Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives by Marco Cascella, Daniela Schiavo, Arturo Cuomo, Alessandro Ottaiano, Francesco Perri, Renato Patrone, Sara Migliarelli, Elena Giovanna Bignami, Alessandro Vittori, Francesco Cutugno

“…Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. …”
Get full text

Laser-Induced Breakdown Spectroscopy vs. Fluorescence Spectroscopy for Olive Oil Authentication by Marios Bekogianni, Theodoros Stamatoukos, Eleni Nanou, Stelios Couris

“…The acquired spectroscopic data were analyzed by several machine learning algorithms, and the constructed predictive models are evaluated thoroughly for their reliability and robustness. …”
Get full text

A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Sardar Sadaqat Ali

“…This study proposes a novel technique utilizing ML and DL algorithms for threat detection. We began with data preprocessing, which included cleansing the data, addressing missing values through Multiple Imputation by Chained Equations (MICE), and applying transformations such as encoding and standard scaling. …”
Get full text

Epigenetic modifications of cfDNA in liquid biopsy for the cancer care continuum by Jodie Wong, Rohit Muralidhar, Liang Wang, Chiang-Ching Huang

“…It advocates for the integration of multi-omic data through machine learning algorithms to refine predictive models and stresses the importance of collaboration among clinicians, researchers, and data scientists. …”
Get full text

Harnessing Artificial Intelligence in Cardiology: Advancements in Diagnosis, Treatment, and Patient Care by Muhammad Hamza Shuja, Firzah Shakil, Syed Hasan Shuja, Minal Hasan, Maliha Edhi, Abeera Farooq Abbasi, Afia Jawaid, Shajia Shakil

“…While AI’s potential in cardiology is vast, ethical considerations and challenges such as data privacy, bias, and the “black box” nature of some algorithms must be addressed. …”
Get full text

Counting of Underwater Static Objects Through an Efficient Temporal Technique by Atif Naseer, Enrique Nava

“…The proposed method employs deep learning techniques to detect objects over time and an efficient spatial–temporal algorithm to track them, allowing for accurate counting of objects within a given area. …”
Get full text

Compact classification using the biomimetic properties of ultrafast spiking microlaser neurons by Gibaek Kim, Matthieu Dubernard, Sami V El-Nakouzi, Amir-Hossein Masominia, Sylvain Barbay, Laurie E Calvet

“…Here we show that a surrogate model based on a machine learning algorithm can accurately and efficiently describe a microlaser neuron’s biomimetic behavior for a specific targeted application. …”
Get full text

Real-Time Progress Monitoring of Bricklaying by Ramez Magdy, Khaled A. Hamdy, Yasmeen A. S. Essawy

“…Improvements in deep learning and Computer Vision (CV) algorithms provide promising results in detecting objects in real time. …”
Get full text

The role of neurovisualization in monitoring stroke risk among athletes: a review by Bauyrzhan Omarov, Akbayan Aliyeva

“…Discussion: the findings were consistent with previous studies that supported the use of multimodal imaging and computational tools in stroke risk evaluation. however, data heterogeneity and algorithmic transparency were identified as persistent challenges across the reviewed literature. …”
Get full text

Evolving techniques in sentiment analysis: a comprehensive review by Mahander Kumar, Lal Khan, Hsien-Tsung Chang

“…This review comprehensively examines the evolving techniques in sentiment analysis, detailing foundational processes such as data gathering and feature extraction. It explores a spectrum of methodologies, from classical word embedding techniques and machine learning algorithms to recent contextual embedding and advanced transformer models like Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), and T5. …”
Get full text

A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines by Mathew Habyarimana, Abayomi A. Adebiyi

“…This paper provides a detailed examination of artificial intelligence (AI) applications in the prediction of electrical device failures, with a focus on techniques such as fuzzy systems, expert systems, artificial neural networks (ANNs), and complex machine-learning algorithms. These methods use both historical and present data to identify and predict problems and allow timely actions. …”
Get full text

Visual analysis of research trends in diabetes-associated dry eye via bibliometrics by Zhe Yang, Jia-Yi Jiang, Xiao-Hui Zhang

“…Building risk prediction models using machine learning algorithms is a promising future research direction, enabling physicians to identify high-risk individuals and implement early interventions.…”
Get full text

Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study by Wenting Feng, Wen Zhang, Yan Guo, Naixing Zhang, Liang Zhou, Dafeng Lin, Linlin Chen, Caiping Li, Liuwei Shi, Xiangli Yang, Peimao Li, Dianpeng Wang

Get full text

Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention by Dorota Bartusik-Aebisher, Kacper Rogóż, David Aebisher

“…This article explores innovation trends such as genetic data integration, digital twins, federated learning, and local signal processing. …”
Get full text

AI-Driven Accounting and Sensing Applications for Investment Management by Foziljonov Ibrohimjon, Yuldashev Jahongir, Turgunov Jasurbek, Khujamurodov Askarjon, Rozhkova Elena

“…As little is known about where AI-led innovation is gaining momentum beyond institutional finance and automated trading systems, the purpose of this study is to map in what subsectors of investment management it is perceived to gain traction. Drawing on data from regression analysis and correlation matrices in emerging market contexts, we identify a long tail of niche applications and sector-specific tools in which a total of 142 unique AI-enabled platforms operate, including use cases such as fraud detection, asset rebalancing, and environmental, social, and governance (ESG) forecasting. …”
Get full text

On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

“…The methodology consists of three main phases: (i) data preprocessing, including anomaly detection and missing value handling; (ii) exploratory analysis to identify trends and correlations between PM concentrations (PMs) and predictor variables; and (iii) model development using 23 machine learning and deep learning algorithms, enhanced by 50 feature selection techniques. …”
Get full text

Application and prospect of artificial intelligence in empowering the operation and managment of oil and gas pipelines by Qi LIAO, Chunying LIU, Jian DU, Hao LAN, Yongtu LIANG, Haoran ZHANG

“…The evolution of AI-driven research has transitioned from conventional approaches like neural networks, expert systems, fuzzy logic, and wavelet analysis towards new-generation algorithms, including deep learning, transfer learning, and reinforcement learning. …”
Get full text

Classification of diabetes mellitus disease at Rato Ebuh Hospital-Indonesia using the K-Nearest neighbors method based on missing value by Putro Sigit Susanto, Putra Moh Abdan Syakura, Fatah Doni Abdul, Asmara Yuli Panca, Fauzan Hermawan Bin, Rochman Eka Mala Sari, Rachmad Aeri

“…The aim of this research is to create a machine learning model that can detect diabetes early. The study was conducted at Syarifah Ambami Rato Ebu Hospital in Bangkalan, utilizing data from 120 patients in 2019, employing data mining techniques to classify diabetes mellitus patients. …”
Get full text

Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

“…Radiomic features were extracted from the bone marrow of thoracic vertebrae on non-contrast chest CT scans. Multiple machine learning algorithms were utilized to construct various radiomics models for predicting the risk of bone metastasis, and the model with optimal performance was integrated with clinical features to develop a nomogram. …”
Get full text

AI-powered visual E-monitoring system for cattle health and wealth by Aung Si Thu Moe, Pyke Tin, Masaru Aikawa, Ikuo Kobayashi, Thi Thi Zin

“…Through integrated deep learning algorithms, the platform performs key health-related tasks, including ear-tag, body-based, and face-based cattle identification, body condition scoring (BCS), lameness detection, feeding time estimation, and real-time localization. …”
Get full text