Search

The objective of this study was to identify the manufacturer of Hanji, Korean handmade paper widely used in conservation science. To achieve this, machine learning models utilizing attenuated total reflectance–infrared spectroscopy (ATR–IR) were developed to assess the robustness and effectiveness of the computed models. Principal component analysis (PCA), partial least squares–discriminant analysis (PLS–DA), decision tree (DT), and k-NN models were constructed using IR spectral data, with the spectral region between 1800 and 1500 cm⁻1 identified as the critical input variable through Variable Importance in Projection (VIP) scores. The transformation of the obtained spectra into second derivative spectra proved beneficial in this key spectral region, leading to significant improvements in model performance. Additionally, the application of DBSCAN for outlier detection was effective in refining the dataset, further enhancing the performance of the models. Specifically, the k-NN model, when applied to the selected variables and preprocessed with the second derivative transformation, achieved an F1 score of 0.92. These findings underscore the importance of focusing on the 1800–1500 cm⁻1 spectral range and applying outlier detection techniques, such as DBSCAN, to enhance the robustness and accuracy of the Hanji classification models by eliminating the influence of atypical data points. [ABSTRACT FROM AUTHOR]

The rapid discrimination of bacteria is currently an emerging trend in the fields of food safety, medical detection, and environmental observation. Traditional methods often require lengthy culturing processes, specialized analytical equipment, and bacterial recognition receptors. In response to this need, we have developed a paper-based fluorescence sensor array platform for identifying different bacteria. The sensor array is based on three unique carbon quantum dots (CQDs) as sensing units, each modified with a different antibiotic (polymyxin B, ampicillin, and gentamicin). These antibiotic-modified CQDs can aggregate on the bacterial surface, triggering aggregation-induced fluorescence quenching. The sensor array exhibits varying fluorescent responses to different bacterial species. To achieve low-cost and portable detection, CQDs were formulated into fluorescent ink and used with an inkjet printer to manufacture paper-based sensor arrays. A smartphone was used to collect the responses generated by the bacteria and platform. Diverse machine learning algorithms were utilized to discriminate bacterial types. Our findings showcase the platform's remarkable capability to differentiate among five bacterial strains, within a detection range spanning from 1.0 × 103 CFU/mL to 1.0 × 107 CFU/mL. Its practicality is further validated through the accurate identification of blind bacterial samples. With its cost-effectiveness, ease of fabrication, and high degree of integration, this platform holds significant promise for on-site detection of diverse bacteria. [ABSTRACT FROM AUTHOR]

The article discusses the potential of advanced technologies in the field of cultural heritage. It highlights how these technologies, such as virtual reality, artificial intelligence, and 3D printing, can be used to understand, preserve, and enhance cultural heritage. The article also presents scientific papers from the Florence Heri-Tech International Conference, showcasing the various applications of these technologies. The papers cover topics such as the use of hyperspectral imaging for hieroglyph recognition, the enhancement of user experience in cultural spaces through advanced systems, and the use of non-invasive techniques for conservation. Overall, the article emphasizes the significant impact of technology on the research, preservation, and promotion of cultural heritage. [Extracted from the article]

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings. [ABSTRACT FROM AUTHOR]

Real-world information is often characterized by uncertainty and partial reliability, which led Zadeh to introduce the concept of Z-numbers as a more appropriate formal structure for describing such information. However, the computation of Z-numbers requires solving highly complex optimization problems, limiting their practical application. Although linguistic Z-numbers have been explored for their computational straightforwardness, they lack theoretical support from Z-number theory and exhibit certain limitations. To address these issues and provide theoretical support from Z-numbers, we propose a Z-number linguistic term set to facilitate more efficient processing of Z-number-based information. Specifically, we redefine linguistic Z-numbers as Z-number linguistic terms. By analyzing the hidden probability density functions of these terms, we identify patterns for ranking them. These patterns are used to define the Z-number linguistic term set, which includes all Z-number linguistic terms sorted in order. We also discuss the basic operators between these terms. Furthermore, we develop a multi-criteria group decision-making (MCGDM) model based on the Z-number linguistic term set. Applying our method to predict the acceptance of academic papers, we demonstrate its effectiveness and superiority. We compare the performance of our MCGDM method with five existing Z-number-based MCGDM methods and eight traditional machine learning clustering algorithms. Our results show that the proposed method outperforms others in terms of accuracy and time consumption, highlighting the potential of Z-number linguistic terms for enhancing Z-number computation and extending the application of Z-number-based information to real-world problems. [ABSTRACT FROM AUTHOR]

This study conducts an analysis of artificial intelligence (AI) and machine learning (ML) applications in natural disaster prediction using a scientometric approach. The Web of Science Core Collection served as the primary data source, yielding 38,456 records spanning from 2003 to 2022. The analysis concentrated on highly influential research, defined by papers garnering 100 or more citations, resulting in a final set of 1,637 publications. VOSviewer software facilitated the exploration of collaboration patterns among authors, institutions, and countries, along with the identification of emerging research topics and the most impactful articles. These highly cited papers were distributed across various sources (625). A total of 443,502 citations were counted, with an average of 270.92 citations per document. Interestingly, the average annual citation growth rate exhibited a negative trend (-1.02%), suggesting a potential shift in citation patterns over time. The average document age of 6.9 years indicates that the majority of the research is relatively recent. Collaboration emerges as a prominent feature within the field, with an average of 5.09 co-authors per document and 46.55% of collaborations being international. This underscores the collaborative nature inherent in research within this domain. Scholarly articles (1263) represent the predominant document type, followed by reviews (323), indicative of the field's solid foundation in peer-reviewed literature. The study's findings hold significant implications for future research and practical applications, identifying gaps in the literature and underscoring the necessity for further exploration in developing AI and ML models tailored to specific types of natural disasters, as well as assessing these models in real-world scenarios. International collaboration and interdisciplinary approaches are highlighted as pivotal components in advancing this critical field. While providing valuable insights, this approach acknowledges limitations associated with its focus on highly cited papers and a single database. Future research could address these limitations by incorporating additional databases, employing broader search criteria, and utilising alternative methodologies to attain a more comprehensive understanding of the evolving research landscape. [ABSTRACT FROM AUTHOR]

