The importance of precise and suitable descriptors in data‐driven approach to boost development of lithium batteries: A perspective by Zehua Wang, Li Wang, Hao Zhang, Hong Xu, Xiangming He

“…This paper provides a review of previous research studies conducted on AI, ML, and descriptors, which have been used to address challenges at various levels, ranging from materials development to battery performance prediction. Additionally, it introduces the basics of AI and ML to assist materials and battery developers in comprehending their operational mechanisms. …”
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Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice by Yuke Ren, Yuke Ren, Shuangxing Li, Di Zhang, Yongtian Zhao, Yanwei Yang, Guitao Huo, Xiaobing Zhou, Xingchao Geng, Zhi Lin, Zhe Qu

“…Five different convolutional neural networks (CNNs)-FCN, LR-ASPP, DeepLabv3+, U-Net, and DenseNet were applied to identify GSCC and non-GSCC regions. Tumor prediction images (algorithm results shown as overlays) derived from the slide images were compared, and the performance of the constructed models was evaluated using Precision, Recall, and F1-score.ResultsThe Precision, Recall, and F1-scores of DenseNet, U-Net, and DeepLabv3 + algorithms were all above 90%. …”
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A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data by Longfei Cui, Xinyu Niu, Haizhong Qian, Xiao Wang, Junkui Xu

“…This study introduces a novel approach called “Pre-Trained Shape Feature Representations from Transformers (PSRT)”, which utilizes transformer encoders designed with three self-supervised pre-training tasks: coordinate masking prediction, coordinate offset correction, and coordinate sequence rearrangement. …”
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Assessment of pulmonary embolism probability using a machine learning model by D. V. Gavrilov, A. E. Andreichenko, A. D. Ermak, T. Yu. Kuznetsova, A. V. Gusev

“…The following signs had the greatest prediction value: cough, respiratory disorders, blood creatinine, body temperature, general weakness, heart rate, respiratory rate, edema, antihypertensive therapy, saturation and age.Conclusion. …”
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Research progress on intelligent coal caving theory and technology by WANG Jiachen, YANG Shengli, LI Lianghui, ZHANG Jinwang, WEI Weijie

“…This paper reviews the research progress on the "Four elements" coal caving theory, the relationship between the top coal recovery rate and the rock mixed ratio, a recovery rate prediction model based on block distribution, and the relationship between instantaneous rock mixed ratio and cumulative rock mixed ratio. …”
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A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios by Jiu Yong, Jianwu Dang, Wenxuan Deng

“…Finally, Focal IoU Loss is introduced to more accurately define the scale information of the prediction box, alleviate the problem of imbalanced positive and negative samples, and improve the relative ambiguity of CIoU Loss in YOLOv8s on the definition of aspect ratio. …”
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Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments by Zichong Wang, Yingying Zheng

“…The Long Short-Term Memory–Parallel Multi-Objective Energy Valley Optimizer (LSTM-PMOEVO) framework incorporates energy load prediction using LSTM and scheduling planning solved via PMOEVO. …”
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MiR-155 has a protective role in the development of non-alcoholic hepatosteatosis in mice. by Ashley M Miller, Derek S Gilchrist, Jagtar Nijjar, Elisa Araldi, Cristina M Ramirez, Christopher A Lavery, Carlos Fernández-Hernando, Iain B McInnes, Mariola Kurowska-Stolarska

“…Using miRNA target prediction algorithms and the microarray transcriptomic profile of miR-155(-/-) livers, we identified and validated that Nr1h3 (LXRα) as a direct miR-155 target gene that is potentially responsible for the liver phenotype of miR-155(-/-) mice. …”
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The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models by Zulfikar Ali Ansari, Manish Madhava Tripathi, Rafeeq Ahmed

“…Although artificial intelligence (AI) has showed amazing promise in breast cancer prediction mainly machine learning (ML) algorithms as well as deep learning (DL), practical use of these models is greatly hampered by their lack of interpretability and transparency. …”
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Optimal micro-grid battery scheduling within a comprehensive smart pricing scheme by Mohammed Ashraf Ali, Ahmad H. Besheer, Hassan M. Emara, Ahmed Bahgat

