Biobanks and Biobank-Based Artificial Intelligence (AI) Implementation Through an International Lens

Artificial intelligence (AI) is already part of our everyday life. Nowadays, facial recognition and autonomous driving are common applications of AI. It has now gone on to invade the field of medicine, and many studies showed promising applications of AI in radiology, pathology, endoscopy, and so on. Artificial intelligence (AI) has been a disruptive innovation in all areas of medicine including surgery. Not only has it been applied for research purpose but also AI can already provide significant solutions in clinical settings. Attention is now turning to the potential of AI in the field of endoscopic surgery. It is essential to understand how machine learning (ML) and deep learning (DL) work, understand current applications in surgery, and prepare for the future of surgery in the era of AI.

Artificial Intelligence (AI) and Extended Reality (XR) technologies present regulators with powerful tools to manipulate human behaviour, but more perniciously, human desire and indeed human being. This is because AI applications can interfere with our internal decision-making processes, and XR applications can affect our sensations by creating and mediating our experiences of the external world. When these technologically driven affordances are strained through contemporary cognitive science research to ground the notion that perceptions are processes for prediction error minimisation, such interferences can amount to designing and engineering the regulatee herself. Effectively, regulation no longer needs to be signalled in a normative regulatory environment, nor must it hardcoded into the architecture or technologically managed. Instead, AI and XR make it possible to design and create regulatees that embody the desired regulatory outcome. Paradoxically, however, such regulatory incorporation looks very much like the exercise of the agency of an agent and therefore is not recognised as a problem by contemporary legal principles and processes which seek to push back against obviously external influences or pressures. As a result, it is immensely difficult to articulate the legal or regulatory challenges posed by these developments, and to identify the harms through these doctrinal lenses.

Artificial Intelligence (AI) and related technologies like Data Mining, Machine Learning or Neural Networks became very popular in recent years. Many IT companies today require graduated students to understand and be able to apply these technologies. Application potential of AI is not limited only to robotics, image processing or intelligent agents, but also in engineering areas like computer networking and communication. However, on most universities, networking courses focus mainly on transmission protocols, network services and hardware design only while AI, machine learning or neural networks are taught separately. This causes a gap that emerges between AI theory and engineering approach. Thus, teachers of engineering courses are challenged how to introduce their students to an application of AI in the engineering areas, e.g., electronics, communication, embedded systems, power grids, etc. This paper shows how selected AI techniques presently used in computer networks can be incorporated into networking curriculum and demonstrated to students which extends student competencies and prepares them better into future jobs. We also present two case studies where AI techniques are applied on networking data in order to solve typical engineering problems.

Artificial intelligence (AI) is changing many aspects of education and is gradually being introduced to language education. This article reviews the literature to examine main trends and common findings in relation to AI technologies and applications for second and foreign language learning and teaching. With special reference to computer-assisted language learning (CALL), the article explores natural language processing (NLP), data-driven learning (DDL), automated writing evaluation (AWE), computerized dynamic assessment (CDA), intelligent tutoring systems (ITSs), automatic speech recognition (ASR), and chatbots. It contributes to discussions on understanding and using AI-supported language learning and teaching. It suggests that AI will be continuously integrated into language education, and AI technologies and applications will have a profound impact on language learning and teaching. Language educators need to ensure that AI is effectively used to support language learning and teaching in AI-powered contexts. More rigorous research on AI-supported language learning and teaching is recommended to maximise second and foreign language learning and teaching with AI.

Artificial intelligence (AI) and machine learning (ML) are opening up important avenues of value creation inside organisations. Value creation will be strengthened if AI and ML are conceptualised using theoretical anchors rooted in a strong understanding of employee psychology. Organisations need to address anxieties of employees and assure them that AI and ML are aids in improving their productivity. They need to communicate to employees that AI and ML are not meant to displace employees from their jobs. Organisations face another dilemma regarding how to use AI and ML to draw upon the tacit knowledge and judgement of human actors to make more effective business decisions.

Artificial intelligence (AI) is rapidly developing and being implemented in every area of society, including higher education (HE). Evidence shows that AI has become an important subject among the academia representing about 2.2% of all scientific publications in 2018 and counting. Higher education institutions (HEIs), especially in developed countries, have already started to use AI technology in various areas such as teaching and learning and management. At the same time, AI has been increasingly appearing in curricula, in an attempt to prepare current students with the skills required by the changing job market. HEIs need to gather the infrastructure, digital and human capacity, and frameworks to properly and safely use such technology while putting students at the center. As such, this chapter provides an initial overview of the practical applications of AI in HEIs, particularly in the areas of teaching, learning, and administration.

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. It is a growing discipline that has enabled computer to undertake activities like facial recognition and game playing in a manner that, to an outsider, looks intelligent. There are many different toolsets that are called AI including neural networks, machine learning, expert systems, fuzzy logics, swarm intelligence and many others. These tools are being used in a wide variety of situations to solve a wide variety of complex problems, problems which have been difficult to solve using traditional algorithmic processes. Human intelligence and AI are sufficiently similar that tools developed to understand human intelligence can be applied to computer-based AI systems as well. Blooms Taxonomy is used to provide a definition of what constitutes intelligent computer applications. The article then considers the relationships between applications and intelligent systems.