Over the years the number of research publications per year is growing exponentially. Finding research papers of quality from the massive literature of relevant articles is a challenging and time-consuming task. The approaches in the latest literature address citation recommendation by utilizing large bibliographic information and use machine learning and deep learning methods for the task. These techniques clearly require a large amount of training data as well as machines with high processing power. To overcome these issues, we propose a novel method by modifying the popular hyperlink induced topic search (HITS), a web page ranking algorithm, as citation recommendation using hyperlink induced topic search (CR-HITS) that works on a directed and weighted heterogeneous bibliographic network containing diverse types of nodes and edges. We define effective scoring schemes for nodes and edges based on basic bibliographic information like citations of papers, number of publications of an author, etc. Given a few seed papers, the citation recommendation algorithm CR-HITS is run on small neighborhoods of the seed papers and hence the time taken by the execution is very small to yield the final recommendations. To the best of our knowledge, HITS has been used for the first time for the citation recommendation problem. We perform extensive experimentation on DBLP (version-11) and ACM (version-9) datasets and compare the results with many baseline methods in terms of MAP, MRR, and recall@N measures. The performance of the proposed algorithms is superior with respect to the MAP metric and matches the second best for the other two metrics. Since the top two algorithms use deep learning methods and use much larger bibliographic information including abstracts of the papers, we claim that our approach utilizes very low resources, yet yields recommendations that are very close to the top recommendations. [ABSTRACT FROM AUTHOR]

Highly cited papers are influenced by external factors that are not directly related to the document's intrinsic quality. In this study, 50 characteristics for measuring the performance of 68 highly cited papers, from the Journal of The American Medical Informatics Association indexed in Web of Science (WOS), from 2009 to 2019 were investigated. In the first step, a Pearson correlation analysis is performed to eliminate variables with zero or weak correlation with the target ("dependent") variable (number of citations in WOS). Consequently, 32 variables are selected for the next step. By applying the Ridge technique, 13 features show a positive effect on the number of citations. Using three different algorithms, i.e., Ridge, Lasso, and Boruta, 6 factors appear to be the most relevant ones. The "Number of citations by international researchers", "Journal self-citations in citing documents", and "Authors' self-citations in citing documents", are recognized as the most important features by all three methods here used. The "First author's scientific age", "Open-access paper", and "Number of first author's citations in WOS" are identified as the important features of highly cited papers by only two methods, Ridge and Lasso. Notice that we use specific machine learning algorithms as feature selection methods (Ridge, Lasso, and Boruta) to identify the most important features of highly cited papers, tools that had not previously been used for this purpose. In conclusion, we re-emphasize the performance resulting from such algorithms. Moreover, we do not advise authors to seek to increase the citations of their articles by manipulating the identified performance features. Indeed, ethical rules regarding these characteristics must be strictly obeyed. Highlights: Comparing 3 feature selection methods: Ridge, Lasso, Boruta Analyzed data set relies on a representative sample from a health informatics journal Highly cited papers are studied "External" features which affect a high citation level are addressed [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI. [ABSTRACT FROM AUTHOR]

This document is a special issue of the Journal of Intelligent Information Systems, focusing on the selected papers from the International Symposium on Methodologies for Intelligent Systems (ISMIS 2022). The symposium, held in Cosenza, Italy, showcased research on various topics related to artificial intelligence, including decision support, knowledge representation, machine learning, computer vision, and more. The special issue includes eleven papers that have undergone rigorous peer-reviewing and cover a wide range of research topics, such as deep learning, anomaly detection, malware detection, sentiment classification, and healthcare professionals' burnout. The authors express their gratitude to the contributors and reviewers for their valuable contributions. [Extracted from the article]

Artificial intelligence (AI) has acquired notorious relevance in modern computing as it effectively solves complex tasks traditionally done by humans. AI provides methods to represent and infer knowledge, efficiently manipulate texts and learn from vast amount of data. These characteristics are applicable in many activities that human find laborious or repetitive, as is the case of the analysis of scientific literature. Manually preparing and writing a systematic literature review (SLR) takes considerable time and effort, since it requires planning a strategy, conducting the literature search and analysis, and reporting the findings. Depending on the area under study, the number of papers retrieved can be of hundreds or thousands, meaning that filtering those relevant ones and extracting the key information becomes a costly and error-prone process. However, some of the involved tasks are repetitive and, therefore, subject to automation by means of AI. In this paper, we present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies. This survey also provides a historical perspective of the evolution of the field and the role that humans can play in an increasingly automated SLR process. [ABSTRACT FROM AUTHOR]

The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of "emerging topics" share the similar characters with the "cover papers", this study present a novel approach to translate the "emerging topics" detection to "cover paper" prediction. By using "AdaBoost model" and topic model, we construct a machine learning framework to imitate the top journal (chief) editor's judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals. [ABSTRACT FROM AUTHOR]

