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Computational Intelligence: A Logical Approach
... Artificial intelligence (AI) refers to simulation of human intelligence by computer based systems (19). On the other hand, machine learning (ML) is a subdivision of AI focused on learning from previous events and applying this knowledge to future decision making (20). ...
Introduction
Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.
Methods
We applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.
Results
135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).
Conclusion
Artificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.
Systematic review registration
https://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.
... Deep learning techniques based on convolutional neural networks (CNN) provide new research ideas to solve this dilemma. Machine Learning [22,23] (ML) was first introduced in 1959 by Samuel [24], a pioneer scholar in artificial intelligence. Machine Learning can find better solutions by analyzing and learning from historical and behavioral data and can simplify many complicated manual tasks. ...
Measuring the human perception of urban street space and exploring the street space elements that influence this perception have always interested geographic information and urban planning fields. However, most traditional efforts to investigate urban street perception are based on manual, usually time-consuming, inefficient, and subjective judgments. This shortcoming has a crucial impact on large-scale street spatial analyses. Fortunately, in recent years, deep learning models have gained robust element extraction capabilities for images and achieved very competitive results in semantic segmentation. In this paper, we propose a Street View imagery (SVI)-driven deep learning approach to automatically measure six perceptions of large-scale urban areas, including “safety”, “lively”, “beautiful”, “wealthy”, “depressing”, and “boring”. The model was trained on millions of people’s ratings of SVIs with a high accuracy. First, this paper maps the distribution of the six human perceptions of urban street spaces within the third ring road of Wuhan (appearing as Wuhan later). Secondly, we constructed a multiple linear regression model of “street constituents–human perception” by segmenting the common urban constituents from the SVIs. Finally, we analyzed various objects positively or negatively correlated with the six perceptual indicators based on the multiple linear regression model. The experiments elucidated the subtle weighting relationships between elements in different street spaces and the perceptual dimensions they affect, helping to identify the visual factors that may cause perceptions of an area to be involved. The findings suggested that motorized vehicles such as “cars” and “trucks” can negatively affect people’s perceptions of “safety”, which is different from previous studies. We also examined the influence of the relationships between perceptions, such as “safety” and “wealthy”. Finally, we discussed the “perceptual bias” issue in cities. The findings enhance the understanding of researchers and city managers of the psychological and cognitive processes behind human–street interactions.
... Scientists approach complex problems regarding health, energy, and environments, and the increasing complexity of these problems has facilitated new methods and innovative technologies that help tackle these challenging problems. Among these, Artificial Intelligence (AI), a subfield of computer science in which intelligence is exhibited by machines or software to make human-like decisions and analyses (Legg et al., 2007;McCarthy et al., 1956;Nilsson & Nilsson, 1998;Norvig & Intelligence, 2002;Poole et al., 1998), is particularly resonating. The most substantial discoveries in science in recent years have benefited from AI (Gil et al., 2014). ...
The rapid evolution of Artificial Intelligence (AI) has ushered in transformative shifts in various sectors, with science education emerging as a pivotal domain of its influence. This study delves into the integration of AI (Google Teachable Machine) within scientific inquiry activities, examining its impact on student engagement and understanding. Through a nuanced exploration, three distinct student archetypes—Pragmatic Innovators, Foundational Explorers, and Holistic Visionaries emerged, offering insights into diverse learning trajectories in the context of AI. The findings underscore the need for adaptive pedagogical strategies that resonate with the multifaceted science learning needs of students in an AI-centric world. By proposing AI-based scientific inquiry, this study not only highlights the transformative potential of AI in reshaping science education but also charts a visionary path forward. This research serves as a seminal contribution to the academic discourse, setting the stage for a new era in science education that is both responsive and forward-thinking.
... To operate like humans, computers need to have four capabilities: natural language processing, knowledge representation, automated reasoning, and machine learning (Russell & Norvig, 2010). Acting rationally indicates that computational intelligence has the potential to result in the construction of intelligent agents (Poole, Goebel, & Mackworth, 1998). ...
