Methodolo- gical Aspects of Semantic Relationship Extraction for Automatic Thesaurus Generation by N. S. Lagutina, K. V. Lagutina, E. I. Mamedov, I. V. Paramonov

“…The main algorithm of generation consists of three stages: selection and preprocessing of a text corpus, recognition of thesaurus terms, and extraction of relations among terms. …”
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Graph-Based Warm Solutions for Optimal Resilience Enhancement of Water Distribution Networks by Mohsen Hajibabaei, Amin Minaei, Mohsen Shahandashti, Robert Sitzenfrei

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Artificial intelligence-driven fuzzy logic approach for optimal well selection in gas lift design: A brown field case study by Nnaemeka Princewill Ohia, Chadi Paul, Emmanuel Asolo, Taiwo Adetomiwa Adewa, Chidimma Favour Chukwu, Paschal Ateb Ubi, Daniel Hogan Itam, Daniel Ugochukwu Nnaji

“…Gas lift is a widely used artificial lift method for enhancing oil production, particularly in wells with insufficient natural reservoir pressure. However, selecting suitable wells for gas lift is complex due to the inherent uncertainty and variability in petroleum production data. …”
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Optimal selection of high-production well targets for fault-controlled fractured-vuggy reservoir in Shunbei oil and gas field, Tarim Basin by Lijun GAO, Haiying LI, Wei GONG, Wei YANG, Hongyan LI

“…This process included methods for optimizing well trajectories, selecting well locations, predicting formation pressures before drilling, and predicting wellbore stability, which improved drilling safety and efficiency. …”
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A Framework for Data‒Driven Fault Diagnosis of Numerical Spacecraft Propulsion Systems by Kazushi Adachi, Samir Khan, Shinichi Nakasuka, Kohji Tominaga, Seiji Tsutsumi, Yu Daimon, Taiichi Nagata

“…The increasing complexity of space exploration missions introduces significant challenges to maintaining spacecraft health, particularly in the propulsion systems, due to the inherent communication delays with Earth. …”
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Selection of favorable shale lithofacies based on an integrated geology and engineering approach: a case study of Lishu Fault Depression in Songliao Basin by Xuan LIN, Jianfeng ZHU, Haiming PANG, Zhongbo LI, Wei WANG, Shuo LIU, Haibin LI, Zhenxue JIANG, Zhuo LI, Chunyu QIN, Kangbo CHEN

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Channel estimation method based on compressive sensing for FBMC/OQAM system by Weina YUAN, Qiu YAN

“…In mobile-to-mobile sensor networks,the channel estimation for FBMC/OQAM system can be investigated as a compressive sensing problem to raise frequency spectrum efficiency by exploiting the sparse nature of wireless channels.Firstly,a novel orthogonal matching pursuit algorithm with selection weak strategy and regularization based on Tanimoto coefficient (T-SWROMP) was proposed to improve the accuracy of LS channel estimation.Then,T-SWROMP methods with auxiliary pilot and coding were used to estimate channel frequency response for FBMC/OQAM system.The experimental results demonstrate the proposed method has lower complexity than the traditional SWOMP method.In addition,it achieve best performance among the traditional OMP,SWOMP and ROMP methods under dual-selective channels.…”
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Joint antenna selection and resource allocation for mm‐wave directional D2D communications using distributed deep reinforcement learning by Pouya Akhoundzadeh, Ghasem Mirjalily

“…Abstract In this paper, with the promising assumption of using adaptive directional microstrip antenna on user equipment, the problem of joint antenna selection, spectrum assignment, and transmit power allocation for mm‐wave device‐to‐device communications underlying cellular networks is tackled, with the goal of enhancing system throughput and energy efficiency. …”
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Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model by Jeong-Gi Lee, Deok-Joo Lee

“…On the other hand, MIBM is advantageous in scenarios where computational efficiency and robustness are prioritized. To support method selection, we also analyze the computational complexity of both approaches and derive error bounds. …”
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Optimizing Machine Learning Models with Data-level Approximate Computing: The Role of Diverse Sampling, Precision Scaling, Quantization and Feature Selection Strategies by Ayad M. Dalloo, Amjad J. Humaidi

