Artificial intelligence and machine learning in modern cardiology: Advancements in diagnosis, treatment and patient monitoring by Szymon Kopciał, Dawid Piecuch, Edyta Hańczyk, Karolina Kornatowska, Natalia Pawelec, Weronika Mazur

“…Additionally, AI-driven algorithms support personalized treatment strategies, real-time remote monitoring, and precision-guided coronary interventions, reducing procedural complications. …”
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Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s by Xiuhong Wang, Qingjie Wang, Yichen Qiao, Xinyue Zhang, Caiyun Lu, Chao Wang

“…Secondly, we proposed an improved YOLO v5s network, incorporating a Convolutional Block Attention Module (CBAM), FasterNet feature extraction network, and a loss function to optimize the network structure and training strategy. …”
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Application of artificial intelligence in triage in emergencies and disasters: a systematic review by Azadeh Tahernejad, Ali Sahebi, Ali Salehi Sahl Abadi, Mehdi Safari

“…AI algorithms like OpenPose and YOLO enhanced efficiency in mass casualty incidents, while e-triage systems allowed for continuous vital sign monitoring and faster triaging. …”
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Application of machine learning and temporal response function modeling of EEG data for differential diagnosis in primary progressive aphasia by Heather Dial, Lokesha S. Pugalenthi, G. Nike Gnanateja, Junyi Jessy Li, Maya L. Henry

“…Early diagnosis is essential for optimal provision of care but differential diagnosis by PPA subtype can be difficult and time consuming. …”
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Forecasting motion trajectories of elbow and knee joints during infant crawling based on long–short-term memory (LSTM) networks by Jieyi Mo, Qiliang Xiong, Ying Chen, Yuan Liu, Xiaoying Wu, Nong Xiao, Wensheng Hou

“…It experimentally explores how different input and output time-frames affect prediction accuracy and sets the stage for future research focused on optimizing models and developing effective control strategies to improve assistive crawling devices.…”
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Toward Greener Matrix Operations by Lossless Compressed Formats by Francesco Tosoni, Philip Bille, Valerio Brunacci, Alessio de de Angelis, Paolo Ferragina, Giovanni Manzini

“…Moreover, our experiments reveal that while data parallelism can improve execution speed and energy efficiency, optimizing both simultaneously poses interesting challenges. …”
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Harnessing AI forward and backward chaining with telemetry data for enhanced diagnostics and prognostics of smart devices by Muhammad Shoaib Farooq, Rizwan Pervez Mir, Atif Alvi, Kilian Tutusaus, Eduardo Garcia Villena, Fadwa Alrowais, Hanen Karamti, Imran Ashraf

“…Accurate early diagnosis and prediction of potential failures have the capability to greatly improve maintenance efficiency, minimize downtime, and optimize cost.…”
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Artificial intelligence in hospital infection prevention: an integrative review by Rabie Adel El Arab, Zainab Almoosa, May Alkhunaizi, May Alkhunaizi, Fuad H. Abuadas, Joel Somerville, Joel Somerville

“…Advanced algorithms, including neural networks, decision trees, and random forests, significantly improved detection rates when integrated with EHRs, enabling real-time surveillance and timely interventions. …”
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Incidence, Pathogenesis, Risk Factors, and Treatment of Cystoid Macula Oedema Following Cataract Surgery: A Systematic Review by Lorenzo Ferro Desideri, Kirupakaran Arun, Enrico Bernardi, Nicola Sagurski, Rodrigo Anguita

“…Further research is needed to establish optimal treatment algorithms and improve outcomes for patients with post-operative CMO…”
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Thematic Analysis of Clinician Documentation Surrounding Treatment Discussions and Decision‐Making in Patients With Desmoid Tumors by Victoria Wytiaz, Tianyi Wang, Scott Schuetze, Nina J. Francis‐Levin, Rashmi Chugh

“…The emergent themes provide novel guidance and support for optimizing treatment discussions and decisions. More robust community peer support groups specific to patients with desmoid tumors and clinician “support groups” may provide a way for both patients and clinicians practicing in a disease state with uncertainty to share experiences and improve resilience. …”
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Radiology AI and sustainability paradox: environmental, economic, and social dimensions by Burak Kocak, Andrea Ponsiglione, Valeria Romeo, Lorenzo Ugga, Merel Huisman, Renato Cuocolo

