Enhancing Software Requirements Classification with Semisupervised GAN-BERT Technique by Gregorius Airlangga

“…However, our analysis has identified substantial gaps in these studies, including (a) a limited dataset volume, (b) the absence of an evaluation study for cross-domain test sets, (c) the problem of real-time prediction scenarios where a vast amount of unlabeled data floods the system each second, and (d) a dearth of comparative studies scrutinizing diverse software requirements datasets and multiple machine learning models, with particular emphasis on in-domain and cross-domain testing. …”
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A comparative analysis of LSTM, GRU, and Transformer models for construction cost prediction with multidimensional feature integration by Tang Shi, Kazuya Shide

“…Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer are advanced machine learning regression models widely utilized for data prediction tasks. …”
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Hepatitis C Virus–Pediatric and Adult Perspectives in the Current Decade by Nanda Kerkar, Kayla Hartjes

“…Artificial intelligence, machine learning, liver organoids, and liver-on-chip are some examples of techniques that have the potential to contribute to our understanding of the disease and treatment process in HCV. …”
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Detecting Anomaly Classification Using PCA-Kmeans and Ensembled Classifier for Wind Turbines by Prince Waqas Khan, Yung-Cheol Byun

“…The primary objective is to improve the precision of anomaly detection in wind turbines by leveraging machine-learning techniques. The proposed methodology utilizes the output of the PCA-Kmeans model to label supervisory control and data acquisition (SCADA) data. …”
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FL-Joint: joint aligning features and labels in federated learning for data heterogeneity by Wenxin Chen, Jinrui Zhang, Deyu Zhang

“…Abstract Federated learning is a distributed machine learning paradigm that trains a shared model using data from various clients, it faces a core challenge in data heterogeneity arising from diverse client settings and environments. …”
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Emerging technologies and language learning: mining the past to transform the future by Hubbard Philip

“…Today’s emerging technologies—artificial intelligence, machine learning, conversational robots, virtual worlds, virtual reality, augmented reality, automated assessment, and so on—are full of promise and seem poised to revolutionize language teaching and learning over the next decade. …”
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Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety by Xiao-chi Ma, Jian Lu, Yiik Diew Wong

“…The behavioral RUSBoost model surpasses other models, achieving an AUC prediction metric of 0.782 and outperforming traditional traffic-flow-driven machine learning models. PDP analysis demonstrates that the sudden-braking behavior is the leading contributory factor of expressway crashes, particularly when the acceleration exceeds 0.5 G. …”
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The Machine as an Autonomous Explanatory Agent by Dilek Yargan

“…The third part delves into whether and to what extent the state-of-the-art machine learning models function as autonomous explanatory agents, based on the exploration in the second part and considering the field of Human-Computer Interaction.…”
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Precomputed Clustering for Movie Recommendation System in Real Time by Bo Li, Yibin Liao, Zheng Qin

“…In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. …”
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Reliable detection of doppelgängers based on deep face representations by Christian Rathgeb, Daniel Fischer, Pawel Drozdowski, Christoph Busch

“…The proposed detection system employs a machine learning‐based classifier, which is trained with generated doppelgänger image pairs utilising face morphing techniques. …”
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Research on image generation technology based on deep learning by Li Jinchen

“…In the realm of image creation, deep learning stands out as an effective and valuable machine learning technique. Deep learning can automatically learn the intrinsic features of images， reaching the goal of generating high-quality images by utilizing multi-layer neural network models. …”
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Experimental and computational investigation of the effect of machining parameters on the turning process of C45 steel by Tien-Thinh Le, Hang Thi Pham, Hiep Khac Doan, Panagiotis G. Asteris

“…Finally, for practical purposes, an artificial neural network model based on machine learning is developed to predict the average temperature near the turning insert nose.…”
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Development and validation of an automated machine for self-injury assessment via young Koreans' natural writings. by Seoyoung Kim, Dong-Gwi Lee

“…Based on 16,645 online posts, Study 1 developed a machine called the Korean Self-Injurious Text Reviewer (K-SITR) using Latent Dirichlet Allocation topic modeling and machine learning. The K-SITR's text-assessment results were statistically indistinguishable from those of professional counselors. …”
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Detection of Data Integrity Attack Using Model and Data-Driven-Based Approach in CPPS by G. Y. Sree Varshini, S. Latha

“…The convolutional neural network- (CNN-) based data-driven anomaly detection technique outperforms other machine learning (ML) techniques such as support vector machine (SVM), K-nearest neighbour (KNN), and random forest (RF). …”
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Lip print‐based identification using traditional and deep learning by Wardah Farrukh, Dustin van derHaar

“…The first pipeline is a traditional method with Speeded Up Robust Features with either an SVM or K‐NN machine learning classifier, which achieved an accuracy of 95.45% and 94.31%, respectively. …”
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AI-Powered Sustainable Environmental Practices using Laser-Induced Breakdown Spectroscopy (LIBS) by Al-Juboori Haider, Rizvi Syed Zuhaib H., bin Roslan Muhammad S., Liew Josephine Y.

“…The research will focus on designing an innovative system architecture that integrates laser-induced breakdown spectroscopy (LIBS) with a robust machine learning (ML) framework, significantly advancing sustainable environmental practices, especially since LIBS offers rapid and precise multi-elemental analysis, while AI enhances data processing and predictive capabilities. …”
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More than just sentiment: Using social, cognitive, and behavioral information of social media to predict stock markets with artificial intelligence and big data by Yunus Emre Akdogan, Adem Anbar

“…Relationships between Twitter-obtained features and BIST indices were analyzed using machine learning methods such as linear regression, Lasso regression, random forest, and XGBoost. …”
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An Overview of Structured Lipid in Food Science: Synthesis Methods, Applications, and Future Prospects by Chi Rac Hong, Byeong Jun Jeon, Kyung-Min Park, Eun Ha Lee, Sung-Chul Hong, Seung Jun Choi

“…In addition, we discuss innovative approaches, including metagenomics, and machine learning, to discover, and classify new lipases and the use of gene editing technologies for lipase engineering. …”
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Credit Risk Prediction Using Fuzzy Immune Learning by Ehsan Kamalloo, Mohammad Saniee Abadeh

“…Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. …”
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Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks by Huu-Thanh Duong, Tram-Anh Nguyen-Thi, Vinh Truong Hoang

“…Several experiments have been performed for both well-known machine learning-based classifiers and deep learning models. …”
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