Artificial intelligence (AI) solutions are used to help make choices that include a high precision of choices they recommend and a deep understanding of choices, so that the chiefs can trust them. Verifiable, non-emblematic learning methods have greater perceptive accuracy. Express, fair representation information learning method produces increasingly justifiable Approach’s. Hybrid AI systems analyses the data and exploratory characteristics of approach types. The fundamental purpose of this commitment is to differentiate between a proper AI strategy for choice of assistance that produces reliable and fairoutcomes, depending on the various AI techniques, which provide an analysis of different approaches either with special or similar datasets. Comprehensibility is measured subjectively, however, by the form of learning process and the scale of the resulting representation of knowledge. We need a hybrid approach for classification using feature extraction in preprocessing of imbalanced dataset (raw dataset) by normalization for better classification, accuracy, and minimization of Error Rate for the smaller amount of training of significant dataset and lesser amount of training time for a good result. Moreover, data preprocessing play very important role in the field of machine learning, the result is more accurate if your data is cleanedotherwise some misclassification occurs which tend to be problematic in classification problems. Therefore, hybrid comparative analysis and approach is required for the selection of dataset with respect to machine learning classifier that will having different results with different data sets based on the hybrid approach to achieve maximum best results against any dataset. Input with maximum accurate result will be reproduced from our hybrid approach because this approach shows which type of classifiers should be used under what type of dataset you have, meanwhile exception of the generous fact based on results should be different among different classifiers when applied to different dataset. After that, a comparative analysis of different algorithms with different dataset has made and a comparison shows the enhanced significant generic approach with the hybrid approach, a clear result in the form of accuracy, precision, recall and f1 score shows the results against the specific techniques against the dataset and that tell us about the accuracy and rate of misclassification error. At the end we will see which machine learning algorithm improves the accuracy for which type of dataset.

Artificial intelligence (AI) is a set of technologies that allows robots to detect, understand, act, and learn at human-like levels. The vast bulk of the AI that surrounds us now is powered by ineffective AI. This sort of AI is demonstrated by IBM Watson, self-driving cars, and Apple's Siri. Machine learning includes deep learning as a subset which is commonly utilised in predictive modelling. It functions in the same manner that neurons do in our brains. Its structure and networking are also influenced by neural networks found in our brains. Since the 1950s, artificial intelligence (AI) has been applied in medicine. The use of artificial intelligence in ophthalmology is mostly focused on high-incidence illnesses such as diabetic retinopathy, glaucoma, age-related macular degeneration, and cataract. The chapter reviews research demonstrating AI enhancing medical diagnosis with a focus on diabetic retinopathy.

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques wont to solve various real-world problems. For a primer on machine learning, you'll want to read this seven-part series that I wrote. While human-like deductive reasoning, presumption, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms

Artificial Intelligence (AI) has significant potential to optimize practices, processes, and energy consumption along with maximizing yield, quality, and uptime. This has substantial impact on putting organizations on the path to net-zero, as such optimizations can reduce greenhouse gas emissions by 20% with minimal capital investments. This comprehensive study presents proven industrial case studies that delivered economically strong strategies coupled with sustainability practice and providing strategic insights to identify, manage and/or attenuate the associated impacts. Environomics presented in this study is a novel framework which deals with unifying economic strategies with sustainability practices (through artificial intelligence) for optimal business performance in terms of finances but also environmental impact. This is achieved through a track, trace, and optimize approach for resources (particularly emissions, energy, water, waste, materials,, and safety) This was achieved through a combination of AI methods such as unsupervised machine learning, multi-variate optimization, and the implementation of similarity measures. A few of the inputs included well data (including production data, drilling data, completion data etc.), logistics/supply chain data (scheduling data, production inventory, mobilization data etc.), safety data (near-miss, observations, hazards, disciplines and insights etc.) with associated costs and emission data. Multiple industrial case studies are presented where sustainability metrics are identified through validated AI models to optimize productivity while reducing emissions and inventory. For instance, well profiling can be used to identify historical parameters that have maximized production potential while optimizing for aspects such as cost or emissions. Furthermore, we can identify the optimal completion parameters for a new well which satisfies carbon targets, use well profiles to build an optimized drilling schedule that meets budget or production criteria while still achieving production targets and optimizing drilling rig routes. Thus, the approach can quickly (within run time) solve interrelated environomic challenges in the reservoir studies space and the field development space. Further case studies indicate that the supply chain can have immense optimization impact on scope 3 aspects with results indicating 30-50% asset utilization improvement with respect to fleets (Vessel, Truck, Rigs). With respect to materials, a 10-20% reduction of material inventory levels all improved through AI. As the workforce are also part of the environment it has been observed that identifying unsafe behaviors within a large operation, also leads to enhanced sustainability behaviors. The models indicate potential of overall emission reduction ranging from 12-20%. This led to the comprehensive framework presented in this study to support sustainable practices that are also economically feasible and deployable. The real-time sustainability metrics generated has immense values in terms of decision-making processes and scenario generation in a fraction of the time that is required using traditional approaches. In addition to assessing the scope of impact, a novel multidisciplinary study and framework is presented to analyze environomic strategies to propose a market-oriented approach through the application of artificial intelligence. Furthermore, industrial, and academic case studies have been evaluated to identify, predict, and optimize the crucial parameters within such workflows that are effective in reducing resources utilized and associated emissions.

