Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. by Son Q Duong, Le Zheng, Minjie Xia, Bo Jin, Modi Liu, Zhen Li, Shiying Hao, Shaun T Alfreds, Karl G Sylvester, Eric Widen, Jeffery J Teuteberg, Doff B McElhinney, Xuefeng B Ling

“…<h4>Conclusions</h4>Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.…”
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Optimization strategies in NOMA-based vehicle edge computing network by Jianbo DU, Nana XUE, Yan SUN, Jing JIANG, Shulei LI, Guangyue LU

“…Nowadays, vehicular network is confronting the challenges to support ubiquitous connections and vast computation-intensive and delay-sensitive smart service for numerous vehicles.To address these issues, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are considered as two promising technologies by letting multiple vehicles to share the same wireless resources, and the powerful edge computing resources were adopted at the edge of vehicular wireless access network respectively.A NOMA-based vehicular edge computing network was studied.Under the condition of guaranteeing task processing delay, the joint optimization problem of task offloading, user clustering, computing resource allocation and transmission power control was proposed to minimize the task processing cost.Since the proposed problem was difficult to solve, it was divided into sub-problems, and a low-complexity and easy-to-implement method was proposed to solve it.The simulation results show that compared with other benchmark algorithms, the proposed algorithm performs well in minimizing costs.…”
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Optimization strategies in NOMA-based vehicle edge computing network by Jianbo DU, Nana XUE, Yan SUN, Jing JIANG, Shulei LI, Guangyue LU

“…Nowadays, vehicular network is confronting the challenges to support ubiquitous connections and vast computation-intensive and delay-sensitive smart service for numerous vehicles.To address these issues, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) are considered as two promising technologies by letting multiple vehicles to share the same wireless resources, and the powerful edge computing resources were adopted at the edge of vehicular wireless access network respectively.A NOMA-based vehicular edge computing network was studied.Under the condition of guaranteeing task processing delay, the joint optimization problem of task offloading, user clustering, computing resource allocation and transmission power control was proposed to minimize the task processing cost.Since the proposed problem was difficult to solve, it was divided into sub-problems, and a low-complexity and easy-to-implement method was proposed to solve it.The simulation results show that compared with other benchmark algorithms, the proposed algorithm performs well in minimizing costs.…”
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Task Scheduling in Cloud Environment&#x2013;Techniques, Applications, and Tools: A Systematic Literature Review by Olanrewaju L. Abraham, Md Asri Bin Ngadi, Johan Bin Mohamad Sharif, Mohd Kufaisal Mohd Sidik

“…Cloud computing has become a revolutionary model for providing computational resources and services via the internet. As the volume of tasks and the dynamic nature of cloud resources increase, several critical challenges emerge, including load balancing, resource utilization, task allocation, and system performance. …”
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Scheduling Jobs with Variable Job Processing Times on Unrelated Parallel Machines by Guang-Qian Zhang, Jian-Jun Wang, Ya-Jing Liu

“…m unrelated parallel machines scheduling problems with variable job processing times are considered, where the processing time of a job is a function of its position in a sequence, its starting time, and its resource allocation. The objective is to determine the optimal resource allocation and the optimal schedule to minimize a total cost function that dependents on the total completion (waiting) time, the total machine load, the total absolute differences in completion (waiting) times on all machines, and total resource cost. …”
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Study on multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning by Xiaojin DING, Yehui XU, Wen BAO, Gengxin ZHANG