Citation information in scholarly data is an important source of insight into the reception of publications and the scholarly discourse. Outcomes of citation analyses and the applicability of citation-based machine learning approaches heavily depend on the completeness of such data. One particular shortcoming of scholarly data nowadays is that non-English publications are often not included in data sets, or that language metadata is not available. Because of this, citations between publications of differing languages (cross-lingual citations) have only been studied to a very limited degree. In this paper, we present an analysis of cross-lingual citations based on over one million English papers, spanning three scientific disciplines and a time span of three decades. Our investigation covers differences between cited languages and disciplines, trends over time, and the usage characteristics as well as impact of cross-lingual citations. Among our findings are an increasing rate of citations to publications written in Chinese, citations being primarily to local non-English languages, and consistency in citation intent between cross- and monolingual citations. To facilitate further research, we make our collected data and source code publicly available. [ABSTRACT FROM AUTHOR]

Traditionally, specific bioreceptors such as antibodies have rapidly identified bacterial species in environmental water samples. However, this method has the disadvantages of requiring an additional process to conjugate or immobilize bioreceptors on the assay platform, which becomes unstable at room temperature. Here, we demonstrate a novel mix-and-match method to identify bacteria species by loading the bacterial samples with simple bacteria interacting components (not bioreceptors), such as lipopolysaccharides, peptidoglycan, and bovine serum albumin, and carboxylated particles, all separately on multiple channels. Neither covalent conjugation nor surface immobilization was necessary. Interactions between bacteria and the above bacteria interacting components resulted in varied surface tension and viscosity, leading to various flow velocities of capillary action through the paper fibers. The smartphone camera and a custom Python code recorded multiple channel flow velocity, each loaded with different bacteria interacting components. A multi-dimensional data set was obtained for a given bacterial species and concentration and used as a machine learning training model. A support vector machine was applied to classify the six bacterial species: Escherichia coli, Salmonella Typhimurium, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, and Bacillus subtilis. Under optimized conditions, the training model predicts the bacterial species with an accuracy of > 85% of the six bacteria species. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (μPADs) have been widely used in point-of-care testing owing to their simple operation, low volume of the sample required, and the lack of the need for an external force. To obtain accurate semi-quantitative or quantitative results, μPADs need to respond to the challenges posed by differences in reaction conditions. In this paper, multi-layer μPADs are fabricated by the imprinting method for the colorimetric detection of C-reactive protein (CRP). Different lighting conditions and shooting angles of scenes are simulated in image acquisition, and the detection-related performance of μPADs is improved by using a machine learning algorithm. The You Only Look Once (YOLO) model is used to identify the areas of reaction in μPADs. This model can observe an image only once to predict the objects present in it and their locations. The YOLO model trained in this study was able to identify all the reaction areas quickly without incurring any error. These reaction areas were categorized by classification algorithms to determine the risk level of CRP concentration. Multi-layer perceptron, convolutional neural network, and residual network algorithms were used for the classification tasks, where the latter yielded the highest accuracy of 96%. It has a promising application prospect in fast recognition and analysis of μPADs. [ABSTRACT FROM AUTHOR]

Investigating the relevant literature is very important for research activities. However, it is difficult to select the most appropriate and important academic papers from the enormous number of papers published annually. Researchers search paper databases by combining keywords, and then select papers to read using some evaluation measure—often, citation count. However, the citation count of recently published papers tends to be very small because citation count measures accumulated importance. This paper focuses on the possibility of classifying high-quality papers superficially using attributes such as publication year, publisher, and words in the abstract. To examine this idea, we construct classifiers by applying machine-learning algorithms and evaluate these classifiers using cross-validation. The results show that our approach effectively finds high-quality papers. [ABSTRACT FROM AUTHOR]

This review focuses on the usage of machine learning methods in sports. It closely follows the PRISMA framework for writing systematic reviews. We introduce the broader field of using sensor data for feedback in sport and cite similar reviews, that focus on other aspects of the field. With its focus on machine learning models that use signals from simple sensors, this review covers a very focused area that has not yet been covered by any other review. As described in problem definition, we use well-defined inclusion criteria, we have reviewed 72 papers. They present existing solutions, that use machine learning to extract useful information from data collected using various sensors in sports. To be included, papers had to use machine learning methods using data collected from sensors during sports, had to focus on sports-related applications and the result of machine learning had to be some information that can be used in real-time. We have found that the field is rapidly developing as 46 of the 72 included papers were from the last four years. Furthermore, we have found that the field is moving from using classical machine learning techniques to using deep learning. We analyze which data is used as input for machine learning, and we find that the most commonly used sensor is the accelerometer, closely followed by the gyroscope. The most common sensor platform is using a single wearable sensor, however, the studies that used deep learning, use multiple wearable sensors most often. Dataset sizes of sports papers are relatively small compared to other fields, but datasets are on average slightly larger in studies that use deep learning than in those that do not. We analyze the most common preprocessing methods and find that low-pass filtering and feature extraction are commonly used. We compare different machine learning models and the results of the studies that have tested multiple models on the same data, where we find that deep learning proved to be better than classical machine learning. Most studies show classification accuracy of over 90%, showing that machine learning is a useful tool for the researched problems. We end the review by researching how far the machine learning methods were implemented. Twenty of the included papers used their machine learning models in applications beyond a research paper and provided some sort of feedback back to athletes or coaches. After completing the review of the field, we propose a solution – a plan for future research. The proposed solution is to use a combination of best practices from the included paper and methods that we found are not yet implemented in the field of sports. We further elaborate, where we see the current state of the field. We conclude the article with short summary of the findings. [ABSTRACT FROM AUTHOR]

