Evaluation of the predictors of tooth loss using artificial intelligence-based machine learning approach: A retrospective study by Amit Rajabhau Pawar, Sankari Malaiappan, Pradeep Kumar Yadalam, P R Ganesh

“…While challenges remain, such as data quality and privacy concerns, integrating machine learning algorithms in dentistry can revolutionize dental healthcare, improve patient outcomes, and reshape the future of periodontics.…”
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Sparse support path generation for multi-axis curved layer fused filament fabrication by Tak Yu Lau, Dong He, Yamin Li, Yihe Wang, Danjie Bi, Lulu Huang, Pengcheng Hu, Kai Tang

“…Currently, most support generation algorithms are for the conventional 2.5D printing, which are not applicable to multi-axis printing. …”
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Interacting Large Language Model Agents. Bayesian Social Learning Based Interpretable Models by Adit Jain, Vikram Krishnamurthy

“…This paper discusses the theory and algorithms for interacting large language model agents (LLMAs) using methods from statistical signal processing and microeconomics. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The presence of software defects significantly impacts the quality of software systems and increases development and maintenance costs. To improve system quality and reduce costs, it is necessary to predict software defects in the early stages of the software development lifecycle. …”
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Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities by Gift Echefu, Rushabh Shah, Zanele Sanchez, John Rickards, Sherry-Ann Brown

“…While these AI applications may help improve preventive measures and facilitate tailored treatment to patients, they are also capable of perpetuating and exacerbating healthcare disparities, if trained on limited, homogenous datasets. …”
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Breast Cancer Classification with Various Optimized Deep Learning Methods by Mustafa Güler, Gamze Sart, Ömer Algorabi, Ayse Nur Adıguzel Tuylu, Yusuf Sait Türkan

“…<b>Conclusions:</b> In conclusion, this study highlights the potential of deep learning algorithms in breast cancer classification. Future research should focus on integrating multi-modal imaging data, refining ensemble learning methodologies, and expanding dataset diversity to further improve the classification accuracy and real-world clinical applicability.…”
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Clinical Validation of a Machine Learning-Based Biomarker Signature to Predict Response to Cytotoxic Chemotherapy Alone or Combined with Targeted Therapy in Metastatic Colorectal C... by Duilio Pagano, Vincenza Barresi, Alessandro Tropea, Antonio Galvano, Viviana Bazan, Adele Caldarella, Cristina Sani, Gianpaolo Pompeo, Valentina Russo, Rosa Liotta, Chiara Scuderi, Simona Mercorillo, Floriana Barbera, Noemi Di Lorenzo, Agita Jukna, Valentina Carradori, Monica Rizzo, Salvatore Gruttadauria, Marco Peluso

“…This research aims to use artificial intelligence (AI) to improve treatment by predicting which therapies will work best for individual patients. …”
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Optimized Two-Stage Anomaly Detection and Recovery in Smart Grid Data Using Enhanced DeBERTa-v3 Verification System by Xiao Liao, Wei Cui, Min Zhang, Aiwu Zhang, Pan Hu

“…The second stage leverages a modified DeBERTa-v3 architecture with comprehensive 25-dimensional feature engineering per variable to verify potential anomalies, improving the precision to 95.1% while maintaining 84.1% for recall. …”
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The Risk of Arrhythmias in Patients with COVID-19 by Lina Haj Ali, Loredana Suhov, Adrian Apostol, Larissa Dăniluc, Oana Sandu, Carina Bogdan, Viviana Mihaela Ivan

“…Additional mechanisms include the proarrhythmic effects of COVID-19 pharmacotherapies, drug–drug interactions, and associated autonomic dysfunction. To improve future risk stratification and clinical management, it is essential to accurately assess the risk of arrhythmia in the context of severe infections, in order to develop diagnostic and therapeutic algorithms that support the fastest and safest possible interventions. …”
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Biaxial Solar Tracking System Based on the MPPT Approach Integrating ICTs for Photovoltaic Applications by Raúl Gregor, Yoshihiko Takase, Jorge Rodas, Leonardo Carreras, Derlis Gregor, Andrés López