“…To solve the problem of the weak spectrum-cognitive ability caused by monitoring angle, direction resolution, limited processing ability and peak power for a low-earth-orbit (LEO) satellite, a multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning was proposed.Firstly, considering the available computing resource, cognitive performance, processing and transmission delay of each spectrum cognitive satellite, a cooperative-satellite selection and computing-resource allocation algorithm was built for multiple spectrum-cognitive tasks.Secondly, based on the selected satellites and the allocated computing resources, a low-complexity multi-satellite cooperative spectrum cognitive strategy was further designed, which could automatically sense the spectrum holes, and detect interference as well as identify the modulation mode.Simulation results demonstrate that compared to the single-node cognitive method, the designed multi-satellite cooperative spectrum cognitive strategy can obtain a better cognitive performance.Moreover, comparing with the existing model, the model utilized in the designed strategy can effectively achieve 96.69% and 93.32% lower number of parameters and required floating point operations per second, whilst maintaining the performance.…”
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Gemini: A Cascaded Dual-Agent DRL Framework for Task Chain Planning in UAV-UGV Collaborative Disaster Rescue by Mengxuan Wen, Yunxiao Guo, Changhao Qiu, Bangbang Ren, Mengmeng Zhang, Xueshan Luo

“…Specifically, this framework comprises a chain selection agent and a resource allocation agent: The chain selection agent plans paths for task chains, and the resource allocation agent distributes platform loads along generated paths. …”
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Interference Mitigation in mmWave Heterogeneous Cloud-Radio Access Network: For Better Performance and User Connectivity by Najwan M. Swadi, Firas A. Sabir, Hamed S. Al-Raweshidy

“…It integrates User-RRH associations to mitigate interference, enhance network throughput (via Heuristic Algorithm) and RRH-BBU clustering (via k-means) to manage resources in the network. …”
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Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

“…Assist yield projections may provide governments and policymakers with valuable information to make well-informed choices about food security, import–export policies, and resource allocation. It facilitates national- and regional-level food supply planning. …”
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Deep Learning-Driven Geospatial Modeling of Elderly Care Accessibility: Disparities Across the Urban-Rural Continuum in Central China by Yi Yu, Tian Dong

“…Taking Changsha as a case study, this research constructs an accessibility evaluation system based on the 15-min life circle theory, utilizing multi-source data. Spatial weighting characteristics of elderly care facility locations were analyzed through machine learning algorithms, and service coverage disparities between urban districts and suburban towns were assessed under 5-, 10-, and 15-min walking thresholds. …”
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Perceptual-Preference-Based Touring Routes in Xishu Gardens Using Panoramic Digital-Twin Modeling by Xueqian Gong, Zhanyuan Zhu, Li Guo, Yong Zhong, Deshun Zhang, Jing Li, Manqin Yao, Wei Yong, Mengjia Li, Yujie Huang

“…Accordingly, the proposed model synchronizes Type II peak-hour throughput with Type I off-peak experiential depth using dynamic path allocation algorithms. This study underscores the strong spatial guidance mechanisms of Xishu Gardens, supporting tourism management and heritage conservation.…”
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Genomic selection optimization in blueberry: Data‐driven methods for marker and training population design by Paul Adunola, Luis Felipe V. Ferrão, Juliana Benevenuto, Camila F. Azevedo, Patricio R. Munoz

“…Our contribution in this study is threefold: (i) for the genotyping resource allocation, the use of genetic data‐driven methods to select an optimal set of markers slightly improved prediction results for all the traits; (ii) for the long‐term implication, we carried out a simulation study and emphasized that data‐driven method results in a slight improvement in genetic gain over 30 cycles than random marker sampling; and (iii) for the phenotyping resource allocation, we compared different optimization algorithms to select training population, showing that it can be leveraged to increase predictive performances. …”
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Dynamic Subchannel Assignment-Based Cross-Layer MAC and Network Protocol for Multihop Ad Hoc Networks by Khanh Nguyen Quang, Van Duc Nguyen, Hyunseung Choo

“…The proposed dynamic sub-channel assignment algorithm provides a new interference avoidance mechanism which solves several drawbacks of existing radio resource allocation techniques in wireless networks using OFDMA/TDD, such as the hidden node and exposed node problems, mobility, and cochannels interference in frequency (CCI). …”
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Intelligent scheduling mechanism of time-sensitive network modal in polymorphic network by Sijin YANG, Lei ZHUANG, Yu SONG, Jiaxing WANG, Xinyu YANG

