Auditory sensory processing measures using EEG and MEG predict symptom recovery in first-episode psychosis with a single-tone paradigm by F. López-Caballero, B.A. Coffman, M. Curtis, A.L. Sklar, S. Yi, D.F. Salisbury

“…Predicting symptom progression in first-episode psychosis (FEP) is crucial for tailoring treatment and improving outcomes. …”
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Optimization of Penta helix in disaster risk reduction at disaster risk reduction forum F-PRB West Sumatra Province by Yulivestra Malse, Novalinda

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Process Monitoring and Control in Dermatological Diagnosis: A Deep Learning Approach Using Python, NumPy, Pandas, Keras, and TensorFlow by Neelamadhab Padhy, T. Bhubaneswari, Sushree Sangita Das, Meetali Sinha

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Abstinence-based treatment of comorbid eating disorders and ultra-processed food addiction by Susan Peirce Thompson, Susan Peirce Thompson, M. Joy Jacobs, M. Joy Jacobs, David A. Wiss

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Analytical prediction of void distribution and a minimum-void angle in anisotropic fabrics for radial injection resin transfer molding by R. Matsuzaki, M. Naito, D. Seto, A. Todoroki, Y. Mizutani

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Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media by Imran Shafi, Sadia Din, Zahid Hussain, Imran Ashraf, Gyu Sang Choi

“…Results suggest that the proposed approach consumes less energy on text visualization, pre-processing, and dimensionality reduction. It also reduces the time-space complexity of the features extraction process and is capable to steer according to the changing strategies of the active criminal groups. …”
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Predicting Health Spending in Turkey Using the GPR, SVR, and DT Models by Didem Güleryüz

“…Consequently, the Health Spendings of Turkey can be predicted by employing SVR with high accuracy.…”
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Opportunities for the Use of Business Data Analysis Technologies by Bāliņa Signe, Žuka Rita, Krasts Juris

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As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference by Peter Thestrup Waade, Christoffer Lundbak Olesen, Jonathan Ehrenreich Laursen, Samuel William Nehrer, Conor Heins, Karl Friston, Christoph Mathys

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Rapid defect prediction and optimisation method for Ti-modified AlCuMg alloys processed by laser powder-bed fusion by Ziqian Wang, Yuhan Qian, Tengteng Sun, Yakai Xiao, Zijue Tang, Yi Wu, Hua Sun, Xingtian Liu, Haowei Wang, Hongze Wang

“…Ti-modified AlCuMg alloys have demonstrated great potential in improving the printability and mechanical properties of conventional Al alloys processed by laser powder-bed fusion (LPBF). However, an effective material design method, which considers processing parameters and solidification microstructure, is currently absent. …”
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A novel prediction method for low wind output processes under very few samples based on improved W‐DCGAN by Shihua Liu, Han Wang, Weiye Song, Shuang Han, Jie Yan, Yongqian Liu

“…Accurate prediction of LWOP is crucial for maintaining the stable operation of the power system. …”
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Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process by A. Bolève, R. Eddies, M. Staring, Y. Benboudiaf, H. Pournaki, M. Nepveaux

“…Additionally, we demonstrate that the use of a prior GS phase could be a promising and interesting means to make the prediction of ground properties more robust, especially for this specific case study described in this paper. …”
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Infant diet quality index predicts nutrients of concern and ultra-processed food intake in low-income children in the United States by Alana M Chaney, Charles D Arnold, Edward A Frongillo, Lorrene D Ritchie, Euridice M Steele, Lauren E Au

“…Background: Diet quality during infancy can influence nutrient intake and ultra-processed foods (UPFs) consumption throughout later childhood. …”
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A Novel Approach to the Prediction of Alzheimer’s Disease Progression by Leveraging Neural Processes and a Transformer Encoder Model by Emad Al-Anbari, Hossein Karshenas, Bijan Shoushtarian

“…In this work, a novel approach to meta-learning for the prediction of AD is proposed, which leverages the combined power of neural processes (NPs) and transformer architectures. …”
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VSG-FC: A Combined Virtual Sample Generation and Feature Construction Model for Effective Prediction of Surface Roughness in Polishing Processes by Dapeng Yang, Shenggao Ding, Lifang Pan, Yong Xu

“…Data-driven methods are efficient ways for predicting surface roughness in polishing processes, which generally depend on large-scale samples for model training. …”
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A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes by Hossein Moayedi, Atefeh Ahmadi Dehrashid, Binh Nguyen Le

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A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…This study establishes a machine learning model incorporating a crude estimation of property (CEP) strategy to enhance prediction accuracy and precisely control process parameters. …”
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Development of an Artificial Neural Network Model to Predict the Tensile Strength of Friction Stir Welding of Dissimilar Materials Using Cryogenic Processes by Mingoo Cho, Jinsu Gim, Ji Hoon Kim, Sungwook Kang

“…The objective of this study was to develop an artificial neural network (ANN) model for predicting the tensile strength of friction stir welding (FSW) joints between dissimilar materials, with a particular focus on aluminum and copper, using cryogenic processes. …”
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Using Sono-Electro-Persulfate Process for Atenolol Removal from Aqueous Solutions: Prediction and Optimization with the ANFIS Model and Genetic Algorithm by Nasrin Zahedi, Bahare Dehdashti, Farzaneh Mohammadi, Maryam Razaghi, Zeynab Moradmand, Mohammad Mehdi Amin

“…The sono-electro persulfate process can be mentioned as a new and effective method for removing ATN from water sources.…”
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Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study by Tahir Abbas Khan, Areej Fatima, Tariq Shahzad, Atta-Ur-Rahman, Khalid Alissa, Taher M. Ghazal, Mahmoud M. Al-Sakhnini, Sagheer Abbas, Muhammad Adnan Khan, Arfan Ahmed

“…Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. …”
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