Simple Artificial Intelligence Research Articles

Liability of Health Professionals Using Sensors, Telemedicine and Artificial Intelligence for Remote Healthcare.

In the last few decades, there has been an ongoing transformation of our healthcare system with larger use of sensors for remote care and artificial intelligence (AI) tools. In particular, sensors improved by new algorithms with learning capabilities have proven their value for better patient care. Sensors and AI systems are no longer only non-autonomous devices such as the ones used in radiology or surgical robots; there are novel tools with a certain degree of autonomy aiming to largely modulate the medical decision. Thus, there will be situations in which the doctor is the one making the decision and has the final say and other cases in which the doctor might only apply the decision presented by the autonomous device. As those are two hugely different situations, they should not be treated the same way, and different liability rules should apply. Despite a real interest in the promise of sensors and AI in medicine, doctors and patients are reluctant to use it. One important reason is a lack clear definition of liability. Nobody wants to be at fault, or even prosecuted, because they followed the advice from an AI system, notably when it has not been perfectly adapted to a specific patient. Fears are present even with simple sensors and AI use, such as during telemedicine visits based on very useful, clinically pertinent sensors; with the risk of missing an important parameter; and, of course, when AI appears "intelligent", potentially replacing the doctors' judgment. This paper aims to provide an overview of the liability of the health professional in the context of the use of sensors and AI tools in remote healthcare, analyzing four regimes: the contract-based approach, the approach based on breach of duty to inform, the fault-based approach, and the approach related to the good itself. We will also discuss future challenges and opportunities in the promising domain of sensors and AI use in medicine.

인공지능 이미지 생성 과정에서의 심미적 경험 고찰

Today, art is expanding in terms of economic efficiency with the development of artificial intelligence(AI) technology and opening up opportunities for creation to the general public, just as it has very effi ciently replaced human labor and time. Amid growing interest in copyright and execution subjects in images generated by AI behind the economic benefi ts of the breakthrough development of artificial intelligence technology, this study examines the difference between the aesthetic experience when the researcher drew an actual picture himself and when creating images through AI programs.
 With the development of ChatGPT utilizing AI, data information processing technology between language and images opens the horizon for new art creation. The implications of this study can be presented in three dimensions in terms of diff erentiated directions from existing art education. First, in relation to developing creativity through thought that can be made up of accumulation of knowledge and long experiential learning, image creation by AI lacks a process of humanistic thinking and needs to be reinforced in terms of learning. Second, the subject of education related to the creation of professional AI images and the setting of educational content and direction according to the subject are required through discussions on whether art AI utilization technology should be taught in engineering fi elds or in art fi elds as a matter of subjectivity and execution power. Finally, in art education, AI should drive a reconsideration of what kind of teaching methods are eff ective and what concepts are required for AI learning in cognitive science and pedagogical theories and methods.
 In conclusion;with the development of ChatGPT;artificial intelligence opens up the horizon for new art creation through data information processing between language and images;and the purpose and meaning of traditional art education need to accommodate this change and transform in the practice of liberal arts education at a time when the development of these technologies is required. The implications of this study show that by identifying the unique attributes of art regardless of the major area;understanding the functions of valuable works;and providing aesthetic experiences;a new horizon must be devised for liberal arts education that enables humanities;sociological;and engineering combinations as well as simple artificial intelligence technologies.

AI-Aided Search for New HIV-1 Protease Ligands.

The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. The search for new potential ligands for proteins involved in the life cycle of pathogens is, therefore, an extremely important research field today. In this work, we have considered the HIV-1 protease, one of the main targets for AIDS therapy. Several drugs are used today in clinical practice whose mechanism of action is based on the inhibition of this enzyme, but after years of use, even these molecules are beginning to be interested by resistance phenomena. We used a simple artificial intelligence system for the initial screening of a data set of potential ligands. These results were validated by docking and molecular dynamics, leading to the identification of a potential new ligand of the enzyme which does not belong to any known class of HIV-1 protease inhibitors. The computational protocol used in this work is simple and does not require large computational power. Furthermore, the availability of a large number of structural information on viral proteins and the presence of numerous experimental data on their ligands, with which it is possible to compare the results obtained with computational methods, make this research field the ideal terrain for the application of these new computational techniques.

Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.

The Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing. In this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard "Fully Connected" neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19. With the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies. These findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.