“…Socially, AI risks perpetuating healthcare disparities through biases in algorithms and unequal access to technology. On the other hand, AI has the potential to improve sustainability in healthcare by reducing low-value imaging, optimizing resource allocation, and improving energy efficiency in radiology departments. …”
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Application of LiDAR and SLAM Technologies in Autonomous Systems for Precision Grapevine Pruning and Harvesting by Obaid Saif, Mohammed G., Nagaraju Krishna Chythanya

“…The project aims to integrate advanced technologies in grapevine pruning and harvesting to optimize these processes. The system improves accuracy and speed, reducing labor costs while enhancing grape yield and quality, representing a promising approach to vineyard management. …”
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Artificial Intelligence and Smart Technologies in Safety Management: A Comprehensive Analysis Across Multiple Industries by Jiyoung Park, Dongheon Kang

“…AI-driven solutions, such as predictive analytics, machine learning algorithms, IoT sensor integration, and digital twin models, are shown to proactively identify and mitigate potential hazards, optimize energy consumption, and enhance operational efficiency. …”
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Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis by Simone Mari, Giovanni Bucci, Fabrizio Ciancetta, Edoardo Fiorucci, Andrea Fioravanti

“…These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual contaminants critical for quality assurance. …”
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A novel hybrid deep learning approach for super-resolution and objects detection in remote sensing by Muhammad Asif, Mohammad Abrar, Faizan Ullah, Abdu Salam, Farhan Amin, Isabel de la Torre, Mónica Gracia Villar, Helena Garay, Gyu Sang Choi

“…This study proposes a robust object detection framework integrating super-resolution techniques with advanced feature extraction algorithms for remote sensing images. The hybrid model combines Advanced StyleGAN and Swin Transformer. …”
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Hierarchical Sensing Framework for Polymer Degradation Monitoring: A Physics-Constrained Reinforcement Learning Framework for Programmable Material Discovery by Xiaoyu Hu, Xiuyuan Zhao, Wenhe Liu

“…Our method combines three key innovations: (1) a dual-channel sensing architecture that fuses spectroscopic signatures from Graph Isomorphism Networks with temporal degradation patterns captured by transformer-based models, enabling comprehensive molecular state detection across multiple scales; (2) a physics-constrained policy network that ensures sensor measurements adhere to thermodynamic principles while optimizing the exploration of degradation pathways; and (3) a hierarchical signal processing system that balances multiple sensing modalities through adaptive weighting schemes learned from experimental feedback. …”
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IoT Based Health Monitoring with Diet, Exercise and Calories recommendation Using Machine Learning by Muhammad Hassaan Naveed, Omar Bin Samin, Muhammad Bilal, Mustehsum Waseem

“…Additionally, machine learning algorithms recommend tailored exercise, diet type, Basal Metabolic Rate (BMR), and daily caloric intake based on individual member data. …”
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Robust zero-watermarking for color images using hybrid deep learning models and encryption by Hager A. Gharib, Noha M. M. Abdelnapi, Khalid M. Hosny

“…Traditional zero-watermarking techniques often struggle to maintain resilience against geometric and signal processing attacks while ensuring high security and imperceptibility. …”
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Lightweight Brain Tumor Segmentation Through Wavelet-Guided Iterative Axial Factorization Attention by Yueyang Zhong, Shuyi Wang, Yuqing Miao, Tao Zhang, Haoliang Li

“…Conclusions: This study underscores the advantages of integrating frequency-domain analysis with optimized attention mechanisms, paving the way for scalable, high-performance medical image segmentation algorithms with broader clinical diagnostic applications.…”
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Clinical application and immune infiltration landscape of stemness‐related genes in heart failure by Wenting Yan, Yanling Li, Gang Wang, Yuan Huang, Ping Xie

“…Conclusions Our study identified a clinically significant stemness‐related signature for predicting HF risk, with the potential to improve early disease diagnosis, optimize risk stratification and provide new strategies for treating patients with HF.…”
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