ABSTRACT
Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and ed it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing details, communicating uncertainties, drawing conclusions, and simulating personal tones. While existing models remained capable of classifying AI-misinfo, a significant performance drop compared to human-misinfo was observed. Results suggested that existing information assessment guidelines had questionable applicability, as AI-misinfo tended to meet criteria in evidence credibility, source transparency, and limitation acknowledgment. We discuss implications for practitioners, researchers, and journalists, as AI can create new challenges to the societal problem of misinformation. © 2023 Owner/Author.
ABSTRACT
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
In the modern age, the context of health, energy, environment, climate crisis, and global Covid-19 pandemic, managing Big Data demands via Sustainable Development Goals and disease mitigation supported by Artificial Intelligence, present significant challenges for a given territory or national boundaries' policies, legal systems, energy infrastructure, societal cohesion, internal and external national security. We look at policy, technical, and legal applications alongside ramifications of relevant policies and practices to highlight key challenges from a global and societal context. This review contributes to developing further awareness of the complexity and demands on policy and technology. In the long term due to these significant changes, inferences of multifaceted policy and data acquisition could present additional compounding challenges regarding civil liberties, data privacy law, and equitable health outcomes, whilst implementing continually evolving policies, practices, and techniques that can weaken infrastructure and present cyber-attack vulnerabilities. As a consequence of local, regional, and international paradigm shifts, Blockchain and Smart Contracts are suggested as part of a solution in providing data protection, transparency, and validity with transactional data to enable further trust between private and public sectors during times of crisis and technological transition processes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
Past research has discovered that the shape design and interaction process design of AI robots, as well as the users' constant features, are the major factors that affect users' willingness to interact with AI robots. Currently, AI robots that play a vital part in the daily activities of our society are becoming increasingly prevalent, thus things about AI robots have gone from mythic to prosaic. But when and where people are more likely to adopt AI robots remains an important research topic. With the development of online technology and the long-term impact of COVID-19, there has been a recent trend in the lower frequency of socializing. To investigate whether a state of low socializing frequency is a robotic moment and whether it affects people's willingness to interact with AI robots, we conducted two-wave questionnaire surveys to collect data from 300 participants from 23 provinces in China. The results showed that the frequency of socializing had a significant negative correlation with the willingness to interact with the AI robots via the mediation role of social compensation. Furthermore, the relationship between social compensation and willingness to interact with the AI robots was demonstrated to be stronger, when participants had a lower anthropomorphic tendency. These findings have theoretical implications for the human-computer interaction literature and managerial implications for the robotics industry. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
After the outbreak of corona virus, all counties are paying special attention to their healthcare infrastructure. During second phase of covid-19, entire world has seen health care crisis. Large number of people died globally. Entire world was affected mentally or physically. There is a great need to strengthen the healthcare infrastructure, to vaccinate the population against covid virus infection and to take proper precaution to avoid spread of the virus, so that the world will not see such deadly days again. This paper discusses how technologies like Internet of Things (IoT), Artificial Intelligence (AI), Drones etc can help in remote monitoring of patients, judicious hospital admission, conscious distribution of lifesaving drugs etc. Investment in technology with not only help in the reduction of spread of the virus but will also help in fighting with all other future pandemics. All the countries must have to invest more on latest technologies in their healthcare to make themselves ready for such future pandemics. When the things will improve, the new normal will be very much different from the life that was before pandemic. IoT, AI and other technologies will become the non-separatable part of our life. © 2023 Bharati Vidyapeeth, New Delhi.
ABSTRACT
COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection. © 2023 IEEE.
ABSTRACT
A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient's health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.
