Supervised Learning-Based Fault Classification in Industrial Rotating Equipment Using Multi-Sensor Data by Aziz Kubilay Ovacıklı, Mert Yagcioglu, Sevgi Demircioglu, Tugberk Kocatekin, Sibel Birtane

“…The results demonstrate the machine learning model’s ability to retrieve valuable information from multi-sensor data integration, improving predictive maintenance strategies. The presented study contributes a practical framework in intelligent fault diagnosis as it presents an example of a real-world implementation while enabling future improvements in industrial condition-based maintenance systems.…”
Get full text

Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques by Abbas Jafar, Myungho Lee

“…Therefore, early detection is crucial to reducing complications and improving the lives of those impacted. However, the performance of previous automated approaches has often been hindered by suboptimal feature selection and algorithms’ “black-box” nature, which adversely affect their interpretability and clinical applicability. …”
Get full text

The Development of an Intelligent System Architecture for Autonomous Care (ISAAC) by Logan Gisick, Alyssa Tanaka, Bob Marinier, Robert Picking, Cameron Copland

“…To accomplish this, we conducted research to explore how intelligent algorithms can be used to supplement the current capabilities by improving the diagnosis, intervention, and monitoring of combat trauma care. …”
Get full text

Hybrid Mode Sensor Fusion for Accurate Robot Positioning by Viktor Masalskyi, Andrius Dzedzickis, Igor Korobiichuk, Vytautas Bučinskas

“…The focus is on the investigated sensors, the applied synthesis methods, and the developed algorithms and their practical application to identify the existing gaps for future system improvements. …”
Get full text

Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems by Federico Amadei, Michele Quercio, Francesco Riganti Fulginei

“…The article discusses various approaches, including supervised learning, regression models, and Q-learning algorithms, highlighting their ability to reduce design time, improving system efficiency, and mitigate risks associated with misalignment and object interference. …”
Get full text

Lensless camera: Unraveling the breakthroughs and prospects by Shuowen Li, Yunhui Gao, Jiachen Wu, Mingjie Wang, Zhangcheng Huang, Shumei Chen, Liangcai Cao

“…The opportunities and challenges associated with lensless camera are discussed for further improving its performance.…”
Get full text

Active Inference-Driven Multi-Armed Bandits: Superior Performance through Dynamic Correlation Adjustments by Lin Xiaoqi

“…In recent years, Multi-Armed Bandit (MAB) algorithms have gained substantial attention due to their effectiveness in real-world applications, such as recommendation systems, autonomous systems, and dynamic resource allocation. …”
Get full text

Transformer-Based Power Conversion System with Synchronous Adjustment of Modulated Inverters by Oleschuk V.

“…These medium-power and high-power systems are characterized by relatively low switching frequency of inverters. Modification of algorithms of space-vector modulation for transformer-based system with several converters allows providing an improvement of spectral composition of winding voltages of the system. …”
Get full text

Operation of the Synchronous Motor with Surface Permanent Magnet in a Sensorless System with Speed Estimation by CRĂCIUNAŞ Gabriela

“…The Gopinath observer plays a crucial role in improving the stability and accuracy of control by reducing estimation errors and ensuring a fast reaction to dynamic load changes. …”
Get full text

Metaheuristics in automated machine learning: Strategies for optimization by Francesco Zito, El-Ghazali Talbi, Claudia Cavallaro, Vincenzo Cutello, Mario Pavone

“…We examine various metaheuristic algorithms employed and, in particular, their effectiveness in improving model performance across diverse applications. …”
Get full text

Predictive analysis of heart disease using quantum-assisted machine learning by Mehroush Banday, Sherin Zafar, Parul Agarwal, M. Afshar Alam, Siddhartha Sankar Biswas, Imran Hussain, K. M. Abubeker

“…Abstract Coronary heart disease (CHD) is a severe cardiac disease, and hence, its early diagnosis is essential as it improves treatment results and saves money on medical care. …”
Get full text

Unsupervised clustering based coronary artery segmentation by Belén Serrano-Antón, Manuel Insúa Villa, Santiago Pendón-Minguillón, Santiago Paramés-Estévez, Alberto Otero-Cacho, Diego López-Otero, Brais Díaz-Fernández, María Bastos-Fernández, José R. González-Juanatey, Alberto P. Muñuzuri

“…The study demonstrates the potential of clustering algorithms for improving segmentation performance in coronary artery imaging.…”
Get full text

Quantum simulations of chemistry in first quantization with any basis set by Timothy N. Georges, Marius Bothe, Christoph Sünderhauf, Bjorn K. Berntson, Róbert Izsák, Aleksei V. Ivanov

“…Practical applications require development of quantum algorithms with reduced resource requirements. Previous work has mainly focused on quantum algorithms where the Hamiltonian is represented in second quantization with compact basis sets while existing methods in first quantization are limited to a grid-based basis. …”
Get full text

Automatic Detection and Correction of Detuned Singing System for Use With Query-By-Humming Applications by Michał LECH

“…Conclusions are drawn and proposals of further improvements are provided.…”
Get full text

Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning by I Nyoman Mahayasa Adiputra, Paweena Wanchai, Pei-Chun Lin

“…Additionally, this study provided a robustness measurement for algorithms, demonstrating that CostLearnGAN outperforms other sampling methods in improving the performance of classical machine learning models with a 5.68 robustness value on average.…”
Get full text

A systematic review of AI-powered collaborative learning in higher education: Trends and outcomes from the last decade by Attila Kovari

“…Given the rapid advances in technology, AI has enormous potential for application in educational settings, with benefits in terms of personalizing learning, better engaging learners and improving learning outcomes. Artificial intelligence tools, in particular machine learning, natural language processing and recommender algorithms, facilitate collaborative learning by enabling personalized learning through feedback and group work. …”
Get full text

Deep Learning-Based Control of Active Power Filters Using LSTM and GRU Networks for Harmonic and Frequency Estimation by Juan L. Flores-Garrido, Patricio Salmeron, Juan A. Gomez-Galan, Alejandro Perez-Valles

“…Therefore, in this work, a new neural controller is presented for further improvement, using previously trained NNs, without need of adaptive algorithms. …”
Get full text

Boosting brain-computer interface performance through cognitive training: A brain-centric approach by Ziyuan Zhang, Ziyu Wang, Kaitai Guo, Yang Zheng, Minghao Dong, Jimin Liang

“…The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. …”
Get full text

Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning by Zhixiang Wang, Qingqing Li, Yiran Wang, Linxue Qian, Xiangdong Hu, Dong Liu

“…It can assist doctors in developing more effective treatment plans, improving patient outcomes. Future studies should expand sample sizes, include multicenter data, and explore advanced algorithms to further enhance diagnostic performance.…”
Get full text

Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

“…The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I’m Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.…”
Get full text