Automatic identification of conodont species using fine-grained convolutional neural networks

Conodonts are jawless vertebrates deposited in marine strata from the Cambrian to the Triassic that play an important role in geoscience research. The accurate identification of conodonts requires experienced professional researchers. The process is time-consuming and laborious and can be subjective and affected by the professional level and opinions of the appraisers. The problem is exacerbated by the limited number of experts who are qualified to identify conodonts. Therefore, a rapid and simple artificial intelligence method is needed to assist with the identification of conodont species. Although the use of deep convolutional neural networks (CNN) for fossil identification has been widely studied, the data used are usually from different families, genera or even higher-level taxonomic units. However, in practical geoscience research, geologists are often more interested in classifying species belonging to the same genus. In this study, we use five fine-grained CNN models on a dataset consisting of nine species of the conodont genus Hindeodus. Based on the cross-validation results, we show that using the Bilinear-ResNet18 model and transfer learning generates the optimal classifier. Area Under Curve (AUC) value of 0.9 on the test dataset was obtained by the optimal classifier, indicating that the performance of our classifier is satisfactory. In addition, although our study is based on a very limited taxa of conodonts, our research principles and processes can be used as a reference for the automatic identification of other fossils.

The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data.

Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared, ultraviolet light radiation, or gas. Smoke detectors are mostly used in residential areas while fire alarm systems (heat, smoke, flame, and fire gas detectors) are used in commercial, industrial and municipal areas. However, in addition to smoke, heat, infrared, ultraviolet light radiation, or gas, other parameters could indicate a fire, such as air temperature, air pressure, and humidity, among others. Collecting these parameters requires the development of a sensor fusion system. However, with such a system, it is necessary to develop a simple system based on artificial intelligence (AI) that will be able to detect fire with high accuracy using the information collected from the sensor fusion system. The novelty of this paper is to show the procedure of how a simple AI system can be created in form of symbolic expression obtained with a genetic programming symbolic classifier (GPSC) algorithm and can be used as an additional tool to detect fire with high classification accuracy. Since the investigation is based on an initially imbalanced and publicly available dataset (high number of samples classified as 1-Fire Alarm and small number of samples 0-No Fire Alarm), the idea is to implement various balancing methods such as random undersampling/oversampling, Near Miss-1, ADASYN, SMOTE, and Borderline SMOTE. The obtained balanced datasets were used in GPSC with random hyperparameter search combined with 5-fold cross-validation to obtain symbolic expressions that could detect fire with high classification accuracy. For this investigation, the random hyperparameter search method and 5-fold cross-validation had to be developed. Each obtained symbolic expression was evaluated on train and test datasets to obtain mean and standard deviation values of accuracy (ACC), area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score. Based on the conducted investigation, the highest classification metric values were achieved in the case of the dataset balanced with SMOTE method. The obtained values of ACC¯±SD(ACC), AUC¯±SD(ACU), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1-score¯±SD(F1-score) are equal to 0.998±4.79×10-5, 0.998±4.79×10-5, 0.999±5.32×10-5, 0.998±4.26×10-5, and 0.998±4.796×10-5, respectively. The symbolic expression using which best values of classification metrics were achieved is shown, and the final evaluation was performed on the original dataset.

A simple AI-enabled method for quantifying bacterial adhesion on dental materials

Purpose Measurement of bacterial adhesion has been of great interest for different dental materials. Various methods have been used for bacterial counting; however, they are all indirect measurements with estimated results and therefore cannot truly reflect the adhesion status. This study provides a new direct measurement approach by using a simple artificial intelligence (AI) method to quantify the initial bacterial adhesion on different dental materials using Scanning Electron Microscope (SEM) images. Materials and Methods Porphyromonas gingivalis (P.g.) and Fusobacterium nucleatum (F.n.) were used for bacterial adhesion on dental zirconia surfaces, and the adhesion was evaluated using SEM images at time points of one, seven, and 24 h(s). Image pre-processing and bacterial area measurement were performed using Fiji software with a machine learning plugin. The same AI method was also applied on SEM with Streptococcus mutans (S.m.) inoculated PMMA nano-structured surface at 1, 24, 72, and 168 h(s), and then further compared with the CLSM method. Results For both P.g. and F.n. initiation adhesion on zirconia, a new linear correlation (r2 > 0.98) was found between bacteria adhered area and time, such that: For S.m. on PMMA surface, live/dead staining CLSM method and the newly proposed AI method on SEM images were strongly and positively associated (Pearson's correlation coefficient r > 0.9), i.e. both methods are comparable. Conclusions SEM images can be analyzed directly for both morphology and quantifying bacterial adhesion on different dental materials’ surfaces by the simple AI-enabled method with reduced time, cost, and labours.