ABSTRACT
Due to the recent COVID-19 pandemic, people tend to wear masks indoors and outdoors. Therefore, systems with face recognition, such as FaceID, showed a tendency of decline in accuracy. Consequently, many studies and research were held to improve the accuracy of the recognition system between masked faces. Most of them targeted to enhance dataset and restrained the models to get reasonable accuracies. However, not much research was held to explain the reasons for the enhancement of the accuracy. Therefore, we focused on finding an explainable reason for the improvement of the model's accuracy. First, we could see that the accuracy has actually increased after training with a masked dataset by 12.86%. Then we applied Explainable AI (XAI) to see whether the model has really focused on the regions of interest. Our approach showed through the generated heatmaps that difference in the data of the training models make difference in range of focus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Coronavirus 2019 (COVID-19) medical images detection and classification are used in artificial intelligence (AI) techniques. Few months back, from the observation it is witnessed that there is a rapid increase in using AI techniques for diagnosing COVID-19 with chest computed tomography (CT) images. AI more accurately detects COVID-19;moreover efficiently differentiates this from other lung infection and pneumonia. AI is very useful and has been broadly accepted in medical applications as its accuracy and prediction rates are high. This paper is developed and aims to fight against corona through AI using computational intelligence in detecting and classifying COVID-19 using Densnet-121 architecture on chest CT images from a global diverse multi-institution dataset. Furthermore, data from clinics and images from medical applications improve the performance of the proposed approach and provide better response with practical applications. Classification performance was evaluated by confusion matrices followed by overall accuracy, precision, recall and specificity for precisely classifying COVID-19 against any condition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
A horrifying number of people died because of the COVID-19 pandemic. There was an unexpected threat to food systems, public health, and the workplace. The pandemic has severely disturbed society and there was a serious impediment to the economy. The world went through an unprecedented state of chaos during this period. To avoid anything similar, we can only be cautious. The project aims to develop a web application for the preliminary detection of COVID-19 using Artificial Intelligence(AI). This project would enable faster coordination, secured data storage, and normalized statistics. First, the available chest X-ray datasets were collected and classified as Covid, Non-Covid, and Normal. Then they were trained using various state-of-the-art pre-trained Convolutional Neural Network (CNN) models with the help of Tensor-flow. Further, they were ranked based on their accuracy. The best-performing models were ensembled into a single model to improve the performance. The model with the highest accuracy was transformed into an application programming interface (API) and integrated with the Decentralized application (D-App). The user needs to upload an image of their chest X-ray, and the D-App then suggests if they should take a reverse transcription-polymerase chain reaction (RT-PCR) test for confirmation. © 2022 IEEE.
ABSTRACT
The Corona Virus Disease 2019 (COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches, thereby highlighting a scenario where artificial intelligence (AI) merges with traditional Chinese medicine (TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors, nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors' authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users' lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.Copyright © 2022 Digital Chinese Medicine
ABSTRACT
The integration of AI and the IoT in education has the potential to revolutionize the way we learn. Personalized learning, real-time feedback and support, and immersive learning experiences are some of the benefits that AI and the IoT can bring to the education system. In this regard, this research paper aims to investigate how AI and the IoT can be integrated into sustainable education in order to provide students with personalized and immersive learning experiences during pandemics, such as COVID-19, for smart cities. The study's key findings report that AI can be employed in sustainable education through personalized learning. AI-powered algorithms can be used to analyze student data and create personalized learning experiences for each student. This includes providing students with tailored content, assessments, and feedback that align with their unique learning style and pace. Additionally, AI can be used to communicate with students in a more natural and human-like way, making the learning experience more engaging and interactive. Another key aspect of the integration of AI and the IoT in education obtained from this research is the ability to provide real-time feedback and support. IoT-enabled devices, such as smart cameras and microphones, can be used to monitor student engagement and provide real-time feedback. AI algorithms can then use these data to adapt the learning experience in real time. IoT-enabled devices, such as tablets and laptops, can be used to collect and process student work, allowing for the automatic grading of assignments and assessments. Additionally, IoT technology can facilitate remote monitoring and grading of student work, which would be particularly useful for students who cannot attend traditional classroom settings. Furthermore, AI and the IoT can also be used to create intelligent personal learning environments (PLEs) that provide students with personalized, adaptive, and engaging learning experiences. IoT-enabled devices, such as smart cameras and microphones, combined with AI-powered algorithms, can provide real-time feedback and support, allowing the PLE to adapt to the student's needs and preferences. It is concluded that integrating AI and the IoT in sustainable education can revolutionize the way people learn, providing students with personalized, real-time feedback and support and opening up new opportunities for remote and disadvantaged students. However, it will be important to ensure that the use of AI and the IoT in education is ethical and responsible to ensure that all students have equal access to the benefits of these technologies.
