Design Features of a Removable Module Intended for Securing Containers When Transported in an Open Wagon by Alyona Lovska, Juraj Gerlici, Ján Dižo, Mykhailo Pavliuchenkov, Pavlo Rukavishnikov

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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Radiomic features were extracted from the bone marrow of thoracic vertebrae on non-contrast chest CT scans. Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
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Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection by Ying Zhao, Sen Wang, Sen Wang, Yue Wang, Jun Li, Jinghong Liu, Yuhui Liu, Haitong Ji, Wenhan Su, Qinhe Zhang, Qingwei Song, Yu Yao, Yu Yao, Ailian Liu, Ailian Liu

“…The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. …”
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Prediction of Low-Temperature Rheological Properties of SBS Modified Asphalt by Qian Chen, Chaohui Wang, Liang Song

“…The extreme learning machine (ELM) algorithm optimized by genetic algorithm (GA) was used to quickly predict the low-temperature rheological properties of styrenic block copolymer (SBS) modified asphalt through the properties of the raw materials. …”
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Detection of Attacks in Network Traffic with the Autoencoder-Based Unsupervised Learning Method by Yalçın Özkan

“…The threshold value is calculated as a value that will optimize the model performance. It is observed that supervised learning methods lead to difficulties and cost increases in the detection of cyber-attacks and the labeling process. …”
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Is Malware Detection Needed for Android TV? by Gokhan Ozogur, Zeynep Gurkas-Aydin, Mehmet Ali Erturk

“…We proposed a machine learning approach to detecting malware and evaluated our model. …”
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Classification of Bitter gourd leaf disease using deep learning architecture ResNet50 by Artika Artika, Wikky Fawwaz Al Maki

“…The proposed ResNet50 architecture accomplished 98% accuracy with the Adam optimizer. The ResNet101 architecture achieves an average accuracy of 94% with the Adam optimizer. …”
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Boosting EEG and ECG Classification with Synthetic Biophysical Data Generated via Generative Adversarial Networks by Archana Venugopal, Diego Resende Faria

“…Combining real and synthetic datasets improved classification accuracy from 92% to 98.45%, highlighting the benefits of dataset augmentation for machine learning performance. The WGAN-GP model achieved 96.84% classification accuracy for synthetic EEG data representing relaxation states and optimal accuracy for concentration states when classified using a fusion of convolutional neural networks (CNNs). …”
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A Trusted Federated Learning Method Based on Consortium Blockchain by Xiaojun Yin, Xijun Wu, Xinming Zhang

“…Federated learning (FL) has gained significant attention in distributed machine learning due to its ability to protect data privacy while enabling model training across decentralized data sources. …”
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Data-Driven Method for Probabilistic Response Prediction of Cable-Stayed Bridges by Minsun Kim, Jaebeom Lee, Kyoung-Chan Lee, Jeong Hwan Jang, Young-Joo Lee

“…The proposed method is used to construct an optimal prediction model based on a data-driven machine-learning method. …”
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基于IMU信号的人工智能上肢多关节运动状态识别系统构建——卒中后人工智能运动功能评估与检测系统建设前导研究 Construction of an Artificial Intelligence Upper Limb Multi-Joint Motion State Recognition System Based on IMU Signals—A Pre... by 程相鑫1，张烁1，杜松骏1，刘子阳1，周宏宇2，3，贾伟丽2，3，李子孝2，3，刘涛1 (CHENG Xiangxin1, ZHANG Shuo1, DU Songjun1, LIU Ziyang1, ZHOU Hongyu2,3, JIA Weili2,3, LI Zixiao2,3, LIU Tao1 )

“…The average accuracy of elbow joint state classification by the traditional machine learning methods was 79.37%, while the deep learning model IBNet reached 87.5%, indicating a stronger pattern-learning capability. …”
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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…It is required to enhance data processing, improve model generalization by preprocessing and synthesizing data, and optimize model adaptability in changing environments. …”
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Canopy-Level Rice Yield and Yield Component Estimation Using NIR-Based Vegetation Indices by Hyeok-Jin Bak, Eun-Ji Kim, Ji-Hyeon Lee, Sungyul Chang, Dongwon Kwon, Woo-Jin Im, Do-Hyun Kim, In-Ha Lee, Min-Ji Lee, Woon-Ha Hwang, Nam-Jin Chung, Wan-Gyu Sang

“…Machine learning regression models were then used to predict yield and yield components using the log-normal parameters and individual VIs as input. …”
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Pre-Symptomatic Detection of Nicosulfuron Phytotoxicity in Vegetable Soybeans via Hyperspectral Imaging and ResNet-18 by Yun Xiang, Tian Liang, Yuanpeng Bu, Shiqiang Cai, Jingjie Guo, Zhongjing Su, Jinxuan Hu, Chang Cai, Bin Wang, Zhijuan Feng, Guwen Zhang, Na Liu, Yaming Gong

“…We developed predictive models for herbicide phytotoxicity through advanced machine learning and deep learning frameworks. …”
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IHML: Incremental Heuristic Meta-Learner by Onur Karadeli, Kıymet Kaya, Şule Gündüz Öğüdücü

“…The results show that the proposed model achieves more accuracy (in average % 10 and at most % 71 improvements) compared to the baseline machine learning models in the literature.…”
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Know your competitor! Analyzing and predicting the location of competing stores: The case study of Valora at Swiss railway stations by Thomas Wieland

“…Machine learning algorithms are used to check and optimize the predictive ability of the models. …”
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The Application of Artificial Intelligent Algorithms in Electric Propulsion by Tian Bin, An Bingchen, Xie Kan, Yang Sulan

“…These algorithms can not only train models based on data to optimize the performance of electric thrusters, but also analyze and solve the mathematical and physical models of plasmas within electric thrusters. …”
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Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output by Arlanova A. A., Hojamkuliyeva B. A., Babanazarov N. Sh., Arlanov M. S.

“…Existing approaches to using artificial intelligence in agricultural technologies for predicting water needs and regulating irrigation are examined. A mathematical model based on machine learning algorithms is developed to predict the optimal water volume required for irrigation of agricultural crops. …”
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Artificial intelligence in the food industry: innovations and applications by Hang Yang, Wenxuan Jiao, Lingyun Zouyi, Hongli Diao, Shibin Xia

“…This review assesses the current state and future prospects of AI applications in key areas such as food traceability, safety, quality control, supply chain optimization, and intelligent packaging solutions. AI technologies, including machine learning (ML) algorithms and computer vision systems, are widely used to optimize supply chains, predict demand, reduce waste, and enhance food safety and quality monitoring. …”
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Outdoor RGB and Point Cloud Depth Dataset for Palm Oil Fresh Fruit Bunch Ripeness Classification and Localization by Jin Yu Goh, Mohamed Sultan Mohamed Ali, Yusri Md Yunos, Usman Ullah Sheikh, Muhamad Salman Khan

“…This dataset enables the development of advanced machine learning models for automated ripeness classification and localization, contributing to precision agriculture implementation, harvest optimization, and sustainable production practices in the oil palm industry. …”
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