Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development by Tahir Rashid, Inam Illahi, Qasim Umer, Muhammad Arfan Jaffar, Waheed Yousuf Ramay, Hanadi Hakami

“…The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction.…”
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Supporting self-management with an internet intervention for low back pain in primary care: a RCT (SupportBack 2) by Adam W A Geraghty, Taeko Becque, Lisa C Roberts, Jonathan Hill, Nadine E Foster, Lucy Yardley, Beth Stuart, David A Turner, Gareth Griffiths, Frances Webley, Lorraine Durcan, Alannah Morgan, Stephanie Hughes, Sarah Bathers, Stephanie Butler-Walley, Simon Wathall, Gemma Mansell, Malcolm White, Firoza Davies, Paul Little

“…A cost-effectiveness study estimated cost per point improvement in the Roland–Morris Disability Questionnaire. …”
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Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods by Ankita Lawarde, Ankita Lawarde, Masuma Khatun, Prakash Lingasamy, Prakash Lingasamy, Andres Salumets, Andres Salumets, Andres Salumets, Vijayachitra Modhukur, Vijayachitra Modhukur

“…Using transcriptomic profiles from 14 cancer types in The Cancer Genome Atlas (TCGA), we constructed co-expression networks and applied multiple feature selection techniques including recursive feature elimination (RFE), random forest (RF), Boruta, and linear discriminant analysis (LDA) to identify a minimal yet informative subset of miRNA features. Ensemble ML algorithms were trained and validated with stratified five-fold cross-validation for robust performance assessment across class distributions.ResultsOur models achieved an overall 99% classification accuracy, distinguishing 14 cancer types with high robustness and generalizability. …”
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From Misinformation to Insight: Machine Learning Strategies for Fake News Detection by Despoina Mouratidis, Andreas Kanavos, Katia Kermanidis

“…Through extensive experimentation across multiple datasets, our results demonstrate that BERT-based models consistently achieve superior performance, significantly improving detection accuracy in complex misinformation scenarios. …”
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Review on Key Technologies for Autonomous Navigation in Field Agricultural Machinery by Hongxuan Wu, Xinzhong Wang, Xuegeng Chen, Yafei Zhang, Yaowen Zhang

“…Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. …”
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Adaptation of mathematical educational content in e-learning resources by Yuliya V. Vainshtein, Victoria A. Shershneva, Roman V. Esin, Tatyana V. Zykova

“…For each student it was formed a personal space of mathematical educational content that “adapts” to its level of mastering the material, which contributed to improving the quality of the educational process in mathematical disciplines.In this paper, the methods of mathematical modeling and logicalgnosiological analysis, the theory of graphs and hypergraphs, system analysis, dynamic processes and systems control theory, complex systems design and imitation modelling methods were used.Approbation of the proposed algorithms for the educational content organization of adaptation in the adaptive electronic learning resource for the discipline “Discrete mathematics” showed the productivity of the proposed approach in the teaching process. …”
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Forecasting motion trajectories of elbow and knee joints during infant crawling based on long–short-term memory (LSTM) networks by Jieyi Mo, Qiliang Xiong, Ying Chen, Yuan Liu, Xiaoying Wu, Nong Xiao, Wensheng Hou

“…It experimentally explores how different input and output time-frames affect prediction accuracy and sets the stage for future research focused on optimizing models and developing effective control strategies to improve assistive crawling devices.…”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. …”
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Voltage Control Nonlinearity in QZSDMC Fed PMSM Drive System with Grid Filtering by Przemysław Siwek, Konrad Urbanski

“…To enhance voltage control, a tunable controller with optimized parameters was proposed. The conducted studies demonstrated a 16.5% improvement in the IAE index and faster settling time for Quasi-Z-Source voltage control using the proposed controller compared to the reference controller.…”
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Cross-Scene Multi-Object Tracking for Drones: Leveraging Meta-Learning and Onboard Parameters with the New MIDDTD by Chenghang Wang, Xiaochun Shen, Zhaoxiang Zhang, Chengyang Tao, Yuelei Xu

“…Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields.…”
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A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

“…A five-fold cross-validation procedure was applied to estimate the model's performance and hyperparameter tuning was conducted to optimize classifier efficiency. …”
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The Impact of AI-Personalized Recommendations on Clicking Intentions: Evidence from Chinese E-Commerce by Jiwang Yin, Xiaodong Qiu, Ya Wang

“…It offers valuable insights for e-commerce platforms to continuously optimize personalized recommendation algorithms and boost the click conversion rate of online shopping.…”
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Personal health data protection and intelligent healthcare applications under generative adversarial network by Xiaoyuan Gao, Wei Mi, Xirui Feng

“…Additionally, the model’s accuracy gradually improves as the privacy budget increases. …”
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Applications of artificial intelligence in neurosurgical education: a scoping review by Hector Julio Piñera-Castro, Christian Borges-García

“…Abstract Background Artificial intelligence (AI) has transformed medical education through optimized instruction, competency assessment, and personalized learning. …”
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Cooling Load Forecasting Method for Central Air Conditioning Systems in Manufacturing Plants Based on iTransformer-BiLSTM by Xiaofeng Huang, Xuan Zhou, Junwei Yan, Xiaofei Huang

“…Finally, BiLSTM is applied for temporal modeling, leveraging its bidirectional recurrent structure to capture both forward and backward dependencies in time series, thereby improving forecasting accuracy. …”
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Pharmacological potential of 6,11-dihydro[1,2,4]triazolo[4’,3’:1,6]-pyrido[3,4-b]-5-carboxylic acid and its esters by S. O. Fedotov, A. S. Hotsulia

“…The combination of the indole fragment, which demonstrates activity due to its aromatic structure, with the 1,2,4-triazole nucleus, which is characterised by chemical stability and the ability to form hydrogen bonds, creates a promising basis for the development of new therapeutic agents with improved properties. The active implementation of various modifications of indole and 1,2,4-triazole frameworks is aimed at optimizing the pharmacokinetic and pharmacodynamic characteristics of medicinal products. …”
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Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review by Platon S. Papageorgiou, Rafail Christodoulou, Panagiotis Korfiatis, Dimitra P. Papagelopoulos, Olympia Papakonstantinou, Nancy Pham, Amanda Woodward, Panayiotis J. Papagelopoulos

“…Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. …”
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Joint Tomek Links (JTL): An Innovative Approach to Noise Reduction for Enhanced Classification Performance by Goksu Tuysuzoglu, Yunus Dogan, Elife Ozturk Kiyak, Mustafa Ersahin, Bita Ghasemkhani, Kokten Ulas Birant, Derya Birant

“…Noisy data is a prevalent issue in data mining, significantly impacting the performance of classification algorithms. Mathematical methods are crucial in tackling this obstacle, particularly in optimizing noise detection and data preprocessing. …”
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Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review by Shaohua Wang, Dachuan Xu, Haojian Liang, Yongqing Bai, Xiao Li, Junyuan Zhou, Cheng Su, Wenyu Wei

“…Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. …”
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Dual-Path Large Kernel Learning and Its Applications in Single-Image Super-Resolution by Zhen Su, Mang Sun, He Jiang, Xiang Ma, Rui Zhang, Chen Lv, Qiqi Kou, Deqiang Cheng

“…To enhance the performance of super-resolution models, neural networks frequently employ module stacking. …”
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