ABSTRACT
This chapter explores trustworthiness in AI and penetrates the black-box opacity through explainable, fair, and ethical AI solutions. AI remains a spirited topic within academic, government, and industrial literature. Much has occurred since the last AI winter in the early 1990's;yet, numerous sources indicate the initial successes solving problems like computer vision, speech recognition, and natural sciences may wane — plunging AI into another winter. Many factors contributed to advances in AI: more data science courses in universities producing data-science capable graduates, high venture capital funding levels encouraging startups, and a decade of broadening awareness among corporate executives about AI promises, real or perceived. Nonetheless, could sources like Gartner be right? Are we approaching another AI winter? As the world learned during the COVID-19 pandemic, when we find ourselves in a crisis, focusing on the fundamentals can have a powerful effect to easing the troubles. As AI makes history, it relies on progress from other domains such as data availability, computing power, and algorithmic advances. Balance among elements maintains a healthy system. AI is no different. Too much or too little of any elemental capability can slow down overall progress. This chapter integrates fundamental ideas from psychology (heuristics and bias), mindfulness in modeling (conceptual models in group settings), and inference (both classical and contemporary). Practitioners may find the techniques proposed in this chapter useful next steps in AI evolution aimed at understanding human behavior. The techniques we discuss can protect against negative impacts resulting from a future AI winter through proper preparation: appreciating the fundamentals, understanding AI assumptions and limitations, and approaching AI assurance in a mindful manner as it evolves. This chapter will address the fundamentals in a unifying example focused on healthcare, with opportunities for trustworthy AI that is impartial, fair, and unbiased. © 2023 Elsevier Inc. All rights reserved.
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During the COVID-19 pandemic it became evident that outcome prediction of patients is crucial for triaging, when resources are limited and enable early start or increase of available therapeutic support. COVID-19 demographic risk factors for severe disease and death were rapidly established, including age and sex. Common Clinical Decision Support Systems (CDSS) and Early Warning Systems (EWS) have been used to triage based on demographics, vital signs and laboratory results. However, all of these have limitations, such as dependency of laboratory investigations or set threshold values, were derived from more or less specific cohort studies. Instead, individual illness dynamics and patterns of recovery might be essential characteristics in understanding the critical course of illness.The pandemic has been a game changer for data, and the concept of real-time massive health data has emerged as one of the important tools in battling the pandemic. We here describe the advantages and limitations of established risk scoring systems and show how artificial intelligence applied on dynamic vital parameter changes, may help to predict critical illness, adverse events and death in patients hospitalized with COVID-19.Machine learning assisted dynamic analysis can improve and give patient-specific prediction in Clinical Decision Support systems that have the potential of reducing both morbidity and mortality. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
Although many AI-based scientific works regarding chest X-ray (CXR) interpretation focused on COVID-19 diagnosis, fewer papers focused on other relevant tasks, like severity estimation, deterioration, and prognosis. The same holds for explainable decisions to estimate COVID-19 prognosis as well. The international hackathon launched during Dubai Expo 2020, aimed at designing machine learning solutions to help physicians formulate COVID-19 patients' prognosis, was the occasion to develop a machine learning model capable of predicting such prognoses and justifying them through interpretable explanations. The large hackathon dataset comprised subjects characterized by their CXR and numerous clinical features collected during triage. To calculate the prognostic value, our model considered both patients' CXRs and clinical features. After automatic pre-processing to improve their quality, CXRs were processed by a Deep Learning model to estimate the lung compromise degree, which has been considered as an additional clinical feature. Original clinical parameters suffered from missing values that were adequately handled. We trained and evaluated multiple models to find the best one and fine-tune it before the inference process. Finally, we produced novel explanations, both visual and numerical, to justify the model predictions. Ultimately, our model processes a CXR and several clinical data to estimate a patient's prognosis related to the COVID-19 disease. It proved to be accurate and was ranked second in the final rankings with 75%, 73.9%, and 74.4% in sensitivity, specificity, and balanced accuracy, respectively. In terms of model explainability, it was ranked first since it was agreed to be the most interpretable by health professionals. © 2023 SPIE.
