Application, opportunities, and challenges of digital technologies in the decarbonizing shipping industry: a bibliometric analysis by Guangnian Xiao, Lei Pan, Fengbo Lai

“…Ultimately, it examines research gaps in speed optimization, emission prediction, and autonomous ships by integrating keyword co-occurrence analysis with the content of recent publications, and then proposes prospective research options.DiscussionsFuture studies on ship speed optimization could benefit from adopting multi-objective optimization methods, combining more machine-learning techniques with the FCP model, etc. Concerning emission prediction, future research efforts could focus on integrating more diverse external data sources into emission prediction models, adopting emerging technology applications, such as ship-based carbon capture (SBCC), introducing blockchain into smart emission monitoring systems, etc. …”
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The Influence of Personality Traits and Domain Knowledge on the Quality of Decision-Making in Engineering Design by Muhammad Ahmad, Guoxin Wang

“…The analysis of personality traits was carried out utilizing the complete Big Five model, while the estimate of the structural equation model was executed by employing partial least squares structural equation modeling (PLS-SEM) and a machine learning model for quality estimation. The available empirical research indicates that individuals who have a lower degree of extraversion and agreeableness, and higher levels of conscientiousness and openness, are more likely to make decisions of higher quality. …”
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Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system by M. Nuthal Srinivasan, Mohamed Yacin Sikkandar, Maryam Alhashim, M. Chinnadurai

“…Addressing the shortcomings of traditional Machine Learning and Deep Learning models, our ESACN model utilizes the dynamic routing and spatial hierarchy capabilities of CapsNets to differentiate complex patterns such as those seen in monkeypox, chickenpox, measles, and normal skin presentations. …”
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Data on battery health and performance: Analysing Samsung INR21700-50E cells with advanced feature engineering by Sahar Qaadan, Aiman Alshare, Alexander Popp, Myrel Tiemann, Utz Spaeth, Benedikt Schmuelling

“…This dataset is particularly valuable for advanced machine learning applications, enabling accurate battery state-of-health estimation and predictive maintenance. …”
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An Incremental Learning Ensemble Strategy for Industrial Process Soft Sensors by Huixin Tian, Minwei Shuai, Kun Li, Xiao Peng

“…The ability to handle large amounts of data incrementally and efficiently is indispensable for modern machine learning (ML) algorithms. According to the characteristics of industrial production process, we address an ILES (incremental learning ensemble strategy) that incorporates incremental learning to extract information efficiently from constantly incoming data. …”
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A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies by Devotha G. Nyambo, Edith T. Luhanga, Zaipuna Q. Yonah

“…Characterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. …”
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Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition by Sota Nemoto, Shunsuke Kitada, Hitoshi Iyatomi

“…Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). …”
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A SuperLearner-based pipeline for the development of DNA methylation-derived predictors of phenotypic traits. by Dennis Khodasevich, Nina Holland, Lars van der Laan, Andres Cardenas

“…<h4>Conclusions</h4>We introduce a novel method for the development of DNAm-based predictors that combines the improved reliability conferred by training on principal components with advanced ensemble-based machine learning. Coupling SuperLearner with PCA in the predictor development process may be especially relevant for studies with longitudinal designs utilizing multiple array types, as well as for the development of predictors of more complex phenotypic traits.…”
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Predicting accrual success for better clinical trial resource allocation by Sisi Ma, Yinzhao Wang, John Wagner, Steve Johnson, Serguei Pakhomov, Constantin Aliferis

“…We built predictive models for accrual failure using state-of-the-art supervised machine learning protocols and methods. Models resulted in good predictive performance that was stable over a 10-year time period, with predictive performance of cross-validation AUC = 0.744 (+/-0.018) and prospective validation AUC = 0.737 (+/-0.038). …”
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ElectroCom61: A multiclass dataset for detection of electronic componentsMendeley Data by Md. Faiyaz Abdullah Sayeedi, Anas Mohammad Ishfaqul Muktadir Osmani, Taimur Rahman, Jannatul Ferdous Deepti, Raiyan Rahman, Salekul Islam

