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New generation techniques are emerging in museum exhibitions. These technologies include advanced technologies such as 3D animation, VR/augmented reality, robots, artificial intelligence, and face reconstruction. Museums undertake large projects where different disciplines work together harmoniously.The use of face reconstruction and artificial intelligence in New Generation Museum Projects is quite common around the world. In the post-truth era, museums, in order to maintain their identities, use artificial intelligence to reformat their archaeological findings by identifying them.Museum collections use facial recognition using the face reconstruction technique. Collections have become new interactive presentations that speak to visitors simultaneously with facial expressions, sound, and movement effects, directing them, and calling themselves "bugs." Even individuals freely participate in the experience of these museum works. Technology will continue to bring us new developments beyond the reality of three-dimensional, five-sensory reality outside museum showcases.Are the museums still the home of source of inspiration? Are they still evaluated again and again like former, or else are they place which have old, broken-down ruins, hovels or shambles? In this article, we try to find an answer for these questions. In 2024, the year which advancement of technology is at a good point, museums exhibitions are places which new technological developments are used in museums. With this purpose, increase in the number of new projects in museums is seen. A lot of areas of profession of different disciplines is obliged to work together.

The research paper on Digital Transformation in Industry focuses on the study of the latest technologies and trends in industry. The research methodology used is a combination of primary and secondary research. Employees of different organizations provided insights on the recent trends in their respective industry. This helped to gather real, accurate, and meaningful data for data analysis. The research paper helps to understand the shifts in digital transformation in different industries like service industry, manufacturing, transport, construction, insurance & government, etc. The study has included how companies like Netflix, Tata Motors, Amazon, etc.have successfully undergone digital transformation to elevate customer experience. In order to measure the success of digital transformation efforts, organizations need to develop digital transformation success metrics. This has also been covered. The digital transformation technologies embraced by organizations are Machine Learning, Artificial Intelligence, Big Data, Internet of Things, Cloud Computing, Augmented Reality, Robotics and Robotic Process Automation etc. Customers, employees, technology, and a culture of change are the pillars of today's digital revolution. While embracing digitization, companies face various barriers like resistance to change, scarcity of funds, lack of technical expertise, lack of co-ordination and communication among departments, absence of a leader to handle the transition, etc. Companies need to overcome these hurdles to be successful in data driven transformation. Finally, the research paper delves on the recommendations for a thriving digitalization strategy. These include hiring competitive talent to drive the change, improving cyber security and enhancing data protection, leveraging efficient technology which is effective, developing simple, easy to use systems, encouraging people of the organization to embrace the digital initiatives as usually company culture resists new initiatives. While introducing digital initiatives, companies need to align their business objectives with the current trends and use only those technologies which are relevant for one's business. Else, the costs of digitization may pile up leading to unnecessary cost escalation. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the survey is to determine how theatre directors’ perceive the impact of ongoing technological change on key aspects of theatre management. Design/methodology/approach: The study adopted a qualitative perspective. Empirical data was obtained through interviews with theatre directors. The interviews were in-depth, partly standardised and unstructured. The research sample included theatre directors from different countries. Findings: The research identified aspects of theatre management that are particularly susceptible to the dynamic development of next-generation technology. The theatre managers most frequently perceive the impact of technology in relation to: the meaning and function of the theatre; funding of theatre operations; stakeholder relations; maintaining audience relations; aspects of the production of theatre productions; and staffing policy and the functioning of theatre companies. Strongly negative perceptions of the impact of technology are held by theatre directors on, among other things, the functions and relevance of the theatre, the impact of cultural policy, and the autonomy of arts. In contrast, positive perceptions of the impact of technology mainly related to aspects of the production of theatre performances. Research limitations/implications: The research is primarily limited to theatre directors' perspectives and the purposive nature of the sample cautions against broad claims about the entire population. Moreover, private and not-for-profit theatres are underrepresented. However, these findings could serve as a foundation for quantitative research aiming to generalize and explore new technology implementation processes in theatres. Further research into the implementation of artificial intelligence, perceived as a threat to the arts and theatre companies, seems crucial. Practical implications: The results of the research can be taken into account by theatre directors as they implement next-generation technologies. Originality/value: To the best of the author's knowledge, this article presents the first research on theatre managers' perceptions of ongoing technological changes in key management areas grounded in literature. The study's international scope enhances its value. The findings could benefit theatre directors, cultural organizers, researchers in theatre and cultural management, third-sector organizations in the theatre industry, and developers of new cultural technologies. [ABSTRACT FROM AUTHOR]