This study aims to investigate the phenomena of how artificial intelligence (AI) as one of the cutting-edge technologies benefits restaurant services and what the obstacles to implementing AI in a restaurant are. Due to the rapid pace of life, people tend to have less and less eating time, especially white-collar workers. More and more restaurants implement AI technology to improve their cooking efficiency and reduce service time, such as fast payment systems (QR and facial recognition payment), AI-enabled bots, AI-powered self-ordering kiosks, and robot chefs. Since the COVID-19 pandemic begins in early 2020, food safety and sanitation become increasingly important when people eat outside. Chef and waiter robots are good options for avoiding intimate contact. This study employed qualitative research with in-depth interviews. We interviewed three restaurant managers in China. The findings suggest that adopting AI technology in restaurant services can minimize high costs, better manage customer relationships, and provide more convenient in-store services. This study contributes to the managerial gap in AI restaurants.
... It is the discipline of computer science, and its capabilities are based on the learning experience that helps increase the chances of success in solving environmental problems. According to Poole (1998), the intelligence of sophisticated machines, demonstrated by the innate intelligence of animals and humans, could be AI, scientific and technical information that allows devices to be as intelligent as humans (Wang & Srinivasan 2017). Researchers have also found that AI systems can learn from experience to create artificial services and adapt inputs to changing environmental issues (Nishant et al. 2020). ...
Environmental issues have continued to spur discussions, debates, public outrages, and awareness campaigns, inciting interest in emerging technologies such as Artificial Intelligence. Its usage is spread across many environmental industries, including wildlife protection, natural resource conservation, clean energy, agriculture, energy management, pollution control, and waste management. In 2017, at the United Nations Artificial Intelligence Summit in Geneva, the UN acknowledged that AI could be an enabler in the sustainable development process towards peace, prosperity, and dignified life for humankind and proposed to refocus on the application of AI in assisting global efforts on sustainable development to eradicate poverty, hunger and to protect the environment as well as to conserve natural resources. It is vital to address environmental sustainability concerns; however, with the advent of AI, most common environmental issues are now solvable by prioritizing human interests. Sustainability encompasses the interrelated areas of the environment, society, and economy. According to the United Nations’ “Our Common Future,” also known as the “Brundtland Report,” it is defined as “development that satisfies current needs without compromising the ability of future generations to meet their own needs.” Unfortunately, the Earth is currently facing serious consequences from global warming and climate change, and immediate action is required to encourage the use of environmentally friendly and sustainable products to address these issues. Environmental degradation and climate change are numerous environmental concerns requiring novel and intelligent artificial intelligence solutions. The literature on AI and environmental sustainability encompasses various domains. Notably, AI is being used to address the bulk of regional and global environmental concerns, including energy, water, biodiversity, and transportation, even though many of these sectors have permeated and evolved. However, there is a need to combine current literature on the application of AI, particularly in relation to environmental sustainability in areas such as energy, water, biodiversity, and transportation. There is a significant lack of research on how AI can promote environmental sustainability. This research aims to explore how AI can be applied to address environmental issues in various sectors to achieve the Sustainable Development Goals (SDGs).
... Good policy analysis is accurate, reliable, practical, 123 relevant, comprehensive, clear, succinct, and timely [ 5 ]. 124 Responding to the "clear need for better empirical research into the sociology of policy analysis" [ 29 :8], and the 125 observation that most research relies on limited survey results, anecdotal case studies, and interview research 126 [ 57 ], some efforts have been made to define and explain the position of the policy analyst. Surveys of public 127 servants to examine the background and training of policy analysts, the techniques they used in their jobs, their 128 typical workday, and how their work related to the needs and prerequisites of evidence-based policy-making 129 revealed that most analysts are short-term, project-oriented "troubleshooters" rather than long-term strategic 130 "planners" [ 57 , 157 ]. ...