To achieve clinical impact in daily oncological practice, emerging AI-based cancer imaging research needs to have clearly defined medical focus, AI methods, and outcomes to be estimated. AI-supported cancer imaging should predict major relevant clinical endpoints, aiming to extract associations and draw inferences in a fair, robust, and trustworthy way. AI-assisted solutions as medical devices, developed using multicenter heterogeneous datasets, should be targeted to have an impact on the clinical care pathway. When designing an AI-based research study in oncologic imaging, ensuring clinical impact in AI solutions requires careful consideration of key aspects, including target population selection, sample size definition, standards, and common data elements utilization, balanced dataset splitting, appropriate validation methodology, adequate ground truth, and careful selection of clinical endpoints. Endpoints may be pathology hallmarks, disease behavior, treatment response, or patient prognosis. Ensuring ethical, safety, and privacy considerations are also mandatory before clinical validation is performed. The Artificial Intelligence for Health Imaging (AI4HI) Clinical Working Group has discussed and present in this paper some indicative Machine Learning (ML) enabled decision-support solutions currently under research in the AI4HI projects, as well as the main considerations and requirements that AI solutions should have from a clinical perspective, which can be adopted into clinical practice. If effectively designed, implemented, and validated, cancer imaging AI-supported tools will have the potential to revolutionize the field of precision medicine in oncology. [ABSTRACT FROM AUTHOR]

Science as a whole is organized into broad fields, and as a consequence, research, resources, students, etc., are also classified, assigned, or invited following a similar structure. Some fields have been established for centuries, and some others are just flourishing. Funding, staff, etc., to support fields are offered if there is some activity on it, commonly measured in terms of the number of published scientific papers. How to find them? There exist well-respected listings where scientific journals are ascribed to one or more knowledge fields. Such lists are human-made, but the complexity begins when a field covers more than one area of knowledge. How to discern if a particular paper is devoted to a field not considered in such lists? In this work, we propose a methodology able to classify the universe of papers into two classes; those belonging to the field of interest, and those that do not. This proposed procedure learns from the title and abstract of papers published in monothematic or "pure" journals. Provided that such journals exist, the procedure could be applied to any field of knowledge. We tested the process with Geographic Information Science. The field has contacts with Computer Science, Mathematics, Cartography, and others, a fact which makes the task very difficult. We also tested our procedure and analyzed its results with three different criteria, illustrating its power and capabilities. Interesting findings were found, where our proposed solution reached similar results as human taggers also similar results compared with state-of-the-art related work. [ABSTRACT FROM AUTHOR]

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques. [ABSTRACT FROM AUTHOR]

In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting. Key Points • Machine learning is new and rather complex for the radiology community. • Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting. • Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) offers the advantage of producing complex parts more efficiently and in a lesser production cycle time as compared to conventional subtractive manufacturing processes. It also provides higher flexibility for diverse applications by facilitating the use of a variety of materials and different processing technologies. With the exceptional growth of computing capability, researchers are extensively using machine learning (ML) techniques to control the performance of every phase of AM processes, such as design, process parameters modeling, process monitoring and control, quality inspection, and validation. Also, ML methods have made it possible to develop cybermanufacturing for AM systems and thus revolutionized Industry 4.0. This paper presents the state-of-the-art applications of ML in solving numerous problems related to AM processes. We give an overview of the research trends in this domain through a systematic literature review of relevant journal articles and conference papers. We summarize recent development and existing challenges to point out the direction of future research scope. This paper can provide AM researchers and practitioners with the latest information consequential for further development. [ABSTRACT FROM AUTHOR]

Low energy surface coatings have found wide range of applications for generating hydrophobic and superhydrophobic surfaces. Most of the studies have been related to use of a single coating material over a single substrate or using a single technique. The degree of hydrophobicity is highly dependent on fabrication processes as well as materials being coated and as such warrants a high-level study using experimental optimization leading to the evaluation of the parametric behavior of coatings and their application techniques. Also, a single platform or system which can predict the required set of parameters for generating hydrophobic surface of required nature for given substrate is of requirement. This work applies the powerful machine learning algorithms (Levenberg Marquardt using Gauss Newton and Gradient methods) to evaluate the various processes affecting the anti-wetting behavior of coated printable paper substrates with the capability to predict the most optimized method of coating and materials that may lead to a desirable surface contact angle. The major application techniques used for this study pertain to dip coating, spray coating, spin coating and inkjet printing and silane and sol–gel base coating materials. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has made tremendous advances in recent years and will presumably have a major impact in health care. These advancements are expected to affect different aspects of clinical medicine and lead to improvement of delivered care but also optimization of available resources. As a modern specialty that extensively relies on imaging, interventional radiology (IR) is primed to be on the forefront of this development. This is especially relevant since IR is a highly advanced specialty that heavily relies on technology and thus is naturally susceptible to disruption by new technological developments. Disruption always means opportunity and interventionalists must therefore understand AI and be a central part of decision-making when such systems are developed, trained, and implemented. Furthermore, interventional radiologist must not only embrace but lead the change that AI technology will allow. The CIRSE position paper discusses the status quo as well as current developments and challenges. [ABSTRACT FROM AUTHOR]

