Deep Learning–Based Enhanced Optimization for Automated Rice Plant Disease Detection and Classification by P. Preethi, R. Swathika, S. Kaliraj, R. Premkumar, J. Yogapriya

“…EASSO optimizes the DNN's parameters, maximizing its accuracy in disease classification. …”
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Investigating the correlation between smoking and blood pressure via photoplethysmography by Q. Qananwah, H. Quran, A. Dagamseh, V. Blazek, S. Leonhardt

“…The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological signals or parameters such as heart rate, blood pressure (BP), Electrocardiogram (ECG), and Photoplethysmogram (PPG), which subtly encode smoking-related effects. …”
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Uncertainty-aware deep learning in healthcare: A scoping review. by Tyler J Loftus, Benjamin Shickel, Matthew M Ruppert, Jeremy A Balch, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, William R Hogan, Parisa Rashidi, Gilbert R Upchurch, Azra Bihorac

“…Overall, the use of model learning curves to quantify epistemic uncertainty (attributable to model parameters) was sparse. Heterogeneity in reporting methods precluded the performance of a meta-analysis. …”
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Enhancing Wind Turbine Power Output Estimation Using Causal Inference and Adaptive Neuro-Fuzzy Inference System ANFIS by Ahmed A. Mostfa, Nawfal A. Zakar, Rasha Raad Al-Mola, Abdel-Nasser Sharkawy

“…To meet the demand for renewable energy at the lowest cost, wind energy became the target of machine learning algorithms and was employed to predict the output power of wind turbines. …”
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Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring by Fabiane Gallucci, Gustavo Fonseca, Danilo C Vieira, Luciana Erika Yaginuma, Paula Foltran Gheller, Simone Brito, Thais Navajas Corbisier

“… This study aims model the distribution of meiofauna indicators in relation to environmental variables from the Santos Basin continental margin, SE Brazil, using machine learning techniques, to provide baseline information and foster future monitoring programs. …”
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Data-driven causal behaviour modelling from trajectory data: A case for fare incentives in public transport by Yuanyuan Wu, Alex Markham, Leizhen Wang, Liam Solus, Zhenliang Ma

“…Behaviour modelling has been widely explored using both statistical and machine learning techniques, primarily relying on analyzing correlations to understand passenger responses under different conditions and scenarios. …”
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The Impact of Real-World Interaction on the Perception of a Humanoid Social Robot in Care for Institutionalised Older Adults by Slawomir Tobis, Joanna Piasek-Skupna, Aleksandra Suwalska, Katarzyna Wieczorowska-Tobis

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Microstrip Patch Sensor for Characterizing Saline Solution Based on Complimentary Split-Ring Resonators (SC-SRRs) by Hussein Jasim, Sadiq Ahmed, Iulia Andreea Mocanu, Amer Abbood Al-Behadili

“…This sensor operates via the turbulence technique, utilizing its resonant properties as indicators to find the parameters of the liquid under test (LUT), which arise due to the variations in the salt concentration altering the complex permittivity. …”
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A Hybrid Approach of DenseNet121 with Attention and Bi-LSTM for Yoga Pose Estimation by Aarthy K., Alice Nithya

“…The system is designed to integrate advanced AI techniques, providing an innovative approach to pose recognition that leverages several sophisticated machine learning models and algorithms to enhance performance. …”
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The impact of tractor drawbar height on performance and optimization using response surface methodology by Ali Yavuz Şeflek, Nuri Orhan, Mete Özkan

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Development of an optimized deep learning model for predicting slope stability in nano silica stabilized soils by Ishwor Thapa, Sufyan Ghani, Prabhu Paramasivam, Mitiku Adare Tufa

