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Self-Adjusting Network Slicing for Dynamic Heterogeneous Task Offloading in UAV-Enabled Mobile Edge Computing

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Li, Xulong Mao, Wencan Xu, Xinyi Liu, Yaxi Zhang, Huifeng Huangfu, Wei Long, Keping University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan Zhejiang Lab Hangzhou311500 China

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has received wide attention for its ability to significantly reduce latency with flexible mobile aerial nodes. However, the existing works merely consider a fixed periodic network slicing and usually neglect the characterization of computing tasks in practical applications. Motivated by this, we envision a novel scenario where the dynamics and heterogeneity of users and tasks are considered in UAV-enabled MEC networks. We propose a novel self-adjusting two-timescale network slicing scheme based on the internal mechanism in deep reinforcement learning (DRL) to automatically determine whether and when network slicing needs to be reconstructed. Such a novel scheme balances reconstruction cost and system performance compared with traditional fixed schemes. We build an optimization to minimize total system cost for joint UAV trajectory planning and resource allocation at both network and user slice levels. To tackle this problem, we design a novel self-adjusting two-timescale proximal policy optimization (PPO) algorithm that utilizes the property of the critic network, referred to as SaTPPO. Experiment results manifest the effectiveness of the proposed SaTPPO, and it has a lower system cost than the traditional fixed schemes. Moreover, the proposed SaTPPO outperforms the state-of-the-art baselines concerning effectiveness and efficiency. © 2015 IEEE.

关键词： Micro air vehicle (MAV)

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Hierarchical Deep Learning Framework for Multi-Scale Energy Modeling in Smart Building Environments 7

Hierarchical Deep Learning Framework for Multi-Scale Energy ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Raj, J. Relin Francis Nikshya, J. Ebens Vinothini, S. Umesh, R. Krishnan, R. Santhana Gopikumar, S. Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Electronics And Communication Engineering Chennai India Scad College of Engineering And Technology Department of Electronics And Communication Engineering Cheranmahadevi India Psna College of Engineering And Technology Department of Computer Applications Dindigul India Velammal College of Engineering And Technology Department of Information Technology Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Civil Engineering Chennai Avadi India

ISBN: (纸本)9798350359299

In contemporary urban landscapes, the quest for sustainable energy consumption has propelled the evolution of smart building technologies. These structures are equipped with advanced sensors, actuators, and control systems to optimize energy utilization while ensuring occupant comfort and operational efficiency. Central to this paradigm is energy modeling, a process that entails the analysis and prediction of energy consumption patterns within building environments. Efficient energy modeling holds paramount significance for devising effective strategies to mitigate energy wastage, reduce operational costs, and minimize environmental footprints. This research presents a novel approach, the Hierarchical Deep Learning Framework for Multi-scale Energy Modeling in Smart Building Environments, aimed at enhancing energy modeling efficiency. The Deep Energy Fusion (DEF) algorithm is introduced by integrating Deep Belief Networks (DBNs) with a hierarchical energy modeling architecture. DEF leverages DBNs' capability to learn complex hierarchical representations of energy data across various scales, facilitating a comprehensive understanding of energy consumption patterns. To evaluate DEF's performance, the simulation analysis are conducted by comparing it with existing algorithms. Metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R^2) are employed to assess prediction accuracy. Our results demonstrate that DEF outperforms traditional algorithms in capturing both local and global dependencies within the energy data, thereby improving energy modeling accuracy. Moreover, DEF showcases superior performance in terms of computational efficiency and scalability. This research contributes to advancing energy modeling techniques in smart buildings, offering a robust framework for optimizing energy management strategies © 2024 IEEE.

关键词： Energy efficiency

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Optimizing Blood Bank Management with Cloud-Hosted Long Short-Term Memory Models for Inventory Forecasting and Utilization 3

Optimizing Blood Bank Management with Cloud-Hosted Long Shor...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Devi, R. Aiyshwariya Sree Southry, S. Rajanarayanan, S. Sekaran, S. Chandra Sivaraman, Audithan Srinivasan, S. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Department of Computer Science and Engineering Tamil Nadu Salem India P.S.V College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Krishnagiri India Department of Information Technology Jafferpur New Delhi India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509675

This research presents an innovative method for blood bank management using Cloud-based Long Short-Term Memory (LSTM) models for precise inventory forecasting and optimization. The objective of this research is to increase blood bank efficiency by utilizing LSTM models to accurately estimate demand, optimize inventory levels, and enhance overall resource utilization. The system uses cloud computing to provide real-time demand forecasting, minimizing blood waste and assuring a reliable supply for healthcare requirements. LSTM models use historical data to identify long-term trends and variations in blood demand, facilitating accurate inventory forecasting. This predictive system optimizes the allocation process, resulting in improve resource utilization and increased operational efficiency. In compared to traditional techniques, the proposed system provides a more scalable and flexible solution by using cloud technology to provide continuous monitoring and dynamic modifications of inventory levels. It provides a robust framework for management of blood supply and enhancing patient care for improved resource management. Future enhancements may include broadening the system to incorporate real-time multi-source data integration and investigating hybrid machine learning models to better optimize demand forecasts. © 2025 IEEE.