With the continuous deepening of academic research in various disciplines and the continuous increase in the number of scientific researchers, exploring the mechanism of matching scientific research results and academic journal subjects is a key topic that can assist researchers in selecting suitable journals for submission. The classification and recommendation of academic journals based on a traditional text representation model cannot take advantage of the semantic relationship between words and cannot take into account the diversity of topics received by different journals, which affects the classification and recommendation effect. To solve these problems, this paper uses doc2vec to perform distributed representation of the bibliographic text so that the semantics between the text features are fully preserved. Then, the XGBoost algorithm is used to consider the impact of the different characteristics of the title, abstract, and keywords of the bibliography on the published journal. The academic journal submission recommendation model proposed in this paper can solve the problem that traditional methods cannot make full use of the contextual semantic information and improve the efficiency of scientific research personnel's academic achievement publications. Experiments on Common SCI English journals in the computer field show that when recommending three candidate journals, the accuracy rate reached 84.24%. [ABSTRACT FROM AUTHOR]

Researchers, science managers and evaluation professionals face a problem when determining the alignment between research results and publication targets. How does a manuscript's content fit a given journal's stated purpose? We develop a framework for understanding how past published papers reveal the actual interests and editorial profile of journal. We articulate an answer to the question by using a total of 16,803 abstracts from articles published from 2010 to 2019 in 20 top innovation-oriented journals. Through a machine learning approach, we trained a text classification algorithm on these materials. The supervised model matched the published contents (abstracts) with journal blurbs with an accuracy rate of 80%. We discover that the content of 25% of the outlet sample might have been of greater interest elsewhere (i.e. to other journals), according to the official editorial positioning available in their homepages. Our conclusions suggest that more can be learned from exploring the abstract-blurb nexus. [ABSTRACT FROM AUTHOR]

In the paper we present an approach to the automatic detection and identification of important elements in paper documents. This includes stamps, logos, printed text blocks, signatures and tables. Presented approach consists of two stages. The first one includes object detection by means of AdaBoost cascade of weak classifiers and Haar-like features. Resulting image blocks are, at the second stage, subjected to verification based on selected features calculated from recently proposed low-level descriptors combined with certain classifiers representing current machine-learning approaches. The training phase, for both stages, uses bootstrapping, i.e., integrative process, aiming at increasing the accuracy. Experiments performed on large set of digitized paper documents showed that adopted strategy is useful and efficient. [ABSTRACT FROM AUTHOR]

We introduce a comprehensive benchmark for local features and robust estimation algorithms, focusing on the downstream task—the accuracy of the reconstructed camera pose—as our primary metric. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods and heuristics. This is demonstrated by embedding dozens of popular algorithms and evaluating them, from seminal works to the cutting edge of machine learning research. We show that with proper settings, classical solutions may still outperform the perceived state of the art. Besides establishing the actual state of the art, the conducted experiments reveal unexpected properties of structure from motion pipelines that can help improve their performance, for both algorithmic and learned methods. Data and code are online (https://github.com/ubc-vision/image-matching-benchmark), providing an easy-to-use and flexible framework for the benchmarking of local features and robust estimation methods, both alongside and against top-performing methods. This work provides a basis for the Image Matching Challenge (https://image-matching-challenge.github.io). [ABSTRACT FROM AUTHOR]

The paper "Consumer choices under new payment methods" suggests a payment portfolio model that includes new payment methods that have emerged from the development of cryptocurrency markets and central bank digital currencies (CBDCs). The 35th issue of Financial Innovation (FIN), Volume 8, No.5 (2022) presents 19 papers contributed by authors and co-authors from sixteen countries and areas: Australia, Brazil, Canada, China, Hong Kong, Iran, Italy, Japan, Korea, Netherlands, Saudi Arabia, South Africa, Taiwan, Türkiye, Tunisia and USA. The paper "Predicting cash holdings using supervised machine learning algorithms" implies that with more advanced algorithms, considerable improvements are observed with a maximum of 42% improvement in RMSE values. [Extracted from the article]

Professor Grimes summarizes the winning paper and the two runners-up:"Machine learning is certainly of great current interest to address the multivariable materials design challenge. 10.1007/s10853-022-07499-9 5 Wang Z, Lai A, Schuh CA, Radovitzky R. Phase transformation and incompatibility at grain boundaries in zirconia-based shape memory ceramics: a micromechanics-based simulation study. The winner of the 2022 Robert W. Cahn Best Paper award is "Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys" by Udesh M.H.U. Kankanamge, Johannes Reiner, Xingjun Ma, Santiago Corujeira Gallo, and Wei Xu. [Extracted from the article]

Industry involvement in the machine learning (ML) community seems to be increasing. However, the quantitative scale and ethical implications of this influence are rather unknown. For this purpose, we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR, and ICML of the last 5 years—almost 11,000 papers in total. Our statistical approach focuses on conflicts of interest, innovation, and gender equality. We have obtained four main findings. (1) Academic–corporate collaborations are growing in numbers. At the same time, we found that conflicts of interest are rarely disclosed. (2) Industry papers amply mention terms that relate to particular trending machine learning topics earlier than academia does. (3) Industry papers are not lagging behind academic papers with regard to how often they mention keywords that are proxies for social impact considerations. (4) Finally, we demonstrate that industry papers fall short of their academic counterparts with respect to the ratio of gender diversity. We believe that this work is a starting point for an informed debate within and outside of the ML community. [ABSTRACT FROM AUTHOR]

