A CLUSTERING TECHNIQUE FOR THE VIETNAMESE WORD CATEGORIZATION by Nguyễn Minh Hiệp, Nguyễn Thị Minh Huyền, Ngô Thế Quyền, Trần Thị Phương Linh

“…This could be solved using unsupervised machine learning methods. This paper presents an application of the DBSCAN clustering algorithm to classify Vietnamese words from a large corpus. …”
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International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm by Wanbo Lu, Tingting Qiu, Wenhui Shi, Xiaojun Sun

“…This hybrid model combines the methodology of complex systems with machine learning techniques, making it more appropriate for analyzing relationships such as high-frequency dependences and solving complex nonlinear problems. …”
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A Customized Deep Neural Network Approach to Investigate Travel Mode Choice with Interpretable Utility Information by Zhengchao Zhang, Congyuan Ji, Yineng Wang, Yanni Yang

“…This is because, by using DNNs, mode choice can be assimilated with the classification problems within the machine learning community. This article proposes a newly designed DNN framework for traffic mode choice in the style of two hidden layers. …”
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Feasibility Study of Real-Time Speech Detection and Characterization Using Millimeter-Wave Micro-Doppler Radar by Nati Steinmetz, Nezah Balal

“…Future research will explore diverse real-world scenarios and the integration of advanced signal processing and machine learning techniques to further enhance accuracy and robustness.…”
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Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization by S. Sakinah S. Ahmad, Witold Pedrycz

“…Finally, a series of numeric experiments using several machine learning data sets is presented.…”
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A Model and Complexity Analysis of the Relationship Based on Organizational Justice and Embeddedness Theories by Xin Su, Hui Zhang, Shubing Guo

“…Taking 245 members of 18 agricultural cooperatives as samples, this paper makes an empirical test by using the machine learning method. Through the analysis of the data, we find that interactive justice has a significant positive impact on the governance performance. …”
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Advancement of Hydraulic Fracture Diagnostics in Unconventional Formations by Ali Mahmoud, Ahmed Gowida, Murtada Saleh Aljawad, Mustafa Al-Ramadan, Ahmed Farid Ibrahim

“…Hence, the applications of machine learning in fracture diagnostics and DFIT analysis were discussed. …”
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Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine by Zeinab Hassani, Mahin Khosravi

“…In recent years, machine learning algorithms are widely used for diagnosis and timely treatment of diseases. …”
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A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering by Alam Zeb, Fakhrud Din, Muhammad Fayaz, Gulzar Mehmood, Kamal Z. Zamli

“…These metaheuristics have applications in various domains such as global optimization, bioinformatics, power engineering, networking, machine learning, image processing, and environmental applications. …”
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Link Quality Prediction via a Neighborhood-Based Nonnegative Matrix Factorization Model for Wireless Sensor Networks by Yuxin Zhao, Shenghong Li, Jia Hou

“…By introducing complex network theory and machine learning techniques, we propose a neighborhood-based nonnegative matrix factorization model to predict link quality in wireless sensor networks. …”
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Development of Integrated Actinide Chemistry Application, AACE, for Acceleration of Actinide Chemistry Experiments by Nakase Masahiko, Nishihara Takahiro, Ikhwan Fauzia Hanum, Okamura Tomohiro, Matsui Kota

“…To efficiently find the candidate molecules we developed Acceleration of Actinide Chemistry Experiment (AACE), which can deploy transfer learning (TL) and human-in-the-loop machine learning (HITL-ML). Our approach utilizes Hansen’s solubility parameters derived from molecular structures to predict solubility and extractability, create extraction models for MA surrogate Lanthanide (Ln) and MA. …”
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An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset by Senthilkumar Devaraj, S. Paulraj

“…Multidimensional medical data classification has recently received increased attention by researchers working on machine learning and data mining. In multidimensional dataset (MDD) each instance is associated with multiple class values. …”
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Does Price Really Matter for Generic Alternatives? Supervised learning Approaches in Deciding the Right Price for Acceptable Quality Attributes of Amlodipine Besylate among Generic... by Laltanpuii Chenkual, Mahindran Mariyappan, Dimple S. Lalchandani, Khajapeer Shaikh, Pavan Kumar Sathala, Pawan Kumar Porwal

“…The price and safety of finished pharmaceutical preparations are two major concerns while prescribing medicine. In this work, machine learning-based classification models were developed with respect to the quality attributes of 258 samples covering 9 marketed amlodipine (AMLO) formulations. …”
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The Emerging Role of Sonoelastography in Pregnancy: Applications in Assessing Maternal and Fetal Health by Abdulrahman M. Alfuraih

“…Future advancements include refining protocols, integrating machine learning, and combining sonoelastography with other diagnostic methods to enhance its predictive power. …”
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Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions by S. Dutta, M. B. Cohen

“…Abstract We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical‐Canadian High Arctic Ionospheric Model (E‐CHAIM). …”
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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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Privacy-enhanced federated learning scheme based on generative adversarial networks by Feng YU, Qingxin LIN, Hui LIN, Xiaoding WANG

“…Federated learning, a distributed machine learning paradigm, has gained a lot of attention due to its inherent privacy protection capability and heterogeneous collaboration.However, recent studies have revealed a potential privacy risk known as “gradient leakage”, where the gradients can be used to determine whether a data record with a specific property is included in another participant’s batch, thereby exposing the participant’s training data.Current privacy-enhanced federated learning methods may have drawbacks such as reduced accuracy, computational overhead, or new insecurity factors.To address this issue, a differential privacy-enhanced generative adversarial network model was proposed, which introduced an identifier into vanilla GAN, thus enabling the input data to be approached while satisfying differential privacy constraints.Then this model was applied to the federated learning framework, to improve the privacy protection capability without compromising model accuracy.The proposed method was verified through simulations under the client/server (C/S) federated learning architecture and was found to balance data privacy and practicality effectively compared with the DP-SGD method.Besides, the usability of the proposed model was theoretically analyzed under a peer-to-peer (P2P) architecture, and future research work was discussed.…”
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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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