Prevention of falls in hospitalized patients—evaluation of the effectiveness of a monitoring system (Verso Vision) developed with artificial intelligence by Corrado Gervasi, Erik Perego, Francesca Galli, Valter Torri, Massimo Castoldi, Emilio Bombardieri

“…The Verso Vision System (VS) is an artificial intelligence-based system for accidental fall prevention and management, which uses computer vision algorithms to monitor environments and people in real time.MethodsThe efficacy of VS monitoring in terms of reduction of accidentals falls was retrospectively evaluated in a group of 362 hospitalized patients at Humanitas Gavazzeni Hospital.ResultsOf the 362 patients included in the analysis, 580 statistical units, 228 monitored with VS and 355 without VS were obtained splitting the observation of each patient based on the presence of VS monitoring and the Stratify score. …”
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An Optimization Method for Operation Adjustment of High-Speed Delayed Trains by Xiaojuan Li, Yueying Huo, Zhenying Yan, Baoming Han

“…Six operation adjustment strategies are analyzed including section acceleration, operation based on the original timetable, dwell time reduction, increase in overtaking, reduction in overtaking, and train postponement. …”
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Estimating shear strength of dredged soils for marine engineering: experimental investigation and machine learning modeling by Zheng Yao, Kaiwei Xu, Zejin Wang, Haodong Sun, Peng Cui, Peng Cui

“…Comparative analysis demonstrated that the LDA–BA–ANN configuration delivered the highest prediction precision and computational speed over traditional models. …”
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Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study by Katerina Lawrie, Petr Waldauf, Peter Balaz, Radoslav Bortel, Ricardo Lacerda, Emma Aitken, Krzysztof Letachowicz, Mario D’Oria, Vittorio Di Maso, Pavel Stasko, Antonio Gomes, Joana Fontainhas, Matej Pekar, Alena Srdelic, VAVASC Study Group, Stephen O’Neill

“…Demographics, risk factors, vessels parameters, types of predicted and created VA (pVA, cVA) were collected. …”
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Cyber threat intelligence for smart grids using knowledge graphs, digital twins, and hybrid machine learning in SCADA networks by Nabeel Al-Qirim, Munir Majdalawieh, Anoud Bani-hani, Hussam Al Hamadi

“…This study presents a novel “Digital-twin and Machine Learning-based SCADA Cyber Threat Intelligence (DT-ML-SCADA-CTI)” approach, which utilizes an innovative algorithm to visualize and predict the effects of cyber-attacks, including FDIA, RTCI, and SRA, on SCADA systems. …”
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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops by Tariq Ali, Saif Ur Rehman, Shamshair Ali, Khalid Mahmood, Silvia Aparicio Obregon, Rubén Calderón Iglesias, Tahir Khurshaid, Imran Ashraf

“…Abstract Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. …”
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Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach by Dong Zhan, Ai-Qing Tian, Shao-Quan Ni

“…Experimental validation demonstrates that, compared to the Snow Goose Algorithm (SGA), the IBM-Dy-SGA achieves a 10%–30% reduction in average fitness values and a 20% improvement in convergence speed. …”
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Heuristic Optimization-Based Trajectory Planning for UAV Swarms in Urban Target Strike Operations by Chen Fei, Zhuo Lu, Weiwei Jiang

“…The MATLAB(R2024b) simulation platform is used to compare the performance of five optimization algorithms—SO, SCA, WOA, MFO, and HHO—against the proposed Electric Eel Foraging Optimization (EEFO) algorithm for UAV swarm target strike missions. …”
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Classification evaluation and improvement of airborne PolSAR images for land use mapping using deep learning by Jiafeng Wang, Yongjiu Feng, Xiaohua Tong, Zhenkun Lei, Mengrong Xi, Yi Zhou, Panli Tang

“…The classification performance of different algorithms was evaluated by the overall accuracy. The results showed that the machine learning algorithm Random Forest (RF) produced the lowest overall accuracy of 71.93%. …”
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Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective... by Vivek Suresh Raj, Ruba Priyadharshini, Saranya Rajiakodi, Bharathi Raja Chakravarthi

