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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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Computational Optimization of RIS-Enhanced Backscatter and Direct Communication for 6G IoT:A DDPG-Based Approach with Physical Layer Security

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computer Modeling in Engineering & sciences 2025年第3期142卷 2191-2210页

作者： Syed Zain Ul Abideen Mian Muhammad Kamal Eaman Alharbi Ashfaq Ahmad Malik Wadee Alhalabi Muhammad Shahid Anwar Liaqat Ali College of Computer Science and Technology Qingdao UniversityQingdao266071China School of Electronic Science and Engineering Southeast UniversityNanjing210018China Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah80200Saudi Arabia Department of Quality Assurance Al-Kawthar UniversityKarachi75300Pakistan Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah80200Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea Department of Electrical Engineering University of Science and TechnologyBannu28100Pakistan

The rapid evolution of wireless technologies and the advent of 6G networks present new challenges and opportunities for Internet ofThings(IoT)applications,particularly in terms of ultra-reliable,secure,and energyefficient *** study explores the integration of Reconfigurable Intelligent Surfaces(RIS)into IoT networks to enhance communication *** traditional passive reflector-based approaches,RIS is leveraged as an active optimization tool to improve both backscatter and direct communication modes,addressing critical IoT challenges such as energy efficiency,limited communication range,and double-fading effects in backscatter *** propose a novel computational framework that combines RIS functionality with Physical Layer Security(PLS)mechanisms,optimized through the algorithm known as Deep Deterministic Policy Gradient(DDPG).This framework adaptively adapts RIS configurations and transmitter beamforming to reduce key challenges,including imperfect channel state information(CSI)and hardware limitations like quantized RIS phase *** optimizing both RIS settings and beamforming in real-time,our approach outperforms traditional methods by significantly increasing secrecy rates,improving spectral efficiency,and enhancing energy ***,this framework adapts more effectively to the dynamic nature of wireless channels compared to conventional optimization techniques,providing scalable solutions for large-scale RIS *** results demonstrate substantial improvements in communication performance setting a new benchmark for secure,efficient and scalable 6G *** work offers valuable insights for the future of IoT networks,with a focus on computational optimization,high spectral efficiency and energy-aware operations.

关键词： Computational optimization reconfigurable intelligent surfaces(RIS) 6G networks IoT and DDPG physical layer security(PLS) backscatter communication

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CK-index: A Distribution-Aware Learned Index for Composite Keys 22

CK-index: A Distribution-Aware Learned Index for Composite K...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wei, Zhengyang Ye, Baoliu Cai, Miao Hohai University College of Computer and Software China Nanjing University Department of Computer Science and Technology China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798331509712

The learned index is a high-performance index structure that uses machine learning methods to predict key positions in a large key space efficiently. Existing learned indexes suffer from underfitting of key-to-position mapping, leading to poor lookup performance. This paper finds that a data distribution property in the widely-used composite key schema addresses this issue effectively. Specifically, the composite key consists of an agglomerate of attributes. Keys with the same attribute value have a regular data distribution, which leads to a higher fitness of key-to-position mapping. Applying the property, we introduce CK-index, a distribution-aware learned index for composite keys. CK-index divides the key space according to attribute values and trains each learned model separately for an attribute to achieve high fitness of key-to-position mapping. Furthermore, it achieves low data storage consumption via storing composite key's attributes instead of the whole keys. We evaluate the CK-index using real-world datasets. Evaluation results demonstrate that CK-index performs much better in lookup performance, bulk loading time and space consumption compared to B+Tree, RMI, PGM-index and ALEX. © 2024 IEEE.

关键词： Composite key Data distribution Learned index

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Performance Analysis of Machine Learning Based On Optimized Feature Selection for Type II Diabetes Mellitus

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Multimedia Tools and Applications 2025年第8期84卷 4945-4964页

作者： Bhat, Salliah Shafi Ansari, Gufran Ahmad Ansari, Mohd Dilshad B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai48 India Department of Computer Science and Software Engineering University of Hail Hail55424 Saudi Arabia Department of Computer Science & Engineering SRM University Delhi-NCR Harayana Sonepat131029 India

Type 2 Diabetes Mellitus (T2DM) is a common chronic illness caused by variations in the secretion of insulin. T2DM will be treated early with the help of an early diagnosis reducing the risk of early death and controlling is course of illness. In this Paper Machine Learning Algorithms (MLA) for T2DM diagnosis are proposed. The major objective of this research is to assess use of MLA and its methods to forecast different illnesses at an initial stage. In this research, we examine most recent proposed strategies with some limitations and potential improvements for future work. The data is collected from Kaggle (University of California) *. Researchers highlight how important is to identify important features that enhance outcomes suggested by recent methods. Six MLA were used in this paper such as Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbour (KNN) were applied in Jupiter Notebook. Feature sets are selected using three feature selection methodologies such as information gain-based selection, correlation-based selection and sequential feature selection. On these feature subsets a variety of MLA are applied and optimal feature subset is chosen based on their performance. Finally a DT is suggested as the top model among the top six performances. The suggested method provides an improved performance label with an average accuracy of 96.10%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability

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Biomedical Materials and Devices 2024年第2期2卷 1225-1245页

