LncRNAs regulates cell death in osteosarcoma by Ping’an Zou, Zhiwei Tao, Zhengxu Yang, Tao Xiong, Zhi Deng, Qincan Chen, Li Niu

“…Three machine learning algorithms—Support Vector Machine, Random Forest, and Generalized Linear Model—were utilized to select feature genes. …”
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L2R-MLP: a multilabel classification scheme for the detection of DNS tunneling by Emmanuel Oluwatobi Asani, Mojiire Oluwaseun Ayoola, Emmanuel Tunbosun Aderemi, Victoria Oluwaseyi Adedayo-Ajayi, Joyce A. Ayoola, Oluwatobi Noah Akande, Jide Kehinde Adeniyi, Oluwambo Tolulope Olowe

“…This highlights the effectiveness of L2 regularization in improving predictive capabilities and model generalization for unseen instances.…”
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Optoelectronic optimization of graded-bandgap CdTeSe thin-film solar cells by Faiz Ahmad, Akhlesh Lakhtakia, Peter B Monk

“…Recent research on CdTeSe thin-film solar cells has indicated that front-junction grading suppresses nonradiative recombination and improves carrier collection, thereby improving the solar-cell efficiency. …”
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UAV-Based Intelligent Sensing Over CF-mMIMO Communications: System Design and Experimental Results by Jian Sun, Wei Shi, Jiacheng Yao, Wei Xu, Dongming Wang, Yang Cao

“…Moreover, we propose an enhanced target tracking algorithm, which is further integrated with Kalman filter-based trajectory prediction for performance improvement. …”
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Maglev Derived Systems: An Interoperable Freight Vehicle Application Focused on Minimal Modifications to the Rail Infrastructure and Vehicles by Jesus Felez, Miguel A. Vaquero-Serrano, William Z. Liu, Carlos Casanueva, Michael Schultz-Wildelau, Gerard Coquery, Pietro Proietti

“…Target speed profiles were precomputed using dynamic programming, while a model predictive control algorithm determined the optimal train state and control trajectories. …”
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MoNetViT: an efficient fusion of CNN and transformer technologies for visual navigation assistance with multi query attention by Liliek Triyono, Liliek Triyono, Rahmat Gernowo, Prayitno

“…Experiments show MoNetViT outperforms other semantic segmentation algorithms in efficiency and effectiveness, particularly in detecting Aruco markers, making it a promising tool to improve navigation aids for the visually impaired.…”
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Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends by Md Amran Hossen, Pg Emeroylariffion Abas

“…Future research directions emphasise the need for improved multimodal sensor fusion, adaptive and personalised models, and the integration of edge computing for real-time analysis. …”
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Source reconstruction accuracy of MEG and EEG Bayesian inversion approaches. by Paolo Belardinelli, Erick Ortiz, Gareth Barnes, Uta Noppeney, Hubert Preissl

“…We also tested whether the use of additional bilateral priors specifying source covariance for ARD and GS algorithms improved performance. Our results show that MNM proves effective only with single source configurations. …”
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Differential Evolution with Autonomous Selection of Mutation Strategies and Control Parameters and Its Application by Zhenyu Wang, Zijian Cao, Zhiqiang Du, Haowen Jia, Binhui Han, Feng Tian, Fuxi Liu

“…Compared with some state-of-the-art intelligence algorithms on 15 functions of CEC2015, 28 functions of CEC2017, and parameter extraction problems of the photovoltaic model, ASDE has the advantages of solution accuracy, convergence speed, and robustness in solving black box problems with unknown features.…”
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An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection. by Ali Nawaz, Amir Ahmad, Shehroz S Khan, Mohammad Mehedy Masud, Nadirah Ghenimi, Luai A Ahmed

“…The proposed interpretation methodology is validated by the clinician experts and reveals promise for early intervention strategies and improved neonatal care.…”
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ML-based top taggers: Performance, uncertainty and impact of tower & tracker data integration by Rameswar Sahu, Kirtiman Ghosh

“…Machine learning algorithms have the capacity to discern intricate features directly from raw data. …”
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A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology. by Duhita Sengupta, Sk Nishan Ali, Aditya Bhattacharya, Joy Mustafi, Asima Mukhopadhyay, Kaushik Sengupta

“…This novel Deep Hybrid Learning model, though derived from classical machine learning algorithms and standard CNN, showed a training and validation AUC score of 0.99 whereas the test AUC score turned out to be 1.00. …”
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Shoulder–Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration by Siyu Zong, Wei Li, Dawen Sun, Zhuoda Jia, Zhengwei Yue

“…To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. …”
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Neural Network Method of Controllers’ Parametric Optimization with Variable Structure and Semi-Permanent Integration Based on the Computation of Second-Order Sensitivity Functions... by Serhii Vladov, Lukasz Scislo, Nina Szczepanik-Ścisło, Anatoliy Sachenko, Victoria Vysotska

“…The model achieved a maximum accuracy of 0.993 with 160 training epochs, minimizing the error function to 0.005. …”
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A Proactive Caching Approach to Boost Network Offloading in Urban Areas by Ahmed Hassan, Khaled Metwally, Tamer Mekkawy, Ashraf Mahran

“…This study marks a paradigm advancement in proactive caching, setting the groundwork for future-ready 6G networks by combining user-centric data models, scalable algorithms, and adaptive file exchange mechanisms to address ever-increasing traffic needs.…”
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Leadership in the Research: Determinants of Quality, Standards and Best Practices by Henryk Dźwigoł

“…The first part addresses the importance of approaches, processes, methods and techniques in research in the field of management and quality sciences; the second – includes questions on improving the research process; the third is demographic. …”
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Class‐specific data augmentation for plant stress classification by Nasla Saleem, Aditya Balu, Talukder Zaki Jubery, Arti Singh, Asheesh K. Singh, Soumik Sarkar, Baskar Ganapathysubramanian

“…Abstract Data augmentation is a powerful tool for improving deep learning‐based image classifiers for plant stress identification and classification. …”
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Experimental assessment of communication delay's impact on connected automated vehicle speed volatility and energy consumption by Wan Li, Jackeline Rios-Torres, Boyu Wang, Zulqarnain H. Khattak

“…To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. …”
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Neural network technologies for forecasting and controlling electricity consumption in energy systems by the genetic method by Nikolay K. Poluyanovich, Oleg V. Kachelaev, Marina N. Dubyago, Talia Hernandez Falcón

“…We consider the task of improving the accuracy of short-term forecasting of electricity consumption using deep machine learning methods. …”
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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…The combination of integrated machine learning methods and IRI models greatly improves the accuracy of electron density measurements obtained by m-NLPs. …”
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