Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables by Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres

“…For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. …”
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Machine Learning Approaches to Predict Patient’s Length of Stay in Emergency Department by Mohammad A. Shbool, Omar S. Arabeyyat, Ammar Al-Bazi, Abeer Al-Hyari, Arwa Salem, Thana’ Abu-Hmaid, Malak Ali

“…That is, by focusing on these factors and analyzing the effect of each. This research aims to determine the critical factors that predict the outcome: the length of stay, i.e., the predictor variables. …”
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Multi-label software requirement smells classification using deep learning by Ashagrew Liyih Alem, Ketema Keflie Gebretsadik, Shegaw Anagaw Mengistie, Muluye Fentie Admas

“…Therefore, Bi-LSTM achieved a greater F1-score macro average than the other algorithms.…”
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Interpretable artificial intelligence (AI) for cervical cancer risk analysis leveraging stacking ensemble and expert knowledge by Priyanka Roy, Mahmudul Hasan, Md Rashedul Islam, Md Palash Uddin

“…The research integrates global and local XAI techniques, including individual feature contributions and tree-based explanations, to interpret model decisions. …”
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Prediction of the remaining useful life of a milling machine using machine learning by Abbas Al-Refaie, Majd Al-atrash, Natalija Lepkova

“…Hence, an adequate predictive maintenance (PdM) strategy for the cutting tools becomes necessary. This research seeks to develop a smart maintenance web application that utilizes Machine Learning (ML) supervised models to predict the Remaining Useful Life (RUL) for milling operations. …”
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Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results by Andy Victor Pakpahan, Fahmi Reza Ferdiansyah, Robby Gustian, Muhammad Nur Faiz, Sukma Aji

“…Machine learning is commonly applied in this area, with algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Random Forest being frequently used. …”
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Insight Thoughts for Intelligent Traffic Management-Based SDN by Sara Sadiq Jawad, Dheyaa Jasim Kadhim, Yusmadi Yah Bt Jusoh

“…The main focus is on comprehensive integration so that research is an effective contribution and keeps pace with the continuous development of smart grid management strategies. …”
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UAV Mission Computer Operation Mode Optimization Focusing on Computational Energy Efficiency and System Responsiveness by Oleksandr Liubimov, Ihor Turkin, Valeriy Cheranovskiy, Lina Volobuieva

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Leveraging Multilingual Transformer for Multiclass Sentiment Analysis in Code-Mixed Data of Low-Resource Languages by Muhammad Kashif Nazir, Cm Nadeem Faisal, Muhammad Asif Habib, Haseeb Ahmad

“…The evaluation results showed a significant improvement in accuracy (+22.55%), precision (+21.06%), recall (+22.55%), and F-measure (+25.50%) compared to benchmark algorithms. Additionally, the proposed model outperformed other transformer-based models, as well as deep learning and machine learning algorithms in sentiment extraction from code-mixed data. …”
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Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. …”
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Advancing lakes algal chlorophyll estimation in the contiguous USA: A comparative study of machine learning models and satellite data by Md Mamun, Xiao Yang

“…Overall, acknowledging the existing limitations and challenges of such approaches, this research illustrates the potential of utilizing open-source data with ML to facilitate large-scale estimation of lake CHL-a.…”
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Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks by Sharaf Aldeen Abdulkadhum Abbas, Abdullahi Abdu Ibrahim

“…In addition, Algorithms perform quality control to determine the optimal hyperplane for the given data, Find neighboring data points and preserve the structure of the tree. …”
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Discovering User Sentiment Patterns in Libraries with a Hybrid Machine Learning and Lexicon-Based Approach by Dini Nurmalasari, Dini Hidayatul Qudsi, Nessa Chairani, Heri R Yuliantoro

“…The high accuracy and effectiveness of the employed algorithms highlight the potential of this methodology for libraries. …”
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Assuring assistance to healthcare and medicine: Internet of Things, Artificial Intelligence, and Artificial Intelligence of Things by Poshan Belbase, Rajan Bhusal, Sapana Sharma Ghimire, Shreesti Sharma, Bibek Banskota

“…Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. …”
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Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities by Gift Echefu, Rushabh Shah, Zanele Sanchez, John Rickards, Sherry-Ann Brown

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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis by S. Jeba Priya, C. Anand Deva Durai, M. S. P. Subathra, S. Thomas George, Andrew Jeyabose

“…Machine learning (ML) and deep learning (DL) algorithms are widely used to enhance classification accuracy. …”
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Multilayer Network Modeling for Brand Knowledge Discovery: Integrating TF-IDF and TextRank in Heterogeneous Semantic Space by Peng Xu, Rixu Zang, Zongshui Wang, Zhuo Sun

“…Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. …”
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Multimodal dataset for sensor fusion in fall detection by Carla Taramasco, Miguel Pineiro, Pablo Ormeño-Arriagada, Diego Robles, David Araya

“…Our research introduces a novel multisensor dataset designed to support the creation and evaluation of advanced multisensor fall detection algorithms. …”
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Breast Cancer Classification with Various Optimized Deep Learning Methods by Mustafa Güler, Gamze Sart, Ömer Algorabi, Ayse Nur Adıguzel Tuylu, Yusuf Sait Türkan

“…<b>Conclusions:</b> In conclusion, this study highlights the potential of deep learning algorithms in breast cancer classification. Future research should focus on integrating multi-modal imaging data, refining ensemble learning methodologies, and expanding dataset diversity to further improve the classification accuracy and real-world clinical applicability.…”
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Monitoring Pine Wilt Disease Using High-Resolution Satellite Remote Sensing at the Single-Tree Scale with Integrated Self-Attention by Wenhao Lv, Junhao Zhao, Jixia Huang

“…This study introduces several advanced self-attention algorithms into the task of satellite-based monitoring of pine wilt disease to enhance detection performance. …”
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