关键词： Efficiency

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Explainable Artificial Intelligence Models for Birth Weight Prediction Based on Maternal Parameters in Ethiopia 5

Explainable Artificial Intelligence Models for Birth Weight ...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Setegn, Gizachew Mulu Engda, Abebaw Agegne Worku, Wasihun Kindalem Dejene, Belayneh Endalamaw Salau, Ayodeji Olalekan Asnake, Nigus Wereta Debark University Department of Computer Science Debark Ethiopia University of Gondar Department of Information Science Gondar Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Debark University Department of Information Technology Debark Ethiopia

ISBN: (纸本)9798331542559

Abnormal birth can have negative impacts on both mothers and infants, leading to complex deliveries and potential long-term health issues such as obesity during childhood and adolescence. This study aimed to develop explainable artificial intelligence models for the prediction of birth weight based on maternal parameters in Ethiopia. The data set was collected from the Ethiopian Demographic Health Survey (EDHS). Homogenous ensemble machine learning algorithms with class decomposition (one versus one and one versus the rest) and without class decomposition were employed after step-forward feature selection techniques. The model was further explained using the LIME, Eli5, and SHAP XAI tools to achieve model-specific interpretability. The performance of the models was evaluated and compared using the standard metrics of accuracy, precision, recall, and F1 score. The overall accuracy of the random forest, bagging, cat boosting, and extra tree with one-versus-rest methods was 95.0%, 95.3%, 95.8%, and 97.4%, respectively. The experimental results showed that the extra tree with one versus the rest outperformed with 97.4% accuracy compared to others, and the researchers decided to use the extra tree further using the XAI tool. The presented XAI techniques provide useful information to understand abnormal birth weight risk and predict results for family members and healthcare providers. Based on the XAI results, it was clear that the greatest risk factors for birth weight prediction were age, sex of the child, birth order, number of households, residency, education level, and religion. © 2024 IEEE.

关键词： Birth Weight Eli5 Explainable Artificial Intelligence LIME Machine Learning SHAP

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Bacterial population dynamics and biosafety assessment in a closed Bioregenerative Life Support System during the “Lunar Palace 365” experiment

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science China Life sciences 2025年第7期68卷 2137-2149页

作者： Yuming Fu Yueying Lu Jianlou Yang Hong Liu Key Laboratory of Biomechanics and Mechanobiology (Beihang University) Ministry of Education Key Laboratory of Innovation and Transformation of Advanced Medical Devices Ministry of Industry and Information Technology National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Inte rdiscipline of Medicine and Engineering) School of Biological Science and Medical Engineering Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University International Joint Research Center of Aerospace Biotechnology & Medical Engineering Beihang University School of Sport Management Shan Dong Sport University Innovation Center for Medical Engineering & Engineering Medicine Hangzhou International Innovation Institute Beihang University

Our study investigates the dynamic variation of bacterial populations within the Bioregenerative Life Support System(BLSS), a critical component for sustainable habitats in extraterrestrial environments supporting human survival. We utilized the ground-based BLSS test platform, referred to as “Lunar Palace 1”(LP1), to examine bacterial dynamics in an ecosystem integrating plant production, human cultivation, and equipment maintenance. Our findings reveal distinct bacterial compositions among crew members and functional areas within the LP1 system while preserving temporal stability across the 370-day experiment. Importantly, we observed a low abundance of potential pathogens, minimal antibiotic resistance, and negligible impact of the isolated bacterial strains on equipment materials, suggesting favorable biosafety attributed to plant integration. Our work enhances the understanding of microbial communities in BLSS and underscores the importance of incorporating green plants to bolster environmental biosafety and sustainability in long-term space exploration missions.

关键词： bacterial community dynamics Bioregenerative Life Support System biosafety antibiotic resistance genes biodegradation

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Two-Stream Holographic Convolutional Network for Speech Emotion Recognition Using Korean Audio 4

Two-Stream Holographic Convolutional Network for Speech Emot...

