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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Mohan, Ramya Narayanan, Mathiyazhagan Rajinikanth, Venkatesan Department of Computer Science and Engineering Center for Research and Innovation TN Chennai602105 India

ISBN: (纸本)9798350377972

Breast Cancer (BC) is a predominant cause of mortality among women globally, with its incidence progressively rising due to various causes. Early detection of BC is crucial to plan and execute appropriate treatment to cure the disease. The clinical level examination of the BC involves in;personal check by the doctor, screening using non-invasive medical imaging modality, and biopsy test to confirm the BC and its stage. Compared to the radiological imaging modalities, the breast-ultrasound (BU) based screening is widely preferred due to its simplicity. The abnormality recorded with BU is then examined to classify the tumor as benign/malignant, an essential process during BC detection. This research aims to propose a deep-learning (DL) tool to segment the tumor from the chosen BU-image. To enhance the segmentation accuracy, every image is preprocessed using tri-level thresholding with Tsallis's Entropy and Hummingbird-Algorithm (TE+HA). The performance of proposed UNet/SegNet is verified on both unprocessed and preprocessed images. The experimental outcome of this research confirms that the VGG-UNet offered an improved mean accuracy (>97%) when the preprocessed BU is considered for examination. © 2024 IEEE.

关键词： Ultrasonic imaging

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital Communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller Data security Cloud computing Cloud management

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Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1386-1397页

作者： Vahidian, Saeed Morafah, Mahdi Chen, Chen Shah, Mubarak Lin, Bill Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States Center for Research in Computer Vision University of Central Florida OrlandoFL32816-2365 United States Center for Research in ComputerVision University of Central Florida OrlandoFL32816 United States

Though successful, federated learning (FL) presents new challenges for machine learning, especially when the issue of data heterogeneity, also known as Non-IID data, arises. To cope with the statistical heterogeneity, previous works incorporated a proximal term in local optimization or modified the model aggregation scheme at the server side or advocated clustered federated learning approaches, where the central server groups agent population into clusters with jointly trainable data distributions to take the advantage of a certain level of personalization. While effective, they lack a deep elaboration on what kind of data heterogeneity and how the data heterogeneity impacts the accuracy performance of the participating clients. In contrast to many of the prior FL approaches,wedemonstrate not only the issue of data heterogeneity in current setups is not necessarily a problem but also in fact it can be beneficial for *** are intuitive: 1) Dissimilar labels of clients (label skew) are not necessarily considered data heterogeneity, and 2) the principal angle between the clients' data subspaces spanned by their corresponding principal vectors of data is a better estimate of the data heterogeneity. Impact Statement-FL is becoming a compelling learning paradigm in the artificial intelligence (AI) area. However, FL suffers from a notorious issue which is the existence of statistical Non-IID data across different distributed clients. Due to diverse participants, severe data heterogeneity can be present in different clients data, which has been demonstrated to result in unstable and slow convergence. For instance, training a global model across hospitals to identify brain/cancer tumors where every hospitals? images come froma different domain/distribution. To simulate this statistical data heterogeneity, data heterogeneity has been simply modeled as Non-IID label skewwhich tends to be a rigid data partitioning and is hardly representative and th

关键词： Artificial intelligence

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Multilevel Self-Training Approach for Cross-Domain Semantic Segmentation in Intelligent Vehicles

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IEEE Intelligent Transportation Systems Magazine 2024年第1期16卷 148-161页

作者： Chen, Yung-Yao Jhong, Sin-Ye National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Smart Electric Vehicle Research Center Taipei106335 Taiwan National Cheng Kung University Department of Engineering Science Tainan City701 Taiwan

The use of intelligent vehicle technology is increasing;however, this technology requires further improvement. Semantic segmentation enables intelligent vehicles to understand the environment. Although advances have been achieved in a deep learning model for semantic segmentation, large-scale real-world datasets with manual pixel-level annotations, which are expensive, are required for adequately training these models. Unsupervised domain adaptation (UDA) for semantic segmentation enables training on a synthetic source domain and allows a model to be adapted to the real-world target domain without target domain annotations. Self-training is a highly effective UDA method;however, current UDA methods struggle to shift domains effectively and rely heavily on aggressive image-based perturbations for consistency training, resulting in poor-quality pseudolabels during the early training phase and decreased training efficiency. Therefore, we propose a novel multilevel self-training UDA method for semantic segmentation. First, a feature guidance and resampling strategy is adopted to improve the learning of rare classes in the source domain, and a spatial-aware mixing method is then used for gradually and smoothly eliminating the domain gap through training in various cross-domain intermediate spaces. In addition, we developed an instance and prototype feature consistency technique that explores optimization in the feature space to improve the training process by increasing model consistency. We conducted large-scale experiments on two challenging UDA benchmarks, 'GTA5-to-Cityscapes' and 'SYNTHIA-to-Cityscapes.' Our method achieved similar or superior performance to that of previously developed UDA methods for semantic segmentation and has advantages in terms of training stability and convergence efficiency. © 2009-2012 IEEE.