A principled governance for emerging AI regimes: lessons from China, the European Union, and the United States

Artificial intelligence (AI) governance is anticipated to have a transformative impact on humanity which has prompted researchers to analyze its implementation and use to ensure that the technology advances ethically and is beneficial for society. Though countries have begun to develop governance initiatives to regulate AI, the number of emerging AI regimes with an established structure is still relatively low. Meanwhile, the technology is advancing rapidly and has already caused harm inequitably to underrepresented communities. Thus, there is an urgent need to establish robust governance to mitigate the issues and risks attendant when deploying AI.While numerous ethics, principles, and structures have been recommended, this article intends to address the policy lag by providing policymakers with a simple and compelling AI governance framework that situates AI principles as the guiding baseline for developing and evaluating policies. Rather than devising new policy recommendations, the most recent (at the time of writing) and comprehensive governance documents from China, the European Union, and the United States were systematically selected, and examined in a comparative analysis to study how the three regimes address AI principles. Based on the comparative analysis, the most comprehensive and effective recommendations were selected to produce seven broad policy recommendations. The governance framework and recommendations are intentionally broad so that they can be adapted to adequately address AI principles across diverse contexts, encouraging the implementation of AI principles, increasing the likelihood of beneficial AI, and reducing the risks and harms associated with the technology. Nevertheless, the recommendations provided should not be considered exhaustive as the technology has an immense reach and new AI governance initiatives are developing continuously in this growth period in AI governance. It is thus essential for policymakers to survey the most current and relevant governance landscape to identify the best practices that are suitable for their specific context and need.

The Application of Artificial Intelligence in the Analysis of Biomarkers for Diagnosis and Management of Uveitis and Uveal Melanoma: A Systematic Review

PurposeThis study aims to identify the available literature describing the utilization of artificial intelligence (AI) as a clinical tool in uveal diseases.MethodsA comprehensive literature search was conducted in 5 electronic databases, finding studies relating to AI and uveal diseases.ResultsAfter screening 10,258 studies,18 studies met the inclusion criteria. Uveal melanoma (44%) and uveitis (56%) were the two uveal diseases examined. Ten studies (56%) used complex AI, while 13 studies (72%) used regression methods. Lactate dehydrogenase (LDH), found in 50% of studies concerning uveal melanoma, was the only biomarker that overlapped in multiple studies. However, 94% of studies highlighted that the biomarkers of interest were significant.ConclusionThis study highlights the value of using complex and simple AI tools as a clinical tool in uveal diseases. Particularly, complex AI methods can be used to weigh the merit of significant biomarkers, such as LDH, in order to create staging tools and predict treatment outcomes.

The engagement of students when learning to use a personal audio classifier to control robot cars in a computational thinking board game

This research explored the creative thinking, learning achievement, and engagement of students when they integrated the application of the personal audio classifier (PAC) into the competition of a computational thinking (CT) board game (i.e., the experimental group), or did not integrate it into the competition but only collaborated with peers to test the function of the program which they had developed (i.e., the control group). The students had experienced popular speech recognition usage in their daily life, such as Siri and Google Assistant; therefore, this study developed instructional material for university freshmen to learn to develop their own artificial intelligence (AI) application (app) on a smart phone with PAC in MIT App Inventor. The PAC platform and the learning material cultivated students to train their own voice classification model, which is a form of supervised machine learning in the AI domain. The results showed that both groups, who had successfully trained computers to distinguish received voice commands with PAC receiving the human voice spectrogram via the cloud platform developed by MIT, made significant progress in their learning effectiveness in AI education. When the students employed the AI app on smartphones in the CT board game, the students’ voice commands could be classified, and then the corresponding command could be executed through the program to control the action of the robot car on the map, regardless of whether they were competing or not. This study not only successfully provided the students with simple AI learning material, but also cultivated their creative thinking, as identified in the survey of the computational thinking self-efficacy scale. During the process of completing a mobile phone application with AI, students should know and use the function of voice classification to achieve goals and expand their cognition of AI applications. This study concluded that the AI learning material for general students rather than students in the department of computer science facilitated the students’ engagement.

