Dual-Ascent-Inspired Transformer for Compressed Sensing by Rui Lin, Yue Shen, Yu Chen

“…However, most deep neural network-based CS models are pre-trained for specific compression ratios (CS ratios), limiting their flexibility compared to traditional iterative algorithms. To address this limitation, we propose the Dual-Ascent-Inspired Transformer (DAT), a novel architecture that maintains stable performance across different compression ratios with minimal training costs. …”
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A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security by Ersin Enes Eryılmaz, Erdal Kılıç, Yankı Ertek, Sedat Akleylek

“…IIoT aims to make manufacturing facilities, energy systems, transportation networks, and other industrial systems smarter, more efficient and connected. IIoT aims to reduce costs, increase productivity, and support more sustainable operations by making industrial processes more efficient. …”
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Resource-Efficient Personalization in Federated Learning With Closed-Form Classifiers by Eros Fani, Raffaello Camoriano, Barbara Caputo, Marco Ciccone

“…We demonstrate that <monospace>Fed3R</monospace> serves as a robust initialization for further fine-tuning with various FL and PFL algorithms, accelerating convergence and boosting performance. …”
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Practical Quantum Advantage on Partially Fault-Tolerant Quantum Computer by Riki Toshio, Yutaro Akahoshi, Jun Fujisaki, Hirotaka Oshima, Shintaro Sato, Keisuke Fujii

“…Our framework is based on partially fault-tolerant logical operations to minimize spatial overhead and avoids the costly distillation techniques typically required for executing non-Clifford gates. …”
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Wetland Scene Segmentation of Remote Sensing Images Based on Lie Group Feature and Graph Cut Model by Canyu Chen, Guobin Zhu, Xiliang Chen

“…However, wetland monitoring still faces significant challenges such as high manual costs and low image monitoring accuracy, primarily due to the complex composition of wetland terrain and diverse spectral characteristics, especially located in remote suburban areas. …”
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Artificial intelligence in personalized rehabilitation: current applications and a SWOT analysis by Elpidio Attoh-Mensah, Arnaud Boujut, Arnaud Boujut, Mikaël Desmons, Anaick Perrochon

“…However, notable challenges persist. High implementation costs, ethical concerns such as algorithmic bias, and risks of increasing healthcare disparities remain major barriers. …”
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Socio-Economical Analysis of a Green Reverse Logistics Network under Uncertainty: A Case Study of Hospital Constructions by Alireza Alibakhshi, Amirreza Saffarian, Erfan Hassannayebi

“…Effective waste management is not only essential for minimizing costs but also for enhancing sustainability and reducing environmental impact. …”
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Progress in plant genome sequencing: research directions by M. K. Bragina, D. A. Afonnikov, E. A. Salina

“…As a result of an improved genome assembly and analysis methods, genome sequencing costs plummeted and the number of high-quality plant genome sequences is constantly growing. …”
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Hardware-software simulator for modeling indoor temperature and humidity processes by О.M. Yevseienko

“…The conclusions confirm that the developed model aims to reduce costs for the development and testing of the control system and has proven its effectiveness. …”
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Adaptive continuous-discrete variables optimization for active learning with extremely sparse data in optical material design by Serang Jung, Eungkyu Lee

“…In the adaptive scheme, we examine the performance of three machine learning (ML) models—Gaussian process regression, factorization machines (FM), and field-aware FM—for a surrogate function, and ML model-specific optimization algorithms such as discrete particle swarm optimization, artificial bee colony optimization, and simulated annealing. …”
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A New Approach for Brain Tumor Detection Using Machine Learning by Elsadek Hussien Ibrahim, Shaaban Ebrahim Abo-Youssef, Khaled El-Bahnasy, Khaled Ahmed Mohamed Fathy

“…As patient numbers and data volumes rise, traditional methods have become costly and inefficient. Methods: Researchers have developed algorithms for detecting and classifying brain tumors and prioritizing accuracy and efficiency. …”
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Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management by Vikram Pasupuleti, Bharadwaj Thuraka, Chandra Shikhi Kodete, Saiteja Malisetty

“…Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis. …”
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Big data transfer service architecture for cloud data centers: problems, methods, applications, and future trends by Muhammad Umar Majigi, Ismaila Idris, Shafi’i Muhammad Abdulhamid, Richard A. Ikuesan

“…After thoroughly screening selected peer-reviewed articles, the primary open issues are: incorporating a data placement module in the data transfer service, providing end-to-end safeguards for packet delivery, improving data transfer time and speed, minimizing costs and implementation overhead, ensuring secure data transfer between servers in the cloud, demonstrating effective big data transfer architectures, considering topology-specific extensions to reduce the busty nature of data centers, and enhancing data transfer services using machine learning algorithms during upload and download operations. …”
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Expedited re-design of multi-band passive microwave circuits using orthogonal scaling directions and gradient-based tuning by Slawomir Koziel, Anna Pietrenko-Dabrowska, Ubaid Ullah

“…Nonetheless, if the intended frequencies are distant from the ones at the starting point, local search procedures tend to fail, whereas global search algorithms are computationally expensive. As recently demonstrated, a combination of large-scale concurrent geometry parameter scaling with intermittent local tuning allows for dependable re-design of high-frequency circuits at low CPU costs. …”
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Emerging Imaging Technologies in Forensic Medicine: A Systematic Review of Innovations, Ethical Challenges, and Future Directions by Feras Alafer

“…Emerging imaging modalities enhance diagnostic precision and facilitate non-invasive examinations, offering culturally sensitive alternatives to traditional autopsies. However, high costs, algorithmic biases, data security risks, and the lack of standardized forensic imaging protocols present significant challenges. …”
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Efficiency of the Photoluminescent Method for Monitoring the Homogeneity of Feed Mixtures in Animal Husbandry by M. V. Belyakov, E. A. Nikitin, I. Yu. Efremenkov

“…It was found that the use of the proposed method in the technological process of preparing the feed mixture will reduce the energy costs.…”
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Unveiling innovation imperatives in agriculture: A data-driven framework for identifying latent needs and regional priorities by Andrea Bonfiglio

“…An iterative approach is used for this purpose. Mixed matching algorithms are employed to form the comparison groups, while G-computation is used to calculate the differences. …”
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Conditional Tabular Generative Adversarial Net for Enhancing Ensemble Classifiers in Sepsis Diagnosis by Ahmed Alfakeeh, Mhd Saeed Sharif, Abin Daniel Zorto, Thiago Pillonetto

“…The study revealed that data balancing and augmentation on the ensemble machine learning algorithms boost the efficacy of clinical predictive models and can help clinics decide which data types are most important when examining patients and diagnosing sepsis early through intelligent human-machine interface.…”
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Development of a Smart Waste Management System for Route Optimization and Adaptive Demand Management in Dubai by H. Hassan, T. Ali, A. Tamimi

“…This paper presents an overview of the development of a Smart Waste Management System (SWMS) that integrates Geographic Information System (GIS) technology with waste route optimization algorithms and adaptive demand management strategies. …”
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Methodology of Determining the Location of the Distribution Center of Material Flows in a Combined Scheme of Goods Delivery by Svetlana A. Zhestkova

“…Namely, existing algorithms do not take into account the need to visit the branches of the transport graph several times and the curvature of the movement trajectory.The developed technique has greater practical application because it deals with determining the optimal location of the distribution center based on the criterion of full vehicle loading, using piecewise linear approximation to account for the curvature of routes, forming delivery routes using the accurate fictitious nodes and branches’ method (FNB) to optimize transportation and minimize overall logistical costs. …”
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