Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index by Dania AL-Najjar, Hazem AL-Najjar, Nadia Al-Rousan, Hamzeh F. Assous

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AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides by Saptashwa Datta, Jen-Chieh Yu, Yi-Hsiang Lin, Yun-Chen Cheng, Ching-Tai Chen

“…Abstract Aging is a natural phenomenon characterized by the loss of normal morphology and physiological functioning of the body, causing wrinkles on the skin, loss of hair, and compromised immune systems. …”
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Adaptive Path Planning for Multi-Drone Systems Based on SFS, DPF, and Learning Based Refinement by Srwa Ahmed Mustafa, Amin Salih Kakshar

“…The Adaptive Stochastic Fractal Algorithm integrates Stochastic Fractal Search for global pathfinding, Dynamic Potential Fields for real-time obstacle avoidance, and Learning Based Refinement using reinforcement learning for adaptive behaviour. …”
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LatentPINNs: Generative physics-informed neural networks via a latent representation learning by Mohammad H. Taufik, Tariq Alkhalifah

“…Motivated by the recent progress on generative models, we promote using latent diffusion models to learn compressed latent representations of the distribution of PDE parameters as they act as input parameters for NN functional solutions. …”
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Comparative analysis of the performance of regression machine learning models for indoor visible light positioning systems by Mohamed Hussien Moharam

“…Abstract This paper presents an Indoor Visible Light Positioning (VLP) system designed for deployment in enclosed environments, using four ceiling-mounted Light Emitting Diodes (LEDs) to serve both illumination and positioning functions. Each LED is fixed at predefined coordinates and transmits unique signals to a floor-level receiver via Visible Light Communication (VLC) technology. …”
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Experiential learning, practices, and space for change: The institutional preconfiguration of community participation in flood risk reduction by Brian R. Cook, Isabel Cornes, Paula Satizábal, Maria deLourdes Melo Zurita

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NOVEL MULTI-MODAL OBSTRUCTION MODULE FOR DIABETES MELLITUS CLASSIFICATION USING EXPLAINABLE MACHINE LEARNING by Reehana SHAIK, Ibrahim SIDDIQUE

“…Furthermore, the detection and classification of DM using PPG and ECG can involve analyzing the functional performance of these modalities. By extracting the features like R wave (W1) and QRS complex (W2) in the ECG signals and Pulse Width (S1) and Pulse Amplitude Variation (S2) can detect DM and can be classified into DM and Non-DM. …”
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Intelligent Optimization of OSPF Path Selection Using Machine Learning Models for Adaptive Network Routing by Rebeen Rebwar Hama Amin

“…Four important ML functions namely traffic forecast, anomaly detection, failure prediction, and dynamic cost optimization—have been used to improve OSPF performance. …”
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Filter Learning-Based Partial Least Squares Regression and Its Application in Infrared Spectral Analysis by Yi Mou, Long Zhou, Weizhen Chen, Jianguo Liu, Teng Li

“…In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. …”
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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…The model first uses CEEMDAN to decompose PV power data into Intrinsic Mode Functions (IMFs), capturing complex nonlinear features. …”
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Harnessing Federated Learning for Digital Forensics in IoT: A Survey and Introduction to the IoT-LF Framework by Hania Mohamed, Nickolaos Koroniotis, Nour Moustafa, Francesco Schiliro, Albert Y. Zomaya

“…The feasibility and functionality of this framework are validated by a Proof of Concept, achieving a detection accuracy of approximately 81.69%, when trained on the TON-IoT dataset. …”
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Machine learning-based meta-analysis reveals gut microbiome alterations associated with Parkinson’s disease by Stefano Romano, Jakob Wirbel, Rebecca Ansorge, Christian Schudoma, Quinten Raymond Ducarmon, Arjan Narbad, Georg Zeller

“…Here, we present a machine learning meta-analysis of PD microbiome studies of unprecedented scale (4489 samples). …”
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Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J. Senthil Murugan, K. G. Chandi Priya

“…This article presents a reproducible machine learning methodology for the early prediction of Alzheimer’s disease (AD) using clinical and behavioural data. …”
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Machine learning unveils hypoxia-immune gene hub for clinical stratification of thyroid-associated ophthalmopathy by Lu Chen, Sha Wang, Jinwei Wang, Bei Xu, Jia Tan, Xiaobo Xia

“…Functional enrichment revealed hypoxia response, apoptosis, and programmed cell death. …”
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Population modeling with machine learning can enhance measures of mental health - Open-data replication by Ty Easley, Ruiqi Chen, Kayla Hannon, Rosie Dutt, Janine Bijsterbosch

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Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach by Martina Srutova, Vaclav Kremen, Lenka Lhotska

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Information-extreme machine learning of wrist prosthesis control system based on the sparse training matrix by Suprunenko M. K., Zborshchyk O. P., Sokolov O.

“…The article considers the problem of machine learning of a wrist prosthesis control system with a non-invasive biosignal reading system. …”
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Estimation of Missing DICOM Windowing Parameters in High-Dynamic-Range Radiographs Using Deep Learning by Mateja Napravnik, Natali Bakotić, Franko Hržić, Damir Miletić, Ivan Štajduhar

“…This study evaluates traditional histogram-based methods and explores the potential of deep learning for predicting window parameters in radiographs where such information is missing. …”
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EmbryoNet-VGG16 framework for deep learning-based embryo classification with Otsu segmentation by M. Saraniya, J. Anitha Ruth

“…The authors develop EmbryoNet-VGG16, a system that functions as an automated embryo quality evaluation tool, combining Otsu segmentation with a modified Visual Geometry Group-16 (VGG16) Convolutional Neural Network (CNN) architecture. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…These datasets serve two functions: improving illness prediction and examining large data reservoirs to identify previously unknown disease patterns. …”
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