Fault-Tolerant Control for Nonsinusoidal Multiphase Drives With Minimum Torque Ripple

For nonsinusoidal electromotive force (NS-EMF) multiphase machines, this article proposes a new strategy and control scheme to guarantee smooth torque under an open-phase fault. Notably, the conventional proportional–integral controllers implemented for vector control in healthy mode can be used in the faulty mode. The strategy is based on reduced-order transformations while the control scheme applies a simple artificial intelligence algorithm using a specific online-trained adaptive linear neuron (ADALINE). Indeed, the inputs of ADALINE require the knowledge of rotor position and NS-EMF harmonic rank to optimize the learning time. The proposed strategy and control scheme are tested on a seven-phase machine with a strong total harmonic distortion (THD) of NS-EMFs, containing numerous harmonics H <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> (THD = 38% with 100% H1, 32.3% H3, 9.4% H7, 12.5% H9, 10.3% H11). Numerical and experimental results are presented in this article. This article is accompanied by a video demonstrating the experimental results.

Implementation of multimarker augmented reality on solar system simulations

The solar system is one of the natural phenomena taught at school. However, delivering the material is still text-based. One of the current technology-based learning uses technology Augmented Reality as a support for learning aid. Augmented Reality is an integrated two worlds, the real and the virtual. Augmented Reality for the solar system learning application was developed by applying the concept of the Rule-Based System algorithm as a simple artificial intelligence that aims to help augmented reality systems in simulating knowledge and experience from humans with several rules prepared. The existence of Augmented Reality facilitates the process of learning on specific topics such as the solar system more attractive and interactive, with aims to inspire students to learn the solar system. Based on the testing results at SDN Purwantoro 2 Malang, Indonesia 95% of respondents are interested and captivated by learning media applications using Augmented Reality technology.

Implementation of multimarker augmented reality on solar system simulations

The solar system is one of the natural phenomena taught at school. However, delivering the material is still text-based. One of the current technology-based learning uses technology Augmented Reality as a support for learning aid. Augmented Reality is an integrated two worlds, the real and the virtual. Augmented Reality for the solar system learning application was developed by applying the concept of the Rule-Based System algorithm as a simple artificial intelligence that aims to help augmented reality systems in simulating knowledge and experience from humans with several rules prepared. The existence of Augmented Reality facilitates the process of learning on specific topics such as the solar system more attractive and interactive, with aims to inspire students to learn the solar system. Based on the testing results at SDN Purwantoro 2 Malang, Indonesia 95% of respondents are interested and captivated by learning media applications using Augmented Reality technology.

Artificial intelligence in the diagnosis and management of arrhythmias.

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.

ROLE OF AI-BASED VIRTUAL ASSISTANTS IN ORAL AND MAXILLOFACIAL RADIOLOGY REPORT WRITING

<h3>BACKGROUND</h3> Artificial Intelligence (AI) encompasses an array of devices, solutions, and codes ranging from a simple calculator to complex machine learning devices. A simple and successful AI system that is being routinely used is a voice-activated virtual assistant. There are several such devices in the market, including Alexa, Siri, Cortana, and Google Assistant. These devices offer a very simple voice-activated front end powered by a database at the back end. There are several instances where radiologists refer to a textbook or visit a website to look for disease-specific features, genetic mutations, and differential diagnoses. <h3>OBJECTIVE(S)</h3> The objective of this study was to evaluate the role and status of 4 voice-based virtual assistants in responding to radiology related queries. <h3>STUDY DESIGN</h3> A questionnaire consisting of 100 questions was queried to 4 commercially available voice-based virtual assistants: Alexa, Siri, Cortana, and Google Assistant. The questions were categorized based on 5 categories. The categorization was based on the frequency and reason for a radiologist to refer either to a textbook or an online resource before diagnosing and finalizing a radiology report. The evaluators queried the devices and rated them on a 4-point modified Likert scale: (1) The device responded with adequate information, (2) The device responded but did not provide adequate information, (3) The device did not know the response to the question, and (4) The device recognized but did not provide adequate information and led to a series of web pages. <h3>RESULTS</h3> The order of efficiency was Google Assistant, Cortana, Siri, and Alexa. A significant difference between the examiners was observed with Cortana in anatomy, dental anatomy, differential diagnosis, and pathology. There was a significant difference between the examiners with Google Assistant for queries about medication. <h3>DISCUSSION/CONCLUSIONS</h3> There is a significant gap in retrieving simple to multilevel queries on oral radiology topics for all of the virtual assistants evaluated in this study.

