LPRLC: Linear predictive run length coding to improve energy consumption of wireless body area networks by Mahdieh Hajiloo Vakil, Zahra Shirmohammadi

“…First- Linear Predictive Coding (LPC) for data prediction, Second- Error calculation, Third- Error coding with Run Length Encoding (RLE) for data compression. …”
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Predicting Firefighter Injury and Entrapment in Urban Firefighting Operations: An Investigation Into the Effectiveness of Modified Fire Time Stages and Machine Learning by Mohammad Mahdi Barati Jozan, Hamed Khosravi, Aynaz Lotfata, Krzysztof J. Cios, Hamed Tabesh

“…Methods In this study, we compare the performance of eight machine learning algorithms in predicting the occurrence of firefighter injuries and entrapment during urban fire incidents. …”
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Heating, Ventilation, and Air Conditioning (HVAC) Temperature and Humidity Control Optimization Based on Large Language Models (LLMs) by Xuanrong Zhu, Hui Li

“…Existing HVAC control methods, such as Proportional–Integral–Derivative (PID) control or Model Predictive Control (MPC), face limitations in understanding high-level information, handling rare events, and optimizing control decisions. …”
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Backstepping Predictive Direct Power Control of Grid-Connected Photovoltaic System Considering Power Quality Issue by Brahim Elkhalil Youcefa, Ahmed Massoum, Said Barkat, Patrice Wira

“…Processor in the Loop (PIL) co-simulation results prove the performances efficiency of the implemented control algorithms under a nonlinear load operating condition.…”
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A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations by Thanh Hai Nguyen, Phuong Le, Tuyen Thanh Thi Nguyen, Anh Kim Su

“…Consequently, many institutions are focused on identifying key factors that contribute to dropout and implementing strategies to mitigate them. This study aims to predict student dropout rates using classical machine learning algorithms while analyzing the key factors influencing these outcomes in higher education. …”
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A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations by Thanh Hai Nguyen, Phuong Le, Tuyen Thanh Thi Nguyen, Anh Kim Su

“…Consequently, many institutions are focused on identifying key factors that contribute to dropout and implementing strategies to mitigate them. This study aims to predict student dropout rates using classical machine learning algorithms while analyzing the key factors influencing these outcomes in higher education. …”
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Enhancing three-phase induction motor reliability with health index and artificial intelligence-driven predictive maintenance by Felipe Lima Aires, Gabriel Dias Galeno, Fernando Nunes Belchior, Antonio Melo Oliveira, Julian David Hunt

“…The proposed approach is based on power quality concepts, the creation of an algebraic algorithm to determine the health index and the use of artificial intelligence algorithms for modelling time series, such as Autoregressive Integrated Moving Average and Facebook Prophet, to predict the future health of the motor based on its historical data. …”
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Predictive modeling of PMMA-based polymer composites reinforced with hydroxyapatite: a machine learning and FEM approach by Rohit Kumar Singh, Khyati Verma, G. C. Mohan Kumar

“…Various machine learning algorithms, such as Feedforward Neural Network (FFNN), Radial Basis Neural Network (RBNN), and Support Vector Machine (SVM), were used to predict the mechanical properties. …”
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A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction by Dabin Zhang, Yufeng Ye, Yongmei Fang, Jing Zhou

“…Finally, the final forecasting values of point prediction, interval prediction and comprehensive probability were obtained through the linear superposition of each subsequence, respectively. …”
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Optimization and predictive modelling for the diameter of nylon-6,6 nanofibers via electrospinning for coronavirus face masks by Malihe Zeraati, Rana Pourmohamad, Bahareh Baghchi, Narendra Pal Singh Chauhan, Ghasem Sargazi

“…The present study used artificial intelligence such as gene expression programming (GEP) and genetic algorithms (GA) were used to predict and optimize the diameter of Nylon-6,6 nanofibers via electrospinning for protection against coronavirus. …”
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Development of Machine Learning Prediction Models to Predict ICU Admission and the Length of Stay in ICU for COVID‑19 Patients Using a Clinical Dataset Including Chest Computed Tom... by Seyed Salman Zakariaee, Negar Naderi, Hadi Kazemi-Arpanahi

“…The imbalance in the data numbers of groups was resolved using the synthetic minority over-sampling technique algorithm. Two sets of prediction models were separately developed to predict ICU admission and ICU LOSs of COVID‑19 patients. …”
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Can heart rate sequences from wearable devices predict day-long mental states in higher education students: a signal processing and machine learning case study at a UK university by Tianhua Chen

“…Abstract The mental health of students in higher education has been a growing concern, with increasing evidence pointing to heightened risks of developing mental health condition. …”
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Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques by Nima Khosravi, Adel Oubelaid, Youcef Belkhier

“…The HDL approach uses predictive analysis real-time data, and layered control algorithms to improve energy distribution strategies, make operations more flexible, and help provide grid support services. …”
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CrySPAI: A New Crystal Structure Prediction Software Based on Artificial Intelligence by Zongguo Wang, Ziyi Chen, Yang Yuan, Yangang Wang

“…Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. …”
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The application of artificial intelligence models in predicting the risk of diabetic foot: a multicenter study by Yao Li, Siyuan Zhou, Bichen Ren, Shuai Ju, Xiaoyan Li, Wenqiang Li, Bingzhe Li, Yunmin Cai, Chunlei Chang, Lihong Huang, Zhihui Dong

“…Abstract This study explores diabetic foot (DF), a severe complication in diabetes, by combining deep learning (DL) and machine learning (ML) to develop a multi-model prediction tool. Early identification of high-risk DF patients can reduce disability and mortality. …”
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Developing a Predictive Model for Stroke Disease Detection Using a Scalable Machine Learning Approach by Assefa Senbato Genale, Tsion Ayalew Dessalegn

“…To address this issue, a scalable stroke disease prediction model for a multinode distributed environment, which was developed by combining big data analytics concepts with machine learning to handle extensive healthcare datasets, an aspect not seen in the prior literature on stroke disease detection, is presented in this work. …”
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Creativity and Awareness in Co-Creation of Art Using Artificial Intelligence-Based Systems in Heritage Education by Francesca Condorelli, Francesca Berti

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Modeling of Global and Individual Kinetic Parameters in Wheat Straw Torrefaction: Particle Swarm Optimization and Its Impact on Elemental Composition Prediction by Ismael Urbina-Salas, David Granados-Lieberman, Martín Valtierra-Rodríguez, Claudia Adriana Ramírez-Valdespino, David Aarón Rodríguez-Alejandro

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Enhancing predictive maintenance in automotive industry: addressing class imbalance using advanced machine learning techniques by Yashashree Mahale, Shrikrishna Kolhar, Anjali S. More

“…Abstract Predictive maintenance is an important application in the automotive industry to enhance vehicle reliability and reducing operational downtime. …”
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Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review by Jie Li, Manli Zhu, Li Yan

“…Then, we comprehensively extracted relevant data related to machine learning algorithms, predictors, and predicted objectives. We subsequently performed a critical evaluation of research quality, data aggregation, and analyses.Results We screened 25 studies on predictive models for sepsis-associated acute kidney injury from a total of originally identified 2898 studies. …”
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