Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

Software Defined Networking (SDN) is a paradigm shift in the network industry with decoupling of control and data plane. This helps network engineers to control and manage their network from centralized controller and troubleshoot the network at much better pace. With the benefits that SDN provides, it also has some vulnerable to attacks like Distributed Denial of Service (DDoS) that can disrupt the availability of the network. Most of the recent research work in DDoS detection and mitigation is done over Traditional network environments and packet limit strategy is used for mitigation by researchers with very less focus on redirection of illegitimate traffic which can result in less downtime and helpful in network analysis. The major contribution of this paper is detection and mitigation of DDoS in Software Defined Networks. We have created our own DDoS dataset with over 1.7 million entries. For Detection, we have used two different methods: (1) Snort (IDS) and (2) Machine Learning. In Machine Learning, we have used eight different Machine Learning algorithms that also include Ensemble Classifier and a Hybrid method of SVM-RF. We were able to get 99.1% accuracy which is a substantial growth as compared to recent works. For Mitigation, we have again used two methods: (1) Drop illegitimate traffic and (2) Redirect Illegitimate traffic. Results showed that hybrid SVM-RF algorithm works better as compared to individual Machine Learning algorithms and also as mitigation depends on Detection accuracy, so, the better the detection, the better the mitigation. In this paper detection and mitigation of DDoS has been performed over a SDN network. In which We have created our own dataset with over 1.7 million entries. For detection we have used two method: Snort (IDS), and Machine Learning and for Mitigation, Drop illegitimate traffic, and Redirect Illegitimate traffic. [ABSTRACT FROM AUTHOR]

This paper simulates the relative performance of various artificial intelligence (AI) techniques when applied to nonlinear distortion compensation in wavelength division multiplexing (WDM) optical communication systems. These procedures are less complex than state-of-the-art compensation methods and do not necessitate prior knowledge about the properties of data in neighboring WDM channels, which can be practically challenging. In this study, Neural Networks (NNs) were integrated into both the transmitter and receiver sections of 3- and 5-channel WDM systems, and the resulting enhancement in performance (Q-factor) was assessed across varying levels of fiber nonlinearities. While the NN stage enhances the system performance, the improvement decreases as expected with the channel number and γ . Next, two-stage architectures that employ a transmitter side NN together with a classifier at the receiver side were modeled. For the systems examined in this paper simple decision tree structures, boosting, forests, extra trees, and multi-layer perceptron (MLP) classifiers all yielded enhanced system performance compared to simple chromatic dispersion compensation (CDC) with the only exception being Ada boosting which decreased the Q-factor for γ = 14 W - 1 k m - 1 . The outcomes of these investigations show that the most effective performance in highly nonlinear WDM systems is attained by employing two-stage systems, with the incorporation of random forest or extra tree AI methods at the receiver side yielding the highest results. [ABSTRACT FROM AUTHOR]

For years, the number of opaque algorithmic decision-making systems (ADM systems) with a large impact on society has been increasing: e.g., systems that compute decisions about future recidivism of criminals, credit worthiness, or the many small decision computing systems within social networks that create rankings, provide recommendations, or filter content. Concerns that such a system makes biased decisions can be difficult to investigate: be it by people affected, NGOs, stakeholders, governmental testing and auditing authorities, or other external parties. Scientific testing and auditing literature rarely focuses on the specific needs for such investigations and suffers from ambiguous terminologies. With this paper, we aim to support this investigation process by collecting, explaining, and categorizing methods of testing for bias, which are applicable to black-box systems, given that inputs and respective outputs can be observed. For this purpose, we provide a taxonomy that can be used to select suitable test methods adapted to the respective situation. This taxonomy takes multiple aspects into account, for example the effort to implement a given test method, its technical requirement (such as the need of ground truth) and social constraints of the investigation, e.g., the protection of business secrets. Furthermore, we analyze which test method can be used in the context of which black box audit concept. It turns out that various factors, such as the type of black box audit or the lack of an oracle, may limit the selection of applicable tests. With the help of this paper, people or organizations who want to test an ADM system for bias can identify which test methods and auditing concepts are applicable and what implications they entail. [ABSTRACT FROM AUTHOR]

