Baseline high-resolution maps of soil nutrients in Morocco to support sustainable agriculture by Yassine Bouslihim, Abdelkrim Bouasria, Ahmed Jelloul, Lotfi Khiari, Sara Dahhani, Rachid Mrabet, Rachid Moussadek

“…Model validation using independent test datasets demonstrated strong performance, with R2 values of 0.78 for P and 0.80 for K. …”
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Adjusted imbalance ratio leads to effective AI-based drug discovery against infectious disease by Ons Masmoudi, Afef Abdelkrim, Emna Harigua-Souiai

“…Abstract The success of AI-based pipelines in Drug Discovery (DD) heavily depends on three components: the dataset (size, content, imbalance ratio), the encoding system and the predictive model. This study focuses on optimizing these elements to develop robust predictive models for the biological activity of chemical molecules. …”
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Confidence-Based Fusion of AC-LSTM and Kalman Filter for Accurate Space Target Trajectory Prediction by Caiyun Wang, Jirui Zhang, Jianing Wang, Yida Wu

“…A confidence-weighted fusion mechanism adaptively integrates the predictions from both models, significantly improving overall prediction performance. …”
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MUF-Net: A Novel Self-Attention Based Dual-Task Learning Approach for Automatic Left Ventricle Segmentation in Echocardiography by Juan Lyu, Jinpeng Meng, Yu Zhang, Sai Ho Ling

“…Experimental results demonstrate that our model outperforms existing segmentation methods. …”
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Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents: a longitudinal study based on positive youth develop... by Jiahe Liu, Lang Chen, Yuxin Chen, Jingsong Luo, Kexin Yu, Linlin Fan, Chan Yong, Huiyu He, Simei Liao, Zongyuan Ge, Lihua Jiang, Lihua Jiang

“…Feature selection and SHapley Additive exPlanations (SHAP) analysis were utilised for model improvement and interpretability, respectively.ResultsExtraRFC achieved the best performance (Test AUC = 0.854, Accuracy = 0.798, F1 = 0.659), outperforming all other models across most metrics and external validations. …”
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Unsignalized intersection vehicle platoon formation scheduling method based on mixed traffic by Junfeng Jiang, Yikang Rui, Bin Ran

“…In deciding the passing order, we propose the Mixed Platoon Scheduling Model (MPSM) to improve the traffic safety and efficiency of unsignalized intersections in mixed traffic environments and to obtain the optimal vehicle passing order without collisions. …”
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A dual scheduling framework for task and resource allocation in clouds using deep reinforcement learning by Jiahui Pan, Yi Wei, Lei Meng, Xiangxu Meng

“…Meanwhile, as service providers for end users, they are responsible for handling changing workloads consisting of user-submitted tasks with some QoS requirements. Under this business model, how to minimize the cost of leasing VM instances while guaranteeing the quality of service for SaaS applications is a challenging issue. …”
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Optimization of microwave components using machine learning and rapid sensitivity analysis by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…Domain confinement reduces the cost of surrogate model establishment and improves its predictive power. …”
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Enhancing environmental sustainability and operational efficiency in a case study of limestone quarry in an arid climate by Hussein A. Saleem, Abebe Temesgen Ayalew

“…Automated environmental monitoring systems, incorporating IoT sensors and machine-learning algorithms, provide real-time data on air quality, dust levels, and noise pollution. …”
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Enhanced engine misfire diagnosis through integration of vibration and acoustic emission signals using artificial neural networks by Mohamed H. Abdelati, M. Mourad, Al-Hussein Matar, M. Rabie

“…After all the features were put into a complete matrix, the selection algorithms chose the most essential ones for classifying the data. …”
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Predicting software reuse using machine learning techniques-A case study on open-source Java software systems. by Matthew Yit Hang Yeow, Chun Yong Chong, Mei Kuan Lim, Yuen Yee Yen

“…Software reuse research mainly addresses two hurdles: 1) identifying reusable candidates effectively and efficiently, and 2) selecting high-quality software components that improve maintainability and extensibility. This paper proposes automating software reuse prediction by leveraging machine learning (ML) algorithms, enabling future research and practitioners to better identify highly reusable software. …”
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Digital Image Copyright Protection and Management Approach—Based on Artificial Intelligence and Blockchain Technology by Jikuan Xu, Jiamin Zhang, Junhan Wang

“…It introduces an originality detection model based on deep learning technology after conducting both off-chain and on-chain detection of unidentified images, providing triple protection for digital image copyright infringement detection and enabling efficient active defense and passive evidence storage. …”
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Detecting Unbalanced Network Traffic Intrusions With Deep Learning by S. Pavithra, K. Venkata Vikas

“…It enables the system to prioritize and focus on these important features during model training, thereby enhancing detection accuracy while reducing computational complexity. …”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. …”
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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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Predicting cancer risk using machine learning on lifestyle and genetic data by Mohamed Abdelmoaty Ahmed, Ahmed AbdelMoety, Asmaa Mohamed Ahmed Soliman

“…Abstract Cancer remains one of the leading causes of mortality worldwide, where early detection significantly improves patient outcomes and reduces treatment burden. …”
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Climate risk and low-carbon policies: implications for sports economics and global events by Siyu Fu, Chen Kong

“…ICSF, on the other hand, leverages game theory and optimization algorithms to enhance decision-making in areas such as resource allocation, salary cap management, and sustainability-oriented strategic planning.ResultsEmpirical results from multiple datasets and experiments confirm that the integration of climate risk and low-carbon policy variables significantly improves the predictive accuracy and resilience of economic planning in the sports sector.DiscussionThis research provides theoretical advancements and practical insights for policymakers, sports managers, and investors seeking to navigate the complex interplay between sustainability and profitability in a rapidly changing global environment.…”
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Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las... by Jingyi Liu, Yuxuan Cai, Xiwei Shen

“…To improve model transparency, the SHAP (SHapley Additive exPlanations) method was employed, identifying key variables influencing segregation outcomes. …”
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Unlocking biological complexity: the role of machine learning in integrative multi-omics by Ravindra Kumar, Rajrani Ruhel, Andre J. van Wijnen

“…Machine learning and ensemble methods allow researchers to model nonlinear relationships and manage high-dimensional data, improving the precision of predictions. …”
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