Global intraspecific diversity of marine forests of brown macroalgae predicted by past climate conditions by Eliza Fragkopoulou, Ester A. Serrão, Olivier De Clerck, Jorge Assis

“…Here, we explain and predict the genetic diversity of cold and temperate brown macroalgae using genetic data from 29 species and a machine-learning algorithm that incorporates contemporary and past climate conditions during the Last Glacial Maximum (~20,000 years ago) based on the niche centroid hypothesis. …”
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N6-methyladenine identification using deep learning and discriminative feature integration by Salman Khan, Islam Uddin, Sumaiya Noor, Salman A. AlQahtani, Nijad Ahmad

“…A multilayer DNN serves as the classification algorithm to identify N6-methyladenine sites accurately. …”
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Research on load labeling method for big data in novel power system by ZHANG Ximing, XU Huan, YANG Qiuyong

“…The synthetic dataset is compared with the real dataset, and the smart grid machine learning algorithm is trained and tested on this basis. …”
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Subgrouping autism and ADHD based on structural MRI population modelling centiles by Clara Pecci-Terroba, Meng-Chuan Lai, Michael V. Lombardo, Bhismadev Chakrabarti, Amber N. V. Ruigrok, John Suckling, Evdokia Anagnostou, Jason P. Lerch, Margot J. Taylor, Rob Nicolson, Stelios Georgiades, Jennifer Crosbie, Russell Schachar, Elizabeth Kelley, Jessica Jones, Paul D. Arnold, Jakob Seidlitz, Aaron F. Alexander-Bloch, Edward T. Bullmore, Simon Baron-Cohen, Saashi A. Bedford, Richard A. I. Bethlehem

“…We use HYDRA, a novel semi-supervised machine learning algorithm which clusters based on differences to controls and compare its performance to a traditional clustering approach. …”
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Regional trending topics mining from real time Twitter data for sentiment, context, network and temporal analysis by Mousumi Hasan, Mujiba Shaima, Quazi Saad ul Mosaher

“…Unlike other social media platforms, Twitter offers researchers an open data source for multifaceted studies, motivating us to delve into regional trending topics, analyzing associated sentiments, context, networks, and temporal patterns. Using machine learning techniques combined with the VADER algorithm, we conducted a comprehensive analysis involving text, metadata, contextual cues, media, links, and historical data. …”
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Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System by Nasrettin Koksal, AbdulRahman Ghannoum, William Melek, Patricia Nieva

“…In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). …”
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Infant rat ultrasonic vocalizations in the neurodevelopmental model of schizophrenia by Agnieszka Potasiewicz, Zuzanna Mincikiewicz, Piotr Popik, Agnieszka Nikiforuk

“…USV characteristics, temporal organization, clustering, and syntax were analyzed by DeepSqueak’s machine-learning algorithms. Unlike controls, which showed an increasing USV rate associated with proper vocal development, MAM-exposed pups displayed a stable emission rate across days and emitted fewer USVs on the 12th PND. …”
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Artificial Intelligence Driven Human Identification by Purushottam Sharma, Aazar Imran Khan, Samyak Jain, Abhishek Srivastava

“…Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. …”
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Applications of artificial intelligence in neurosurgical education: a scoping review by Hector Julio Piñera-Castro, Christian Borges-García

“…Simulation training utilized neural networks to classify expertise and deliver individualized feedback, though rigid metrics risked oversimplifying skill progression. Machine learning algorithms assessed surgical performance, identifying metrics. …”
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Integrative Role of RNA N7-methylguanosine in epilepsy: Regulation of neuronal oxidative phosphorylation, programmed death and immune microenvironment. by Jiangli Zhao, Qingyuan Sun, Xuchen Liu, Jiwei Wang, Ning Yang, Chao Li, Xinyu Wang