[Purpose] The paper aims to understand the relationship between Artificial Intelligence, Law and Discourse (namely, the one used in judicial decisions). To then analyze how the interference of Artificial Intelligence in judicial decisions can guarantee impartiality and if that interference should be the intention. [Methodology/Approach/Design] For this purpose, a theoretical reflection was carried out around the central concepts: Law, judicial decisions, impartiality, and artificial intelligence. For this, bibliographical research was carried out. [Findings] Artificial Intelligence (AI) is a current reality. A tautology that has been repeated so that individuals do not forget the challenges they face as a result. Thus, the courts, the judicial professions, and the law, are being confronted with this “new” reality, whether in a challenge format or from the perspective of the facilities that AI brings coupled. It remains to be seen, then, what will be the principles of law. If AI will interfere in judicial decision-making (as has already been tested), what will become of the principle of impartiality? And is this a principle that is intended to be maintained or even currently exists? The article goes through all these corners: the intertwining between discourse, law and AI; impartiality and AI as a decision-maker.

Copyright of Law, State & Telecommunications Review / Revista de Direito, Estado e Telecomunicações is the property of Grupo de Estudos em Direito das Telecomunicacoes (GETEL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

ChatGPT is a chatbot that is based on the generative pretrained transformer architecture as an artificial inteligence-based large language model. Its widespread use in healthcare practice, research, and education seems to be (increasingly) inevitable. Also considering the relevant limitations regarding privacy, ethics, bias, legal, and validity, in this article, its use as a supplement (for sure not as a substitute for physicians) is discussed in light of the recent literature. Particularly, the "opinion" of ChatGPT about how it can help/harm physiatry is exemplified. [ABSTRACT FROM AUTHOR]

Background and Aims: This study presents a comprehensive overview of a well‐recognized critical pathogen Acinetobacter baumannii and demonstrates the potential of artificial intelligence (AI) in discovering new antibiotics. It is well‐known for causing serious infections, especially in patients admitted to intensive care units, which is further compromised by bacterium's resistance to even last‐resort antibiotics like carbapenems, posing critical challenges in its treatment and eradication. This article discusses Abaucin, a revolutionary antibiotic created from artificial intelligence, as a creative and potential treatment for A. baumannii. Methods: A comprehensive literature search was conducted using various databases including PubMed, Google Scholar, Cochrane, Science Direct, MEDLINE. Only articles written in English were considered. This correspondence was written after a thorough evaluation of all the articles that discussed Abaucin's discovery, potency against A. baumannii, and the potential of AI in discovering new antibiotics. In addition, references from the selected articles were also examined to ensure a comprehensive review of the literature. Results: This article highlights the discovery of a novel AI‐derived antibiotic, Abaucin that offers a promising solution for A. baumannii infections. Abaucin has a narrow range of action that minimizes the risk of inter‐pathogen spread of resistance, making the drug more appealing and effective. Its potential to suppress wound infections caused by multidrug‐resistant A. baumannii paves a hope for the eradication of superbugs. Further investigation and trials are needed to determine its effectiveness in human subjects and its integrity in the clinical workflow. machine learning (ML) and precision approach could revolutionize treatment strategies. Conclusion: The discovery of Abaucin as an antibiotic to combat drug‐resistant A. baumannii bacteria showcases AI and ML's potential to uncover novel antibiotics, thereby offering alternative treatment options. This novel inception of the AI‐derived antibiotic, Abaucin marks the first step in introducing an innovative treatment option and represents a substantial leap in the treatment of drug‐resistant illnesses and the use of AI in medical advancements. [ABSTRACT FROM AUTHOR]

