Decoding Poultry Welfare from Sound—A Machine Learning Framework for Non-Invasive Acoustic Monitoring by Venkatraman Manikandan, Suresh Neethirajan

“…The pipeline employs a multistage process comprising high-fidelity signal acquisition, feature extraction (e.g., mel-frequency cepstral coefficients, spectral contrast, zero-crossing rate), and classification using models including Random Forest, HistGradientBoosting, CatBoost, TabNet, and LSTM. …”
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Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS by Ahmed M. Elshewey, Enas Selem, Amira Hassan Abed

“…Traditional machine learning models often pose difficulties in comprehending the intricate processes behind specific classification choices due to their intricate and obscure nature. …”
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An Investigation Towards Resampling Techniques and Classification Algorithms on CM1 NASA PROMISE Dataset for Software Defect Prediction by Agung Fatwanto, Muh Nur Aslam, Rebbecah Ndugi, Muhammad Syafrudin

“…Software defect prediction is a practical approach to improving the quality and efficiency of software testing processes. However, establishing robust and trustworthy models for software defect prediction is quite challenging due to the limitation of historical datasets that most developers are capable of collecting. …”
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The Impact of Digital Transformation on the Investment Potential of the Russian Cities by E. G. Kiseleva

“…Petersburg is improving by boosting the “e-business” component which reflects the use of ERP and CRM systems by organizations, as well as by the e-commerce. …”
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Comparison of mask R-CNN and YOLOv8-seg for improved monitoring of the PCB surface during laser cleaning by Lu Qiao, Markus Veltrup, Bernd Mayer

“…This study confirms YOLOv8-seg’s effectiveness in real-time PCB monitoring during laser cleaning, boosting automation and efficiency in PCB recycling.…”
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Uncovering the Diagnostic Power of Radiomic Feature Significance in Automated Lung Cancer Detection: An Integrative Analysis of Texture, Shape, and Intensity Contributions by Sotiris Raptis, Christos Ilioudis, Kiki Theodorou

“…The analysis of feature interpretability ascertains its potential in early detection and boosting diagnostic confidence. Conclusions: The current work identifies the most important radiomic features and quantifies their diagnostic significance through a properly conducted feature selection process reflecting stability analysis. …”
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Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization by Saâd Soulaimani, Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman, Abdelhalim Miftah, Mohammed S. Fnais, Biraj Kanti Mondal

“…Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this strategy is leveraging population search algorithms to fine-tune hyperparameters, thus boosting prediction accuracy. …”
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Reverse cholesterol transport: current assay methods, alterations with disease and response to therapeutic intervention by Martin P. Playford, Edward B. Neufeld, Rafael Zubirán, Alan T. Remaley

“…The removal of excess cholesterol from the body by High-density lipoprotein (HDL) in a process termed reverse cholesterol transport (RCT) has long been proposed to play a critical role in reduction of the lipid burden in arterial wall atherosclerotic lesions. …”
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Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth by B. S. Saruk, G. Mokesh Rayalu

“…Abstract Agriculture is one of the sectors that has an important impact, taking into account the problem of sufficient food supply on a global level. The process of predicting the yield of crops is among the most challenging undertakings in the agricultural industry. …”
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Engaging Students in Audiovisual Coding Through Interactive MATLAB GUIs by Carlos Cuevas, Carlos Cortes, Narciso Garcia

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Regulation of CAR transgene expression to design semiautonomous CAR-T by Paweł Głowacki, Cezary Tręda, Piotr Rieske

“…It turns out that various constitutive promoters used in clinical products may influence CAR-T cell antitumor effectiveness and impact the manufacturing process. Furthermore, next-generation CAR-T starts to install remotely controlled inducible promoters or even autonomous expression systems, opening new ways of priming, boosting, and increasing the safety of CAR-T. …”
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Towards Seamless Human–Robot Interaction: Integrating Computer Vision for Tool Handover and Gesture-Based Control by Branislav Malobický, Marián Hruboš, Júlia Kafková, Jakub Krško, Mário Michálik, Rastislav Pirník, Pavol Kuchár

“…The results show that this approach enhances the efficiency and reliability of human–robot cooperation, particularly in manufacturing settings, by streamlining tasks and boosting productivity. By integrating real-time computer vision into the robot’s decision-making process, the system demonstrates heightened adaptability and responsiveness, creating the way for more natural and effective human–robot collaboration in industrial contexts.…”
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Reliable prediction for TBM energy consumption during tunnel excavation: A novel technique balancing explainability and performance by Wenli Liu, Yafei Qi, Fenghua Liu

“…Although data-driven models exhibit strong predictive capabilities, their outputs derived from “black box” processes are challenging to interpret and generalize. …”
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Detection of false data injection in electric energy metering platforms using gradient lifting decision trees and MLP neural networks by Yakui Zhu, Yangrui Zhang, Chao Zhang, Bingyu Zhang, Hongying Wang, Shaokang Feng

“…The predictor was based on a gradient boosting decision tree to predict potential data injection anomalies. …”
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Driving efficiency through innovation: exploring dynamic capabilities in Indian manufacturing small and medium scale enterprise by Mubarak Rahman P, Shakeb Akhtar, Shahbaz Babar Khan, Mohsin Khan, Manoharan M, Mohd Afjal

“…Unlike prior studies, this work uniquely dissects the impact of various types of innovations—product, process, marketing, and organizational—on SME efficiency. …”
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Enhancing electrolysis performance: Nanoparticles and micro-arc oxidation in green hydrogen generation by Mohammad Hamdan, Randa Abulail, Osama Ayadi, Khadeejeh Afaneh, Ubiedullah Al-Qawabeha, Ahmad Sakhrieh

“…MAO-treated electrodes improved hydrogen output by up to 30 % at lower potassium hydroxide (KOH) concentrations and maintained a 5 % boost even at higher concentrations. Including nanoparticles provided remarkable results: copper oxide increased hydrogen production by 107 %, while zinc oxide achieved an astounding 745 % improvement. …”
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Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning by Suranjana V. Mayani, Hessan Mohammad, Soumya V. Menon, Rishabh Thakur, Abdulqader Faris Abdulqader, S. Supriya, Prabhat Kumar Sahu, Kamal Kant Joshi

“…A membrane contactor was employed for the separation and mass transfer analysis for the removal of organic molecules from water. The process is simulated via computational fluid dynamics and machine learning. …”
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Single-Frame Infrared Small Target Detection Network Based on Multibranch Feature Aggregation by Ziqiang Hao, Zheng Jiang, Xiaoyu Xu, Zhuohao Wang, Zhicheng Sun

“…Enhancing both the accuracy and speed of this detection process can substantially improve the overall performance of infrared target detection and tracking. …”
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Machine Learning‐Assisted Prediction and Generation of Antimicrobial Peptides by Sukhvir Kaur Bhangu, Nicholas Welch, Morgan Lewis, Fanyi Li, Brint Gardner, Helmut Thissen, Wioleta Kowalczyk

“…The eXtreme Gradient Boosting model achieves an accuracy of ≈87% in distinguishing between AMPs and non‐AMPs. …”
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Prediction of coronary heart disease based on klotho levels using machine learning by Yuan Yao, Ying Zhao, Haifeng Li, Yanlin Han, Yue Wu, Renwei Guo, Mingfeng Ma, Lixia Bu

“…We analyzed data from 11,583 persons in US NHANES and entered 13 potential predictive variables, including klotho and other clinically relevant indicators, into the feature screening process. We established that the five ML models could predict the onset of CHD. …”
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