“…The smart grid and distributed generation based on renewable energy applications often involve the use of information and communication technology (ICT) coupled with advanced control and monitoring algorithms to improve the efficiency and reliability of the electrical grid and renewable generation systems. …”
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Machine learning models for predicting the risk of depressive symptoms in Chinese college students by Chengfu Yu, Xiangxuan Kong, Weijie Yu, Xingcan Ni, Jing Chen, Xiaoyan Liao

“…Future studies should improve generalizability by incorporating more diverse samples and physiological biomarkers.…”
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Quantum AI: A Cognitive Machine Learning Technique based on Nurturing Food Security Sustainability Predictive Analysis for Life Science - Bioengineering in Healthcare by Senthil G.A., Monica K.M., Prabha R., Prinslin L., Elavarasi R.

“…Disease prevention and increased food security This article combines image analysis with quantum algorithms for precise food insights, introducing an advanced quantum-enhanced AI system. …”
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Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance by Yinghui Le, Chongshang Zhao, Jing An, Jiali Zhou, Dongdong Deng, Yi He

“…Artificial intelligence (AI) can simplify and automate work flows and improve image post-processing speed and diagnostic accuracy; thus, it greatly affects many aspects of CMR. …”
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HSI Reconstruction: A Spectral Transformer With Tensor Decomposition and Dynamic Convolution by Le Sun, Xihan Ma, Xinyu Wang, Qiao Chen, Zebin Wu

“…Conversely, traditional iterative optimization algorithms are built upon sound mathematical derivations. …”
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AI-driven video summarization for optimizing content retrieval and management through deep learning techniques by Deepali Vora, Payal Kadam, Dadaso D Mohite, Nilesh Kumar, Nimit Kumar, Pratheeik Radhakrishnan, Shalmali Bhagwat

“…Existing methods, often reliant on human intervention or low-complexity algorithms, are observed to struggle with the growing demands of online video quantity and quality. …”
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Benchmark dataset on feeding intensity of the pearl gentian grouper(Epinephelus fuscoguttatus♀×E. lanceolatus♂) by Haijing Qin, Yunchen Tian, Jianing Quan, Xueqi Cong, Qingfei Li, Jinzhu Sui

“…In addition, training on this benchmark dataset proposes an improved feeding intensity evaluation network, which achieves a good balance in prediction accuracy and parameter memory and offers the possibility of subsequent deployment of the model on mobile. …”
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A Lightweight YOLO-Based Architecture for Apple Detection on Embedded Systems by Juan Carlos Olguín-Rojas, Juan Irving Vasquez, Gilberto de Jesús López-Canteñs, Juan Carlos Herrera-Lozada, Canek Mota-Delfin

“…In Mexico, the manual detection of damaged apples has led to inconsistencies in product quality, a problem that can be addressed by integrating vision systems with machine learning algorithms. The YOLO (You Only Look Once) neural network has significantly improved fruit detection through image processing and has automated several related tasks. …”
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Electrical discharge machining: Recent advances and future trends in modeling, optimization, and sustainability by Muhamad Taufik Ulhakim, Sukarman, Khoirudin, Dodi Mulyadi, Hendri Susilo, Rohman, Muji Setiyo

“…Advanced modeling techniques, such as finite element analysis (FEA) and artificial intelligence (AI)-driven simulations, have improved the accuracy of process predictions, enabling real-time adjustments and precise control of machining parameters. …”
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Predicting Survival Rates in Brain Metastases Patients from Non‐Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity by Fuxing Deng, Gang Xiao, Guilong Tanzhu, Xianjing Chu, Jiaoyang Ning, Ruoyu Lu, Liu Chen, Zijian Zhang, Rongrong Zhou

“…These findings highlight the potential of combining radiomic and RNA sequencing data for improved survival predictions and personalized treatment strategies in BM patients from NSCLC.…”
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Advances in Surface-Enhanced Raman Spectroscopy for Urinary Metabolite Analysis: Exploiting Noble Metal Nanohybrids by Ningbin Zhao, Peizheng Shi, Zengxian Wang, Zhuang Sun, Kaiqiang Sun, Chen Ye, Li Fu, Cheng-Te Lin

“…We explore the diverse range of hybrid materials, including carbon-based, metal–organic-framework (MOF), silicon-based, semiconductor, and polymer-based systems, which have significantly improved SERS performance for detecting key urinary biomarkers. …”
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