Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models by Md. Mahfuzul Islam Shamim, Abu Bakar bin Abdul Hamid, Tadiwa Elisha Nyamasvisva, Najmus Saqib Bin Rafi

“…Key AI techniques, including support vector machines (SVMs) (7.90% of studies), decision trees, and gradient-boosting models, offer substantial improvements in cost prediction and resource optimization. …”
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AI and Machine Learning in V2G technology: A review of bi-directional converters, charging systems, and control strategies for smart grid integration by Nagarajan Munusamy, Indragandhi Vairavasundaram

“…We explore the potential of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to optimize V2 G performance. …”
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A Systematic Review of Gen-AI Applications in Education: Rewards, Challenges and Future Prospects by Everleen Nekesa Wanyonyi, Millicent Kathambi Murithi

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Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

“…The positive correlation between judge evaluations and sensor data indicates judges’ ability to evaluate overall gait quality. Three different algorithms were employed to predict the judges score from the IMU measurements: Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and K-Nearest Neighbors (KNN). …”
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Research on Reservoir Identification of Gas Hydrates with Well Logging Data Based on Machine Learning in Marine Areas: A Case Study from IODP Expedition 311 by Xudong Hu, Wangfeng Leng, Kun Xiao, Guo Song, Yiming Wei, Changchun Zou

“…This article selects six ML methods, including Gaussian process classification (GPC), support vector machine (SVM), multilayer perceptron (MLP), random forest (RF), extreme gradient boosting (XGBoost), and logistic regression (LR). …”
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Prediction of hydrogen production in proton exchange membrane water electrolysis via neural networks by Muhammad Tawalbeh, Ibrahim Shomope, Amani Al-Othman, Hussam Alshraideh

“…The integration of artificial intelligence (AI) and machine learning (ML) appears to be effective in optimization within the energy sector. …”
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Workplace Preference Analytics Among Graduates by Sin-Yin Ong, Choo-Yee Ting, Hui-Ngo Goh, Albert Quek, Chin-Leei Cham

“…Feature selection was used to identify top-10 predictors that influence the selection of jobs in graduates' desired sectors. Various analytics methods such as Decision Tree Analysis, Random Forest Model selection, Naive Bayes Classification Method, Support Vector Machines and K-Nearest Neighbor Algorithms were employed for comparative evaluations within the workplace analytics scope. …”
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A comprehensive survey on customer churn analysis studies by Maryam Shahabikargar, Amin Beheshti, Xuyun Zhang, Jin Foo, Alireza Jolfaei

“…The survey then reviews a range of machine learning algorithms used for churn prediction and explores the various feature types employed in this domain. …”
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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…Support vector machine (SVM) and optimization algorithms (Wrapper) were found to be the best learner and feature selection techniques, respectively, out of all the available feature selection algorithms. …”
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The Role of Education in Building National Soft Power: An Empirical Analysis From a Global Perspective Using Deep Neural Networks by Yun Bai

“…Finally, we compare the performance of our proposed DNN model with other machine learning algorithms, such as Random Forest and Support Vector Machines, demonstrating superior predictive accuracy. …”
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A Data-Driven Model for the Energy and Economic Assessment of Building Renovations by Giuseppe Piras, Francesco Muzi, Zahra Ziran

“…However, defining effective and economically sustainable interventions remains a challenge, partly due to the variability of building characteristics and the lack of digital tools to support data-driven decision making. This research aims to identify the main factors influencing the energy consumption of buildings by analyzing a large database of building characteristics using machine learning algorithms. …”
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Artificial Intelligence in Breast Reconstruction: A Narrative Review by Andrei Iulian Rugină, Andreea Ungureanu, Carmen Giuglea, Silviu Adrian Marinescu

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Breaking Down Walls: A Literature Review on the Power of Artificial Intelligence in Enhancing Universal Language Connection by Sheida Jamalnia, Zahra Karimian

“…The search focused on peer-reviewed articles published in English from January 2020 to June 2024, using keywords like ‘artificial intelligence,’ ‘communication,’ ‘natural language processing,’ and ‘machine translation.’ Studies were selected based on methodological rigor, with data extracted independently by two reviewers and synthesized narratively to identify key themes.Results: AI-driven technologies, including machine translation and NLP tools, significantly enhance multilingual communication across sectors such as business, healthcare, and education. …”
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A Survey on Data Mining for Data-Driven Industrial Assets Maintenance by Eduardo Coronel, Benjamín Barán, Pedro Gardel

“…The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. …”
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Integrated Technologies for Smart Building Energy Systems Refurbishment: A Case Study in Italy by Lorenzo Villani, Martina Casciola, Davide Astiaso Garcia

“…This model enabled energy simulations using the Carrier–Pizzetti method and supported the design of a hybrid HVAC system—integrating VRF and hydronic circuits—further enhanced by a custom ML algorithm for adaptive, predictive energy management through BIM and IoT data fusion. …”
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Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

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Studying the Impact of Changing Consumer Behavior During Crisis Periods Through Store Classification by Kiymet Tabak Kızgın, Selçuk Alp

“…Then, pre-pandemic, pandemic period and both periods data were analyzed using 7 different machine learning classification algorithms. The results obtained were compared. …”
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Innovative approaches in marketing for the development of the tourism industry by Yuliia Stasiuk, Andrii Kobchenko

“…For instance, AI-powered chatbots and virtual assistants provide real-time customer support, while machine learning algorithms analyze booking patterns to offer personalized travel recommendations. …”
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Leveraging artificial intelligence to assess the impact of COVID-19 on the teacher-student relationship in higher education. by Md Juwel Ahmed Sarker, Mahmudul Hasan, Alamgir Kabir, Abdullah Haque

“…COVID-19 disrupted teacher-student interaction and hindered the flow of teacher's support to students. The damage caused by the pandemic to the higher education sector has mostly recovered. …”
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Predicting Patients’ Revisit Intention Based on Satisfaction Scores: Combination of Penalized Regression and Neural Networks by Farshid Abdi, Shaghayegh Abolmakarem, Amir Karbassi Yazdi, Paul Leger, Yong Tan, Giuliani Coluccio

“…In addition to feature selection models such as Random Forest, Genetic Algorithm, and Lasso Regression, the study employs various methods, including Neural Networks, Support Vector Machines, Decision Trees, k-Nearest Neighbors, Rule-based systems, and Naive Bayes algorithms. …”
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