Semi-supervised prediction of protein fitness for data-driven protein engineering by Alicia Olivares-Gil, José A. Barbero-Aparicio, Juan J. Rodríguez, José F. Díez-Pastor, César García-Osorio, Mehdi D. Davari

“…However, the combinatorial complexity of the protein sequence space and the limited availability of assay-labelled data hinder the efficient optimization of protein properties. Data-driven strategies utilizing machine learning methods have emerged as a promising solution, yet their dependence on labelled training datasets poses a significant obstacle. …”
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Blood pressure abnormality detection and interpretation utilizing explainable artificial intelligence by Hedayetul Islam, Md. Sadiq Iqbal, Muhammad Minoar Hossain

“…Explainable artificial intelligence (XAI) is a state-of-the-art ML toolset that helps us understand and explain the prediction of an ML model. This research aims to build an automatic blood pressure anomaly detection system with maximum accuracy using the fewest features and learn why a model arrived at a particular result using XAI. …”
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An Enhanced Deep Learning Approach to Potential Purchaser Prediction: AutoGluon Ensembles for Cross-Industry Profit Maximization by Hashibul Ahsan Shoaib, Md Anisur Rahman, Jannatul Maua, Ashifur Rahman, M. F. Mridha, Pankoo Kim, Jungpil Shin

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Predicting pile bearing capacity using gene expression programming with SHapley Additive exPlanation interpretation by Adil Khan, Majid Khan, Waseem Akhtar Khan, Muhammad Ali Afridi, Khawaja Atif Naseem, Ayesha Noreen

“…A dataset of 472 reinforced concrete piles obtained from literature, was employed for training, and validating the model. The ten most optimal parameters were selected as inputs. …”
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

“…Federated learning (FL) is an emerging machine learning (ML) paradigm used to train models across multiple nodes (i.e., clients) holding local data sets, without explicitly exchanging the data. …”
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Enhanced predictability and interpretability of COVID-19 severity based on SARS-CoV-2 genomic diversity: a comprehensive study encompassing four years of data by Miao Miao, Yonghong Ma, Jiao Tan, Renjuan Chen, Ke Men

“…Four machine learning methods were employed to construct prediction models. …”
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Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing by Shizhe Li, Chunzhi Fan, Ali Kargarandehkordi, Yinan Sun, Christopher Slade, Aditi Jaiswal, Roberto M. Benzo, Kristina T. Phillips, Peter Washington

“…Results: Among the 9 participants, we achieved an average area under the receiver operating characteristic curve score across participants of 0.695 for the supervised CNNs and 0.729 for the SSL models. Strategic selection of an optimal threshold enabled us to optimize either sensitivity or specificity while maintaining reasonable performance for the other metric. …”
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Flight-Safe Inference: SVD-Compressed LSTM Acceleration for Real-Time UAV Engine Monitoring Using Custom FPGA Hardware Architecture by Sreevalliputhuru Siri Priya, Penneru Shaswathi Sanjana, Rama Muni Reddy Yanamala, Rayappa David Amar Raj, Archana Pallakonda, Christian Napoli, Cristian Randieri

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Enhancing Deep Learning Sustainability by Synchronized Multi Augmentation with Rotations and Multi-Backbone Architectures by Nikita Gordienko, Yuri Gordienko, Sergii Stirenko

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A Novel Transformer-Based Approach for Reliability Evaluation of Composite Systems With Renewables and Plug-in Hybrid Electric Vehicles by Chiranjeevi Yarramsetty, Tukaram Moger, Debashisha Jena, Veeranki Srinivasa Rao

“…The Transformer model identified 1,788 loss-of-load states compared to 1,510 actual instances, requiring only 176 minutes of computation. …”
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Use of n-grams and K-means clustering to classify data from free text bone marrow reports by Richard F. Xiang

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Investigation of the Effects of 3D Printing Parameters on the Mechanical Properties of Bone Scaffolds: Experimental Study Integrated with Artificial Neural Networks by Rixiang Quan, Sergio Cantero Chinchilla, Fengyuan Liu

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Short-term Power Load Forecasting for a 33/11 KV Sub-Station by Utilizing Attention-Based Hybrid Deep Learning Architectures by Mukkamala R.

“…Estimating electric power load at substations is a fundamental task for system operators, as it is es-sential for the reliable and optimal operation of the power system. Effective load forecasting is criti-cal for optimal power generation, as precise predictions facilitate the economical use of electrical infrastructure. …”
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Classifying schizophrenia using functional MRI and investigating underlying functional phenomena by Yangyang Liu, Bi Wan, Zixuan Liu, Shuaiqi Zhang, Pei Liu, Ningning Ding, Yuxin Wang, Jun Dong, Moiz Kabeer Ahmad, Haisan Zhang

“…Statistically significant metrics were selected as features, and machine learning models were used to distinguish between patients and controls. …”
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A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction by Yongjian Li, Weihua Zhang, Qing Xiong, Tianwei Lu, Guiming Mei

“…In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM) using vibration signals is proposed which is conducted in three main steps. …”
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Explainable Artificial Intelligence for predicting the compressive strength of soil and ground granulated blast furnace slag mixtures by Ahmed Mohammed Awad Mohammed, Omayma Husain, Muyideen Abdulkareem, Nor Zurairahetty Mohd Yunus, Nadiah Jamaludin, Elamin Mutaz, Hashim Elshafie, Mosab Hamdan

“…This study aims to predict the UCS of soft soil stabilized with GGBS using various machine learning models. A database of 200 samples was compiled from the literature, and six ML models—linear regression, decision trees, random forest, artificial neural networks, gradient boosting, and extreme gradient boosting were developed and evaluated. …”
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Marginal land identification and grain production capacity prediction of the coverage area of western route of China’s South-to-North Water Diversion Project by Heng Zhou, Jun Zhou, Jun Zhou, Kunming Lu, Minghui Niu, Chenyi Wang, Gaofeng Zhang, Jiawei Kou

“…To assess the grain production potential of these lands, we used maize and wheat as representative crops. Three modeling approaches—random forest regression, gradient boosted regression trees, and two-point machine learning (TPML)—were compared for their predictive accuracy. …”
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High-accuracy lung sound classification for healthy versus unhealthy diagnosis using artificial neural network by Weiwei Zhang, Xinyu Li, Qiao Liu, Xiangyang Zheng, Yisu Ge, Xiaotian Pan, Xiaotian Pan, Yu Zhou

“…The dataset was obtained using a 3M Littmann® Electronic Stethoscope Model 3,200, employing three types of filters (Bell, Diaphragm, and Extended) to capture sounds across different frequency ranges. …”
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HEVERL – Viewport Estimation Using Reinforcement Learning for 360-degree Video Streaming by Nguyen Viet Hung, Pham Tien Dat, Nguyen Tan, Nguyen Anh Quan, Le Thi Huyen Trang, Le Mai Nam

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Coupling effects of laser assistance and tool rake angle on brittle-ductile transition in monocrystalline silicon by Guohui Li, Gangjie Luo, Jiafu Zhou, Yang Ou, Cheng Huang, Chaoliang Guan, Yifan Dai, Xiaoqiang Peng, Yupeng Xiong

“…The tool rake angle and laser power need to be properly matched to provide the optimal DBT depth. In addition, a DBT depth model related to temperature and rake angle was established to accurately describe and predict the experimental results. …”
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