Empowering Healthcare: TinyML for Precise Lung Disease Classification by Youssef Abadade, Nabil Benamar, Miloud Bagaa, Habiba Chaoui

“…These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. …”
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Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review by Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian

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Prediction of suspended sediment concentration in fluvial flows using novel hybrid deep learning model by Sadra Shadkani, Yousef Hemmatzadeh, Amirreza Pak, Soroush Abolfathi

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Leveraging machine learning techniques to analyze nutritional content in processed foods by K. A. Muthukumar, Soumya Gupta, Doli Saikia

“…With protein deficiencies being prevalent among Indians, it is crucial to understand the impact of food processing on nutrient retention. This research integrates machine learning with food science to develop a comprehensive AI framework for forecasting the protein content of various plant-based sources following both traditional and non-conventional processing methods. …”
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Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach by Mehdi Fasihi, Mirko Sodini, Alex Falcon, Francesco Degano, Paolo Sivilotti, Giuseppe Serra

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Predicting neoadjuvant chemotherapy response in locally advanced gastric cancer using a machine learning model combining radiomics and clinical biomarkers by Tong Ling, Zhichao Zuo, Liucheng Wu, Jie Ma, Tingan Wang, Mingwei Huang

“…A variety of ML algorithms were applied to integrate the rad score with clinical biomarkers, resulting in the construction of a hybrid model. …”
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An improved performance model for artificial intelligence-based diabetes prediction by Ugwu Hillary Okwudili, Oparaku Ogbonna Ukachukwu, V. C. Chijindu, Michael Okechukwu Ezea, Buhari Ishaq

“…XB demonstrates remarkable predictive performance, bolstered by its built-in cross-validation and parallel processing; nevertheless, it remains sensitive to outliers, highlighting the importance of thorough data processing. By integrating these algorithms into an ensemble framework, this study effectively mitigated their individual limitations, leading to a more accurate and improved reliable prediction model. …”
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Facial emotion based smartphone addiction detection and prevention using deep learning and video based learning by C. Joseph, P. Uma Maheswari

“…The TMVM framework leverages Theory of Mind AI to analyze students’ facial emotions via smartphone cameras while watching videos. …”
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Architecture-aware minimization (A2M): how to find flat minima in neural architecture search by Matteo Gambella, Fabrizio Pittorino, Manuel Roveri

“…We will open-source our code at https://github.com/AI-Tech-Research-Lab/AsquaredM .…”
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Ultrasound combined with serological markers for predicting neonatal necrotizing enterocolitis: a machine learning approach by Yi Yang, Shoulan Zhou, Xiaomin Liu, Yanhong Zhang, Liping Lin, Chenhan Zheng, Xiaohong Zhong

“…Model performance was assessed on an independent validation set (30%). Explainable AI (XAI) using SHAP values was employed to identify key predictive features.ResultsXGBoost demonstrated the highest performance (AUC = 0.97, 95% CI: 0.92–0.99) during cross-validation. …”
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Research on global ship cargo capacity prediction based on multi-source heterogeneous data by Shuhang Chen, Zhihuan Wang, Zhihuan Wang, Tianye Lu, Jiayang Zhu, Chunchang Zhang, Chunchang Zhang, Xiangming Zeng, Xiangming Zeng, Jiayi Wang, Zandi Shang, Zandi Shang

“…This study addresses this gap by leveraging maritime big data to compare traditional empirical methods with machine learning approaches, integrating multi-source datasets (ship inbound/outbound records, vessel archives, and AIS data). …”
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A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves by James M. Trauer, Angus E. Hughes, David S. Shipman, Michael T. Meehan, Alec S. Henderson, Emma S. McBryde, Romain Ragonnet

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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Abstract Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. …”
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PlantGaussian: Exploring 3D Gaussian splatting for cross-time, cross-scene, and realistic 3D plant visualization and beyond by Peng Shen, Xueyao Jing, Wenzhe Deng, Hanyue Jia, Tingting Wu

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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

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Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review by Florin-Stefan Zamfir, Madalina Carbureanu, Sanda Florentina Mihalache

“…Thus, this paper identifies gaps in the current research, discusses ML and DL algorithms in the context of optimizing wastewater treatment processes, and identifies future directions for optimizing these processes through data-driven methods. …”
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Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads by Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat, Ahmad Mousa

“…Machine and deep learning algorithms have recently been more instrumental for forecasting pavement conditions. …”
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Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review by Zia‐Ur‐Rehman, Mohd Khalid Awang, Ghulam Ali, Muhammad Faheem

“…Neuroimaging methods, when integrated with DL techniques, have demonstrated considerable potential in enhancing the diagnosis and categorization of AD. …”
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Identifying COVID-19-Infected Segments in Lung CT Scan Through Two Innovative Artificial Intelligence-Based Transformer Models by Zeinab Momeni pour, Ali Asghar Beheshti Shirazi

“… Introduction: Automatic systems based on Artificial intelligence (AI) algorithms have made significant advancements across various domains, most notably in the field of medicine. …”
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Unsupervised person re‐identification based on adaptive information supplementation and foreground enhancement by Qiang Wang, Zhihong Huang, Huijie Fan, Shengpeng Fu, Yandong Tang

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