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4th International Conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2024

作者： Venkateswaran, N. Mudadla, Balaji. Jasmine, R. Rajitha Nalini, J. Rajasekar, M. Jayasudha, T. Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India CMR Technical Campus KandlaKoya Medchal Telangana Hyderabad501401 India R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai601206 India Vignan's Institute of Engineering for Women Department of Computer Science Andhra Pradesh Visakhapatnam530049 India Saveetha College of Liberal arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Computer Science And Application Tamil Nadu Chennai602105 India Kpr Institute of Engineering and Technology Department of English Tamil Nadu Coimbatore641407 India

ISBN: (纸本)9798331529635

Speech Emotion Recognition (SER) has become an important area of research in the AI domain, helping systems comprehend people's feelings with speech, in such areas as healthcare, automated customer support, and virtual companionship. Emotion detection from Korean speech raises learning issues and demand an understanding of all high-frequency spectrum and time selective features. Despite that these layers work effectively, typical convolutional networks may lose phase information which is so important for emotion distinction especially in languages with tone and phonetics. For this research, the AI-Hub Speech Emotional Database, a large scale Korean speech dataset, is employed to create a Two-Stream Holographic Convolutional Network (TSHCN) for SER. However, the TSHCN architecture employed a two-path structure in which one branch encoded the spectral feature and the other branch encoded the temporal dynamics in sequence, while the phase information was retained using holodynamic convolutions for better classification of emotions. In order to increase the efficiency of the model, the Kookaburra Optimization Algorithm (KOA) is used on the parameters of the weight network, reducing the number of mistakes and the load of calculations as well as increasing the accuracy of the model. As more evident from the previously discussed results, SER performance is notably enhanced by the use of TSHCN with KOA;specifically, the accurate representation of Korean speakers' speech emotions will help pave the way for the development of SER for the Korean language and other complex languages as well. This study thus takes up the shortcoming in the conventional approaches to the task and proposes an optimal and language-independent SER model. The introduced approach attains higher accuracy as 99%. © 2024 IEEE.

关键词： Emotion Recognition

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LiDAR-Based Climate Change Imaging in Geoscience Using Spatio Extreme Fuzzy Gradient Model

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 465-472页

作者： Krushnasamy, V.S. Al-Omari, Omaia Sundaram, Ajith Varghese, Issac K. Muniyandy, Elangovan Rao, M. V. A. L. Narasimha Dept. of EIE Dayananda Sagar College of Engineering Bangalore India Information Systems Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India T A Pai Management Institute Manipal Academy of Higher Education Manipal India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of MBA Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur 522302 India

In order to estimate high-resolution air temperatures, computationally demanding procedures are required because climate models do not provide the resolution required for urban climate studies. Data-driven methods, on the other hand, provide air temperature downscaling that is quicker and more precise. Lidar technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. This research proposes a novel technique in urban–rural interface analysis in remote sensing by machine learning (ML) model. Here the input is collected as urban–rural landscape data and processed for noise removal as well as normalisation. Then these data features have been extracted and classified using a spatio extreme fuzzy gradient model with reinforcement Markov vector perceptron neural networks. Experimental analysis has been carried out for various geographical regions in terms of training accuracy, average precision, recall, RMSE, and F-1 score. The proposed model attained an F-1 score of 92%, training accuracy of 98%, average precision of 95%, recall of 93%, and RMSE of 55%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Lidar technology Machine learning model Reinforcement Markov vector Remote sensing Spatio extreme fuzzy gradient

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The Effect of Digital Marketing on Micro, Small and Medium Enterprise in Indonesia 1

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International Conference on Applied informatics and Media Design, AIMD 2019

作者： Sasongko, Alvin Igo Widjaja, Gregorius Christian Theodore, Jason Afriliana, Nunik Matsuo, Tokuro Gaol, Ford Lumban School of Information System Bina Nusantara University Jakarta Indonesia Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9783031307690

ISBN: (纸本)9783031307683

Digital Marketing is a product marketing strategy using digital media and internet networks. Nowadays, digital marketing is a necessity for companies that thrive in the digital world. It greatly affects the growth of MSME (Micro, Small, Medium Enterprises) especially when it comes to increasing their sales performance by enhancing their brand awareness through various media or instruments such as a Facebook ad, Instagram advertising, Search Engine Optimization, and Search Engine Marketing. This research is aimed to measure how much impact of digital marketing utilization into Micro, Small, Medium enterprises. On the other hand, it also provides an understanding on what are the best digital marketing method to be utilized in the current situation. The method we used in this paper is quantitative. Data were collected through various questions constructed in an electronic questionnaire. This research provides a reference of digital marketing strategies for Micro, Small and Medium business owners. The result shows that digital marketing has a huge impact on the growth of Micro, Small and Medium Enterprise from increasing sales, increasing brand awareness, and gaining customers based on the data collected from both customer and Micro, Small, and Medium Enterprise players in Indonesia. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital storage

