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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Viveka S Prabhuranjith P Madhubala. C K. Hemalatha A. Ramya N.G. Gayathri Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Artificial Intelligence and Data Science Velalar college of Engineering and Technology Erode Tamilnadu India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamilnadu India Department of Information Technology Sona College of Technology Tamilnadu India Department of Computer Science and Engineering Indra Ganesan College of Engineering Trichy Tamilnadu India Department of Computer Science and Business Systems K.Ramakrishnan college of Engineering Trichy Tamilnadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

Liver segmentation is one of the crucially important components in clinical-decision support system. Improving the tasks of early diagnosis of the critical liver infections and diseases such as jaundice, hepatitis infections and carcinoma can result in effectual outcomes and higher rates of mortality. Infection segmentation from liver is one of the challenging tasks to due to their varying structures, sizes and the location of infections. These are also affected by the low ranges of contrast and to their blurred boundaries. To resolve these pitfalls encountered by the existing approaches, compiled with less accuracy rates, higher model complexity and with higher time for both detection and segmentation. Thus, the proposed method using the U-Net with the Extended Echo Skip based ResoReplicate Layer for segmenting the infectious regions from the livers, using the image dataset from two various sources comprising various CT scan images of livers and images containing hepatic carcinomas of both the gender, 2 anonymous CT-scans and DEPOLL dataset. Thus, the segmentation aids in the appropriate and earlier diagnosis of the infections and for a delivery of precise treatment in aspects of avoiding further complications. These modifications in U-net results in replicating retrieving only the important and the precise features for the detection and are replicated in the following layers for a precise segmentation of infectious areas from liver. The overall segmentation ability of the proposed model will be validated using the probabilistic metrics comprising the Dice Coefficient rates and the Intersection over Union (IOU) score.

关键词： Jaundice Image segmentation Accuracy Liver diseases Computational modeling Computed tomography Rendering (computer graphics) Real-time systems Complexity theory Reliability

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An Enhanced Residual Networks Based Framework for Early Alzheimer’s Disease Classification and Diagnosis 6th

An Enhanced Residual Networks Based Framework for Early Alzh...

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6th International Conference on Communication, Devices and Networking, ICCDN 2022

作者： Awotunde, Joseph Bamidele Sur, Nath Samarendra Imoize, Agbotiname Lucky Misra, Sanjay Gaber, Tarek Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electronics and Communication Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University East Sikkim Rangpo Majitar737136 India Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum44801 Germany Department of Applied Data Science Institute of Energy Technology Halden Norway Faculty of Computers and Informatics Suez Canal University Ismailia41522 Egypt School of Science Engineering and Environment University of Salford ManchesterM5 4WT United Kingdom

ISBN: (纸本)9789819919826

Alzheimer's disease (AD) is a long-lasting, degenerative brain illness for which there is now no successful treatment. However, there are medications that can delay its growth and stop its tracks. Therefore, stopping and regulating the progression of AD depends greatly on its early diagnosis. computer-aided diagnosis (CAD) in health care has become more and more dependent on deep learning technologies. This has help in early diagnosis and detection of various disease in health care. New techniques for the early diagnosis of neurocognitive abnormalities are now possible thanks to advancements in medical imaging and computational capacity in an effort to stop or slow down cognitive aging. Therefore, this study proposes a residual network (ResNet) type 50 for early diagnosis of AD. Convolutional neural networks (CNNs) are first used in the study for features extraction and then make the ultimate diagnosis using the ResNet-50. To create a better architecture, the problem of AD diagnosis will be addressed by utilizing and incorporating ResNet-50, which improves the current categorization problem's accuracy to its maximum. With a 98.8% accuracy rate, the proposed model using ResNet-50 produces encouraging findings for AD prediction. The study reveals that the model can effectively circumvent CNNs for the diagnostic issue with AD. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer aided diagnosis

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Developing the Role of Firewalls in Enhancing Web Security for Wireless Networks

Developing the Role of Firewalls in Enhancing Web Security f...

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

作者： Ritushree Narayan N. V. Balaji Kalanandhini G Parikshit N. Mahalle Javed Wasim Roma Singh Department of Coputing & Information Technology Usha Martin University Ranchi India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science Mangalayatan University Aligarh India Department of Computer Science Himalayan University Itanagar India

Firewalls play a critical role in controlling incoming and outgoing community site visitors and shielding a wireless network from malicious assaults. This technical summary focuses on the development of firewalls as a way to beautify the internet safety for Wi-Fi networks. Contemporary Wi-Fi networks are increasingly more vulnerable to malicious cyber-attacks. Firewalls are one of the simplest strategies of safety, and are able to restricting visitors among networks or from the net. They also can be used to come across and perceive malicious pastime. The development of firewalls has improved at a fast pace in current years. New functions along with Intrusion Detection and Prevention systems (IDS/IPS), URL filtering, virus scanning, user authentication, and application layer filtering, have vastly advanced the effectiveness of firewalls. Advanced strategies such as deploy Rector Reactive tracking (IRM), and community get right of entry to manage (NAC) also are getting used to detect suspicious community requests and put into effect coverage. NAC may be used to both control get admission to network resources, and respond to malicious site visitors. The enhanced internet protection supplied by firewalls is useful for both home and employer networks. Via implementing those capabilities, organizations can lessen the danger of cyberattacks and guard their resources.

