The recycling of construction waste asphalt mixes – study case by Mengya Jiang, Xiaoqing Cai

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IDMPF: intelligent diabetes mellitus prediction framework using machine learning by Leila Ismail, Huned Materwala

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MLCRP: ML-Based GPU Cache Performance Modeling Featured With Reuse Profiles by Minjung Cho, Eui-Young Chung

“…MLCRP consists of three main stages: data preparation, training, and inference. In the data preparation stage, synthetic RP-based traces are generated from parameterized distributions to simulate diverse and non-stationary memory patterns. …”
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View adaptive multi-object tracking method based on depth relationship cues by Haoran Sun, Yang Li, Guanci Yang, Zhidong Su, Kexin Luo

“…Abstract Multi-object tracking (MOT) tasks face challenges from multiple perception views due to the diversity of application scenarios. Different views (front-view and top-view) have different imaging and data distribution characteristics, but the current MOT methods do not consider these differences and only adopt a unified association strategy to deal with various occlusion situations. …”
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Reducing Defense Vulnerabilities in Federated Learning: A Neuron-Centric Approach by Eda Sena Erdol, Hakan Erdol, Beste Ustubioglu, Guzin Ulutas, Iraklis Symeonidis

“…Additionally, we perform simulations on the GTSR dataset as a real-world application. Experimental results demonstrate that NC-FLD successfully defends against diverse attack scenarios in both IID and non-IID dataset distributions, maintaining accuracy above 70% with 40% malicious participation, a 5–15% improvement over the state-of-the-art method, showing enhanced robustness across diverse data distributions while effectively mitigating the impacts of both data and model poisoning attacks.…”
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EarlyExodus: Leveraging early exits to mitigate backdoor vulnerability in deep learning by Salmane Douch, M. Riduan Abid, Khalid Zine-Dine, Driss Bouzidi, Fatima Ezzahra El Aidos, Driss Benhaddou

“…Extensive experiments on LeNet-5, ResNet-32, and GhostNet across MNIST, CIFAR-10, and GTSRB reduce the average attack success rate of seven recent backdoor attacks to about 3%, with clean-data accuracy degradation kept below 2%. These results demonstrate a practical, architecture-agnostic pathway toward trustworthy edge-AI systems and lay the foundation for extending backdoor defenses beyond image models to broader application domains.…”
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Access control relationship prediction method based on GNN dual source learning by Dibin SHAN, Xuehui DU, Wenjuan WANG, Aodi LIU, Na WANG

“…With the rapid development and wide application of big data technology, users’ unauthorized access to resources becomes one of the main problems that restrict the secure sharing and controlled access to big data resources.The ReBAC (Relationship-Based Access Control) model uses the relationship between entities to formulate access control rules, which enhances the logical expression of policies and realizes dynamic access control.However, It still faces the problems of missing entity relationship data and complex relationship paths of rules.To overcome these problems, a link prediction model LPMDLG based on GNN dual-source learning was proposed to transform the big data entity-relationship prediction problem into a link prediction problem with directed multiple graphs.A topology learning method based on directed enclosing subgraphs was designed in this modeled.And a directed dual-radius node labeling algorithm was proposed to learn the topological structure features of nodes and subgraphs from entity relationship graphs through three segments, including directed enclosing subgraph extraction, subgraph node labeling calculation and topological structure feature learning.A node embedding feature learning method based on directed neighbor subgraph was proposed, which incorporated elements such as attention coefficients and relationship types, and learned its node embedding features through the sessions of directed neighbor subgraph extraction and node embedding feature learning.A two-source fusion scoring network was designed to jointly calculate the edge scores by topology and node embedding to obtain the link prediction results of entity-relationship graphs.The experiment results of link prediction show that the proposed model obtains better prediction results under the evaluation metrics of AUC-PR, MRR and Hits@N compared with the baseline models such as R-GCN, SEAL, GraIL and TACT.The ablation experiment results illustrate that the model’s dual-source learning scheme outperforms the link prediction effect of a single scheme.The rule matching experiment results verify that the model achieves automatic authorization of some entities and compression of the relational path of rules.The model effectively improves the effect of link prediction and it can meet the demand of big data access control relationship prediction.…”
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The Impact of Countries’ Credit Rating Scores on the Export Performance of Companies by Ruhan İri, Esen Gürbüz

“…In the field research covering the second stage of the research, ISO 500 enterprises constitute the universe in which the data is collected. A questionnaire regarding the perceptions of business executives about the export performance of companies in 2013, when Turkey’s credit rating score was raised up to investable level, and in 2016, when Turkey’s credit rating score was lowered, was developed based on the EXPERF scale, and the export performance was measured with online or faceto-face application of the questionnaire to the business executives who participated in the study voluntarily. …”
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Comparative analysis of machine learning techniques for temperature and humidity prediction in photovoltaic environments by Montaser Abdelsattar, Ahmed AbdelMoety, Ahmed Emad-Eldeen

