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Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

Computers, Materials & Continua 2022年第7期72卷 281-299页

作者： S.Sreedhar Kumar Syed Thouheed Ahmed Qin Xin S.Sandeep M.Madheswaran Syed Muzamil Basha Dr.T.Thimmaiah Institute of Technology VTUKGFKarnatakaIndia School of Computing&Information Technology REVA UniversityBengaluruIndia Faculty of Science and Technology University of the Faroe IslandsFaroe IslandsDenmark K S School of Engineering BengaluruIndia Muthayammal Engineering College RasipuramTamil NaduIndia

This paper presents,a new approach of Medical Image Pixels Clustering(MIPC),aims to trace the dissimilar patterns over the Magnetic Resonance(MR)image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment,pattern predication and deeper *** proposed MIPC consists of two stages:clustering and *** the clustering stage,the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering(iLIAC),Dynamic Automatic Agglomerative Clustering(DAAC)and Optimum N-Means(ONM).In the second stage,the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure(SICM).Experimental results showthat the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.

关键词： Magnetic resonance image unsupervised clustering scheme intra intimacy intra contrast iLIAC shreekum intra cluster measure medical image clustering

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Killing Many Birds with One Stone: Single-Source to Multiple-Target Domain Adaptation for Extractive Question Answering 9

Killing Many Birds with One Stone: Single-Source to Multiple...

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9th IEEE Smart World Congress, SWC 2023

作者： Yao, Xun Ma, Junlong Hu, Xinrong Wang, Hailun Huang, Gang Zhang, Lu Yang, Jie Li, Wanqing Wuhan Textile University School of Computer Science and Artificial Intelligence Wuhan China Quzhou University College of Electrical and Information Engineering Quzhou China University of Wollongong School of Computing and Information Technology Wollongong Australia

ISBN: (纸本)9798350319804

Extractive Question Answering (EQA) is one of fundamental problems in Natural Language Understanding. This paper deals with the problem of transferring an EQA model trained on a single (probably large) dataset, known as a source, to multiple new and unlabeled datasets, known as targets. Specifically, a novel single-source to multiple-target domain adaptation method is proposed to address the cross-domain EQA task. The method forms the shared feature space across different domains via minimizing the training loss on the source and the feature discrimination loss between source and target samples, and importantly, a syntax alignment loss is also considered to regulate sample representations from the source-and-target domains. Experimental results on several highly-competitive EQA datasets demonstrate the proposed method outperforms state-of-the-art models by a large margin. Intensive ablation studies are also offered to examine the impact from the integration of source-target domains, investigate the model breakdown, and visualize the intermediate shared latent subspace. © 2023 IEEE.

关键词： Domain Discrimination Extractive Question Answering Machine Reading Comprehension Multiple-Target Domain Adaptation Syntax Alignment

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Blockchain Adaptation of Remote Patient Monitoring with Internet of Medical Things

Blockchain Adaptation of Remote Patient Monitoring with Inte...

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2022 International Conference on computing, Communication, Security and Intelligent Systems, IC3SIS 2022

作者： Godly, Cinthia Joy Balasubramanian, Venki Jinila, Bevish Y. Federation University Australia School of Engineering Information Technology and Physical Sciences Ballarat Australia Sathyabama Institute of Science and Technology School of Computing Chennai India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

IoT is transforming the world of information significantly. It has allowed administration services without human interventions for a range of applications including healthcare with Remote Patient Monitoring (RPM). Blockchain is broadly utilized in IoT applications like smart health monitoring that provides privacy and security. Blockchain innovation presents freedom for the medical services industry, for example, reduced transaction costs, transparency for reporting regulatory entities, productive medical care information, privacy, confidentiality and universality of healthcare records. This chapter deals with the collaboration of blockchain innovation in the IoT security in terms of the RPM framework. Patient Centric Agent is used for end-To-end design for nonstop tolerant checking. The patient agent deals with a segment of blockchain to provide protection when streaming information from body area sensors that need to be securely stored and analysing critical parts of blockchain to a wellbeing application network where patient's information can be utilized to make alarms that are essential to validate medical services suppliers in a protected way. This methodology is valuable as they assemble information for longer time frames. The access control manager stores and safely accesses information needed by the classifier during retraining progressively in real-Time from an external data storage. © 2022 IEEE.

