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The Internet of Things in Elderly Healthcare Applications: A Systematic Review and Future Directions

IEEE Access 2025年 13卷 71335-71373页

作者： Shirvanian, Naser Shams, Maryam Masoud Rahmani, Amir Lansky, Jan Mildeova, Stanislava Hosseinzadeh, Mehdi Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran14778-93855 Iran Kurdistan University Department of Physics Faculty of Basic Science Sanandaj66177-15175 Iran National Yunlin University of Science and Technology Future Technology Research Center Douliu64002 Taiwan University of Finance and Administration Department of Computer Science and Mathematics Faculty of Economic Studies Prague101 00 Czech Republic Duy Tan University School of Computer Science Da Nang550000 Viet Nam Jadara University Jadara Research Center Irbid21110 Jordan

The increase in the elderly population globally and the prevalence of chronic diseases among the elderly have resulted in rising healthcare costs worldwide. Utilizing the Internet of Things (IoT) and smart sensors to reduce healthcare costs and enhance the quality of life for the elderly is a highly effective and valuable solution. Effective monitoring can improve resource management in IoT-based healthcare systems. We conducted a systematic literature review (SLR) for elderly healthcare on the IoT to evaluate its various facets and key areas. The taxonomy of elderly healthcare studies in the IoT comprises six categories: smart homes, security and privacy, remote healthcare system, wearable devices, smartphone, healthcare and healthcare costs. Management of chronic diseases has the highest percentage of evaluation statistics in the field of smart homes, at 61%. In addition, 26% of smart homes in healthcare are devoted to behavioral characteristics and 13% to biological characteristics. Finally, this study highlights the visions and challenges of elderly healthcare articles and future work. © 2013 IEEE.

关键词： Medical services Internet of Things Older adults Security Monitoring Diseases Costs Privacy Biomedical monitoring Smart homes

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A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

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Journal of Experimental and Theoretical Artificial Intelligence 2025年第2期37卷 307-342页

作者： Tabagchi Milan, Sara Jafari Navimipour, Nima Lohi Bavil, Hamed Yalcin, Senay Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Engineering University of Applied Sciences & Technology Tabriz Iran Department of Computer Engineering Nisantasi University Istanbul Turkey

Nowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Cloud computing

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Step-by-Step and Tailored Teaching: Dynamic Knowledge Distillation 18th

Step-by-Step and Tailored Teaching: Dynamic Knowledge Disti...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zhang, Zhenqiang Geng, Liting Du, Wenqing Li, Feng Wang, Chunxiao Zhao, Zhigang Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714634

Knowledge distillation typically involves transferring knowledge from large-scale teacher models to student models, garnering widespread attention in the fields of model compression and knowledge transfer. However, existing research often encounters several challenges. Firstly, the transmission process usually fixes the teacher model, making it difficult to adjust the learning situation of the student model according to individual needs. Secondly, the difficulty of tasks affects the learning ability of the student model. In training tasks, difficulty levels are often coupled with data, posing a challenge in guiding the student model to gradually master knowledge. To address these issues, this study proposes a dynamic learning temperature-based meta-learning knowledge distillation method, namely Temperature Meta-learning Knowledge Distillation (TMKD). Inspired by meta-learning heuristics, this algorithm enables the teacher model to dynamically adjust knowledge transfer strategies based on student feedback, facilitating tailored teaching. Furthermore, a Dynamic Temperature Regulation Module (DTRM) is constructed to flexibly control the difficulty level of tasks, allowing the student model to progressively learn knowledge. Finally, we design a Selective Insight Attention mechanism to ensure that the student network focuses more on key information during learning and inference, thereby enhancing overall performance. Extensive experiments on CIFAR-100 and ImageNet demonstrate the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Benchmarking the Performance of Pre-trained LLMs across Urdu NLP Tasks 1

Benchmarking the Performance of Pre-trained LLMs across Urdu...

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1st Workshop on Challenges in Processing South Asian Languages, CHiPSAL 2025, co-located with the 31st International Conference on Computational Linguistics, COLING 2025

作者： Tahir, Munief Hassan Shams, Sana Fiaz, Layba Adeeba, Farah Hussain, Sarmad Center for Language Engineering Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan

ISBN: (纸本)9798891762015

Large Language Models (LLMs) pre-trained on multilingual data have revolutionized natural language processing research, by transitioning from languages and task specific model pipelines to a single model adapted on a variety of tasks. However majority of existing multilingual NLP benchmarks for LLMs provide evaluation data in only few languages with little linguistic diversity. In addition these benchmarks lack quality assessment against the respective state-of the art models. This study presents an in-depth examination of 7 prominent LLMs: GPT-3.5-turbo, Llama 2-7B-Chat, Llama 3.1-8B, Bloomz 3B, Bloomz 7B1, Ministral-8B and Whisper (Large, medium and small variant) across 17 tasks using 22 datasets, 13.8 hours of speech, in a zero-shot setting, and their performance against state-of-the-art (SOTA) models, has been compared and analyzed. Our experiments show that SOTA models currently outperform encoder-decoder models in majority of Urdu NLP tasks under zero-shot settings. However, comparing Llama 3.1-8B over prior version Llama 2-7B-Chat, we can deduce that with improved language coverage, LLMs can surpass these SOTA models. Our results emphasize that models with fewer parameters but richer language-specific data, like Llama 3.1-8B, often outperform larger models with lower language diversity, such as GPT-3.5, in several tasks. © 2025 International Committee on Computational Linguistics.

