Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

“…Abstract Agriculture is one of the sectors that has an important impact, taking into account the problem of sufficient food supply on a global level. The process of predicting the yield of crops is among the most challenging undertakings in the agricultural industry. …”
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A Three-Dimensional Ply Failure Model for Composite Structures by Maurício V. Donadon, Sérgio Frascino M. de Almeida, Mariano A. Arbelo, Alfredo R. de Faria

“…A fully 3D failure model to predict damage in composite structures subjected to multiaxial loading is presented in this paper. …”
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European sovereign debt control through reinforcement learning by Tato Khundadze, Willi Semmler, Willi Semmler, Willi Semmler

“…We demonstrate that the Soft Actor-Critic algorithm provides comparable or, in some cases, better solutions to multi-objective macroeconomic optimization problems, in comparison to Nonlinear Model Predictive Control (NMPC) algorithm.…”
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Mining patterns of comorbidity evolution in patients with multiple chronic conditions using unsupervised multi-level temporal Bayesian network. by Syed Hasib Akhter Faruqui, Adel Alaeddini, Carlos A Jaramillo, Jennifer S Potter, Mary Jo Pugh

“…Our findings show that the unsupervised approach has noticeably accurate predictive performance that is comparable to the best performing semi-supervised and the second-best performing supervised approaches. …”
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Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems by T. Kadeethum, V. L. S. Silva, P. Salinas, C. C. Pain, H. Yoon

“…To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. …”
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A Golden Jackal based optimal operation and optimization of a virtual power plant involving renewable resources by Anubhav Kumar Pandey, Chaima Mansour, Nandini K. K., Vinay Kumar Jadoun, Jayalakshmi N. S.

“…In particular, there is a reduction in cost by 2.1% and 0.62% when compared with the salp swarm algorithm and beluga whale algorithm followed by 2.12% and 1.18% in emission. …”
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Two-Layer Intelligent Learning Control Using Output Recurrent Fuzzy Neural Long Short-Term Memory Broad Learning System With RMSprop by Ali Rospawan, Ching-Chih Tsai, Chi-Chih Hung

“…Simulation and experimental results validate that, compared to conventional PID, the proposed method achieves superior tracking accuracy, with the best case showing an 82% reduction in RMSE, and enhanced control efficiency, with an 85% reduction in ITAE. …”
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Quantification of evaporation water losses from nearly 5000 reservoirs in China by Yinuo Zhu, Aizhong Ye, Wei Tian

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Lightweight CNC digital process twin framework: IIoT integration with open62541 OPC UA protocol by Arivazhagan Anbalagan, Waqir Yusuf Zanhar, Shone George, Marcos Kauffman, Tengfei Long

“…This data trained five ML models to predict sensor positions with high accuracies (Random-Forest: R²(0.9994), KNN: R²(0.9998). …”
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Optimal Task Offloading Strategy for Vehicular Networks in Mixed Coverage Scenarios by Xuewen He, Yuhao Cen, Yinsheng Liao, Xin Chen, Chao Yang

“…This study employs long short-term memory networks to predict the loading status of base stations. Then, based on the prediction results, we propose an optimized task offloading strategy using the proximal policy optimization algorithm. …”
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Design and Deployment of ML in CRM to Identify Leads by Alonso Yocupicio-Zazueta, Agustin Brau-Avila, Federico Cirett-Galán, Margarita Valenzuela-Galván

“…In Jupyter Notebooks, logistic regression was utilized to design and to train a model to accurately predict whether a lead will convert into a client or not. …”
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A cellular automata coupled multi-objective optimization framework for blue-green infrastructure spatial allocation by Qinghe Hou, Hanwen Xu, Mingkun Xie, Pingjia Luo, Yuning Cheng

“…The optimized BGI allocation solutions achieved an average increase of 7.45 % in water bodies and 19.92 % in green stormwater infrastructures, alongside a 20.15 % reduction in impervious surfaces. These improvements corresponded to a 19.73 % increase in landscape objective, a 27.55 % improvement in hydrology performance, and a 26.59 % reduction in life-cycle cost (LCC). …”
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Tri-manual interaction in hybrid BCI-VR systems: integrating gaze, EEG control for enhanced 3D object manipulation by Jian Teng, Jian Teng, Sukyoung Cho, Shaw-mung Lee

“…A soft maximum weighted arbitration algorithm resolves spatiotemporal conflicts between manual and virtual inputs with 92.4% success rate. …”
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Efficient Task Scheduling and Load Balancing in Fog Computing for Crucial Healthcare Through Deep Reinforcement Learning by Prashanth Choppara, Bommareddy Lokesh

“…Comparative performance analysis indicated a 30% reduction in task completion times, a 40% reduction in operational latency, and a 25% improvement in fault tolerance relative to traditional scheduling approaches. …”
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Dynamic Water Scheduling in the Northwest River Delta Basin Based on Minimum Discharge Flow Control in Cross-section by WU Di, FANG Chunyan, SHANG Yunfei

“…In response to the problem of insufficient future demand forecasting and poor scheduling balance caused by traditional watershed water scheduling methods relying on historical data and fixed rules, a dynamic water scheduling method for the Northwest River Delta Basin based on minimum discharge flow control of cross-sections was proposed. By using the grey prediction model, the production and domestic water consumption in the downstream areas of the Northwest River Delta Basin was accurately predicted. …”
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Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application by Shiguang Zhang, Ge Feng, Feng Yuan, Shuangle Guo

“…The main purpose of twin support vector regression (TSVR) is to find linear or nonlinear relationships in sample data, and then predict future data. TSVR is the decomposition of a large convex quadratic programming problem into two small convex quadratic programming problems. …”
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Applications, Challenges, and Future Perspectives of Artificial Intelligence in Psychopharmacology, Psychological Disorders and Physiological Psychology: A Comprehensive Review by Mohammad Hossein Salemi, Elham Foroozandeh, Molouk Khademi Ashkzari

“…Personalized medicine, powered by AI, predicts individual medication responses, minimizing side effects and optimizing outcomes. …”
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Analytical approach for modelling of thread crimp in jacquard woven two-dimensional fabrics by Brigita Kolcavová Sirková, Iva Mertová

“…The theoretical predicted values of thread crimp in jacquard fabrics were compared with experimentally obtained values. …”
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The System Research and Implementation for Autorecognition of the Ship Draft via the UAV by Wei Zhan, Shengbing Hong, Yong Sun, Chenguang Zhu

“…In order to solve the above problems, this paper introduces the computer image processing technology based on deep learning, and the specific process is divided into three steps: first, the video sampling is carried out by the UAV to obtain a large number of pictures of the ship draft reading face, and the images are preprocessed; then, the deep learning target detection algorithm of improved YOLOv3 is used to process the images to predict the position of the waterline and identify the draft characters; finally, the prediction results are analyzed and processed to obtain the final reading results. …”
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Forecasting Ultrafine Dust Concentrations in Seoul: A Machine Learning Approach by Sophia Park, Myeong Jun Kim

“…Using daily data from 1 January 2018 to 30 June 2023, this study employed the Boruta algorithm, a variable selection technique based on the random forest model, to identify the most influential predictors for predicting PM2.5 concentrations. …”
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