“…In this paper, we propose a framework that uses data-level approximate computing techniques, including by diverse sampling strategies, precision scaling, quantization, and feature selection methods, to evaluate the impact of these techniques on the computational efficiency and accuracy of KNN and SVM models. …”
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Improving inverter efficiency for electric vehicles: Experimental validation of the neural network-based SHE technique using RT-LAB by Seyf Eddine Bechekir, Mokhtaria Jbilou, Mostefa Brahami, Fatima Zohra Boudjella, Imen Souhila Bousmaha, Mimouna Oukli, Said Nemmich

“… Inverters are essential for converting direct current to alternating current in electric vehicles, relying on pulse width modulation (PWM) for efficiency. This study presents a real-time Selective Harmonic Elimination PWM (SHE-PWM) algorithm using artificial neural networks, validated with the OP5600 RT LAB simulator. …”
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Sequence-based engineering of pH-sensitive antibodies for tumor targeting or endosomal recycling applications by Wanlei Wei, Traian Sulea

“…This method, called Sequence-based Identification of pH-sensitive Antibody Binding (SIpHAB), was trained on 3D-structure-based calculations of 3,490 antibody-antigen complexes with solved experimental structures. SIpHAB was parametrized to enhance preferential binding either toward or against the acidic pH, for selective targeting of solid tumors or for antigen release in the endosome, respectively. …”
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HERCULE: High-Efficiency Resource Coordination Using Kubernetes and Machine Learning in Edge Computing for Improved QoS and QoE by Garrik Brel Jagho Mdemaya, Miguel Landry Foko Sindjoung, Milliam Maxime Zekeng Ndadji, Mthulisi Velempini

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Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models by Samira Mohammadi, Vahid Rahmanian, Sasan Sattarpanah Karganroudi, Mehdi Adda

“…We focused on metrics such as accuracy, precision, recall, and loss to compare the performance of models VGG19 and DeiT (data-efficient image transformer). RandomSearchCV was used for hyperparameter optimization, and we selectively froze some layers during training to help better tailor the models to our dataset. …”
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Increasing efficiency and sustainability: A comparative analysis of concrete 3D printing and traditional methods based on case studies by Karamara Merve, Bogdanski Moritz-Ole, Zöller Raphael, Albrecht Sophie Viktoria, Linner Thomas, Bock Thomas, Braml Thomas

“…Concrete 3D printing offers several significant advantages, including the ability to create complex geometries, increased material efficiency, faster build times and cost savings. …”
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A lightweight Xray-YOLO-Mamba model for prohibited item detection in X-ray images using selective state space models by Kai Zhao, Shufan Peng, Yujin Li, Tianliang Lu

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Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments by G. Nalinipriya, S. Rama Sree, K. Radhika, E. Laxmi Lydia, Faten Khalid Karim, Mohamad Khairi Ishak, Samih M. Mostafa

“…The Mayfly Optimization Algorithm (MOA) is then utilized for feature selection, effectively mitigating computational complexity. …”
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Low‐power fast Fourier transform hardware architecture combining a split‐radix butterfly and efficient adder compressors by Guilherme Ferreira, Guilherme Paim, Leandro M. G. Rocha, Gustavo M. Santana, Renato H. Neuenfeld, Eduardo A. C. Costa, Sergio Bampi

“…Results reveal that the proposed FFT hardware architecture using the split‐radix butterfly is 13.28% more power efficient than the radix‐4 one. The results further show that, by combining 5‐2 AC within the split‐radix butterfly, our proposal saves up to 43.1% of the total power dissipation considering the whole FFT hardware architecture, compared with the state‐of‐the‐art radix‐4 butterfly employing the adder automatically selected by the logic synthesis tool.…”
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An efficient deep learning prognostic model for remaining useful life estimation of high speed CNC milling machine cutters by Hamdy K. Elminir, Mohamed A. El-Brawany, Dina Adel Ibrahim, Hatem M. Elattar, E.A. Ramadan

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Optimizing linear/non-linear Volterra-type integro-differential equations with Runge–Kutta 2 and 4 for time efficiency by Martin Ndi Azese

“…Additionally, a complex VTIDE is constructed featuring nonlinearities both within and outside the convolutions, as well as a derivative-of-dependent-variable integrant. …”
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