Deep Learning in Power Systems: A Bibliometric Analysis and Future Trends by Seyed Mahdi Miraftabzadeh, Andrea Di Martino, Michela Longo, Dario Zaninelli

“…Advancements in deep learning enable sophisticated algorithms to learn from vast data sets, driving innovation and efficiency in power systems. …”
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CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection by Chuhe Lin, Zhijun Xie, Xing Jin, Hangjuan Lin, Renguang Shan

“…However, the performance of current algorithms remains unsatisfactory. To address this, we have constructed a diverse CHDP dataset and proposed a lightweight network for CHDP detection, named CHDPL-Net. …”
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Unveiling innovation imperatives in agriculture: A data-driven framework for identifying latent needs and regional priorities by Andrea Bonfiglio

“…An iterative approach is used for this purpose. Mixed matching algorithms are employed to form the comparison groups, while G-computation is used to calculate the differences. …”
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CARE to Compare: A Real-World Benchmark Dataset for Early Fault Detection in Wind Turbine Data by Christian Gück, Cyriana M. A. Roelofs, Stefan Faulstich

“…Early fault detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain-specific public datasets are scarce. …”
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Predicting Drivers’ Eyes-Off-Road Duration in Different Driving Scenarios by Wei Yuan, Zhuofan Liu, Rui Fu

“…Moreover, HMM-based algorithms that fed up with both glance duration and glance location sequences resulted in a highest accuracy of 92.7% in driver’s eyes-off-road durations prediction. …”
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Facilitating real-time LED-based photoacoustic imaging with DenP2P: An optimized conditional generative adversarial deep learning solution by Avijit Paul, Srivalleesha Mallidi

“…Photoacoustic imaging (PAI) benefits from the optical absorption contrast of the tissue while achieving greater depth information with ultrasound resolution than the other optical imaging platforms. …”
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Incorporating Contextual Factors into a Comprehensive Analysis of Operational Efficiency and Service Quality in Healthcare Sector by Utsav Pandey, Sanjeet Singh

“…In the first stage, an output-oriented Data Envelopment Analysis is employed to model the interdependency between operational efficiency and service quality by assessing the allocation of the input resources for achieving these two objectives. While accounting for the external influences and avoiding the ‘best practice trap’ in the healthcare sector, a set of classification algorithms are used to quantify the impact of external factors on efficiency levels. …”
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Analysis of the Influence of Image Resolution in Traffic Lane Detection Using the CARLA Simulation Environment by Aron Csato, Florin Mariasiu, Gergely Csiki

“…Results indicate that a resolution of 512 × 256 yields the best trade-off, offering high mIoU and F1 scores while maintaining real-time processing speeds on a standard CPU. …”
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High-speed threat detection in 5G SDN with particle swarm optimizer integrated GRU-driven generative adversarial network by R. Shameli, Sujatha Rajkumar

“…The attack detection in 5G SDN involves Machine learning (ML) and Deep learning (DL) algorithms to analyze large volumes of network data and identify patterns indicative of attacks. …”
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Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review by Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen, Shuangquan Liu

“…Our analysis reveals that while detection algorithms for ramp events have matured and the overall predictive performance of power forecasting models has improved, existing approaches often struggle to capture localized ramp phenomena, resulting in persistent deviations. …”
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Judges versus artificial intelligence in juror decision-making in criminal trials: Evidence from two pre-registered experiments. by Eiichiro Watamura, Yichen Liu, Tomohiro Ioku

“…These findings suggest that jurors do not inherently avoid algorithmic judgments and may consider AI opinions on par with those of human judges in certain contexts. …”
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Identifying the optimal number of topics in text mining: a case study on reindeer pastoralism literature by Barbara Contiero, Øystein Holand, Giulio Cozzi

“…Text mining and topic analysis algorithms which group textual contents in the most efficient way, are becoming increasingly useful to summarise the main information contained in large data corpus of complex scientific fields. …”
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Development of a Smart Waste Management System for Route Optimization and Adaptive Demand Management in Dubai by H. Hassan, T. Ali, A. Tamimi

“…This paper presents an overview of the development of a Smart Waste Management System (SWMS) that integrates Geographic Information System (GIS) technology with waste route optimization algorithms and adaptive demand management strategies. …”
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Multi-View Collaborative Training and Self-Supervised Learning for Group Recommendation by Feng Wei, Shuyu Chen

“…One of the key challenges facing recommendation algorithms is data sparsity, a limitation that is even more severe in group recommendation than in traditional recommendation tasks. …”
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Comparing Xenium 5K and Visium HD data from identical tissue slide at a pathological perspective by Mengping Long, Taobo Hu, Weixin Wang, Junshun Gao, Nan Wang, Mats Nilsson

“…Abstract Recent advancements in spatial transcriptomics have been largely triggered by two high-resolution technologies: Visium-HD and Xenium in-situ. While sequencing-based Visium HD features a refined bin size of 2 µm and transcriptome wide coverage, Xenium in-situ is a targeted imaging-based detection technology with sub-micron resolution. …”
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COSMIC-Based Early Software Size Estimation Using Deep Learning and Domain-Specific BERT by Yohannes Sefane Molla, Esubalew Alemneh, Samuel Temesgen Yimer

“…In this study, we proposed an automated COSMIC-based early software size estimation approach using a domain-specific BERT called RE-BERT and deep learning algorithms. Two major experiments were employed in the research: the first was further pretraining of the generic BERT model over requirement engineering domain texts to produce a domain-specific pre-trained model called RE-BERT, while the second experiment was developing a deep learning regressor model for COSMIC size estimations using RE-BERT. …”
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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 GEO-based feature selection method effectively identified the most relevant predictors, while the Deep-NARMAX model captured temporal dynamics for accurate forecasting. …”
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Presenting a prediction model for HELLP syndrome through data mining by Boshra Farajollahi, Mohammadjavad Sayadi, Mostafa Langarizadeh, Ladan Ajori

“…Results A total of 21 variables were included in this study after the first stage. Among all the ML algorithms, multi-layer perceptron and deep learning performed the best, with an F1 score of more than 99%.In all three evaluation scenarios of 5fold and 10fold cross-validation, the K-nearest neighbors (KNN), random forest (RF), AdaBoost, XGBoost, and logistic regression (LR) had an F1 score of over 0.95, while this value was around 0.90 for support vector machine (SVM), and the lowest values were below 0.90 for decision tree (DT). …”
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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.…”
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Skin region images extracted from 3D total body photographs for lesion detection by Anup Saha, Joseph Adeola, Nuria Ferrera, Adam Mothershaw, Gisele Rezze, Séraphin Gaborit, Brian D’Alessandro, Robert Voskanyan, Gyula Szabó, Balázs Pataki, Hayat Rajani, Sana Nazari, Hassan Hayat, Laura Serra-García, Clare Primiero, Serena Bonin, Iris Zalaudek, H. Peter Soyer, Josep Malvehy, Rafael Garcia

“…In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the centre of the image. While these lesion-centric datasets have been fundamental for developing diagnostic algorithms, they lack the context of the surrounding skin, which is critical for improving lesion detection. …”
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