Artificial intelligence (AI) technology–based intelligent robots are constructed using different technologies, such as Internet of Things (IoT), big data, deep learning, machine learning, neural network, and expert system. This particular type of robots can increase the work efficiency of humans and improve their quality of life. From the industry perspective, AI robots possess unlimited potential for development, and they are projected to be a 10-trillion-dollar industry. In this study, the critical technology of IoT is applied to develop a teaching module for an IoT smart home robot. Teaching and evaluation are performed through an embedded thematic-approach teaching strategy in the course named Automatic Measurement and Monitoring. This research aims to teach students how to integrate IoT technology into robot design and construction to build IoT smart home robots. This cross-disciplinary research incorporates emerging technology—integration of smart home, robot construction, and IoT technologies—into industrial education, teaching material and equipment development, and experimental teaching and evaluation. The participating students were juniors or seniors from the Department of Electrical Engineering or Electromechanical Engineering at the University of Technology.

Artificial intelligence (AI) has achieved significant progress in recent years and its applications cover a wide range of fields such as computer vision, natural language processing, autonomous driving and medical diagnosis. In industry, the rapid development of real-time-sensor measurement techniques promotes equipment surveillance and maintenance into the era of big data. It is still challenging to manually analyze this big data and establish general physical modelling by using the information hidden in these data. To this end, AI technology, such as machine learning and neural networks, has emerged as a promising tool to extract useful knowledge from measured data, and to tackle the real-time monitoring, diagnosis problems as well as enhancing the health management and reliability of modern industrial equipment. With the vision of establishing a strong link between AI and industrial equipment surveillance and maintenance, this special feature is designated to select, organize and exhibit the latest research progress on the cutting-edge research topics relevant to AI-Enabled Industrial Equipment Monitoring, Diagnosis and Health Management. Submissions for this topic in Measurement Science and Technology are open from 23 March 2022 to 30 September 2022 and contains 42 outstanding papers in above research fields.

Artificial intelligence (AI) and machine learning (ML) have been growing at an incredible rate in recent years and they show no sign of stopping. Manufacturing, educational systems, transportation architecture, and genetic research are industries where artificial intelligence algorithms have been developed and found practical applications in which they can increase task efficiency and reduce cost through process optimization, pattern recognition, and automation. At NASA, one of the goals of the cognitive communications project has been to find applications for such algorithms to next-generation communication systems. The goal of this effort is to identify areas and approaches to intelligent system design and implementation which could allow NASA to support a larger space-and ground-based network while simultaneously reducing the operational costs involved with maintaining such a system This paper will evaluate the state of various approaches by searching for algorithms which are feasible to deploy directly onto future space systems with improved processing requirements. We begin by describing a set of heuristics through which algorithms may be compared, emphasizing memory and computational requirements, and heuristic bounds. We then evaluate general-purpose processing platforms onto which such algorithms may be deployed. We also evaluate how such systems may be packaged so as to offer a deterministic set of performance and decision metrics, to make the devices easier for system designers to include in present and future systems. We conclude the paper with a discussion of our findings, as well as where and how this study might continue in the future.

Artificial Intelligence (AI) methods, which are often based on Machine Learning (ML) algorithms, are also applied in the healthcare domain to provide predictions to physicians and patients based on electronic health records (EHRs), such as history of laboratory values, applied procedures and diagnoses. The question about these predictions “Why Should I Trust You?” encapsulates the issue with ML black boxes. Therefore, explaining the reasons for these ML predictions to physicians and patients is crucial to allow them to decide whether the prediction is applicable or not. In this paper, we explained and evaluated two prediction explanation methods for healthcare professionals (physicians and nurses). We compared two model-agnostic explanation methods based on global feature importance and local feature importance. We evaluated the user trust and reliance (UTR) for the explanation results of each method in a user study based on real patients’ electronic health records (EHR) and the feedback of healthcare professionals. Based on the user study, we observed that both methods have strengths and weaknesses according to the patients’ data, especially based on the data size of the patient. When the amount of data is small, global feature importance is enough to use. However, when the patient’s data size is big, using a local feature importance method makes more sense. As future work, we will develop a hybrid explanation method (by combining these methods automatically with a smart setting) to obtain higher and more stable performance results in terms of user trust and reliance.