This paper provides an empirical and conceptual account on seeing machine learning models as part of a sociotechnical system to identify relevant vulnerabilities emerging in the context of use. As ML is increasingly adopted in socially sensitive and safety-critical domains, many ML applications end up not delivering on their promises, and contributing to new forms of algorithmic harm. There is still a lack of empirical insights as well as conceptual tools and frameworks to properly understand and design for the impact of ML models in their sociotechnical context. In this paper, we follow a design science research approach to work towards such insights and tools. We center our study in the financial industry, where we first empirically map recently emerging MLOps practices to govern ML applications, and corroborate our insights with recent literature. We then perform an integrative literature research to identify a long list of vulnerabilities that emerge in the sociotechnical context of ML applications, and we theorize these along eight dimensions. We then perform semi-structured interviews in two real-world use cases and across a broad set of relevant actors and organizations, to validate the conceptual dimensions and identify challenges to address sociotechnical vulnerabilities in the design and governance of ML-based systems. The paper proposes a set of guidelines to proactively and integrally address both the dimensions of sociotechnical vulnerability, as well as the challenges identified in the empirical use case research, in the organization of MLOps practices. [ABSTRACT FROM AUTHOR]

This paper explores the intersection of art history and AI technology. Special attention is paid to Generative Adversarial Networks (GANs), a machine learning technology widely used in AI art. This technology is particularly interesting to art history and the philosophy of art because it raises enduring questions about the creative process of artmaking, especially what constitutes a new and original work of art. While this is a relatively new area, it is possible to discern emerging directions where art and AI intersect. One pressing issue concerns the use (or misuse) of algorithmically enabled technologies and datasets to perpetuate longstanding art-historical biases. The critical position adopted in this paper shares this concern. I examine a notable example of GAN technology that claims to create original art styles. While such experiments in computational creativity are technically interesting, their artistic aims can be questionable. I argue a mechanical model of artistic development based solely on the autonomy of style is problematic for at least two reasons: 1) it uncritically reinstates a formalist view of art history; 2) it recreates a narrow style-centric ‘historical modernism.’ However, despite voicing these reservations, I also discuss the potential of this technology to engage with topics of critical relevance to art history and the philosophy of art. [ABSTRACT FROM AUTHOR]

This paper discusses some societal implications of the most recent and publicly discussed application of advanced machine learning techniques: generative AI models, such as ChatGPT (text generation) and DALL-E (text-to-image generation). The aim is to shift attention away from conceptual disputes, e.g. regarding their level of intelligence and similarities/differences with human performance, to focus instead on practical problems, pertaining the impact that these technologies might have (and already have) on human societies. After a preliminary clarification of how generative AI works (Sect. 1), the paper discusses what kind of transparency ought to be required for such technologies and for the business model behind their commercial exploitation (Sect. 2), what is the role of user-generated data in determining their performance and how it should inform the redistribution of the resulting benefits (Sect. 3), the best way of integrating generative AI systems in the creative job market and how to properly negotiate their role in it (Sect. 4), and what kind of “cognitive extension” offered by these technologies we ought to embrace, and what type we should instead resist and monitor (Sect. 5). The last part of the paper summarizes the main conclusions of this analysis, also marking its distance from other, more apocalyptic approaches to the dangers of AI for human society. [ABSTRACT FROM AUTHOR]

Background: Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal's changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers' performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. Results: The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases' results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm's capabilities in precisely determining the emotion label for each participant's EEG features' instance. Conclusion: The findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm's performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a collaborative artificial intelligence (AI) approach that enables distributed training of AI models without data sharing, thereby promoting privacy by design. However, it is essential to acknowledge that FL only offers a partial solution to safeguard the confidentiality of AI and machine learning (ML) models. Unfortunately, many studies fail to report the results of privacy measurement when applying FL, mainly due to assumptions that privacy is implicitly achieved as FL is a privacy-by-design approach. This trend can also be attributed to the complexity of understanding privacy measurement metrics and methods. This paper presents a survey of privacy measurement in FL, aimed at evaluating its effectiveness in protecting the privacy of sensitive data during the training of AI and ML models. While FL is a promising approach for preserving privacy during model training, ensuring privacy is genuinely achieved in practice is crucial. By evaluating privacy measurement metrics and methods in FL, we can identify the gaps in existing approaches and propose new techniques to enhance FL's privacy. A comprehensive study investigating "privacy measurement and metrics" in FL is therefore required to support the field's growth. Our survey provides a critical analysis of the current state of privacy measurement in FL, identifies gaps in existing research, and offers insights into potential research directions. Moreover, this paper presents a case study that evaluates the effectiveness of various privacy techniques in a specific FL scenario. This case study serves as tangible evidence of the real-world implications of privacy measurements, providing insightful and practical guidelines for researchers and practitioners to optimize privacy preservation while balancing other crucial factors such as communication overhead and accuracy. Finally, our paper outlines a future roadmap for advancing privacy in FL, combining traditional techniques with innovative technologies such as quantum computing and Trusted Execution Environments to fortify data protection. [ABSTRACT FROM AUTHOR]