Using sentiment analysis to predict opinion inversion in Tweets of political communication

Social media networks have become an essential tool for sharing information in political discourse. Recent studies examining opinion diffusion have highlighted that some users may invert a message's content before disseminating it, propagating a contrasting view relative to that of the original author. Using politically-oriented discourse related to Israel with focus on the Israeli–Palestinian conflict, we explored this Opinion Inversion (O.I.) phenomenon. From a corpus of approximately 716,000 relevant Tweets, we identified 7147 Source–Quote pairs. These Source–Quote pairs accounted for 69% of the total volume of the corpus. Using a Random Forest model based on the Natural Language Processing features of the Source text and user attributes, we could predict whether a Source will undergo O.I. upon retweet with an ROC-AUC of 0.83. We found that roughly 80% of the factors that explain O.I. are associated with the original message's sentiment towards the conflict. In addition, we identified pairs comprised of Quotes related to the domain while their Sources were unrelated to the domain. These Quotes, which accounted for 14% of the Source–Quote pairs, maintained similar sentiment levels as the Source. Our case study underscores that O.I. plays an important role in political communication on social media. Nevertheless, O.I. can be predicted in advance using simple artificial intelligence tools and that prediction might be used to optimize content propagation.

Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

Utilization of Artificial Intelligence for Diagnosis and Management of Urinary Incontinence in Women Residing in Areas with Low Resources: An Overview

Urinary incontinence (UI) is a distressing condition involving involuntary loss of urine from the body. Urinary incontinence can negatively impact a person’s overall quality of life and lead them into stages of embarrassment and depression. It is an underrepresented and undertreated condition prevalent in women, especially in low socioeconomic regions where women may not be able to express their concerns due to unawareness of diagnosis and treatment/management options. There are different diagnostic and management protocols for UI; however, utilizing artificially intelligent systems is not standard care. This paper overviews the use of artificial intelligence in women’s health and as a means of cost-effectively diagnosing patients, and as an avenue for providing low-cost treatments to women that suffer from urinary incontinence in low-resource communities. Studies found that these systems, mainly utilizing artificial neural networks (ANNs) and convolutional neural networks (CNNs), served to be an effective method in diagnosing patients and providing an avenue for personalized treatment for improved patient outcomes. A simple artificial intelligence (AI) model utilizing Multilayer Perceptron (MLP) Networks was proposed to diagnose and manage urinary incontinence.

Network Engineering Using Autonomous Agents Increases Cooperation in Human Groups.

SummaryCooperation in human groups is challenging, and various mechanisms are required to sustain it, although it nevertheless usually decays over time. Here, we perform theoretically informed experiments involving networks of humans (1,024 subjects in 64 networks) playing a public-goods game to which we sometimes added autonomous agents (bots) programmed to use only local knowledge. We show that cooperation can not only be stabilized, but even promoted, when the bots intervene in the partner selections made by the humans, re-shaping social connections locally within a larger group. Cooperation rates increased from 60.4% at baseline to 79.4% at the end. This network-intervention strategy outperformed other strategies, such as adding bots playing tit-for-tat. We also confirm that even a single bot can foster cooperation in human groups by using a mixed strategy designed to support the development of cooperative clusters. Simple artificial intelligence can increase the cooperation of groups.

Finite State Machines for Building Believable Non-Playable Character in the Game of Khalid ibn Al-Walid

The character recognition game Khalid ibn al-Walid is an interactive Drama game application with the perspective of First Person Shooter introducing a hero in Islam named Khalid ibn al-Walid. To make the game interesting, the Non-Playable Character must be given artificial intelligence so that it can behave intelligently and make the game atmosphere more lively. One of the simple artificial intelligence that is applied in NPC games is Finite State Machine (FSM). In this game, FSM is applied to the enemy behavior of the character Khalid ibn al-Walid. The trial results show that NPC can behave independently and smartly in Search Player, Move To Player, Wait to Attack and Attack.