Policy advising in government centers on the analysis of public problems and the developing of recommendations for dealing with them. In carrying out this work, policy analysts consult a variety of sources and work to synthesize that body of evidence into useful decision support documents commonly called briefing notes. Advances in natural language processing (NLP) have led to the continuing development of tools that can undertake a similar task. Given a brief prompt, a large language model (LLM) can synthesize information in content databases. This article documents the findings from an experiment that tested whether contemporary NLP technology is capable of producing public policy relevant briefing notes that expert evaluators judge to be useful. The research involved two stages. First, briefing notes were created using three models: NLP generated; human generated; and NLP generated / human edited. Next, two panels of retired senior public servants (with only one panel informed of the use of NLP in the experiment) were asked to judge the briefing notes using a heuristic evaluation rubric. The findings indicate that contemporary NLP tools were not able to, on their own, generate useful policy briefings. However, the feedback from the expert evaluators indicates that automatically-generated briefing notes might serve as a useful supplement to the work of human policy analysts. And the speed with which the capabilities of NLP tools are developing, supplemented with access to a larger corpus of previously prepared policy briefings and other policy-relevant material, suggests that the quality of automatically-generated briefings may improve significantly in the coming years. The article concludes with reflections on what such improvements might mean for the future practice of policy analysis.
... This term is applied when a machine mirrors cognitive functions such as learning and problem-solving [4]. AI research has been divided into subfields [5] that include reasoning, planning, and learning [4,[6][7][8]. Machine learning investigates the study and development of algorithms to learn from and make predictions on data [9]. Machine learning can accelerate and improve investigations by building a model from example inputs to make data-driven predictions instead of time-consuming and intensive deterministic analytical or computational approaches. ...
Determination of suitable sites for small hydropower projects could offer new opportunities for sustainable developments. However, the non-scalable initial investigation costs are one of the biggest burdens when planning small projects. Moreover, solving a complex problem with only a few available parameters is almost impossible with many traditional models, and lack of data may make many design studies infeasible for remote, hard to access or developing areas. Artificial neural networks (ANNs) could help reduce investigation costs and make many projects feasible to study by acting as input–output mapping algorithms. This study provides an easy to understand and implement method to develop fast ANN-based estimation models using the multilayer perceptron (MLP) neural network and extended Kalman filter (EKF) or gradient descent (GD) as the training algorithm. Also, three approaches to feeding training data to the models were studied. Estimating runoff is an important challenge in water resources engineering, especially for development and operation plans. Therefore, the proposed method is applied for a runoff estimating problem using only easily measured precipitation and temperature. Results of this case study indicate that for a relatively similar performance, ANN models using EKF required a fewer number of neurons and training epochs than GD. Compared to the prior research in this study area, the methods in this study are much easier to understand and implement and are not dependent on data mining techniques or continuous long-term time series. Based on the results, a combination of the proposed data feeding methods and the EKF training algorithm improved estimation models by reducing the number of training epochs and the size of the network.
—— Keywords: Artificial Neural Network; ANN; Extended Kalman Filter; EKF; Gradient descent; Multilayer Perceptron; MLP; Machine learning; Runoff; Taleghan basin.
Artificial intelligence (AI) technology was created to solve problems that are complex to be solved by humans, related to the construction of machines that understand, monitor, reason, predict, interact, learn, develop and work like humans. Thanks to the development AI has achieved in recent years, AI has surpassed its limits in the field of computer engineering and has begun to be effective in almost every field. AI has started to contribute to the management of information in education and directly to the education and training process, with its features such as learning, making predictions, solving complex problems, having experience and adapting to changing conditions. Systems inspired by AI have become very popular and have been applied in almost every field, especially in educational institutions. The biggest impact of this technology on education has been in the delivery of education. Technological developments are starting to change many sectors and the education sector is also keeping up with this change. AI is not just made to support learning. AI is used in all educational institutions such as teaching, evaluation, classroom management, administrative affairs, teacher duties and school management. The aim of the article is to investigate the impact of AI on traditional education, to examine the point traditional education has reached with AI and to put forward assumptions for the future.
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