Different sounds have various effects on human health, and by introducing the ones that are therapeutic, a healing environment can be created. This paper describes the process to train and test a machine learning algorithm to describe and explore the therapeutic nature of Quranic verse. Using a dataset containing four emotional states namely happy, sad, angry, and relaxed, we trained a model and classified different recitations of the Quran into one of these states. This paper proposes the use of Mel-frequency cepstral coefficients (MFCC) to extract features from Quranic audio and classify it with respect to a known dataset. Based on the experiments conducted on Quranic verses, we summarize our results. [ABSTRACT FROM AUTHOR]

In order to achieve an optimal system performance, decision makers are continually faced with the responsibility of making choices that will enhance availability and reduce failures cost. To realize this goal, it is crucial to ensure the timely maintenance of equipment, which often poses a significant challenge. However, the adoption of predictive maintenance (PdM) technology can offer a solution by enabling real-time maintenance, resulting in various benefits such as reduced downtime, cost savings, and enhanced production quality. Machine learning (ML) techniques are increasingly being used in the field of predictive maintenance to predict failures and calculate estimated remaining useful life (RUL) of equipment. A case study is proposed in this research paper based on a maintenance dataset from the aerospace industry. It experiments and compare multiple combination of feature engineering techniques and advanced ML models with the aim to propose the most efficient techniques for prediction. Moreover, future research papers can focus on the challenge of validating this proposed model in different industrial environments. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of vehicles on roadways, accompanied by an escalating demand for augmented safety and refined traffic management, has fueled substantial progress in the domains of computer vision and deep learning methodologies. This technological surge has precipitated ground-breaking applications, with real-time vehicle detection emerging as a linchpin. Capability to accurately detect vehicles in a real-time holds profound implications across diverse domains. In the context of autonomous driving, it underpins essential functions such as path planning, collision avoidance, and decision-making, essential for the safe operation of autonomous vehicles. In the arena of traffic monitoring, it empowers transportation authorities to acquire real-time insights into traffic dynamics, congestion patterns, and incident detection, thereby optimizing traffic flow and resource allocation strategies. Furthermore, within surveillance systems, real-time vehicle detection equips security personnel with the means to swiftly identify potential security threats, enhancing overall situational awareness and safety. This paper contributes to this dynamic landscape by introducing an innovative architecture expressly crafted for vehicle detection. Leveraging state-of-the-art computer vision techniques and deep learning methodologies, this architecture addresses the evolving challenges posed by contemporary traffic scenarios. Through the enhancement of real-time vehicle detection capabilities, our aim is to fortify the foundations of safety, efficiency, and security within vehicular contexts. The paper includes a fusion of AlexNet and Darknet accompanied by a comparative analysis. It is expected to deliver unmatched dependability and accuracy in real-time traffic monitoring, setting a new standard for performance in this application. [ABSTRACT FROM AUTHOR]

Purpose: This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools. Methods: This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included. Results: A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data. Conclusion: AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice. [ABSTRACT FROM AUTHOR]

Combining the advantages of quantum computing and machine learning, quantum machine learning is anticipated to advance the field of artificial intelligence further. Recent research has demonstrated, however, that the state-of-the-art quantum classifiers can be deceived by adversarial examples, leading to incorrect classifications by quantum models. This paper proposes the quantum fuzzy convolutional neural network, which is used to detect adversarial examples, by combining the benefits of fuzzy systems and the quantum convolutional neural network. This defensive strategy does not modify the structure and training process of the quantum classifier that requires protection. Simulation experiments show that the adversarial example detection approach proposed in this paper can successfully separate the real data distribution from the adversarial data distribution and detect the adversarial examples generated by a specific attack method on a specific quantum classifier. [ABSTRACT FROM AUTHOR]

