Grape Leaf Diseases Identification System Using Convolutional Neural Networks and LoRa Technology by Zinon Zinonos, Socratis Gkelios, Ala F. Khalifeh, Diofantos G. Hadjimitsis, Yiannis S. Boutalis, Savvas A. Chatzichristofis

“…The technique is both efficient and adaptive to the specifics of each leaf disease, while it does not need any training data to adjust parameters. It is worth noting that today, end-user trust in Machine and Deep Learning models has increased significantly because of novel solutions in the field of Explainable Artificial Intelligence (XAI). …”
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Next-generation cutting-edge hybrid AI frameworks for predicting rheological properties and CO₂ emissions in alkali-activated concrete by Lu Zhang, Kangning Liu, Ali H. AlAteah, Sadiq Alinsaif, Muhammad Sufian, Ayaz Ahmad

“…These models were trained on experimental data to estimate the key rheological parameters and further validated using statistical metrics such as MSE, MAE, RMSE, MARE, precision, recall and F1 score. …”
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Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression by Haoshen Qin, Lal Hussain, Ziang Liu, Xu Yan, Fuad A. Awwad, Faisal Mehmood Butt, Umair Ahmad Salaria, Emad A.A. Ismail

“…Methods This study investigated deep learning methods for ALS progression prediction using the publicly available PRO-ACT dataset. Initially, machine learning models (XGBoost, LightGBM) and a deep learning sequential model were evaluated with default parameters, using R-squared (R2) and Root Mean Squared Error (RMSE) as performance metrics. …”
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IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective by Dhruv Parikh, Avaneesh Karthikeyan, V. Ravi, Merin Shibu, Riya Singh, Reka S. Sofana

“…In this study, we present a potential application of the Internet of Things (IoT) and machine learning to prevent the spread of COVID-19. …”
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Optimal Thermal Output of an Absorption Heat Pump with Steam Heating Integrated in a PT-60/70-130/13 Steam Turbine by Oleksandr L. Shubenko, Viktoriia O. Tarasova, Mykola Yu. Babak, Oleksii Yu. Boiarshynov

“…Modes with steam consumption in the production selection of the turbine for the consumers were studied: 15, 30 and 45 t/h (with parameters: 1.296 MPa, 280 ºС) and mains water: 1600, 1650 and 1700 m3/h. …”

Construction and application of a digital twin model for multi-objectiveoptimization of intelligent tape conveyor system by Wei CHEN, Jingzhao LI, Qing SHI, Jichao LIU, Huashun LI

“…In this conceptual model, an Online Sequential Extreme Learning Machine (OS-ELM) is applied within the virtual component to construct a near real-time model predicting sweeping force, using physical feedback data as input parameters. …”
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Synergistic Mechanisms Between Elderly Oriented Community Activity Space Morphology and Microclimate Performance: An Integrated Learning and Multi-Objective Optimization Approach by Fang Wen, Lu Zhang, Ling Jiang, Rui Tang, Bo Zhang

“…It then integrated ensemble learning and the interpretable machine learning model SHAP to reveal nonlinear relationships and boundary effects. …”
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Concept of PPI energy technology complex upgrading by P. V. Lukanin, V. G. Kazakov, L. O. Zverev

“…The positive results of research in water chemistry under supercritical parameters give promise to predict success in developing the energy efficient pulp technology by exposing pulpchips to water at the subcritical and supercritical parameters. …”
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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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Processor-in-the-Loop validation of direct power control based on fractional-order modified super-twisting algorithm for doubly-fed induction generators by Mourad Yessef, Habib Benbouhenni, Ahmed Lagrioui, Nicu Bizon, Badre Bossoufi, Saad F. Al-Gahtani, Z. M. S. Elbarbary

“…This technique is notable for its straightforwardness, minimal gain parameters, ease of implementation, compatibility with embedded systems, and economic efficiency. …”
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The effect of the method of joining layers in two-layer fabrics on their physical and mechanical properties by Katsiarina S. Mileeva

“…The fabrics are produced sequentially, on the same two-layer Picanol loom with a Bonas jacquard machine with the same filling parameters. Dyed cotonine-containing linen yarn with a linear density of 50 tex is used in the warp and weft, the warp and weft threads of the upper and lower layers differ from each other only in color, but are identical for each of the methods of connecting the layers. …”
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Chronic lung lesions in COVID-19 survivors: predictive clinical model by Paulo A Lotufo, Juliana C Ferreira, Eloisa Bonfa, Anna S Levin, Rodrigo Caruso Chate, Marta Imamura, Esper G Kallas, Roger Chammas, Thais Mauad, Izabel Marcilio, Nelson Gouveia, Ricardo Nitrini, José Eduardo Krieger, Marcio Valente Yamada Sawamura, Michelle Louvaes Garcia, Cristiano Gomes, Guilherme Fonseca, Jorge Hallak, Luis Yu, Marcio Mancini, Maria Elizabeth Rossi, Thiago Avelino-Silva, Edivaldo M Utiyama, Aluisio C Segurado, Beatriz Perondi, Anna Miethke-Morais, Amanda C Montal, Leila Harima, Marjorie F Silva, Marcelo C Rocha, Maria Amélia de Jesus, Carolina Carmo, Clarice Tanaka, Julio F M Marchini, Thaís Guimarães, Ester Sabino, Carlos Roberto Ribeiro Carvalho, Celina Almeida Lamas, Diego Armando Cardona Cardenas, Daniel Mario Lima, Paula Gobi Scudeller, João Marcos Salge, Cesar Higa Nomura, Marco Antonio Gutierrez, Adriana L Araújo, Bruno F Guedes, Carolina S Lázari, Cassiano C Antonio, Claudia C Leite, Emmanuel A Burdmann, Euripedes C Miguel, Fabio R Pinna, Fabiane Y O Kawano, Geraldo F Busatto, Giovanni G Cerri, Heraldo P Souza, Izabel C Rios, Larissa S Oliveira, Linamara R Batisttella, Luiz Henrique M Castro, Marcello M C Magri, Maria Cassia J M Corrêa, Maria Cristina P B Francisco, Maura S Oliveira, Orestes V Forlenza, Ricardo F Bento, Rodolfo F Damiano, Rossana P Francisco, Solange R G Fusco, Tarcisio E P Barros-Filho, Wilson J Filho

