A quality exploration of machine learning’s ability to predict predatory dental publications

Spatio-temporal distribution analysis of circulating genotypes of dengue virus type 1 in western and southern states of India by a one-step real-time RT-PCR assay

Artificial Intelligence-related Literature in Transplantation: A Practical Guide.

In the internet era spam has become a big problem. Researchers are troubled with unsolicited or bulk spam emails inviting them to publish. However, this strategy has helped predatory journals hunt their prey and earn money. These journals have grown tremendously during the past few years despite serious efforts by researchers and scholarly organizations to hinder their growth. Predatory journals and publishers are often based in developing countries, and they potentially target researchers from these counties by using different tactics identified in previous research. In response to the spread of predatory publishing, scientists are trying to develop criteria and guidelines to help avoid them-for example, the recently reported "predatory rate". This article attempts to (a) highlight the strategies used by predatory journals to convince researchers to publish with them, (b) report their article processing charges, (c) note their presence in Jeffrey Beall's List of Predatory Publishers, (d) rank them based on the predatory rate, and (e) put forward suggestions for junior researchers (especially in developing counties), who are the most likely targets of predatory journals.

Machine Learning Applications in Orthopaedic Imaging.

Purpose Coronary artery disease (CAD) is one of the major cardiovascular diseases and the leading cause of death globally. Blood lipid profile is associated with CAD early risk. Therefore, we aim to establish machine learning models utilizing blood lipid profile to predict CAD risk. Methods In this study, 193 non-CAD controls and 2001 newly-diagnosed CAD patients (1647 CAD patients who received lipid-lowering therapy and 354 who did not) were recruited. Clinical data and the result of routine blood lipids tests were collected. Moreover, low-density lipoprotein cholesterol (LDL-C) subfractions (LDLC-1 to LDLC-7) were classified and quantified using the Lipoprint system. Six predictive models (k-nearest neighbor classifier (KNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)) were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), recall (sensitivity), accuracy, precision, and F1 score. The selected features were analyzed and ranked. Results While predicting the CAD development risk of the CAD patients without lipid-lowering therapy in the test set, all models obtained AUC values above 0.94, and the accuracy, precision, recall, and F1 score were above 0.84, 0.85, 0.92, and 0.88, respectively. While predicting the CAD development risk of all CAD patients in the test set, all models obtained AUC values above 0.91, and the accuracy, precision, recall, and F1 score were above 0.87, 0.94, 0.87, and 0.92, respectively. Importantly, small dense LDL-C (sdLDL-C) and LDLC-4 play pivotal roles in predicting CAD risk. Conclusions In the present study, machine learning tools combining both clinical data and blood lipid profile showed excellent overall predictive power. It suggests that machine learning tools are suitable for predicting the risk of CAD development in the near future.

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study’s objectives. This study identifies gaps in utilizing ML techniques in cloud cyber security. Moreover, this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of precision, accuracy, and recall values, as well as F1 scores, R.O.C. curves, and confusion matrices. The results demonstrated that the X.G.B. model outperformed, in terms of all the matrices, with an accuracy of 97.50%, a precision of 97.60%, a recall value of 97.60%, and an F1 score of 97.50%. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.

ADRD caregivers increasingly use social media to meet their health information wants (HIW). Machine learning (ML) tools may help understand caregivers’ HIW as expressed via social media. This pilot study explored a collaborative, iterative process between domain experts and ML tools to identify ADRD caregivers’ HIW from social media data. The HIW-ADRD framework was adapted from an existing HIW framework. Through multiple rounds of iteration between the experts and the ML tools, the framework was expanded to include 11 types of health information. Each type included corresponding keywords developed through a hybrid approach that included keywords from both the theoretical constructs (top-down) and caregivers’ posts (bottom-up). These keywords were then used to enhance the ML tools’ ability to code 106 recent posts extracted from an ADRD social media group in March 2020. When compared with expert coding results, ML tools accurately predicted 56% of HIW. Further work is underway.

The process of automatically recognising significant patterns within large amounts of data is called "machine learning." Throughout the last couple of decades, it has evolved into a tool used in almost every activity requiring the extraction of information from large data sets. We are surrounded by technology that is based on machine learning: Search engines are learning how to bring us the best results (while placing profitable ads), antispam software is learning how to filter our email messages, and credit card transactions are secured by software that learns how to detect frauds. Intelligent personal assistance software on smartphones can learn to recognise voice commands, and digital cameras can train themselves to identify faces. Accident-prevention systems in vehicles are constructed with the help of machine-learning algorithms. These systems are installed in modern automobiles. In addition, machine learning is extensively utilised in various scientific applications, including bioinformatics, medicine, and astronomy. One aspect that is shared by all of these applications is the fact that, in contrast to more conventional applications of computers, in these situations, due to the complexity of the patterns that need to be detected, a human programmer is unable to provide an explicit, fine-detailed specification of how such tasks should be carried out. This is one of the characteristics that make all of these applications unique. Taking cues from other intelligent beings, most of our capabilities have been obtained or improved via learning from our experiences (rather than following explicit instructions). Tools for machine learning are used to give computer programmes the capacity to "learn" and modify their behaviour on their own. The first objective of this book is to provide the fundamental ideas that comprehensively underpin machine learning while still being simple to understand. The process of automatically recognising significant patterns within large amounts of data is called "machine learning." Throughout the last couple of decades, it has evolved into a tool used in almost every activity requiring the extraction of information from large data sets. We are surrounded by technology that is based on machine learning: Search engines are learning how to bring us the best results (while placing profitable ads), antispam software is learning how to filter our email messages, and credit card transactions are secured by software that learns how to detect frauds. Intelligent personal assistance software on smartphones can learn to recognise voice commands, and digital cameras can train themselves to identify faces. Accident-prevention systems in vehicles are constructed with the help of machine-learning algorithms. These systems are installed in modern automobiles. In addition, machine learning is extensively utilised in various scientific applications, including bioinformatics, medicine, and astronomy. One aspect that is shared by all of these applications is the fact that, in contrast to more conventional applications of computers, in these situations, due to the complexity of the patterns that need to be detected, a human programmer is unable to provide an explicit, fine-detailed specification of how such tasks should be carried out. This is one of the characteristics that make all of these applications unique. Taking cues from other intelligent beings, most of our capabilities have been obtained or improved via learning from our experiences (rather than following explicit instructions). Tools for machine learning are used to give computer programmes the capacity to "learn" and modify their behaviour on their own. The first objective of this book is to provide the fundamental ideas that comprehensively underpin machine learning while still being simple to understand.