It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its existence. However, the question remains whether we possess all the pieces of the holistic puzzle. To fill the gap, in this paper, we test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision. This is done by training seven supervised machine learning models on the compilation of data from the 2019 Special Eurobarometer on undeclared work and relevant figures from other sources. The conducted analysis not only does attest to the completeness of our knowledge concerning the drivers of undeclared work but also paves the way for wide usage of artificial intelligence in monitoring and confronting this detrimental practice. The study, however, exposes the necessity of having at disposal considerably larger datasets compared to those currently available if successful real-world applications of machine learning are to be achieved in this field. Alongside the apparent theoretical contribution, this paper is thus also expected to be of particular importance for policymakers, whose efforts to tackle tax evasion will have to be expedited in the period after the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The issue of traffic mismanagement is getting bad to worse as the trend of urbanization is increasing and posing significant threats to the urban areas. This paper addresses this issue of traffic mismanagement by proposing a Fog-Cloud centric Internet of Things (IoT)-based collaborative framework that enables machine learning-based situation-aware traffic management. This framework monitors the traffic dynamics at the fog layer for providing real-time analytics to the cloud and enabling the traffic situation-aware routing of the vehicles. Whereas the framework at the cloud layer determines the situation-aware analytics by predicting Waiting Time (WaT), Waiting Queue Length (WQueL) and next green phase duration (G) for each Traffic Movement Signal Point (TMSP) based on the real-time data and analytics provided by the fog layer. The road-infrastructure hosted fog layer classifies the In-schedule Reachability Status (IRS) that determines the ability of the vehicle to reach the respective TMSP down the lane before the start of the next green phase. After classifying all such vehicles on a lane by the fog layer, the cloud layer predicts the optimal duration of the next green phase at that respective TMSP to reduce the traffic congestion on the respective junction point and subsequently provides these area-wide analytics to the vehicle hosted fog nodes and traffic controllers on the respective junction points. The vehicles use these analytics for enabling situation-aware vehicle routing functionality by selecting a time optimized path and enabling balanced traffic load on all possible paths. The framework employs Logistic Regression (LR) for IRS classification at the fog layer and Artificial Neural Network (ANN) for traffic prediction at the respective TMSP at the cloud layer. The result depiction acknowledges LR's efficiency compared to other employed classifiers in terms of various statistical parameters. The optimal performance of ANN at the learning rate (LeR) of 0.1, momentum rate (MoR) of 0.95, and 500 epochs depict the prediction efficiency of ANN as compared to other used prediction approach. The contribution of this paper is two functionalities-based working of fog computing for providing real-time and situation-aware traffic analytics, adaptive traffic movement phase planning, time optimized navigation, and optimal traffic load balancing. [ABSTRACT FROM AUTHOR]

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]

The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]

Optical tracking sensor technology has been widely used in the field of motion. However, there are still some challenges in the recognition of badminton players, and more accurate methods are needed to capture the dynamic characteristics of badminton players. The aim of this study is to design an optical tracking sensor system based on image feature extraction for badminton player motion recognition. In this paper, a high resolution camera is used to collect the image sequence of badminton match. Then through image processing and computer vision technology, the key image features are extracted from the image sequence. Then, machine learning algorithm is used to classify and recognize the extracted features to achieve accurate recognition of badminton players' movements. The experimental results show that the optical tracking sensor system can effectively extract the movement features of badminton players and identify their movements accurately. Compared with the traditional method, the system in this paper has higher precision and real-time performance, and can meet the needs of practical applications. [ABSTRACT FROM AUTHOR]

Protein structure expectation is a particularly mind boggling issue that it is frequently assaulted and disintegrated using four distinct levels and they are: 1-D forecast of under- lying highlights along the essential succession of amino acids sequences, 2-D forecast of spatial connections between the sequence of amino acids, 3-D forecast of a tertiary structure of protein and quaternary structure of protein. This paper also try to introduce some assessment tools for finding the accuracy of result from applying ML and DL tools. And try to analyses and compare various algorithms based on deep learning methods verses machine learning methods used for sequence prediction. This paper also examines the turn of events and utilization of concealed Markov model, uphold vector machines, Bayesian techniques, and grouping strategies. This investigation will be helpful in creating future strategies to improve the exactness of protein auxiliary structure expectation. In this paper, also introduce and summarize the problem of quantum essential elements of: (1) Variational auto-encoder (2) GAN, generative adversarial network (3) RNN, recurrent neural (4) CNN, convolutional neural networks protein structure prediction. Later on also summarizes the evolution of predictive algorithms for 1-4D structure of protein from Amino Acid Sequences and summarize the deep learning ideas to prediction of structure of protein and learned algorithms of the last decade. [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]