“…The algorithm utilizes day-ahead forecasts for MG load profiles and photovoltaic output power, enabling the prediction of BESS’s optimal power profile a day in advance. …”
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Effective Parallel Processing Social Media Analytics Framework by Ravindra Kumar Singh, Harsh Kumar Verma

“…In recent years lots of research were conducted and various machine learning algorithms were developed around the processing of data to achieve higher accuracy while reducing the processing time is still challenging. …”
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Impact of missing electronic fetal monitoring signals on perinatal asphyxia: a multicohort analysis by Debjyoti Karmakar, Lochana Mendis, Emerson Keenan, Marimuthu Palaniswami, Roxanne Hastie, Enes Makalic, Fiona Brownfoot

“…Abstract Cardiotocography (CTG) is essential for monitoring high-risk pregnancies, yet perinatal asphyxia prediction accuracy remains limited to 50–55%. Regions of artifacts (missing valid signals)-including signal processing aberrations-possibly contribute to this limitation, highlighted by 40% of FDA reports on intrapartum stillbirths. …”
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Lipoprotein(a) levels in a sample of 115,197 subjects from the largest Brazilian private laboratory. by Maria Helane Costa Gurgel Castelo, Isac de Castro, Priscila Raupp-da-Rosa, Patrícia Cristina Grenzi, Eduardo Gomes Lima, Andreza Almeida Senerchia, Érica Ferreira, Flavia Paiva Proença Lobo Lopes

“…Lipoprotein(a) (Lp(a)) is an independent risk factor for atherosclerotic disease and is increasingly being incorporated into clinical algorithms of cardiovascular risk prediction. However, the epidemiology of Lp(a) in Brazil remains unknown. …”
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Exploring dynamical whole-brain models in high-dimensional parameter spaces. by Kevin J Wischnewski, Florian Jarre, Simon B Eickhoff, Oleksandr V Popovych

“…Applying the modeling results to phenotypical data, we found significantly higher prediction accuracies for sex classification when the GoF or coupling parameter values optimized in the high-dimensional spaces were considered as features. …”
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Understanding Precipitation Moisture Sources and Their Dominant Factors During Droughts in the Vietnamese Mekong Delta by Keke Zhou, Xiaogang Shi

“…These findings are of great significance for understanding the moisture sources of precipitation and further improving drought prediction in the VMD.…”
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Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. by Mabirizi, Vicent, Kawuma, Simon, Kyarisiima, Addah, Bamutura, David, Atwiine, Barnabas, Nanjebe, Deborah, Oyesigye, Adolf Mukama

“…This is evidenced by some models and algorithms with ≥90% prediction accuracy. From the literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3, and ReNet. …”
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Artificial Intelligence in Virtual Screening: Transforming Drug Research and Discovery—A Review by Sayantani Roy, Karuppiah Nagaraj, Amit Mittal, Flora C. Shah, Kaliyaperumal Raja

“…Virtual screening (VS) has become an essential computational tool in drug discovery that helps to identify bioactive compounds by predicting their interactions with biological targets. …”
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Uncertainty Quantification in Data Fusion Classifier for Ship-Wake Detection by Maice Costa, Daniel Sobien, Ria Garg, Winnie Cheung, Justin Krometis, Justin A. Kauffman

“…Using deep learning model predictions requires not only understanding the model’s confidence but also its uncertainty, so we know when to trust the prediction or require support from a human. …”
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Potential Health Risks of Chloroacetanilide Herbicides: An In Silico Analysis by Nihan Akıncı Kenanoğlu, Ahmet Ali Berber, Şefika Nur Demir

“…This article proposes to use tools to predict their potential toxicities based on their chemical structure. …”
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Data fusion-based improvements in empirical regression and machine learning for global daily ∼ 8 km resolution sea surface nitrate estimation and interpretation by Aifen Zhong, Difeng Wang, Fang Gong, Jingjing Huang, Zhuoqi Zheng, Xianqiang He, Qing Zhang, Qiankun Zhu

“…While various global-scale SSN regression and machine learning algorithms based on SSN-environment variable relationships have been developed, the prediction accuracy and spatiotemporal resolution of their applications continue to face limitations. …”
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