In silico prediction of HIV-1-host molecular interactions and their directionality. by Haiting Chai, Quan Gu, Joseph Hughes, David L Robertson

“…Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). …”
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Information and Communication Technologies (ICT) for the intelligent operation of building energy systems: design, implementation and evaluation in a living lab by Florian Redder, Philipp Althaus, Eziama Ubachukwu, Maximilian Mork, Sascha Johnen, Christian Küpper, Paul Lieberenz, Marieluise Oden, Lidia Westphal, Thomas Storek, André Xhonneux, Dirk Müller

“…We demonstrate the proposed ICT setup through an experimental study that applies a cloud-based Model Predictive Controller (MPC) to a real building space. …”
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Biopsy image-based deep learning for predicting pathologic response to neoadjuvant chemotherapy in patients with NSCLC by Yibo Zhang, Shuaibo Wang, Xinying Liu, Yang Qu, Zijian Yang, Yang Su, Bin Hu, Yousheng Mao, Dongmei Lin, Lin Yang, Meng Zhou

“…In this study, we propose a weakly supervised deep learning model, DeepDrRVT, which integrates self-supervised feature extraction and attention-based deep multiple instance learning, to improve NAC decision making from pretreatment biopsy images. …”
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Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic by Muhammed Ballı, Asli Ercan Dogan, Sevin Hun Senol, Hale Yapici Eser

“…To ensure model generalizability, the best-performing model was externally validated on a separate dataset of 361 individuals. …”
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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment by Alanoud Al Mazroa, Fahad R. Albogamy, Mohamad Khairi Ishak, Samih M. Mostafa

“…In addition, the binary grey wolf optimizer (BGWO) model is utilized to choose an optimal feature subset. …”
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An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment by M. Karpagam, S. Sarumathi, A. Maheshwari, K. Vijayalakshmi, K. Jagadeesh, V. Bereznychenko, R. Narayanamoorthi

“…Further, the research will extend the approach to other CD like cancer and heart disease to improve the predictive performance.…”
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Analysing and Forecasting the Energy Consumption of Healthcare Facilities in the Short and Medium Term. A Case Study by Ali Koç, Serap Ulusam Seçkiner

“…Nevertheless, when considering the computational time for all regression models, the single models have better computational times, although the error metrics are not as good as for the ensemble models. …”
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Variational autoencoder-based multipath clustering algorithm for terahertz channels by HAO Xinyu, LIAO Xi, ZHENG Xiangquan, WANG Yang, LIN Feng, CHEN Qianbin, ZHANG Jie

“…To address the shortcomings of multipath clustering algorithms in terahertz channel modeling, particularly in terms of multidimensional parameter adaptability and unsupervised feature separation, a variational autoencoder-based latent space multipath clustering (VAE-LMC) model was proposed. …”
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Cooling Load Reduction in Courtyard Houses: Examining the Role of Courtyard Configuration in a Hot, Arid Climate by Essam Almahmoud, Lobna Elgheriani, Abdulbasit Almhafdy

“…Two semi-detached house configurations, each featuring either a square or rectangular U-shaped courtyard, were modeled and analyzed. …”
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Single target tracking in high-resolution satellite videos: a comprehensive review by Xin Huang, Ding Wang, Qiqi Zhu, Ying Zheng, Qingfeng Guan

“…We provide an overview as well as a meta-analysis of satellite video targets, data sources, performance metrics, state-of-art models, and the trend of the target-specific comparative methods. …”
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Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests by Lei Yafei, Jiang Wanlu, Niu Hongjie, Shi Xiaodong, Yang Xukang

“…Using the test and verifying the data set for comparative testing, the fault diagnosis precision rates of the model are above 90.6%, the recall rates are more than 90.9%, the F1 score is higher than 90.7%, and the accuracy rate of this model reached 97.14%. …”
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Quality Evaluation Methods for Cytopathological Cell Image by PENGChenhui, LIUHaochen, GAOWeining, HEYongjun

