Multiple PM Low-Cost Sensors, Multiple Seasons’ Data, and Multiple Calibration Models by S Srishti, Pratyush Agrawal, Padmavati Kulkarni, Hrishikesh Chandra Gautam, Meenakshi Kushwaha, V. Sreekanth

“…Abstract In this study, we combined state-of-the-art data modelling techniques (machine learning [ML] methods) and data from state-of-the-art low-cost particulate matter (PM) sensors (LCSs) to improve the accuracy of LCS-measured PM2.5 (PM with aerodynamic diameter less than 2.5 microns) mass concentrations. …”
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Ferroelectric capacitive memories: devices, arrays, and applications by Zuopu Zhou, Leming Jiao, Zijie Zheng, Yue Chen, Kaizhen Han, Yuye Kang, Dong Zhang, Xiaolin Wang, Qiwen Kong, Chen Sun, Jiawei Xie, Xiao Gong

“…In addition, we present the computing-in-memory applications of the FCMs to realize ultra-low-power machine learning acceleration for future computing systems.…”
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Advancements in Physics-Informed Neural Networks for Laminated Composites: A Comprehensive Review by Salman Khalid, Muhammad Haris Yazdani, Muhammad Muzammil Azad, Muhammad Umar Elahi, Izaz Raouf, Heung Soo Kim

“…Physics-Informed Neural Networks (PINNs) integrate physics principles with machine learning, offering innovative solutions for complex modeling challenges. …”
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Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance by Jirakorn Jamrasnarodom, Pharuj Rajborirug, Pises Pisespongsa, Kitsuchart Pasupa

“…Colorectal cancer, arising from adenomatous polyps, is a leading cause of cancer-related mortality, making early detection and removal crucial for preventing cancer progression. Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. …”
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From Data to Decisions: A Smart IoT and Cloud Approach to Environmental Monitoring by Guerbaoui Mohammed, El Faiz Samira, Ed-Dahhak Abdelali, Lachhab Abdeslam, Benhala Bachir, Bakziz Zakaria, Ichou Ismail, Selmani Abdelouahed

“…Future enhancements could include the integration of additional sensors and the application of machine learning algorithms for predictive analytics. Overall, the project demonstrates the potential of IoT and data analytics in addressing real-world challenges related to environmental monitoring.…”
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Multi-Granularity User Anomalous Behavior Detection by Wenying Feng, Yu Cao, Yilu Chen, Ye Wang, Ning Hu, Yan Jia, Zhaoquan Gu

“…To address these challenges, this paper presents a novel approach for insider threat detection, leveraging machine learning techniques to conduct multi-granularity anomaly detection. …”
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Discovering patient groups in sequential electronic healthcare data using unsupervised representation learning by Jingteng Li, Kimberley R. Zakka, John Booth, Louise Rigny, Samiran Ray, Mario Cortina-Borja, Payam Barnaghi, Neil Sebire

“…The latent patient features obtained via the embedding process enabled direct applications of other machine learning algorithms. Future work will focus on utilising the temporal information within EHR and extending EHR embedding algorithms to develop personalised patient journey predictions.…”
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Data Mining Evidences Variabilities in Glucose and Lipid Metabolism among Fish Strains: A Case Study on Three Genotypes of Gibel Carp Fed by Different Carbohydrate Sources by Xuerong Song, Haokun Liu, Junyan Jin, Dong Han, Xiaoming Zhu, Yunxia Yang, Shouqi Xie

“…The results of the growth and physical responses were analysed by data visualization and unsupervised machine learning. As revealed by a self-organizing map (SOM) and the cluster of growth and biochemical indicators, CASV had superior growth and feed utilization and better regulation of postprandial glucose, followed by CASIII, while Dongting showed a high level of plasma glucose with poor growth performance. …”
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Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Mo... by Y. Fang, H. V. Tran, L. R. Leung

“…However, uncertainties in modeling the hydrologic response to hurricanes may limit the modeling of nutrient losses during such events. Using a machine learning approach, we calibrated the land component of the Energy Exascale Earth System Model (E3SM), or ELM, version 2.1, based on the water table depth (WTD) of a calibrated 3D subsurface hydrology model. …”
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Learning to Boost the Performance of Stable Nonlinear Systems by Luca Furieri, Clara Lucia Galimberti, Giancarlo Ferrari-Trecate

“…The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. However, maintaining closed-loop stability while boosting the performance of nonlinear control systems using data-driven and deep-learning approaches stands as an important unsolved challenge. …”
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Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models by Syed Mohsin Bokhari, Muhammad Shafi, Fazal Noor, Sarmad Sohaib, Saad Alqahtany, Mark Donnelly

“…The suggested SNN, SCNN, RSNN, and SCLSTM models attained accuracies of 84.35%, 93.04%, 94.68%, 99.76% respectively, exceeding standard machine learning approaches in both precision and recall for identifying expertise levels based on visual attention patterns. …”
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A conceptual approach to material detection based on damping vibration-force signals via robot by Ahmad Saleh Asheghabadi, Mohammad Keymanesh, Saeed Bahrami Moqadam, Saeed Bahrami Moqadam, Saeed Bahrami Moqadam, Jing Xu

“…After recording the damping force signal and vibration data from the load cell and accelerometer caused by the metal appendage's impact, features such as vibration amplitude, damping time, wavelength, and force amplitude were retrieved. Three machine-learning techniques were then used to classify the objects' materials according to their damping rates. …”
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Development of Stacked Long Short-Term Memory Neural Networks with Numerical Solutions for Wind Velocity Predictions by Chih-Chiang Wei

“…For more accurate wind speed predictions during a typhoon episode, we used cutting-edge machine learning techniques to construct a wind speed prediction model. …”
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Multi-Disaster Hazard Analysis, The Case of Elazığ Province by Fethi Ahmet Canpolat

“…Various methods, such as the Analytic Hierarchy Process (AHP) and machine learning models, including the Random Forest algorithm, were employed to assess the severity and probability of exposure for each hazard type. …”
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CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins by Reny Pratiwi, Aijaz Ahmad Malik, Nalini Schaduangrat, Virapong Prachayasittikul, Jarl E. S. Wikberg, Chanin Nantasenamat, Watshara Shoombuatong

“…This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance. …”
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Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach. by Paul Blanc-Durand, Axel Van Der Gucht, Eric Guedj, Mukedaisi Abulizi, Mehdi Aoun-Sebaiti, Lionel Lerman, Antoine Verger, François-Jérôme Authier, Emmanuel Itti

“…<h4>Conclusion</h4>We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using 18F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.…”
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A Metaheuristic Approach to Detecting and Mitigating DDoS Attacks in Blockchain-Integrated Deep Learning Models for IoT Applications by Manal Alkhammash

“…Besides, this use makes vast data quantities. Machine Learning (ML) provides broad sovereignty in the study of big data, and abilities of decision making and so it is employed as a critical device. …”
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Addressing Data Imbalance in Crash Data: Evaluating Generative Adversarial Network&#x2019;s Efficacy Against Conventional Methods by Bei Zhou, Qianxi Zhou, Zongzhi Li

“…In the realm of traffic safety analysis, the inherent imbalance in crash datasets, particularly in terms of injury severity, poses a significant challenge for machine learning-based classification models. This study delves into the efficacy of Generative Adversarial Networks (GANs), with a specific focus on Conditional Tabular GAN (CTGAN), for synthesizing minority crash data to address this imbalance. …”
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Large Language Models for UAVs: Current State and Pathways to the Future by Shumaila Javaid, Hamza Fahim, Bin He, Nasir Saeed

“…Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. …”
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