Avances en el aprovechamiento de biopolímeros y productos peruanos by Erika Del Milagro Lozano-Flores

“…Asimismo, el análisis de palabras clave destaca la relevancia de técnicas como "machine learning", "deep learning" y "neural networks". …”
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Evaluating Urban Community Sustainability by Integrating Housing, Ecosystem Services, and Landscape Configuration by Liang Zhou, Haowei Mu, Bao Wang, Bo Yuan, Xuewei Dang

“…In this study, we perform a sustainable-oriented land use scheme using multisource remote sensing, machine learning, and object-based postclassification refinement. …”
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Water quality assessment for aquaculture using deep neural network by Rajeshwarrao Arabelli, T. Bernatin, Venkataramana Veeramsetty

“…The proposed model is validated by comparing with other machine learning models like support vector machine, K-Nearest Neighbour and Naive Bayes classifier in terms of metrics like accuracy, f1-score, precision and recall. …”
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Explainable vision transformer for automatic visual sleep staging on multimodal PSG signals by Hyojin Lee, You Rim Choi, Hyun Kyung Lee, Jaemin Jeong, Joopyo Hong, Hyun-Woo Shin, Hyung-Sin Kim

“…Abstract Polysomnography (PSG) is crucial for diagnosing sleep disorders, but manual scoring of PSG is time-consuming and subjective, leading to high variability. While machine-learning models have improved PSG scoring, their clinical use is hindered by the ‘black-box’ nature. …”
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Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture by H. V. Ramachandra, Pundalik Chavan, S. Supreeth, H. C. Ramaprasad, K. Chatrapathy, G. Balaraju, S. Rohith, H. S. Mohan

“…DINN is evaluated considering the various machine learning attack such as basic_iterative_method attack, momentum_iterative_method attack, post_gradient_descent attack, and C&W attack; comparison is carried out on existing and DINN, considering attack success rate and MSE. …”
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Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection by wafiq azizah, soffiana agustin

“…The proposed methodology integrates two feature extraction techniques, Histogram of Oriented Gradients (HOG) and Texture Moments, with the Random Forest machine learning algorithm. The research process encompasses four key stages: pre-processing, feature extraction, classification, and system evaluation, where the accuracy of individual and combined feature extraction methods is analyzed. …”
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AI Methods for Antimicrobial Peptides: Progress and Challenges by Carlos A. Brizuela, Gary Liu, Jonathan M. Stokes, Cesar de laFuente‐Nunez

“…However, the high cost of extensive wet‐lab screening has made AI methods for identifying and designing AMPs increasingly important, with machine learning (ML) techniques playing a crucial role. …”
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Monitoring Population Phenology of Asian Citrus Psyllid Using Deep Learning by Maria Bibi, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Muhammad Irfan Khan, Shouket Zaman Khan, Casper Shikali Shivachi, Asad Anees

“…In the current study, several prediction models were developed based on regression algorithms of machine learning to monitor different phenological stages of Asian citrus psyllid to predict its population about different abiotic variables (average maximum temperature, average minimum temperature, average weekly temperature, average weekly relative humidity, and average weekly rainfall) and biotic variable (host plant phenological patterns) in citrus-growing regions of Pakistan. …”
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A CNN-LSTM-Based Model to Forecast Stock Prices by Wenjie Lu, Jiazheng Li, Yifan Li, Aijun Sun, Jingyang Wang

“…At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. …”
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Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks by Shougi S. Abosuliman, Saleem Abdullah, Nawab Ali

“…Abstract Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. …”
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3D Reconstruction of a Nuclear Reactor by Muon Tomography: Structure Validation and Anomaly Detection by Baptiste Lefevre, Julien Vogel, Héctor Gomez, David Attié, Laurent Gallego, Philippe Gonzales, Bertrand Lesage, Philippe Mas, Daniel Pomarède

