Multimodal Classification of Alzheimer’s Disease Using Longitudinal Data Analysis and Hypergraph Regularized Multi-Task Feature Selection by Shuaiqun Wang, Huan Zhang, Wei Kong

“…The selected features are subsequently integrated via multi-kernel support vector machines for final classification. We used functional magnetic resonance imaging and structural functional magnetic resonance imaging data from Alzheimer’s Disease Neuroimaging Initiative at four different time points (baseline (T1), 6th month (T2), 12th month (T3), and 24th month (T4)) to evaluate our method. …”
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Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives by Mario Della Mura, Joana Sorino, Anna Colagrande, Maged Daruish, Giuseppe Ingravallo, Alessandro Massaro, Gerardo Cazzato, Carmelo Lupo, Nadia Casatta, Domenico Ribatti, Angelo Vacca

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A Review of Vision-Based Multi-Task Perception Research Methods for Autonomous Vehicles by Hai Wang, Jiayi Li, Haoran Dong

“…Moreover, we discuss the definition, role, and classification of multi-task learning. In addition, we analyze the design of classical network architectures and loss functions for multi-task perception, introduce commonly used datasets and evaluation metrics, and discuss the current challenges and development prospects of multi-task perception. …”
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A Prediction of the Shooting Trajectory for a Tuna Purse Seine Using the Double Deep Q-Network (DDQN) Algorithm by Daeyeon Cho, Jihoon Lee

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Ikke skrivekyndig? by Mia Franzisca Fasting

“…The article further discusses how maintaining a less logocentric view of text production, including iconic pictures and other multimodal signs, and the possibility of using familiar digital tools, could be a resource in adult immigrants learning and participation. The article concludes that multimodal communication via smartphone is important as a text practice, and it functions to solve specific tasks both in everyday life and in formal learning situations. …”
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Toward Causal and Evidential Open-Set Temporal Action Detection by Zhuoyao Wang, Rui-Wei Zhao, Rui Feng, Cheng Jin

“…It accomplishes expressive video feature pyramid extraction, discriminative causal action feature representation learning, and reliable EDL-based prediction uncertainty estimation with our tailored network architectures and modified loss functions. …”
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A Comprehensive Review on Next-Generation Modeling and Optimization for Semiconductor Devices by Pratikhya Raut, Deepak Kumar Panda, Amit Kumar Goyal

“…Novel AI-driven approaches, including as deep learning, physics-informed neural networks (PINNs), and alternative modelling, provide innovative ways to address these difficulties. …”
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Deep fusion of incomplete multi-omic data for molecular mechanism of Alzheimer’s disease by Linhui Xie, Yash Raj, Mingzhao Tong, Kwangsik Nho, Paul Salama, Andrew J. Saykin, Shiaofen Fang, Jingwen Yan

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Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis by Serena Dattola, Augusto Ielo, Giuseppe Varone, Giuseppe Varone, Alberto Cacciola, Angelo Quartarone, Lilla Bonanno

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Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review by Mahdieh Poodineh Moghadam, Zabih Allah Moghadam, Mohammad Reza Chalak Qazani, Pawel Plawiak, Roohallah Alizadehsani

“…Despite these advancements, the current systematic literature predominantly focuses on machine learning (ML) applications for a limited set of chronic diseases, often overlooking the extensive capabilities of deep learning (DL) technologies. …”
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Long Run Relationship Between Exports, Imports and its Determinants of Medical Instruments: Case of Pakistan by Fazal-ur-Rehman

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Physics-Guided Machine Learning-Based Forward-Modeling of Radar Observables: A Case Study on Sentinel-1 Observations of Corn-Fields by Tina Nikaein, Paco Lopez-Dekker

“…This article aims to develop a new physics-guided machine learning approach that combines neural networks and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. …”
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Memory-like states created by the first ethanol experience are encoded into the Drosophila mushroom body learning and memory circuitry in an ethanol-specific manner. by Caleb Larnerd, Maria Nolazco, Ashley Valdez, Vanessa Sanchez, Fred W Wolf

“…Thus, the first ethanol exposure creates distinct memory-like states in ethanol-specific circuits and impacts the function of learning and memory circuitry in ways that might influence the formation and retention of other memories.…”
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Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach. by Chen Chen, Bupachad Khanthiyong, Benjamard Thaweetee-Sukjai, Sawanya Charoenlappanit, Sittiruk Roytrakul, Phrutthinun Surit, Ittipon Phoungpetchara, Samur Thanoi, Gavin P Reynolds, Sutisa Nudmamud-Thanoi

“…By targeting a specific Thai cohort, this research provides novel insights into the link between neuroinflammation and cognitive performance, advancing our understanding of cognitive variability, highlighting the role of biological markers in cognitive function, and contributing to developing more accurate machine learning models for diverse populations.…”
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Multi-objective optimization of corrosion resistance, strength, ductility properties of weathering steel utilizing interpretable attention-based deep learning model by Bingxiao Shi, Xin Guo, Luntao Wang, Wenbo Huang, Hong Luo, Lizhi Qin, Guowei Yang, Xuequn Cheng, Xiaogang Li

“…Abstract A high-performance, low-cost weathering steel was developed using a deep learning-based interpretable Attention mechanism, which identifies key compositional factors and captures complex feature interactions. …”
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Determination of disintegration time using formulation data for solid dosage oral formulations via advanced machine learning integrated optimizer models by Mohammed Ghazwani, Umme Hani

“…Abstract We evaluated the properties of tablets by artificial intelligence and machine learning computational approach with integration of optimizer. …”
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A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning by Junyao Ding, Jianchao Du, Hejie Wang, Song Xiao

“…This process introduces a multi-objective fitness function to guide the feature subset, minimizing the number of features in the subset while enhancing classification accuracy. …”
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Leveraging Subjective Parameters and Biomarkers in Machine Learning Models: The Feasibility of <i>lnc-IL7R</i> for Managing Emphysema Progression by Tzu-Tao Chen, Tzu-Yu Cheng, I-Jung Liu, Shu-Chuan Ho, Kang-Yun Lee, Huei-Tyng Huang, Po-Hao Feng, Kuan-Yuan Chen, Ching-Shan Luo, Chien-Hua Tseng, Yueh-His Chen, Arnab Majumdar, Cheng-Yu Tsai, Sheng-Ming Wu

“…Clinical assessment approaches, including pulmonary function tests and high-resolution computed tomography, are limited by accessibility constraints and radiation exposure. …”
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Enhancing Trajectory Tracking Performance of Underwater Gliders Using Finite-Time Sliding Mode Control Within a Reinforcement Learning Framework by Guohui Wang, Jianing Yu, Yanan Yang

“…It incorporates a radial basis function neural network in a critic–actor reinforcement learning framework, enhancing navigational performance in difficult conditions. …”
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GAPO: A Graph Attention-Based Reinforcement Learning Algorithm for Congestion-Aware Task Offloading in Multi-Hop Vehicular Edge Computing by Hongwei Zhao, Xuyan Li, Chengrui Li, Lu Yao

“…A multi-objective reward function, designed to minimize task latency and to alleviate link congestion, guides the entire learning process. …”
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