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]

Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26–0.81, mean reader-AI kappa = 0.49, 95% CI 0.27—0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform. [ABSTRACT FROM AUTHOR]

The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation-how to accurately account for the inherent stochastic nature of certain devices. While conventional deterministic models have served as indispensable tools for circuit designers, they fall short when it comes to capturing the subtle yet critical variability exhibited by many electronic components. In this paper, we present an innovative approach that transcends the limitations of traditional modeling techniques by harnessing the power of machine learning, specifically Mixture Density Networks (MDNs), to faithfully represent and simulate the stochastic behavior of electronic devices. We demonstrate our approach to model heater cryotrons, where the model is able to capture the stochastic switching dynamics observed in the experiment. Our model shows 0.82% mean absolute error for switching probability. This paper marks a significant step forward in the quest for accurate and versatile compact models, poised to drive innovation in the realm of electronic circuits. [ABSTRACT FROM AUTHOR]

While traveling, people need to utilize smart tourism service systems based on the Internet of Things (IoT) to enhance their journeys' convenience. However, existing IoT-based smart tourism service systems still possess certain shortcomings. In light of this, this paper delves into the integration of IoT and machine learning (ML) technologies to improve the design of smart tourism service systems and establish a platform for intelligent tourism services. Simulated datasets are employed to evaluate the performance and functionality of the smart tourism service system. The primary contributions, as inferred from the test results, are as follows: In area 1, the model achieves a congestion recognition rate of 92.6% with an error rate of only 1.5% in congestion judgment. In area 2, congestion is accurately identified 93.5% of the time, with a misjudgment rate of just 1.2%. In area 3, the model achieves a congestion identification rate of 94.2% along with a 1.3% misjudgment rate, effectively identifying congestion situations in scenic spots. When comparing the research and design system with the system based on the support vector machine (SVM) algorithm, the results demonstrate that the smart tourism service system designed in this paper, leveraging IoT technology and ML algorithms, achieves higher prediction accuracy than the SVM-based system. These findings can serve as a valuable guide for designing future smart tourism service systems. [ABSTRACT FROM AUTHOR]

This paper presents a cutting-edge Sustainable Power Management System for Light Electric Vehicles (LEVs) using a Hybrid Energy Storage Solution (HESS) integrated with Machine Learning (ML)-enhanced control. The system's central feature is its ability to harness renewable energy sources, such as Photovoltaic (PV) panels and supercapacitors, which overcome traditional battery-dependent constraints. The proposed control algorithm orchestrates power sharing among the battery, supercapacitor, and PV sources, optimizing the utilization of available renewable energy and ensuring stringent voltage regulation of the DC bus. Notably, the ML-based control ensures precise torque and speed regulation, resulting in significantly reduced torque ripple and transient response times. In practical terms, the system maintains the DC bus voltage within a mere 2.7% deviation from the nominal value under various operating conditions, a substantial improvement over existing systems. Furthermore, the supercapacitor excels at managing rapid variations in load power, while the battery adjusts smoothly to meet the demands. Simulation results confirm the system's robust performance. The HESS effectively maintains voltage stability, even under the most challenging conditions. Additionally, its torque response is exceptionally robust, with negligible steady-state torque ripple and fast transient response times. The system also handles speed reversal commands efficiently, a vital feature for real-world applications. By showcasing these capabilities, the paper lays the groundwork for a more sustainable and efficient future for LEVs, suggesting pathways for scalable and advanced electric mobility solutions. [ABSTRACT FROM AUTHOR]

Ekman and Friesen's (1969) seminal theoretical paper on the leakage hierarchy sparked decades of research on the relationship between nonverbal cues and deception. Yet skepticism over the strength and reliability of behavioral cues to deception has been building over the years (DePaulo et al., 2003; Patterson et al., 2023; Vrij et al., 2019). However, the last two decades have seen dramatic growth in research paradigms, interviewing techniques, integration of technology, automated coding methods, and facial research, suggesting a need for reexamination of the current state of the field. This special issue includes theoretical and empirical papers that advance our understanding of the link between nonverbal cues and deception. This collection of papers suggests there is cause for some optimism in the field of nonverbal deception detection and signals some fruitful avenues for future research. Specifically, deception research in ecologically valid, high-stakes lie-detection situations using a multi-modal approach has good promise for differentiating truth-tellers from liars. [ABSTRACT FROM AUTHOR]

