Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

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Efficient hybrid heuristic adopted deep learning framework for diagnosing breast cancer using thermography images by Ahmad Y. A. Bani Ahmad, Jafar A. Alzubi, Manimaran Vasanthan, Suresh Babu Kondaveeti, J. Shreyas, Thella Preethi Priyanka

“…Then, the optimal binary thresholding is done to segment the preprocessed images, where optimized the thresholding value using developed Rock Hyraxes Dandelion Algorithm Optimization (RHDAO). …”
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Design of Dual-Channel Supply Chain Network Based on the Internet of Things Under Uncertainty by Hamed Nozari, Hossein Abdi, Agnieszka Szmelter-Jarosz, Seyyed Hesamoddin Motevalli

“…In this paper, a mathematical model of a dual-channel supply chain network (DCSCN) based on the Internet of Things (IoT) under uncertainty is presented, and its solution using algorithms based on artificial intelligence such as genetic algorithm (GA), particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and gray wolf optimizer (GWO). …”
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Optimizing EV charging stations and power trading with deep learning and path optimization. by Qing Zhu

“…Path optimization, using the Dijkstra algorithm, minimized travel times for EV users by 11.4%. …”
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Enhancing Global Optimization through the Integration of Multiverse Optimizer with Opposition-Based Learning by Vu Hong Son Pham, Nghiep Trinh Nguyen Dang, Van Nam Nguyen

“…The effectiveness of iMVO is assessed through a series of tests involving both classical and IEEE CEC 2021 benchmark functions, demonstrating competitive performance against established algorithms. Moreover, the applicability of iMVO to real-world challenges is validated through its successful deployment in civil engineering tasks, particularly in optimizing truss designs and managing time-cost tradeoffs. …”
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Book Recommendation Using Collaborative Filtering Algorithm by Esmael Ahmed, Adane Letta

“…Moreover, using hyperparameter tuning with SVD also has an improvement on model performance compared with the existing SVD algorithm.…”
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An Underwater Localization Algorithm Based on the Internet of Vessels by Ziqi Wang, Ying Guo, Fei Li, Yuhang Chen, Jiyan Wei

“…The algorithm is composed of three stages: crowdsensing, denoising, and aggregation-based optimization. …”
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Mathematical Models for Management Information Systems on Digital Platforms: from Resource Management to Demand Forecasting by Viktor Godliuk

“…The use of optimization methods, graph algorithms, forecasting, and machine learning to improve the efficiency of digital systems is investigated. …”
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RTRS algorithm in low-power Internet of things by Yuchen CHEN, Yuan CAO, Laipeng ZHANG, Lianghui DING, Feng YANG

“…Considering the feature of periodical uplink data transmission in IEEE 802.11ah low-power wide area network (LWPAN),a real-time RAW setting (RTRS) algorithm was proposed.Multiple node send data to an access point (AP),and the uplink channel resources were divided into Beacon periods in time.During a Beacon period,AP firstly predicted the next data uploading time and the total amount of devices that will upload data in the next Beacon period.The AP calculated the optimal RAW parameters for minimum energy cost and broadcasted the information to all node.Then all devices upload data according to the RAW scheduling.The simulation results show that the current network state can be predicted accurately according to the upload time of the terminal in the last period.According to the predicted state,raw configuration parameters can be dynamically adjusted and the energy efficiency can be significantly improved.…”
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RTRS algorithm in low-power Internet of things by Yuchen CHEN, Yuan CAO, Laipeng ZHANG, Lianghui DING, Feng YANG

“…Considering the feature of periodical uplink data transmission in IEEE 802.11ah low-power wide area network (LWPAN),a real-time RAW setting (RTRS) algorithm was proposed.Multiple node send data to an access point (AP),and the uplink channel resources were divided into Beacon periods in time.During a Beacon period,AP firstly predicted the next data uploading time and the total amount of devices that will upload data in the next Beacon period.The AP calculated the optimal RAW parameters for minimum energy cost and broadcasted the information to all node.Then all devices upload data according to the RAW scheduling.The simulation results show that the current network state can be predicted accurately according to the upload time of the terminal in the last period.According to the predicted state,raw configuration parameters can be dynamically adjusted and the energy efficiency can be significantly improved.…”
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A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data by Zhibo Cui, Bifeng Hu, Songchao Chen, Nan Wang, Defang Luo, Jie Peng

“…The primary objective was to determine the optimal monitoring period for SOC. Within this period, optimal feature subsets were extracted using variable selection algorithms. …”
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Fault Location and Route Selection Strategy of Distribution Network Based on Distributed Sensing Configuration and Fuzzy C-Means by Bo Li, Guochao Qian, Lijun Tang, Peng Sun, Zhensheng Wu

“…Firstly, the DBPSO algorithm is used to optimize the configuration scheme of the distributed voltage and current sensing device, which reduces the number of measuring devices and system cost on the premise of ensuring the global observability of the distribution network. …”
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A Quantum-Inspired Optimization Strategy for Optimal Dispatch to Increase Heat and Power Efficiency by K. Vanitha, B. Jyothi, R. Seshu Kumar, V. S. Chandrika, Arvind R. Singh, R. M. Naidoo

“…Future research can further explore the application of quantum-inspired optimization algorithms in other energy systems and optimize the algorithm’s parameters to improve its performance.…”
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An Inter-Regional Lateral Transshipment Model to Massive Relief Supplies with Deprivation Costs by Shuanglin Li, Na Zhang, Jin Qin

“…Specifically, JADE is employed in the first stage to allocate the volume of massive relief supplies, aiming to minimize deprivation costs, while IALNS optimizes intermodal routing in the second stage to minimize the weighted sum of transportation time and cost. …”
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Stochastic Explicit Calibration Algorithm for Survival Models by Jeongho Park, Sangwook Kang, Gwangsu Kim

“…In addition, we highlight how improved calibration can improve downstream tasks, including optimizing resource allocation and improving patient care decisions. …”
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Subsampling Algorithms for Irregularly Spaced Autoregressive Models by Jiaqi Liu, Ziyang Wang, HaiYing Wang, Nalini Ravishanker

“…These methods use A-optimality or D-optimality criteria to assess the usefulness of each data point and prioritize the inclusion of the most informative ones. …”
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The Application of Artificial Intelligent Algorithms in Electric Propulsion by Tian Bin, An Bingchen, Xie Kan, Yang Sulan

“…These studies not only demonstrate the great potential of artificial intelligence algorithms in enhancing the performance of electric propulsion systems, optimizing design, and reducing computational costs, but also provide new directions for the future development of electric propulsion technologies.…”
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Flexible Demand Side Management in Smart Cities: Integrating Diverse User Profiles and Multiple Objectives by Nuno Souza e Silva, Paulo Ferrão

“…Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. …”
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Low-cost Cascaded prototype for DC-DC converters for Renewable energy applications by Rerhrhaye Fathallah, Ennaciri Yassine, Rerhrhaye Badr, Lahlouh Ilyas, Khouili Driss, El Martaoui Chaymae, El Akkary Ahmed, Sefiani Nacer

“…The proposed prototype offers a cost-effective and highly efficient solution for implementing various Maximum Power Point Tracking (MPPT) algorithms and advanced control methods. …”
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Anchor Dragging Risk Estimation Strategy from Supervised Cost-Sensitive Learning by Sang-Lok Yoo, Shem Otoi Onyango, Joo-Sung Kim, Kwang-Il Kim

“…This suggested that the optimal ML strategy would involve undersampling the majority class and cost-sensitive learning. …”
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