关键词： Semantics

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3-Multi ranked encryption with enhanced security in cloud computing

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Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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Enhanced memory window and efficient resistive switching in stabilized BaTiO3-based RRAM through incorporation of Al2O3 interlayer

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材料科学技术（英文版） 2025年第10期213卷 125-134页

作者： Akendra Singh Chabungbam Minjae Kim Atul Thakre Dong-eun Kim Hyung-Ho Park Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCAUSA Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Centre of Functional Materials Vellore Institute of TechnologyTamil Nadu 632014India Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Aerogel Materials Research Center Yonsei UniversitySeoul 03722Korea

As artificial intelligence and big data become increasingly prevalent,resistive random-access memory(RRAM)has become one of the most promising alternatives for storing massive amounts of *** this study,we employed high-quality crystalline TiN/Al2O3/BaTiO3/Pt RRAM with an optimized thin Al2O3 in-terlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the inter-layer affects the memory window *** insertion of the Al2O3 interlayer,the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in ***,the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magni-tude,respectively,thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude,*** device also exhibits multilevel resistive switching behavior when varying the applied ***,we also developed a 6 × 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal ***,our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.

关键词： Resistive random-access memory Resistive switching Atomic layer deposition Al2O3 interlayer

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Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

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computers, Materials & Continua 2023年第3期74卷 6823-6833页

作者： Sunhwa Annie Nam Kyungwoon Cho Hyokyung Bahn Department of Computer Science and Engineering Ewha UniversitySeoul03760Korea Embedded Software Research Center Ewha UniversitySeoul03760Korea

AI(Artificial Intelligence)workloads are proliferating in modernreal-time *** the tasks of AI workloads fluctuate over time,resourceplanning policies used for traditional fixed real-time tasks should be *** particular,it is difficult to immediately handle changes inreal-time tasks without violating the deadline *** cope with thissituation,this paper analyzes the task situations of AI workloads and findsthe following two ***,resource planning for AI workloadsis a complicated search problem that requires much time for ***,although the task set of an AI workload may change over time,thepossible combinations of the task sets are known in *** on theseobservations,this paper proposes a new resource planning scheme for AIworkloads that supports the re-planning of *** of generatingresource plans on the fly,the proposed scheme pre-determines resourceplans for various combinations of ***,in any case,the workload isimmediately executed according to the resource plan ***,the proposed scheme maintains an optimized CPU(Central Processing Unit)and memory resource plan using genetic algorithms and applies it as soonas the workload *** proposed scheme is implemented in the opensourcesimulator SimRTS for the validation of its *** show that the proposed scheme reduces the energy consumptionof CPU and memory by 45.5%on average without deadline misses.

关键词： Resource planning artificial intelligence real-time system task scheduling optimization problem genetic algorithm

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HPPH: computer-vision-Based Service for High-Performance Pavement Health Recognition

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IEEE Internet of Things Journal 2025年第11期12卷 15987-15996页

作者： Tsung, Chen-Kun Kristiani, Endah Chiu, Chen-Kang Liu, Jung-Chun Yang, Chao-Tung National Chin-Yi University of Technology Department of Computer Science and Information Engineering Taichung City411030 Taiwan Tunghai University Department of Computer Science Taichung407224 Taiwan Krida Wacana Christian University Department of Informatics Jakarta11470 Indonesia Tunghai University Department of Computer Science Research Center of Smart Sustainable Circular Economy Taichung407224 Taiwan

Identifying pavement damage is a crucial component in road maintenance and infrastructure management. Prompt detection and corrective action of pavement defects can prevent severe deterioration, maintain safety, and prolong the useful life of road infrastructure. computer vision is widely applied to vehicle applications, such as driver assistance and self-driving, and that is a good platform to equip the pavement damage recognition service. Therefore, this work applies computer vision to develop the pavement damage detection service named high-performance pavement health recognition (HPPH) to detect common pavement damages, including longitudinal cracks, transverse cracks, alligator cracks, and potholes. The proposed HPPH can be deployed on a vehicle for regular road detection. To fit the requirements of vehicle applications, HPPH considers state-of-the-art techniques to optimize the performance of the recognition service on vehicles, e.g., DeepStream is applied to increase the inference performance. At the same time, YOLOv8s (you only look once, YOLO) provides real-time inference. The experimental results reveal that HPPH provides an accuracy of 90.3% and an average precision 0.5 of 74.3%. Moreover, the optimized HPPH provides three times better than the pure YOLOv8s in terms of the inference speed. In summary, the proposed HPPH provides high recognition accuracy with high efficiency and is feasible to be applied to vehicle applications to realize distributed road maintenance. © 2014 IEEE.

关键词： Deep learning

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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An End-to-End Deep Learning QoS Prediction Model Based on Temporal Context and Feature Fusion

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IEEE Transactions on Services Computing 2025年第03期18卷 1232-1244页

作者： Zhang, Peiyun Fan, Jiajun Chen, Yutong Huang, Wenjun Zhu, Haibin Zhao, Qinglin Nanjing University of Information Science & Technology Engineering Research Center of Digital Forensics of Ministry of Education School of Computer Science Nanjing210044 China Nipissing University Department of Computer Science and Mathematics North BayONP1B 8L7 Canada Macau University of Science and Technology School of Computer Science and Engineering Taipa999078 China

Existing end-to-end quality of service (QoS) prediction methods based on deep learning often use one-hot encodings as features, which are input into neural networks. It is difficult for the networks to learn the information that is conducive to prediction. Aiming at the above problem, an end-to-end deep learning QoS prediction model based on a temporal context and feature fusion is proposed. In the proposed model, three blocks are designed for QoS prediction. Firstly, a user-service encoding conversion block is designed to convert the one-hot encodings of users and services into the latent features of users and services, which can make full use of the data in sparse matrices. Then a time feature extraction block is designed to extract time features based on the time-varying characteristics of QoS values. Finally, the time features are fused with the latent features of users and services to predict QoS values. The experimental results show that on existing datasets, the proposed model has better prediction accuracy than other advanced methods in response time and throughput. © 2025 IEEE.

关键词： Prediction models

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