The world is witnessing a rise in speech-enabled devices serving as epistemic informants to their users. Some philosophers take the view that because the utterances produced by such machines can be phenomenologically similar to an equivalent human speech, and they may deliver the same function in terms of delivering content to their audience, such machine utterances should be conceptualized as “assertions”. This paper argues against this view and highlights the theoretical and pragmatic challenges faced by such a conceptualization which seems to be based on a “functionalist” account of assertion. Instead, the paper argues that the view that some such machine utterances can be “proxy assertions”, made on behalf of the designers and/or deployers of such machines is more tenable. Existing accounts of such a view, however, need further refinement, which is provided here. The paper then discusses implications of this view for design of such machines, particularly those enabled and equipped with machine learning capabilities. [ABSTRACT FROM AUTHOR]

Increasing computational power has led to the expansion of civil engineering research into using machine learning concepts for developing improved design strategies. These strategies are particularly useful for the design of extra-terrestrial habitats under uncertain environmental conditions. This paper focuses on building an unsupervised machine learning model (convolutional autoencoder) capable of detecting patterns in arch shapes and differentiating between their stress and displacement contours. Foremost, detailed discussions of the model's architecture and input data are presented. The variation of arch shapes and contours between cluster centroids in the latent space is determined, proving the capability of optimisation by moving towards clusters with optimal contours. Finally, a regression model is built to investigate the relationship between the input geometric variables and the latent space representation. We prove that the autoencoder and regression models produce arch shapes with logical structural contours given a set of input geometric variables. The results presented in this paper provide essential tools for the development of an automated design strategy capable of finding optimal arch shapes for extra-terrestrial habitats. [ABSTRACT FROM AUTHOR]

Context: With the union of machine learning (ML) and quantum chemistry, amid the debate between machine-learned functionals and human-designed functionals in density functional theory (DFT), this paper aims to demonstrate the generation of potential energy surfaces using computations with machine-learned density functional approximation (ML-DFA). A recent research trend is the application of ML in quantum sciences in the design of density functionals such as DeepMind's Deep Learning model (DeepMind21, DM21). Though science reported the state-of-the-art performance of DM21, the opportunity to utilize DeepMind's pretrained DM21 neural networks in computations in quantum chemistry has not yet been tapped. So far in the literature, the Deep Learning density functionals (DM21) have not been applied to generate potential energy surfaces. While the superior accuracy of DM21 has been reported, there is still a scarcity of publications that apply DM21 in calculations in the field. In this context, for the first time in literature, neural density functionals inferring 2D potential energy surfaces (ML-DFA-PES) based on machine-learned DFA-based computational method is contributed in this paper. This paper reports the ML-DFA-generated PES for C4H8, H2O, H2, and H2+ by employing a pretrained DM21m TensorFlow model with cc-pVDZ basis set. In addition, we also analyze the long-range behavior of DM21 based PES to investigate the ability to describe a system at long ranges. Furthermore, we compare PES diagrams from DM21 with popular DFT functionals (b3lyp/ PW6B95) and CCSD(T). Methods: In this method, 2D potential energy surfaces are obtained using a method that relies upon the neural network's ability to accurately learn the mapping between 3D electron density and exchange–correlation potential. By inserting Deep Learning inference in DFT with a pretrained neural network, self-consistent field (SCF) energy at different geometries along the coordinates of interest is computed, and then, potential energy surfaces are plotted. In this method, first, the electron density is computed mathematically, and this computed 3D electron density is used as a ML feature vector to predict the exchange correlation potential as a ML inference computed by a forward pass of pre-trained DM21 TensorFlow computational graph, followed by the computation of self-consistent field energy at multiple geometries, and then, SCF energies at different bond lengths/angles are plotted as 2D PES. We implement this in a python source code using frameworks such as PySCF and DM21. This paper contributes this implementation in open source. The source code and DM21-DFA-based PES are contributed at https://sites.google.com/view/MLfunctionals-DeepMind-PES. [ABSTRACT FROM AUTHOR]

The paper examines whether the structure of the risk factor disclosure in an IPO prospectus helps explain the cross-section of first-day returns in a sample of Chinese initial public offerings. This paper analyzes the semantics and content of risk disclosure based on an unsupervised machine learning algorithm. From both long-term and short-term perspectives, this paper explores how the information effect and risk effect of risk disclosure play their respective roles. The results show that risk disclosure has a stronger risk effect at the semantic novelty level and a more substantial information effect at the risk content level. A novel aspect of the paper lies in the use of text analysis (semantic novelty and content richness) to characterize the structure of the risk factor disclosure. The study shows that initial IPO returns negatively correlate with semantic novelty and content richness. We show the interaction between risk effect and information effect on risk disclosure under the nature of the same stock plate. When enterprise information transparency is low, the impact of semantic novelty and content richness on the IPO market is respectively enhanced. [ABSTRACT FROM AUTHOR]

