A comprehensive review on the applications of chemometrics in analytical chemistry by Projesh Saha, Bibhas Pandit, Soma Pramanik, Bhupendra Shrestha

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Applying Machine Learning on Big Data With Apache Spark by Elias Dritsas, Maria Trigka

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Big Data Analytics in IoT, social media, NLP, and information security: trends, challenges, and applications by Kamal Taha

Subjects: “…Big Data Analytics…”
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Machine Learning-Driven Customer Segmentation: A Behavior-Based Approach for F&B Providers by Jacint JUHASZ

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Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward by Sudeep Tanwar, Qasim Bhatia, Pruthvi Patel, Aparna Kumari, Pradeep Kumar Singh, Wei-Chiang Hong

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Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis by Zemin Li, Yulun Wu, Tao Chen, Bo Yan, Chaohai Wei

Subjects: “…Machine learning model…”
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Advances in machine learning for the detection and characterization of microplastics in the environment by M. Maksuda Khanam, M. Khabir Uddin, Julhash U. Kazi

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Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis by Chao Tan, Hao Liu, Zhen Zhang, Xinyu Liu, Yinquan Ai, Xiumin Wu, Enlin Jian, Yongyan Song, Jin Yang

“…ObjectiveDespite growing interest in the application of machine learning (ML) in proteomics, a comprehensive and systematic mapping of this research domain has been lacking. …”
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Machine learning-based detection of medical service anomalies: Kazakhstan’s health insurance data by Maksut Kulzhanov, Alexander Wagner, Abylkair Skakov, Iliyas Mukhamejan, Saya Zhorabek, Ainur B. Qumar

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An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels by Yiannis Kiouvrekis, Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou, Ioannis Filippopoulos

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Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions by Aikaterini Sakagianni, Christina Koufopoulou, Petros Koufopoulos, Sofia Kalantzi, Nikolaos Theodorakis, Maria Nikolaou, Evgenia Paxinou, Dimitris Kalles, Vassilios S. Verykios, Pavlos Myrianthefs, Georgios Feretzakis

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A Data-Driven Machine Learning Framework Proposal for Selecting Project Management Research Methodologies by Otniel Didraga, Andrei Albu, Viorel Negrut, Diogen Babuc, Ovidiu Dobrican

Subjects: “…machine learning…”
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Quantitative Assessment of Data Volume Requirements for Reliable Machine Learning Analysis by Xukuan Xu, Jinghou Bi, Michael Moeckel, Hajo Wiemer, Steffen Ihlenfeldt

“…Applying machine learning (ML) techniques in the context of limited data remains a challenge of practical importance. …”
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Classification Analytics for Wind Turbine Blade Faults: Integrated Signal Analysis and Machine Learning Approach by Waqar Ali, Idriss El-Thalji, Knut Erik Teigen Giljarhus, Andreas Delimitis

“…This paper presents an approach to classify faults in wind turbine blades by leveraging well-known signals and analysis with machine learning techniques. The methodology involves a detailed feature engineering process that extracts and analyzes features from the time and frequency domains. …”
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Quantitative insights into the Winnipeg rental sector: A data-driven analytical approach using geographic and property metrics by Lahiru Wickramasinghe, Aditya Jain

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IDMPF: intelligent diabetes mellitus prediction framework using machine learning by Leila Ismail, Huned Materwala

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Data normalization in machine learning by V. V. Starovoitov, Yu. I. Golub

“…In machine learning, the input data is often given in different dimensions. …”
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IMPROVING PREDICTION OF DEPRESSION AN ANALYTICAL COMPARISON BETWEEN HYBRID AI, MACHINE LEARNING AND DEEP LEARNING APPROACHES by Kanchapogu Naga Raju, Sachi Nandan Mohanty

“…This study provides a substantial addition by conducting an in-depth analysis of individual machine learning and deep learning models and comparing them to the integrated artificial intelligence solutions that we have built. …”
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Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription by Manaf Zargoush, Somayeh Ghazalbash, Mahsa Madani Hosseini, Farrokh Alemi, Dan Perri

Subjects: “…Machine learning…”
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Predicting tennis match outcomes mid-game using machine learning on psychological and physical data by Boyuan Li, Zihui Deng, Gaurav Gupta, Jinger Li, Yixuan Miao

“…Our work presents an AI for Science (AI4Sci) method to use real-time data from each game point to determine essential feature values, formulate and assess the impact of psychological momentum, and employ machine learning methodology on mid-match data for predicting the game’s victor. …”
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