“…First , the effective field of view and grid-like imaging are evaluated by image segmentation and image texture feature extraction; the ResNet-34 model with channel attention is used to evaluate the sharpness; then the color deconvolution is performed on the image. …”
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An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction by Fabio Cassano, Anna Maria Crespino, Mariangela Lazoi, Giorgia Specchia, Alessandra Spennato

“…Experimental results show that the system can predict failure events up to 10 time units in advance, with the best-performing model achieving an AUROC of 0.93 and recall above 90%. …”
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Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer. by Vesna Barros, Nour Abdallah, Michal Ozery-Flato, Avihu Dekel, Moshiko Raboh, Nicholas Heller, Simona Rabinovici-Cohen, Alex Golts, Amilcare Gentili, Daniel Lang, Suman Chaudhary, Varsha Satish, Resha Tejpaul, Ivan Eggel, Itai Guez, Ella Barkan, Henning Müller, Efrat Hexter, Michal Rosen-Zvi, Christopher Weight

“…As a starting point, we perform a clinical feature ablation study by training a logistic regression baseline model to predict renal cancer patients' eligibility for adjuvant therapy. …”
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Instance structure based multi-label learning with missing labels by Tianzhu CHEN, Fenghua LI, Yunchuan GUO, Zifu LI

“…To address the problem that the existing methods in multi-label learning did not efficiently deal with the problems, the instance structure based multi-label learning scheme with missing labels was proposed.By considering the feature and label structure of instance, the similarity of label vectors were exploit to fill the missing labels and the weight rank loss was exploit to reduce the model bias.Meanwhile, the weight rank loss was also exploit to reduce the model bias.More specially, the manifold structure was capture by forcing the consistency of the geometry similarity of labels and one of the predicted labels.By measuring ranking loss for complete labels and incomplete labels, the relevance of label was distinguish to instance.Experiment results show that the superior performances of the proposed approach compared with the state-of-the-art methods and the accuracy is improved by more than 10% compared with the best comparison scheme under some evaluation criteria.…”
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Research on the Power Flow of Distributed Generation Based on Micro-grid Experiment Platform by LI Zhiyong, WU Binbin, WU Hengliang, LI Yao

“…It established distributed generation model and network model, and performed simulation process by Newton-Raphson power flow algorithm. …”
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MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection by Hantao Ding, Shengfeng Lou, Hairong Ye, Yanbing Chen

“…To address these issues, we introduce MT-CMVAD, a hierarchically structured Transformer architecture that makes two key technical contributions: (1) A Context-Aware Dynamic Fusion Module that leverages cross-modal attention with learnable gating coefficients to effectively bridge the gap between RGB and optical flow modalities through adaptive feature recalibration, significantly enhancing fusion performance; (2) A Multi-Scale Spatiotemporal Transformer that establishes global-temporal dependencies via dilated attention mechanisms while preserving local spatial semantics through pyramidal feature aggregation. …”
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Robustness Study of Non-Dimensional Star Pattern Recognition for a Typical Star Tracker by Jafar Roshanian, Shabnam Yazdani, Masoud Ebrahimi

“…In order to evaluate the effect of angle differences on algorithm performance, overall disposition of bright point centers is modeled. …”
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Neurophysiological Approaches to Lie Detection: A Systematic Review by Bewar Neamat Taha, Muhammet Baykara, Talha Burak Alakuş

“…These findings demonstrate the effectiveness of hybrid and deep learning-based models in enhancing classification performance. <b>Conclusions:</b> EEG-based lie detection, particularly using the ERP P300 response to face recognition tasks, shows promising accuracy and robustness compared to traditional polygraph methods. …”
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Receptor activator of nuclear factor-kappa B ligand-derived microglia healing peptide 1-AcN inhibits osteoarthritis progression in mice by Yuji Fukuda, Munehisa Shimamura, Yuki Etani, Takaaki Noguchi, Takuya Kurihara, Atsushi Goshima, Taihei Miura, Makoto Hirao, Nagahiro Ochiai, Nan Ju, Atsushi Sugimoto, Takashi Kanamoto, Ken Nakata, Seiji Okada, Kosuke Ebina

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