Towards solving NLP tasks with optimal transport loss by Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

“…Loss functions are essential to computing the divergence of a model’s predicted distribution from the ground truth. Such functions play a vital role in machine learning algorithms as they steer the learning process. …”
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Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification by Rafiqul Islam, Sazzad Hossain

“…The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms’ accuracy and dependability. …”
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Impact of Advanced Agriculture Technologies and Energy Consumption on Crop Yields in Modern Agriculture Using Deep Learning Techniques by Khan Baz, Zhu Zhen, Hashmat Ali

“…These machine learning techniques have successfully predicted the synergistic relationships, leading to significant improvements in cereal yield forecasting. …”
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Sentiment Analysis of User Reviews of the AdaKami Online Loan App from the App Store Using SVM and Naive Bayes by Wava Lativa Azzahra, Jamaludin Indra, Rahmat Rahmat, Sutan Faisal

“…The SVM model was then applied to predict 971 unlabeled reviews, and the results showed a dominance of negative sentiment. …”
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H-DSAE: a hybrid technique to recognize heart disease by K. Uma Maheswari, A. Valarmathi

“…This method combines various classifiers to achieve highly accurate predictions. Due to the complexity of the task, the researchers decided to use deep learning methods to perform the heart disease classification task. …”
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Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories by Claire-Alice Hébert, Joshua E. Meyers, My H. Do, Patricia R. Burchat, LSST Dark Energy Science Collaboration

“…We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. …”
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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

“…It has already been elucidated that in ovarian cancer, the extent of alteration in nuclear shape and morphology can modulate genetic changes and thus can be utilized to predict the outcome of low to a high form of serous carcinoma. …”
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Machine Learning Creates a Simple Endoscopic Classification System that Improves Dysplasia Detection in Barrett’s Oesophagus amongst Non-expert Endoscopists by Vinay Sehgal, Avi Rosenfeld, David G. Graham, Gideon Lipman, Raf Bisschops, Krish Ragunath, Manuel Rodriguez-Justo, Marco Novelli, Matthew R. Banks, Rehan J. Haidry, Laurence B. Lovat

“…These generate a simple algorithm to accurately predict dysplasia. Once taught to non-experts, the algorithm significantly improves their rate of dysplasia detection. …”
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Diagnostics and treatment of peptic ulcer: clinical guidelines of the Russian gastroenterological Association by V. T. Ivashkin, A. A. Sheptulin, I. V. Mayev, Ye. K. Baranskaya, A. S. Trukhmanov, T. L. Lapina, S. G. Burkov, A. V. Kalinin, A. V. Tkachev

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Power System Operation Stability Assessment Method Based on Deep Convolutional Neural Network by Jinman Luo, Yuqing Li, Qile Wang, Liyuan Liu

“…METHODS: We adopt the hierarchical real-time prediction model to evaluate the stability and instability of the determined stable samples and unstable samples, thereby improving the timeliness and accuracy of transient evaluation. …”
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Small fixed-wing UAV Precision Aerial Drop capability development by Antonić Vuk, Trifković Milenko, Molović Vojimir, Milenković-Babić Miodrag

“…Precision aerial drop software module is presented, focusing on the automated payload drop algorithm with continually calculated impact point prediction (CCIP) and UAV guidance to the continually calculated release point (CCRP). …”
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Forecast-Aided Converter-Based Control for Optimal Microgrid Operation in Industrial Energy Management System (EMS): A Case Study in Vietnam by Yeong-Nam Jeon, Jae-ha Ko

“…The forecasted load data is then used to optimize charge/discharge schedules for energy storage systems (ESS) using a Particle Swarm Optimization (PSO) algorithm. The strategy is validated using real-site data from a Vietnamese industrial complex, where the proposed method demonstrates enhanced load prediction accuracy, cost-effective ESS operation, and multi-microgrid flexibility under weather variability. …”
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A VMD-LMS filtering approach for DAS noise suppression by Lang Xie, Jingcai Xu, Peidong Hou, Juxiang Qiu, Yu Wu

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A Novel Compressive Optical Encryption via Single-Pixel Imaging by Chenggong Zhang, Bennian Han, Wenqi He, Xiang Peng, Chen Xu

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Multiobjective TOU Pricing Optimization Based on NSGA2 by Huilan Jiang, Bingqi Liu, Yawei Wang, Shuangqi Zheng

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Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints by Chenfeng Pan, Wenwen Lin, Jianxing Zhou, Wei Jian, Ka Chun Chan, Yuk Lun Chan, Lu Ren

“…Furthermore, a ML-based Zn alloy composition design system (ZACDS) was proposed by integrating the Bayesian optimization algorithm. A novel high-strength Zn alloy was successfully designed using ZACDS, demonstrating good agreement between predicted and experimental mechanical properties. …”
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Fault diagnosis of DC distribution network based on improved matrix half-tensor product Bayesian network by YU Huanan, QIU Huahua, WANG He, LI Shiqiang, WEI Bo

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Operation strategy analysis for the passenger and freight shared transportation system by Zuoning Jia, Siyuan Yang, Kun An, Wanjing Ma

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Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS by Dongliang Wang, Yikun Nie, Gaolei Hu, Hon Ki Tsang, Chaoran Huang

“…Experimental results demonstrate state-of-the-art performance in prediction, emulation, and classification tasks across various machine learning applications. …”
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A Reduced-Order Model for Active Suppression Control of Vehicle Longitudinal Low-Frequency Vibration by Donghao Hao, Changlu Zhao, Ying Huang

“…Establishing a prediction model, with linearity and few dof (degree of freedom), is a key step for the design of a control algorithm based on the modern control theory. …”
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