Advances in ECG and PCG-based cardiovascular disease classification: a review of deep learning and machine learning methods by Asmaa Ameen, Ibrahim Eldesouky Fattoh, Tarek Abd El-Hafeez, Kareem Ahmed

“…This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. …”
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Exploiting Artificial Neural Networks for the State of Charge Estimation in EV/HV Battery Systems: A Review by Pierpaolo Dini, Davide Paolini

“…By structuring insights from the extensive literature, this paper aims to establish ANNs as a foundational tool in next-generation battery management systems, ultimately supporting safer and more efficient EVs through real-time fault detection, accurate SOC estimation, and robust safety protocols. Future research directions include refining dataset quality, optimizing algorithm selection, and enhancing diagnostic precision, thereby broadening ANNs’ role in ensuring reliable battery management in electric vehicles.…”
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Machine Learning and Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring by Sehat Ullah, Sangeen Khan, David Vanecek, Inam Ur Rehman

“…Machine Learning (ML) can analyze massive amounts of data and perceive patterns to anticipate anomalous health conditions. …”
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Informational Approaches in Modelling Social and Economic Relations: Study on Migration and Access to Services in the European Union by Florentina-Loredana Dragomir-Constantin, Camelia Madalina Beldiman, Monica Laura Zlati

“…The applied methodology includes attribute distribution analysis, identification of hidden patterns through clustering algorithms (K-Means and Expectation-Maximisation) and training of classifiers using regression decision trees with linear leaf models (M5P) corresponding to interdependent data processing and integration modules, exploratory analysis module, machine learning and decision-making modules, oriented to support public policies through explainable scenarios and predictive-evaluative structures. …”
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A Flexible Constrained ICA Approach for Multisubject fMRI Analysis by Hanlu Yang, Trung Vu, Ehsan Ahmed Dhrubo, Vince D. Calhoun, Tülay Adali

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DIODEM – A Diverse Inertial and Optical Dataset of kinEmatic chain Motion by Simon Bachhuber, Dustin Lehmann, Ive Weygers, Thomas Seel

“…The dataset enables researchers to systematically study individual and combined IMT challenges, facilitating algorithm development for applications ranging from biomechanics to autonomous systems.…”
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Marine Soundscape Monitoring Enabled by Artificial Intelligence-Aided Integrated Distributed Sensing and Communications by Juan M. Marin, Wahyu Hendra Gunawan, Alaaeddine Rjeb, Islam Ashry, Baoshi Sun, Talha Ariff, Chun Hong Kang, Tien Khee Ng, Shinkyu Park, Carlos M. Duarte, Boon S. Ooi

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Spatiotemporal evolution of interseismic coupling and stress accumulation near an asperity on a vertical strike-slip fault: Insights from three-dimensional viscoelastic numerical s... by LI Yebo, HUANG Luyuan, YAO Rui, TIAN Yiwei, YANG Shuxin

“…Consequently, the degree of fault-locking displays a ring-shaped attenuation pattern centered on this asperity. Under purely elastic conditions, the ISC and shear stress accumulation rates near the vicinity of the asperity remained constant over time. …”
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The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis. by Toviah Moldwin, Li Shay Azran, Idan Segev

“…Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules to enable artificial neural networks to effectively perform both supervised and unsupervised learning tasks. …”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…Since the outbreak of COVID-19, there has been an influx of research on predictive modelling, with artificial intelligence (AI) techniques, particularly machine learning (ML) methods, becoming the dominant research direction due to their superior capability in processing multidimensional datasets and capturing complex nonlinear transmission patterns. …”
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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

“…Machine learning based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. …”
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Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review by Kapongo D. Lumamba, Gordon Wells, Delon Naicker, Threnesan Naidoo, Adrie J. C. Steyn, Mandlenkosi Gwetu

“…We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. …”
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Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications by You Wu, Kunyu Wang, Yan Song, Bin Li

“…Single-cell analysis exposed cell type-specific expression patterns, and the MFAP4 gene was identified as a potential therapeutic target due to its association with patient prognosis and modulation of cellular behavior. …”
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Chromaticity-Adaptive Generalized Spatial Modulation for MIMO VLC With Multi-Color LEDs by Ye Xiao, Yi-Jun Zhu

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Application of Concentration-Area fractal modeling and artificial neural network to identify Cu, Zn±Pb geochemical anomalies in Hashtjin area, NW of Iran by Ali Imamalipour, Hamed Ebrahimi, Amir reza Abdollahpur

“…Identification of geochemical anomalies plays an essential role in mineral exploration. Recent research investigations have shown that Machine Learning (ML) algorithms can identify geochemical anomalies associated with mineralization that represent targets for mineral exploration. …”
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A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony by Mohammed Hasan Abdulameer, Siti Norul Huda Sheikh Abdullah, Zulaiha Ali Othman

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Analysis of Sulawesi Earthquake Data from 2019 to 2023 using DBSCAN Clustering by Ody Octora Wijaya, Rushendra

“…This study analyses earthquake occurrence data in the Sulawesi region from 2019 to 2023 using clustering methods with the DBSCAN algorithm. The utilization of the DBSCAN algorithm was chosen for its ability to cluster data based on spatial density, well-suited for analyzing the spatial patterns of earthquakes. …”
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Human-Annotated Label Noise and Their Impact on ConvNets for Remote Sensing Image Scene Classification by Longkang Peng, Tao Wei, Xuehong Chen, Xiaobei Chen, Rui Sun, Luoma Wan, Jin Chen, Xiaolin Zhu

“…Additionally, the label noise estimation algorithm (confident learning) cannot fully identify label noise. …”
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Development of a deep learning system for predicting biochemical recurrence in prostate cancer by Lu Cao, Ruimin He, Ao Zhang, Lingmei Li, Wenfeng Cao, Ning Liu, Peisen Zhang

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The role of fMRI in the mind decoding process in adults: a systematic review by Sahal Alotaibi, Maher Mohammed Alotaibi, Faisal Saleh Alghamdi, Mishaal Abdullah Alshehri, Khaled Majed Bamusa, Ziyad Faiz Almalki, Sultan Alamri, Ahmad Joman Alghamdi, Mohammed Alhazmi, Hamid Osman, Mayeen U. Khandaker

“…Studies were selected based on strict inclusion and exclusion criteria: peer-reviewed; published between 2000 and 2024 (in English); focused on adults; investigated mind-reading (mental state decoding, brain-computer interfaces) or related processes; and employed various mind-reading techniques (pattern classification, multivariate analysis, decoding algorithms). …”
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