Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches by M. Sadeghi malekabadi, A.R. Davari

“…Comparisons between CFD results and NN predictions validate the accuracy of the neural network. …”
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Error Data Analytics on RSS Range-Based Localization by Shuhui Yang, Zimu Yuan, Wei Li

“…The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.…”
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Real-time locomotion mode detection in individuals with transfemoral amputation and osseointegration by Bahareh Ahkami, Morten B. Kristoffersen, Max Ortiz-Catalan

“…The method was implemented in LocoD, an existing open-source platform, allowing for further development by the community and allowing easy comparison between different classification algorithms. The evaluation of the platform and prediction algorithm relies on quantifying the transition error, signifying instances where the algorithm falls short in predicting shifts between different walking surfaces. …”
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Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization by Miguel García-Ortegón, Srijit Seal, Carl Rasmussen, Andreas Bender, Sergio Bacallado

“…Overall, our results suggest that NPs on molecular graphs hold great potential for molecular property prediction in the low-data setting. Scientific contribution Neural processes are a family of meta-learning algorithms which deal with data scarcity by transferring information across tasks and making probabilistic predictions. …”
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Identification of a panel of volatile organic compounds in urine for early detection of for bladder cancer by Mai Mao, Yanli Zhang, Haibo Liu, Xiumei Jiang, Yuxiao Zhao, Qi Liu, Zhongfang Niu, Xin Zhang

“…VOCs in urine samples were detected using gas chromatography-ion mobility spectrometry (GC-IMS). Machine learning algorithms were used to establish diagnostic models for predicting BC based on differential expressed VOCs. …”
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Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks by Hanjin Kim, Young-Jin Kim, Won-Tae Kim

“…The rate selection algorithms predict CTI occurrence times and adjust the transmission rate accordingly, enhancing the performance of existing explicit and implicit interference mitigation methods. …”
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Improved Diagnostics Using Polarization Imaging and Artificial Neural Networks by Jianhua Xuan, Uwe Klimach, Hongzhi Zhao, Qiushui Chen, Yingyin Zou, Yue Wang

“…Polarization image analysis algorithms are then developed to analyze Stokes polarization images for cell or tissue classification. …”
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An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition by Zongying Liu, Shaoxi Li, Jiangling Hao, Jingfeng Hu, Mingyang Pan

“…However, these algorithms have their own limitations and their prediction accuracy still has space to improve. …”
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Adaptive Real-Time Transmission in Large-Scale Satellite Networks Through Software-Defined-Networking-Based Domain Clustering and Random Linear Network Coding by Shangpeng Wang, Chenyuan Zhang, Yuchen Wu, Limin Liu, Jun Long

“…Existing research primarily emphasizes traffic prediction and scheduling using spatiotemporal models and machine learning. …”
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Frontier machine learning techniques for melanoma skin cancer identification and categorization: An in-Depth review by Viomesh Singh, Kavita A. Sultanpure, Harshwardhan Patil

“…It's got these brainy models and algorithms that not only soak up information but also play psychic by predicting stuff on brand new data it's never laid eyes on before. …”
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An Efficient Random Forest Classifier for Detecting Malicious Docker Images in Docker Hub Repository by Maram Aldiabat, Qussai M. Yaseen, Qusai Abu Ein

“…Therefore, developing a machine learning classifier that effectively predicts and classifies whether a Docker image contains injected malicious behaviors is crucial as a proactive approach. …”
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Paucity of optineurin gene variants in Indian juvenile open-angle glaucoma patients by Manoj Yadav, Chand Singh Dhull, Sumit Sachdeva, Anshu Yadav, Aarti Bhardwaj, Vishal Panghal, Ankit Kumari, Ritu Yadav, Sapna Sharma, Mukesh Tanwar

“…Methods: Polymerase chain reaction (PCR) amplification and sequencing were employed to identify nucleotide variants within the coding sequence and intron-exon boundaries of the OPTN gene in 85 JOAG patients and 100 controls. A pathogenicity prediction of identified variants was performed by six distinct online algorithms. …”
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Modelling Itasy Lake Water Quality by Long Short Term Memory (LSTM) using Landsat8 Data by Randrianiaina Jerry Jean Christien Frederick, Rakotonirina Rija Itokiana, Jean Robertin Rasoloariniaina, Fils Lahatra Razafindramisa

“…Modeling lake water quality is very important to preserve and protect this resource. Several algorithms can be used to model lake water quality using in-situ measurement data. …”
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Defect detection in textiles using back propagation neural classifier by Subrata Das, Amitabh Wahi, Suresh Jayaram

“…This paper presents a classification method to detect defects such as holes and thick places in knitted fabric by applying artificial neural network algorithm. The artificial neural network algorithms learn from the input data after successful training process, it predicts the nature of the unknown samples in very fast and accurate way. …”
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The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials by Vera Malheiro, Beatriz Santos, Ana Figueiras, Filipa Mascarenhas-Melo

“…AI has revolutionized drug discovery and development by enabling rapid and effective analysis of vast volumes of biological and chemical data during the identification of new therapeutic compounds. The algorithms developed can predict the efficacy, toxicity, and possible adverse effects of new drugs, optimize the steps involved in clinical trials, reduce associated time and costs, and facilitate the implementation of innovative drugs in the market, making it easier to develop precise therapies tailored to the individual genetic profile of patients. …”
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Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence by Yushuang Dong, HuiPing Liao, Feiming Huang, YuSheng Bao, Wei Guo, Zhen Tan

“…Therefore, this study provides a valuable reference for enhancing recurrence risk prediction models in AML and clarifying disease pathogenesis, as well as offering broader insights into mechanisms underlying other major diseases.…”
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Research Progress on Process Optimization of Metal Materials in Wire Electrical Discharge Machining by Xinfeng Zhao, Binghui Dong, Shengwen Dong, Wuyi Ming

“…It highlights that the integration of AI by optimization algorithms (such as Genetic Algorithms, particle swarm optimization, and manta ray foraging optimization) offers an effective path toward the intelligent evolution of WEDM processes. …”
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Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming by Abhishek Raghuvanshi, Umesh Kumar Singh, Guna Sekhar Sajja, Harikumar Pallathadka, Evans Asenso, Mustafa Kamal, Abha Singh, Khongdet Phasinam

“…Crop irrigation is an important step in crop yield prediction. Field harvesting is very reliant on human supervision and experience. …”
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High-Performance Multi-Object Tracking for Autonomous Driving in Urban Scenarios With Heterogeneous Embedded Boards by Alessio Medaglini, Biagio Peccerillo, Sandro Bartolini

“…In this paper, we address the issues behind high-performance Multi-Object Tracking (MOT) algorithms for a real-world urban transportation scenario. …”
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An Intelligent Technique for Android Malware Identification Using Fuzzy Rank-Based Fusion by Altyeb Taha, Ahmed Hamza Osman, Yakubu Suleiman Baguda

“…Second, the fuzzy rank-based fusion approach was employed to adaptively integrate the classification results obtained from the base machine learning algorithms. By leveraging rankings instead of explicit class labels, the proposed ANDFRF method reduces the impact of anomalies and noisy predictions, leading to more accurate ensemble outcomes. …”
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