Harnessing AI for Improved Detection and Classification of Pleural Effusion: Insights and Innovations by Geran Maule, Ahmad Alomari, Abdallah Rayyan, Ogbeide Aghahowa, Mohammad Khraisat, Luis Javier

“…Notably, models such as Light Gradient Boosting Machine (LGB) and XGBoost have achieved accuracy levels up to 96% and high AUC values (e.g., AUC = 0.883 for pleural effusion differentiation). …”
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Invisible Manipulation: Deep Reinforcement Learning-Enhanced Stealthy Attacks on Battery Energy Management Systems by Qi Xiao, Lidong Song, Jong Ha Woo, Rongxing Hu, Bei Xu, Kai Ye, Ning Lu

“…To minimize real-time computational demands, we transform this online optimization problem into an offline DRL training problem, utilizing high-fidelity simulation data from a digital twin-based microgrid testbed. …”
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Identifying Effective Rock-Breaking Ratio Based on Rock Chip Information for Rock-Breaking Efficiency Evaluation of TBM by Chuigang Zeng, Changbin Yan, Gaoliu Li, Xiao Xu, Fengwei Yang, Weilin Su

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Research on Predicting Super-Relational Data Links for Mine Hoists Within Hyper-Relational Knowledge Graphs by Xiaochao Dang, Xiaoling Shu, Fenfang Li, Xiaohui Dong

“…Experimental results demonstrate that the proposed method performs well on both public datasets and a self-constructed hoisting machine dataset. In the Mine Hoist Super-Relationship Dataset (MHSD-100), HyLinker outperforms the latest models, with improvements of 0.142 in MRR (Mean Reciprocal Rank) and 0.156 in Hit@1 (Hit Rate at Rank 1), effectively addressing the knowledge graph completion problem for hoisting machines and providing more accurate information for equipment maintenance and fault prediction. …”
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Frobenius deep feature fusion architecture to detect diabetic retinopathy by C. Priyadharsini, Y. Asnath Victy Phamila

“…Detecting various severity levels helps identify the level of treatment required for each patient, and directing them to the appropriate intensive care unit helps optimize the overall treatment efficacy. Methods This work proposes a multi-model architecture by combining the features extracted from convolutional neural networks using novel Frobenius norm-based feature fusion with an ensemble of machine learning classifiers to perform the classification of binary and multi-class stages of Diabetic Retinopathy. …”
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Seven unique frequency profiles for scoring vigilance states in preclinical electrophysiological data by Freja Gam Østergaard, Martien J. H. Kas

“…Current advances in the application of machine-learning and artificial intelligence to EEG data are moving fast; however, there is still a need for expert raters to validate scoring of EEG data. …”
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Sentiment Analysis of Public Satisfaction with the 'INFO BMKG' Application using Naive Bayes, SVM, and KNN by Natasya Aditiya, Pratomo Setiaji, Supriyono Supriyono

“…This research employs three classification algorithms—Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—to categorize user reviews into positive, neutral, or negative sentiments. …”
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Study on incentive mechanism of reward and punishment on work efficiency of PCB welder based on recurrence quantification analysis and electroencephalogram signals by Zhang Qian, Mingyue Guo, Fuwang Wang

“…To address this issue, this study innovatively combines recurrence quantification analysis (RQA) with electroencephalogram (EEG) signals, proposing a dynamic incentive evaluation model based on the analysis of brain chaos characteristics. …”
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Reconstructing In-Cylinder Pressure from Head Vibrations with Signal-to-Signal Deep Learning Architectures by Mateusz Tabaszewski, Grzegorz M. Szymański, Maciej Tabaszewski, Mikołaj Klekowicki

“…This method of signal-to-signal processing uses deep artificial neural network (ANN) models for signal reconstruction, providing an extensive exploration of the abilities of the presented models in the reconstruction of the pressure measurements. …”
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In-silico study of approved drugs as potential inhibitors against 3CLpro and other viral proteins of CoVID-19. by Imra Aqeel, Abdul Majid, Tahani Jaser Alahmadi, Areej Althubaity

“…In this study, we employed an integrated In-Silico approach using molecular docking and machine learning regression models to explore the potential inhibitors against key proteins of SARS-CoV-2. …”
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Artificial neural networks in predicting impaired bone metabolism in diabetes mellitus by S. S. Safarova

