (from very small objects to entire cities), accurately capturing geometry, materials, and lighting, while meeting the need for fast and efficient rendering-crucial for applications requiring real-time performance. Although High Dynamic Range (HDR) technology, which enables the capture of comprehensive real-world lighting information, has been used in novel view synthesis, several questions remain unanswered. For example, does HDR improve the overall quality of reconstruction? Are 8 bits enough? Can tone mapped images be a balanced compromise regarding quality and details? To answer such questions, in this work, we study the application of HDR technology on the 3DGS method for acquiring real-world scenes. © 2024 The Authors.

关键词： Rendering (computer graphics)

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Improving ECAPA-TDNN Performance with Coordinate Attention

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Journal of Shanghai Jiaotong University (science) 2024年 1-7页

作者： Liu, Shuanghong Song, Zhida He, Liang School of Computer Science and Technology Xinjiang University Urumqi830017 China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi830017 China Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

The current mainstream networks, such as squeeze and excitation residual neural network (SE-ResNet) and emphasized channel attention, propagation and aggregation based time delay neural network (ECAPA-TDNN), enhance the capability of speaker embedding extractors to extract more discriminative speaker embeddings by incorporating squeeze and excitation (SE) attention within the convolutional blocks. However, the SE attention focuses solely on encoding inter-channel information, overlooking the importance of spatial positional information and time-frequency information, which are crucial for the model’s performance. In this paper, we first experimentally compare the effectiveness of several mainstream attention mechanisms in the computer vision domain for the ECAPA-TDNN model. Next, we focus on the substantial improvements that coordinate attention (CA) brings to the ECAPA-TDNN model. The introduction of CA can help the model embed time-frequency information into the channel representation. Even without using AS-Norm, our proposed model achieves relative reductions of about 5.3% equal error rate (EER) and 5.5% minimum detection cost function (minDCF) on both the Voxceleb-O and Voxceleb-H test sets compared to the ECAPA-TDNN baseline model. In addition, the EER is relatively reduced by 9.46% on the CN-Celeb1 test set. This result strongly demonstrates that the CA module can effectively improve the generalization ability of the ECAPA-TDNN model. © Shanghai Jiao Tong University 2024.

关键词： Neural network models

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Ensuring End-to-End Security With Fine-Grained Access Control for Connected and Autonomous Vehicles

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IEEE Transactions on information Forensics and Security 2024年 19卷 6962-6977页

作者： Yu, Donghyun Lee, Sungho Hsu, Ruei-Hau Lee, Jemin the School of Electrical and Electronic Engineering Yonsei University Seoul03722 Korea Republic of Gumi39253 Korea Republic of the Department of Computer Science and Engineering the Information Security Center National Sun Yat-sen University Kaohsiung80424 Taiwan

— As advanced V2X applications emerge in the connected and autonomous vehicle (CAV), the data communications between in-vehicle end-devices and outside nodes increase, which make the end-to-end (E2E) security to in-vehicle end-devices as the urgent issue to be handled. However, the E2E security with fine-grained access control still remains as a challenging issue for resource-constrained end-devices since the existing security solutions require complicated key management and high resource consumption. Therefore, this paper proposes a practical and secure vehicular communication protocol for the E2E security based on a new attribute-based encryption (ABE) scheme. In the ABE scheme, the outsourced computation is provided for encryption, and the computation cost for decryption constantly remains small, regardless of the number of attributes. The policy privacy can be ensured by the proposed ABE to support privacy-sensitive V2X applications, and the existing identity-based signature for outsourced signing is newly reconstructed. The protocol achieves the confidentiality, message authentication, identity anonymity, unlinkability, traceability, and reconfigurable outsourced computation, and this paper also shows the practical feasibility of the protocol via the performance evaluation. © 2024 IEEE.

关键词： Access control

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IoT Attacks Detection Using Supervised Machine Learning Techniques

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 534-550页

作者： Aljabri, Malak Shaahid, Afrah Alnasser, Fatima Saleh, Asalah Alomari, Dorieh Aboulnour, Menna Al-Eidarous, Walla Althubaity, Areej Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah21955 Saudi Arabia College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Depatment of Cybersecurity College of Computing Umm Al-Qura University Makkah21955 Saudi Arabia

In recent times, the growing significance of Internet of Things (IoT) devices in people's lives is undeniable, driven by their myriad benefits. However, these devices confront cybersecurity threats akin to traditional network devices, as they depend on networks for connectivity and synchronization. Artificial Intelligence (AI) techniques, specifically Machine Learning (ML) and Deep Learning (DL), have demonstrated notable reliability in the field of cyberattack detection. This study focuses on detecting Flood and Brute Force cyberattacks using Machine Learning (ML) and Deep Learning (DL) models. The primary emphasis lies in identifying traffic features that significantly detect these types of attacks. The experimental study incorporates eight models: Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Logistic Regression (LR), Gradient Boosting (GB), Naïve Bayes (NB), and Artificial Neural network (ANN). Two sets of experiments were conducted, with the first set involving six features and the subsequent set, after feature selection, focusing on a reduced set of three features. The evaluation of the proposed model's efficiency and performance relied on metrics such as Accuracy, Precision, Recall, and F1-score. Remarkably, all proposed models exhibited high performance in both sets of experiments. However, the Gradient Boosting (GB) classifier suppressed others, attaining an impressive accuracy level of 95.94% and 95.28% in the sets with six features and three features, respectively. © 2024, Ital Publication. All rights reserved.

关键词： Support vector machines

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Accurate Classification of Cervical Cancer Based on Multi-layer Perceptron Hunger Games search Optimization technique 21

Accurate Classification of Cervical Cancer Based on Multi-la...

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21st International Learning and Technology Conference, L and T 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. Youssef, Rasha Y. The Higher Institute for Management and Information Technology Minya Egypt Nahda University Faculty of Computer Science Computer Science Department Beni Suef Egypt Effat University College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Cancer is distinguished by the presence of abnormal cellular proliferation and growth, both of which serve as signs and symptoms for this kind of illness. computer vision, deep learning, and metaheuristics optimization techniques are increasingly important for solving complex medical Artificial Intelligence (AI) problems such as cancer detection. This paper introduces a new methodology for training the Multi-Layer Perceptron (MLP) using the optimization algorithm known as Hunger Games search Optimization technique (HGO) and apply this method to classify the cervical cancer. The main goal of this method is to reduce the error and enhance the classification rate of cervical cancer. The outcomes show that the MLP with HGO algorithm performed better than other algorithms in terms of classification efficacy and accuracy rate. Simulation outcomes indicate that the proposed strategy performs better than previously published research in terms of effectiveness for the classification optimization methods. © 2024 IEEE.

关键词： Diseases

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