“…It comprises different tools based on data augmentation and machine learning, which proved to be very efficient on simulated data and increase the quality of the experimental data analysis. …”
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Predicting wind power using LSTM, Transformer, and other techniques by Arun Kumar M, Rithick Joshua K, Sahana Rajesh, Caroline Dorathy Esther J, Kavitha Devi MK

“…In this study, we bridge the gap by exploring various machine learning (ML) and deep learning (DL) methodologies to enhance wind power forecasts. …”
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A Scalable Framework for Sensor Data Ingestion and Real-Time Processing in Cloud Manufacturing by Massimo Pacella, Antonio Papa, Gabriele Papadia, Emiliano Fedeli

“…Experimental validation using sensor data from the UCI Machine Learning Repository demonstrated substantial improvements in processing efficiency and throughput compared with conventional frameworks. …”
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Investigating Rotor Conditions on Wind Turbines Using Integrating Tree Classifiers by Bikash Chandra Saha, Joshuva Arockia Dhanraj, M. Sujatha, R. Vallikannu, Mohana Alanazi, Ahmad Almadhor, Ravishankar Sathyamurthy, Kuma Gowwomsa Erko, V. Sugumaran

“…This research presents a methodology adaptation on machine learning technique for appropriate classification of different failure conditions on blade during turbine operation. …”
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Spatial Pattern Characteristics and Influencing Factors of Green Use Efficiency of Urban Construction Land in Jilin Province by Huisheng Yu, Ge Song, Tong Li, Yanjun Liu

“…Taking 47 counties and cities in Jilin Province as an example, this paper evaluates the green utilization efficiency of urban construction land (GUEUCL) in 2011 and 2015 by using the unexpected output super-SBM model and explores the spatial-temporal differentiation characteristics and influencing factors of GUEUCL by using GIS and machine learning methods. The results show that (1) the GUEUCL in Jilin Province is low, mainly distributed in small- and medium-sized areas, with significant positive spatial correlation. …”
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A novel ensemble model for fall detection: leveraging CNN and BiLSTM with channel and temporal attention by Sarita Sahni, Sweta Jain, Sri Khetwat Saritha

“…Despite the proliferation of machine learning and deep learning algorithms for fall detection, their efficacy remains hampered by resilience, robustness, and adaptability challenges across varied input scenarios. …”
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Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia by Bojana Ivošević, Nina Pajević, Sanja Brdar, Rana Waqar, Maryam Khan, João Valente

“…This dataset is a valuable resource for researchers working on habitats mapping and assessment for biodiversity monitoring studies on one side and researchers working on novel machine learning methods for land cover classification.…”
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Antimicrobial resistance in diverse urban microbiomes: uncovering patterns and predictive markers by Rodolfo Brizola Toscan, Wojciech Lesiński, Piotr Stomma, Balakrishnan Subramanian, Paweł P. Łabaj, Witold R. Rudnicki, Witold R. Rudnicki

“…Using metagenomic data from the CAMDA 2023 challenge, we applied tools such as AMR++, Bowtie, AMRFinderPlus, and RGI for resistome profiling, along with clustering, normalization, and machine learning techniques to identify predictive markers. …”
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Correlation feature and instance weights transfer learning for cross project software defect prediction by Quanyi Zou, Lu Lu, Shaojian Qiu, Xiaowei Gu, Ziyi Cai

“…Abstract Due to the differentiation between training and testing data in the feature space, cross‐project defect prediction (CPDP) remains unaddressed within the field of traditional machine learning. Recently, transfer learning has become a research hot‐spot for building classifiers in the target domain using the data from the related source domains. …”
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Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals by Arezoo Sanati Fahandari, Sara Moshiryan, Ateke Goshvarpour

“…Furthermore, the SVM classifier surpassed the other machine learning algorithms when all features were integrated, attaining an accuracy of 89.06%, a sensitivity of 88.97%, an F1 score of 94.16%, and a precision of 100% for the discrimination of depression in the gamma band. …”
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