“…Further, the obtained data were processed using the MATLAB software to develop an ANN with a training (80%) and test (20%) set. The ANN model was trained by optimizing the relationship between a set of input data (a number of clinical and laboratory parameters: gender, age, body mass index, duration of diabetes mellitus, etc.) and a set of corresponding output data (variables reflecting the state of bone metabolism: bone mineral density, markers of bone remodeling).Results. …”
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Weather-Baglog Parameters Monitoring System Based IoT-MQTT-Nodered For Mushroom Cultivation Room: A Precision Agriculture by Sumarsono Sumarsono, Nur Muflihah, Hadi Sucipto

“…It seeks to analyze and evaluate the dominant parameters influencing ideal oyster mushroom cultivation room conditions using machine learning classification models and capability process analysis. …”
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Experimental Study on the Impact of Soil Type Variations on Compressive Strength and Settlement Characteristics of Spread Footing Foundations by Ubong Nkamare Tobby, Ben Uchechukwu Ngene

“…The results showed that soil cohesion and internal friction angle had the most significant impact on compressive strength, while moisture content and void ratio were key contributors to settlement behavior. The optimized model achieved high accuracy of 82% in classifying settlement levels, reinforcing the dataset's reliability. …”
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MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction by Yong Zhang, Yong Yin, Ning Xu, Bowen Jia

“…Experimental results demonstrate that the MCE-HGCN model converges effectively with small datasets. …”
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Smooth Guided Adversarial Fully Test-Time Adaptation by Dong Li, Panfei Yang

“…Fully test-time adaptation (FTTA) refers to a specific type of domain adaptation that involves adjusting a pre-trained machine learning model to work with a new target domain, without accessing any data from the source domain. …”
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Protein structural domain-disease association prediction based on heterogeneous networks by Jingpu Zhang, Lianping Deng, Lei Deng

“…The results show that the binary classification model using the XGBOOST algorithm performs significantly better than models using other machine learning algorithms, achieving an AUC (Area Under Curve) score of 0.94 in the leave-one-out cross-validation experiment. …”
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Deep learning-based computational approach for predicting ncRNAs-disease associations in metaplastic breast cancer diagnosis by Saleem Ahmad, Imran Zafar, Shaista Shafiq, Laila Sehar, Hafsa Khalil, Nida Matloob, Mehvish Hina, Sidra Tul Muntaha, Hamid Khan, Najeeb Ullah Khan, Samreen Rana, Ahsanullah Unar, Muhammad Azmat, Muhammad Shafiq, Yousef A. Bin Jardan, Musaab Dauelbait, Mohammed Bourhia

“…This study introduces a Deep Reinforcement Learning (DRL)-based framework for predicting ncRNA–disease associations in metaplastic breast cancer (MBC) using a multi-dimensional descriptor system (ncRNADS) integrating 550 sequence-based features and 1,150 target gene descriptors (miRDB score ≥ 90). The model achieved 96.20% accuracy, 96.48% precision, 96.10% recall, and a 96.29% F1-score, outperforming traditional classifiers such as support vector machines (SVM) and neural networks. …”
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A Novel Evolutionary Deep Learning Approach for PM_2.5 Prediction Using Remote Sensing and Spatial–Temporal Data: A Case Study of Tehran by Mehrdad Kaveh, Mohammad Saadi Mesgari, Masoud Kaveh

“…The performance of the proposed OA-LSTM model is compared to five advanced machine learning (ML) algorithms. …”
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Analysis of Three Dimensional Thermal and Flow Fields for the Brushless Exciter with Full Axial Ventilation under Multi-scheme by HAN Jia-de, ZHANG Yu-nan, LIU Yan-hao, LI Hui-lan, LU Yi-ping, FU Xiu-lan

“…According to the existing scheme of 7.8MW brushless exciter , the ventilation and cooling system is optimized and a variety of schemes are proposed to change the ventilation structure, rotation direction and inlet position of the present scheme in this paper.The fluid-solid coupling model of the whole machine is established. ?…”
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Shared gene signatures and molecular mechanisms link ankylosing spondylitis and rheumatoid arthritis by Boli Qin, Xiaopeng Qin, Jie Ma, Chenxing Zhou, Tianyou Chen, Jichong Zhu, Chengqian Huang, Shaofeng Wu, Rongqing He, Songze Wu, Sitan Feng, Jiarui Chen, Jiang Xue, Wendi Wei, Tengxiang Long, Quan Pan, Kechang He, Zhendong Qin, Tiejun Zhou, Jiayan Jiang, Xinli Zhan, Chong Liu

“…The CBC data of 23,289 patients were collected, and six machine learning algorithms were applied to develop disease prediction models for AS and RA. …”
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