“…Patients with abnormalities in at least one of these parameters underwent chest CT. mMRC scale, SpO2, FVC and CXR findings were used to build a machine learning model for lung lesion detection on CT.Setting A tertiary hospital in Sao Paulo, Brazil.Participants 749 eligible RT-PCR-confirmed SARS-CoV-2-infected patients aged ≥18 years.Primary outcome measure A predictive clinical model for lung lesion detection on chest CT.Results There were 470 patients (63%) that had at least one sign of pulmonary involvement and were eligible for CT. …”
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Use of ICT to Confront COVID-19 by Yousry Saber El Gamal

“…. , AI techniques, particularly machine learning algorithms, can also be used to correlate the patient’s data parameters with a specific drug’s usage. …”
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Analyzing canopy structure effects based on LiDAR for GPP-SIF relationship and GPP estimation by Shuo Shi, Shuo Shi, Shuo Shi, Shuo Shi, Zixi Shi, Fangfang Qu, Wei Gong, Wei Gong, Wei Gong, Wei Gong, Lu Xu, Chenxi Liu

“…In this study, we used multi-source data, particularly LiDAR-derived canopy structure products, to analyze the annual variations in canopy structural parameters and GPP/SIF across different forest types, investigate the response of canopy structure to the GPP-SIF relationship, and employ machine learning models to estimate GPP and assess the contribution of canopy structural factors. …”
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Video-urodynamic Profile in Postoperative Cases of Lumbosacral Myelomeningocele with Neurogenic Bladder by Dipanjan Goswami, Gaurav Saxena, Sujoy Neogi, Simmi K Ratan, Prafull Kumar, Chiranjiv Kumar, Md. Fahim Ahmad

“…Based on the VUDS parameters, we tried to categorize the type of neurogenic bladder and also establish a correlation between the VUDS profile and upper tract changes if any. …”
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Robotic monitoring of European habitats: a labeled dataset for plant detection in Annex I habitats of Italy by Giovanni Di Lorenzo, Franco Angelini, Michele Pierallini, Simone Tolomei, Davide De Benedittis, Agnese Denaro, Giovanni Rivieccio, Maria Carmela Caria, Federica Bonini, Anna Grassi, Leopoldo de Simone, Emanuele Fanfarillo, Tiberio Fiaschi, Simona Maccherini, Barbara Valle, Marina Serena Borgatti, Simonetta Bagella, Daniela Gigante, Claudia Angiolini, Marco Caccianiga, Manolo Garabini

“…Researchers in vegetation science, habitat monitoring, robotics, machine learning, and biodiversity conservation can access the dataset through Zenodo. …”
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Efficient Soil Temperature Profile Estimation for Thermoelectric Powered Sensors by Jiri Konecny, Jaromir Konecny, Kamil Bancik, Miroslav Mikus, Jan Choutka, Jiri Koziorek, Ibrahim A. Hameed, Algimantas Valinevicius, Darius Andriukaitis, Michal Prauzek

“…Beyond achieving a lower temperature prediction error, the proposed solution simplifies the input parameters of the model to only ambient temperature and solar irradiance. …”
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A multi-objective grey wolf optimizer for energy planning problem in smart home using renewable energy systems by Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Feras Al-Obeidat, Osama Ahmad Alomari, Ammar Kamal Abasi, Mohammad Tubishat, Zenab Elgamal, Waleed Alomoush

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Assessing Sternal Dimensions for Sex Classification: Insights from a Greek Computed Tomography-Based Study by Konstantina Vatzia, Michail Fanariotis, Maciej Bugajski, Ioannis V. Fezoulidis, Maria Piagkou, Marianna Vlychou, George Triantafyllou, Ioannis Vezakis, George Botis, Stavroula Papadodima, George Matsopoulos, Katerina Vassiou

“…A secondary objective was to develop and evaluate statistical and machine learning models based on these measurements for forensic identification. …”
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Mechanical characterization of an origami-inspired super deformable metamaterial with high tunability for tissue engineering by M.A. Bagheri, C.E. Aubin, M.L. Nault, I. Villemure

“…A parametric design explored the effects of changing geometrical parameters on the mechanical properties of the metamaterial. …”
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