Hybrid POF-VLC Systems: Recent Advances, Challenges, Opportunities, and Future Directions by Rola Abdallah, Mohamed Atef, Nasir Saeed

“…Moreover, this paper presents several promising research directions, such as optimizing training algorithms, exploring deeper neural network architectures, and integrating POF-VLC systems with emerging technologies like beyond 5G, improving energy efficiency, and addressing scalability and complexity in real-time adaptive POF-VLC systems.…”
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Binary program taint analysis optimization method based on function summary by Pan YANG, Fei KANG, Hui SHU, Yuyao HUANG, Xiaoshao LYU

“…Taint analysis is a popular software analysis method, which has been widely used in the field of information security.Most of the existing binary program dynamic taint analysis frameworks use instruction-level instrumentation analysis methods, which usually generate huge performance overhead and reduce the program execution efficiency by several times or even dozens of times.This limits taint analysis technology’s wide usage in complex malicious samples and commercial software analysis.An optimization method of taint analysis based on function summary was proposed, to improve the efficiency of taint analysis, reduce the performance loss caused by instruction-level instrumentation analysis, and make taint analysis to be more widely used in software analysis.The taint analysis method based on function summary used function taint propagation rules instead of instruction taint propagation rules to reduce the number of data stream propagation analysis and effectively improve the efficiency of taint analysis.For function summary, the definition of function summary was proposed.And the summary generation algorithms of different function structures were studied.Inside the function, a path-sensitive analysis method was designed for acyclic structures.For cyclic structures, a finite iteration method was designed.Moreover, the two analysis methods were combined to solve the function summary generation of mixed structure functions.Based on this research, a general taint analysis framework called FSTaint was designed and implemented, consisting of a function summary generation module, a data flow recording module, and a taint analysis module.The efficiency of FSTaint was evaluated in the analysis of real APT malicious samples, where the taint analysis efficiency of FSTaint was found to be 7.75 times that of libdft, and the analysis efficiency was higher.In terms of accuracy, FSTaint has more accurate and complete propagation rules than libdft.…”
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Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction Using Machine Learning Technique by Muhammad Ibrahim, Sagheer Abbas, Areej Fatima, Taher M. Ghazal, Muhammad Saleem, Meshal Alharbi, Fahad Mazaed Alotaibi, Muhammad Adnan Khan, Muhammad Waqas, Nouh Elmitwally

“…Thalassemia is considered a common genetic blood condition that has received extensive investigation in medical research globally. Likely, inherited disorders will be passed down to children from their parents. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

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Debris Flow Susceptibility Prediction Using Transfer Learning: A Case Study in Western Sichuan, China by Tiezhu Li, Qidi Huang, Qigang Chen

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View Score: An early warning score to detect possible complications among COVID-19 patients by Meenakshi Bhakare, Rajkumar Nikalje, Reshma Patil, Urvi Shukla, Rupesh Bokade, Gajanan Sakhare, Shardul Joshi, Aditi Pais

“…Introduction: Understanding pulmonary function at various phases after coronavirus disease 2019 (COVID-19) infection is critical for determining the exact pathophysiological mechanism of COVID-19. Research Question: What is the correlation between spirometry indices and clinical indicators in COVID-19 patients over a 6-week follow-up? …”
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Surrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement by Kaustab C. Sahu, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…However, building accurate surrogates is a daunting task beyond simple cases (low dimensionality, narrow geometry parameter and frequency ranges). This research suggests a new technique for dependable modeling of microwave circuits. …”
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Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…There has been relatively little study on explainability, especially when dealing with malware imagery data, irrespective of the fact that DL/ML algorithms have revolutionized malware detection. Explainability techniques such as SHAP, LIME, and Grad-CAM approaches are employed to present a complete comprehension of feature significance and local or global predictive behavior of the model over various malware categories. …”
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Early detection of bloodstream infection in critically ill children using artificial intelligence by Hye-Ji Han, Kyunghoon Kim, June Dong Park