Cancer is characterized as a heterogeneous disease of various types. The early detection and prognosis of a cancer type have turned into a major requirement, as it facilitates successive medical treatment of patients. The research team has classified the cancer patients into high or low-risk groups. This makes it a significant task for the medical teams to study the application of deep learning and machine learning models. As a result, such techniques have been employed for modeling the development and treatment of cancer conditions. Additionally, the machine learning tools can have the ability the significant detection features from complex datasets. Numerous techniques like Support Vector Machines (SVM), Bayesian Networks (BN), Decision Trees (DT), Artificial Neural Networks (ANN), Recurrent Neural Network (RNN), and Deep Neural Network (DNN) has been broadly utilized in cancer research. As per the current survey, the detection rate is about 99.89%, which shows the prediction models' efficiency and precise decision making. However, it is proven that deep learning and machine learning approaches can enhance cancer progression. An adequate level of estimation is required for such approaches for considering the daily medical practice. This survey analyzes and learns the diverse contributions of cancer prediction models using intelligent approaches. Further, the paper tries to categorize the different algorithms, the utilized datasets, and utilized environments. Along with this, various performance measures evaluated in each contribution is sorted out. An extensive search is conducted relevant to machine learning and deep learning methods in cancer susceptibility, recurrence, and survivability prediction, and the existing challenges in this area are clearly described. However, ML models are still in the testing as well as the experimentation phase for cancer prognoses. As the datasets are getting larger with higher quality, researchers are building increasingly accurate models. Moreover, ML models have a long way to go, and most of the models still lack sufficient data and suffer from bias. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models have gained significant attention in a variety of applications, from computer vision to natural language processing, and are almost always based on big data. There are a growing number of applications and products with built-in machine learning models, and this is the area where software engineering, artificial intelligence and data science meet. The requirement for a system to operate in a real-world environment poses many challenges, such as how to design for wrong predictions the model may make; How to assure safety and security despite possible mistakes; which qualities matter beyond a model's prediction accuracy; How can we identify and measure important quality requirements, including learning and inference latency, scalability, explainability, fairness, privacy, robustness, and safety. It has become crucial to test thoroughly these models to assess their capabilities and potential errors. Existing software testing methods have been adapted and refined to discover faults in machine learning and deep learning models. This paper covers a taxonomy, a methodologically uniform presentation of all presented solutions to the aforementioned issues, as well as conclusions about possible future development trends. The main contributions of this paper are a classification that closely follows the structure of the ML-pipeline, a precisely defined role of each team member within that pipeline, an overview of trends and challenges in the combination of ML and big data analytics, with uses in the domains of industry and education. [ABSTRACT FROM AUTHOR]

Headline generation is a challenging subtask of abstractive text summarization, which its output should be a summary, shorter than one sentence. It would be precious to develop a dataset for the evaluation of abstractive summarization methods on this task in the Persian language. There are several datasets for headline generation in Persian, most of which are not large enough to be used by more sophisticated methods of text summarization, such as deep learning models. Moreover, all of these datasets are focused on daily news and there is no dataset for summarizing scientific Persian papers. In this article, we present "ElmNet," a headline generation dataset of about 400,000 abstract/headline pairs of scientific papers, gathered from six major publishers for scientific articles in Persian. We, moreover, evaluate the performance of the most important deep learning-based headline generation methods, on the proposed dataset. The results prove the comparability of the performance of the state-of-the-art methods on this task, to their results on the existing English datasets. [ABSTRACT FROM AUTHOR]

Current research on Digital Twin (DT) is largely focused on the performance of built assets in their operational phases as well as on urban environment. However, Digital Twin has not been given enough attention to construction phases, for which this paper proposes a Digital Twin framework for the construction phase, develops a DT prototype and tests it for the use case of measuring the productivity and monitoring of earthwork operation. The DT framework and its prototype are underpinned by the principles of versatility, scalability, usability and automation to enable the DT to fulfil the requirements of large-sized earthwork projects and the dynamic nature of their operation. Cloud computing and dashboard visualisation were deployed to enable automated and repeatable data pipelines and data analytics at scale and to provide insights in near-real time. The testing of the DT prototype in a motorway project in the Northeast of England successfully demonstrated its ability to produce key insights by using the following approaches: (i) To predict equipment utilisation ratios and productivities; (ii) To detect the percentage of time spent on different tasks (i.e., loading, hauling, dumping, returning or idling), the distance travelled by equipment over time and the speed distribution; and (iii) To visualise certain earthwork operations. [ABSTRACT FROM AUTHOR]

This paper investigates the art design of user interaction experience in mobile systems through the methods of contextual perception and machine learning. The theoretical foundations for the design of intangible cultural heritage interactive display resources include digital display theory, intangible cultural heritage education theory, embodied cognition theory, and gamification design theory. Based on the modelling and analysis of the theoretical foundations, the design principles are derived, including the heritage education principle, the somatic interaction design principle, and the content design principle. In this paper, we use ontologies to construct user knowledge models, fuse multi-situational similarity metrics, screen out candidate neighbour sets through preliminary screening, then combine the user's activity to construct time-based weight tensor scores, use tensor decomposition to obtain recommendation evaluation values, and finally use the recommendation evaluation values to make artistic recommendations. The experimental results show that the algorithm can still obtain a good recommendation implementation in the case of extremely sparse data. The analysis of the fit between augmented reality and folk art appreciation class, as well as the reference to relevant application cases, make design and practice of augmented reality application in folk art appreciation class, try to solve the common problems in folk art appreciation class, analyse the feedback effect and make a summary and outlook. [ABSTRACT FROM AUTHOR]

Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time-consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, which utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements, are learned and inferred from data. During the inference stage, the maximum a posterior (MAP) estimation framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Based on the applications of BSO algorithms for KSP, the properties of different swarm optimization algorithms can be understood better. The comparison result of the proposed algorithm with other algorithms proves that EDOLSCA also has advantages in heat exchanger optimal design. Finally, the experimental results confirm the efficiency of the proposed scheme in real-world urban scenarios; The last paper by Lidong Zhang et al. on "Elite and dynamic opposite learning enhanced sine cosine algorithm for application to plat-fin heat exchangers design problem" proposes a novel variant of sine and cosine algorithm named EDOLSCA enhanced by dynamic opposite learning algorithm and the elite strategy. [Extracted from the article]

Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. [ABSTRACT FROM AUTHOR]

Particularly, the relational database management systems and the big data systems (e.g., Key-Value stores, Document stores, Graph stores and Graph Computation Systems, Spark, MapReduce/Hadoop, or Data Stream Processing Systems) have evolved with novel additions and extensions. The paper by Harish Kumar Harihara Subramanian et al. on supporting DBMS GPU-based operations with out-of-the-box libraries describes the ongoing research in the database community regarding the use of GPUs for query processing. Data management systems have evolved in terms of functionality, performance characteristics, complexity, and variety during the last 40 years. [Extracted from the article]

Objective: To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations. Methods: Google Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM's use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed. Results: A total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively. Conclusion: Only a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists. Clinical relevance statement: Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities. Key Points: • Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously. [ABSTRACT FROM AUTHOR]

This comprehensive review paper examines the challenges faced by blockchain technology in terms of scalability and proposes potential solutions and future research directions. Scalability poses a significant hurdle for Bitcoin and Ethereum, manifesting as low throughput, extended transaction delays, and excessive energy consumption, thereby compromising efficiency. The current state of blockchain scalability is analyzed, encompassing the limitations of existing solutions such as Sharding and off-chain scaling. Various proposed remedies, including layer 2 scaling solutions, consensus mechanisms, and alternative approaches, are investigated. The paper also explores the impact of scalability on diverse blockchain applications and identifies potential future research directions by integrating data science techniques with blockchain technology. Notably, nearly 110 primary research papers from reputable scientific databases like Scopus, IEEE Explore, Science Direct, and Web of Science were reviewed, demonstrating scalability in blockchain comprising several elements. Transaction throughput and network latency emerge as the most prominent concerns. Consequently, this review offers future research avenues to address scalability challenges by leveraging data science techniques like distributed computing and parallel processing to divide and process vast datasets across multiple machines. The synergy between data science and blockchain holds promise as an optimal solution. Overall, this up-to-date understanding of blockchain scalability is invaluable to researchers, practitioners, and policy makers engaged in this domain. [ABSTRACT FROM AUTHOR]

Addressing the pressing demand for rapid and inexpensive coagulation testing in cardiovascular care, this study introduces a novel application of repurposed COVID-19 rapid antigen tests (RATs) as paper-based lateral flow assays (LFAs) combined with machine learning for coagulation status evaluation. By further developing a mobile app prototype, we present a platform that enables clinicians to perform immediate and accurate anticoagulant dosing adjustments using existing post-pandemic resources. Our proof-of-concept employs a random forest machine learning classifier to interpret image feature variations on RAT NC membrane, correlating red blood cell (RBC) wicked diffusion distance in recalcified citrated whole blood with changes in coagulative viscosity, easily interpreted. Enhanced by confocal imaging studies of paper microfluidics, our approach provides insights into the mechanisms dissecting coagulation components, achieving high classification precision, recall, and F1-scores. The inverse relationship between RBC wicked diffusion distance and enoxaparin concentration paves the way for machine learning to inform real-time dose prescription adjustments, aligning with individual patient profiles to optimize therapeutic outcomes. This study not only demonstrates the potential of leveraging surplus RATs for coagulation management but also exemplifies a cost-effective, rapid, and smart strategy to enhance clinical decision-making in the post-pandemic era. Highlights: • Repurposed COVID-19 RATs provide an ideal platform for observing differences in blood coagulability. • Random Forest image classification algorithms can facilitate rapid coagulation status assessment on a paper-based LFA platform. [ABSTRACT FROM AUTHOR]

Summary: Due to the convenience of virtualization, the live migration of virtual machines is widely used to fulfill optimization objectives in cloud/edge computing. However, live migration may lead to side effects and performance degradation when migration is overused or an unreasonable migration process is carried out. One pressing challenge is how to capture the best opportunity for virtual machine migration. Leveraging rough sets and AI, this paper provides an innovative strategy based on Q-learning that is designed for migration decisions. The highlight of our strategy is the harmonious mechanism for applying rough sets and Q-learning. For the ABDS (adaptive boundary decision system) strategy in this paper, the exploration space of Q learning is confined by the boundary region of rough sets, while the thresholds of the boundary region can be dynamically adjusted by the reaction results from the computing cluster. The structure and mechanism of the ABDS strategy are described in this paper. The corresponding experiments show a firm advantage for the cooperation of rough sets and reinforcement learning algorithms. Considering both the energy consumption and application performance, the ABDS strategy in this paper outperforms the benchmark strategies in comprehensive performance. [ABSTRACT FROM AUTHOR]