关键词： Computational linguistics

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Online Queue-Aware Service Migration and Resource Allocation in Mobile Edge Computing

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8063-8078页

作者： Du, An Jia, Jie Chen, Jian Wang, Xingwei Huang, Ming Northeastern University School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Key Agreement and Management Scheme Based on Blockchain for 5G-Enabled Vehicular Networks

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China Communications 2025年第3期22卷 270-287页

作者： Wang Zhihua Wang Shuaibo Wang Haofan Li Jiaze Yao Yizhe Wang Yongjian Yang Xiaolong School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Cyber Science and Engineering Zhengzhou UniversityZhengzhou 449001China Lab of National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029China

5G technology has endowed mobile communication terminals with features such as ultrawideband access,low latency,and high reliability transmission,which can complete the network access and interconnection of a large number of devices,thus realizing richer application scenarios and constructing 5G-enabled vehicular ***,due to the vulnerability of wireless communication,vehicle privacy and communication security have become the key problems to be solved in vehicular ***,the large-scale communication in the vehicular networks also makes the higher communication efficiency an inevitable *** order to achieve efficient and secure communication while protecting vehicle privacy,this paper proposes a lightweight key agreement and key update scheme for 5G vehicular networks based on ***,the key agreement is accomplished using certificateless public key cryptography,and based on the aggregate signature and the cooperation between the vehicle and the trusted authority,an efficient key updating method is proposed,which reduces the overhead and protects the privacy of the vehicle while ensuring the communication ***,by introducing blockchain and using smart contracts to load the vehicle public key table for key management,this meets the requirements of vehicle traceability and can dynamically track and revoke misbehaving ***,the formal security proof under the eck security model and the informal security analysis is conducted,it turns out that our scheme is more secure than other authentication schemes in the vehicular *** analysis shows that our scheme has lower overhead than existing schemes in terms of communication and computation.

关键词： blockchain certificateless public key cryptography 5G vehicular networks key agreement key management

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Attribute-Based Access Control Encryption

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2227-2242页

作者： Wang, Xiuhua Yu, Mengyang Wang, Yaohui Pi, Yinjia Xu, Peng Wang, Shuai Jin, Hai Han, Min Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Wuhan430074 China State Key Laboratory of Cryptology Beijing100878 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Huazhong University of Science and Technology 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 Wuhan430074 China

The burgeoning complexity of communication necessitates a high demand for security. Access control encryption is a promising primitive to meet the security demand but the bulk of its constructions rely on formulating the access control policy with identities. Attribute-based access control policy in attribute-based encryption (ABE) is known to be more expressive without relying on enumerating identities. We propose a generic framework to build attribute-based access control encryption from ciphertext-policy ABE. Our instantiations prioritize different emphases on expressiveness and efficiency. The first instantiation supports multi-valued AND-gate access control structures, while the second supports the linear-secret-sharing access structure. Both are prototyped with efficiency validated empirically. © 2024 IEEE.

关键词： Ciphertext

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Estimating the Dst index using machine learning from IMAGE/HENA observations

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Advances in Space Research 2025年第6期75卷 4786-4794页

作者： Yan, Yuheng Yu, Xiancai Zhang, Xianguo Sun, Tianran Beijing Key Laboratory of Space Environment Exploration National Space Science Center Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Space Weather National Space Science Center CAS Beijing China

This study presents a novel approach to estimating the Disturbance Storm Time (Dst) index, a critical measure of geomagnetic storm activity, using Energetic Neutral Atom (ENA) observations from the High Energy Neutral Atom (HENA) imager on the IMAGE satellite. By applying machine learning techniques—specifically multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) to ENA data from 2000 to 2005, we developed models that achieve a root mean square error (RMSE) of 18.96 nT and a correlation coefficient (R) of 0.87 in predicting the Dst index. This method offers a new, global perspective on geomagnetic storm dynamics by leveraging satellite-based ENA data, enhancing spatial and temporal resolution beyond ground-based magnetometer measurements. These findings show the potential of machine learning in improving space weather forecasting and deepening our understanding of geomagnetic storm dynamics. © 2024

关键词： Convolutional neural networks

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Anomaly Detection Method for Meteorological Data Based on Neural Networks

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Russian Meteorology and Hydrology 2025年第2期50卷 102-114页

作者： Guan, A. Zhu, Ch. Kong, L. Nan, Y. Lu, P. Liu, Sh. Zhao, Y. School of Computer Science and Communication Lanzhou University of Technology Lanzhou730050 China Gansu Meteorological Information and Technology Equipment Support Center Lanzhou730020 China

Abstract: Time series anomaly detection aims to discover specific patterns in time series data that do not conform to general rules and to identify abnormal states in specific time steps. Meteorological data is a type of multivariate time series. Aiming at the problems of poor anomaly detection performance caused by complex time correlation and high dimension of multivariate time series, a multivariate time series anomaly detection model based on the U-MambaFormer and TCN (MTAD_UMFT) networks is proposed. The time series correlation of data and the dependency between multivariate features are captured by the multi-view embedding method. The signal decomposition technology is introduced to reveal different patterns in time series, reduce the interference of the seasonal and periodic characteristics of data on detection results, and improve the accuracy and robustness of the model. The decomposed data are input into the TCN and U-MambaFormer module, respectively. The former is used to examine the time series pattern of data, and the local and global features of data are studied by the latter. The joint error of the two components is used as the final anomaly score. The performance of the model is tested on the MSL, SWAT, PSM, SMD, and Weather datasets. The results show that the anomaly detection accuracy of the model proposed in this paper is better than that of most other baseline models. The F1-score for the real meteorological dataset reaches 98.23%, which verifies the effectiveness of the model. © Allerton Press, Inc. 2025.

关键词： Anomaly detection

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