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Segmentation and Rigid Registration of Liver Dynamic Computed Tomography Images for Diagnostic Assessment of Fatty Liver Disease

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Journal of Computing science and engineering 2023年第3期17卷 117-126页

作者： Koo, Kyoyeong Lee, Jeongjin Hwang, Jiwon Park, Taeyong Jeong, Heeryeol Khang, Seungwoo Lee, Jongmyoung Kwon, Hyuk Na, Seungwon Lee, Sunyoung Kim, Kyoung Won Kim, Kyung Won School of Computer Science and Engineering Soongsil University Seoul Korea Republic of Hanwha Vision Co. Ltd. Seongnam Korea Republic of Department of Biomedical Informatics Hallym University Medical Center Anyang Korea Republic of SKIA Inc. Seoul Korea Republic of Department of Radiology and Research Institute of Radiological Science Severance Hospital Yonsei University College of Medicine Seoul Korea Republic of Department of Radiology and Research Institute of Radiology Asan Medical Center University of Ulsan College of Medicine Seoul Korea Republic of iAID Inc. Seoul Korea Republic of

This study presents a method for diagnosing fatty liver disease by using time-difference liver computed tomography (CT) images of the same patient to perform segmentation and rigid registration on liver regions, excluding the vascular regions. The proposed method comprises three main steps. First, the liver region is segmented in the precontrast phase, and the liver and liver vessel regions are segmented in the portal phase. Second, rigid registration is performed between the liver regions to align the liver positions affected by the patient's posture or breathing. Finally, fatty liver diagnosis is performed with the average Hounsfield unit (HU) value calculated using only the area removed from the vessel area segmented in the portal phase after registration in the precontrast liver area. The mean distance error between the points corresponding to the liver boundary was 3.136 mm and the mean error between the anatomic landmarks was 4.166 mm. A fatty liver diagnosis was confirmed in a total of 18 cases, and the results were identical to the histology results. This technique may be valuable in clinically diagnosing fatty liver using liver CT imaging, which is widely available and more commonly used than abdominal magnetic resonance. © 2023. The Korean institute of information Scientists and Engineers

关键词： Diagnosis

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Micro-hyperspectral Imaging Methodology for Characterization of Lung Cancer Cell 9

Micro-hyperspectral Imaging Methodology for Characterization...

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9th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Cui, Xiaoyu Zhang, Zhe Li, Zhenhao Cheng, Xi-Yu Qi, Zhi-Mei Institute of Medical Intelligence School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China College of Life Sciences and Bioengineering School of Physical Science and Engineering Beijing Jiaotong University Beijing100044 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781510664432

Micro-hyperspectral technology combines hyperspectral imaging and microscopic imaging technology to obtain the spatial and spectral information of samples simultaneously. Because of its rapidity and nondestructive character, micro-hyperspectral technology has shown immense potential in the biomedical field. In this study, we employed self-assembled micro-hyperspectral imaging (MHSI) system to image A549 cells (human non-small cell lung cancer), introducing gold nanoparticles (AuNPs) as the contrast agents to increase the image signal-to-noise ratio. The result shows that spectrum information distinguishes the optical absorption capabilities of nanoparticles from that of other sources in the cellular environment. Currently, given the experimental restrictions, AuNPs cannot be observed directly, but the location of gold nanoparticles can be identified by the specific spectrum. To analyze hyperspectral three-dimensional cell images and quantitatively analyze the submicroscopic structure of lung cancer cells, the space-spectral feature fusion algorithm is utilized. According to the preliminary results, micro-hyperspectral imaging technology highlights the potential application for subcellular diagnosis, but the image segmentation of cells without contrast agents yields erroneously classified results;the outcomes of cell segmentation for A549 cells' uptake of AuNPs indicate that the cellular structure is distinct. However, there are still misclassifications since AuNPs lack a specialized targeting function. Taking into consideration the current challenges, our next objective is to execute surface modification on AuNPs as probes to target particular sections of living cells to improve the resolution of cell submicroscopic structure imaging. In conclusion, MHSI technology can effectively characterize the submicroscopic structure of cells and show a wide range of potential bioimaging applications. © 2023 SPIE.

关键词： Bioimaging

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