Investigation of TsGAN-based multimodal image fusion to augment image pre-processing abilities by Priyanka Bhatambarekar, Gayatri Phade

“…Image fusion offers robust solution by integrating the data from multiple sources to produce more comprehensive and informative output. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. …”
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Research Status and Prospects of Key Technologies for Rice Smart Unmanned Farms by YU Fenghua, XU Tongyu, GUO Zhonghui, BAI Juchi, XIANG Shuang, GUO Sien, JIN Zhongyu, LI Shilong, WANG Shikuan, LIU Meihan, HUI Yinxuan

“…Rice yield estimation technology is mainly used to predict yield by combining multi-source data and algorithms, but there are still problems such as the difficulty of integrating multi-source data, which requires further research. …”
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Automated Image-Based Wound Area Assessment in Outpatient Clinics Using Computer-Aided Methods: A Development and Validation Study by Kuan-Chen Li, Ying-Han Lee, Yu-Hsien Lin

“…K-means clustering is a machine learning algorithm that segments the wound region by grouping pixels in an image according to their color similarity. …”
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Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study by Guangyu Lu, Zhixia Su, Xiaoping Yu, Yuhang He, Taining Sha, Kai Yan, Hong Guo, Yujian Tao, Liting Liao, Yanyan Zhang, Guotao Lu, Weijuan Gong

“…Methods Employing a prospective study design from June 2023 to January 2024 at the Affiliated Hospital of Yangzhou University, we assessed the malignancy of pulmonary nodules using the Mayo Clinic model and collected exhaled breath samples alongside lifestyle and health examination data. We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. …”
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Feasibility of Digitally Identifying and Minimizing Stressors in Palliative Care Workplaces by Measuring Stress Continuously for Nurses Through Wearable Sensors (DiPa): Protocol fo... by Aaron Seehausen, Wencke Chodan, Florian Höpfner, Carolin Schneider, Sabine Felser, Hugo Murua Escobar, Mario Aehnelt, Christian Junghanss

“…Comparisons between subjective and objective measures and group comparisons between variables of interest will be made using inferential statistics, including regression analyses and analyses of variance. Data analysis using machine learning algorithms will be implemented once sufficient data are gathered. …”
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High-Quality Multispectral Image Reconstruction for the Spectral Camera Based on Ghost Imaging via Sparsity Constraints Using CoT-Unet by Tao Hu, Jianxia Chen, Shu Wang, Jianrong Wu, Ziyan Chen, Zhifu Tian, Ruipeng Ma, Di Wu

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Development of an Efficient and Generalized MTSCAM Model to Predict Liquid Chromatography Retention Times of Organic Compounds by Mengdie Fan, Chenhui Sang, Hua Li, Yue Wei, Bin Zhang, Yang Xing, Jing Zhang, Jie Yin, Wei An, Bing Shao

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Predicting Olive Tree Chlorophyll Fluorescence Using Explainable AI with Sentinel-2 Imagery in Mediterranean Environment by Leonardo Costanza, Beatriz Lorente, Francisco Pedrero Salcedo, Francesco Pasanisi, Vincenzo Giannico, Francesca Ardito, Carlota María Martí Martínez, Simone Pietro Garofalo

“…Using spectral bands preserves more information per observation, enabling models to detect variations that VIs might miss. Additionally, raw reflectance data minimizes potential bias that could arise from selecting specific indices. …”
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Developing a Non-Invasive Intelligent System for Blood Glucose Level Estimation Using Reflective Optical Sensors by Mihaela MARIN, Teodora Alexandra DOBRIN, Florin Bogdan MARIN

“…The system integrates a detection unit equipped with an infrared LED, phototransistor, microcontroller, OLED display, and Bluetooth module-with realtime data visualization and wireless transmission to mobile devices. …”
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Effective Classification of Plant Diseases Using Blend Unity Resqueeze With ResNet Model by Reena S, Rathinasabapathy

“…However, the task becomes complex and erroneous due to the complex nature of data. Consequently Deep Learning (DL) models tend to outperform others traditional methods through utilizing network topology with convolution layers in core. …”
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Feature fusion with attributed deepwalk for protein–protein interaction prediction by Mei-Yuan Cao, Suhaila Zainudin, Kauthar Mohd Daud

“…These complementary features are fused through the weighting mechanism before being processed by the Attributed DeepWalk algorithm, which enhances protein representations by learning low-dimensional embeddings. …”
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Identification Method of Dynamic Propagation Process of Rock Fracture Based on Ground Penetrating Radar by CHEN Jun, ZHANG Bo, ZHUANG Xingyue, SONG Zhishu, ZHENG Jun

“…Additionally, the application of deep learning algorithms, such as convolutional neural networks and Transformers, can automate the process of GPR image analysis, reducing human bias and improving the efficiency of data interpretation.ConclusionsThe findings of this study demonstrate the significant potential of GPR technology for dynamic monitoring of rock mass fractures. …”
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Intraoperative hypotension prediction in cardiac and noncardiac procedures: is HPI truly worthwhile? A systematic review and meta-analysis by Erfan Shirmohamadi, Reza Hosseini Dolama, Narjes Mohammadzadeh, Navid Ebrahimi, Negar Ghasemloo

“…Studies were included if they utilized machine learning algorithms, including HPI, to predict or detect IOH in adult surgical patients. …”
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Securing fruit trees future: AI-driven early warning and predictive systems for abiotic stress in changing climate by Muhammad Ahtasham Mushtaq, Muhammad Ateeq, Muhammad Ikram, Shariq Mahmood Alam, Muhammad Mohsin Kaleem, Muhammad Atiq Ashraf, Muhammad Asim, Khalid F. Almutairi, Mahmoud F. Seleiman, Fareeha Shireen

“…Specifically, multi-omics, data accessibility, algorithmic biases, the cost of implementation and requirements for robust training programs need to integrate for sustainable agriculture. …”
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Telemedicine Screening for Diabetic Retinopathy Using Digital Technology: Foreign Experience by D. A. Andreev, N. N. Kamynina

“…Many modern technologies such as portable mobile devices could be adapted and used in the implementation of telemedicine programs. Deep machine learning algorithms and other artificial intelligence-based approaches demonstrate excellent results. …”
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Digital Twin for a Frequency Mixer Used as a Phase Sensor by Carlos Pires, Manuel Abreu, Isabel Godinho, Rui Agostinho, João A. Sousa

“…The DT was trained with two machine learning algorithms, in particular, ARIMA and LSTM networks. …”
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Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training by Kamelia Sepanloo, Daniel Shevelev, Young-Jun Son, Shravan Aras, Janine E. Hinton

“…The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. Machine learning algorithms were then used to analyze the nurse’s physiological data and to classify different levels of stress. …”
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Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases by Hua Liu, Sheng Lin, Pei-Xuan Chen, Juan Min, Xia-Yang Liu, Ting Guan, Chao-Ying Yang, Xiao-Juan Xiao, De-Hui Xiong, Sheng-Jie Sun, Ling Nie, Han Gong, Xu-Sheng Wu, Xiao-Feng He, Jing Liu

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Potential of solar-induced chlorophyll fluorescence for monitoring long-term dynamics of soil salinity in Central Asia the Xinjiang Region China by Kuangda Cui, Kuangda Cui, Jianli Ding, Jianli Ding, Jinjie Wang, Jinjie Wang, Jiao Tan, Jiao Tan, Lijing Han, Jiangtao Li, Jiangtao Li

“…Using a random forest algorithm, soil salinity was classified into five levels based on satellite data and ground references from 2000–2020. …”
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