Abstract There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly. The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices. To maximise the data rate fairness of Narrow Band IoT devices, a multi‐dimensional indoor localisation model is devised, consisting of transmission power, data scheduling, and time slot scheduling, based on a network model that employs non‐orthogonal multiple access via a relay. Based on this network model, the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors, while taking into account the Narrow Band IoT network: The multi‐dimensional indoor localisation optimisation model of equipment tends to minimize data rate, energy constraints and EH relay energy and data buffer constraints, data scheduling and time slot scheduling. As a result, each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised. We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion. The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay. However, the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference, which impacts NOMA's performance enhancement. Through simulation, the proposed approach is successfully shown. These improvements have boosted the network's energy efficiency by 44.1%, data rate proportional fairness by 11.9%, and spectrum efficiency by 55.4%.

Copyright of Uporabna Informatika is the property of Slovensko Drustvo Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly. The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices. To maximise the data rate fairness of Narrow Band IoT devices, a multi‐dimensional indoor localisation model is devised, consisting of transmission power, data scheduling, and time slot scheduling, based on a network model that employs non‐orthogonal multiple access via a relay. Based on this network model, the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors, while taking into account the Narrow Band IoT network: The multi‐dimensional indoor localisation optimisation model of equipment tends to minimize data rate, energy constraints and EH relay energy and data buffer constraints, data scheduling and time slot scheduling. As a result, each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised. We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion. The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay. However, the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference, which impacts NOMA's performance enhancement. Through simulation, the proposed approach is successfully shown. These improvements have boosted the network's energy efficiency by 44.1%, data rate proportional fairness by 11.9%, and spectrum efficiency by 55.4%. [ABSTRACT FROM AUTHOR]

We call artificial intelligence any machine that processes information with some purpose, complying with the logical rules of Turing's computation described more than 70 years ago. These machines work with instructions called algorithms, a finite and well-defined sequence of information processing implemented by automata (computers) or any digital technology to optimize a process. This means that the purpose of artificial intelligence is optimization

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

The article discusses the relationship between artificial intelligence (AI) and journalism, focusing on the practice of the opinion journalism. The aim is to identify the possibilities and limitations of using this tool to produce reflective articles dedicated to judge controversial issues. Supported by the historical-dialectic materialist method, the approach is based on a pedagogical experience in which students of an undergraduate course were encouraged to compare their own texts with elaborations requested from the ChatGPT chatbot, launched in November 2022. As a result, we consider that AI, by itself, offers little to the exercise of opinion, since it pasteurizes the information retrieved by the algorithms, does not assimilate socio-cultural filters and, above all, generates superficial essays that fall far short of the human capacity to analyze the emerging agendas of the social world. [ABSTRACT FROM AUTHOR]

This study refers to the common carp (Cyprinus carpio), which is an adaptable species that enriches the variability of quantitative and qualitative characteristics and increases genetic diversity. Local aquaculture populations of common carp, called "landraces," have developed due to different environmental conditions and breeding efforts. However, the introduction of carp in some areas has led to negative impacts on natural aquatic ecosystems. To improve the quality of economically important fish species, the variation in morphological, physiological, and biochemical characteristics is utilized. In this study, a patent application for a system for reproduction, selection, and growth of fish fry with the simulation of natural conditions is described. The article explains the method used to replicate the natural aquatic environment and create viable products with high genetic adaptability to its conditions. The process falls into the category of extensive aquaculture, promoting sustainable aquaculture by increasing the percentage of ecological and environmentally friendly productions. The study concludes with the results and the development of a set-up of the station for laboratory use. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI), with an increase in serum creatinine, is a common adverse drug event. Although various clinical studies have investigated whether a combination of two nephrotoxic drugs has an increased risk of AKI using traditional statistical models such as multivariable logistic regression (MLR), the evaluation metrics have not been evaluated despite the fact that traditional statistical models may over-fit the data. The aim of the present study was to detect drug-drug interactions with an increased risk of AKI by interpreting machine-learning models to avoid overfitting. Methods: We developed six machine-learning models trained using electronic medical records: MLR, logistic least absolute shrinkage and selection operator regression (LLR), random forest, extreme gradient boosting (XGB) tree, and two support vector machine models (kernel = linear function and radial basis function). In order to detect drug-drug interactions, the XGB and LLR models that showed good predictive performance were interpreted by SHapley Additive exPlanations (SHAP) and relative excess risk due to interaction (RERI), respectively. Results: Among approximately 2.5 million patients, 65,667 patients were extracted from the electronic medical records, and assigned to case (N = 5,319) and control (N = 60,348) groups. In the XGB model, a combination of loop diuretic and histamine H2 blocker [mean (|SHAP|) = 0.011] was identified as a relatively important risk factor for AKI. The combination of loop diuretic and H2 blocker showed a significant synergistic interaction on an additive scale (RERI 1.289, 95% confidence interval 0.226-5.591) also in the LLR model. Conclusion: The present population-based case-control study using interpretable machine-learning models suggested that although the relative importance of the individual and combined effects of loop diuretics and H2 blockers is lower than that of well-known risk factors such as older age and sex, concomitant use of a loop diuretic and histamine H2 blocker is associated with increased risk of AKI. [ABSTRACT FROM AUTHOR]