“…Our findings also suggested that active m7G levels could promote oxidative phosphorylation in the neurons of epilepsy patients and decrease neuronal necroptosis activity. Machine learning algorithms were used to identify key m7G regulators (EIF4E3, NUDT3, SNUPN, LSM1, and METTL1), and a nomogram model was constructed based on these findings. …”
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Landscape analysis of m6A modification regulators reveals LRPPRC as a key modulator in tubule cells for DKD: a multi-omics study by Li Jiang, Hongda Yu, Jie Jian, Xulin Sai, Yudian Wang, Yufei Zhang, Xiai Wu

“…The significance of N6-methyladenosine (m6A) modification in the pathogenesis of DKD has become increasingly prominent.AimThis study aimed to investigate the specific expression patterns of the m6A geneset in the pathogenesis of DKD.MethodBulk RNA, single-cell and spatial transcriptome were utilized to clarify the hub gene. 3 types of machine learning algorithms were applied. …”
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A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…This method leverages the advantages of multiple machine-learning algorithms, demonstrating strong operability and improving the accuracy of CBM dynamic predictions. …”
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Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification by Chia‐Hung Lin, Hsiang‐Yueh Lai, Ping‐Tzan Huang, Pi‐Yun Chen, Chien‐Ming Li

“…For IVR and ASR applications, speaker classification into different ages and gender groups can be applied in human–machine interaction or computer‐based interaction systems for customised advertisement, translation (text generation), machine dialog systems, or self‐service applications. …”
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Application of the LDA model to identify topics in telemedicine conversations on the X social network by Mario Sierra Martín, Fang-Wei Chen, Pilar Alarcón Urbistondo

“…In the present analysis, we focus on unstructured tweet texts written by Internet users and apply both machine learning and the Latent Dirichlet Allocation algorithm to model X databases and identify tweet topics. …”
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Consensus-Based Intelligent Distributed Secondary Control for Multiagent Islanded Microgrid by Ali M. Jasim, Basil H. Jasim, Flah Aymen, Hossam Kotb, Ahmed Althobaiti

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Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma. by José Fernando García Molina, Lei Zheng, Metin Sertdemir, Dietmar J Dinter, Stefan Schönberg, Matthias Rädle

“…Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. …”
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A Deep Learning-Based Framework for Bearing RUL Prediction to Optimize Laser Shock Peening Remanufacturing by Yuchen Liang, Yuqi Wang, Anping Li, Chengyi Gu, Jie Tang, Xianjuan Pang

“…This study introduces a novel methodology integrating advanced machine learning and optimization techniques to address this challenge. (1) A transformer-attention model was developed to process segmented vibration signals, effectively capturing complex patterns. …”
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A hybrid ensemble framework with particle swarm optimization for network anomaly detection by Narinder Verma, Neerendra Kumar, Gourav Kumar, Kuljeet Singh

“…By incorporating adaptive mechanisms within the PSO algorithm, our framework dynamically adjusts hyperparameters during optimization, enhancing model robustness and convergence speed. …”
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Enhancing Latent Defect Detection in Built-In Spindle Assembly Lines Through Vibration Data Analysis by Kuo-Hao Li, Chao-Nan Wang, Yao-Chi Tang

“…The inclusion of the DBI in the k-means++ clustering algorithm facilitated an objective evaluation of cluster quality, ensuring that the selected number of clusters accurately represents the underlying data patterns. …”
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Describing the performance of U.S. hospitals by applying big data analytics. by Nicholas S Downing, Alexander Cloninger, Arjun K Venkatesh, Angela Hsieh, Elizabeth E Drye, Ronald R Coifman, Harlan M Krumholz

“…Accordingly, we developed a novel approach for characterizing hospital performance that highlights the similarities and differences between hospitals and identifies common patterns of hospital performance. Specifically, we built a semi-supervised machine learning algorithm and applied it to the publicly-available quality measures for 1,614 U.S. hospitals to graphically and quantitatively characterize hospital performance. …”
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