In-depth Analysis on Machine Learning Approaches by Abdulhady A. Abdullah, Nergz S. Mohammed, Maryam Khanzadi, Safar M. Asaad, Zrar Kh. Abdul, Halgurd S. Maghdid

“… Machine learning (ML) approaches cover several aspects of daily life tasks, including knowledge representation, data analysis, regression, classification, recognition, clustering, planning, reasoning, text recommendation, and perception. …”
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Correlation analysis and recurrence evaluation system for patients with recurrent hepatolithiasis: a multicentre retrospective study by Zihan Li, Zihan Li, Yibo Zhang, Zixiang Chen, Jiangming Chen, Hui Hou, Cheng Wang, Zheng Lu, Xiaoming Wang, Xiaoping Geng, Fubao Liu

“…This study aimed to develop a model that dynamically predicts the risk of hepatolithiasis recurrence using a machine-learning (ML) approach based on multiple clinical high-order correlation data.Materials and methodsData from patients with RH who underwent surgery at five centres between January 2015 and December 2020 were collected and divided into training and testing sets. …”
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A Bibliometric Analysis on Federated Learning by Ersin Namlı, Yusuf Sait Türkan, Mesut Ulu, Ömer Algorabi

“…With the rapid advancement of technology and growing concerns about data privacy, federated learning (FL) has attracted considerable attention from the scientific community. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. …”
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Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation by Amna Faisal, Rafia Mumtaz, Muhammad Deedahwar Mazhar Qureshi, Muhammad Ali Tahir, N. Z. Jhanjhi, Mehedi Masud, Mohammad Shorfuzzaman

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Enhancing Comminution Process Modeling in Mineral Processing: A Conjoint Analysis Approach for Implementing Neural Networks with Limited Data by Carlos Moraga, César A. Astudillo, Rodrigo Estay, Alicia Maranek

“…An alternative to simplifying the complexity of these stages is adopting machine learning (ML) techniques; however, ML often requires a substantial amount of data for effective training and validation. …”
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A Machine-Learning-Based Data Science Framework for Effectively and Efficiently Processing, Managing, and Visualizing Big Sequential Data by Alfredo Cuzzocrea, Islam Belmerabet, Abderraouf Hafsaoui, Carson K. Leung

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Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis by Arun Kodirekka, Ayyagari Srinagesh

“…The second step is the data normalization task, and the final step is classification, which can be achieved using three different methods: lexicon, machine learning, and deep learning. …”
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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

“…Three datasets were constructed based on organic types: biodegradable organic compounds, biorefractory organic compounds and combined two types organic compounds. Two machine learning models were employed to predict Anammox performance, with Random Forest (RF) identified as the optimal model, subsequently validated using real coking industry wastewater treatment data. …”
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Exploring learning analytics practices and their benefits through the lens of three case studies in UK higher education by Neil Dixon, Rob Howe, Uwe Matthias Richter

“…Following this, this article analyses the panel discussion themes in relation to the literature, covering both the data quality procedures and practices for learning, teaching and assessment. …”
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A hybrid deep learning model for predicting atmospheric corrosion in steel energy structures under maritime conditions based on time-series data by Mohamed El Amine Seghier Ben, Tam T. Truong, Christian Feiler, Daniel Höche

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A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India by Abhijeet Das

“…Our research in Mahanadi River Basin, Odisha, presents an enhanced methodology based on data, specifically designed to be beneficial for Water Quality (WQ) based on Synthetic Pollution Index (SPI) and machine learning models such as Long Short-Term Memory (LSTM) and Sparrow Search Algorithm (SSA), for its analysis and interpretation of extensive, intricate data sets on water quality, as well as the allocation of pollution sources or contributing elements, in order to improve knowledge of the water quality and the planning of monitoring networks for efficient water resource management. …”
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Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Hamza Imran, Miguel Angel Duque Vaca, Greys Carolina Herrera Morales, Nestor Ulloa, Krishna Prakash Arunachalam

“…Additionally, the integration of SHAP analysis for feature importance ranking provides an interpretable machine learning approach to concrete mix design, which enhances decision-making for engineers and researchers. …”
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Evaluating the Effect of Surrogate Data Generation on Healthcare Data Assessment by Saeid Sanei, Tracey K. M. Lee, Issam Boukhennoufa, Delaram Jarchi, Xiaojun Zhai, Klaus McDonald-Maier

“…In healthcare applications, often it is not possible to record sufficient data as required for deep learning or data-driven classification and feature detection systems due to the patient condition, various clinical or experimental limitations, or time constraints. …”
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Explainable AI Highlights the Most Relevant Gait Features for Neurodegenerative Disease Classification by Gianmarco Tiddia, Francesca Mainas, Alessandra Retico, Piernicola Oliva

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Chemical Nose-Based Non-Invasive Detection of Breast Cancer Using Exhaled Breath by Yosef Matana, Shai Libson, Barak Amihood, Zvi Boger, David Lieberman, Offer Zeiri, Yehuda Zeiri

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Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare by Kamini Lamba, Shalli Rani, Mohammad Shabaz

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A dataset of Roman Urdu text with spelling variations for sentence level sentiment analysisMendeley Data by Mudasar Ahmed Soomro, Rafia Naz Memon, Asghar Ali Chandio, Mehwish Leghari, Muhammad Hanif Soomro

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Development of an IMU-Based Post-Stroke Gait Data Acquisition and Analysis System for the Gait Assessment and Intervention Tool by Yu-Chi Wu, Yu-Jung Huang, Chin-Chuan Han, Yuan-Yang Cheng, Chao-Shu Chang

“…In this paper, we developed a gait data acquisition and analysis system based on IMU wearable devices, proposed a simple yet accurate calibration process to reduce the IMU drifting errors, designed a machine learning algorithm to obtain real-time coordinates from IMU data, computed gait parameters, and derived a formula for G.A.I.T. scores with significant correlation with the physician’s observational scores.…”
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The Attitudes of the Telecommunication Customers in the COVID-19 Outbreak: The Effect of the Feature Selection Approach in Churn Analysis by Handan Donat, Saliha Karadayı Usta

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