This manuscript presents the results obtained in a study of the importance of a system based on live simulation resources in training Defence University students in combat drills. This training is essential for understanding how to conduct combat operations and how the commander should manage the activity. In confronting the considerable sums spent on the acquisition of modern simulation systems, a research question was formulated to ascertain the impact of these resources on the educational process. The results of the research will be used as an important argument for the level of application and possibly further acquisition of systems. A research sample of almost 200 students was used for the research. The results were statistically processed and conclusions were formulated and discussed. The main result of the article is the statistical difference found between the units using the means of live simulation and the lower results of the groups that did not have these means during training. [ABSTRACT FROM AUTHOR]

The use of plastic in modern life is increasing rapidly, causing the number of people who use plastic to increase, one of which is when shopping. The function of plastic bags as packaging for luggage is not comparable to the impact caused by plastic waste in the years to come. Plastic bags take a long time, even hundreds to thousands of years, to completely decompose. In order to support the government's program to reduce the use of plastic bags, this study will discuss how to detect cloth bags as a substitute for plastic bags. In this research, a system will be implemented to detect the use of cloth bags with Roboflow and Yolo v5. After carrying out all stages of the research, it can be concluded that the goodie bag detection model has been successfully created. The detection model was created using the YOLOV5 algorithm. The dataset used consists of 102 goodie bag images. The process model uses 100 epochs with the training result mAP@0.5 is 89.8%. So, in other words, it can be said that YOLO v5 can detect goodie bags very well.

Drug information service is an assistance service to handle the needs of pharmacists related to medicines consumed by patients at the Lasi Health Center, Agam Regency. Nowadays, most of drug information services always require pharmacists to carry out their services, although there is limited number of pharmacists for providing drug information services at the Lasi Health Center, Agam Regency. This study aims to optimize drug information services so that the services can be carried out without the direct presence of a pharmacist. The data used in this study were drug prescription data available at the Pharmacy of Lasi Health Center Agam for the last 12 months and drug information services provided by pharmacists at the Lasi Health Center Agam Regency. This study used the backward chaining method to identify the drugs prescribed to the patients. The result achieved by this study were 356 Rules that could be applied directly to drug information services, with an accuracy rate of 100%. The rules generated using the backward chaining method can be used to optimize drug information services at the Lasi Health Center in Agam Regency without having to be served directly by pharmacists.

Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultrawide- band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers.

We present a Machine Learning algorithm based on Python which can be used to aid COVID-19 diagnosis. This algorithm employs Convolutional Neural Networks (CNN) of ResNet-18 architecture from thoracic X-ray images to build a trained dataset that enables further comparisons between common pulmonary diseases and COVID-19 diagnosed patients to classify the radiological findings as being due the COVID-19 or other pathologies. We discuss the importance of setting the right parameters related to training and what they might represent in clinical procedures. We used a dataset containing 942 COVID-19 labeled radiographs from HCPA - Hospital das Clínicas de Porto Alegre and compared it to a public dataset from NIH Clinical Center containing images of pulmonary diseases. Lastly, our trained model had an accuracy of 81.76% for the imbalanced classes and an accuracy of 46.94% for the balanced classes, when compared to other pulmonary diseases such as pneumonia, edema, mass, consolidation, and fibrosis. These results disclose the difficulty of diagnosing COVID-19 from a chest radiograph as it resembles other pulmonary illnesses and makes room for further research in this matter.