“…For the problems of uncertain forwarding scheduling and long solving time of time-sensitive network modal in polymorphic network, a joint routing and scheduling mechanism of time-sensitive network modal based on CSQF was proposed.Considering the requirement of bounded delay, network state and different routing mechanisms, a hybrid resource scheduling problem of joint cache queue and routing was formulated to optimize the resource usage of the entire network.Then, the traffic characteristics and cache queue utilization was used to predict the cache utilization of the next cycle, which was based on deep reinforcement learning.In addition, by using multi-queue CSQF forwarding scheduling mechanism and explicit routing algorithm based on cache utilization, an iterative scheduling algorithm was proposed to achieve deterministic forwarding and resource allocation.Simulation results show that the mechanism can effectively adjust the transmission scheduling of deterministic applications according to the resource usage of the network, and has better schedulability compared with other off-line scheduling mechanisms.…”
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Intelligent scheduling mechanism of time-sensitive network modal in polymorphic network by Sijin YANG, Lei ZHUANG, Yu SONG, Jiaxing WANG, Xinyu YANG

“…For the problems of uncertain forwarding scheduling and long solving time of time-sensitive network modal in polymorphic network, a joint routing and scheduling mechanism of time-sensitive network modal based on CSQF was proposed.Considering the requirement of bounded delay, network state and different routing mechanisms, a hybrid resource scheduling problem of joint cache queue and routing was formulated to optimize the resource usage of the entire network.Then, the traffic characteristics and cache queue utilization was used to predict the cache utilization of the next cycle, which was based on deep reinforcement learning.In addition, by using multi-queue CSQF forwarding scheduling mechanism and explicit routing algorithm based on cache utilization, an iterative scheduling algorithm was proposed to achieve deterministic forwarding and resource allocation.Simulation results show that the mechanism can effectively adjust the transmission scheduling of deterministic applications according to the resource usage of the network, and has better schedulability compared with other off-line scheduling mechanisms.…”
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Rescheduling of Multi-Scenario and Multi-Objective Dynamic Changes of Ship Group Construction by ZHANG Aoyuan, HU Xiaofeng, ZHANG Yahui

“…First, the objective function is selected based on different production stage and abnormal disturbance, and a mathematical model is then developed, incorporating site constraints, task precedence constraints, and human resource constraints. Next, a site allocation algorithm is introduced, and an improved non-dominated sorting algorithm based on reference points is adopted to solve the problem. …”
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Crop yield prediction using machine learning: An extensive and systematic literature review by Sarowar Morshed Shawon, Falguny Barua Ema, Asura Khanom Mahi, Fahima Lokman Niha, H.T. Zubair

“…In order to ensure food security and optimize resource allocation, precise crop yield prediction has become essential due to the growing global population and the effects of climate change on agricultural production. …”
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Machine learning in predicting firm performance: a systematic review by Yaseen Hezam, Hoa Luong, Lilian Anthonysamy

“…By adopting a more comprehensive ML-based approach to performance prediction, these stakeholders can make more informed decisions regarding resource allocation, capital budgeting, investment strategies, and policy formulation. …”
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Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector by Utsav Pandey, Sanjeet Singh

“…In the first stage, an output-oriented Data Envelopment Analysis is employed to model the interdependency between operational efficiency and service quality by assessing the allocation of the input resources for achieving these two objectives. …”
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Coordinated Scheduling of Automated Loading Platforms in Commercial Logistics by Xiaoqian Nie, Lu Qin, Zixuan Peng, Chuanhan Zhou

“…Taking the intelligent demonstration warehouse in a commercial logistics park in Shandong, China as the background, this paper constructs a platform resource scheduling model under the Automatic Guided Vehicle (AGV) sharing mode to solve the problems of platform allocation and equipment scheduling, and solves it using the simulated annealing algorithm. …”
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