“…Most Reinforcement Learning (RL) algorithms optimize a single-objective function, whereas real-world decision-making involves multiple aspects. …”
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Providing a framework to optimize the sustainable construction material supply chain with the possibility of horizontal transfers by Hamed Ghamari, Hamidreza Abbasianjahromi, Seyed Mohammad Mirhosseini

“…The results show that while both the ant-lion and gray wolf algorithms deliver comparable average performance, the gray wolf algorithm excels in terms of solution time, making it more effective for real-time problem-solving. …”
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A Hardware Architecture of a Dynamic Ranking Packet Scheduler for Programmable Network Devices by Mostafa Elbediwy, Bill Pontikakis, Jean-Pierre David, Yvon Savaria

“…The proposed DR-PIFO’s hardware implementation is shown to closely approach the behavior and performance of its algorithmic model by efficiently executing various scheduling algorithms, leading to precise bandwidth distribution among traffic flows. …”
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A comprehensive review of bibliometric and methodological approaches in flood mitigation studies: Current trends and future directions by Funmilayo Ebun Rotimi, Roohollah Kalatehjari, Taofeeq Durojaye Moshood, George Dokyi

“…The findings emphasize the need for an integrated and interdisciplinary approach to flood reduction research, considering the complex interplay of social, ecological, and physical dimensions in flood risk management. …”
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PCES-YOLO: High-Precision PCB Detection via Pre-Convolution Receptive Field Enhancement and Geometry-Perception Feature Fusion by Heqi Yang, Junming Dong, Cancan Wang, Zhida Lian, Hui Chang

“…The performance of PCES-YOLO is also evaluated against mainstream object detection algorithms, including Faster R-CNN, SSD, YOLOv8n, etc. …”
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Machine learning for Internet of Things (IoT) device identification: a comparative study by Hamid Tahaei, Anqi Liu, Hamid Forooghikian, Mehdi Gheisari, Faiz Zaki, Nor Badrul Anuar, Zhaoxi Fang, Longjun Huang

“…Furthermore, it demonstrates the effectiveness of wrapper methods in the reduction of feature sets.…”
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Identification of Postural Deviations and Trunk Asymmetries in Physical Therapy Students Using an AI-Based Mobile Application by Liviu Moraru, Adela Badau, Florin Marin Litoi, Andrei Madalin Petrescu, Catalin Octavian Manescu, Neluta Smidu

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Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis by Jiarui Wang, Shiyan Jiang, Jiaxin Shi, Jing Wang, Shengrong Du, Zufang Huang

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Boosting skin cancer diagnosis accuracy with ensemble approach by Priya Natha, Sivarama Prasad Tera, Ravikumar Chinthaginjala, Safia Obaidur Rab, C. Venkata Narasimhulu, Tae Hoon Kim

“…Overall performance was increased by the combined predictions made with the Max Voting technique. Moreover, feature vectors that were optimally produced from image data by a Genetic Algorithm (GA) were given to the ML models. …”
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Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings by Rajan Bhatt, Akbar Hossain, Debjyoti Majumder, Mandapelli Sharath Chandra, Rajiv Ghimire, Muhammad Faisal Shahzad, Krishan K. Verma, Amarinder Singh Riar, Vishnu D. Rajput, Mauro Wagner Oliveira, Adel Nisi, Riyadh S. Almalki, Viliam Bárek, Marian Brestic, Sagar Maitra

“…This study discusses the latest research on artificial intelligence (AI) in sugarcane farming, with particular attention given to soil biochemistry, disease detection, climate-smart technology for greenhouse gas emissions, yield and water productivity prediction, and cane juice biochemistry. Artificial intelligence (AI) tools, such as machine learning algorithms, can optimize irrigation, increase yields, save water, and properly estimate sugarcane production. …”
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Poverty‐Armed Conflict Nexus: Can Multidimensional Poverty Data Forecast Intrastate Armed Conflicts? by Çağlar Akar, Doğa Başar Sarıipek, Gökçe Cerev

“…Poverty, arising from factors such as gender inequality and limited access to education and public services, profoundly affects social cohesion. …”
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