Diagnostic accuracy of artificial intelligence for the screening of prostate cancer in biparametric magnetic resonance imaging: a systematic review by Oksana V. Kryuchkova, Elena V. Schepkina, Natalia A. Rubtsova, Boris Y. Alekseev, Anton I. Kuznetsov, Svetlana V. Epifanova, Elena V. Zarya, Ali E. Talyshinskii

“…AIM: This systematic review aimed to assess the quality of prediction models designed to detect prostate cancer during initial presentation. …”
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Non-Invasive Monitoring of Cerebral Edema Using Ultrasonic Echo Signal Features and Machine Learning by Shuang Yang, Yuanbo Yang, Yufeng Zhou

“…We utilized support vector machine (SVM), logistic regression (LogR), decision tree (DT), and random forest (RF) algorithms for classifying cerebral edema types, and SVM, RF, linear regression (LR), and feedforward neural network (FNNs) for predicting the cerebral infarction volume ratio. …”
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Spike Stall Precursor Detection in a Single-Stage Axial Compressor: A Data-Driven Dynamic Modeling Approach by Anish Thapa, Jichao Li, Marco P. Schoen

“…Reducing this margin through active control requires stall precursor detection and mitigation mechanisms. While several algorithms have shown promising results in predicting modal stalls, predicting spike stalls remains a challenge due to their rapid onset, leaving little time for corrective actions. …”
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A new deep learning-based fast transcoding for internet of things applications by Jia Yang, Yonghong Peng, Linbo Qing, Yajuan Xue, Hong Yang

“…At the CU level, it reduces HEVC encoding complexity by accurately predicting CU partitions. At the PU level, predicting PU partition modes for non-split CUs further streamlines the encoding process. …”
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Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos by Lisa Boucret, Floris Chabrun, Magalie Boguenet, Pascal Reynier, Pierre-Emmanuel Bouet, Pascale May-Panloup

“…Abstract Time-lapse imaging and deep-learning algorithms are promising tools to assess the most viable embryos and improve embryo selection in IVF laboratories. …”
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Recent Advances in Creep Modelling Using the <i>θ</i> Projection Method by William Harrison

“…Using a power law approach along with optimisation algorithms, the residual error between predicted and experimentally observed creep curves is reduced. …”
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Macro-microscopic study on the damage threshold strain of particle-filled polymer composites by Wang Jiaxiang, Qiang Hongfu, Wang Xueren, Wang Zhejun, Li Shiqi

“…This study proposes a damage threshold strain prediction model for typical particle filled polymer composites (composite solid propellants) based on the theory of particle inclusion micromechanics. …”
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Study on the inversion and spatiotemporal variation mechanism of soil salinization at multiple depths in typical oases in arid areas: A case study of Wei-Ku Oasis by Jinming Zhang, Jianli Ding, Zihan Zhang, Jinjie Wang, Xu Zeng, Xiangyu Ge

“…However, accurately modeling and predicting soil salinity at multiple depths over time remains a challenge due to complex interactions among environmental factors and limited ground observations. …”
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Prognosis model of patients with breast cancer based on metabolism-related LncRNAs by Dan Zhang, Shiwei Ma, Liling Yang, Hongyuan Liu, Han Jiang, Yan Wang

“…This study aims to identify metabolism-related lncRNAs with high predictive value for prognosis and to construct a model that can predict the prognosis of breast cancer individually. …”
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Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization. by Huazan Liu, Yukang He, Qichao Hu, Jianfei Guo, Lan Luo

“…Then, based on the BP neural network and MTLBO algorithm, we established the MTLBO-BP neural network prediction model and tested its performance. …”
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Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…Comparative analysis demonstrated that the LDA–BA–ANN configuration delivered the highest prediction precision and computational speed over traditional models. …”
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Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study by Katerina Lawrie, Petr Waldauf, Peter Balaz, Radoslav Bortel, Ricardo Lacerda, Emma Aitken, Krzysztof Letachowicz, Mario D’Oria, Vittorio Di Maso, Pavel Stasko, Antonio Gomes, Joana Fontainhas, Matej Pekar, Alena Srdelic, VAVASC Study Group, Stephen O’Neill

“…Demographics, risk factors, vessels parameters, types of predicted and created VA (pVA, cVA) were collected. …”
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Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks by Nabeel Al-Qirim, Munir Majdalawieh, Anoud Bani-hani, Hussam Al Hamadi

“…This study presents a novel “Digital-twin and Machine Learning-based SCADA Cyber Threat Intelligence (DT-ML-SCADA-CTI)” approach, which utilizes an innovative algorithm to visualize and predict the effects of cyber-attacks, including FDIA, RTCI, and SRA, on SCADA systems. …”
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Identification of Postural Deviations and Trunk Asymmetries in Physical Therapy Students Using an AI-Based Mobile Application by Liviu Moraru, Adela Badau, Florin Marin Litoi, Andrei Madalin Petrescu, Catalin Octavian Manescu, Neluta Smidu

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Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis by Jiarui Wang, Shiyan Jiang, Jiaxin Shi, Jing Wang, Shengrong Du, Zufang Huang

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Boosting skin cancer diagnosis accuracy with ensemble approach by Priya Natha, Sivarama Prasad Tera, Ravikumar Chinthaginjala, Safia Obaidur Rab, C. Venkata Narasimhulu, Tae Hoon Kim

“…Overall performance was increased by the combined predictions made with the Max Voting technique. Moreover, feature vectors that were optimally produced from image data by a Genetic Algorithm (GA) were given to the ML models. …”
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Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings by Rajan Bhatt, Akbar Hossain, Debjyoti Majumder, Mandapelli Sharath Chandra, Rajiv Ghimire, Muhammad Faisal Shahzad, Krishan K. Verma, Amarinder Singh Riar, Vishnu D. Rajput, Mauro Wagner Oliveira, Adel Nisi, Riyadh S. Almalki, Viliam Bárek, Marian Brestic, Sagar Maitra

“…This study discusses the latest research on artificial intelligence (AI) in sugarcane farming, with particular attention given to soil biochemistry, disease detection, climate-smart technology for greenhouse gas emissions, yield and water productivity prediction, and cane juice biochemistry. Artificial intelligence (AI) tools, such as machine learning algorithms, can optimize irrigation, increase yields, save water, and properly estimate sugarcane production. …”
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Poverty‐Armed Conflict Nexus: Can Multidimensional Poverty Data Forecast Intrastate Armed Conflicts? by Çağlar Akar, Doğa Başar Sarıipek, Gökçe Cerev

“…Poverty, arising from factors such as gender inequality and limited access to education and public services, profoundly affects social cohesion. …”
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A Review on Current Technologies and Future Direction of Water Leakage Detection in Water Distribution Network by Mohammed Rezwanul Islam, Sami Azam, Bharanidharan Shanmugam, Deepika Mathur

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Design and optimization of distributed energy management system based on edge computing and machine learning by Nan Feng, Conglin Ran

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