“…The model’s training and verification were used with the data file containing 3,159 cases of inclination with different percentages of NS, and geotechnical parameters. The results show that RNN-CNN-LSTM, optimized through OPTUNA algorithms, overcomes conventional machine learning models and achieves an accuracy of 99.4% on unseen test data, supported by stable validation trends and robust predictive performance. …”
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Dexmedetomidine reduces in-hospital mortality in aneurysmal subarachnoid hemorrhage patients by modulating three key genes and inflammatory pathways: insights from clinical and bio... by Zhi-ang Li, Hong-cai Wang, Xue-wei Zhang, Li-hong Hu

“…Patients in the in-hospital mortality group exhibited older age, higher SAPS II scores, and altered physiological parameters. Dexmedetomidine was the most influential treatment variable, significantly associated with reduced in-hospital mortality. …”
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Quality by Design and In Silico Approach in SNEDDS Development: A Comprehensive Formulation Framework by Sani Ega Priani, Taufik Muhammad Fakih, Gofarana Wilar, Anis Yohana Chaerunisaa, Iyan Sopyan

“…By integrating molecular simulations with machine learning, this approach enables rational and efficient optimization. …”
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Design and validation of a novel multiple sites signal acquisition and analysis system based on pressure stimulation for human cardiovascular information by Gaiqin Liu, Yuan Li, Longcong Chen, Juan Jiang, Jie Tian, Panpan Feng

“…Our results demonstrate that the system can achieve simultaneous acquisition of 27-channel signals during each sub-process, yielding both novel and traditional cardiovascular parameters with high accuracy and good stability. Furthermore, the results suggest that the system can facilitate in-depth research into the relationships between collected signals and CVDs, provide rich raw data for cardiovascular health assessment and disease prediction models based on machine learning algorithms, and offer a new non-invasive method for early diagnosis, evaluation, and prediction of CVDs.…”
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Systematic Review of Image Segmentation Programs in Craniomaxillofacial Surgery by Khawla Rasheed, Auns Al-Neami, Haider Abbas

“…Various image segmentation programs that use different techniques, including thresholding, edge-based methods, region-based methods and machine learning-based methods, were investigated. …”
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Bio-based polyurethane as a sustainable coating material for controlled-release fertilizer by Lyu Yao, Azizah Baharum, Lih Jiun Yu, Zibo Yan, Khairiah Haji Badri

“…In this study, a bio-based PU coating synthesized from palm kernel oil-based polyol and methylene diphenyl diisocyanate (MDI) was coated onto urea granules using a micro scale coating machine. Two key parameters were studied, which are the NCO/OH ratio (0.8:1, 1:1, 1.2:1) and coating amount (4%, 9%, 14%). …”
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HOLESOM: Constraining the Properties of Slowly Accreting Massive Black Holes with Self-organizing Maps by Valentina La Torre, Fabio Pacucci

“…We present HOLESOM (HOLESOM is publicly available at: http://github.com/valentinalatorre/holesom ), a machine learning-powered tool based on the self-organizing maps (SOMs) algorithm, specifically designed to identify slowly accreting MBHs using sparse photometric data. …”
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Influence of Piston Lubricant on the Distribution of Defects in Cold Chamber High Pressure Die Casting by Jingzhou Lu, Ewan Lordan, Yijie Zhang, Zhongyun Fan, Kun Dou

“…To further investigate this issue, a pilot scale HPDC machine is used and the lubricant burning issue is studied based on material characterization and numerical modelling. …”
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Speech emotion recognition algorithm of intelligent robot based on ACO-SVM by Xueliang Kang

“…To solve this problem, genetic algorithm is used to fine-tune SVM model parameters, and multi-level SVM classification architecture is constructed to enhance the accuracy of emotion recognition. …”
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Advances and Challenges in Automated Drowning Detection and Prevention Systems by Maad Shatnawi, Frdoos Albreiki, Ashwaq Alkhoori, Mariam Alhebshi, Anas Shatnawi

“…Automatic drowning detection approaches could be further categorized into computer vision-based approaches, where camera-captured images are analyzed by machine learning algorithms to detect instances of drowning, and sensing-based approaches, where sensing instruments are attached to swimmers to monitor their physical parameters. …”
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