Preventing Publication of Falsified and Fabricated Data: Roles of Scientists, Editors, Reviewers, and Readers.

The scientific community depends on high-quality peer-reviewed research, which is being polluted with pseudoscience published in fake journals that have exploited the open-access model. This "predatory publishing" has made its way into the field of dermatology. In a recent study, we identified and listed these journals. The "predatory rate" was calculated for 76 journals in order to rank the journals based on specific criteria associated with unethical publishing. Of the 76 journals, 89.5% were classified as predatory journals and the remaining as journals involved in predatory practices. The field of dermatology is not immune to predatory publishers. This study validates Beall's list as well as other previous studies. Strategies to a solution include spreading awareness throughout academic institutions and dermatology departments as well as avoiding publishers that are involved in predatory practices. However, some journals may be able to make necessary adjustments and become legitimate contributors to the field.

No-code machine learning (ML) tools provide an avenue for individuals who lack advanced ML skills to develop ML applications. Extant literature indicates that by using such tools, individuals can acquire relevant ML skills. However, no explanation has been provided of how the use of no-code ML tools leads to the generation of these skills. Using the theory of technology affordances and constraints, this article undertakes a qualitative evaluation of publicly available no-code ML tools to explain how their usage can lead to the formation of relevant ML skills. Subsequently, the authors show that no-code ML tools generate familiarization affordances, utilization affordances, and administration affordances. Subsequently, they provide a conceptual framework and process model that depicts how these affordances lead to the generating of ML skills.

Machine learning (ML) tools reduce the costs of performing repetitive, time-consuming tasks yet run the risk of introducing systematic unfairness into organizational processes. Automated approaches to achieving fairness often fail in complex situations, leading some researchers to suggest that human augmentation of ML tools is necessary. However, our current understanding of human–ML augmentation remains limited. In this paper, we argue that the Information Systems (IS) discipline needs a more sophisticated view of and research into human–ML augmentation. We introduce a typology of augmentation for fairness consisting of four quadrants: reactive oversight, proactive oversight, informed reliance, and supervised reliance. We identify significant intersections with previous IS research and distinct managerial approaches to fairness for each quadrant. Several potential research questions emerge from fundamental differences between ML tools trained on data and traditional IS built with code. IS researchers may discover that the differences of ML tools undermine some of the fundamental assumptions upon which classic IS theories and concepts rest. ML may require massive rethinking of significant portions of the corpus of IS research in light of these differences, representing an exciting frontier for research into human–ML augmentation in the years ahead that IS researchers should embrace. 1

Machine-Learning Implementation in Clinical Anesthesia: Opportunities and Challenges.

In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools to be interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London’s position. In particular, we make two claims. First, we claim that London’s solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London’s views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like.

The advent of cloud-based super-computing platforms has given rise to a Data Science (DS) boom. Many types of technological problems that were once considered prohibitively expensive to tackle are now candidates for exploration. Machine Learning (ML) tools that were valued only in academic environments are quickly being embraced by industrial giants and tiny startups alike. Coupled with modern-day computing power, ML tools can be looked at as hammers that can deal with even the most stubborn nails. ML tools have become so ubiquitous that the current industrial expectation is that they should not only deliver accurate and intelligent solutions but also do so rapidly. In order to keep pace with these requirements, a new enterprise, referred to as MLOps has blossomed in recent years. MLOps combines the process of ML and DS with an agile software engineering technique to develop and deliver solutions in a fast and iterative way. One of the key challenges to this is that ML and DS tools should be efficient and have better usability characteristics than were traditionally offered. In this paper, we present a novel software for Grammatical Evolution (GE) that meets both of these expectations. Our tool, GELAB, is a toolbox for GE in Matlab which has numerous features that distinguish it from existing contemporary GE software. Firstly, it is user-friendly and its development was aimed for use by non-specialists. Secondly, it is capable of hybrid optimization, in which standard numerical optimization techniques can be added to GE. We have shown experimentally that when hybridized with meta-heuristics GELAB has an overall better performance as compared with standard GE.