Artificial intelligence and machine learning algorithms have an increasingly pervasive presence in all fields of science due to their ability to find patterns, model dynamic systems, and make predictions of complex processes. This review aims at providing the researchers in the mineral processing area with structured knowledge about the applications of machine learning algorithms to the leaching process, showing the applications of techniques such as artificial neural networks (ANN), support vector machines (SVM), or Bayesian networks (BN), among others. Additionally, future perspectives are indicated, emphasizing both the generalization of the algorithms and the productive potential of the application of modeling, simulation, and optimization of the tools studied to industrial processes.

Nowadays, there is a trend to employ adaptive solutions in mobile communication. The adaptive transmission systems seem to answer the need for highly reliable communication that serves high data rates. For efficient adaptive transmission, the future Channel State Information (CSI) has to be known. The various prediction methods can be applied to estimate the future CSI. However, each method has its bottlenecks. The task is even more challenging while considering the future 5G/6G communication where the employment of sub-6 GHz and millimetre waves (mmWaves) in narrow-band, wide-band and ultra-wide-band transmission is considered. Thus, author describes the differences between sub-6 GHz/mmWave and narrow-band/wide-band/ultrawide- band channel prediction, provide a comprehensive overview of available prediction methods, discuss its performance and analyse the opportunity to use them in sub-6 GHz and mmWave systems. We select Long Short-Term Memory Recurrent Neural Network (RNN) as the most promising technique for future CSI prediction and propose optimising two of its parameters - the number of input features, which was not yet considered as an opportunity to improve the performance of CSI prediction, and the number of hidden layers. [ABSTRACT FROM AUTHOR]

Background: Despite a 2010 Kenyan constitutional amendment limiting members of elected public bodies to < two-thirds of the same gender, only 22 percent of the 12th Parliament members inaugurated in 2017 were women. Investigating gender bias in the media is a useful tool for understanding socio-cultural barriers to implementing legislation for gender equality. Natural language processing (NLP) methods, such as word embedding and sentiment analysis, can efficiently quantify media biases at a scope previously unavailable in the social sciences. Methods: We trained GloVe and word2vec word embeddings on text from 1998 to 2019 from Kenya's Daily Nation newspaper. We measured gender bias in these embeddings and used sentiment analysis to predict quantitative sentiment scores for sentences surrounding female leader names compared to male leader names. Results: Bias in leadership words for men and women measured from Daily Nation word embeddings corresponded to temporal trends in men and women's participation in political leadership (i.e., parliamentary seats) using GloVe (correlation 0.8936, p = 0.0067, r 2 = 0.799) and word2vec (correlation 0.844, p = 0.0169, r 2 = 0.712) algorithms. Women continue to be associated with domestic terms while men continue to be associated with influence terms, for both regular gender words and female and male political leaders' names. Male words (e.g., he, him, man) were mentioned 1.84 million more times than female words from 1998 to 2019. Sentiment analysis showed an increase in relative negative sentiment associated with female leaders (p = 0.0152) and an increase in positive sentiment associated with male leaders over time (p = 0.0216). Conclusion: Natural language processing is a powerful method for gaining insights into and quantifying trends in gender biases and sentiment in news media. We found evidence of improvement in gender equality but also a backlash from increased female representation in high-level governmental leadership. [ABSTRACT FROM AUTHOR]