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

arXiv

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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Fixed-Time Consensus Tracking of Nonlinear Multi-Agent Systems Under DoS Attacks

Fixed-Time Consensus Tracking of Nonlinear Multi-Agent Syste...

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IEEE Conference on Decision and Control

作者： Xinhe Wang Guanghui Wen Wei Xing Zheng School of Cyber Science and Engineering Southeast University Nanjing China Department of Systems Science School of Mathematics Southeast University Nanjing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies the fixed-time consensus tracking problem of nonlinear multi-agent systems, where communication links are subjected to denial-of-service (DoS) attacks. The DoS attacks make the communication networks switch arbitrarily and therefore the network connectivity may be broken, which poses a serious challenge to the consensus tracking problem within the fixed-time framework. To deal with this issue, a switching-based fixed-time control strategy is proposed. Critical conditions for fixed-time consensus tracking of nonlinear multi-agent systems under DoS attacks are developed. In particular, the adverse impact of DoS attacks on the settling time can be clearly reflected by the fixed-time strategy derived in this paper. Finally, numerical examples are provided to validate the theoretical analysis.

关键词： Sufficient conditions Protocols Decentralized control Switches Denial-of-service attack Communication networks Cyberattack Multi-agent systems

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Classification of Acute Intracerebral Hemorrhage Using Radiomics on Brain Computed Tomography Images

Classification of Acute Intracerebral Hemorrhage Using Radio...

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International Conference on Information Technology and Electrical engineering (ICITEE)

作者： Phattanun Thabarsa Salita Angkurawaranon Chakri Madla Withawat Vuthiwong Kittisak Unsrisong Papangkorn Inkeaw Master's Degree Program in Data Science Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Radiology Faculty of Medicine Chiang Mai University Chiang Mai Thailand Department of Computer Science Data Science Research Center Faculty of Science Chiang Mai University Chiang Mai Thailand

Acute intracerebral hemorrhage (ICH) entity accounts for 10 to 15% of all strokes and is associated with a higher mortality rate ischemic stroke or subarachnoid hemorrhage. Causes of ICH are divided into primary, and secondary, including vascular malformation and tumorous. Primary ICH accounts for approximately 80% of all ICH cases. Vascular anomalies rank as the second most common cause of spontaneous ICH overall. Furthermore, hemorrhage resulting from brain tumors can occur in up to 10% of all primary or metastatic tumors. Early recognizing of these three causes of bleeding is critical for clinicians in precise diagnosis, effective treatment management, and helps avoid delayed diagnosis. We proposed a radiomics approach for classifying multiple causes of acute ICH as vascular malformation, tumorous, and primary-related hematoma. Non-contrast brain computed tomography with clinical features was used as input. The regions of both hematoma and perihematomal edema were delineated by using manual segmentation approach. Four feature selection methods were adopted. Also, three classification models were investigated in this study. The results showed that using the features selected by F-value applied with SVM classifier outperformed the other models, achieving weighted average accuracy (± SD) of 0.84 (± 0.07). Additionally, the model demonstrated average sensitivity and positive predictive value of 0.84 (± 0.06) and 0.86 (± 0.05), respectively. We also evaluate the overall performance of discriminating each class from the rest using AUC. The result suggested that our proposed model achieved the weighted average AUC of 0.90. Our proposed method highlights the potential in identifying multiple causes of acute and nontraumatic ICH, which has not been previously explored.

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A Survey on Deep Learning Techniques for Secure and Accurate Mobile Sink Position Prediction in Vehicular Pattern WSN

A Survey on Deep Learning Techniques for Secure and Accurate...

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International Conference on Inventive Systems and Control (ICISC)

作者： D.K. Shareef V. Jyothsna Department of Computer Science and Engineering School of Computing MOHAN BABU UNIVERSITY (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)) Tirupati Andhra Pradesh India Department of Data Science School of Computing MOHAN BABU UNIVERSITY (Erstwhile Sree Vidyanikethan Engineering College (Autonomous)) Tirupati Andhra Pradesh India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs.

关键词： Deep learning Surveys Wireless communication Wireless sensor networks Technological innovation Accuracy Routing

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization 13

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Li, Yongfeng Zhang, Yuze Ma, Lijia Ji, Junkai Liu, Dugang Leung, Victor C. M. Li, Jianqiang Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen518123 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

ISBN: (纸本)9798350308365

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lin Gu Junhao Huang Shaoxing Huang Deze Zeng Bo Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions.

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FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion

arXiv

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arXiv 2024年

作者： Han, Xing Nguyen, Huy Harris, Carl Ho, Nhat Saria, Suchi Department of Computer Science Johns Hopkins University United States Department of Statistics and Data Sciences The University of Texas Austin United States Department of Biomedical Engineering Johns Hopkins University United States

As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due to missing elements, and the temporal irregularity and sparsity of collected samples. Successfully leveraging this complex data, while overcoming the scarcity of high-quality training samples, is key to improving these models’ predictive performance. We introduce "FuseMoE", a mixture-of-experts framework incorporated with an innovative gating function. Designed to integrate a diverse number of modalities, FuseMoE is effective in managing scenarios with missing modalities and irregularly sampled data trajectories. Theoretically, our unique gating function contributes to enhanced convergence rates, leading to better performance in multiple downstream tasks. The practical utility of FuseMoE in the real world is validated by a diverse set of challenging prediction tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： data reduction

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