In this survey paper, we review recent advances of compressed sensing applied to systems and control. Compressed sensing has been actively researched in the field of signal processing and machine learning. More recently, the method has been applied to systems and control problems, such as sparse feedback gain design, reduced-order control, and maximum hands-off control. This paper introduces these important applications of compressed sensing to systems and control. MATLAB programs for the numerical examples shown in this survey paper are available as supplementary materials. [ABSTRACT FROM AUTHOR]

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals. [ABSTRACT FROM AUTHOR]

In this paper, I explore Derrida’s concept of exteriorization in relation to texts generated by machine learning. I first discuss Heidegger’s view of machine creation and then present Derrida’s criticism of Heidegger. I explain the concept of iterability, which is the central notion on which Derrida’s criticism is based. The thesis defended in the paper is that Derrida’s account of iterability provides a helpful framework for understanding the phenomenon of machine learning–generated literature. His account of textuality highlights the incalculability and mechanical elements characteristic of all texts, including machine-generated texts. By applying Derrida’s concept to the phenomenon of machine creation, we can deconstruct the distinction between human and non-human creation. As I propose in the conclusion to this paper, this provides a basis on which to consider potential positive uses of machine learning. [ABSTRACT FROM AUTHOR]

In recent years, there has been a rapid increase in the application of machine learning methods about predicting the incidence of dengue fever. However, the predictive factors and models employed in different studies vary greatly. Hence, we conducted a systematic review to summarize machine learning methods and predictors in previous studies. We searched PubMed, ScienceDirect, and Web of Science databases for articles published up to July 2023. The selected papers included not only the forecast of dengue incidence but also machine learning methods. A total of 23 papers were included in this study. Predictive factors included meteorological factors (22, 95.7%), historical dengue data (14, 60.9%), environmental factors (4, 17.4%), socioeconomic factors (4, 17.4%), vector surveillance data (2, 8.7%), and internet search data (3, 13.0%). Among meteorological factors, temperature (20, 87.0%), rainfall (20, 87.0%), and relative humidity (14, 60.9%) were the most commonly used. We found that Support Vector Machine (SVM) (6, 26.1%), Long Short-Term Memory (LSTM) (5, 21.7%), Random Forest (RF) (4, 17.4%), Least Absolute Shrinkage and Selection Operator (LASSO) (2, 8.7%), ensemble model (2, 8.7%), and other models (4, 17.4%) were identified as the best models based on evaluation metrics used in each article. These results indicate that meteorological factors are important predictors that cannot be ignored and SVM and LSTM algorithms are the most commonly used models in dengue fever prediction with good predictive performance. This review will contribute to the development of more robust early dengue warning systems and promote the application of machine learning methods in predicting climate-related infectious diseases. [ABSTRACT FROM AUTHOR]

In the process of calisthenics training, athletes' movements need to be monitored and analyzed. The traditional monitoring method has the problem of insufficient resolution, which limits the improvement of accuracy and reliability. In this paper, a high dimensional image super resolution model is developed by using infrared spectral imaging technology to improve the monitoring and simulation results in the process of aerobics training. In this paper, infrared spectral imaging technology combined with deep learning algorithm is used to design a high dimensional image super resolution model. Infrared spectral imaging data were collected during aerobics training, and the data were trained and learned to extract features and realize super-resolution reconstruction. Finally, the model is tested and verified to evaluate its monitoring and simulation effect in aerobics training. The experimental results show that the high-dimensional image super resolution model has good monitoring and simulation effects in aerobics training. By increasing the detail and clarity of the image, the model can provide more accurate motion analysis and motion suggestions, so as to help the trainer improve the movement skills and improve the training effect. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is a specialized domain within the broader science of Artificial Intelligence. Machine Learning facilitates the acquisition of knowledge by machines through the analysis of data. As a result, ML empowering them to generate predictions or make informed decisions. Machine learning has found extensive application across diverse healthcare domains, encompassing the areas of disease diagnosis, prognosis, treatment, and disease management. Nevertheless, the integration and implementation of machine learning models in healthcare face numerous challenges and limitations. These include issues related to data governance, data quality, legal considerations, ethical concerns, and interpretability. This article presents a thorough examination of diverse machine learning concepts and their potential implications in the healthcare domain. Furthermore, the paper explores various categories of machine learning models and examines the diverse range of algorithms that can be employed to address specific tasks. In addition, this paper presents a precis of various machine learning techniques and their respective applications in the field of healthcare, utilizing diverse datasets and performance metrics. Ultimately, this study examines the barriers and potential remedies surrounding the implementation and utilization of machine learning in the healthcare sector. Additionally, offers suggestions for future research avenues. [ABSTRACT FROM AUTHOR]

Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear information on when researchers should stop the reviewing process. In this study, we conducted a retrospective screening simulation using 27 systematic reviews in education and educational psychology. We evaluated the sensitivity, specificity, and estimated time savings of several learning algorithms and heuristic stopping criteria. The results showed, on average, a 58% (SD = 19%) reduction in the screening workload of irrelevant records when using learning algorithms for abstract screening and an estimated time savings of 1.66 days (SD = 1.80). The learning algorithm random forests with sentence bidirectional encoder representations from transformers outperformed other algorithms. This finding emphasizes the importance of incorporating semantic and contextual information during feature extraction and modeling in the screening process. Furthermore, we found that 95% of all relevant abstracts within a given dataset can be retrieved using heuristic stopping rules. Specifically, an approach that stops the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers yielded the most significant gains in terms of specificity (M = 42%, SD = 28%). However, the performance of the heuristic stopping criteria depended on the learning algorithm used and the length and proportion of relevant papers in an abstract collection. Our study provides empirical evidence on the performance of machine learning screening algorithms for abstract screening in systematic reviews in education and educational psychology. [ABSTRACT FROM AUTHOR]

Forecasting of load is essential for operating power systems. India recently witnessed one of the worst power crisis with the highest ever power demand of 207 GW on April 29, 2022. The demand in the month of May and June 2022 was estimated to reach 215 GW. The peak demand this year 2023, according to the electricity ministry, is predicted to be around 230 GW from April to June. The inability to meet certain fundamental issues as power can take a toll on any country's economy. Proper prediction helps in proper decision making and planning. The main objective of this paper is to predict day ahead electrical load demand for Assam. Statistical and Machine Learning Algorithms has been studied. The study has been carried out using real-time data for the years 2016, 2017 and 2018. The paper presents a detailed analysis of the different hyper parameters of the deep learning models and their effect is seen on the learning efficiency. A novel stacked forecasting model is proposed using neural networks as base learners and CatBoost as the meta-learner. The performance of the proposed model has been evaluated and compared with individual models in terms of training time and accuracy using different error metrics namely MAE, MSE, RMSE, MAPE and R2 score. A comparison of the proposed prediction model with the prediction models available in literature has been presented. The conclusion states that both the statistical and machine learning algorithms used in this study act as useful tools for daily load forecasting with considerable accuracy; yet machine learning algorithm outperforms the statistical methods. The entire work has been done in Google Colaboratory using Python as the programming language. [ABSTRACT FROM AUTHOR]

The present work serves as a theoretical commentary over the Himalayan Re-gridded and Observational EXperiment (HiROX) and distributes the conceived framework grown under the two-phase experiment. HiROX is a scientific effort aimed at revising the existing precipitation knowledge over the Central Himalayas over the state of Uttarakhand, including the Upper Ganga Basin. The first phase of the study is experimenting with machine learning methods aimed at developing re-gridded precipitation datasets capable of incorporating selective precipitation features and distributing the developed machine learning framework to the research community. The model is designed for easy reproduction of re-gridded precipitation data and the ability to adapt to the available precipitation datasets subject to missing and/or modified inputs. The model is capable of ingesting gridded precipitation datasets at varying spatial resolutions, along with point data values, and can return gridded precipitation information at a spatial resolution of 5 × 5 km2. The second objective of the companion paper under HiROX is the realization of the features of the developed model and exercising it in the development of two high-resolution precipitation datasets at daily timesteps, namely HiROX-1 (~50 yrs) and HiROX-2 (~20 yrs). The present paper limits the discourse to robust development, and the produced datasets are discussed and distributed in the joint paper – Himalayan Re-gridded and Observational Experiment (HiROX): Part II – Application. [ABSTRACT FROM AUTHOR]