“…Conclusions We developed a machine learning model that predicts BSI with acceptable performance. Further research is necessary to validate its effectiveness.…”
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Behavior Modeling and Bio-Hybrid Systems: Using Reinforcement Learning to Enhance Cyborg Cockroach in Bio-Inspired Swarm Robotics by Le Minh Triet, Nguyen Truong Thinh

“…Future work will explore scaling the swarm system and integrating advanced sensors and AI algorithms.…”
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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…The DNN with the Adam optimizer achieved the highest accuracy (R² = 0.98423), demonstrating superior predictive capability compared to other ML and DL models. This novel research optimizes hBN exfoliation and establishes a new framework for yield prediction using machine and deep learning, empowering researchers for targeted hBNNs production.…”
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Handwritten W-Net for High-Frequency Guided Single-Image Super-Resolution by Xiang Yang, Chen Fang, Xiaolan Xie, Minggang Dong

“…Recent advances in intelligent image reconstruction algorithms have been accompanied by impressive visual outcomes in SR generative adversarial networks (GANs). …”
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Conceptual model of collaborative pharmaceutical practice in healthcare and social care for the elderly by Ilić Velibor O., Marinković Valentina, Tasić Ljiljana

“…Methods. Using two search algorithms that were created to search articles published in English, a comprehensive search of the bibliographic databases Web of Science and PubMed was undertaken (up to June 2015). …”
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Inversion model of stress state reconstruction for geological hazard pipelines based on digital twin by Xue Luning, Tian Mingliang, Zhao Juncheng

“…The results indicate that the research model has high prediction accuracy and efficiency and can effectively handle the problem of predicting pipeline stress states under different geological disasters, providing a reliable method for evaluating pipeline stress states in geological disasters.…”
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Analysis and Prediction of Wear in Interchangeable Milling Insert Tools Using Artificial Intelligence Techniques by Sonia Val, María Pilar Lambán, Javier Lucia, Jesús Royo

“…It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. …”
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Machine Learning-Based Carbon Compliance Forecasting and Energy Performance Assessment in Commercial Buildings by Aditya Ramnarayan, Felipe de Castro, Andres Sarmiento, Michael Ohadi

“…The forecasts from the machine learning algorithms predicted that the portfolio of buildings would incur an annual average penalty of $31.7 million ($1.09/sq. ft.) and ~$348.7 million ($12.03/sq. ft.) over 11 years. …”
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THE ADOPTION AND HARMONISATION OF REGULATION (EU) 2024/1689 (AI ACT) AND REGULATION (EU) 2018/1725 (EUDPR): CHALLENGES AND BEST PRACTICES by Emilian MATEICIUC

“…This paper critically examines the interplay between the AI Act and Regulation (EU) 2018/1725 (EUDPR), highlighting challenges and proposing best practices for their effective harmonization. The research identifies key intersections and potential conflicts between the two regulations, particularly regarding data processing, transparency, and algorithmic accountability. …”
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Deep-Learning-Based Solar Flare Prediction Model: The Influence of the Magnetic Field Height by Lei Hu, Zhongqin Chen, Long Xu, Xin Huang

“…Most research has focused on designing or selecting the right deep network for the task. …”
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Rethinking model prototyping through the MedMNIST+ dataset collection by Sebastian Doerrich, Francesco Di Salvo, Julius Brockmann, Christian Ledig

“…In addition, the field has increasingly prioritized marginal performance gains on a few, narrowly scoped benchmarks over clinical applicability, slowing down meaningful algorithmic progress. This trend often results in excessive fine-tuning of existing methods on selected datasets rather than fostering clinically relevant innovations. …”
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Emotion-Based Music Recommendation System Integrating Facial Expression Recognition and Lyrics Sentiment Analysis by V. S. G. S. Phaneendra Bottu, K. Ragavan

“…These systems can improve adaptable user interfaces and support music therapy. While prior research has explored algorithms for FER and their effectiveness in identifying emotions, existing solutions often lack optimal accuracy in pairing emotion recognition with music recommendations, particularly in real-world contexts with diverse user preferences. …”
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