The pH detection helps control food quality, prevent spoilage, determine storage methods, and monitor additive levels. In the previous studies, colorimetric pH detection involved manual capture of target regions and classification of acid–base categories, leading to time-consuming processes. Additionally, some researchers relied solely on R*G*B* or H*S*V* to build regression models, potentially limiting their generalizability and robustness. To address the limitations, this study proposed a colorimetric method that combines pH paper, smartphone, computer vision, and machine learning for fast and precise pH detection. Advantages of the computer vision model YOLOv5 include its ability to quickly capture the target region of the pH paper and automatically categorize it as either acidic or basic. Subsequently, recursive feature elimination was applied to filter out irrelevant features from the R*G*B*, H*S*V*, L*a*b*, Gray, XR, XG, and XB. Finally, the support vector regression was used to develop the regression model for pH value prediction. YOLOv5 demonstrated exceptional performance with mean average precision of 0.995, classification accuracy of 100%, and detection time of 4.9 ms. The pH prediction model achieved a mean absolute error (MAE) of 0.023 for acidity and 0.061 for alkalinity, signifying a notable advancement compared to the MAE range of 0.03–0.46 observed in the previous studies. The proposed approach shows potential in improving the dependability and effectiveness of pH detection, specifically in resource-constrained scenarios. [ABSTRACT FROM AUTHOR]

Forest fire is a kind of natural disaster that is destructive, easy to spread, and difficult to extinguish. It greatly harms the balance of ecosystem and human life and property. The prediction and risk assessment of forest fire accidents can find forest fires as early as possible and then take corresponding remedial measures; the loss of forest fires will be minimized. This paper first collected the relevant data information in the Lesser Hinggan Mountain region, established the fire driving factor database, and then analyzed the impact and distribution characteristics of forest fire driving factors such as temperature and rainfall. Finally, a fire accident prediction model was built based on a deep neural network, and the model's performance was compared with the SVM and RF models. The analysis results show that this paper's fire accident prediction model is more accurate. [ABSTRACT FROM AUTHOR]

Nowadays, literature review is a necessary task when trying to solve a given problem. However, an exhaustive literature review is very time-consuming in today's vast literature landscape. It can take weeks, even if looking only for abstracts or surveys. Moreover, choosing a method among others, and targeting searches within relevant problem and solution domains, are not easy tasks. These are especially true for young researchers or engineers starting to work in their field. Even if surveys that provide methods used to solve a specific problem already exist, an automatic way to do it for any use case is missing, especially for those who don't know the existing literature. Our proposed tool, SARBOLD-LLM, allows discovering and choosing among methods related to a given problem, providing additional information about their uses in the literature to derive decision-making insights, in only a few hours. The SARBOLD-LLM comprises three modules: (1: Scopus search) paper selection using a keyword selection scheme to query Scopus API; (2: Scoring and method extraction) relevancy and popularity scores calculation and solution method extraction in papers utilizing OpenAI API (GPT 3.5); (3: Analyzes) sensitivity analysis and post-analyzes which reveals trends, relevant papers and methods. Comparing the SARBOLD-LLM to manual ground truth using precision, recall, and F1-score metrics, the performance results of AI in the oncology case study are 0.68, 0.9, and 0.77, respectively. SARBOLD-LLM demonstrates successful outcomes across various domains, showcasing its robustness and effectiveness. The SARBOLD-LLM addresses engineers more than researchers, as it proposes methods and trends without adding pros and cons. It is a useful tool to select which methods to investigate first and comes as a complement to surveys. This can limit the global search and accumulation of knowledge for the end user. However, it can be used as a director or recommender for future implementation to solve a problem. Highlights: Automated support for literature choice and solution selection for any use case. A generalized keyword selection scheme for literature database queries. Trends in literature: detecting AI methods for a case study using Scopus and OpenAI. A better understanding of the tool by sensitivity analyzes for Scopus and OpenAI. Robust tool for different domains with promising OpenAI performance results. [ABSTRACT FROM AUTHOR]

We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular disease (CVD). A machine learning-based framework was developed to (1) determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a sensing membrane, and (2) to accurately infer target analyte concentration. Using a custom-designed handheld VFA reader, a clinical study with 85 human samples showed a competitive coefficient-of-variation of 11.2% and linearity of R2 = 0.95 among blindly-tested VFAs in the hsCRP range (i.e., 0–10 mg/L). We also demonstrated a mitigation of the hook-effect due to the multiplexed immunoreactions on the sensing membrane. This paper-based computational VFA could expand access to CVD testing, and the presented framework can be broadly used to design cost-effective and mobile point-of-care sensors. [ABSTRACT FROM AUTHOR]

Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many science subfields have recently faced a reproducibility crisis, eroding trust in processes and results, and influencing the rise in retractions of scientific papers. For the same reasons, conducting research in deep learning (DL) also requires reproducibility. Although several valuable manuscript checklists for AI in medical imaging exist, they are not focused specifically on reproducibility. In this study, we conducted a systematic review of recently published papers in the field of DL to evaluate if the description of their methodology could allow the reproducibility of their findings. We focused on the Journal of Digital Imaging (JDI), a specialized journal that publishes papers on AI and medical imaging. We used the keyword "Deep Learning" and collected the articles published between January 2020 and January 2022. We screened all the articles and included the ones which reported the development of a DL tool in medical imaging. We extracted the reported details about the dataset, data handling steps, data splitting, model details, and performance metrics of each included article. We found 148 articles. Eighty were included after screening for articles that reported developing a DL model for medical image analysis. Five studies have made their code publicly available, and 35 studies have utilized publicly available datasets. We provided figures to show the ratio and absolute count of reported items from included studies. According to our cross-sectional study, in JDI publications on DL in medical imaging, authors infrequently report the key elements of their study to make it reproducible. [ABSTRACT FROM AUTHOR]