作者： Biswas, Shuvo Mostafiz, Rafid Paul, Bikash Kumar Uddin, Khandaker Mohammad Mohi Hadi, Md. Abdul Khanom, Fahmida Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Institute of Information Technology Noakhali Science and Technology University Department of Software Engineering Daffodil International University Department of Computer Science and Engineering Dhaka International University Department of Computer Science: Information Technology University of Nebraska Omaha Department of Mathematics American International University – Bangladesh

Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2024.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Explainable AI

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CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for Large Language Models

CHAmbi: A New Benchmark on Chinese Ambiguity Challenges for ...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Zhang, Qin Cai, Sihan Zhao, Jiaxu Pechenizkiy, Mykola Fang, Meng College of Computer Science and Software Engineering Shenzhen University China Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9798891761681

Ambiguity is an inherent feature of language, whose management is crucial for effective communication and collaboration. This is particularly true for Chinese, a language with extensive lexical-morphemic ambiguity. Despite the wide use of large language models (LLMs) in numerous domains and their growing proficiency in Chinese, there is a notable lack of datasets to thoroughly evaluate LLMs' ability to handle ambiguity in Chinese. To bridge this gap, we introduce the CHAmbi dataset, a specialized Chinese multi-label disambiguation dataset formatted in Natural Language Inference. It comprises 4,991 pairs of premises and hypotheses, including 824 examples featuring a wide range of ambiguities. In addition to the dataset, we develop a series of tests and conduct an extensive evaluation of pre-trained LLMs' proficiency in identifying and resolving ambiguity in the Chinese language. Our findings reveal that GPT-4 consistently delivers commendable performance across various evaluative measures, albeit with limitations in robustness. The performances of other LLMs, however, demonstrate variability in handling ambiguity-related tasks, underscoring the complexity of such tasks in the context of Chinese. The overall results highlight the challenge of ambiguity handling for current LLMs and underscore the imperative need for further enhancement in LLM capabilities for effective ambiguity resolution in the Chinese language. © 2024 Association for Computational Linguistics.

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Medicine and Disease Association Prediction via Attention-Based Medical Heterogeneous Information Network Representation Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 69-78页

作者： Zhang, Bin Yang, Dan Lin, Zhihuang Master of Computer Science and Software Engineering Department University of Science and Technology Liao Ning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liao Ning Anshan China

Knowledge of medication and disease has been rapidly accumulated. Also, an increasing number of researchers have paid more attention to predicting medicine-disease associations by machine learning methods. The associations of entities in the medical heterogeneous information network involve different association types. In this paper, we propose MHIN-MD, a representation learning of medical heterogeneous network model to solve the association between medicines and diseases in medical heterogeneous information network. Specifically, we first construct a medical heterogeneous information network contains diseases, medicines, diagnosis, etc. Next, we design a neural network model to learn the feature vector representation of medical nodes information. We try to find the association between medicines and diseases through the characteristics of medicines structure and the interaction between medicines. Using the Euclidean distance to calculate medicine similarity and disease similarity, generates a set of similar nodes between medicines and diseases to prediction association between medicines and diseases. Finally, our extensive experiments on MIMIC- III datasets and DrugBank datasets, the result demonstrates that MHIN-MD can outperform baselines in the association prediction. © 2022. All Rights Reserved.

关键词： Diagnosis

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of Information technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Scale Feature-Aware Generative Adversarial Network Improve MRI Device Data Imbalance for Healthy Consumption

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 984-996页

作者： Zhao, Yawu Wang, Shudong Qiao, Sibo Zhang, Yulin Wang, Jiehuan Yin, Wenjing Liu, Tiyao Pang, Shanchen China University of Petroleum Qingdao Institute of Software School of Computer Science and Technology Shandong Qingdao China Tiangong University The School of Software Tianjin China Shandong University of Science and Technology College of Mathematics and System Science Shandong Qingdao China Affliated Hospital of Jining Medical University Department of Medical Imaging Shandong Jining China

In consumer healthcare, the imbalance between the imaging data of MRI devices creates a significant barrier to the generalization ability of medical image segmentation. We employ generative adversarial networks to address the challenges inherent in the heterogeneity of data distribution among different MRI devices. First, we propose the Scale Feature Awareness Module (SFAM), which can skillfully capture image details at different scales and broader contextual information. Then, we propose the Dynamic Scale Attention Module (DSAM), which aims to combine feature mappings at different scales for different paths dynamically. We can assign different dynamic weights to each scale to enhance the feature representation. Finally, we propose a multi-scale discriminator to guide the generation of aneurysm images with different diameters. Based on the above modules, we designed Scale Feature Aware Generative Adversarial Network (SFAGAN) to generate medical images with the same distribution. It is experimentally demonstrated that SFAGAN improves PSNR, SSIM, and FID values by 0.64, 0.62, and 7.99, respectively, over the SOTA method. In addition, we use the generated data for downstream segmentation tasks to demonstrate the quality of the generated images. Notably, our SFAGAN has significant performance, making its application in medical systems very feasible. © 1975-2011 IEEE.

关键词： Dynamic contrast enhanced MRI

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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of Information technology (Singapore) 2024年 1-18页

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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