Background: Current digital cell imaging systems perform peripheral blood smear (PBS) analysis in limited regions of the PBS and require the support of manual microscopy without achieving full digital microscopy. We report a multicenter study that validated the Scopio Labs X100 Full Field PBS, a novel digital imaging system that utilizes a full field view approach for cell recognition and classification, in a decision support system mode. Methods: We analyzed 335 normal and 310 abnormal PBS from patients with various clinical conditions and compared the performance of Scopio's Full Field PBS as the test method, with manual PBS analysis as the reference method. Deming regression analysis was utilized for comparisons of WBC and platelet estimates. Measurements of WBC and platelet estimation accuracy along with the agreement on RBC morphology evaluation were performed. Reproducibility and repeatability (R&R) of the system were also evaluated. Results: Scopio's Full Field PBS WBC accuracy was evaluated with an efficiency of 96.29%, sensitivity of 87.86%, and specificity of 97.62%. The agreement between the test and reference method for RBC morphology reached 99.77%, and the accuracy for platelet estimation resulted in an efficiency of 94.89%, sensitivity of 90.00%, and specificity of 96.28%, with successful R&R tests. The system enabled a comprehensive review of full field PBS as shown in representative samples. Conclusions: Scopio's Full Field PBS showed a high degree of correlation of all tested parameters with manual microscopy. The novel full field view of specimens facilitates the long‐expected disengagement between the digital application and the manual microscope. [ABSTRACT FROM AUTHOR]

Crowdsourcing and human computational outputs provide a scalable and convenient way to perform different human generated tasks use to evaluate the effectiveness of different crowdsourcing platforms based on artificial intelligence. The crowdsourcing generated datasets depend on multiple factors including quality, task reward and accuracy measuring filters. The present was designed to evaluate reliability and stability and consistency of crowdsourcing platforms outputs. We conduct a longitudinal experiment over specified time and two crowdsourcing platforms, Amazon Mechanical Turk and CrowdFlower to demonstrate how the outcomes of reliability varies considerably across platforms whereas repeating tasks over same platform yields consistent results. Different tasks on three different datasets were performed to evaluate the quality of the task interface, employees' experience level supplied by the platform and the evaluation of aquracy of outcomes dependent on the task completion time. The outcomes revealed significant (p<0.05) preeminence of MTurk over CrowdFlower in terms of reliability, accuracy and completion time taken for a task. The tasks replicated on these two platforms showed significant difference in quality based outcomes. The data quality of same repeated tasks over different time was stable in the same platform while it's was different for different crowdsourcing platform over differ time span. It was concluded from the findings that by employing standard platform crowdsourcing settings varying order and magnitude of task completion on different platforms can easily be achieved with varying levels of accuracy. [ABSTRACT FROM AUTHOR]

Cardiac diastolic dysfunction is prevalent and is a diagnostic criterion for heart failure with preserved ejection fraction—a burgeoning global health issue. As gold-standard invasive haemodynamic assessment of diastolic function is not routinely performed, clinical guidelines advise using echocardiography measures to determine the grade of diastolic function. However, the current process has suboptimal accuracy, regular indeterminate classifications and is susceptible to confounding from comorbidities. Advances in artificial intelligence in recent years have created revolutionary ways to evaluate and integrate large quantities of cardiology data. Imaging is an area of particular strength for the sub-field of machine-learning, with evidence that trained algorithms can accurately discern cardiac structures, reliably estimate chamber volumes, and output systolic function metrics from echocardiographic images. In this review, we present the emerging field of machine-learning based echocardiographic diastolic function assessment. We summarise how machine-learning has made use of diastolic parameters to accurately differentiate pathology, to identify novel phenotypes within diastolic disease, and to grade diastolic function. Perspectives are given about how these innovations could be used to augment clinical practice, whilst areas for future investigation are identified.

The authors present a method of supporting decision-making processes with the use of predictive analytics. The method is presented with an example of investment project portfolios of a mining company. In the research multiple predictive models have been tested to determine most effective algorithms and key project attributes allowing to predict possible budget deviations. The proposed approach can be applied in other organizations managing projects according to project methodologies with access to structured project data. [ABSTRACT FROM AUTHOR]

Modeling is essential for modern science, and science-based policies are directly affected by the reliability of model outputs. Artificial intelligence has improved the accuracy and capability of model simulations, but often at the expense of a rational understanding of the systems involved. The lack of transparency in black box models, artificial intelligence based ones among them, can potentially affect the trust in science driven policy making. Here, we suggest that a broader discussion is needed to address the implications of black box approaches on the reliability of scientific advice used for policy making. We argue that participatory methods can bridge the gap between increasingly complex scientific methods and the people affected by their interpretations