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Metody analizy danych na potrzeby audytu wewnętrznego by Piotr Buda, Agata Szczerbetka

“…Purpose: The research objective of the article is to assess the effectiveness and practical application of various data analysis methods in internal audit processes. …”
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Developing a large language model for oil- and gas-related rock mechanics: Progress and challenges by Botao Lin, Yan Jin, Qianwen Cao, Han Meng, Huiwen Pang, Shiming Wei

“…However, there are three primary challenges in building this domain-specific LLM: data standardization, data security and access, and striking a compromise between physics and data when building the model structure. …”
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Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu, Lauren Y. Gómez-Zamorano

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Advanced Plant Phenotyping: Unmanned Aerial Vehicle Remote Sensing and CimageA Software Technology for Precision Crop Growth Monitoring by Hongyu Fu, Jianning Lu, Guoxian Cui, Jihao Nie, Wei Wang, Wei She, Jinwei Li

“…The development of UAV (unmanned aerial vehicle) remote sensing technology provides a new means for the large-scale, efficient, and accurate acquisition of crop phenotypes, but its practical application and popularization are hindered due to the complicated data processing required. …”
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Promoting Computational Access to Digital Collections in the Nordic and Baltic Countries: An Icelandic Use Case by Gustavo Candela, Olga Holownia, Max Odsbjerg, Mirjam Cuper, Nele Gabriëls, Katrine Hofmann, Edward J. Gray, Sally Chambers, Mahendra Mahey

“…Recent initiatives such as GLAM Labs and Collections as data promote the reuse of and computational access to digital collections. …”
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Low-Memory-Footprint CNN-Based Biomedical Signal Processing for Wearable Devices by Zahra Kokhazad, Dimitrios Gkountelos, Milad Kokhazadeh, Charalampos Bournas, Georgios Keramidas, Vasilios Kelefouras

“…The rise of wearable devices has enabled real-time processing of sensor data for critical health monitoring applications, such as human activity recognition (HAR) and cardiac disorder classification (CDC). …”
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A Review of Research on SLAM Technology Based on the Fusion of LiDAR and Vision by Peng Chen, Xinyu Zhao, Lina Zeng, Luxinyu Liu, Shengjie Liu, Li Sun, Zaijin Li, Hao Chen, Guojun Liu, Zhongliang Qiao, Yi Qu, Dongxin Xu, Lianhe Li, Lin Li

“…Specific solutions for current problems (complexity of data fusion, computational burden and real-time performance, multi-scenario data processing, etc.) are examined by categorizing and summarizing the body of the extant literature and, at the same time, discussing the trends and limitations of the current research by categorizing and summarizing the existing literature, as well as looks forward to the future research directions, including multi-sensor fusion, optimization of algorithms, improvement of real-time performance, and expansion of application scenarios. …”
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Applying machine learning to high-dimensional proteomics datasets for the identification of Alzheimer’s disease biomarkers by Christoffer Ivarsson Orrelid, Oscar Rosberg, Sophia Weiner, Fredrik D. Johansson, Johan Gobom, Henrik Zetterberg, Newton Mwai, Lena Stempfle

“…Results We present a machine learning workflow for working with high-dimensional TMT proteomics data that addresses their inherent data characteristics. …”
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Klasifikasi Program Bantuan Sosial Menggunakan Algoritma K-Nearest Neighbor (K-NN)(Studi Kasus Kecamatan Malangbong Kabupaten Garut) by Hamzah Nurrifqi Fakhri Fikrillah

“…From the results of research conducted using the Rapidminer application with a total of 19,943 families of data and a division ratio of 70% for training data, 30% for test data and a value of K=598 or 3% of the total dataset, a model accuracy rate of 86.70% was obtained. …”
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The international multi-system OSEs/OSSEs by the UN Ocean Decade Project SynObs and its early results by Yosuke Fujii, Yosuke Fujii, Elisabeth Remy, Magdalena Alonso Balmaseda, Shoichiro Kido, Jennifer Waters, K. Andrew Peterson, Gregory C. Smith, Ichiro Ishikawa, Kamel Chikhar

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Knowledge, interest and perspectives on Artificial Intelligence in Neurosurgery. A global survey by A. Boaro, E. Mezzalira, F. Siddi, C. Bagattini, N. Gabrovsky, N. Marchesini, M. Broekman, F. Sala, N. Gabrovsky, Marcel Ivanov, Florian Ringel, Enrico Tessitore, Nicolas Sampron, Alessandro Boaro, Victor E. Staartjes

“…Introduction: Artificial Intelligence (AI) applications in healthcare are growing exponentially. …”
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