关键词： Blockchain

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Fall Detection Algorithm for Elderly People Living Alone Based on ARKit and YOLOv5 12

Fall Detection Algorithm for Elderly People Living Alone Bas...

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12th International Conference on Orange technology, ICOT 2024

作者： Jin, Hui Lin, Yuh-Chung Athamneh, Odai Zhou, Weijun Xu, Huiting School of Computing and Engineering University of Missouri-Kansas City Kansas City United States School of Information Science and Technology Sanda University Shanghai China School of Law University of Missouri-Kansas City Kansas City United States

ISBN: (纸本)9798331523602

The article presents a fall detection algorithm for elderly people living alone based on ARKit and YOLOv5. By combining ARKit’s spatial perception capabilities with YOLOv5’s high-speed visual analysis, the algorithm can quickly and accurately identify elderly people falling. It comprises functional modules such as data collection and fall detection. It adopts the operating environment of the iOS platform and is developed using Swift, Object-C++, and Python languages. The algorithm demonstrates strong fall detection performance with high accuracy, recall, and mAP. However, limitations in the versions of YOLO as well as the size and diversity of the dataset. Future work could focus on using different YOLO versions, expanding the dataset, changing the network structure, and introducing more techniques to improve the accuracy and performance of the algorithm. © 2024 IEEE.

关键词： Fall detection

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Context-guided Triple Matching for Multiple Choice Question Answering 9

Context-guided Triple Matching for Multiple Choice Question ...

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9th IEEE Smart World Congress, SWC 2023

作者： Yao, Xun Ma, Junlong Hu, Xinrong Liu, Junping Wang, Hailun Huang, Gang Yang, Jie Li, Wanqing Wuhan Textile University School of Computer Science and Artificial Intelligence Wuhan China Quzhou University College of Electrical and Information Engineering Quzhou China University of Wollongong School of Computing and Information Technology Wollongong Australia

ISBN: (纸本)9798350319804

The task of multiple choice question answering (MCQA) is to identify the correct answer from multiple candidates given a passage and a question. It is typically approached by estimating the matching score among the triple of the passage, question and candidate answers. Existing methods decouple this estimation into pairwise or dual matching ignoring the third component. This paper introduces a Context-guided Triple Matching algorithm, which models the matching among the triple simultaneously. Specifically, the proposed matching takes one component from the triple as the context, and estimates its semantic matching between the other two. Additionally, a contrastive term is adopted to model the dissimilarity between the correct answer and distractive ones. The proposed algorithm is validated on several benchmarking MCQA datasets and outperforms the state-of-the-art models by a large margin. © 2023 IEEE.

关键词： Contrastive regularization multiple Choice Question Answering Reasoning Triple matching

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Privacy-preserving data aggregation achieving completeness of data queries in smart grid

Privacy-preserving data aggregation achieving completeness o...

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作者： Li, Xinyang Zhao, Meng Ding, Yong Yang, Changsong Wang, Huiyong Liang, Hai Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine-grained data. To address these challenges, we propose a privacy-preserving data aggregation scheme supporting data query (PAQ). We designed a multi-level data aggregation mechanism based on Paillier semi-homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency. © 2024 John Wiley & Sons, Ltd.

关键词： Smart power grids

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FluE: A Resource Fluid Equilibrium Strategy for AIGC Within Evolving computing Power Networks

FluE: A Resource Fluid Equilibrium Strategy for AIGC Within ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Zejun Qiu, Chao Ren, Xiaoxu Wang, Xiaofei Xiong, Zehui Yao, Haipeng Niyato, Dusit Tianjin University College of Intelligence and Computing Tianjin China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing China Singapore University of Technology and Design Singapore Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350351255

The presence of Artificial Intelligence Generated Content (AIGC) has garnered widespread interest. AIGC enables content creation by analyzing big data, leveraging the capabilities of extensive AI models, and substantial AI computing. computing power networks (CPNs) represent an excellent approach for offering pervasive AI computing resources to AIGC. However, these characteristics have posed unprecedented challenges to the CPNs helped AIGC, including the uncertainty of prompts' information value, the inability to model the continuity of computing resources, and the incapacity to represent complex multi-dimensional spaces. In this paper, we propose a computing resources equilibrium strategy based on the fluid model for AIGC helped by CPNs, namely FluE. This mechanism obtains information entropy by constructing an AIGC prompt tree to measure the information value of AIGC prompts. In addition, we model the continuity of computing resources by the fluid model. A fluid-stopping equilibrium strategy is formulated to obtain the average fluid level of computing resources based on the Laplace-Stieltjes transform. To solve the equilibrium strategy, we develop a diffusion-based algorithm for FluE to adjust the fluid policy dynamically to maximize resource rewards. Finally, the evaluations demonstrate improvements in average social welfare. © 2024 IEEE.

