Mapping Data to Concepts: Enhancing Quantum Neural Network Transparency with Concept-Driven Quantum Neural Networks by Jinkai Tian, Wenjing Yang

“…CD-QNN merges the representational capabilities of QNNs with the transparency of self-explanatory models by mapping input data into a human-understandable concept space and making decisions based on these concepts. The algorithmic design of CD-QNN is comprehensively analyzed, detailing the roles of the concept generator, feature extractor, and feature integrator in improving and balancing model expressivity and interpretability. …”
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Exploring the Role of Artificial Intelligence in Detecting Advanced Persistent Threats by Pedro Ramos Brandao

“…By leveraging machine learning algorithms and data analytics, AI systems can identify patterns and anomalies that are indicative of sophisticated cyber-attacks. …”
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Review on the Innovation of Investment Banks’ Credit Risk Assessment System in a Highly Volatile Market by Wei Yakun

“…Results highlight significant improvements in real-time risk identification and adaptability to dynamic environments. …”
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Cardiac computed tomography: Current practice, guidelines, applications, and prospects by Yu-Tai Shih, Jia-Hao Zhou, Jong-Kai Hsiao

“…Cardiac computed tomography (CT) has evolved significantly as a critical tool in diagnosing and managing cardiac diseases, greatly facilitated by technological advancements in multidetector systems, dose-reduction techniques, and sophisticated imaging algorithms. This article discusses the historical progression and technological evolution in cardiac CT (CCT), focusing on the impact of 64-multidetector row CT and dual-energy CT systems on improving spatial and temporal resolutions and reducing radiation exposure. …”
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Scheduling framework based on reinforcement learning in online-offline colocated cloud environment by Ling MA, Qiliang FAN, Ting XU, Guanchen GUO, Shenglin ZHANG, Yongqian SUN, Yuzhi ZHANG

“…Some reinforcement learning-based scheduling algorithms for cloud computing platforms barely considered one scenario or ignored the resource constraints of jobs and treated all machines as the same type, which caused low resource utilization or insufficient scheduling efficiency.To address the scheduling problems in online-offline colocated cloud environment, a framework named JobFusion was proposed.Firstly, an efficient resource partitioning scheme was built in the cloud computing platform supporting virtualization technology by integrating the hierarchical clustering method with connectivity constraints.Secondly, a graph convolutional neural network was utilized to embed the attributes of elastic dimension with various constraints and the jobs with various numbers, to capture the critical path information of workflow.Finally, existing high-performance reinforcement learning methods were integrated for scheduling jobs.According to the results of evaluation experiments, JobFusion improves the resource utilization by 39.86% and reduces the average job completion time by up to 64.36% compared with baselines.…”
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L’efficacité visuelle des cartogrammes animés en question. Une piste d’amélioration à travers l’exemple de la ségrégation à Bogotá (1993-2005) by Florent Demoraes, Mégane Bouquet, Boris Mericskay

“…In this article we experiment with a way of improving the effectiveness of such animated cartograms, by conjointly using spatial smoothing. …”
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Application of Artificial Intelligence in Support of NAFLD Diagnosis by Jakub Płudowski, Jan Mulawka

“…Those models have achieved diagnostic accuracy higher than 95%, and the system is designed for continuous improvement by aggregating more data and automatically retraining models. …”
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Research on Functional Modularity and Health Monitoring Design of Home Fitness Equipment by Xinyue Song, Cuiyu Li

“…The system achieves 95% monitoring accuracy for key metrics (heart rate: 97–147 bpm, energy consumption: 216–550 kcal) and improves user satisfaction by 40% compared to conventional equipment. …”
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Filter-enhanced adiabatic quantum computing on a digital quantum processor by Erenay Karacan, Conor Mc Keever, Michael Foss-Feig, David Hayes, Michael Lubasch

“…We demonstrate a significant improvement in ground-state accuracies for paradigmatic quantum spin models.…”
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The use of web resources for metabolomics in horticultural crops by Esra Karakas, Mustafa Bulut, Alisdair R. Fernie

“…Moreover, the application of machine learning algorithms to these web resources has further optimized data interpretation, enabling more accurate prediction of metabolic profiles. …”
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Precision Analysis of Multi-Parameter Multi-Epoch Emitter Localization Radar in Three-Satellite Formation by Yiming Lian, Yuxuan Wu, Yaowen Chen, Xian Liu, Liming Jiang

“…The combined multi-parameter multi-epoch method shows substantial advantages in improving both accuracy and robustness, providing valuable insights for future satellite-based emitter localization technologies.…”
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A Novel Temperature Reconstruction Method for Acoustic Pyrometry Under Strong Temperature Gradients and Limited Measurement Points by Jingkao Tan, Lehang Chen, Na Li, Qulan Zhou, Zhongquan Gao, Jie Zhou

“…However, most of the existing acoustic reconstruction algorithms are developed and utilized in relatively uniform temperature distributions. …”
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Computer Vision-Based Safety Monitoring of Mobile Scaffolding Integrating Depth Sensors by Muhammad Sibtain Abbas, Rahat Hussain, Syed Farhan Alam Zaidi, Doyeop Lee, Chansik Park

“…The qualitative analysis further confirms substantial improvements in worker position detection and safety compliance using 3D data over 2D approaches. …”
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Effective Practices of Macroprudential Stress Testing as a Tool of Increasing the Stability of Russian Financial System in the Context of Macroeconomic Shocks by D. Yu. Desyatnichenko, O. V. Ryabov, O. Yu. Desyatnichenko

“…The authors share the point of view that the existing methods and practices of stress testing still need improvements and methodological improvements, since they regularly allow the practical implementation of adverse scenarios leading to financial shocks and global crises. …”
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High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach by Sunny Md. Saber, Kya Zaw Thowai, Muhammad Asifur Rahman, Md. Mehedi Hassan, A.B.M. Mainul Bari, Asif Raihan

“…In conclusion, this study improves predictive analytics in marine logistics by giving useful information about real-time ETA estimates. …”
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Organ‐based clues for diagnosis of inborn errors of immunity: A practical guide for clinicians by Fatemeh Mohammadi, Amirhossein Yadegar, Mahta Mardani, Aryan Ayati, Hassan Abolhassani, Nima Rezaei

“…The last decade has witnesses improving in the molecular diagnosis of IEI patients. …”
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Small Target Ewe Behavior Recognition Based on ELFN-YOLO by Jianglin Wu, Shufeng Li, Baoqin Wen, Jing Nie, Na Liu, Honglei Cen, Jingbin Li, Shuangyin Liu

“…In response to the poor performance of long-distance small target recognition tasks and real-time intelligent monitoring, this paper proposes a deep learning-based recognition method aimed at improving the ability to recognize and monitor various behaviors of captive ewes. …”
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Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…For the comparison between Logistic Regression (LR) and non-LR algorithms, LR-based algorithms exhibited numerically higher AUC and sensitivity; however, these differences were not statistically significant. …”
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Exploring the Applications of Explainability in Wearable Data Analytics: Systematic Literature Review by Yasmin Abdelaal, Michaël Aupetit, Abdelkader Baggag, Dena Al-Thani

“…Post hoc methods such as Shapley Additive Explanations have gained traction for their adaptability in explaining complex algorithms visually. However, user evaluation remains an area in which improvement is needed, and involving users in the development process can contribute to more transparent and reliable artificial intelligence models in health care applications. …”
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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Background and Objective: In recent years, DNA methylation-tumor classification based on artificial intelligence algorithms has led to a notable improvement in diagnostic accuracy compared to traditional machine learning methods. …”
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