“…We ensured that these images reflect real-world conditions, incorporating varied lighting, backgrounds, distances, and camera angles to bolster the potential machine learning model's robustness. We also divided the dataset into training, validation, and test sets to facilitate deep learning model development. …”
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Features to Text: A Comprehensive Survey of Deep Learning on Semantic Segmentation and Image Captioning by Ariyo Oluwasammi, Muhammad Umar Aftab, Zhiguang Qin, Son Tung Ngo, Thang Van Doan, Son Ba Nguyen, Son Hoang Nguyen, Giang Hoang Nguyen

“…Specifically, image captioning has become an attractive focal direction for most machine learning experts, which includes the prerequisite of object identification, location, and semantic understanding. …”
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A 20 m spatial resolution peatland extent map of Alaska by Mark J. Lara, Roger Michaelides, Duncan Anderson, Wenqu Chen, Emma C. Hall, Caroline Ludden, Aiden I. G. Schore, Umakant Mishra, Sarah N. Scott

“…Ground-data were used to train machine learning classifiers to detect peatlands using a fusion of Sentinel-1 (Dual-polarized Synthetic Aperture Radar), Sentinel-2 (Multi-Spectral Imager), and derivatives from the Arctic Digital Elevation Model (ArcticDEM), that were spatially constrained by a peatland suitability model. …”
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A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting by Bin Li, Yuqing He

“…The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. …”
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Decoding Sentiment in Online Communication: A Social Media Analysis of Fashion Companies by Karolina Mania, Monika Jedynak, Aneta Kuźniarska, Karolina Woszczyna

“…Analysis was performed at sentence, document, and aspect levels, employing both lexicon-based methods and machine learning. Findings: The results showed a preponderance of strong positive sentiment among the messages seen on the social media channels of the companies analyzed. …”
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Physics-Informed Denoising Model for Dynamic Data Recovery of Power Systems by Jian Li, Guoqiang Lu, Yongbin Li, Dongning Zhao, Huaiyuan Wang, Yucheng Ouyang

“…In light of the poor interpretability exhibited by traditional machine learning (ML) methods in denoising, a physics-informed denoising model (PIDM) for dynamic data recovery is proposed. …”
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Mapping fruit tree dynamics using phenological metrics from optimal Sentinel-2 data and Deep Neural Network by Yingisani Chabalala, Elhadi Adam, Mahlatse Kganyago

“…However, the heterogeneity and complexity of the study area—composed of smallholder mixed cropping systems with overlapping spectra—constituted an obstacle to the application of optical pixel-based classification using machine learning (ML) classifiers. Given the socio-economic importance of fruit tree crops, the research sought to map the phenological dynamics of these crops using deep neural network (DNN) and optical Sentinel-2 data. …”
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Self-driving lab for the photochemical synthesis of plasmonic nanoparticles with targeted structural and optical properties by Tianyi Wu, Sina Kheiri, Riley J. Hickman, Huachen Tao, Tony C. Wu, Zhi-Bo Yang, Xin Ge, Wei Zhang, Milad Abolhasani, Kun Liu, Alan Aspuru-Guzik, Eugenia Kumacheva

“…Here, we introduce the Autonomous Fluidic Identification and Optimization Nanochemistry (AFION) self-driving lab that integrates a microfluidic reactor, in-flow spectroscopic nanoparticle characterization, and machine learning for the exploration and optimization of the multidimensional chemical space for the photochemical synthesis of plasmonic nanoparticles. …”
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Convolutional Neural Networks for Direction of Arrival Estimation Compared to Classical Estimators and Bounds by Christopher J. Bell, Kaushallya Adhikari, Andrew Brown

“…Recently, there has been a proliferation of applied machine learning (ML) research, including the use of convolutional neural networks (CNNs) for direction of arrival (DoA) estimation. …”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…For classification, the review covers machine learning techniques such as random forests, support vector machines, and convolutional neural networks, among others. …”
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Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks by Tatjana Gligorijević, Zoran Ševarac, Branislav Milovanović, Vlado Đajić, Marija Zdravković, Saša Hinić, Marina Arsić, Milica Aleksić

“…Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. …”
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