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Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena Optimizer Combined with Transfer Learning Approaches 15th

Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena O...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Balakrishnan, Roshni M. Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Patil, Swetha Rajaguru, Harikumar Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidhyapeetham Bengaluru India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering PES University EC Campus Bengaluru India

ISBN: (纸本)9783031810794

Histopathological oral images are one of the popular methodologies to diagnose oral squamous cell carcinoma. Computerized diagnostic systems need to be developed in order to process huge number of histopathological oral images for each subject and helping the clinician in classifying the oral images as either normal class or oral squamous cell carcinoma class. This research work uses deep transfer learning approaches namely MobileNetV3, InceptionV2, and EfficientNetB3 for extracting the features from histopathological oral images. Spotted Hyena Optimizer is used as feature transform for improving classification accuracy. Two dense layers are used for classifying the transformed features. 2784 & 3632 number of histopathological oral images are considered in this study under the two classes as normal and oral squamous cell carcinoma respectively. With the proposed spotted Hyena Optimizer, the maximum accuracy attained is 93% for the features extracted through InceptionV2 model. Relatively, the highest accuracy attained is 89% if the optimizer is not used for the features extracted through MobileNetV3 model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transfer learning

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Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification 39

Bidirectional Logits Tree: Pursuing Granularity Reconcilemen...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

ISBN: (纸本)157735897X

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Boosting cationic and anionic redox activity of Li-rich layered oxide cathodes via Li/Ni disordered regulation

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Journal of Energy Chemistry 2025年第1期100卷 533-543页

作者： Zewen Liu Zhen Wu Hao Wang Xudong Zhang Yuanzhen Chen Yongning Liu Shengwu Guo Shenghua Chen Yanli Nan Yan Liu State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina School of Materials Science and Engineering Xi’an University of Architecture and TechnologyXi’an 710055ShaanxiChina Hefei Advanced Computing Center Operation Management Corp Ltd Hefei 230088AnhuiChina Network Information Center Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina

Lithium-rich layered oxides (LLOs) are increasingly recognized as promising cathode materials for nextgeneration high-energy-density lithium-ion batteries (LIBs).However,they suffer from voltage decay and low initial Coulombic efficiency (ICE) due to severe structural degradation caused by irreversible O ***,we introduce a three-in-one strategy of increasing Ni and Mn content,along with Li/Ni disordering and TM–O covalency regulation to boost cationic and anionic redox activity simultaneously and thus enhance the electrochemical activity of *** target material,Li_(1.2)Ni_(0.168)Mn_(0.558)Co_(0.074)O_(2)(L1),exhibits an improved ICE of 87.2%and specific capacity of 293.2 mA h g^(-1)and minimal voltage decay of less than 0.53 m V cycle-1over 300 cycles at 1C,compared to Li_(1.2)Ni_(0.13)Mn_(0.54)Co_(0.13)O_(2)(Ls)(274.4 mA h g^(-1)for initial capacity,73.8%for ICE and voltage decay of 0.84 mV/cycle over 300 cycles at 1C).Theoretical calculations reveal that the density of states (DOS) area near the Fermi energy level for L1 is larger than that of Ls,indicating higher anionic and cationic redox reactivity than ***,L1 exhibits increased O-vacancy formation energy due to higher Li/Ni disordering of 4.76%(quantified by X-ray diffraction Rietveld refinement) and enhanced TM–O covalency,making lattice O release more difficult and thus improving electrochemical *** increased Li/Ni disordering also leads to more Ni^(2+)presence in the Li layer,which acts as a pillar during Li+de-embedding,improving structural *** research not only presents a viable approach to designing low-Co LLOs with enhanced capacity and ICE but also contributes significantly to the fundamental understanding of structural regulation in high-performance LIB cathodes.

关键词： Low-Co Li-rich layered oxides Li/Ni disordering TM-O covalency Cationic and anionic redox activity

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