ABSTRACT
The spread of COVID-19 has resulted in several changes worldwide. In particular, border closures and economic stagnation have significantly affected societies. Although the implementation of preventive measures has improved the pandemic scenario in several countries, the effectiveness of vaccines has decreased with the emergence of mutant viruses. With this background, the use of masks is considered the best method for preventing the spread of the virus. Notably, public transportation is closely related to socioeconomic activities, and the spread of infectious diseases is more likely in closed, dense, and congested areas. Moreover, the probability of infection during public transportation also depends on the proportion of commuters wearing masks. Based on the closed-circuit television footage of various public transportation spaces, the number of mask wearers can be analysed using artificial intelligence deep learning, and the probability of COVID-19 spread can be predicted by determining the proportion of mask wearers among the commuters. With this background, in this study, the importance of masks in controlling the spread of the virus is confirmed. In conclusion, appropriate measures can be implemented by determining the probability of infection according to the mask-wearing rate in public transportation spaces. © 2023 ICE Publishing: All rights reserved.
ABSTRACT
This paper proposes a novel and robust technique for remote cough recognition for COVID-19 detection. This technique is based on sound and image analysis. The objective is to create a real-time system combining artificial intelligence (AI) algorithms, embedded systems, and network of sensors to detect COVID-19-specific cough and identify the person who coughed. Remote acquisition and analysis of sounds and images allow the system to perform both detection and classification of the detected cough using AI algorithms and image processing to identify the coughing person. This will give the ability to distinguish between a normal person and a person carrying the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available data commons as well as a sequestered commons for performance evaluation of algorithms. This work sought to evaluate the currently implemented methodology for apportioning data to the public and sequestered data commons by investigating the resulting distributions of joint demographic characteristics between the public and sequestered commons. 54,185 patients whose de-identified imaging studies and metadata had been submitted to MIDRC were previously separated into public and sequestered commons using a multi-dimensional stratified sampling method, resulting in 41,556 patients (77%) in the public commons and 12,629 patients (23%) in the sequestered commons. To compare the balance obtained in the joint distributions of patient characteristics from use of the developed sequestration method, patients from each commons were separated into bins, representing a unique combination of the demographic variables of COVID-19 status, age, race, and sex assigned at birth. The joint distributions of patients were visualized, and the absolute and percent difference in each bin from an exact 77:23 split of the data were calculated. Results indicated 75.9% of bins obtained differences of less than 15 patients, with a median difference of 3.6 from the total data for both public and sequestered commons. Joint distributions of patient characteristics in both the public and sequestered commons closely matched each other as well as that of the total data, indicating the sequestration by stratified sampling method has operated as intended. © 2023 SPIE.
ABSTRACT
The Internet of Medical Things (IoMT) has been applied to provide health care facilities for elders and parents. Remote health care is essential for providing scarce resources and facilities to coronavirus patients. Ongoing IoMT communication is susceptible to potential security attacks. In this research, an artificial intelligence-driven security model of the IoMT is also proposed to simulate and analyses the results. Under the proposed plan, only authorized users will be able to access private and sensitive patient information, and unauthorized users will be unable to access a secure healthcare network. The various phases for implementing artificial intelligence (AI) techniques in the IoMT system have been discussed. AI-driven IoMT is implemented using decision trees, logistic regression, support vector machines (SVM), and k-nearest neighbours (KNN) techniques. The KNN learning models are recommended for IoMT applications due to their low consumption time with high accuracy and effective prediction. © 2023 IEEE.
ABSTRACT
Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.